<div class="socmaildefaultfont" dir="ltr" style="font-family:Arial, Helvetica, sans-serif;font-size:10pt" ><div dir="ltr" ><font face="Courier New, Courier, monospace" >Hi Graham,</font></div>
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<div dir="ltr" ><font face="Courier New, Courier, monospace" >This issue is again specific to no proper handling for bug endian environment.</font></div>
<div dir="ltr" > </div>
<div dir="ltr" ><font face="Courier New, Courier, monospace" >There is a fix from pandas library maintainers for this issue.</font></div>
<div dir="ltr" > </div>
<div dir="ltr" ><font face="Courier New, Courier, monospace" ><a href="https://github.com/pandas-dev/pandas/pull/40116">https://github.com/pandas-dev/pandas/pull/40116</a></font></div>
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<div dir="ltr" ><font face="Courier New, Courier, monospace" >Please find the <span style="font-size: 13.3333px;" >link</span> above and check if this resolves your issue.</font></div>
<div dir="ltr" > </div>
<div dir="ltr" ><font face="Courier New, Courier, monospace" >Thanks,</font></div>
<div dir="ltr" ><font face="Courier New, Courier, monospace" >Rajendra</font></div>
<div dir="ltr" > </div>
<blockquote data-history-content-modified="1" dir="ltr" style="border-left:solid #aaaaaa 2px; margin-left:5px; padding-left:5px; direction:ltr; margin-right:0px" >----- Original message -----<br>From: Rajendra Kharat1/India/Contr/IBM<br>To: ginggs@debian.org, 1004870@bugs.debian.org<br>Cc: submit@bugs.debian.org<br>Subject: Re: [EXTERNAL] Bug#1004870: python-xarray: autopkgtest regression on s390x<br>Date: Thu, Feb 3, 2022 3:34 PM<br>
<div dir="ltr" style="font-family:Arial, Helvetica, sans-serif;font-size:10pt" ><div dir="ltr" >We are having a look. Will check and update.</div>
<div dir="ltr" > </div>
<div dir="ltr" >Thanks,</div>
<div dir="ltr" >Rajendra</div>
<div dir="ltr" > </div>
<blockquote data-history-content-modified="1" dir="ltr" style="border-left:solid #aaaaaa 2px; margin-left:5px; padding-left:5px; direction:ltr; margin-right:0px" >----- Original message -----<br>From: "Graham Inggs" <ginggs@debian.org><br>To: "Debian Bug Tracking System" <submit@bugs.debian.org><br>Cc:<br>Subject: [EXTERNAL] Bug#1004870: python-xarray: autopkgtest regression on s390x<br>Date: Thu, Feb 3, 2022 1:06 AM<br>
<div><font face="Default Monospace,Courier New,Courier,monospace" size="2" >Source: python-xarray<br>Version: 0.21.0-1<br>X-Debbugs-CC: debian-ci@lists.debian.org, debian-s390@lists.debian.org<br>Severity: serious<br>User: debian-ci@lists.debian.org<br>Usertags: regression<br><br>Hi Maintainer<br><br>python-xarray's autopkgtests are failing on the big-endian s390x<br>architecture [1].<br>I've copied what I hope is the relevant part of the log below.<br><br>Regards<br>Graham<br><br><br>[1] <a href="https://ci.debian.net/packages/p/python-xarray/unstable/s390x/" target="_blank">https://ci.debian.net/packages/p/python-xarray/unstable/s390x/</a> <br><br><br>=================================== FAILURES ===================================<br>_______________________ test_calendar_cftime_2D[365_day] _______________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[0.25602205, 0.47375523, 0.88418655, ..., 0.19579452,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: -259805407763208-03-07 00:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime<br>_______________________ test_calendar_cftime_2D[360_day] _______________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[0.3348676 , 0.8813548 , 0.07158625, ..., 0.12469613,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: 768533895196513-09-16 16:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime<br>_______________________ test_calendar_cftime_2D[julian] ________________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[0.05513783, 0.72362925, 0.78967474, ..., 0.8560986 ,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: 904522921033531-11-08 08:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime<br>______________________ test_calendar_cftime_2D[all_leap] _______________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[0.02927022, 0.10328084, 0.12428704, ..., 0.83960594,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: -77577995854656-10-03 16:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime<br>_______________________ test_calendar_cftime_2D[366_day] _______________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[0.32570151, 0.71143133, 0.43459037, ..., 0.14784034,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: 391106800438843-10-05 00:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime<br>______________________ test_calendar_cftime_2D[gregorian] ______________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[0.91161183, 0.42436822, 0.53522578, ..., 0.36468928,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: -690921531052547-07-02 08:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime<br>_________________ test_calendar_cftime_2D[proleptic_gregorian] _________________<br><br>data = <xarray.DataArray 'data' (lon: 10, lat: 10, time: 100)><br>array([[[8.84930980e-01, 9.76547499e-01, 4.34131057e-01, ...,<br>...0.0 2.222 4.444 6.667 ... 13.33 15.56 17.78 20.0<br> * time (time) object 2000-01-01 00:00:00 ... 2000-01-05 03:00:00<br><br> @requires_cftime<br> def test_calendar_cftime_2D(data) -> None:<br> # 2D np datetime:<br>> data = xr.DataArray(<br> np.random.randint(1, 1000000, size=(4,<br>5)).astype("<M8[h]"), dims=("x", "y")<br> )<br><br>/usr/lib/python3/dist-packages/xarray/tests/test_accessor_dt.py:426:<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>/usr/lib/python3/dist-packages/xarray/core/dataarray.py:400: in __init__<br> data = as_compatible_data(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:232: in<br>as_compatible_data<br> data = _possibly_convert_objects(data)<br>/usr/lib/python3/dist-packages/xarray/core/variable.py:176: in<br>_possibly_convert_objects<br> return np.asarray(pd.Series(values.ravel())).reshape(values.shape)<br>/usr/lib/python3/dist-packages/pandas/core/series.py:439: in __init__<br> data = sanitize_array(data, index, dtype, copy)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:545: in<br>sanitize_array<br> subarr = _try_cast(data, dtype, copy, raise_cast_failure)<br>/usr/lib/python3/dist-packages/pandas/core/construction.py:704: in _try_cast<br> return sanitize_to_nanoseconds(arr, copy=copy)<br>/usr/lib/python3/dist-packages/pandas/core/dtypes/cast.py:1740: in<br>sanitize_to_nanoseconds<br> values = conversion.ensure_datetime64ns(values)<br>pandas/_libs/tslibs/conversion.pyx:256: in<br>pandas._libs.tslibs.conversion.ensure_datetime64ns<br> ???<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br><br>> ???<br>E pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds<br>nanosecond timestamp: -67784665697082-12-03 00:00:00<br><br>pandas/_libs/tslibs/np_datetime.pyx:120: OutOfBoundsDatetime</font><br> </div></blockquote>
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