diff options
Diffstat (limited to 'numpy/lib/tests/test_nanfunctions.py')
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 266 |
1 files changed, 259 insertions, 7 deletions
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index af01a7167..c5af61434 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -5,21 +5,21 @@ import warnings import numpy as np from numpy.testing import ( run_module_suite, TestCase, assert_, assert_equal, assert_almost_equal, - assert_raises + assert_raises, assert_array_equal ) # Test data _ndat = np.array([[0.6244, np.nan, 0.2692, 0.0116, np.nan, 0.1170], - [0.5351, 0.9403, np.nan, 0.2100, 0.4759, 0.2833], - [np.nan, np.nan, np.nan, 0.1042, np.nan, 0.5954], + [0.5351, -0.9403, np.nan, 0.2100, 0.4759, 0.2833], + [np.nan, np.nan, np.nan, 0.1042, np.nan, -0.5954], [0.1610, np.nan, np.nan, 0.1859, 0.3146, np.nan]]) # Rows of _ndat with nans removed _rdat = [np.array([ 0.6244, 0.2692, 0.0116, 0.1170]), - np.array([ 0.5351, 0.9403, 0.2100, 0.4759, 0.2833]), - np.array([ 0.1042, 0.5954]), + np.array([ 0.5351, -0.9403, 0.2100, 0.4759, 0.2833]), + np.array([ 0.1042, -0.5954]), np.array([ 0.1610, 0.1859, 0.3146])] @@ -114,6 +114,31 @@ class TestNanFunctions_MinMax(TestCase): assert_(res.shape == (3, 1)) res = f(mat) assert_(np.isscalar(res)) + # check that rows of nan are dealt with for subclasses (#4628) + mat[1] = np.nan + for f in self.nanfuncs: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + res = f(mat, axis=0) + assert_(isinstance(res, np.matrix)) + assert_(not np.any(np.isnan(res))) + assert_(len(w) == 0) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + res = f(mat, axis=1) + assert_(isinstance(res, np.matrix)) + assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0]) + and not np.isnan(res[2, 0])) + assert_(len(w) == 1, 'no warning raised') + assert_(issubclass(w[0].category, RuntimeWarning)) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + res = f(mat) + assert_(np.isscalar(res)) + assert_(res != np.nan) + assert_(len(w) == 0) class TestNanFunctions_ArgminArgmax(TestCase): @@ -130,8 +155,8 @@ 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(): - warnings.simplefilter('ignore') + with warnings.catch_warnings(record=True): + warnings.simplefilter('always') ind = f(row) val = row[ind] # comparing with NaN is tricky as the result @@ -502,5 +527,232 @@ class TestNanFunctions_MeanVarStd(TestCase): assert_(np.isscalar(res)) +class TestNanFunctions_Median(TestCase): + + def test_mutation(self): + # Check that passed array is not modified. + ndat = _ndat.copy() + np.nanmedian(ndat) + assert_equal(ndat, _ndat) + + def test_keepdims(self): + mat = np.eye(3) + for axis in [None, 0, 1]: + tgt = np.median(mat, axis=axis, out=None, overwrite_input=False) + res = np.nanmedian(mat, axis=axis, out=None, overwrite_input=False) + assert_(res.ndim == tgt.ndim) + + d = np.ones((3, 5, 7, 11)) + # Randomly set some elements to NaN: + 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) + 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) + assert_equal(res.shape, (1, 1, 7, 11)) + res = np.nanmedian(d, axis=(0, 3), keepdims=True) + assert_equal(res.shape, (1, 5, 7, 1)) + res = np.nanmedian(d, axis=(1,), keepdims=True) + assert_equal(res.shape, (3, 1, 7, 11)) + res = np.nanmedian(d, axis=(0, 1, 2, 3), keepdims=True) + assert_equal(res.shape, (1, 1, 1, 1)) + res = np.nanmedian(d, axis=(0, 1, 3), keepdims=True) + assert_equal(res.shape, (1, 1, 7, 1)) + + def test_out(self): + mat = np.random.rand(3, 3) + nan_mat = np.insert(mat, [0, 2], np.nan, axis=1) + resout = np.zeros(3) + tgt = np.median(mat, axis=1) + res = np.nanmedian(nan_mat, axis=1, out=resout) + assert_almost_equal(res, resout) + assert_almost_equal(res, tgt) + # 0-d output: + resout = np.zeros(()) + tgt = np.median(mat, axis=None) + res = np.nanmedian(nan_mat, axis=None, out=resout) + assert_almost_equal(res, resout) + assert_almost_equal(res, tgt) + res = np.nanmedian(nan_mat, axis=(0, 1), out=resout) + assert_almost_equal(res, resout) + assert_almost_equal(res, tgt) + + def test_small_large(self): + # test the small and large code paths, current cutoff 400 elements + for s in [5, 20, 51, 200, 1000]: + d = np.random.randn(4, s) + # Randomly set some elements to NaN: + w = np.random.randint(0, d.size, size=d.size // 5) + d.ravel()[w] = np.nan + d[:,0] = 1. # ensure at least one good value + # use normal median without nans to compare + tgt = [] + for x in d: + nonan = np.compress(~np.isnan(x), x) + tgt.append(np.median(nonan, overwrite_input=True)) + + assert_array_equal(np.nanmedian(d, axis=-1), tgt) + + def test_result_values(self): + tgt = [np.median(d) for d in _rdat] + res = np.nanmedian(_ndat, axis=1) + assert_almost_equal(res, tgt) + + 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') + assert_(np.isnan(np.nanmedian(mat, axis=axis)).all()) + if axis is None: + assert_(len(w) == 1) + else: + assert_(len(w) == 3) + assert_(issubclass(w[0].category, RuntimeWarning)) + # Check scalar + assert_(np.isnan(np.nanmedian(np.nan))) + if axis is None: + assert_(len(w) == 2) + else: + assert_(len(w) == 4) + assert_(issubclass(w[0].category, RuntimeWarning)) + + def test_empty(self): + mat = np.zeros((0, 3)) + for axis in [0, None]: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + assert_(np.isnan(np.nanmedian(mat, axis=axis)).all()) + assert_(len(w) == 1) + assert_(issubclass(w[0].category, RuntimeWarning)) + for axis in [1]: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + assert_equal(np.nanmedian(mat, axis=axis), np.zeros([])) + assert_(len(w) == 0) + + def test_scalar(self): + assert_(np.nanmedian(0.) == 0.) + + def test_extended_axis_invalid(self): + d = np.ones((3, 5, 7, 11)) + assert_raises(IndexError, np.nanmedian, d, axis=-5) + assert_raises(IndexError, np.nanmedian, d, axis=(0, -5)) + assert_raises(IndexError, np.nanmedian, d, axis=4) + assert_raises(IndexError, np.nanmedian, d, axis=(0, 4)) + assert_raises(ValueError, np.nanmedian, d, axis=(1, 1)) + + +class TestNanFunctions_Percentile(TestCase): + + def test_mutation(self): + # Check that passed array is not modified. + ndat = _ndat.copy() + np.nanpercentile(ndat, 30) + assert_equal(ndat, _ndat) + + def test_keepdims(self): + mat = np.eye(3) + for axis in [None, 0, 1]: + tgt = np.percentile(mat, 70, axis=axis, out=None, + overwrite_input=False) + res = np.nanpercentile(mat, 70, axis=axis, out=None, + overwrite_input=False) + assert_(res.ndim == tgt.ndim) + + d = np.ones((3, 5, 7, 11)) + # Randomly set some elements to NaN: + 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) + 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) + assert_equal(res.shape, (1, 1, 7, 11)) + res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True) + assert_equal(res.shape, (1, 5, 7, 1)) + res = np.nanpercentile(d, 90, axis=(1,), keepdims=True) + assert_equal(res.shape, (3, 1, 7, 11)) + res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True) + assert_equal(res.shape, (1, 1, 1, 1)) + res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True) + assert_equal(res.shape, (1, 1, 7, 1)) + + def test_out(self): + mat = np.random.rand(3, 3) + nan_mat = np.insert(mat, [0, 2], np.nan, axis=1) + resout = np.zeros(3) + tgt = np.percentile(mat, 42, axis=1) + res = np.nanpercentile(nan_mat, 42, axis=1, out=resout) + assert_almost_equal(res, resout) + assert_almost_equal(res, tgt) + # 0-d output: + resout = np.zeros(()) + tgt = np.percentile(mat, 42, axis=None) + res = np.nanpercentile(nan_mat, 42, axis=None, out=resout) + assert_almost_equal(res, resout) + assert_almost_equal(res, tgt) + res = np.nanpercentile(nan_mat, 42, axis=(0, 1), out=resout) + assert_almost_equal(res, resout) + assert_almost_equal(res, tgt) + + def test_result_values(self): + tgt = [np.percentile(d, 28) for d in _rdat] + res = np.nanpercentile(_ndat, 28, axis=1) + assert_almost_equal(res, tgt) + tgt = [np.percentile(d, (28, 98)) for d in _rdat] + res = np.nanpercentile(_ndat, (28, 98), axis=1) + assert_almost_equal(res, tgt) + + 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') + assert_(np.isnan(np.nanpercentile(mat, 60, axis=axis)).all()) + if axis is None: + assert_(len(w) == 1) + else: + assert_(len(w) == 3) + assert_(issubclass(w[0].category, RuntimeWarning)) + # Check scalar + assert_(np.isnan(np.nanpercentile(np.nan, 60))) + if axis is None: + assert_(len(w) == 2) + else: + assert_(len(w) == 4) + assert_(issubclass(w[0].category, RuntimeWarning)) + + def test_empty(self): + mat = np.zeros((0, 3)) + for axis in [0, None]: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all()) + assert_(len(w) == 1) + assert_(issubclass(w[0].category, RuntimeWarning)) + for axis in [1]: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([])) + assert_(len(w) == 0) + + def test_scalar(self): + assert_(np.nanpercentile(0., 100) == 0.) + + def test_extended_axis_invalid(self): + d = np.ones((3, 5, 7, 11)) + assert_raises(IndexError, np.nanpercentile, d, q=5, axis=-5) + assert_raises(IndexError, np.nanpercentile, d, q=5, axis=(0, -5)) + assert_raises(IndexError, np.nanpercentile, d, q=5, axis=4) + assert_raises(IndexError, np.nanpercentile, d, q=5, axis=(0, 4)) + assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1)) + + if __name__ == "__main__": run_module_suite() |