Bug#1004870: marked as done (python-xarray: autopkgtest regression on s390x)

Debian Bug Tracking System Tue, 06 Sep 2022 05:51:21 -0700

Your message dated Tue, 06 Sep 2022 12:49:49 +0000
with message-id <e1ovy1v-00ecpk...@fasolo.debian.org>
and subject line Bug#1004870: fixed in python-xarray 2022.06.0-6
has caused the Debian Bug report #1004870,
regarding python-xarray: autopkgtest regression on s390x
to be marked as done.


This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.

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-- 
1004870: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1004870
Debian Bug Tracking System
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--- Begin Message ---

Source: python-xarray
Version: 0.21.0-1
X-Debbugs-CC: debian...@lists.debian.org, debian-s...@lists.debian.org
Severity: serious
User: debian...@lists.debian.org
Usertags: regression

Hi Maintainer

python-xarray's autopkgtests are failing on the big-endian s390x
architecture [1].
I've copied what I hope is the relevant part of the log below.

Regards
Graham


[1] https://ci.debian.net/packages/p/python-xarray/unstable/s390x/


=================================== FAILURES ===================================
_______________________ test_calendar_cftime_2D[365_day] _______________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[0.25602205, 0.47375523, 0.88418655, ..., 0.19579452,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: -259805407763208-03-07 00:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime
_______________________ test_calendar_cftime_2D[360_day] _______________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[0.3348676 , 0.8813548 , 0.07158625, ..., 0.12469613,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: 768533895196513-09-16 16:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime
_______________________ test_calendar_cftime_2D[julian] ________________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[0.05513783, 0.72362925, 0.78967474, ..., 0.8560986 ,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: 904522921033531-11-08 08:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime
______________________ test_calendar_cftime_2D[all_leap] _______________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[0.02927022, 0.10328084, 0.12428704, ..., 0.83960594,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: -77577995854656-10-03 16:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime
_______________________ test_calendar_cftime_2D[366_day] _______________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[0.32570151, 0.71143133, 0.43459037, ..., 0.14784034,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: 391106800438843-10-05 00:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime
______________________ test_calendar_cftime_2D[gregorian] ______________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[0.91161183, 0.42436822, 0.53522578, ..., 0.36468928,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: -690921531052547-07-02 08:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime
_________________ test_calendar_cftime_2D[proleptic_gregorian] _________________

data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)>
array([[[8.84930980e-01, 9.76547499e-01, 4.34131057e-01, ...,
...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0
  * time     (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00

    @requires_cftime
    def test_calendar_cftime_2D(data) -> None:
        # 2D np datetime:
>       data = xr.DataArray(
            np.random.randint(1, 1000000, size=(4,
5)).astype("<M8[h]"), dims=("x", "y")
        )

/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__
    data = as_compatible_data(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in
as_compatible_data
    data = _possibly_convert_objects(data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in
_possibly_convert_objects
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__
    data = sanitize_array(data, index, dtype, copy)
/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in
sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast
    return sanitize_to_nanoseconds(arr, copy=copy)
/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in
sanitize_to_nanoseconds
    values = conversion.ensure_datetime64ns(values)
pandas/_libs/tslibs/conversion.pyx:256: in
pandas._libs.tslibs.conversion.ensure_datetime64ns
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds
nanosecond timestamp: -67784665697082-12-03 00:00:00

pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime

--- End Message ---

--- Begin Message ---

Source: python-xarray
Source-Version: 2022.06.0-6
Done: Alastair McKinstry <mckins...@debian.org>

We believe that the bug you reported is fixed in the latest version of
python-xarray, which is due to be installed in the Debian FTP archive.

A summary of the changes between this version and the previous one is
attached.

Thank you for reporting the bug, which will now be closed.  If you
have further comments please address them to 1004...@bugs.debian.org,
and the maintainer will reopen the bug report if appropriate.

Debian distribution maintenance software
pp.
Alastair McKinstry <mckins...@debian.org> (supplier of updated python-xarray 
package)

(This message was generated automatically at their request; if you
believe that there is a problem with it please contact the archive
administrators by mailing ftpmas...@ftp-master.debian.org)


-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256

Format: 1.8
Date: Tue, 06 Sep 2022 13:29:36 +0100
Source: python-xarray
Architecture: source
Version: 2022.06.0-6
Distribution: unstable
Urgency: medium
Maintainer: Debian Science Maintainers 
<debian-science-maintain...@lists.alioth.debian.org>
Changed-By: Alastair McKinstry <mckins...@debian.org>
Closes: 1004870
Changes:
 python-xarray (2022.06.0-6) unstable; urgency=medium
 .
   * Patch for cftime test failure on s390x. Closes: #1004870
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--- End Message ---

Bug#1004870: marked as done (python-xarray: autopkgtest regression on s390x)

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