From 396050855b9f4b1a84b6794f0ff5da53dac7e2ef Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Thu, 31 Oct 2024 12:29:18 +0100 Subject: [PATCH] Development environment updates, io, and docs (#4) * add funtion to api docs * update env, add sliderule io * optional imports, more docs * matplotlib optional * github actions updates * github actions updates * fix links * temp disable pixi cache * actually disable cache, add optional pkgs to docs * better optional imports * better optional imports * try locked=false * back to lockfile * better wesm remote search * folium for docs * OGR aws sessions management * set GDAL ENV via pyogrio * disable pam --- .copier-answers.yml | 12 - .git_archival.txt | 3 - .gitattributes | 3 - .github/workflows/ci.yml | 12 +- .github/workflows/pixi.yml | 14 +- .github/workflows/upstream.yml | 48 + .pre-commit-config.yaml | 10 + README.md | 26 +- docs/api.rst | 2 +- docs/datasets.md | 21 + docs/index.md | 9 + docs/installation.md | 4 +- docs/introduction.md | 27 +- docs/quickstart.ipynb | 627 ++ noxfile.py | 107 - pixi.lock | 6622 +++++++--------------- pyproject.toml | 77 +- src/coincident/__init__.py | 6 +- src/coincident/_utils.py | 33 + src/coincident/datasets/maxar.py | 21 +- src/coincident/datasets/usgs.py | 3 +- src/coincident/io/__init__.py | 9 + src/coincident/io/sliderule.py | 70 + src/coincident/overlaps/__init__.py | 2 +- src/coincident/search/main.py | 2 +- src/coincident/search/stac.py | 26 +- src/coincident/search/swath_polygons.csv | 610 ++ src/coincident/search/wesm.py | 113 +- tests/test_search.py | 17 +- 29 files changed, 3853 insertions(+), 4683 deletions(-) delete mode 100644 .copier-answers.yml delete mode 100644 .git_archival.txt delete mode 100644 .gitattributes create mode 100644 .github/workflows/upstream.yml create mode 100644 docs/datasets.md create mode 100644 docs/quickstart.ipynb delete mode 100644 noxfile.py create mode 100644 src/coincident/_utils.py create mode 100644 src/coincident/io/__init__.py create mode 100644 src/coincident/io/sliderule.py create mode 100644 src/coincident/search/swath_polygons.csv diff --git a/.copier-answers.yml b/.copier-answers.yml deleted file mode 100644 index a56561a..0000000 --- a/.copier-answers.yml +++ /dev/null @@ -1,12 +0,0 @@ -# Changes here will be overwritten by Copier; NEVER EDIT MANUALLY -_commit: 2024.08.19 -_src_path: gh:scientific-python/cookie -backend: hatch -email: scottyh@uw.edu -full_name: Scott Henderson -license: MIT -org: uw-cryo -project_name: coincident -project_short_description: Search and analysis of STV Precursor Coincident Datasets -url: https://github.com/uw-cryo/coincident -vcs: true diff --git a/.git_archival.txt b/.git_archival.txt deleted file mode 100644 index 7c51009..0000000 --- a/.git_archival.txt +++ /dev/null @@ -1,3 +0,0 @@ -node: $Format:%H$ -node-date: $Format:%cI$ -describe-name: $Format:%(describe:tags=true,match=*[0-9]*)$ diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 0d3a9b6..0000000 --- a/.gitattributes +++ /dev/null @@ -1,3 +0,0 @@ -.git_archival.txt export-subst -# GitHub syntax highlighting -pixi.lock linguist-language=YAML linguist-generated=true diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1f23528..badd57a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -30,9 +30,6 @@ jobs: - uses: pre-commit/action@v3.0.1 with: extra_args: --hook-stage manual --all-files - - name: Run PyLint - continue-on-error: true - run: pipx run nox -s pylint -- --output-format=github checks: name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }} @@ -68,7 +65,8 @@ jobs: python -m pytest -ra --cov --cov-report=xml --cov-report=term --durations=20 - - name: Upload coverage report - uses: codecov/codecov-action@v4.5.0 - with: - token: ${{ secrets.CODECOV_TOKEN }} + # NOTE: need an account for this... + # - name: Upload coverage report + # uses: codecov/codecov-action@v4.5.0 + # with: + # token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/pixi.yml b/.github/workflows/pixi.yml index 5090c97..db93c29 100644 --- a/.github/workflows/pixi.yml +++ b/.github/workflows/pixi.yml @@ -15,16 +15,24 @@ jobs: - uses: prefix-dev/setup-pixi@v0.8.1 with: + pixi-version: v0.34.0 environments: dev - cache: true - cache-write: - ${{ github.event_name == 'push' && github.ref_name == 'main' }} + manifest-path: pyproject.toml + cache: false + locked: false + #cache-write: + # ${{ github.event_name == 'push' && github.ref_name == 'main' }} # NOTE: https://github.com/prefix-dev/setup-pixi/issues/136 - name: Ensure Dynamic version run: | pip install -e . + - name: Run Pylint + continue-on-error: true + run: | + pixi run lint + - name: Run Pytest env: MAXAR_API_KEY: ${{ secrets.MAXAR_API_KEY}} diff --git a/.github/workflows/upstream.yml b/.github/workflows/upstream.yml new file mode 100644 index 0000000..612c54a --- /dev/null +++ b/.github/workflows/upstream.yml @@ -0,0 +1,48 @@ +# Test against the latest versions of Python and other libraries +name: Upstream versions + +on: + workflow_dispatch: + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + # Many color libraries just need this to be set to any value, but at least + # one distinguishes color depth, where "3" -> "256-bit color". + FORCE_COLOR: 3 + +jobs: + checks: + name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }} + runs-on: ${{ matrix.runs-on }} + strategy: + fail-fast: false + matrix: + python-version: ["3.13", "3.14"] + # windows-latest, + runs-on: [ubuntu-latest, macos-14] + #include: + # - python-version: "pypy-3.10" + # runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + allow-prereleases: true + + - name: Install package + run: python -m pip install .[dev] + + - name: Test package + env: + MAXAR_API_KEY: ${{ secrets.MAXAR_API_KEY}} + run: >- + python -m pytest -ra --cov --cov-report=xml --cov-report=term + --durations=20 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 90f6435..cf0abb4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -87,3 +87,13 @@ repos: - id: check-dependabot - id: check-github-workflows - id: check-readthedocs + + # https://pylint.pycqa.org/en/latest/user_guide/installation/pre-commit-integration.html#pre-commit-integration + # - repo: local + # hooks: + # - id: pylint + # name: pylint + # entry: pylint + # language: system + # types: [python] + # require_serial: true diff --git a/README.md b/README.md index affff7b..5224122 100644 --- a/README.md +++ b/README.md @@ -27,8 +27,10 @@ Such datasets are intended to be used by the NASA STV community for calibration/validation, fusion algorithm development, and discipline-specific scientific analysis. +See here for more information: + + **This tool is under active development, there are no stable releases yet!** -https://science.nasa.gov/earth-science/decadal-surveys/decadal-stv/coincident-datasets/ ## Development @@ -41,8 +43,9 @@ git checkout -b newfeature pixi shell --environment dev # type `exit` to deactivate pre-commit install -# Or run pre-configured environments and commands -pixi run test +# Or run pre-configured commands: +pixi run networktest # or 'test' +pixi run precommit # also runs automatically upon commits pixi run lint pixi run docs ``` @@ -54,12 +57,21 @@ authentication to _download_ data (NASA). `coincident` assumes you have the following Environment Variables defined: ```bash -export EARTHDATA_USERNAME=xxxxx -export EARTHDATA_PASSWORD=yyyyy -export MAXAR_API_KEY=zzzzz +export EARTHDATA_USERNAME=aaaaa +export EARTHDATA_PASSWORD=bbbbb +export MAXAR_API_KEY=ccccc +export PC_SDK_SUBSCRIPTION_KEY=ddddd ``` +Sign up for credentials at the following webpages: + +- [](https://urs.earthdata.nasa.gov) +- [](https://developers.maxar.com/docs/authentication/guides/api-key) +- [](https://planetarycomputer.developer.azure-api.net) + ### Acknowledgements - Python packaging template provided by - https://github.com/scientific-python/cookie + + +- Funding for this effort was provided by NASA Grant 80NSSC22K1094 diff --git a/docs/api.rst b/docs/api.rst index cde540a..520d9f1 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -19,7 +19,6 @@ Search wesm.get_swath_polygons - Overlaps -------- @@ -29,6 +28,7 @@ Overlaps :toctree: generated/ geographic_area + subset_by_minimum_area subset_by_temporal_overlap diff --git a/docs/datasets.md b/docs/datasets.md new file mode 100644 index 0000000..9fd80be --- /dev/null +++ b/docs/datasets.md @@ -0,0 +1,21 @@ +# Supported datasets + +Below we provide a short table summarizing datasets that are searchable with +`coincident`. Note that many of these datasets (or subsets) are available from +different providers, the _provider_ column identifies the source of the data +used by this library. + +| Dataset | Alias | Type | Start | End | Extent | Source | +| -------------- | ---------- | --------- | ---------- | ---------- | ------------- | --------------------------------------------------------------------------- | +| TanDEM-X | tdx | SAR | 2007-07-01 | | global | [NASA CSDAP](https://csdap.earthdata.nasa.gov/stac/collections/airbus) | +| Maxar Stereo | maxar | VHR | 2007-07-01 | | global | [Maxar](https://developers.maxar.com/docs/discovery/) | +| Coperincus DEM | cop30 | SAR | 2021-04-22 | | global | [Microsoft](https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-30) | +| ICESat-2 ATL06 | atl06 | Altimeter | 2018-10-13 | | global | [NASA](https://nsidc.org/data/atl03) | +| GEDI L2A | gedi | Altimeter | 2019-04-04 | 2023-03-17 | mid-latitudes | [NASA](https://lpdaac.usgs.gov/products/gedi02_av002/) | +| 3DEP LiDAR | 3dep | LiDAR | 2000-12-01 | | CONUS | [USGS](https://www.usgs.gov/3d-elevation-program) | +| ESA WorldCover | worldcover | LULC | 2020-01-01 | 2021-12-31 | global | [Microsoft](https://planetarycomputer.microsoft.com/dataset/esa-worldcover) | + +## Other data sources + +If you are interested in working with additional data, feel free to open an +[issue](https://github.com/uw-cryo/coincident/issues). diff --git a/docs/index.md b/docs/index.md index fb42c3b..e5cecd0 100644 --- a/docs/index.md +++ b/docs/index.md @@ -10,6 +10,15 @@ installation introduction +datasets +``` + +```{toctree} +:maxdepth: 2 +:hidden: +:caption: Examples + +quickstart ``` ```{toctree} diff --git a/docs/installation.md b/docs/installation.md index 1acc022..2ed0b0a 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -7,8 +7,8 @@ coincident directly from GitHub: pip install git+https://github.com/uw-cryo/coincident.git@main ``` -Alternatively, you can install a fresh locked environment with -[pixi.sh](https://pixi.sh/latest/): +Alternatively, you can install a fresh locked environment using the +[GitHub CLI](https://cli.github.com) and [pixi.sh](https://pixi.sh/latest/): ```bash gh repo clone uw-cryo/coincident diff --git a/docs/introduction.md b/docs/introduction.md index b8abca1..2161aca 100644 --- a/docs/introduction.md +++ b/docs/introduction.md @@ -1,7 +1,13 @@ # Introduction `coincident` simplifies access to a curated set of datasets of relevance to NASA -STV studies +STV studies. It is designed to simplify working with disparate metadata for +areal and satellite remote sensing datasets. `coincident` relies heavily on +[GeoPandas](https://geopandas.org/en/stable/index.html) in that metadata records +are always returned as GeoDataFrame objects, and most methods are written to +operate either on entire dataframes or single rows within a dataframe. + +## Dataset aliases ```python import coincident @@ -9,19 +15,28 @@ import coincident coincident.datasets.aliases ``` -the `coincident` package provides a `search()` method that has the same syntax -regardless of which dataset you are searching. Behind the scenes, polygons -intersecting your area of interest are efficiently located and returned as a -geodataframe. +## Unified search function + +the `coincident` package provides a [search()](#coincident.search.search) method +that has the same syntax regardless of which dataset you are searching. Behind +the scenes, polygons intersecting your area of interest are efficiently located +and returned as a geodataframe. ```python aoi = gpd.read_file( "https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/CO/shape.geojson" ) -gf = coincident.search.search( +gf = coincident.search( dataset="3dep", intersects=aoi, datetime=["2018", "2024"], ) gf.explore(column="workunit", popup=True) ``` + +## Convenience functions + +`coincident` also provides a number of convenience functions, some of which only +pertain to specific datasets. For example, loading raster imagery via +[Xarray](https://docs.xarray.dev/en/stable) or creating visualizations of browse +imagery. Refer to [the API Docs](./api) for a listing of functions. diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb new file mode 100644 index 0000000..863be35 --- /dev/null +++ b/docs/quickstart.ipynb @@ -0,0 +1,627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quickstart\n", + "\n", + "This library provides functions to hone in on coincident datasets and visualize their coverage. \n", + "\n", + "The current recommended workflow is to:\n", + "\n", + "1. start with a lidar dataset \n", + "1. reduce to region with coincident maxar stereo within an acceptable temporal range\n", + "1. optionally reduce further with additional datasets such as icesat-2 and gedi altimetry\n", + "\n", + "This notebook provides and example starting from USGS 3DEP LiDAR in Colorado, USA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import coincident\n", + "\n", + "print(coincident.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define an area of interest\n", + "\n", + "Searches often start with an spatial subset, in this notebook we know we are interested in datasets in Colorado. We recommend restricting searches to areas at the 'State' scale rather than Country or Global scales" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aoi = gpd.read_file(\n", + " \"https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/CO/shape.geojson\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aoi.explore(color=\"black\", style_kwds=dict(fill=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{note}\n", + "this polygon for the state of Colorado is simple and has only 4 vertices in the corners, but it's good practice good to check the number of vertices you have before searching. The simpler your search polygons are the faster your searches will be!\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Original number of vertices:\", aoi.count_coordinates().iloc[0])\n", + "aoi = aoi.simplify(0.01)\n", + "print(\"Simplified number of vertices:\", aoi.count_coordinates().iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uniform search method\n", + "\n", + "the `coincident` package provides a [search()](#coincident.search.search) method that has the same syntax regardless of which dataset you are searching. Behind the scenes, polygons intersecting your area of interest are efficiently located and returned as a geodataframe. \n", + "\n", + "For 3DEP LiDAR, we start by searching bounding boxes for each 'workunit'. Once we identify a workunit, we load a precise polygon delineating the extent of LiDAR measurements.\n", + "\n", + "The search function is essentially a [pystac_client.ItemSearch](#pystac_client.ItemSearch) with an extra argument `dataset`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coincident.datasets.aliases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf = coincident.search.search(\n", + " dataset=\"3dep\",\n", + " intersects=aoi,\n", + " datetime=[\"2018\", \"2024\"],\n", + ")\n", + "gf.explore(column=\"workunit\", popup=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# From this search we identify a specific lidar acquisition of interest\n", + "gf = gf[gf.workunit == \"CO_WestCentral_2019\"]\n", + "gf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each Workunit has a unique 'Feature ID', or 'FID' that can be used to efficiently retrieve the full-resultion detailed MultiPolygon footprint from the USGS's WESM.gpkg file in AWS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf.index.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### USGS 3DEP Lidar\n", + "\n", + "Some datasets have additional functions available to load auxiliary data. For example, we can load original high resolution polygon. In addition to loading \"swath\" polygons to understand exact days when acquisitions were made.\n", + "\n", + "```{warning}\n", + "Swath polygons are not available for all LiDAR workunits. They are more likely to exist for acquisitions after 2018.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf_wesm = coincident.search.wesm.load_by_fid(fids=gf.index.values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf_wesm.explore(column=\"workunit\", popup=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf_wesm[[\"workunit\", \"start_datetime\", \"end_datetime\", \"duration\"]] # duration in days" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this 'workunit' data was collected over 29 days, in order to see when data was collected within this polygon we need to load a corresponding 'swath' polygon. `coincident` provides a helper function for this, which loads the swath polygon for a given workunit\n", + "\n", + "```{note}\n", + "Swath polygons have detailed timing information for individual LiDAR flight lines composing a given 'workunit'. Not all workunits have swath polygons. They tend to be available for data collected after 2019.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: be patient here, reading shapefiles from s3 can be slow depending on your bandwidth\n", + "gf_swath = coincident.search.wesm.get_swath_polygons(gf.workunit.iloc[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Number of swath polygons:\", len(gf_swath))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot them, simplify first for faster plotting\n", + "gf_swath[\"geometry\"] = gf_swath.simplify(0.01)\n", + "gf_swath.explore(column=\"dayofyear\", cmap=\"plasma\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see in the above figure, the actual day of observation for any point on the ground can vary in complex ways based on the flight paths taken during the LiDAR collection period." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Maxar stereo search\n", + "\n", + "Now that we understand our lidar collection, let's return to the uniform search function, and now search for Maxar stereo pairs. We are only interested in pairs in the same date range of the lidar. Note that searching by a simplified polygon (in this case back to the convex hull we started with) is recommended as most APIs have limits on the number of polygon vertices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simplify search polygon and add date range\n", + "pad = gpd.pd.Timedelta(days=14)\n", + "date_range = [gf.start_datetime.iloc[0] - pad, gf.end_datetime.iloc[0] + pad]\n", + "aoi = gf.geometry\n", + "\n", + "gf_maxar = coincident.search.search(\n", + " dataset=\"maxar\",\n", + " intersects=aoi,\n", + " datetime=date_range,\n", + " filter=\"eo:cloud_cover < 20\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(len(gf_maxar))\n", + "gf_maxar.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with gpd.pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n", + " print(gf_maxar.iloc[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Group by stereo pair id (NOTE: assumes single id per mono acquisition)\n", + "gf_maxar[\"stereo_pair_id\"] = gf_maxar.stereo_pair_identifiers.str[0]\n", + "\n", + "gf_stereo = gf_maxar.dissolve(by=\"stereo_pair_id\", as_index=False)\n", + "gf_stereo.explore(column=\"stereo_pair_identifiers\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = gf_wesm.explore(color=\"black\")\n", + "gf_maxar.explore(column=\"dayofyear\", cmap=\"plasma\", m=m, popup=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restrict polygon footprints to area of overlap\n", + "\n", + "We will make regular use of the GeoPandas overlay() function to shrink original acquisition footprints so that they only overlap an area of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Consider area of overlap\n", + "m = gf_wesm.explore(color=\"black\")\n", + "# NOTE: don't really need wesm attributes in result, so just use geodataframe w/ geometry column\n", + "# gf_i = gf_wesm[['geometry']].overlay(gf_stereo, how='intersection')\n", + "gf_i = gf_stereo.overlay(gf_wesm[[\"geometry\"]], how=\"intersection\")\n", + "gf_i.explore(column=\"dayofyear\", cmap=\"plasma\", m=m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exclude very small overlaps\n", + "\n", + "Consider further filtering results by a minimum overlap area criteria, or by a more precise estimate of the number of days elapsed for the maxar stereo acquisition relative to lidar estimated from swath polygons" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "min_area_km2 = 20\n", + "gf_i = coincident.overlaps.subset_by_minimum_area(gf_i, min_area_km2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at intersection with swath polygons for exact date difference\n", + "# Since both have 'dayofyear', these cols get expanded to dayofyear_1 and dayofyear_2\n", + "stereo_pair = gf_i.iloc[[0]]\n", + "gf_dt = gf_swath.overlay(stereo_pair, how=\"intersection\")\n", + "print(\"Maxar Stereo Acquisition DOY - Swath Lidar DOY:\")\n", + "print(stereo_pair.stereo_pair_id.values[0])\n", + "(gf_dt.dayofyear_2 - gf_dt.dayofyear_1).describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GEDI\n", + "\n", + "Search for Coincident GEDI L2A." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We've refined our search polygon again, so use a new AOI\n", + "aoi = gpd.GeoSeries(gf_i.union_all().convex_hull, crs=\"EPSG:4326\")\n", + "\n", + "gf_gedi = coincident.search.search(\n", + " dataset=\"gedi\",\n", + " intersects=aoi,\n", + " datetime=date_range,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(len(gf_gedi))\n", + "gf_gedi.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf_gedi.explore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Altimeter granules span a large geographic area! So let's again cut results down to the area of intersection." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize colormap across both dataframes\n", + "vmin, vmax = gpd.pd.concat([gf_i.dayofyear, gf_gedi.dayofyear]).agg([\"min\", \"max\"])\n", + "cmap = \"plasma\"\n", + "\n", + "# NOTE: here we just take the boundary of the union of all maxar+lidar regions to avoid many overlay geometries\n", + "union = gpd.GeoDataFrame(geometry=[gf_i.union_all()], crs=\"EPSG:4326\")\n", + "m = gf_i.explore(column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax)\n", + "gf_gedi = union.overlay(gf_gedi, how=\"intersection\")\n", + "gf_gedi.explore(m=m, column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax, legend=False)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Immediately we see some potential for close-in time acquisitions. \n", + "\n", + "```{note}\n", + "As an additional step, you might want to add another filtering step to remove tiny polygons\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each Stereo pair, describe number of altimeter passes and day offset\n", + "stereo_pair = gf_i.iloc[[0]]\n", + "gf_dt = stereo_pair.overlay(gf_gedi, how=\"intersection\")\n", + "m = stereo_pair.explore(\n", + " column=\"dayofyear\", vmin=vmin, vmax=vmax, cmap=cmap, legend=False\n", + ")\n", + "gf_dt.explore(m=m, column=\"dayofyear_2\", vmin=vmin, vmax=vmax, cmap=cmap)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Maxar Stereo Acquisition DOY - GEDI DOY:\")\n", + "print(stereo_pair.stereo_pair_identifiers.values[0])\n", + "(gf_dt.dayofyear_1 - gf_dt.dayofyear_2).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Consider just the minimum offset for each stereo pair\n", + "# NOTE: probably a fancier way to do this using a multiindex/groupby\n", + "print(\"Maxar Stereo Acquisition DOY - GEDI DOY\")\n", + "for i in range(len(gf_i)):\n", + " stereo_pair = gf_i.iloc[[i]]\n", + " gf_dt = stereo_pair.overlay(gf_gedi, how=\"intersection\")\n", + " min_dt = (gf_dt.dayofyear_1 - gf_dt.dayofyear_2).min()\n", + " print(stereo_pair.stereo_pair_identifiers.values[0], min_dt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{note}\n", + "In this case, we have some altimeter acquisitions that were just 3 days after a maxar stereo acquisition!\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ICESat-2\n", + "\n", + "Search for Coincident ICESat-2 Altimetry (ATL03)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gf_is2 = coincident.search.search(\n", + " dataset=\"icesat-2\",\n", + " intersects=aoi,\n", + " datetime=date_range,\n", + ")\n", + "print(len(gf_is2))\n", + "gf_is2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize colormap across both dataframes\n", + "vmin, vmax = gpd.pd.concat([gf_i.dayofyear, gf_is2.dayofyear]).agg([\"min\", \"max\"])\n", + "cmap = \"plasma\"\n", + "\n", + "# NOTE: here we just take the boundary of the union of all maxar+lidar regions to avoid many overlay geometries\n", + "union = gpd.GeoDataFrame(geometry=[gf_i.union_all()], crs=\"EPSG:4326\")\n", + "m = gf_i.explore(\n", + " column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax, style_kwds=dict(color=\"black\")\n", + ")\n", + "gf_is2 = union.overlay(gf_is2, how=\"intersection\")\n", + "gf_is2.explore(m=m, column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax, legend=False)\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each Stereo pair, describe number of altimeter passes and day offset\n", + "stereo_pair = gf_i.iloc[[0]]\n", + "gf_dt = stereo_pair.overlay(gf_is2, how=\"intersection\")\n", + "m = stereo_pair.explore(\n", + " column=\"dayofyear\",\n", + " vmin=vmin,\n", + " vmax=vmax,\n", + " cmap=cmap,\n", + " legend=False,\n", + " style_kwds=dict(color=\"black\"),\n", + ")\n", + "gf_dt.explore(m=m, column=\"dayofyear_2\", vmin=vmin, vmax=vmax, cmap=cmap)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Maxar Stereo Acquisition DOY - ICESat-2 DOY\")\n", + "for i in range(len(gf_i)):\n", + " stereo_pair = gf_i.iloc[[i]]\n", + " gf_dt = stereo_pair.overlay(gf_is2, how=\"intersection\")\n", + " min_dt = (gf_dt.dayofyear_1 - gf_dt.dayofyear_2).min()\n", + " print(stereo_pair.stereo_pair_identifiers.values[0], min_dt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "- Any of these Maxar stereo pairs could be worth ordering. But if the goal is to have coincident acquisitions as close as possible in time '11a11283-6661-4f91-9bcb-5dab3c6a5d02-inv' seems like a good bet:\n", + " - There is an overlapping ICESat-2 track 2 days later\n", + " - There is an overlapping GEDI track 3 days earlier\n", + " - 3DEP Lidar was acquired 27 to 36 days earlier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Can save this dataframe, or just note STAC ids to work with later ('102001008EC5AC00','102001008BE9BB00')\n", + "pair_id = \"11a11283-6661-4f91-9bcb-5dab3c6a5d02-inv\"\n", + "full_frame = gf_maxar[gf_maxar.stereo_pair_id == pair_id]\n", + "subset = gf_i[gf_i.stereo_pair_id == pair_id]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the original STAC metadata and polygon\n", + "full_frame.to_parquet(\"/tmp/maxar-mono-images.parquet\")\n", + "# Also save the subsetted region for this stereo pair\n", + "subset.to_parquet(\"/tmp/stereo-subset.parquet\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "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": 2 +} diff --git a/noxfile.py b/noxfile.py deleted file mode 100644 index c04839b..0000000 --- a/noxfile.py +++ /dev/null @@ -1,107 +0,0 @@ -from __future__ import annotations - -import argparse -import shutil -from pathlib import Path - -import nox - -DIR = Path(__file__).parent.resolve() - -nox.needs_version = ">=2024.3.2" -nox.options.sessions = ["lint", "pylint", "tests"] -nox.options.default_venv_backend = "uv|virtualenv" - - -@nox.session -def lint(session: nox.Session) -> None: - """ - Run the linter. - """ - session.install("pre-commit") - session.run( - "pre-commit", "run", "--all-files", "--show-diff-on-failure", *session.posargs - ) - - -@nox.session -def pylint(session: nox.Session) -> None: - """ - Run PyLint. - """ - # This needs to be installed into the package environment, and is slower - # than a pre-commit check - session.install(".", "pylint>=3.2") - session.run("pylint", "coincident", *session.posargs) - - -@nox.session -def tests(session: nox.Session) -> None: - """ - Run the unit and regular tests. - """ - session.install(".[test]") - session.run("pytest", *session.posargs) - - -@nox.session(reuse_venv=True) -def docs(session: nox.Session) -> None: - """ - Build the docs. Pass --non-interactive to avoid serving. First positional argument is the target directory. - """ - - parser = argparse.ArgumentParser() - parser.add_argument( - "-b", dest="builder", default="html", help="Build target (default: html)" - ) - parser.add_argument("output", nargs="?", help="Output directory") - args, posargs = parser.parse_known_args(session.posargs) - serve = args.builder == "html" and session.interactive - - session.install("-e.[docs]", "sphinx-autobuild") - - shared_args = ( - "-n", # nitpicky mode - "-T", # full tracebacks - f"-b={args.builder}", - "docs", - args.output or f"docs/_build/{args.builder}", - *posargs, - ) - - if serve: - session.run("sphinx-autobuild", "--open-browser", *shared_args) - else: - session.run("sphinx-build", "--keep-going", *shared_args) - - -@nox.session -def build_api_docs(session: nox.Session) -> None: - """ - Build (regenerate) API docs. - """ - - session.install("sphinx") - session.run( - "sphinx-apidoc", - "-o", - "docs/api/", - "--module-first", - "--no-toc", - "--force", - "src/coincident", - ) - - -@nox.session -def build(session: nox.Session) -> None: - """ - Build an SDist and wheel. - """ - - build_path = DIR.joinpath("build") - if build_path.exists(): - shutil.rmtree(build_path) - - session.install("build") - session.run("python", "-m", "build") diff --git a/pixi.lock b/pixi.lock index a0e428d..5061201 100644 --- a/pixi.lock +++ b/pixi.lock @@ -10,70 +10,48 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/aiobotocore-2.15.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.10.9-py312h66e93f0_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/aioitertools-0.12.0-pyhd8ed1ab_0.conda - 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"Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering", "Typing :: Typed", ] dynamic = ["version"] dependencies = [ + #"aiohttp>=3.10.6,<4", + "cloudpathlib[s3]>=0.20.0,<0.21", + #"fsspec>=2024.9.0,<2025", "geopandas>=1.0.1,<2", - "maxar-platform==1.0.2a2", + "maxar-platform>=1.0.2,<2", "planetary-computer>=1.0.0,<2", + "pyarrow>=18.0.0,<19", "pystac-client>=0.8.3,<0.9", - "fsspec>=2024.9.0,<2025", "requests>=2.32.3,<3", - "aiohttp>=3.10.6,<4", - "pyarrow>=17", - "stac-asset>=0.4.3,<0.5", "rioxarray>=0.17.0,<0.18", + "stac-asset>=0.4.3,<0.5", "stac-geoparquet>=0.6.0,<0.7", - "s3fs>=2024.9.0,<2025", + #"s3fs>=2024.9.0,<2025", ] [project.optional-dependencies] dev = [ "ipykernel>=6.29.5,<7", "mypy>=1.11.2,<2", + "pre-commit>=3.8.0,<4", + "pylint>=3.3.1,<4", "pytest >=6", "pytest-cov >=3", - "pre-commit>=3.8.0,<4", ] docs = [ + "folium", # comes w/ geopandas on conda-forge but not pypi "furo>=2023.08.17", + "mapclassify", + "matplotlib", "myst-nb", "myst_parser>=0.13", "sphinx>=7.0", "sphinx_autodoc_typehints", "sphinx_copybutton", ] +# all = [ +# "matplotlib-base>=3.9.2,<4", +# "sliderule>=4.7.1,<5" +# ] [project.urls] Homepage = "https://github.com/uw-cryo/coincident" @@ -88,7 +96,12 @@ filterwarnings = [ "ignore:maxar_plaform authentication error:UserWarning", # https://github.com/microsoft/planetary-computer-sdk-for-python/issues/58 "ignore::pydantic.warnings.PydanticDeprecatedSince20", - "ignore:Inheritance class OAuth2Client from ClientSession is discouraged:DeprecationWarning" + "ignore:Inheritance class OAuth2Client from ClientSession is discouraged:DeprecationWarning", + # sliderule datetime.datetime.utcnow() is deprecated... + "ignore::DeprecationWarning:sliderule", + # Optional imports + "ignore:Unable to authenticate with Maxar API:UserWarning", + "ignore:.* package not found:UserWarning", ] log_cli_level = "INFO" testpaths = [ @@ -105,6 +118,9 @@ report.exclude_also = [ 'if typing.TYPE_CHECKING:', ] +[tool.codespell] +skip="pixi.lock" + [tool.mypy] files = ["src", "tests"] warn_unused_configs = true @@ -150,7 +166,11 @@ ignore = [ "PLR09", # Too many <...> "PLR2004", # Magic value used in comparison "ISC001", # Conflicts with formatter - "PLC0414" # explicit re-export + "PLC0414", # explicit re-export + "PD901", # df for DataFrame +] +exclude = [ + "docs/*.ipynb", ] isort.required-imports = ["from __future__ import annotations"] # Uncomment if using a _compat.typing backport @@ -158,7 +178,6 @@ isort.required-imports = ["from __future__ import annotations"] [tool.ruff.lint.per-file-ignores] "tests/**" = ["T20"] -"noxfile.py" = ["T20"] [tool.pylint] ignore-paths = [".*/_version.py"] @@ -171,6 +190,8 @@ messages_control.disable = [ "missing-module-docstring", "missing-function-docstring", "wrong-import-position", + "C0103", # Allow ALLCAPS variable names + "W0212", # Access to a protected member _... ] [tool.pixi.project] @@ -185,43 +206,49 @@ docs = { features = ["docs"], solve-group = "default" } # NOTE: pixi will match project.dependencies versions above but get them from conda-forge # If a package is listed in project.dependencies but not repeated here, it is installed from pypi [tool.pixi.dependencies] -aiohttp = "*" -fsspec = "*" +python = "<3.13" # https://github.com/stac-utils/stac-geoparquet/issues/81 +#aiohttp = "*" +#fsspec = "*" geopandas = "*" planetary-computer = "*" pystac-client = "*" requests = "*" rioxarray = "*" # stac-asset = "*" # not on conda-forge +#s3fs = "*" stac-geoparquet = "*" -s3fs = "*" -sliderule = ">=4.7.1,<5" +pyarrow = "*" +#nbconvert = ">=7.16.4,<8" +#cloudpathlib-s3 = ">=0.20.0,<0.21" +#matplotlib-base = ">=3.9.2,<4" +#sliderule = ">=4.7.1,<5" [tool.pixi.feature.dev.dependencies] +# NOTE: ipykernel to run notebooks in vscode +ipykernel = ">=6.29.5,<7" # Bug if=*? Because you require pre-commit>=3.8.0,<4 and pre-commit==4.0.0, we can conclude that your requirements are unsatisfiable. pre-commit = "<4" +pylint = "*" pytest = "*" mypy = "*" -pyarrow = "*" -# NOTE: ipykernel to run notebooks in vscode -ipykernel = ">=6.29.5,<7" +# Testing additional dependencies #rich = ">=13.8.1,<14" # Optional. convenient for rich.print(dataset) -#uv = ">=0.4.14,<0.5" -#stac-geoparquet = "*" -#rasterio = ">=1.4.0,<2" -xvec = ">=0.3.0,<0.4" +#xvec = ">=0.3.0,<0.4" + +[tool.pixi.pypi-dependencies] +coincident = { path = ".", editable = false } +#cloudpathlib = { version = "0.20.0,<0.21", extras = ["s3"]} [tool.pixi.feature.dev.pypi-dependencies] coincident = { path = ".", editable = true } [tool.pixi.feature.dev.tasks] precommit = "pre-commit run --all" -# PyLint run separately https://pylint.pycqa.org/en/latest/user_guide/installation/pre-commit-integration.html -pylint = "pipx run nox -s pylint" +# NOTE: consider --output-format=github for github actions +lint = "pylint src" test = "pytest -o markers=network -m 'not network' --cov --cov-report=xml --cov-report=term" networktest = "pytest --cov --cov-report=xml --cov-report=term" - [tool.pixi.feature.docs.pypi-dependencies] coincident = { path = ".", editable = true } diff --git a/src/coincident/__init__.py b/src/coincident/__init__.py index 36dc3d5..4c1c2ec 100644 --- a/src/coincident/__init__.py +++ b/src/coincident/__init__.py @@ -1,12 +1,10 @@ """ -Copyright (c) 2024 Scott Henderson. All rights reserved. - coincident: Search and analysis of STV Precursor Coincident Datasets """ from __future__ import annotations -from coincident import datasets, overlaps, search +from coincident import datasets, io, overlaps, search from coincident._version import version as __version__ -__all__ = ["__version__", "datasets", "search", "overlaps"] +__all__ = ["__version__", "datasets", "search", "overlaps", "io"] diff --git a/src/coincident/_utils.py b/src/coincident/_utils.py new file mode 100644 index 0000000..f3adff6 --- /dev/null +++ b/src/coincident/_utils.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from collections.abc import Callable +from functools import wraps +from importlib.util import find_spec + +# NOTE: not really sure about type hinting here! +# https://stackoverflow.com/questions/65621789/mypy-untyped-decorator-makes-function-my-method-untyped +from typing import Any, TypeVar, cast + +F = TypeVar("F", bound=Callable[..., Any]) + + +def depends_on_optional(module_name: str) -> Callable[[F], F]: + """borrowed from https://stackoverflow.com/questions/27361427/how-to-properly-deal-with-optional-features-in-python + Usage: add decorator above function that uses optional dependency + Example: @depends_on_optional("matplotlib") + """ + + def decorator(func: F) -> F: + @wraps(func) + def wrapper(*args, **kwargs): # type: ignore[no-untyped-def] + spec = find_spec(module_name) + if spec is None: + message = ( + f"Optional dependency {module_name} not found ({func.__name__})." + ) + raise ImportError(message) + return func(*args, **kwargs) + + return cast(F, wrapper) + + return decorator diff --git a/src/coincident/datasets/maxar.py b/src/coincident/datasets/maxar.py index 26171ff..73afd8f 100644 --- a/src/coincident/datasets/maxar.py +++ b/src/coincident/datasets/maxar.py @@ -13,22 +13,30 @@ from stac_asset import Config import os +import warnings from dataclasses import dataclass, field from enum import Enum from pathlib import Path +import numpy as np import pystac import rasterio import stac_asset import xarray as xr +from coincident._utils import depends_on_optional from coincident.datasets.general import Dataset MAXAR_CONFIG = stac_asset.Config( - http_headers={"MAXAR-API-KEY": os.environ.get("MAXAR_API_KEY")}, - # https://github.com/stac-utils/stac-asset/issues/223 - http_check_content_type=False, + http_headers={"MAXAR-API-KEY": os.environ.get("MAXAR_API_KEY")} ) +try: + import matplotlib.pyplot as plt +except ImportError: + warnings.warn( + "'matplotlib' package not found. Install for plotting functions: https://matplotlib.org/stable/install/index.html", + stacklevel=2, + ) class Collection(str, Enum): @@ -71,6 +79,7 @@ async def download_item( return item # noqa: RET504 +@depends_on_optional("matplotlib") def open_browse(item: pystac.Item, overview_level: int = 0) -> xr.DataArray: """ Open a browse image from a STAC item using the specified overview level. @@ -110,6 +119,7 @@ def open_browse(item: pystac.Item, overview_level: int = 0) -> xr.DataArray: ) +@depends_on_optional("matplotlib") def plot_browse(item: pystac.Item, overview_level: int = 0) -> None: """ Plots a browse image from a STAC item using Matplotlib. @@ -127,13 +137,10 @@ def plot_browse(item: pystac.Item, overview_level: int = 0) -> None: This function does not return any value """ - import matplotlib.pyplot as plt - import numpy as np - da = open_browse(item, overview_level=overview_level) mid_lat = da.y[int(da.y.size / 2)].to_numpy() # PD011 - fig, ax = plt.subplots(figsize=(8, 11)) + fig, ax = plt.subplots(figsize=(8, 11)) # pylint: disable=unused-variable da.plot.imshow(rgb="band", add_labels=False, ax=ax) ax.set_aspect(aspect=1 / np.cos(np.deg2rad(mid_lat))) ax.set_title(item.id) diff --git a/src/coincident/datasets/usgs.py b/src/coincident/datasets/usgs.py index 7be3e89..977b5a0 100644 --- a/src/coincident/datasets/usgs.py +++ b/src/coincident/datasets/usgs.py @@ -18,7 +18,8 @@ class ThreeDEP(Dataset): has_stac_api: bool = False search: str = ( - "https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/WESM.gpkg" + # "https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/WESM.gpkg" + "s3://prd-tnm/StagedProducts/Elevation/metadata/WESM.gpkg" ) start: str = "2000-12-01" end: str | None = None diff --git a/src/coincident/io/__init__.py b/src/coincident/io/__init__.py new file mode 100644 index 0000000..f7e8831 --- /dev/null +++ b/src/coincident/io/__init__.py @@ -0,0 +1,9 @@ +""" +coincident: Search and analysis of STV Precursor Coincident Datasets +""" + +from __future__ import annotations + +from coincident.io.sliderule import load_gedi, load_icesat2 + +__all__ = ["load_icesat2", "load_gedi"] diff --git a/src/coincident/io/sliderule.py b/src/coincident/io/sliderule.py new file mode 100644 index 0000000..4a419ea --- /dev/null +++ b/src/coincident/io/sliderule.py @@ -0,0 +1,70 @@ +""" +Read ICESat-2 & GEDI using Sliderule +""" + +from __future__ import annotations + +import warnings +from typing import Any + +import geopandas as gpd +import pandas as pd + +try: + from sliderule import gedi, icesat2, toregion +except ImportError: + warnings.warn( + "'sliderule' package not found. Install for GEDI & ICESat2 functionality: https://slideruleearth.io/web/rtd/getting_started/Install.html", + stacklevel=2, + ) +from coincident._utils import depends_on_optional + +# TODO: make these configurable? +default_params = { + "ats": 20.0, + "cnt": 5, + "len": 40.0, + "res": 20.0, + "maxi": 6, +} + + +@depends_on_optional("sliderule.gedi") +def load_gedi( + aoi: gpd.GeoDataFrame, + start_datetime: pd.Timestamp, + end_datetime: pd.Timestamp, + quality_flags: bool = True, + **kwargs: Any, +) -> gpd.GeoDataFrame: + # NOTE: allow passing private cluster? + gedi.init("slideruleearth.io") + params = default_params.copy() + if quality_flags: + params["degrade_flag"] = 0 + params["l2_quality_flag"] = 1 + params["poly"] = toregion(aoi)["poly"] + params["t0"] = start_datetime.tz_localize(None).isoformat() + params["t1"] = end_datetime.tz_localize(None).isoformat() + params.update(kwargs) + return gedi.gedi02ap(params) + + +@depends_on_optional("sliderule.icesat2") +def load_icesat2( + aoi: gpd.GeoDataFrame, + start_datetime: pd.Timestamp, + end_datetime: pd.Timestamp, + **kwargs: Any, +) -> gpd.GeoDataFrame: + # NOTE: allow passing private cluster? + # NOTE: deal with kwargs + icesat2.init("slideruleearth.io") + params = default_params.copy() + params["srt"] = icesat2.SRT_LAND + params["cnf"] = icesat2.CNF_SURFACE_HIGH + params["poly"] = toregion(aoi)["poly"] + params["t0"] = start_datetime.tz_localize(None).isoformat() + params["t1"] = end_datetime.tz_localize(None).isoformat() + params.update(kwargs) + return icesat2.atl06p(params) diff --git a/src/coincident/overlaps/__init__.py b/src/coincident/overlaps/__init__.py index 8abad92..f1453d5 100644 --- a/src/coincident/overlaps/__init__.py +++ b/src/coincident/overlaps/__init__.py @@ -71,7 +71,7 @@ def subset_by_temporal_overlap( buffered_start, buffered_end, closed="both" ) search_interval = gpd.pd.Interval(start_datetime, end_datetime, closed="both") - keep = results_intervals.overlaps(search_interval) + keep = results_intervals.overlaps(search_interval) # pylint: disable=no-member return gf.loc[keep, :] diff --git a/src/coincident/search/main.py b/src/coincident/search/main.py index 1694a63..6bf3096 100644 --- a/src/coincident/search/main.py +++ b/src/coincident/search/main.py @@ -115,7 +115,7 @@ def search( # Non-STAC Searches elif dataset.alias == "3dep": - gf = wesm.search_convex_hulls( + gf = wesm.search_bboxes( intersects=intersects, search_start=search_start, search_end=search_end, diff --git a/src/coincident/search/stac.py b/src/coincident/search/stac.py index d61c162..a5c5a9a 100644 --- a/src/coincident/search/stac.py +++ b/src/coincident/search/stac.py @@ -4,6 +4,7 @@ from __future__ import annotations +import warnings from typing import Any import geopandas as gpd @@ -12,16 +13,18 @@ import pystac_client import stac_geoparquet -# Only needed if planetary_computer SDK is optional dependency... -# try: -# import planetary_computer -# modifier = planetary_computer.sign_inplace -# except ModuleNotFoundError as e: -# modifier = None -# message = f'{str(e)}: for authenticated requests install plantary_computer package (https://github.com/microsoft/planetary-computer-sdk-for-python)' -# warnings.warn(message) +# Any import that requires auth to work will be optional +try: + import maxar_platform +except ImportError: + message = "'maxar-platform' package not found. Install for maxar functionality: https://pypi.org/project/maxar-platform/" + warnings.warn(message, stacklevel=2) -# NOTE: add function to keep only single thumbnail in assets from NASA CMR searches? +try: + import maxar_platform.discovery +except maxar_platform.session.NoSessionCredentials: + message = "Unable to authenticate with Maxar API. Please set MAXAR_API_KEY environment variable." + warnings.warn(message, stacklevel=2) def to_geopandas( @@ -69,8 +72,7 @@ def search( def configure_maxar_client(area_based_calc: bool = True) -> pystac_client.client.Client: - """ """ - import maxar_platform.discovery # automatically checks authentication + # automatically checks authentication client = maxar_platform.discovery.open_catalog(catalog="imagery") # Custom Maxar setting @@ -80,10 +82,8 @@ def configure_maxar_client(area_based_calc: bool = True) -> pystac_client.client def configure_stac_client(url: str) -> pystac_client.client.Client: - """ """ return pystac_client.Client.open(url=url) def configure_mspc_client(url: str) -> pystac_client.client.Client: - """ """ return pystac_client.Client.open(url=url, modifier=planetary_computer.sign_inplace) diff --git a/src/coincident/search/swath_polygons.csv b/src/coincident/search/swath_polygons.csv new file mode 100644 index 0000000..adcbd4a --- /dev/null +++ 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+AZ_Southwest_1_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_1_D23/spatial_metadata/USGS/USGS_AZ_Southwest_1_D23_SwathPoly.shp +AZ_Southwest_2_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_2_D23/spatial_metadata/USGS/USGS_AZ_Southwest_2_D23_SWATH_POLY.shp +AZ_Southwest_4_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_4_D23/spatial_metadata/USGS/USGS_AZ_Southwest_4_D23_Swath_Poly/USGS_AZ_Southwest_4_D23_Swath_Poly.shp +AK_NativeAKVillages_5_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_5_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_5_D22_Swath_Poly/USGS_AK_NativeAKVillages_5_D22_Swath_Poly.shp +AK_NativeAKVillages_6_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_6_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_6_D22_Swath_Polygon/USGS_AK_NativeAKVillages_6_D22_Swath_Polygon.shp +NV_USFSR4_1_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/NV_USFSR4_D23/NV_USFSR4_1_D23/spatial_metadata/USGS/USGS_NV_USFSR4_1_D23_SwathPoly/USGS_NV_USFSR4_1_D23_SwathPoly.shp +AR_NorthEast_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AR_NorthEast_D22/AR_NorthEast_1_D22/spatial_metadata/USGS/USGS_AR_NorthEast_1_D22_Swath_poly/USGS_AR_NorthEast_1_D22_Swath_Poly.shp diff --git a/src/coincident/search/wesm.py b/src/coincident/search/wesm.py index 682fdd7..5f69e91 100644 --- a/src/coincident/search/wesm.py +++ b/src/coincident/search/wesm.py @@ -5,24 +5,30 @@ from __future__ import annotations +from importlib import resources from typing import Any -import fsspec -import s3fs -from geopandas import GeoDataFrame, read_file, read_parquet -from pandas import Series, Timedelta, Timestamp +import pandas as pd +import pyogrio +from cloudpathlib import S3Client +from geopandas import GeoDataFrame, read_file +from pandas import Timedelta, Timestamp +from shapely.geometry import box from coincident.datasets import usgs from coincident.overlaps import subset_by_temporal_overlap -defaults = usgs.ThreeDEP() -wesm_gpkg_url = defaults.search +# Cloudpath-based S3 client +client = S3Client(no_sign_request=True) +# Geopandas S3 Client +pyogrio.set_gdal_config_options( + {"AWS_NO_SIGN_REQUEST": True, "GDAL_PAM_ENABLED": False} +) +swath_polygon_csv = resources.files("coincident.search") / "swath_polygons.csv" -def simplify_footprint(gf: GeoDataFrame, tolerance: float = 0.1) -> None: - """complexity of WESM footprints causes slow STAC searches""" - # parts of a simplified geometry will be no more than `tolerance` distance from the original - gf = gf.simplify(tolerance) +defaults = usgs.ThreeDEP() +wesm_gpkg_url = defaults.search def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame: @@ -52,6 +58,8 @@ def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame: "collect_end": "end_datetime", } gf = gf.rename(columns=name_map) + gf["start_datetime"] = pd.to_datetime(gf["start_datetime"]) + gf["end_datetime"] = pd.to_datetime(gf["end_datetime"]) duration = gf.end_datetime - gf.start_datetime gf["datetime"] = gf.start_datetime + duration / 2 gf["dayofyear"] = gf.datetime.dt.dayofyear @@ -60,20 +68,45 @@ def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame: return gf -def search_convex_hulls( - url: str = "https://github.com/uw-cryo/stv-aux-data/releases/download/v0.1/WESM-chulls.geoparquet", +def read_wesm_csv(url: str = wesm_gpkg_url) -> GeoDataFrame: + """ + Read WESM metadata from a remote CSV file. + + The CSV contains up to date metadata and a mapping of workunit to feature ID (FID) + in the main GPKG file. + + Parameters + ---------- + url : str, optional + The URL or file path to the WESM index file + + Returns + ------- + GeoDataFrame + A GeoDataFrame containing the metadata from the CSV file. + """ + # Cache CSV locally, so subsequent reads are faster + wesm_csv = client.CloudPath(url.replace(".gpkg", ".csv")) + df = pd.read_csv(wesm_csv) + df.index += 1 + df.index.name = "fid" + return df + + +def search_bboxes( + url: str = wesm_gpkg_url, intersects: GeoDataFrame | None = None, search_start: Timestamp | None = None, search_end: Timestamp | None = None, # **kwargs: dict[str, Any] | None, ) -> GeoDataFrame: """ - Search GeoParquet WESM index of convex hulls within a time range and spatial intersection. + Search WESM.gpkg bounding boxes for spatial and temporal overlap Parameters ---------- url : str, optional - URL to the GeoParquet file containing the convex hulls, by default "https://github.com/uw-cryo/stv-aux-data/releases/download/v0.1/WESM-chulls.geoparquet". + The URL or file path to the GeoPackage (GPKG) file. intersects : GeoDataFrame, optional A GeoDataFrame to spatially intersect with the convex hulls, by default None. search_start : Timestamp, optional @@ -86,10 +119,24 @@ def search_convex_hulls( GeoDataFrame A GeoDataFrame containing the convex hull geometries and FIDs in EPSG:4326 """ - with fsspec.open(url) as f: - gf = read_parquet(f) + # NOTE: much faster to JUST read bboxes, not full geometry or other columns + sql = "select * from rtree_WESM_geometry" + df = pyogrio.read_dataframe( + url, sql=sql + ) # , use_arrow=True... arrow probably doesn;t matter for <10000 rows? + + bboxes = df.apply(lambda x: box(x.minx, x.miny, x.maxx, x.maxy), axis=1) + gf = ( + GeoDataFrame(df, geometry=bboxes, crs="EPSG:4326") + .rename(columns={"id": "fid"}) + .set_index("fid") + ) + + df = read_wesm_csv(url) + gf = gf.merge(df, right_index=True, left_index=True) gf = stacify_column_names(gf) + if intersects is not None: gf = gf[gf.intersects(intersects.geometry.iloc[0])] @@ -122,6 +169,7 @@ def load_by_fid( modified by :meth:`stacify_column_names`. """ # Format SQL: # special case for (a) not (a,) + # Reading a remote WESM by specific FIDs is fast query = f"fid in ({fids[0]})" if len(fids) == 1 else f"fid in {*fids,}" gf = read_file( @@ -130,7 +178,7 @@ def load_by_fid( **kwargs, # mask=mask, # spatial subset intolerably slow for remote GPKG... # NOTE: worth additional dependencies for speed? - # Only faster I think if GPKG is local https://github.com/geopandas/pyogrio/issues/252 + # Only faster I think if GPKG is local & large https://github.com/geopandas/pyogrio/issues/252 # engine='pyogrio', # pyarrow=True, ) @@ -172,14 +220,16 @@ def swathtime_to_datetime( return stacify_column_names(gf) -def get_swath_polygons(row: Series) -> GeoDataFrame: +def get_swath_polygons( + workunit: str, +) -> GeoDataFrame: """ Retrieve swath polygons from a remote URL and convert swath time to datetime. Parameters ---------- - row : pd.Series - A pandas Series containing the project and workunit information needed to construct the URL. + fid : int + A pandas Series containing WESM feature ID (FID) Returns ------- @@ -188,23 +238,20 @@ def get_swath_polygons(row: Series) -> GeoDataFrame: Notes ----- - - Swath polygons only available for data collected after 2020 - - Usually takes ~10 to 60s to load one of these shapefiles + - Swath polygons not available for all workunits """ - s3 = s3fs.S3FileSystem(anon=True) - url = f"prd-tnm/StagedProducts/Elevation/metadata/{row.project}/{row.workunit}/spatial_metadata/USGS/**/*.shp" + # NOTE: this CSV comes from crawling WESM USGS/spatial_metadata in S3 bucket + df = pd.read_csv(swath_polygon_csv) + try: - files = s3.glob(url) - swath_poly_key = next(x for x in files if "swath" in x.lower()) - except StopIteration: - missing_link = f"http://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/metadata/{row.project}/{row.workunit}" - message = f"Unable to find swath polygon shapefile in {missing_link}" - raise FileNotFoundError(message) from None + url = df[df.workunit == workunit].swathpolygon_link.iloc[0] + except Exception as e: + message = f"No swath polygons found for workunit={workunit}" + raise ValueError(message) from e - uri = swath_poly_key.replace("prd-tnm", "https://prd-tnm.s3.amazonaws.com") - gf = read_file(uri) + # Actually read from S3! + gf = read_file(url) gf = swathtime_to_datetime(gf) - # Swath polygons likely have different CRS (EPSG:6350), so reproject return gf.to_crs("EPSG:4326") diff --git a/tests/test_search.py b/tests/test_search.py index 08f7747..a42d928 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -131,19 +131,13 @@ def test_wesm_search(aoi): dataset="3dep", intersects=aoi, ) - assert len(gf) == 3 + assert len(gf) == 5 # NOTE ~10s on wifi @network def test_get_swath_polygon(): - row = gpd.pd.Series( - dict( # noqa: C408 - project="CO_CameronPeakWildfire_2021_D21", - workunit="CO_CameronPkFire_1_2021", - ) - ) - gf = m.search.wesm.get_swath_polygons(row) + gf = m.search.wesm.get_swath_polygons("CO_CameronPkFire_1_2021") assert isinstance(gf, gpd.GeoDataFrame) assert len(gf) == 51 assert "start_datetime" in gf.columns @@ -152,8 +146,5 @@ def test_get_swath_polygon(): @network def test_swath_polygon_not_found(): - row = gpd.pd.Series(dict(project="AL_SWCentral_B22", workunit="AL_SWCentral_1_B22")) # noqa: C408 - with pytest.raises( - FileNotFoundError, match="Unable to find swath polygon shapefile" - ): - m.search.wesm.get_swath_polygons(row) + with pytest.raises(ValueError, match="No swath polygons found for workunit="): + m.search.wesm.get_swath_polygons("AL_SWCentral_1_B22")