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Index: scipy/scipy/stats/tests/test_axis_nan_policy.py
===================================================================
--- scipy.orig/scipy/stats/tests/test_axis_nan_policy.py 2025-01-21 12:40:26.637357722 +0100
+++ scipy/scipy/stats/tests/test_axis_nan_policy.py 2025-01-21 12:48:43.012564773 +0100
@@ -466,6 +466,144 @@
assert_allclose(res_nd, res_1d, rtol=1e-14)
+ """
+ test_axis_nan_policy_axis_is_None fails with [mixed-*-xp*] on mips64el
+ see https://github.com/scipy/scipy/issues/22360
+
+ The following set of tests tries various permutations of the array
+ aiming to help diagnose the problem.
+ """
+def test_nan_array_asany():
+ import warnings
+ from scipy._lib.array_api_compat import numpy as np
+ nan = np.nan
+ arr = np.asanyarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=None)
+
+def test_nan_array_asarray():
+ import warnings
+ from scipy._lib.array_api_compat import numpy as np
+ nan = np.nan
+ arr = np.asarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=None)
+
+def test_nan_array_asany_numpy():
+ import warnings
+ import numpy as np
+ nan = np.nan
+ arr = np.asanyarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=None)
+
+def test_nan_array_asarray_numpy():
+ import warnings
+ import numpy as np
+ nan = np.nan
+ arr = np.asarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=None)
+
+def test_nan_array_asany_axis0():
+ import warnings
+ from scipy._lib.array_api_compat import numpy as np
+ nan = np.nan
+ arr = np.asanyarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=0)
+
+def test_nan_array_asarray_axis0():
+ import warnings
+ from scipy._lib.array_api_compat import numpy as np
+ nan = np.nan
+ arr = np.asarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=0)
+
+def test_nan_array_asany_numpy_axis0():
+ import warnings
+ import numpy as np
+ nan = np.nan
+ arr = np.asanyarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=0)
+
+def test_nan_array_asarray_numpy_axis0():
+ import warnings
+ import numpy as np
+ nan = np.nan
+ arr = np.asarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr, axis=0)
+
+def test_nan_array_asany_axis_default():
+ import warnings
+ from scipy._lib.array_api_compat import numpy as np
+ nan = np.nan
+ arr = np.asanyarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr)
+
+def test_nan_array_asarray_axis_default():
+ import warnings
+ from scipy._lib.array_api_compat import numpy as np
+ nan = np.nan
+ arr = np.asarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr)
+
+def test_nan_array_asany_numpy_axis_default():
+ import warnings
+ import numpy as np
+ nan = np.nan
+ arr = np.asanyarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr)
+
+def test_nan_array_asarray_numpy_axis_default():
+ import warnings
+ import numpy as np
+ nan = np.nan
+ arr = np.asarray([0.63696169, 0.26978671, 0.04097352, 0.01652764, 0.81327024,
+ 0.91275558, 0.60663578, 0.72949656, 0.543624, nan, 0.13509651,
+ nan, 0.52535432, 0.31024188, 0.48583536, nan, nan, 0.3577952 ])
+ with warnings.catch_warnings():
+ warnings.simplefilter('error')
+ np.mean(arr)
@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
"paired", "unpacker"), axis_nan_policy_cases)
|
