issue_comments: 1264643360

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https://github.com/pydata/xarray/issues/7096#issuecomment-1264643360 https://api.github.com/repos/pydata/xarray/issues/7096 1264643360 IC_kwDOAMm_X85LYO0g 1006477 2022-10-02T13:23:06Z 2022-10-02T13:23:06Z NONE

Hi, we are getting similar failures when building xarray for Arch Linux riscv64.

I'm not sure what that has to do with xarray though? Does this give the same result?

import numpy as np import pandas as pd num_dates = np.asarray([0., np.nan]) flat_num_dates = num_dates.ravel() flat_num_dates_ns_int = (flat_num_dates * (int(1e9) * 60 * 60 * 24)).astype(np.int64) flat_num_dates_ns_int array([ 0, 9223372036854775807])

I got the same result in riscv64. One thing I could guess is that the sign bit of NaN is not kept during conversions. Some more details could be found at: https://sourceware.org/pipermail/libc-alpha/2022-September/142011.html

Repeating the same steps result in array([0, -9223372036854775808]) in x86_64 and array([0, 0]) in aarch64.

Please could you answer the question on whether pandas tests pass?

I have tried pandas' tests and got many failures like:

E AssertionError: Attributes of DataFrame.iloc[:, 4] (column name="date") are different E E Attribute "dtype" are different E [left]: float64 E [right]: datetime64[ns] or E AssertionError: numpy array are different E E numpy array values are different (50.0 %) E [index]: [0, 1] E [left]: [1036713600000, -9223372036854775808] E [right]: [1036713600000000000, -9223372036854775808]

Quite some of the tests are having NaN in the context as well. So you are probably right that pandas or numpy may be where the problem lies.

 
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