pytesmo
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TUW-GEO
python Toolbox for the Evaluation of Soil Moisture Observations
Support `n x m` array in which we have `n` time series of length `m`. The metrics should return arrays of size `n`. If the input is 1D then everything...
Metadata handling in the validation framework should be supported from start to finish. We want to track: - metadata from the input datasets. The `pygeobase.object_base.TS` object already has a `.metadata`...
I've run into ISMN timeseries files that are turned into a series of NaNs by scaling (e.g. ARM/Anthony/ARM_ARM_Anthony_sm_0.025000_0.025000_SMP1-A_19780101_20180712.stm at lon: -98.0969, lat: 37.2134). This seems to happen in `pytesmo.scaling.gen_cdf_match` (scaling.py,...
Update the readthedocs for using dask in the example use case scripts.
As far as I can see: if there's "bad" input data, pytesmo usually either drops it or issues a (sometimes quite generic) warning. Examples are: - `pytesmo.validation_framework.data_manager.DataManager.read_ds`: warnings are given...
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Errors in the metrics calculation are currently caught and [returned as a MetricsCalculationError](https://github.com/TUW-GEO/pytesmo/blob/4a03d4ea151a41297b28d3d71ff88873df92abc6/src/pytesmo/validation_framework/validation.py#L475) without much information. -> improve MetricsCalculationError class to return more information where the exception was thrown and...