pywasp.bwc_from_tswc#

pywasp.bwc_from_tswc(ds, hist=None, normalize=True, revert_to_original=True, wsbin_width=1, n_wsbins=30, n_sectors=12, wind_dir_crs=None)[source]#

Add timeseries to histogram

Converts a time series with wind speed & direction to derive summed histogram of the wind vector.

Warning

This function is experimental and its signature may change.

Parameters:

  • ds (Dataset) – PyWAsP Timeseries dataset. Can have any of the pywasp spatial data structures

  • hist (Dataset, optional) – Histogram with dimensions point, wsbin, sector containing counts to add to this time series. The input is not modified. Default is None, creating a histogram using wsbin_width, n_wsbins, and n_sectors.

  • normalize (bool) – Normalize histogram for each sector, storing the frequency per sector in “wdfreq” variable, Default True

  • revert_to_original (bool) – If True (default), operate on and return ds’s own native spatial structure directly – there is no promotion/reversion round trip to pay for any more, unlike before. If False, promote to point structure and return that; still useful when composing with other point-structured accumulators, e.g. across chunked calls.

  • wsbin_width (float) – width of wind speed bins

  • n_wsbins (int) – Number of wind speed bins

  • n_sectors (int) – Number of sectors (wind direction bins)

  • wind_dir_crs (CRS-like, optional) – CRS that defines the output wind-direction frame. If ds has a wind_dir_crs attribute, input directions are treated as already expressed in that CRS frame. Otherwise, input directions are assumed true-north-relative. Directions are rotated into a projected target CRS’s grid-north frame before binning; a geographic target CRS retains them in the true-north frame.

Returns:

hist (Dataset) – Histogram with dimensions point, wsbin, sector containing the values from the time series. The per-location count_bins_exceeded diagnostic is retained for both normalized and unnormalized output.

Notes

NumPy-backed inputs are evaluated eagerly. Dask-backed inputs remain lazy and histogram each time chunk independently before a tree reduction, preserving the input spatial chunks. Dask is therefore optional. For lazy output, inspect count_bins_exceeded after computation instead of relying on the eager bin-overflow warning.

The result’s count, count_missing and recovery_percentage attributes need the actual data values (a missing-sample count) and are therefore only set for eager input; start_time, end_time and count_expected are always set. For lazy input, or once any chunk in an accumulation has been lazy, those three attributes are omitted rather than reported as a number that could be wrong.

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