feat: station data - #18
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Mirrors the `client.stations` namespace from dclimate-client-js (PR #10), built on tabular-py. Gridded Zarr datasets still come from `load_dataset`; point-observation station datasets (GHCND and friends) read the same way -- degrees, ISO timestamps, chained selections: async with dClimateClient() as client: stations = await client.stations.load(cid) rows = await stations.select("USW00093134").time_range(a, b).rows() # Or resolve dataset and station in one call, which is the question most # callers open a station dataset to ask: st = await client.stations.nearest(cid, lat, lon, columns=["TMAX"]) Two things differ from the JS client, both because the Python side can do better rather than because parity slipped: - Reads prefer the client's own KuboCAS over a second HTTP path, so pinning, retries and configured endpoints apply to station reads too. Outside the async context there is no KuboCAS yet, so it falls back to the plain gateway. - `nearest` takes `columns=` to mean nearest station *with usable data*. Without it, near downtown LA the closest station is 0.63 km away and has never recorded TMAX, while the closest that has is 5.4 km away. Errors are translated at the boundary, so everything escaping the client is a ZarrClientError: an unknown column is InvalidSelectionError, a well-formed query matching nothing is NoDataFoundError, and unreadable bytes are the new DatasetCorruptError -- distinct because it points at the publisher rather than the caller. Transport failures deliberately pass through untranslated: a gateway timeout is retryable and says nothing about the dataset. The translation covers the whole chain, not just `load`. `select`, `where` and `rows` raise tabular's errors too, and they are where a caller is far likelier to meet one; WrappedStationDataset forwards attribute access and re-wraps returned datasets so the boundary does not stop at the first link. tabular-py is not on PyPI yet, so it is deliberately neither a dependency nor an extra: uv resolves every extra when locking, so naming an unpublished package makes `uv sync` fail even for people who never touch station data. `client.stations` raises TabularNotInstalledError with install instructions until then, and the package imports cleanly without it. Verified end to end against bafyr4if2wbttslbxpzmro427j4l4nvcrxqo4tufuffqqmqz7afj2pxyu4a, including every README example and each error-translation path.
PyPI rejects the name `tabular-py`: it normalises onto the unrelated existing `tabularpy` project. The distribution is namespaced to `dclimate-tabular-py` instead, matching @dclimate/tabular on npm. The import name is unchanged -- `tabular_py` -- since PyPI has no opinion about it and renaming the module would churn both repos for nothing. Now that it is published, station support is an ordinary dependency rather than an install-it-yourself note, matching dclimate-client-js's unconditional dependency on @dclimate/tabular. `uv sync` resolves it from the registry, so the earlier problem -- naming an unpublished package made even a plain sync fail -- is gone. `_require_tabular` and TabularNotInstalledError are kept, with the message rewritten. They no longer guard an optional extra; they exist because the import name and the distribution name differ, so a stock ModuleNotFoundError would name `tabular_py`, which `pip install` cannot find. The test now asserts the message names the installable distribution, which is the failure mode that actually matters. Verified from a clean Python 3.12 environment with no cache: the client installs, pulls dclimate-tabular-py 0.1.0 from pypi.org, imports as tabular_py, and resolves client.stations. 298 tests pass there, up from 292 -- six of the previously reported failures were local environment damage (root-owned .venv and build directories), not code.
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This repository does not receive automatic reviews because it has fewer than 10 stars. ⚙️ Run configurationConfiguration used: defaults Review profile: CHILL Plan: Pro Plus Run ID: 📝 WalkthroughWalkthroughChangesStation dataset support
Estimated code review effort: 3 (Moderate) | ~25 minutes Mergeability Score: 🟡 Moderate · up to Station access can fail after leaving an async context because the cached client may continue using a closed transport, and accessing stations before entering the context may leave HTTP resources open. This is a concrete merge-readiness issue that should be fixed before merging. Sequence Diagram(s)sequenceDiagram
participant dClimateClient
participant StationsClient
participant KuboCAS
participant StationDataset
dClimateClient->>StationsClient: access stations
StationsClient->>KuboCAS: select configured CAS or gateway source
StationsClient->>StationDataset: open dataset manifest by CID
StationDataset-->>StationsClient: dataset
StationsClient-->>dClimateClient: WrappedStationDataset
Possibly related PRs
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In `@dclimate_client_py/dclimate_client.py`:
- Around line 225-230: The cached station client must not retain a closed
KuboCAS or leak gateway transports across context transitions. Update the
station-client lifecycle around __aexit__ and the pre-__aenter__ client
replacement path to either reuse one client while updating its transport or
explicitly await aclose() before clearing and recreating it; ensure post-exit
access falls back to the gateway transport. Add a regression test covering
client.stations access before and after an async context.
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README.mddclimate_client_py/__init__.pydclimate_client_py/dclimate_client.pydclimate_client_py/dclimate_zarr_errors.pydclimate_client_py/stations/__init__.pydclimate_client_py/stations/errors.pydclimate_client_py/stations/stations_client.pydclimate_client_py/stations/wrap.pypyproject.tomltests/test_stations.py
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| try: | ||
| if self._stations_client is not None: | ||
| await self._stations_client.aclose() |
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MEDIUM
After this closes station sources, the cached StationsClient still references the KuboCAS that is closed immediately afterward. Subsequent client.stations.load(...) calls after leaving the context reuse that closed transport instead of the documented gateway fallback. Clear or rebind _stations_client during exit.
| if self._stations_client is None or ( | ||
| cas is not None and self._stations_client._cas is not cas | ||
| ): | ||
| self._stations_client = StationsClient( |
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MEDIUM
If station data was loaded before __aenter__, this assignment discards the gateway-backed client—and its owned HTTP sources—without closing it. The replacement is the only instance closed by __aexit__, so the original connection pool leaks. Preserve and close superseded station clients or update the existing client's transport.
| """ | ||
| sources, self._owned_sources = self._owned_sources, [] | ||
| for source in sources: | ||
| await source.aclose() |
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LOW
If one source.aclose() raises, the loop aborts and every remaining source stays open. Because _owned_sources was already cleared, those sources cannot be retried on a later close. Attempt all closes and re-raise or aggregate errors afterward.
Three defects found in review, all about a cached StationsClient outliving the transport it was built for. A KuboCAS never reopens once closed, so __aexit__ now drops the station client rather than only closing it: keeping it would make reads after the context fail on a dead transport instead of falling back to the gateway, as the property documents. Rebuilding the namespace onto the CAS used to discard the previous gateway-backed instance and its open sources. Since the replacement is the only instance __aexit__ closes, those pools leaked; the replacement now adopts them. Building happens in a property, which cannot await, so the sources move rather than being closed there. aclose() stopped at the first failing source, stranding the rest with no way to retry them -- the list is cleared up front. It now closes every source and re-raises the first failure afterwards. Each test fails on the previous commit and passes here.
| source = tabular.GatewayRangeSource(gateway_url or self._gateway_url) | ||
| # Gateway sources own an httpx client, so they have to be closed; CAS | ||
| # sources borrow the client's and must not be. | ||
| self._owned_sources.append(source) |
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MEDIUM
Gateway sources are retained before StationDataset.open() succeeds. If opening a missing or corrupt CID raises, no dataset can use that source, but its httpx client remains open until aclose()—indefinitely for the documented outside-context usage. Repeated failed loads can exhaust connection pools/file descriptors; remove and close the newly created source on the failure path.
| source = tabular.GatewayRangeSource(gateway_url or self._gateway_url) | ||
| # Gateway sources own an httpx client, so they have to be closed; CAS | ||
| # sources borrow the client's and must not be. | ||
| self._owned_sources.append(source) |
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MEDIUM
_source() permanently registers a fresh GatewayRangeSource and its HTTP client for every gateway-backed call. Failed EntityDataset.open() attempts and successful nearest() calls never return a dataset that needs the source, but still retain it until the entire client closes. Repeated failures or nearest lookups can accumulate connection pools; remove and close the per-call source whenever no dataset escapes.
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client.stations.