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  • NotSqrt · 7 ✖

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  • CONTRIBUTOR · 7 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
413678494 https://github.com/pydata/xarray/pull/1953#issuecomment-413678494 https://api.github.com/repos/pydata/xarray/issues/1953 MDEyOklzc3VlQ29tbWVudDQxMzY3ODQ5NA== NotSqrt 1997005 2018-08-16T20:43:27Z 2018-08-16T20:43:27Z CONTRIBUTOR

@shoyer Can you review those changes, please ? Thanks !

 
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   Fix maybe_promote 301856320
394597043 https://github.com/pydata/xarray/issues/553#issuecomment-394597043 https://api.github.com/repos/pydata/xarray/issues/553 MDEyOklzc3VlQ29tbWVudDM5NDU5NzA0Mw== NotSqrt 1997005 2018-06-05T06:32:26Z 2018-06-05T06:32:26Z CONTRIBUTOR

Thanks !

 
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   operation on complex number data 103703011
394431344 https://github.com/pydata/xarray/issues/553#issuecomment-394431344 https://api.github.com/repos/pydata/xarray/issues/553 MDEyOklzc3VlQ29tbWVudDM5NDQzMTM0NA== NotSqrt 1997005 2018-06-04T17:17:41Z 2018-06-04T17:17:51Z CONTRIBUTOR

@shoyer xarray.ufuncs.angle now triggers the DeprecationWarning added in #1962, and numpy/numpy#6266 is still open.

What is the recommended way to keep an DataArray when computing angles ?

Thanks !

 
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   operation on complex number data 103703011
370007209 https://github.com/pydata/xarray/issues/1952#issuecomment-370007209 https://api.github.com/repos/pydata/xarray/issues/1952 MDEyOklzc3VlQ29tbWVudDM3MDAwNzIwOQ== NotSqrt 1997005 2018-03-02T18:21:20Z 2018-03-02T18:21:20Z CONTRIBUTOR

Fixing maybe_promote helps, but then on my example, there's a FutureWarning: xarray/core/duck_array_ops.py:152: FutureWarning: In the future, 'NAT == x' and 'x == NAT' will always be False. flag_array = (arr1 == arr2)

 
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   xarray.merge exception : invalid type promotion 301795163
359839979 https://github.com/pydata/xarray/issues/1064#issuecomment-359839979 https://api.github.com/repos/pydata/xarray/issues/1064 MDEyOklzc3VlQ29tbWVudDM1OTgzOTk3OQ== NotSqrt 1997005 2018-01-23T16:07:44Z 2018-01-23T16:07:44Z CONTRIBUTOR

FYI, merged.time.encoding = {} before calling to_netcdf seems to avoid the RuntimeWarning.

 
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   Differences on datetime values appears after writing reindexed variable on netCDF file 185709414
358570582 https://github.com/pydata/xarray/issues/1064#issuecomment-358570582 https://api.github.com/repos/pydata/xarray/issues/1064 MDEyOklzc3VlQ29tbWVudDM1ODU3MDU4Mg== NotSqrt 1997005 2018-01-18T08:16:06Z 2018-01-18T08:16:06Z CONTRIBUTOR

There you go !

```python import numpy import pandas import tempfile import warnings import xarray

array1 = xarray.DataArray( numpy.random.rand(5), dims=['time'], coords={'time': pandas.to_datetime(['2018-01-01', '2018-01-01 00:01', '2018-01-01 00:02', '2018-01-01 00:03', '2018-01-01 00:04'])}, name='foo' )

array2 = xarray.DataArray( numpy.random.rand(5), dims=['time'], coords={'time': pandas.to_datetime(['2018-01-01 00:05', '2018-01-01 00:05:10', '2018-01-01 00:05:20', '2018-01-01 00:05:30', '2018-01-01 00:05:40'])}, name='foo' )

with tempfile.NamedTemporaryFile() as tmp: # save first array array1.to_netcdf(tmp.name) # reload it array1_reloaded = xarray.open_dataarray(tmp.name)

# the time encoding stores minutes as int, so seconds won't be allowed at next call of to_netcdf
assert array1_reloaded.time.encoding['dtype'] == numpy.int64
assert array1_reloaded.time.encoding['units'] == 'minutes since 2018-01-01 00:00:00'

merged = xarray.merge([array1_reloaded, array2])
array1_reloaded.close()

with warnings.catch_warnings():
    warnings.filterwarnings('error', category=RuntimeWarning)
    merged.to_netcdf(tmp.name)

```

 
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   Differences on datetime values appears after writing reindexed variable on netCDF file 185709414
358324488 https://github.com/pydata/xarray/issues/1064#issuecomment-358324488 https://api.github.com/repos/pydata/xarray/issues/1064 MDEyOklzc3VlQ29tbWVudDM1ODMyNDQ4OA== NotSqrt 1997005 2018-01-17T14:41:14Z 2018-01-17T14:41:14Z CONTRIBUTOR

I faced this issue when switching from a concat to a merge.

The first merged dataset had a time dimension which encoding says {'calendar': 'proleptic_gregorian', 'dtype': dtype('int64'), 'units': 'minutes since 2017-08-20 00:00:00'}, which meant that the data from the second merged dataset could not be stored with a finer resolution than minutes.

If I try to store values like '2017-08-20 00:00:30', I get the warning xarray\conventions.py:1092: RuntimeWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs.

Maybe it is similar in your case: netcdf stored the data as 'hours since XXXX', so you lose the minutes.

 
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   Differences on datetime values appears after writing reindexed variable on netCDF file 185709414

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