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  • Fix an overflow bug in decode_cf_datetime · 4 ✖

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375927133 https://github.com/pydata/xarray/pull/2015#issuecomment-375927133 https://api.github.com/repos/pydata/xarray/issues/2015 MDEyOklzc3VlQ29tbWVudDM3NTkyNzEzMw== fmaussion 10050469 2018-03-24T21:45:31Z 2018-03-24T21:45:31Z MEMBER

I am curious why this test is failing in appveyor. The casting rule of numpy should not depend on the operating system.

I'm curious too, but I don't have a windows machine to test it. The tests are green with the float64 casting though

 
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   Fix an overflow bug in decode_cf_datetime 308284379
375926070 https://github.com/pydata/xarray/pull/2015#issuecomment-375926070 https://api.github.com/repos/pydata/xarray/issues/2015 MDEyOklzc3VlQ29tbWVudDM3NTkyNjA3MA== fujiisoup 6815844 2018-03-24T21:26:54Z 2018-03-24T21:26:54Z MEMBER

I am curious why this test is failing in appveyor. The casting rule of numpy should not depend on the operating system.

 
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   Fix an overflow bug in decode_cf_datetime 308284379
375916591 https://github.com/pydata/xarray/pull/2015#issuecomment-375916591 https://api.github.com/repos/pydata/xarray/issues/2015 MDEyOklzc3VlQ29tbWVudDM3NTkxNjU5MQ== fujiisoup 6815844 2018-03-24T18:58:57Z 2018-03-24T18:58:57Z MEMBER

Thanks @fmaussion .

Is there a way to make sure this line casts to a secure dtype better than my proposed solution to cast everything to a float64 first?

How about np.array([np.int32(2)]) * np.array([np.int64(2)])?

I am not sure whether numpy explains this casting behavior, but I guess this is intentional. This is consistent with python In [11]: (np.array([1.0], dtype=np.float32) * 1.0).dtype Out[11]: dtype('float32') where we expect the array dtype takes precedence.

 
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   Fix an overflow bug in decode_cf_datetime 308284379
375914656 https://github.com/pydata/xarray/pull/2015#issuecomment-375914656 https://api.github.com/repos/pydata/xarray/issues/2015 MDEyOklzc3VlQ29tbWVudDM3NTkxNDY1Ng== fmaussion 10050469 2018-03-24T18:30:17Z 2018-03-24T18:30:17Z MEMBER

The problem comes from a weird numpy casting behavior. Consider:

```python In [1]: import numpy as np

In [2]: (np.int32(2) * np.int64(2)).dtype Out[2]: dtype('int64')

In [3]: (np.array(np.int32(2)) * np.int64(2)).dtype Out[3]: dtype('int64')

In [4]: (np.array([np.int32(2)]) * np.int64(2)).dtype Out[4]: dtype('int32') ```

In the offending line:

python flat_num_dates * _NS_PER_TIME_DELTA[delta] flat_num_dates is an array of dtype int32 and _NS_PER_TIME_DELTA[delta] is a scalar of dtype int64. Is there a way to make sure this line casts to a secure dtype better than my proposed solution to cast everything to a float64 first?

 
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   Fix an overflow bug in decode_cf_datetime 308284379

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