issue_comments

Table actions

3 rows where author_association = "NONE" and issue = 1389019400 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date)

issue 1

  • Handle NaNs when decoding times (failures on riscv64) · 3 ✖

author_association 1

  • NONE · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
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.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   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])

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   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.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Handle NaNs when decoding times (failures on riscv64) 1389019400

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issue_comments] (
   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [updated_at] TEXT,
   [author_association] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);