Comments (4)
I just realized we don't need a special allDatasets
query method, because allDatasets
is just a special dataset, so we can use something like:
kwargs = {
'minTime<=': stop_time,
'maxTime>=': start_time,
}
variables = ['datasetID', 'minLongitude', 'minLatitude']
url = e.get_download_url(dataset_id='allDatasets', variables=variables, response='csv', **kwargs)
to generate ERDDAP URLs like this one, which returns the ID
, min_lon
and min_lat
for all datasets in a certain time range
from erddapy.
url = e.get_download_url(dataset_id='allDatasets', variables=variables, response='csv', **kwargs)
We should document that.
from erddapy.
I forgot to document this. Re-opening so we can do that.
from erddapy.
The docs were added in vinisalazar@f0ec8a6. Closing this now.
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Related Issues (20)
- relative_constraints is used only in download_url HOT 1
- Make joblib an optional dependency
- Conda vs pip last versions are different HOT 5
- errdapy not available for Apple Silicon macOS 12 systems HOT 6
- GSoC 2022 ideas HOT 18
- Problem with 00-quick_intro.ipbgy.ipynb HOT 2
- Remove pagination from all responses but HTML in search_for HOT 3
- griddap opendap request not lazy loading, and failing HOT 4
- Make get_info_url rerun the root info data when no dataset_id is specified. HOT 4
- IOOS RA query/utility package HOT 8
- Move tests that hit a server to the ERDDAP gold standard server HOT 8
- `to_xarray` method error on OS X HOT 3
- Deprecation warnings for downstream packages HOT 7
- Disable timeout tests HOT 1
- Refactor tests and mandatory vs optional dependencies HOT 1
- Impossible to import erddapy with pandas 2.0 HOT 2
- Permission denied when accessing griddap through `.to_xarray()` HOT 1
- e.to_xarray falling for new erddapy version HOT 2
- Timeout on large requests: httpx default timeout bottleneck HOT 1
- Download file utility function HOT 6
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