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Limit public functions and simplify example scripts:
move data retrieve to functions in data_retrieve.py and return ds (download of netcdf file is not present, open if it is present). Something like kw.data.timeseries("HOEKVHLD") and kw.data.extremes("HOEKVHLD")
put default filenames in dict so their strings are not duplicated
Many data_retrieve functions have station_list instead of station argument, do for all functions or not? >> no retrieve/read meas is per station, rest is for entire station_list
solve 5 remaining TODO in kw.data_analysis.plot_measurements() script. They are related to aggers and monthly/yearly averages for ts and ext. Maybe first make hwlwconvert modular so we can also apply it to an xarray.Dataset
prevent exposure of private stats functions, they might be exposed via __all__ now
maybe convert netcdf back to ddlpy-like dataframe in kw.read_measurements(), but how to handle extreme types?
convert kw.derive_statistics() from ds to df
move tidal indicators to separate module, now in slotgemiddelden.py
fix "TypeError: Cannot compare tz-naive and tz-aware timestamps" when running kwk_process.py
check number of stations in locations df only once in data_retrieval.py, so move this check to a function
kw.read_measurements() now returns None if no meas netcdf available, better alternative? Also returning none if no meas/loc available on ddlpy? Make this consistent or raise specific error that can be catched >> fine for now, maybe iterate later
Limit public functions and simplify example scripts:
kw.data.timeseries("HOEKVHLD")
andkw.data.extremes("HOEKVHLD")
kw.data_analysis.plot_measurements()
script. They are related to aggers and monthly/yearly averages for ts and ext. Maybe first make hwlwconvert modular so we can also apply it to anxarray.Dataset
__all__
nowkw.data_analysis.plot_measurements()
script about timezones >> maybe Add timezone toddlpy.dataframe_to_xarray()
Deltares/ddlpy#105 >> by always converting ds to dfkw.read_measurements()
, but how to handle extreme types?kw.derive_statistics()
from ds to df"TypeError: Cannot compare tz-naive and tz-aware timestamps"
when running kwk_process.pykw.read_measurements()
now returnsNone
if no meas netcdf available, better alternative? Also returning none if no meas/loc available on ddlpy? Make this consistent or raise specific error that can be catched >> fine for now, maybe iterate laterFollow up issue: #32
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