diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 0000000..b499bb3 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,62 @@ +name: Bug report +description: Something is broken or behaves unexpectedly. +title: "[Bug]: " +labels: ["bug"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to file a bug. Please fill in what you can — + a minimal reproducible example is the single most helpful thing. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: What did you do, what did you expect, and what happened instead? + placeholder: I called KaguyaTC(...) and expected ..., but got ... + validations: + required: true + - type: textarea + id: reproduce + attributes: + label: Minimal reproducible example + description: The smallest snippet that triggers the bug. It will be rendered as Python. + render: python + placeholder: | + import astrofetch as af + + moondata = af.KaguyaTC(products=["dtm"], bbox=(-26.3, -50.6, -25.5, -49.7)) + next(iter(moondata)) + validations: + required: true + - type: textarea + id: traceback + attributes: + label: Traceback or error output + description: Paste the full traceback if there is one. It will be rendered as a code block. + render: shell + - type: input + id: version + attributes: + label: AstroFetch version + description: "Output of: python -c \"import astrofetch; print(astrofetch.__version__)\"" + placeholder: "0.1.0" + validations: + required: true + - type: input + id: environment + attributes: + label: Python and OS + description: Python version and operating system. + placeholder: "Python 3.12 on macOS 15" + validations: + required: true + - type: checkboxes + id: checks + attributes: + label: Before submitting + options: + - label: I searched existing issues and this is not a duplicate. + required: true + - label: The example above reproduces the problem on a clean environment. + required: false diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..d675436 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,8 @@ +blank_issues_enabled: false +contact_links: + - name: Questions and discussion + url: https://github.com/TechnicToms/AstroFetch/discussions + about: Ask a question or discuss an idea before filing an issue. + - name: Contributing guide + url: https://github.com/TechnicToms/AstroFetch/blob/main/CONTRIBUTING.md + about: How to set up, test, and open a pull request. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 0000000..5d56847 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,54 @@ +name: Feature request +description: Suggest an enhancement or new capability. +title: "[Feature]: " +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + Thanks for the idea! For anything substantial, it helps to discuss the + approach here before a large PR. Note the current phase in the + [roadmap](https://github.com/TechnicToms/AstroFetch/blob/main/AGENTS.md#roadmap). + - type: textarea + id: problem + attributes: + label: What problem does this solve? + description: The use case or pain point motivating the request. + placeholder: When training on lunar patches I need ... + validations: + required: true + - type: textarea + id: proposal + attributes: + label: Proposed solution + description: What would the API or behavior look like? A code sketch is welcome. + render: python + validations: + required: true + - type: textarea + id: alternatives + attributes: + label: Alternatives considered + description: Other approaches you weighed, and why this one. + - type: dropdown + id: scope + attributes: + label: Roughly which area? + options: + - Data path (STAC / COG / grid / cache) + - New instrument dataset + - New body module + - Transforms / samplers + - Docs / examples + - Other + validations: + required: true + - type: checkboxes + id: checks + attributes: + label: Before submitting + options: + - label: I searched existing issues and this is not a duplicate. + required: true + - label: I would be willing to help implement this. + required: false diff --git a/.github/ISSUE_TEMPLATE/new_dataset.yml b/.github/ISSUE_TEMPLATE/new_dataset.yml new file mode 100644 index 0000000..013bb6e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/new_dataset.yml @@ -0,0 +1,71 @@ +name: New dataset or body +description: Propose a new instrument dataset or a new planetary body module. +title: "[Data]: " +labels: ["dataset"] +body: + - type: markdown + attributes: + value: | + New instrument datasets and body modules are exactly the contributions + we want. AstroFetch's quantitative path needs Cloud Optimized GeoTIFFs + (COGs) discoverable via a STAC API. Rendered-only WMS/WMTS layers can be + added as a clearly-labeled non-quantitative source. See the "Adding a + new instrument dataset" section of + [CONTRIBUTING.md](https://github.com/TechnicToms/AstroFetch/blob/main/CONTRIBUTING.md). + - type: input + id: body + attributes: + label: Planetary body + placeholder: "Moon / Mars / ..." + validations: + required: true + - type: input + id: instrument + attributes: + label: Probe and instrument + placeholder: "LRO / LROC WAC" + validations: + required: true + - type: dropdown + id: source-type + attributes: + label: Data source type + options: + - STAC API with COG assets (quantitative — preferred) + - Standalone COG / mosaic (no STAC) + - WMS / WMTS rendered tiles (non-quantitative) + - Other / not sure + validations: + required: true + - type: textarea + id: source + attributes: + label: Where does the data live? + description: STAC catalog root and collection id, or the COG / service URL(s). + placeholder: | + STAC root: https://stac.astrogeology.usgs.gov/api/ + Collection: kaguya_terrain_camera_usgs_dtms + COG asset key(s): dtm, orthoimage + validations: + required: true + - type: textarea + id: products + attributes: + label: Products and units + description: Which products (bands/assets), their physical units, and any scale/offset or nodata quirks. + placeholder: "DTM (metres, nodata -32767); orthoimage (radiance, DN * scale)." + - type: input + id: projection + attributes: + label: Projection / CRS + description: Native CRS of the rasters, if known (equirectangular, polar stereographic, IAU code). + placeholder: "IAU_2015:30110 (equirectangular); polar items use stereographic" + - type: checkboxes + id: checks + attributes: + label: Before submitting + options: + - label: I confirmed the data is openly accessible (no login / paywall). + required: true + - label: I searched existing issues and this is not a duplicate. + required: true diff --git a/AGENTS.md b/AGENTS.md index 7127a8e..de0424e 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -19,7 +19,7 @@ src/astrofetch/ tiles.py # secondary rendered mode: USGS WMS, Moon Trek WMTS moon/ layers.py # layer registry (name -> STAC collection + read config) + Body/Probe/Instrument catalog (MOON) - datasets.py # instrument dataset classes (InstrumentDataset, KaguyaTC, LROCWAC) + IntersectionDataset + datasets.py # instrument dataset classes (InstrumentDataset, KaguyaTC, KaguyaTCImagery) + IntersectionDataset tests/ unit/ # network fully mocked, runs in CI live/ # hits real endpoints, manual trigger only @@ -39,7 +39,7 @@ tests/ - Python 3.10+, src layout, uv-managed: `uv sync` installs the package plus the `dev` group (or `pip install -e .` for the package alone). - Lint: `uv run ruff check` and `uv run ruff format` -- Types: `uv run mypy src` (config in pyproject.toml; keep new code typed) +- Types: `uv run ty check` (config in pyproject.toml; keep new code typed) - Tests: `uv run pytest` (unit only; the `live` marker is deselected by default). Live endpoint tests: `uv run pytest tests/live -m live` (never run these in CI or in loops; they hit real government servers). - Docs: `uv run --group docs mkdocs serve` @@ -93,12 +93,12 @@ Widely-adopted defaults that keep the codebase consistent. When in doubt, match Check the current phase before proposing work; for example, do not build Phase 2 datasets and transforms while Phase 1 (STAC sampler MVP) is incomplete. Everything is a thin layer above existing archive tooling, never a mirror of any archive. -**Current phase: Phase 0 (scaffolding) — the per-instrument API surface is real and stable; the data path returns synthetic placeholder tensors until the Phase 1 STAC sampler lands.** +**Current phase: Phase 1 (STAC sampler). Phase 0 scaffolding is complete. `InstrumentDataset.read` now fetches the real COGs covering a window from the USGS ARD catalog, reprojects them onto a common geographic grid, applies scale/offset, mosaics overlapping items, and caches the result — no more synthetic tensors.** ### Phase 0: Scaffolding (weekend 1) - Repo setup: pyproject.toml (hatchling or setuptools), src layout, MIT or Apache-2.0 license, CITATION.cff. -- CI: GitHub Actions running ruff, mypy (lenient at first), pytest on 3.10 through 3.12. +- CI: GitHub Actions running ruff, ruff format, `ty` type checking, and pytest on 3.10 through 3.14. - Testing policy established early: unit tests mock all network calls; a separate, manually triggered "live" test suite hits real endpoints. - Placeholder docs (mkdocs-material) and a README with the one-line pitch and the target API sketch. @@ -111,23 +111,23 @@ The single most important deliverable. Everything else builds on it. - `astrofetch.data.stac`: query the USGS ARD catalog root with pystac-client, filter by collection and bbox. - `astrofetch.data.raster`: windowed COG reads via rasterio, honoring scale/offset to return physical values, resampling to a requested resolution. - `astrofetch.data.grid`: define a common target grid (equirectangular, IAU 2015 Moon), reproject and stack layers into a (C, H, W) float tensor with a per-channel metadata record. -- The sampler plugs into `InstrumentDataset.read(bbox)`, replacing the Phase 0 synthetic placeholder. Public API target (already the shipped Phase 0 surface): +- The sampler plugs into `InstrumentDataset.read(bbox)`, replacing the Phase 0 synthetic placeholder. Shipped public API: ```python import astrofetch as af -bbox = (-60.0, 5.0, -55.0, 10.0) -moondata = af.KaguyaTC(products=["dtm"], bbox=bbox, resolution=100) & af.LROCWAC( +bbox = (-26.3, -50.6, -25.5, -49.7) # a Kaguya TC USGS DTM footprint +moondata = af.KaguyaTC(products=["dtm"], bbox=bbox, resolution=100) & af.KaguyaTCImagery( bbox=bbox, resolution=100 ) sample = next(iter(moondata)) sample["image"] # torch.Tensor (C, H, W), physical values sample["mask"] # torch.BoolTensor (C, H, W), validity (nodata gaps) -sample["layers"] # ["kaguya_tc_dtm", "lroc_wac"], plus bbox/crs/resolution +sample["layers"] # ["kaguya_tc_dtm", "kaguya_tc_image"], plus bbox/crs/resolution ``` - Local disk cache keyed by (collection, item, window, resolution), transparent and clearable. -Exit criteria: the quickstart notebook fetches a Reiner Gamma patch with two layers and plots it, end to end, on a clean machine. +Exit criteria: `KaguyaTC(products=["dtm", "ortho"], bbox=...)` fetches a real, coregistered two-layer patch from the USGS ARD catalog on a clean machine (covered by `tests/live`). A plotting quickstart notebook is a nice-to-have follow-up. ### Phase 2: Datasets and transforms (2 to 3 weekends) @@ -135,6 +135,7 @@ Exit criteria: the quickstart notebook fetches a Reiner Gamma patch with two lay - Samplers that respect spatial autocorrelation for train/val/test splits (block splitting, not random pixels). - Transforms: per-channel normalization stats, nodata masking, polar/equatorial projection handling made explicit. - Secondary access mode behind the same interface: WMS/WMTS rendered mode, clearly labeled non-quantitative. +- LRO WAC global mosaic: the USGS ARD STAC catalog has no LRO WAC collection, so add it here from a non-STAC source (a public COG mosaic or WMS/WMTS), behind the same instrument-dataset interface as a new `LROCWAC` class. Exit criteria: `DataLoader` trains a toy model on random lunar patches without custom user code. diff --git a/CITATION.cff b/CITATION.cff index a840b15..314cdd9 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -8,6 +8,8 @@ type: software authors: - family-names: Sander given-names: Tom + - family-names: Arnaut + given-names: Mirza license: Apache-2.0 version: 0.1.0 date-released: "2026-07-08" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..7f820f6 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,109 @@ +# Contributing to AstroFetch + +Thanks for your interest in AstroFetch — a PyTorch-friendly, ML-ready gateway to +planetary science data. Contributions are welcome, and **new body modules (Mars +first) and new instrument datasets are especially wanted**. + +This file is the quick-start for contributors. The design rules, architecture, +and phased roadmap live in [`AGENTS.md`](AGENTS.md) — read it before a +non-trivial change; it is the source of truth for how the codebase is meant to +fit together. + +## Ways to contribute + +- **Report a bug** or **request a feature** with the [issue templates](https://github.com/TechnicToms/AstroFetch/issues/new/choose). +- **Add an instrument dataset** (a new STAC-backed sensor) or a **new body module**. +- **Improve docs, examples, or tests.** +- Not sure where to start? Open a discussion issue first — for anything + substantial, please float the idea before writing a large PR. + +## Development setup + +AstroFetch uses [uv](https://docs.astral.sh/uv/) for everything. You do not need +to manage a virtualenv by hand. + +```bash +git clone https://github.com/TechnicToms/AstroFetch.git +cd AstroFetch +uv sync # create the venv and install the package + dev/test groups +``` + +Common commands: + +```bash +uv run pytest # unit tests only — no network (the CI default) +uv run ruff check # lint +uv run ruff format # format (the single source of truth for layout) +uv run ty check # type check +uv run --group docs mkdocs serve # preview the docs site at localhost:8000 +``` + +Live endpoint tests hit real government servers and are **deselected by +default**. Run them deliberately, one at a time, when verifying an endpoint — +never in a loop: + +```bash +uv run pytest tests/live -m live +``` + +## Making a change + +1. **Branch** off `main` (`git switch -c my-change`). +2. **Write the code and its tests together.** Every new public function needs a + NumPy-style docstring with a runnable example and at least one unit test. +3. **Keep unit tests network-free.** Mock STAC responses and read small local + GeoTIFFs; never point a unit test at a live service. If you add or change an + endpoint or query, add or update the corresponding fixture. +4. **Run the full local gate before pushing:** + ```bash + uv run ruff check && uv run ruff format --check && uv run ty check && uv run pytest + ``` +5. **Open a PR** into `main` with a clear description. If you reimplemented + anything instead of wrapping existing archive tooling, justify it there + (see design rule 1 in `AGENTS.md`). + +## Coding conventions + +These are enforced in CI; the short version: + +- **Formatting is automated.** `ruff format` owns layout, quotes, and line + length — do not hand-align or fight it. Run it before every commit. +- **Imports** are sorted and grouped (stdlib, third-party, first-party) by + ruff's isort rules; no wildcard or unused imports. +- **Type hints** on every public function; keep `ty check` clean. +- **Naming** follows PEP 8 (`snake_case`, `PascalCase`, `UPPER_SNAKE_CASE`). +- **Conventional commits** (`feat:`, `fix:`, `docs:`, `test:`, `refactor:`, + `chore:`). +- **No print statements** in library code; use the `astrofetch` logger. Raise + `EndpointError` for remote failures and `ValueError` for bad user input. + +Non-negotiable architecture rules (endpoint URLs live only in +`data/endpoints.py`, the quantitative COG path and rendered tile path never mix, +the cache is disposable, and so on) are spelled out in `AGENTS.md` — please skim +them so review can focus on the substance of your change. + +## Adding a new instrument dataset + +The typical shape of such a change: + +1. Add the dataset class in `src/astrofetch/moon/datasets.py` (subclass + `InstrumentDataset`, declare its `probe`, `instrument`, STAC `collection`, + and `all_products` map of product → `Product(layer, asset)`). +2. Register its layers and wire it into the `MOON` catalog in + `src/astrofetch/moon/layers.py`. +3. Export it from `src/astrofetch/moon/__init__.py` and `astrofetch/__init__.py`. +4. Add unit tests (mocked reads) and, if useful, a single live check. + +A new **body** (e.g. Mars) is a new sibling module under `src/astrofetch/`, not +edits scattered through the Moon code — the body-agnostic `data/` layer is +reused as-is. + +## Code of conduct + +Be kind, be constructive, assume good faith. Planetary science is a small, +collaborative community; let's keep it welcoming. + +## License + +By contributing, you agree that your contributions are licensed under the +project's [Apache 2.0](LICENSE) license. diff --git a/README.md b/README.md index 2503eee..bd5c1a9 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,7 @@ +

+ AstroFetch — Planetary Datasets for PyTorch +

+ # AstroFetch PyTorch-friendly, ML-ready access to planetary science data, starting with the Moon. @@ -14,9 +18,9 @@ rehosted, everything is fetched on demand and cached. It is, roughly, what [TorchGeo](https://github.com/microsoft/torchgeo) is for Earth observation, pointed at the Moon (and Mars next). -> **Status: Phase 0 scaffolding.** The API surface is real and stable; the data path -> currently returns synthetic placeholder tensors until the STAC sampler lands in Phase 1. -> See the [roadmap](#roadmap). +> **Status: Phase 1 (STAC sampler).** The data path is real: a request fetches the +> Cloud Optimized GeoTIFFs covering the window from the USGS ARD catalog, reprojects +> them onto a common grid, and returns physical values. See the [roadmap](#roadmap). ## Install @@ -32,16 +36,26 @@ channels over the overlapping region: ```python import astrofetch as af -bbox = (-60.0, 5.0, -55.0, 10.0) # west, south, east, north (degrees) +bbox = (-26.3, -50.6, -25.5, -49.7) # west, south, east, north (degrees) -moondata = af.KaguyaTC(products=["dtm"], bbox=bbox, resolution=100) & af.LROCWAC( +moondata = af.KaguyaTC(products=["dtm"], bbox=bbox, resolution=100) & af.KaguyaTCImagery( bbox=bbox, resolution=100 ) for sample in moondata: sample["image"] # torch.Tensor (C, H, W), one channel per layer sample["mask"] # torch.BoolTensor (C, H, W), validity (nodata gaps) - sample["layers"] # ["kaguya_tc_dtm", "lroc_wac"], plus bbox/crs/resolution + sample["layers"] # ["kaguya_tc_dtm", "kaguya_tc_image"], plus bbox/crs/resolution +``` + +Each dataset is **map-style**: index it (`moondata[0]`), take its `len()`, and +shuffle or split it like any `torch` dataset. `patch_size` sets the output size — +images are `(C, patch_size, patch_size)`, default 256 — and `length` sets how +many random patches make up one epoch: + +```python +moondata = af.KaguyaTC(products=["dtm"], bbox=bbox, patch_size=128, length=500, seed=0) +moondata[0]["image"].shape # torch.Size([1, 128, 128]); moondata[i] is reproducible ``` It plugs directly into a PyTorch training loop — samples are plain dicts, so @@ -65,7 +79,7 @@ for probe in MOON.probes.values(): for instrument in probe.instruments.values(): print(probe.name, "/", instrument.name, "->", sorted(instrument.products)) -MOON.probes["lro"].instruments["lroc_wac"].dataset # +MOON.probes["kaguya"].instruments["tc_imagery"].dataset # KaguyaTCImagery ``` ## Design principles @@ -87,9 +101,9 @@ MOON.probes["lro"].instruments["lroc_wac"].dataset # =1.24", "torch>=2.0"] +dependencies = [ + "numpy>=1.24", + "torch>=2.0", + "pystac-client>=0.7", + "rasterio>=1.3", +] [project.urls] Homepage = "https://github.com/TechnicToms/AstroFetch" diff --git a/src/astrofetch/__init__.py b/src/astrofetch/__init__.py index 2d36259..e93d6e4 100644 --- a/src/astrofetch/__init__.py +++ b/src/astrofetch/__init__.py @@ -6,8 +6,15 @@ """ from astrofetch import moon -from astrofetch.moon import LROCWAC, MOON, IntersectionDataset, KaguyaTC +from astrofetch.moon import MOON, IntersectionDataset, KaguyaTC, KaguyaTCImagery __version__ = "0.1.0" -__all__ = ["MOON", "IntersectionDataset", "KaguyaTC", "LROCWAC", "moon", "__version__"] +__all__ = [ + "MOON", + "IntersectionDataset", + "KaguyaTC", + "KaguyaTCImagery", + "moon", + "__version__", +] diff --git a/src/astrofetch/data/__init__.py b/src/astrofetch/data/__init__.py new file mode 100644 index 0000000..2ccc52e --- /dev/null +++ b/src/astrofetch/data/__init__.py @@ -0,0 +1,6 @@ +"""Body-agnostic data access: STAC search, windowed COG reads, gridding, cache. + +Moon-specific wiring (which collection backs which product) lives under +``astrofetch/moon``; everything here is body-neutral and reused as new bodies +are added. +""" diff --git a/src/astrofetch/data/cache.py b/src/astrofetch/data/cache.py new file mode 100644 index 0000000..6e545a9 --- /dev/null +++ b/src/astrofetch/data/cache.py @@ -0,0 +1,73 @@ +"""Disposable local cache for reprojected layer windows. + +Never load-bearing (AGENTS rule 4): every entry is re-fetchable from the archive +and safe to delete at any time. A key is a hash of the layer id and the exact +target grid, so an identical request skips the network on a warm cache while any +change to bbox, size, or CRS misses cleanly. +""" + +from __future__ import annotations + +import hashlib +import os +from pathlib import Path + +import numpy as np + +from astrofetch.data.grid import TargetGrid + + +def default_cache_dir() -> Path: + """Cache root: ``$ASTROFETCH_CACHE`` if set, else an XDG cache subdir.""" + override = os.environ.get("ASTROFETCH_CACHE") + if override: + return Path(override) + base = os.environ.get("XDG_CACHE_HOME", str(Path.home() / ".cache")) + return Path(base) / "astrofetch" + + +class WindowCache: + """Filesystem cache of ``(image, mask)`` arrays keyed by layer and grid. + + Args: + root: cache directory; defaults to :func:`default_cache_dir`. + """ + + def __init__(self, root: str | Path | None = None) -> None: + self.root = Path(root) if root is not None else default_cache_dir() + + def _key(self, layer: str, grid: TargetGrid) -> str: + west, south, east, north = grid.bbox + raw = ( + f"{layer}|{grid.crs}|{grid.width}x{grid.height}" + f"|{west:.8f},{south:.8f},{east:.8f},{north:.8f}" + ) + return hashlib.sha256(raw.encode()).hexdigest()[:16] + + def _path(self, key: str) -> Path: + return self.root / f"{key}.npz" + + def get(self, layer: str, grid: TargetGrid) -> tuple[np.ndarray, np.ndarray] | None: + """Return the cached ``(image, mask)`` for ``layer``/``grid``, or None.""" + path = self._path(self._key(layer, grid)) + if not path.exists(): + return None + with np.load(path) as data: + return data["image"], data["mask"] + + def put(self, layer: str, grid: TargetGrid, image: np.ndarray, mask: np.ndarray) -> None: + """Store ``(image, mask)`` for ``layer``/``grid``, replacing any entry.""" + self.root.mkdir(parents=True, exist_ok=True) + path = self._path(self._key(layer, grid)) + # Write to a temp file then atomically replace, so an interrupted write + # never leaves a half-written cache entry that a later read would trust. + tmp = self.root / f"{path.stem}.{os.getpid()}.tmp.npz" + np.savez(tmp, image=image, mask=mask) + os.replace(tmp, path) + + def clear(self) -> None: + """Delete every cache entry. The cache is disposable; this is always safe.""" + if not self.root.exists(): + return + for entry in self.root.glob("*.npz"): + entry.unlink() diff --git a/src/astrofetch/data/endpoints.py b/src/astrofetch/data/endpoints.py new file mode 100644 index 0000000..e7156a3 --- /dev/null +++ b/src/astrofetch/data/endpoints.py @@ -0,0 +1,11 @@ +"""External archive endpoints — the ONLY place URLs live (AGENTS rule 2). + +Every COG asset href is discovered through the STAC API at request time, so the +API root below is the single external URL AstroFetch hard-codes. When a service +moves (as QuickMap's domain once did), this is the only file to change. +""" + +from __future__ import annotations + +STAC_API_ROOT = "https://stac.astrogeology.usgs.gov/api/" +"""USGS Astrogeology Analysis Ready Data STAC API root (pystac-client entry).""" diff --git a/src/astrofetch/data/grid.py b/src/astrofetch/data/grid.py new file mode 100644 index 0000000..af8e840 --- /dev/null +++ b/src/astrofetch/data/grid.py @@ -0,0 +1,78 @@ +"""Target grid definition and unit helpers (body-agnostic). + +A :class:`TargetGrid` is the common raster every layer is reprojected onto so +that channels coregister exactly. Phase 1 uses a plate-carree (equirectangular) +grid in the IAU 2015 Moon geographic CRS; ``data/raster.py`` reprojects each +source COG — whatever its native projection, equirectangular or polar +stereographic — onto it. Keeping the grid geographic means the ``bbox`` a caller +passes and the ``crs`` a sample advertises are the same coordinate system. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +from affine import Affine +from rasterio.transform import from_bounds + +BBox = tuple[float, float, float, float] +"""(west, south, east, north) in degrees.""" + +GEOGRAPHIC_CRS = "IAU_2015:30100" +"""Ocentric IAU 2015 Moon geographic CRS (degrees); the target grid CRS.""" + +MOON_RADIUS_M = 1737400.0 +"""IAU 2015 Moon sphere radius in metres (IAU code 30100; see any ARD COG WKT).""" + + +@dataclass(frozen=True) +class TargetGrid: + """A fixed output raster: a bbox rasterized to ``width`` x ``height`` pixels. + + Args: + bbox: (west, south, east, north) in degrees, west < east, south < north. + width: output width in pixels. + height: output height in pixels. + crs: grid CRS; defaults to the IAU 2015 Moon geographic CRS. + """ + + bbox: BBox + width: int + height: int + crs: str = GEOGRAPHIC_CRS + + def __post_init__(self) -> None: + west, south, east, north = self.bbox + if not (west < east and south < north): + raise ValueError(f"invalid bbox (west, south, east, north): {self.bbox}") + if self.width <= 0 or self.height <= 0: + raise ValueError(f"grid size must be positive, got {self.width}x{self.height}") + + @property + def transform(self) -> Affine: + """Affine mapping pixel (col, row) to grid CRS coordinates.""" + west, south, east, north = self.bbox + return from_bounds(west, south, east, north, self.width, self.height) + + +def meters_to_degrees(meters: float, latitude: float) -> tuple[float, float]: + """Approximate the (dlon, dlat) degree span of a ground distance in metres. + + Spherical arc length on the IAU 2015 Moon sphere, used only to size a + sampling window from ``patch_size * resolution``; the exact pixel geometry + is fixed later by :class:`TargetGrid`, so this need only be close. Longitude + degrees shrink with ``cos(latitude)``. + + Args: + meters: ground distance in metres. + latitude: latitude in degrees where the span is measured. + + Returns: + (dlon, dlat) span in degrees. + """ + dlat = math.degrees(meters / MOON_RADIUS_M) + coslat = math.cos(math.radians(latitude)) + # Guard the poles, where a bounded ground distance spans unbounded longitude. + dlon = dlat / coslat if coslat > 1e-6 else 360.0 + return dlon, dlat diff --git a/src/astrofetch/data/raster.py b/src/astrofetch/data/raster.py new file mode 100644 index 0000000..a9e44e8 --- /dev/null +++ b/src/astrofetch/data/raster.py @@ -0,0 +1,77 @@ +"""Windowed COG reads reprojected onto a :class:`TargetGrid` (rasterio). + +Reads only the region of a Cloud Optimized GeoTIFF that covers the grid — via +HTTP range requests against the right overview level — reprojects it onto the +grid's CRS and resolution, applies the band's scale/offset to recover physical +values, and returns the data with a boolean validity mask (``False`` where the +source had nodata or simply does not cover the grid). + +All map-projection and resampling math is delegated to rasterio/GDAL (AGENTS +rule 1): this module wires it up, it does not reimplement it. +""" + +from __future__ import annotations + +import numpy as np +import rasterio +from rasterio.enums import Resampling +from rasterio.errors import RasterioIOError +from rasterio.vrt import WarpedVRT + +from astrofetch.data.grid import TargetGrid +from astrofetch.errors import EndpointError + +_FILL = np.float32(0.0) +"""Value written into invalid pixels; always paired with a False mask entry.""" + + +def read_window( + href: str, + grid: TargetGrid, + band: int = 1, + resampling: Resampling = Resampling.bilinear, +) -> tuple[np.ndarray, np.ndarray]: + """Read one COG band, reprojected onto ``grid``, in physical units. + + Args: + href: COG URL or local path. + grid: output grid; defines CRS, size, and extent. + band: 1-based band index to read. + resampling: resampling used when reprojecting to the grid. + + Returns: + ``(image, mask)`` where ``image`` is a ``(grid.height, grid.width)`` + float32 array of physical values (scale/offset applied) with invalid + pixels set to 0, and ``mask`` is a same-shaped bool array, ``True`` + where the pixel is valid. + + Raises: + EndpointError: the COG could not be opened or read. + """ + try: + with rasterio.open(href) as src: + nodata = src.nodata + scale = float(src.scales[band - 1]) + offset = float(src.offsets[band - 1]) + with WarpedVRT( + src, + crs=grid.crs, + transform=grid.transform, + width=grid.width, + height=grid.height, + resampling=resampling, + src_nodata=nodata, + nodata=nodata, + ) as vrt: + raw = vrt.read(band) + except RasterioIOError as exc: + raise EndpointError(href, f"could not read COG: {exc}") from exc + + if nodata is not None: + mask = raw != nodata + else: + mask = np.ones(raw.shape, dtype=bool) + + image = raw.astype(np.float32) * np.float32(scale) + np.float32(offset) + image[~mask] = _FILL + return image, mask diff --git a/src/astrofetch/data/stac.py b/src/astrofetch/data/stac.py new file mode 100644 index 0000000..2a444d0 --- /dev/null +++ b/src/astrofetch/data/stac.py @@ -0,0 +1,85 @@ +"""STAC search against the USGS ARD catalog (pystac-client), politely. + +Wraps pystac-client with a retrying, backed-off HTTP session — AGENTS rule 5: +never hammer archive servers — and normalizes failures into :class:`EndpointError` +so callers never have to know pystac-client's exception surface. A single client +is reused across searches (one polite connection pool, not one per request). +""" + +from __future__ import annotations + +from functools import cache + +from pystac_client import Client +from pystac_client.exceptions import APIError +from pystac_client.stac_api_io import StacApiIO +from urllib3.util.retry import Retry + +from astrofetch.data.endpoints import STAC_API_ROOT +from astrofetch.data.grid import BBox +from astrofetch.errors import EndpointError + +_TIMEOUT_S = 30 +"""Per-request timeout; a stuck archive should fail, not hang a dataloader.""" + +_RETRY = Retry( + total=4, + backoff_factor=0.5, + status_forcelist=(429, 500, 502, 503, 504), + allowed_methods=frozenset({"GET", "POST"}), +) +"""Exponential backoff on rate-limit and transient server errors.""" + + +@cache +def catalog(root: str = STAC_API_ROOT) -> Client: + """Open (and memoize) the STAC API client for ``root``. + + Raises: + EndpointError: the catalog root could not be opened. + """ + try: + return Client.open(root, stac_io=StacApiIO(max_retries=_RETRY, timeout=_TIMEOUT_S)) + except APIError as exc: + raise EndpointError(root, f"could not open STAC catalog: {exc}") from exc + + +def find_asset_hrefs( + collection: str, + asset_key: str, + bbox: BBox, + max_items: int = 20, + root: str = STAC_API_ROOT, +) -> list[str]: + """Return hrefs of one asset across items of ``collection`` intersecting ``bbox``. + + Args: + collection: STAC collection id to search. + asset_key: asset key to pull from each matching item, e.g. ``"dtm"``. + bbox: (west, south, east, north) in degrees. + max_items: cap on items returned; bounds the request volume per read. + root: STAC API root; defaults to the configured USGS ARD catalog. + + Returns: + Asset hrefs, one per intersecting item (possibly empty if none overlap). + + Raises: + EndpointError: the search failed or a matched item lacks ``asset_key``. + """ + client = catalog(root) + try: + search = client.search(collections=[collection], bbox=list(bbox), max_items=max_items) + items = list(search.items()) + except APIError as exc: + raise EndpointError(collection, f"STAC search failed: {exc}") from exc + + hrefs = [] + for item in items: + asset = item.assets.get(asset_key) + if asset is None: + raise EndpointError( + collection, + f"item {item.id} has no asset {asset_key!r}; available: {sorted(item.assets)}", + ) + hrefs.append(asset.href) + return hrefs diff --git a/src/astrofetch/errors.py b/src/astrofetch/errors.py new file mode 100644 index 0000000..6afafb0 --- /dev/null +++ b/src/astrofetch/errors.py @@ -0,0 +1,26 @@ +"""AstroFetch exception hierarchy. + +Public errors live here so callers can ``except astrofetch.errors.EndpointError`` +without importing internal modules. +""" + +from __future__ import annotations + + +class AstroFetchError(Exception): + """Base class for every error AstroFetch raises on purpose.""" + + +class EndpointError(AstroFetchError): + """A remote archive endpoint failed or returned something unusable. + + Carries the offending endpoint (a collection id, URL, or asset href) and + always points back at ``astrofetch/data/endpoints.py`` — the single place + external URLs are configured — so a moved or flaky service is easy to trace. + """ + + def __init__(self, endpoint: str, message: str) -> None: + self.endpoint = endpoint + super().__init__( + f"{endpoint}: {message} (endpoint URLs are configured in astrofetch/data/endpoints.py)" + ) diff --git a/src/astrofetch/moon/__init__.py b/src/astrofetch/moon/__init__.py index 80be111..567d295 100644 --- a/src/astrofetch/moon/__init__.py +++ b/src/astrofetch/moon/__init__.py @@ -1,10 +1,10 @@ """Lunar data access: instrument datasets, layer registry, discovery catalog.""" from astrofetch.moon.datasets import ( - LROCWAC, InstrumentDataset, IntersectionDataset, KaguyaTC, + KaguyaTCImagery, ) from astrofetch.moon.layers import LAYERS, MOON, Body, Instrument, LayerSpec, Probe @@ -16,7 +16,7 @@ "InstrumentDataset", "IntersectionDataset", "KaguyaTC", - "LROCWAC", + "KaguyaTCImagery", "LayerSpec", "Probe", ] diff --git a/src/astrofetch/moon/datasets.py b/src/astrofetch/moon/datasets.py index 0283852..850244a 100644 --- a/src/astrofetch/moon/datasets.py +++ b/src/astrofetch/moon/datasets.py @@ -1,31 +1,50 @@ """Torch dataset classes over lunar instrument data. -Phase 0 scaffolding: the API surface is real, the data path is a placeholder. -Instrument datasets will be backed by the STAC sampler (Phase 1); until then -``read`` yields correctly-shaped random tensors so the training loop contract — -``for sample in KaguyaTC(...) & LROCWAC(...)`` — can be exercised end to end. +Each instrument dataset samples random windows inside its ``bbox`` and, for each +window, reads its product COGs from the USGS ARD STAC catalog, reprojects them +onto a common target grid, and stacks them into a coregistered ``(C, H, W)`` +tensor. Combine instruments with ``&`` to stack their channels over the region +they share. + +The Phase 1 read path is real: :mod:`astrofetch.data.stac` finds the COG assets +covering a window, :mod:`astrofetch.data.raster` reprojects and reads them, and +:class:`astrofetch.data.cache.WindowCache` memoizes the result on disk. """ from __future__ import annotations from collections.abc import Iterator -from typing import ClassVar +from typing import ClassVar, NamedTuple +import numpy as np import torch -from torch.utils.data import IterableDataset +from torch.utils.data import Dataset + +from astrofetch.data import raster, stac +from astrofetch.data.cache import WindowCache +from astrofetch.data.grid import GEOGRAPHIC_CRS, TargetGrid, meters_to_degrees BBox = tuple[float, float, float, float] """(west, south, east, north) in degrees, IAU 2015 Moon.""" -CRS = "IAU_2015:30100" -"""Ocentric IAU 2015 Moon CRS carried by every sample.""" +CRS = GEOGRAPHIC_CRS +"""Ocentric IAU 2015 Moon geographic CRS carried by every sample.""" + + +class Product(NamedTuple): + """One user-facing product: its sample-dict layer id and its STAC asset key.""" + layer: str + asset: str -class _WindowedDataset(IterableDataset): - """Shared sampling loop: draw random windows, delegate to ``read``. + +class _WindowedDataset(Dataset[dict]): + """Map-style dataset: index ``i`` deterministically samples one window. Subclasses set ``bbox``, ``resolution``, ``patch_size``, ``length``, - ``seed``, and ``layers``, and implement ``read``. + ``seed``, and ``layers``, and implement ``read``. ``dataset[i]`` and + iteration both work, and because each index maps to a fixed window the + dataset supports ``DataLoader`` shuffling, samplers, and ``random_split``. """ bbox: BBox @@ -34,8 +53,9 @@ class _WindowedDataset(IterableDataset): length: int seed: int | None layers: list[str] + _cached_seed_base: int | None = None - def read(self, bbox: BBox, generator: torch.Generator | None = None) -> dict: + def read(self, bbox: BBox) -> dict: """Read one window as a sample dict. Returns: @@ -44,12 +64,16 @@ def read(self, bbox: BBox, generator: torch.Generator | None = None) -> dict: """ raise NotImplementedError + def __getitem__(self, index: int) -> dict: + if index < 0: + index += self.length + if not 0 <= index < self.length: + raise IndexError(f"index out of range for length {self.length}") + return self.read(self._sample_bbox(index)) + def __iter__(self) -> Iterator[dict]: - generator = torch.Generator() - if self.seed is not None: - generator.manual_seed(self.seed) - for _ in range(self.length): - yield self.read(self._sample_bbox(generator), generator) + for index in range(self.length): + yield self[index] def __len__(self) -> int: return self.length @@ -57,38 +81,58 @@ def __len__(self) -> int: def __and__(self, other: _WindowedDataset) -> IntersectionDataset: return IntersectionDataset(self, other) - def _sample_bbox(self, generator: torch.Generator) -> BBox: + @property + def _seed_base(self) -> int: + # Fixed per instance: an explicit seed makes samples reproducible across + # instances; without one, each instance still gives a stable dataset[i]. + if self._cached_seed_base is None: + if self.seed is not None: + self._cached_seed_base = self.seed + else: + self._cached_seed_base = int(torch.randint(0, 2**31 - 1, (1,)).item()) + return self._cached_seed_base + + def _sample_bbox(self, index: int) -> BBox: + generator = torch.Generator() + generator.manual_seed((self._seed_base * 1_000_003 + index) % (2**63 - 1)) west, south, east, north = self.bbox + span_lon, span_lat = east - west, north - south + # Window ground size from patch_size * resolution, converted to degrees + # at the region's centre latitude; clamp to the bbox so it stays inside. + center_lat = (south + north) / 2.0 + win_lon, win_lat = meters_to_degrees(self.patch_size * self.resolution, center_lat) + win_lon, win_lat = min(win_lon, span_lon), min(win_lat, span_lat) u, v = torch.rand(2, generator=generator).tolist() - # Placeholder: a degenerate point bbox; Phase 1 sizes the window from - # patch_size * resolution on the target grid. - lon = west + u * (east - west) - lat = south + v * (north - south) - return (lon, lat, lon, lat) + west0 = west + u * (span_lon - win_lon) + south0 = south + v * (span_lat - win_lat) + return (west0, south0, west0 + win_lon, south0 + win_lat) class InstrumentDataset(_WindowedDataset): - """Iterable dataset of patches from a single instrument. + """Map-style dataset of patches from a single instrument. - Samples random bounding boxes within ``bbox`` and yields sample dicts: - ``image`` is a (C, H, W) float tensor with one channel per requested - product, ``mask`` a same-shaped bool validity tensor (orbital swaths do - not cover everything), plus ``layers``/``bbox``/``crs``/``resolution`` - provenance. Combine instruments with ``&`` to stack their channels over - the overlapping region. + Each index deterministically samples a bounding box within ``bbox`` and + returns a sample dict: ``image`` is a (C, H, W) float tensor with one + channel per requested product (physical values) where ``H = W = + patch_size``, ``mask`` a same-shaped bool validity tensor (orbital swaths + do not cover everything), plus ``layers``/``bbox``/``crs``/``resolution`` + provenance. Combine instruments with ``&`` to stack their channels over the + overlapping region. Args: products: product names to stack, e.g. ``["dtm"]``; defaults to all products the instrument offers. bbox: region to sample from as (west, south, east, north) degrees. - resolution: target resolution in meters per pixel. + resolution: target resolution in metres per pixel. patch_size: output height and width in pixels. length: number of patches per epoch. seed: RNG seed for reproducible sampling. + max_items: cap on STAC items mosaicked per layer per window. + cache: window cache; defaults to the shared on-disk cache. Example: - >>> moondata = LROCWAC(bbox=(-60.0, 5.0, -55.0, 10.0)) - >>> for sample in moondata: + >>> moondata = KaguyaTC(products=["dtm"], bbox=(-26.4, -50.7, -25.4, -49.6)) + >>> for sample in moondata: # doctest: +SKIP ... sample["image"] # torch.Tensor (C, H, W) """ @@ -98,8 +142,11 @@ class InstrumentDataset(_WindowedDataset): instrument: ClassVar[str] """Human-readable instrument name.""" - all_products: ClassVar[dict[str, str]] - """Product name -> layer identifier (see ``astrofetch.moon.layers``).""" + collection: ClassVar[str] + """USGS ARD STAC collection id backing this instrument's products.""" + + all_products: ClassVar[dict[str, Product]] + """Product name -> (layer id, STAC asset key).""" def __init__( self, @@ -109,6 +156,8 @@ def __init__( patch_size: int = 256, length: int = 1000, seed: int | None = None, + max_items: int = 20, + cache: WindowCache | None = None, ) -> None: if products is None: products = list(self.all_products) @@ -124,41 +173,82 @@ def __init__( if not (west < east and south < north): raise ValueError(f"invalid bbox (west, south, east, north): {bbox}") self.products = list(products) - self.layers = [self.all_products[p] for p in products] + self.layers = [self.all_products[p].layer for p in products] self.bbox = bbox self.resolution = resolution self.patch_size = patch_size self.length = length self.seed = seed - - def read(self, bbox: BBox, generator: torch.Generator | None = None) -> dict: - # Placeholder for the Phase 1 STAC sampler: query the USGS ARD - # catalog, window-read COGs, reproject, and stack. - image = torch.rand(len(self.layers), self.patch_size, self.patch_size, generator=generator) + self.max_items = max_items + self.cache = cache if cache is not None else WindowCache() + + def read(self, bbox: BBox) -> dict: + grid = TargetGrid(bbox=bbox, width=self.patch_size, height=self.patch_size, crs=CRS) + images, masks = [], [] + for product in self.products: + spec = self.all_products[product] + image, mask = self._read_layer(spec, grid) + images.append(image) + masks.append(mask) return { - "image": image, - "mask": torch.ones_like(image, dtype=torch.bool), + "image": torch.from_numpy(np.stack(images)).to(torch.float32), + "mask": torch.from_numpy(np.stack(masks)).to(torch.bool), "layers": list(self.layers), "bbox": bbox, "crs": CRS, "resolution": self.resolution, } + def _read_layer(self, spec: Product, grid: TargetGrid) -> tuple[np.ndarray, np.ndarray]: + cached = self.cache.get(spec.layer, grid) + if cached is not None: + return cached + hrefs = stac.find_asset_hrefs(self.collection, spec.asset, grid.bbox, self.max_items) + image, mask = _mosaic(hrefs, grid) + self.cache.put(spec.layer, grid, image, mask) + return image, mask + + +def _mosaic(hrefs: list[str], grid: TargetGrid) -> tuple[np.ndarray, np.ndarray]: + """Reproject each COG onto ``grid`` and fill invalid pixels from later items. + + Earlier items win where they have data; later items fill only the gaps. + With no items, returns an all-invalid (zero) window — the mask tells the + truth about coverage rather than fabricating data. + """ + image = np.zeros((grid.height, grid.width), dtype=np.float32) + valid = np.zeros((grid.height, grid.width), dtype=bool) + for href in hrefs: + layer_image, layer_mask = raster.read_window(href, grid) + fill = layer_mask & ~valid + image[fill] = layer_image[fill] + valid |= layer_mask + return image, valid + class KaguyaTC(InstrumentDataset): - """Kaguya (SELENE) Terrain Camera: stereo-derived DTM and orthomosaic.""" + """Kaguya (SELENE) Terrain Camera: USGS stereo-derived DTM and orthoimage.""" probe = "Kaguya (SELENE)" - instrument = "Terrain Camera" - all_products = {"dtm": "kaguya_tc_dtm", "ortho": "kaguya_tc_ortho"} + instrument = "Terrain Camera (USGS DTM)" + collection = "kaguya_terrain_camera_usgs_dtms" + all_products = { + "dtm": Product("kaguya_tc_dtm", "dtm"), + "ortho": Product("kaguya_tc_ortho", "orthoimage"), + } + +class KaguyaTCImagery(InstrumentDataset): + """Kaguya (SELENE) Terrain Camera: stereoscopic radiance imagery. -class LROCWAC(InstrumentDataset): - """LRO Wide Angle Camera: global morphologic mosaic.""" + Raw Terrain Camera observations (16-bit DN scaled to radiance), distinct + from the USGS-derived DTM products in :class:`KaguyaTC`. + """ - probe = "Lunar Reconnaissance Orbiter" - instrument = "LROC Wide Angle Camera" - all_products = {"mosaic": "lroc_wac"} + probe = "Kaguya (SELENE)" + instrument = "Terrain Camera (imagery)" + collection = "kaguya_terrain_camera_stereoscopic_uncontrolled_observations" + all_products = {"image": Product("kaguya_tc_image", "image")} class IntersectionDataset(_WindowedDataset): @@ -196,9 +286,9 @@ def __init__(self, first: _WindowedDataset, second: _WindowedDataset) -> None: self.seed = first.seed if first.seed is not None else second.seed self.layers = first.layers + second.layers - def read(self, bbox: BBox, generator: torch.Generator | None = None) -> dict: - left = self.first.read(bbox, generator) - right = self.second.read(bbox, generator) + def read(self, bbox: BBox) -> dict: + left = self.first.read(bbox) + right = self.second.read(bbox) return { "image": torch.cat([left["image"], right["image"]]), "mask": torch.cat([left["mask"], right["mask"]]), diff --git a/src/astrofetch/moon/layers.py b/src/astrofetch/moon/layers.py index b5057ff..8e0dd5f 100644 --- a/src/astrofetch/moon/layers.py +++ b/src/astrofetch/moon/layers.py @@ -1,18 +1,23 @@ """Layer registry and discovery catalog for lunar data. The registry (``LAYERS``) is the single place where user-facing layer -identifiers map to their provenance and, in Phase 1, USGS ARD STAC -collections. The catalog (``MOON``) arranges the same information as a +identifiers map to their provenance and their backing USGS ARD STAC collection +and asset. The catalog (``MOON``) arranges the same information as a Body -> Probe -> Instrument hierarchy for discovery; its nodes hold specs and dataset *classes*, never dataset instances. Flat imports -(``from astrofetch.moon import LROCWAC``) remain the primary path. +(``from astrofetch.moon import KaguyaTC``) remain the primary path. """ from __future__ import annotations from dataclasses import dataclass -from astrofetch.moon.datasets import CRS, LROCWAC, InstrumentDataset, KaguyaTC +from astrofetch.moon.datasets import ( + CRS, + InstrumentDataset, + KaguyaTC, + KaguyaTCImagery, +) @dataclass(frozen=True) @@ -32,12 +37,15 @@ class LayerSpec: """Product name within the instrument, e.g. ``"dtm"``.""" collection: str - """USGS ARD STAC collection id; placeholder until the Phase 1 sampler.""" + """USGS ARD STAC collection id backing this layer.""" + + asset: str + """STAC asset key read from each item, e.g. ``"dtm"``.""" @dataclass(frozen=True) class Instrument: - """Catalog node: one instrument, its products, and its dataset class.""" + """Catalog node: one instrument dataset, its products, and its class.""" name: str products: dict[str, LayerSpec] @@ -47,7 +55,7 @@ class Instrument: @dataclass(frozen=True) class Probe: - """Catalog node: one probe and the instruments it carries.""" + """Catalog node: one probe and the instrument datasets it carries.""" name: str instruments: dict[str, Instrument] @@ -62,24 +70,24 @@ class Body: probes: dict[str, Probe] -def _spec(dataset: type[InstrumentDataset], product: str, collection: str) -> LayerSpec: +def _spec(dataset: type[InstrumentDataset], product: str) -> LayerSpec: + entry = dataset.all_products[product] return LayerSpec( - name=dataset.all_products[product], + name=entry.layer, probe=dataset.probe, instrument=dataset.instrument, product=product, - collection=collection, + collection=dataset.collection, + asset=entry.asset, ) -# Collection ids are placeholders until Phase 1 pins the real USGS ARD -# collections; endpoint URLs will live in data/endpoints.py, never here. LAYERS: dict[str, LayerSpec] = { spec.name: spec for spec in ( - _spec(KaguyaTC, "dtm", "placeholder:kaguya_tc_dtm"), - _spec(KaguyaTC, "ortho", "placeholder:kaguya_tc_ortho"), - _spec(LROCWAC, "mosaic", "placeholder:lroc_wac_mosaic"), + _spec(KaguyaTC, "dtm"), + _spec(KaguyaTC, "ortho"), + _spec(KaguyaTCImagery, "image"), ) } @@ -87,7 +95,7 @@ def _spec(dataset: type[InstrumentDataset], product: str, collection: str) -> La def _instrument(dataset: type[InstrumentDataset]) -> Instrument: return Instrument( name=dataset.instrument, - products={product: LAYERS[layer] for product, layer in dataset.all_products.items()}, + products={product: LAYERS[entry.layer] for product, entry in dataset.all_products.items()}, dataset=dataset, ) @@ -96,8 +104,13 @@ def _instrument(dataset: type[InstrumentDataset]) -> Instrument: name="Moon", crs=CRS, probes={ - "kaguya": Probe(name=KaguyaTC.probe, instruments={"tc": _instrument(KaguyaTC)}), - "lro": Probe(name=LROCWAC.probe, instruments={"lroc_wac": _instrument(LROCWAC)}), + "kaguya": Probe( + name=KaguyaTC.probe, + instruments={ + "tc_dtm": _instrument(KaguyaTC), + "tc_imagery": _instrument(KaguyaTCImagery), + }, + ), }, ) """Discovery catalog for the Moon: enumerate probes, instruments, products.""" diff --git a/tests/live/test_endpoints_live.py b/tests/live/test_endpoints_live.py index fb4e378..f2c4213 100644 --- a/tests/live/test_endpoints_live.py +++ b/tests/live/test_endpoints_live.py @@ -6,18 +6,46 @@ uv run pytest tests/live -m live -Phase 0 ships no network code yet, so this module only establishes the policy -and the ``live`` marker. Real STAC/COG connectivity checks land with the -Phase 1 sampler. +They exercise the Phase 1 STAC/COG path against the USGS ARD catalog over a +small, known-covered lunar region, so they stay fast and single-shot. """ +from __future__ import annotations + +from pathlib import Path + import pytest +import astrofetch as af +from astrofetch.data import stac +from astrofetch.data.cache import WindowCache + +# A small box with known Kaguya TC USGS DTM coverage (near -50 deg latitude). +_COVERED_BBOX = (-26.3, -50.6, -25.5, -49.7) + @pytest.mark.live -def test_live_marker_is_registered() -> None: - """Placeholder asserting the live suite is wired up. +def test_stac_catalog_returns_covering_cogs() -> None: + """The USGS ARD catalog is reachable and returns DTM COGs for a known box.""" + hrefs = stac.find_asset_hrefs(af.KaguyaTC.collection, "dtm", _COVERED_BBOX, max_items=3) + assert hrefs + assert all(href.endswith(".tif") for href in hrefs) + - Replace with a real USGS ARD STAC reachability check in Phase 1. - """ - pytest.skip("no live endpoints until the Phase 1 STAC sampler lands") +@pytest.mark.live +def test_fetches_a_real_two_layer_patch(tmp_path: Path) -> None: + """End to end: a coregistered (2, H, W) DTM+ortho patch of real physical data.""" + moondata = af.KaguyaTC( + products=["dtm", "ortho"], + bbox=_COVERED_BBOX, + resolution=50.0, + patch_size=64, + length=1, + seed=3, + cache=WindowCache(tmp_path), + ) + sample = next(iter(moondata)) + assert sample["image"].shape == (2, 64, 64) + assert sample["layers"] == ["kaguya_tc_dtm", "kaguya_tc_ortho"] + # At least some pixels are valid over a covered region. + assert bool(sample["mask"].any()) diff --git a/tests/unit/test_cache.py b/tests/unit/test_cache.py new file mode 100644 index 0000000..b487190 --- /dev/null +++ b/tests/unit/test_cache.py @@ -0,0 +1,63 @@ +"""Unit tests for the disposable window cache.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest + +from astrofetch.data.cache import WindowCache, default_cache_dir +from astrofetch.data.grid import TargetGrid + + +def _grid(width: int = 8) -> TargetGrid: + return TargetGrid(bbox=(0.0, 0.0, 1.0, 1.0), width=width, height=8) + + +def test_roundtrips_image_and_mask(tmp_path: Path) -> None: + cache = WindowCache(tmp_path) + image = np.arange(64, dtype=np.float32).reshape(8, 8) + mask = image > 30 + + assert cache.get("kaguya_tc_dtm", _grid()) is None + cache.put("kaguya_tc_dtm", _grid(), image, mask) + cached = cache.get("kaguya_tc_dtm", _grid()) + + assert cached is not None + got_image, got_mask = cached + np.testing.assert_array_equal(got_image, image) + np.testing.assert_array_equal(got_mask, mask) + + +def test_distinct_grids_do_not_collide(tmp_path: Path) -> None: + cache = WindowCache(tmp_path) + image = np.ones((8, 8), dtype=np.float32) + cache.put("layer", _grid(width=8), image, image.astype(bool)) + + # Same layer, different grid width -> different key -> miss. + assert cache.get("layer", _grid(width=16)) is None + + +def test_clear_removes_entries(tmp_path: Path) -> None: + cache = WindowCache(tmp_path) + image = np.zeros((8, 8), dtype=np.float32) + cache.put("layer", _grid(), image, image.astype(bool)) + + cache.clear() + + assert cache.get("layer", _grid()) is None + assert not list(tmp_path.glob("*.npz")) + + +def test_put_leaves_no_temp_files(tmp_path: Path) -> None: + cache = WindowCache(tmp_path) + image = np.zeros((8, 8), dtype=np.float32) + cache.put("layer", _grid(), image, image.astype(bool)) + + assert not list(tmp_path.glob("*.tmp.npz")) + + +def test_default_dir_honors_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + monkeypatch.setenv("ASTROFETCH_CACHE", str(tmp_path / "custom")) + assert default_cache_dir() == tmp_path / "custom" diff --git a/tests/unit/test_datasets.py b/tests/unit/test_datasets.py index c3f5cc2..1c0ba5d 100644 --- a/tests/unit/test_datasets.py +++ b/tests/unit/test_datasets.py @@ -1,13 +1,48 @@ +"""Unit tests for the instrument datasets. + +The read path is real (STAC search + COG reproject + cache), so these tests +stub :mod:`astrofetch.data.stac` and :mod:`astrofetch.data.raster` — no network, +no real COGs — and redirect the cache to a temp dir. A deterministic fake read +lets us assert shapes, layer wiring, reproducibility, and composition. +""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np import pytest import torch from torch.utils.data import DataLoader import astrofetch as af from astrofetch.moon import LAYERS, MOON +from astrofetch.moon import datasets as ds SAMPLE_KEYS = {"image", "mask", "layers", "bbox", "crs", "resolution"} +def _fake_find_asset_hrefs( + collection: str, asset: str, bbox: tuple, max_items: int = 20, root: str | None = None +) -> list[str]: + return [f"{collection}|{asset}"] + + +def _fake_read_window(href, grid, band=1, resampling=None): + # Deterministic in (href, window): identical requests read identical data, + # which is what makes seeded sampling reproducible under mocking. + value = float(hash((href, grid.bbox)) % 997) + image = np.full((grid.height, grid.width), value, dtype=np.float32) + return image, np.ones((grid.height, grid.width), dtype=bool) + + +@pytest.fixture(autouse=True) +def _mock_reads(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + monkeypatch.setenv("ASTROFETCH_CACHE", str(tmp_path / "cache")) + monkeypatch.setattr(ds.stac, "find_asset_hrefs", _fake_find_asset_hrefs) + monkeypatch.setattr(ds.raster, "read_window", _fake_read_window) + + def test_instrument_yields_sample_dicts() -> None: moondata = af.KaguyaTC( products=["dtm", "ortho"], @@ -33,10 +68,52 @@ def test_products_default_to_all() -> None: assert moondata.layers == ["kaguya_tc_dtm", "kaguya_tc_ortho"] +def test_read_queries_stac_by_collection_and_asset(monkeypatch: pytest.MonkeyPatch) -> None: + calls: list[tuple[str, str]] = [] + + def _spy(collection, asset, bbox, max_items=20, root=None): + calls.append((collection, asset)) + return [f"{collection}|{asset}"] + + monkeypatch.setattr(ds.stac, "find_asset_hrefs", _spy) + next(iter(af.KaguyaTC(products=["ortho"], patch_size=8, length=1, seed=0))) + assert calls == [("kaguya_terrain_camera_usgs_dtms", "orthoimage")] + + +def test_supports_indexing() -> None: + moondata = af.KaguyaTC(products=["dtm"], patch_size=8, length=5, seed=0) + sample = moondata[0] + assert set(sample) == SAMPLE_KEYS + assert sample["image"].shape == (1, 8, 8) + # Indexing is deterministic and negative indices work. + assert torch.equal(moondata[0]["image"], moondata[0]["image"]) + assert torch.equal(moondata[-1]["image"], moondata[4]["image"]) + + +def test_index_out_of_range_raises() -> None: + moondata = af.KaguyaTC(products=["dtm"], patch_size=8, length=3, seed=0) + with pytest.raises(IndexError): + moondata[3] + + +def test_patch_size_sets_image_dimensions() -> None: + moondata = af.KaguyaTCImagery(patch_size=48, length=1, seed=0) + assert moondata[0]["image"].shape == (1, 48, 48) + + +def test_shuffled_dataloader_covers_every_index() -> None: + moondata = af.KaguyaTC(products=["dtm"], patch_size=8, length=6, seed=0) + loader = DataLoader(moondata, batch_size=2, shuffle=True) + total = sum(batch["image"].shape[0] for batch in loader) + assert total == 6 + + def test_seed_is_reproducible() -> None: - kwargs = dict(products=["mosaic"], patch_size=16, length=2, seed=42) - first = [s["image"] for s in af.LROCWAC(**kwargs)] - second = [s["image"] for s in af.LROCWAC(**kwargs)] + def sampler() -> af.KaguyaTCImagery: + return af.KaguyaTCImagery(products=["image"], patch_size=16, length=2, seed=42) + + first = [s["image"] for s in sampler()] + second = [s["image"] for s in sampler()] for a, b in zip(first, second, strict=True): assert torch.equal(a, b) @@ -44,29 +121,29 @@ def test_seed_is_reproducible() -> None: def test_intersection_stacks_channels() -> None: moondata = af.KaguyaTC( products=["dtm"], bbox=(-30.0, -10.0, -20.0, 0.0), patch_size=16, length=4, seed=1 - ) & af.LROCWAC(bbox=(-25.0, -15.0, -15.0, 5.0), patch_size=16, length=6, seed=1) + ) & af.KaguyaTCImagery(bbox=(-25.0, -15.0, -15.0, 5.0), patch_size=16, length=6, seed=1) assert moondata.bbox == (-25.0, -10.0, -20.0, 0.0) assert len(moondata) == 4 for sample in moondata: assert set(sample) == SAMPLE_KEYS assert sample["image"].shape == (2, 16, 16) assert sample["mask"].shape == (2, 16, 16) - assert sample["layers"] == ["kaguya_tc_dtm", "lroc_wac"] + assert sample["layers"] == ["kaguya_tc_dtm", "kaguya_tc_image"] def test_intersection_nests() -> None: moondata = ( af.KaguyaTC(products=["dtm"], patch_size=16, length=2, seed=0) & af.KaguyaTC(products=["ortho"], patch_size=16, length=2, seed=0) - & af.LROCWAC(patch_size=16, length=2, seed=0) + & af.KaguyaTCImagery(patch_size=16, length=2, seed=0) ) sample = next(iter(moondata)) assert sample["image"].shape == (3, 16, 16) - assert sample["layers"] == ["kaguya_tc_dtm", "kaguya_tc_ortho", "lroc_wac"] + assert sample["layers"] == ["kaguya_tc_dtm", "kaguya_tc_ortho", "kaguya_tc_image"] def test_works_with_default_dataloader_collation() -> None: - moondata = af.LROCWAC(patch_size=16, length=4, seed=0) + moondata = af.KaguyaTCImagery(patch_size=16, length=4, seed=0) loader = DataLoader(moondata, batch_size=2) batches = list(loader) assert len(batches) == 2 @@ -86,26 +163,60 @@ def test_rejects_unknown_product() -> None: def test_rejects_invalid_bbox() -> None: with pytest.raises(ValueError): - af.LROCWAC(bbox=(10.0, 0.0, -10.0, 5.0)) + af.KaguyaTCImagery(bbox=(10.0, 0.0, -10.0, 5.0)) def test_rejects_disjoint_intersection() -> None: with pytest.raises(ValueError): - af.KaguyaTC(bbox=(-60.0, 5.0, -55.0, 10.0)) & af.LROCWAC(bbox=(30.0, -10.0, 40.0, 0.0)) + af.KaguyaTC(bbox=(-60.0, 5.0, -55.0, 10.0)) & af.KaguyaTCImagery( + bbox=(30.0, -10.0, 40.0, 0.0) + ) def test_rejects_mismatched_grids() -> None: with pytest.raises(ValueError): - af.KaguyaTC(patch_size=16) & af.LROCWAC(patch_size=32) + af.KaguyaTC(patch_size=16) & af.KaguyaTCImagery(patch_size=32) def test_catalog_points_at_dataset_classes() -> None: - assert MOON.probes["lro"].instruments["lroc_wac"].dataset is af.LROCWAC - assert MOON.probes["kaguya"].instruments["tc"].dataset is af.KaguyaTC + assert MOON.probes["kaguya"].instruments["tc_dtm"].dataset is af.KaguyaTC + assert MOON.probes["kaguya"].instruments["tc_imagery"].dataset is af.KaguyaTCImagery def test_catalog_and_registry_agree() -> None: - spec = MOON.probes["lro"].instruments["lroc_wac"].products["mosaic"] - assert spec is LAYERS["lroc_wac"] - assert spec.probe == af.LROCWAC.probe - assert spec.instrument == af.LROCWAC.instrument + spec = MOON.probes["kaguya"].instruments["tc_dtm"].products["dtm"] + assert spec is LAYERS["kaguya_tc_dtm"] + assert spec.collection == af.KaguyaTC.collection + assert spec.asset == "dtm" + + +def test_mosaic_prefers_earlier_items_and_fills_gaps(monkeypatch: pytest.MonkeyPatch) -> None: + from astrofetch.data.grid import TargetGrid + + def _coverage(href, grid, band=1, resampling=None): + image = np.full((grid.height, grid.width), float(href[-1]), dtype=np.float32) + mask = np.zeros((grid.height, grid.width), dtype=bool) + if href.endswith("1"): # first item covers only the left half + mask[:, : grid.width // 2] = True + else: # second item covers everything + mask[:] = True + image[~mask] = 0.0 + return image, mask + + monkeypatch.setattr(ds.raster, "read_window", _coverage) + grid = TargetGrid(bbox=(0.0, 0.0, 1.0, 1.0), width=8, height=8) + + image, mask = ds._mosaic(["item1", "item2"], grid) + + assert mask.all() + assert (image[:, :4] == 1.0).all() # earlier item wins where it has data + assert (image[:, 4:] == 2.0).all() # later item fills the gap + + +def test_mosaic_with_no_items_is_all_invalid() -> None: + from astrofetch.data.grid import TargetGrid + + grid = TargetGrid(bbox=(0.0, 0.0, 1.0, 1.0), width=8, height=8) + image, mask = ds._mosaic([], grid) + assert not mask.any() + assert (image == 0.0).all() diff --git a/tests/unit/test_grid.py b/tests/unit/test_grid.py new file mode 100644 index 0000000..89ecc5d --- /dev/null +++ b/tests/unit/test_grid.py @@ -0,0 +1,49 @@ +import math + +import pytest + +from astrofetch.data.grid import ( + GEOGRAPHIC_CRS, + MOON_RADIUS_M, + TargetGrid, + meters_to_degrees, +) + + +def test_transform_spans_bbox() -> None: + grid = TargetGrid(bbox=(-10.0, -5.0, 10.0, 5.0), width=200, height=100) + transform = grid.transform + # Top-left pixel corner maps to (west, north); bottom-right to (east, south). + assert transform * (0, 0) == pytest.approx((-10.0, 5.0)) + assert transform * (200, 100) == pytest.approx((10.0, -5.0)) + + +def test_default_crs_is_geographic() -> None: + assert TargetGrid(bbox=(0.0, 0.0, 1.0, 1.0), width=8, height=8).crs == GEOGRAPHIC_CRS + + +def test_rejects_inverted_bbox() -> None: + with pytest.raises(ValueError): + TargetGrid(bbox=(10.0, 0.0, -10.0, 5.0), width=8, height=8) + + +def test_rejects_nonpositive_size() -> None: + with pytest.raises(ValueError): + TargetGrid(bbox=(0.0, 0.0, 1.0, 1.0), width=0, height=8) + + +def test_meters_to_degrees_at_equator_is_isotropic() -> None: + dlon, dlat = meters_to_degrees(MOON_RADIUS_M * math.radians(1.0), latitude=0.0) + assert dlon == pytest.approx(1.0) + assert dlat == pytest.approx(1.0) + + +def test_longitude_span_grows_toward_the_pole() -> None: + dlon, dlat = meters_to_degrees(1000.0, latitude=60.0) + # cos(60) = 0.5, so a fixed ground distance spans twice the longitude. + assert dlon == pytest.approx(2.0 * dlat) + + +def test_pole_longitude_span_is_clamped() -> None: + dlon, _ = meters_to_degrees(1000.0, latitude=90.0) + assert dlon == 360.0 diff --git a/tests/unit/test_raster.py b/tests/unit/test_raster.py new file mode 100644 index 0000000..baf2c94 --- /dev/null +++ b/tests/unit/test_raster.py @@ -0,0 +1,102 @@ +"""Unit tests for windowed COG reads — local GeoTIFFs only, never the network.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest +import rasterio +from rasterio.enums import Resampling +from rasterio.transform import from_bounds + +from astrofetch.data.grid import GEOGRAPHIC_CRS, TargetGrid +from astrofetch.data.raster import read_window +from astrofetch.errors import EndpointError + + +def _write_geotiff( + path: Path, + data: np.ndarray, + bbox: tuple[float, float, float, float], + *, + crs: str = GEOGRAPHIC_CRS, + nodata: float | None = None, + scale: float = 1.0, + offset: float = 0.0, +) -> str: + height, width = data.shape + west, south, east, north = bbox + with rasterio.open( + path, + "w", + driver="GTiff", + height=height, + width=width, + count=1, + dtype=data.dtype, + crs=crs, + transform=from_bounds(west, south, east, north, width, height), + nodata=nodata, + ) as dst: + dst.write(data, 1) + dst.scales = (scale,) + dst.offsets = (offset,) + return str(path) + + +def test_reads_grid_shape_and_dtype(tmp_path: Path) -> None: + src = np.arange(64, dtype=np.float32).reshape(8, 8) + href = _write_geotiff(tmp_path / "src.tif", src, (0.0, 0.0, 8.0, 8.0)) + grid = TargetGrid(bbox=(0.0, 0.0, 8.0, 8.0), width=8, height=8) + + image, mask = read_window(href, grid) + + assert image.shape == (8, 8) + assert image.dtype == np.float32 + assert mask.dtype == np.bool_ + assert mask.all() + + +def test_applies_scale_and_offset(tmp_path: Path) -> None: + src = np.full((8, 8), 100, dtype=np.int16) + href = _write_geotiff( + tmp_path / "radiance.tif", src, (0.0, 0.0, 8.0, 8.0), scale=0.013, offset=2.0 + ) + grid = TargetGrid(bbox=(0.0, 0.0, 8.0, 8.0), width=8, height=8) + + image, _ = read_window(href, grid, resampling=Resampling.nearest) + + assert image == pytest.approx(100 * 0.013 + 2.0) + + +def test_nodata_becomes_invalid_mask(tmp_path: Path) -> None: + src = np.ones((8, 8), dtype=np.float32) + src[:, :4] = -9999.0 + href = _write_geotiff(tmp_path / "gappy.tif", src, (0.0, 0.0, 8.0, 8.0), nodata=-9999.0) + grid = TargetGrid(bbox=(0.0, 0.0, 8.0, 8.0), width=8, height=8) + + image, mask = read_window(href, grid, resampling=Resampling.nearest) + + assert not mask[:, :4].any() + assert mask[:, 4:].all() + # Invalid pixels are zero-filled, never left as the nodata sentinel. + assert (image[~mask] == 0.0).all() + + +def test_area_outside_source_is_invalid(tmp_path: Path) -> None: + src = np.ones((8, 8), dtype=np.float32) + href = _write_geotiff(tmp_path / "small.tif", src, (0.0, 0.0, 4.0, 4.0), nodata=0.0) + # Grid extends east of the source coverage. + grid = TargetGrid(bbox=(0.0, 0.0, 8.0, 4.0), width=16, height=8) + + _, mask = read_window(href, grid, resampling=Resampling.nearest) + + assert mask[:, :8].all() + assert not mask[:, 8:].any() + + +def test_missing_file_raises_endpoint_error(tmp_path: Path) -> None: + grid = TargetGrid(bbox=(0.0, 0.0, 1.0, 1.0), width=8, height=8) + with pytest.raises(EndpointError): + read_window(str(tmp_path / "does_not_exist.tif"), grid) diff --git a/tests/unit/test_stac.py b/tests/unit/test_stac.py new file mode 100644 index 0000000..9718df8 --- /dev/null +++ b/tests/unit/test_stac.py @@ -0,0 +1,78 @@ +"""Unit tests for the STAC wrapper — the client is stubbed, never the network.""" + +from __future__ import annotations + +import pytest +from pystac_client.exceptions import APIError + +from astrofetch.data import stac +from astrofetch.errors import EndpointError + + +class _Asset: + def __init__(self, href: str) -> None: + self.href = href + + +class _Item: + def __init__(self, item_id: str, assets: dict[str, _Asset]) -> None: + self.id = item_id + self.assets = assets + + +class _Search: + def __init__(self, items: list[_Item]) -> None: + self._items = items + + def items(self) -> list[_Item]: + return self._items + + +class _Client: + def __init__(self, items: list[_Item]) -> None: + self._items = items + self.calls: list[dict] = [] + + def search(self, **kwargs: object) -> _Search: + self.calls.append(kwargs) + return _Search(self._items) + + +def _patch_catalog(monkeypatch: pytest.MonkeyPatch, client: _Client) -> None: + monkeypatch.setattr(stac, "catalog", lambda root=stac.STAC_API_ROOT: client) + + +def test_returns_hrefs_in_item_order(monkeypatch: pytest.MonkeyPatch) -> None: + client = _Client([ + _Item("a", {"dtm": _Asset("http://x/a.tif")}), + _Item("b", {"dtm": _Asset("http://x/b.tif")}), + ]) + _patch_catalog(monkeypatch, client) + + hrefs = stac.find_asset_hrefs("coll", "dtm", (-1.0, -1.0, 1.0, 1.0), max_items=5) + + assert hrefs == ["http://x/a.tif", "http://x/b.tif"] + assert client.calls == [ + {"collections": ["coll"], "bbox": [-1.0, -1.0, 1.0, 1.0], "max_items": 5} + ] + + +def test_no_overlap_returns_empty(monkeypatch: pytest.MonkeyPatch) -> None: + _patch_catalog(monkeypatch, _Client([])) + assert stac.find_asset_hrefs("coll", "dtm", (0.0, 0.0, 1.0, 1.0)) == [] + + +def test_missing_asset_raises_endpoint_error(monkeypatch: pytest.MonkeyPatch) -> None: + _patch_catalog(monkeypatch, _Client([_Item("a", {"thumbnail": _Asset("http://x/a.jpg")})])) + with pytest.raises(EndpointError, match="no asset 'dtm'"): + stac.find_asset_hrefs("coll", "dtm", (0.0, 0.0, 1.0, 1.0)) + + +def test_api_error_becomes_endpoint_error(monkeypatch: pytest.MonkeyPatch) -> None: + class _Boom(_Client): + def search(self, **kwargs: object) -> _Search: + raise APIError("boom") + + _patch_catalog(monkeypatch, _Boom([])) + with pytest.raises(EndpointError, match="STAC search failed"): + stac.find_asset_hrefs("coll", "dtm", (0.0, 0.0, 1.0, 1.0)) diff --git a/uv.lock b/uv.lock index 09fe12a..6ec6907 100644 --- a/uv.lock +++ b/uv.lock @@ -8,6 +8,15 @@ resolution-markers = [ "python_full_version < '3.11'", ] +[[package]] +name = "affine" +version = "2.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" } +wheels = [ + { url = 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