Add support for group-by financial years. #1257
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@@ Coverage Diff @@
## develop #1257 +/- ##
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- Coverage 93.60% 93.58% -0.03%
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Files 129 129
Lines 13145 13331 +186
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+ Hits 12305 12476 +171
- Misses 840 855 +15
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… support financial years.
datacube/virtual/transformations.py
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| """ | ||
| df = pd.Series(time.values) | ||
| years = df.apply(lambda x: np.datetime64(str(x.to_period('Q-JUN').qyear))).values | ||
| ds = xr.DataArray(years, name='time', attrs=time.attrs, coords=time.coords, dims=time.dims) |
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Looks good to me too.
What happens if instead of the two lines defining and modifying ds you try to do it in one go?
ds = xr.DataArray(years, name='time', attrs=time.attrs, coords=years, dims=time.dims)I think that should work!
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The coordinates should be a dictionary so something like below will work but I'm not 100% sure if the coords can contain other variables than time which the following code will exclude:
ds = xr.DataArray(years, name='time', attrs=time.attrs, coords=({"time": years}), dims=time.dims)
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How about writing a test - that way we know for sure if it works the way we expect. :)
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Most of the existing time grouping transforms aren't covered by tests either, but they are trivial compared to this one.
…xtending the fiscal year function to support multi-input time dimensions.
Reason for this pull request
Support virtual products to group-by financial years.
Proposed changes
Added a new function to support this alongside the calendar year.
Closes #xxxx
Tests added / passed
Fully documented, including
docs/about/whats_new.rstfor all changes