diff options
Diffstat (limited to 'numpy/lib/tests')
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 237 |
1 files changed, 171 insertions, 66 deletions
diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 70e7865db..af01a7167 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -7,31 +7,25 @@ from numpy.testing import ( run_module_suite, TestCase, assert_, assert_equal, assert_almost_equal, assert_raises ) -from numpy.lib import ( - nansum, nanmax, nanargmax, nanargmin, nanmin, nanmean, nanvar, nanstd, - NanWarning - ) -_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], - [0.161, np.nan, np.nan, 0.1859, 0.3146, np.nan]] -) +# 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], + [0.1610, 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([0.1610, 0.1859, 0.3146]) -] +# 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([ 0.1610, 0.1859, 0.3146])] class TestNanFunctions_MinMax(TestCase): - nanfuncs = [nanmin, nanmax] + nanfuncs = [np.nanmin, np.nanmax] stdfuncs = [np.min, np.max] def test_mutation(self): @@ -81,22 +75,50 @@ class TestNanFunctions_MinMax(TestCase): mat = np.array([np.nan]*9).reshape(3, 3) for f in self.nanfuncs: for axis in [None, 0, 1]: - assert_(np.isnan(f(mat, axis=axis)).all()) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + assert_(np.isnan(f(mat, axis=axis)).all()) + assert_(len(w) == 1, 'no warning raised') + assert_(issubclass(w[0].category, RuntimeWarning)) + # Check scalars + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + assert_(np.isnan(f(np.nan))) + assert_(len(w) == 1, 'no warning raised') + assert_(issubclass(w[0].category, RuntimeWarning)) def test_masked(self): mat = np.ma.fix_invalid(_ndat) msk = mat._mask.copy() - for f in [nanmin]: + for f in [np.nanmin]: res = f(mat, axis=1) tgt = f(_ndat, axis=1) assert_equal(res, tgt) assert_equal(mat._mask, msk) assert_(not np.isinf(mat).any()) + def test_scalar(self): + for f in self.nanfuncs: + assert_(f(0.) == 0.) + + def test_matrices(self): + # Check that it works and that type and + # shape are preserved + mat = np.matrix(np.eye(3)) + for f in self.nanfuncs: + res = f(mat, axis=0) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (1, 3)) + res = f(mat, axis=1) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (3, 1)) + res = f(mat) + assert_(np.isscalar(res)) + class TestNanFunctions_ArgminArgmax(TestCase): - nanfuncs = [nanargmin, nanargmax] + nanfuncs = [np.nanargmin, np.nanargmax] def test_mutation(self): # Check that passed array is not modified. @@ -120,15 +142,10 @@ class TestNanFunctions_ArgminArgmax(TestCase): def test_allnans(self): mat = np.array([np.nan]*9).reshape(3, 3) - tgt = np.iinfo(np.intp).min for f in self.nanfuncs: for axis in [None, 0, 1]: - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - res = f(mat, axis=axis) - assert_((res == tgt).all()) - assert_(len(w) == 1) - assert_(issubclass(w[0].category, NanWarning)) + assert_raises(ValueError, f, mat, axis=axis) + assert_raises(ValueError, f, np.nan) def test_empty(self): mat = np.zeros((0, 3)) @@ -139,39 +156,83 @@ class TestNanFunctions_ArgminArgmax(TestCase): res = f(mat, axis=axis) assert_equal(res, np.zeros(0)) + def test_scalar(self): + for f in self.nanfuncs: + assert_(f(0.) == 0.) + + def test_matrices(self): + # Check that it works and that type and + # shape are preserved + mat = np.matrix(np.eye(3)) + for f in self.nanfuncs: + res = f(mat, axis=0) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (1, 3)) + res = f(mat, axis=1) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (3, 1)) + res = f(mat) + assert_(np.isscalar(res)) + class TestNanFunctions_IntTypes(TestCase): - int_types = ( - np.int8, np.int16, np.int32, np.int64, np.uint8, - np.uint16, np.uint32, np.uint64) + int_types = (np.int8, np.int16, np.int32, np.int64, np.uint8, + np.uint16, np.uint32, np.uint64) - def setUp(self, *args, **kwargs): - self.mat = np.array([127, 39, 93, 87, 46]) + mat = np.array([127, 39, 93, 87, 46]) def integer_arrays(self): for dtype in self.int_types: yield self.mat.astype(dtype) def test_nanmin(self): - min_value = min(self.mat) + tgt = np.min(self.mat) for mat in self.integer_arrays(): - assert_equal(nanmin(mat), min_value) + assert_equal(np.nanmin(mat), tgt) def test_nanmax(self): - max_value = max(self.mat) + tgt = np.max(self.mat) for mat in self.integer_arrays(): - assert_equal(nanmax(mat), max_value) + assert_equal(np.nanmax(mat), tgt) def test_nanargmin(self): - min_arg = np.argmin(self.mat) + tgt = np.argmin(self.mat) for mat in self.integer_arrays(): - assert_equal(nanargmin(mat), min_arg) + assert_equal(np.nanargmin(mat), tgt) def test_nanargmax(self): - max_arg = np.argmax(self.mat) + tgt = np.argmax(self.mat) for mat in self.integer_arrays(): - assert_equal(nanargmax(mat), max_arg) + assert_equal(np.nanargmax(mat), tgt) + + def test_nansum(self): + tgt = np.sum(self.mat) + for mat in self.integer_arrays(): + assert_equal(np.nansum(mat), tgt) + + def test_nanmean(self): + tgt = np.mean(self.mat) + for mat in self.integer_arrays(): + assert_equal(np.nanmean(mat), tgt) + + def test_nanvar(self): + tgt = np.var(self.mat) + for mat in self.integer_arrays(): + assert_equal(np.nanvar(mat), tgt) + + tgt = np.var(mat, ddof=1) + for mat in self.integer_arrays(): + assert_equal(np.nanvar(mat, ddof=1), tgt) + + def test_nanstd(self): + tgt = np.std(self.mat) + for mat in self.integer_arrays(): + assert_equal(np.nanstd(mat), tgt) + + tgt = np.std(self.mat, ddof=1) + for mat in self.integer_arrays(): + assert_equal(np.nanstd(mat, ddof=1), tgt) class TestNanFunctions_Sum(TestCase): @@ -179,21 +240,21 @@ class TestNanFunctions_Sum(TestCase): def test_mutation(self): # Check that passed array is not modified. ndat = _ndat.copy() - nansum(ndat) + np.nansum(ndat) assert_equal(ndat, _ndat) def test_keepdims(self): mat = np.eye(3) for axis in [None, 0, 1]: tgt = np.sum(mat, axis=axis, keepdims=True) - res = nansum(mat, axis=axis, keepdims=True) + res = np.nansum(mat, axis=axis, keepdims=True) assert_(res.ndim == tgt.ndim) def test_out(self): mat = np.eye(3) resout = np.zeros(3) tgt = np.sum(mat, axis=1) - res = nansum(mat, axis=1, out=resout) + res = np.nansum(mat, axis=1, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) @@ -202,11 +263,11 @@ class TestNanFunctions_Sum(TestCase): codes = 'efdgFDG' for c in codes: tgt = np.sum(mat, dtype=np.dtype(c), axis=1).dtype.type - res = nansum(mat, dtype=np.dtype(c), axis=1).dtype.type + res = np.nansum(mat, dtype=np.dtype(c), axis=1).dtype.type assert_(res is tgt) # scalar case tgt = np.sum(mat, dtype=np.dtype(c), axis=None).dtype.type - res = nansum(mat, dtype=np.dtype(c), axis=None).dtype.type + res = np.nansum(mat, dtype=np.dtype(c), axis=None).dtype.type assert_(res is tgt) def test_dtype_from_char(self): @@ -214,11 +275,11 @@ class TestNanFunctions_Sum(TestCase): codes = 'efdgFDG' for c in codes: tgt = np.sum(mat, dtype=c, axis=1).dtype.type - res = nansum(mat, dtype=c, axis=1).dtype.type + res = np.nansum(mat, dtype=c, axis=1).dtype.type assert_(res is tgt) # scalar case tgt = np.sum(mat, dtype=c, axis=None).dtype.type - res = nansum(mat, dtype=c, axis=None).dtype.type + res = np.nansum(mat, dtype=c, axis=None).dtype.type assert_(res is tgt) def test_dtype_from_input(self): @@ -226,43 +287,65 @@ class TestNanFunctions_Sum(TestCase): for c in codes: mat = np.eye(3, dtype=c) tgt = np.sum(mat, axis=1).dtype.type - res = nansum(mat, axis=1).dtype.type + res = np.nansum(mat, axis=1).dtype.type assert_(res is tgt) # scalar case tgt = np.sum(mat, axis=None).dtype.type - res = nansum(mat, axis=None).dtype.type + res = np.nansum(mat, axis=None).dtype.type assert_(res is tgt) def test_result_values(self): tgt = [np.sum(d) for d in _rdat] - res = nansum(_ndat, axis=1) + res = np.nansum(_ndat, axis=1) assert_almost_equal(res, tgt) def test_allnans(self): - # Check for FutureWarning and later change of return from - # NaN to zero. + # Check for FutureWarning with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') - res = nansum([np.nan]*3, axis=None) + res = np.nansum([np.nan]*3, axis=None) assert_(res == 0, 'result is not 0') assert_(len(w) == 0, 'warning raised') + # Check scalar + res = np.nansum(np.nan) + assert_(res == 0, 'result is not 0') + assert_(len(w) == 0, 'warning raised') + # Check there is no warning for not all-nan + np.nansum([0]*3, axis=None) + assert_(len(w) == 0, 'unwanted warning raised') def test_empty(self): mat = np.zeros((0, 3)) tgt = [0]*3 - res = nansum(mat, axis=0) + res = np.nansum(mat, axis=0) assert_equal(res, tgt) tgt = [] - res = nansum(mat, axis=1) + res = np.nansum(mat, axis=1) assert_equal(res, tgt) tgt = 0 - res = nansum(mat, axis=None) + res = np.nansum(mat, axis=None) assert_equal(res, tgt) + def test_scalar(self): + assert_(np.nansum(0.) == 0.) + + def test_matrices(self): + # Check that it works and that type and + # shape are preserved + mat = np.matrix(np.eye(3)) + res = np.nansum(mat, axis=0) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (1, 3)) + res = np.nansum(mat, axis=1) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (3, 1)) + res = np.nansum(mat) + assert_(np.isscalar(res)) + class TestNanFunctions_MeanVarStd(TestCase): - nanfuncs = [nanmean, nanvar, nanstd] + nanfuncs = [np.nanmean, np.nanvar, np.nanstd] stdfuncs = [np.mean, np.var, np.std] def test_mutation(self): @@ -275,13 +358,13 @@ class TestNanFunctions_MeanVarStd(TestCase): def test_dtype_error(self): for f in self.nanfuncs: for dtype in [np.bool_, np.int_, np.object]: - assert_raises(TypeError, f, _ndat, axis=1, dtype=np.int) + assert_raises( TypeError, f, _ndat, axis=1, dtype=np.int) def test_out_dtype_error(self): for f in self.nanfuncs: for dtype in [np.bool_, np.int_, np.object]: out = np.empty(_ndat.shape[0], dtype=dtype) - assert_raises(TypeError, f, _ndat, axis=1, out=out) + assert_raises( TypeError, f, _ndat, axis=1, out=out) def test_keepdims(self): mat = np.eye(3) @@ -333,14 +416,14 @@ class TestNanFunctions_MeanVarStd(TestCase): mat = np.eye(3, dtype=c) tgt = rf(mat, axis=1).dtype.type res = nf(mat, axis=1).dtype.type - assert_(res is tgt) + assert_(res is tgt, "res %s, tgt %s" % (res, tgt)) # scalar case tgt = rf(mat, axis=None).dtype.type res = nf(mat, axis=None).dtype.type assert_(res is tgt) def test_ddof(self): - nanfuncs = [nanvar, nanstd] + nanfuncs = [np.nanvar, np.nanstd] stdfuncs = [np.var, np.std] for nf, rf in zip(nanfuncs, stdfuncs): for ddof in [0, 1]: @@ -349,7 +432,7 @@ class TestNanFunctions_MeanVarStd(TestCase): assert_almost_equal(res, tgt) def test_ddof_too_big(self): - nanfuncs = [nanvar, nanstd] + nanfuncs = [np.nanvar, np.nanstd] stdfuncs = [np.var, np.std] dsize = [len(d) for d in _rdat] for nf, rf in zip(nanfuncs, stdfuncs): @@ -361,7 +444,7 @@ class TestNanFunctions_MeanVarStd(TestCase): assert_equal(np.isnan(res), tgt) if any(tgt): assert_(len(w) == 1) - assert_(issubclass(w[0].category, NanWarning)) + assert_(issubclass(w[0].category, RuntimeWarning)) else: assert_(len(w) == 0) @@ -379,7 +462,11 @@ class TestNanFunctions_MeanVarStd(TestCase): warnings.simplefilter('always') assert_(np.isnan(f(mat, axis=axis)).all()) assert_(len(w) == 1) - assert_(issubclass(w[0].category, NanWarning)) + assert_(issubclass(w[0].category, RuntimeWarning)) + # Check scalar + assert_(np.isnan(f(np.nan))) + assert_(len(w) == 2) + assert_(issubclass(w[0].category, RuntimeWarning)) def test_empty(self): mat = np.zeros((0, 3)) @@ -389,13 +476,31 @@ class TestNanFunctions_MeanVarStd(TestCase): warnings.simplefilter('always') assert_(np.isnan(f(mat, axis=axis)).all()) assert_(len(w) == 1) - assert_(issubclass(w[0].category, NanWarning)) + assert_(issubclass(w[0].category, RuntimeWarning)) for axis in [1]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_equal(f(mat, axis=axis), np.zeros([])) assert_(len(w) == 0) + def test_scalar(self): + for f in self.nanfuncs: + assert_(f(0.) == 0.) + + def test_matrices(self): + # Check that it works and that type and + # shape are preserved + mat = np.matrix(np.eye(3)) + for f in self.nanfuncs: + res = f(mat, axis=0) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (1, 3)) + res = f(mat, axis=1) + assert_(isinstance(res, np.matrix)) + assert_(res.shape == (3, 1)) + res = f(mat) + assert_(np.isscalar(res)) + if __name__ == "__main__": run_module_suite() |