diff options
Diffstat (limited to 'numpy/lib/tests')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 33 | ||||
| -rw-r--r-- | numpy/lib/tests/test_io.py | 29 | ||||
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 84 | ||||
| -rw-r--r-- | numpy/lib/tests/test_twodim_base.py | 2 |
4 files changed, 73 insertions, 75 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 0d7b11c44..4535c1e7f 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -8,7 +8,7 @@ from numpy.testing import ( run_module_suite, TestCase, assert_, assert_equal, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_allclose, assert_array_max_ulp, assert_warns, - assert_raises_regex, dec, clear_and_catch_warnings + assert_raises_regex, dec, suppress_warnings ) from numpy.testing.utils import HAS_REFCOUNT import numpy.lib.function_base as nfb @@ -320,7 +320,11 @@ class TestAverage(TestCase): a = np.array([[1,2],[3,4]]).view(subclass) w = np.array([[1,2],[3,4]]).view(subclass) - assert_equal(type(np.average(a, weights=w)), subclass) + with suppress_warnings() as sup: + # Note that the warning is spurious, because the test checks + # for weights while a is ignored. + sup.filter(FutureWarning, "np.average currently does not preserve") + assert_equal(type(np.average(a, weights=w)), subclass) # also test matrices a = np.matrix([[1,2],[3,4]]) @@ -1457,12 +1461,12 @@ class TestHistogram(TestCase): a, b = histogram([], bins=([0, 1])) assert_array_equal(a, np.array([0])) assert_array_equal(b, np.array([0, 1])) - + def test_error_binnum_type (self): # Tests if right Error is raised if bins argument is float vals = np.linspace(0.0, 1.0, num=100) histogram(vals, 5) - assert_raises(TypeError, histogram, vals, 2.4) + assert_raises(TypeError, histogram, vals, 2.4) def test_finite_range(self): # Normal ranges should be fine @@ -1792,15 +1796,6 @@ class TestCheckFinite(TestCase): assert_(a.dtype == np.float64) -class catch_warn_nfb(clear_and_catch_warnings): - - """ - Context manager to catch, reset warnings in function_base module - - """ - class_modules = (nfb,) - - class TestCorrCoef(TestCase): A = np.array( [[0.15391142, 0.18045767, 0.14197213], @@ -1837,10 +1832,10 @@ class TestCorrCoef(TestCase): def test_ddof(self): # ddof raises DeprecationWarning - with catch_warn_nfb(): + with suppress_warnings() as sup: warnings.simplefilter("always") assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1) - warnings.simplefilter("ignore") + sup.filter(DeprecationWarning) # ddof has no or negligible effect on the function assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1) assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2) @@ -1849,11 +1844,11 @@ class TestCorrCoef(TestCase): def test_bias(self): # bias raises DeprecationWarning - with catch_warn_nfb(): + with suppress_warnings() as sup: warnings.simplefilter("always") assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0) assert_warns(DeprecationWarning, corrcoef, self.A, bias=0) - warnings.simplefilter("ignore") + sup.filter(DeprecationWarning) # bias has no or negligible effect on the function assert_almost_equal(corrcoef(self.A, bias=1), self.res1) @@ -2316,7 +2311,7 @@ class TestInterp(TestCase): assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.nan assert_almost_equal(np.interp(x0, x, y), x0) - + def test_complex_interp(self): # test complex interpolation x = np.linspace(0, 1, 5) @@ -2335,7 +2330,7 @@ class TestInterp(TestCase): x = [-180, -170, -185, 185, -10, -5, 0, 365] xp = [190, -190, 350, -350] fp = [5+1.0j, 10+2j, 3+3j, 4+4j] - y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j, + y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j, 3.5+3.5j, 3.75+3.75j] assert_almost_equal(np.interp(x, xp, fp, period=360), y) diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py index 1474349de..333891d46 100644 --- a/numpy/lib/tests/test_io.py +++ b/numpy/lib/tests/test_io.py @@ -19,7 +19,7 @@ from numpy.ma.testutils import assert_equal from numpy.testing import ( TestCase, run_module_suite, assert_warns, assert_, assert_raises_regex, assert_raises, assert_allclose, - assert_array_equal, temppath, dec, IS_PYPY + assert_array_equal, temppath, dec, IS_PYPY, suppress_warnings ) @@ -282,8 +282,8 @@ class TestSavezLoad(RoundtripTest, TestCase): # collector, so we catch the warnings. Because ResourceWarning # is unknown in Python < 3.x, we take the easy way out and # catch all warnings. - with warnings.catch_warnings(): - warnings.simplefilter("ignore") + with suppress_warnings() as sup: + sup.filter(Warning) # TODO: specify exact message for i in range(1, 1025): try: np.load(tmp)["data"] @@ -687,9 +687,8 @@ class TestLoadTxt(TestCase): assert_array_equal(x, a) def test_empty_file(self): - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", - message="loadtxt: Empty input file:") + with suppress_warnings() as sup: + sup.filter(message="loadtxt: Empty input file:") c = TextIO() x = np.loadtxt(c) assert_equal(x.shape, (0,)) @@ -826,9 +825,8 @@ class TestLoadTxt(TestCase): assert_(x.shape == (3,)) # Test ndmin kw with empty file. - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", - message="loadtxt: Empty input file:") + with suppress_warnings() as sup: + sup.filter(message="loadtxt: Empty input file:") f = TextIO() assert_(np.loadtxt(f, ndmin=2).shape == (0, 1,)) assert_(np.loadtxt(f, ndmin=1).shape == (0,)) @@ -974,8 +972,8 @@ class TestFromTxt(TestCase): assert_equal(test, ctrl) def test_skip_footer_with_invalid(self): - with warnings.catch_warnings(): - warnings.filterwarnings("ignore") + with suppress_warnings() as sup: + sup.filter(ConversionWarning) basestr = '1 1\n2 2\n3 3\n4 4\n5 \n6 \n7 \n' # Footer too small to get rid of all invalid values assert_raises(ValueError, np.genfromtxt, @@ -1302,9 +1300,8 @@ M 33 21.99 def test_empty_file(self): # Test that an empty file raises the proper warning. - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", - message="genfromtxt: Empty input file:") + with suppress_warnings() as sup: + sup.filter(message="genfromtxt: Empty input file:") data = TextIO() test = np.genfromtxt(data) assert_equal(test, np.array([])) @@ -1751,8 +1748,8 @@ M 33 21.99 assert_raises(ValueError, np.genfromtxt, TextIO(data), max_rows=4) # Test with invalid not raise - with warnings.catch_warnings(): - warnings.filterwarnings("ignore") + with suppress_warnings() as sup: + sup.filter(ConversionWarning) test = np.genfromtxt(TextIO(data), max_rows=4, invalid_raise=False) control = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]]) 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) diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py index 31925d5fe..98b8aa39c 100644 --- a/numpy/lib/tests/test_twodim_base.py +++ b/numpy/lib/tests/test_twodim_base.py @@ -5,7 +5,7 @@ from __future__ import division, absolute_import, print_function from numpy.testing import ( TestCase, run_module_suite, assert_equal, assert_array_equal, - assert_array_max_ulp, assert_array_almost_equal, assert_raises + assert_array_max_ulp, assert_array_almost_equal, assert_raises, ) from numpy import ( |