issue_comments: 429497014
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html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
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https://github.com/pydata/xarray/issues/2481#issuecomment-429497014 | https://api.github.com/repos/pydata/xarray/issues/2481 | 429497014 | MDEyOklzc3VlQ29tbWVudDQyOTQ5NzAxNA== | 6628425 | 2018-10-13T00:42:35Z | 2018-10-13T00:42:35Z | MEMBER | I think this would be a fair amount of work to implement :), but in principle it would be a natural step forward in the pattern we have been following in porting time series functionality from pandas for use with calendars supported by In the short term, at least for the use-case you describe above, I think a potentially simpler option would be to use a call to In [2]: times = pd.date_range('2000', periods=361) In [3]: da = xr.DataArray(range(361), [('time', times)]) In [4]: monthly_count = da.resample(time='M').count() In [5]: end_time = monthly_count.indexes['time'] In [6]: start_time = end_time.shift(-1, 'M') In [7]: expected_days_in_group = end_time - start_time In [8]: expected_days_in_group
Out[8]:
TimedeltaIndex(['31 days', '29 days', '31 days', '30 days', '31 days',
'30 days', '31 days', '31 days', '30 days', '31 days',
'30 days', '31 days'],
dtype='timedelta64[ns]', name=u'time', freq=None)
In [9]: b = a.shift(-1, 'M') In [10]: a - bTypeError Traceback (most recent call last) <ipython-input-10-09bd029d0285> in <module>() ----> 1 a - b /Users/spencerclark/xarray-dev/xarray/xarray/coding/cftimeindex.pyc in sub(self, other) 365 366 def sub(self, other): --> 367 return CFTimeIndex(np.array(self) - other) 368 369 TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'CFTimeIndex' ``` As a side note, support even for standard PeriodIndexes in xarray needs some work -- see the following existing open issues: #1270, #1565. Serialization to netCDF files would also be nice to support (but I don't see an existing issue for that). I'm sure work to fix these issues would also be appreciated! |
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