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  • Handle NaNs when decoding times (failures on riscv64) · 9 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1265711007 https://github.com/pydata/xarray/issues/7096#issuecomment-1265711007 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LcTef dcherian 2448579 2022-10-03T16:22:19Z 2022-10-03T16:22:19Z MEMBER

As in #7098

I think the real solution here is to explicitly handle NaNs during the decoding step. We do want these to be NaT in the output.

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1264693714 https://github.com/pydata/xarray/issues/7096#issuecomment-1264693714 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LYbHS max-sixty 5635139 2022-10-02T17:27:33Z 2022-10-02T17:27:33Z MEMBER

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

Thanks for trying that. Notably, that code doesn't have xarray in. So I'm keen to be part of the solution, but it doesn't look to be a problem with xarray code specifically. Let me know if that makes sense.

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1264643360 https://github.com/pydata/xarray/issues/7096#issuecomment-1264643360 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LYO0g felixonmars 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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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1264535517 https://github.com/pydata/xarray/issues/7096#issuecomment-1264535517 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LX0fd max-sixty 5635139 2022-10-02T02:47:22Z 2022-10-02T02:47:22Z MEMBER

Closing but please feel free to reopen

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1261481952 https://github.com/pydata/xarray/issues/7096#issuecomment-1261481952 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LMK_g max-sixty 5635139 2022-09-28T21:26:09Z 2022-09-28T21:26:09Z MEMBER

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])

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

We're here helping as volunteers; we can only engage on issues if you reciprocate our good faith. Please could you answer this?

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1261436738 https://github.com/pydata/xarray/issues/7096#issuecomment-1261436738 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LL_9C andreas-schwab 2175493 2022-09-28T20:35:34Z 2022-09-28T20:35:34Z NONE

array([ 0, 9223372036854775807])

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1261412104 https://github.com/pydata/xarray/issues/7096#issuecomment-1261412104 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LL58I max-sixty 5635139 2022-09-28T20:07:57Z 2022-09-28T20:07:57Z MEMBER

What are the bogus values?

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

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1261313113 https://github.com/pydata/xarray/issues/7096#issuecomment-1261313113 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LLhxZ andreas-schwab 2175493 2022-09-28T18:31:14Z 2022-09-28T18:31:14Z NONE

On Sep 28 2022, Maximilian Roos wrote:

It looks lie many of these occur in pandas code — do pandas tests pass?

That's because xarray is passing bogus values.

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400
1261237590 https://github.com/pydata/xarray/issues/7096#issuecomment-1261237590 https://api.github.com/repos/pydata/xarray/issues/7096 IC_kwDOAMm_X85LLPVW max-sixty 5635139 2022-09-28T17:34:03Z 2022-09-28T17:34:03Z MEMBER

It looks lie many of these occur in pandas code — do pandas tests pass?

 
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   Handle NaNs when decoding times (failures on riscv64) 1389019400

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