diff options
Diffstat (limited to 'numpy/lib/tests/test_nanfunctions.py')
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 84 |
1 files changed, 45 insertions, 39 deletions
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 03f9beff6..06c0953b5 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -5,7 +5,7 @@ import warnings import numpy as np from numpy.testing import ( run_module_suite, TestCase, assert_, assert_equal, assert_almost_equal, - assert_warns, assert_no_warnings, assert_raises, assert_array_equal + assert_no_warnings, assert_raises, assert_array_equal, suppress_warnings ) @@ -167,8 +167,8 @@ class TestNanFunctions_ArgminArgmax(TestCase): def test_result_values(self): for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]): for row in _ndat: - with warnings.catch_warnings(record=True): - warnings.simplefilter('always') + with suppress_warnings() as sup: + sup.filter(RuntimeWarning, "invalid value encountered in") ind = f(row) val = row[ind] # comparing with NaN is tricky as the result @@ -317,26 +317,34 @@ class SharedNanFunctionsTestsMixin(object): codes = 'efdgFDG' for nf, rf in zip(self.nanfuncs, self.stdfuncs): for c in codes: - tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type - res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type - assert_(res is tgt) - # scalar case - tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type - res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type - assert_(res is tgt) + with suppress_warnings() as sup: + if nf in {np.nanstd, np.nanvar} and c in 'FDG': + # Giving the warning is a small bug, see gh-8000 + sup.filter(np.ComplexWarning) + tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type + res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type + assert_(res is tgt) + # scalar case + tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type + res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type + assert_(res is tgt) def test_dtype_from_char(self): mat = np.eye(3) codes = 'efdgFDG' for nf, rf in zip(self.nanfuncs, self.stdfuncs): for c in codes: - tgt = rf(mat, dtype=c, axis=1).dtype.type - res = nf(mat, dtype=c, axis=1).dtype.type - assert_(res is tgt) - # scalar case - tgt = rf(mat, dtype=c, axis=None).dtype.type - res = nf(mat, dtype=c, axis=None).dtype.type - assert_(res is tgt) + with suppress_warnings() as sup: + if nf in {np.nanstd, np.nanvar} and c in 'FDG': + # Giving the warning is a small bug, see gh-8000 + sup.filter(np.ComplexWarning) + tgt = rf(mat, dtype=c, axis=1).dtype.type + res = nf(mat, dtype=c, axis=1).dtype.type + assert_(res is tgt) + # scalar case + tgt = rf(mat, dtype=c, axis=None).dtype.type + res = nf(mat, dtype=c, axis=None).dtype.type + assert_(res is tgt) def test_dtype_from_input(self): codes = 'efdgFDG' @@ -524,16 +532,16 @@ class TestNanFunctions_MeanVarStd(TestCase, SharedNanFunctionsTestsMixin): dsize = [len(d) for d in _rdat] for nf, rf in zip(nanfuncs, stdfuncs): for ddof in range(5): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') + with suppress_warnings() as sup: + sup.record(RuntimeWarning) + sup.filter(np.ComplexWarning) tgt = [ddof >= d for d in dsize] res = nf(_ndat, axis=1, ddof=ddof) assert_equal(np.isnan(res), tgt) if any(tgt): - assert_(len(w) == 1) - assert_(issubclass(w[0].category, RuntimeWarning)) + assert_(len(sup.log) == 1) else: - assert_(len(w) == 0) + assert_(len(sup.log) == 0) def test_allnans(self): mat = np.array([np.nan]*9).reshape(3, 3) @@ -585,8 +593,8 @@ class TestNanFunctions_Median(TestCase): w = np.random.random((4, 200)) * np.array(d.shape)[:, None] w = w.astype(np.intp) d[tuple(w)] = np.nan - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always', RuntimeWarning) + with suppress_warnings() as sup: + sup.filter(RuntimeWarning) res = np.nanmedian(d, axis=None, keepdims=True) assert_equal(res.shape, (1, 1, 1, 1)) res = np.nanmedian(d, axis=(0, 1), keepdims=True) @@ -642,22 +650,20 @@ class TestNanFunctions_Median(TestCase): def test_allnans(self): mat = np.array([np.nan]*9).reshape(3, 3) for axis in [None, 0, 1]: - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - warnings.simplefilter('ignore', FutureWarning) + with suppress_warnings() as sup: + sup.record(RuntimeWarning) + assert_(np.isnan(np.nanmedian(mat, axis=axis)).all()) if axis is None: - assert_(len(w) == 1) + assert_(len(sup.log) == 1) else: - assert_(len(w) == 3) - assert_(issubclass(w[0].category, RuntimeWarning)) + assert_(len(sup.log) == 3) # Check scalar assert_(np.isnan(np.nanmedian(np.nan))) if axis is None: - assert_(len(w) == 2) + assert_(len(sup.log) == 2) else: - assert_(len(w) == 4) - assert_(issubclass(w[0].category, RuntimeWarning)) + assert_(len(sup.log) == 4) def test_empty(self): mat = np.zeros((0, 3)) @@ -685,8 +691,8 @@ class TestNanFunctions_Median(TestCase): assert_raises(ValueError, np.nanmedian, d, axis=(1, 1)) def test_float_special(self): - with warnings.catch_warnings(record=True): - warnings.simplefilter('ignore', RuntimeWarning) + with suppress_warnings() as sup: + sup.filter(RuntimeWarning) a = np.array([[np.inf, np.nan], [np.nan, np.nan]]) assert_equal(np.nanmedian(a, axis=0), [np.inf, np.nan]) assert_equal(np.nanmedian(a, axis=1), [np.inf, np.nan]) @@ -723,8 +729,8 @@ class TestNanFunctions_Percentile(TestCase): w = np.random.random((4, 200)) * np.array(d.shape)[:, None] w = w.astype(np.intp) d[tuple(w)] = np.nan - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always', RuntimeWarning) + with suppress_warnings() as sup: + sup.filter(RuntimeWarning) res = np.nanpercentile(d, 90, axis=None, keepdims=True) assert_equal(res.shape, (1, 1, 1, 1)) res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True) @@ -819,8 +825,8 @@ class TestNanFunctions_Percentile(TestCase): large_mat[:, :, 3:] *= 2 for axis in [None, 0, 1]: for keepdim in [False, True]: - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') + with suppress_warnings() as sup: + sup.filter(RuntimeWarning, "All-NaN slice encountered") val = np.percentile(mat, perc, axis=axis, keepdims=keepdim) nan_val = np.nanpercentile(nan_mat, perc, axis=axis, keepdims=keepdim) |