tmp
/
pip-install-ghxuqwgs
/numpy_78e94bf2b6094bf9a1f3d92042f9bf46
/numpy
/lib
/tests
/test_nanfunctions.py
| from __future__ import division, absolute_import, print_function | |
| import warnings | |
| import numpy as np | |
| from numpy.testing import ( | |
| run_module_suite, TestCase, assert_, assert_equal, assert_almost_equal, | |
| 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], | |
| [0.1610, 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])] | |
| class TestNanFunctions_MinMax(TestCase): | |
| nanfuncs = [np.nanmin, np.nanmax] | |
| stdfuncs = [np.min, np.max] | |
| def test_mutation(self): | |
| # Check that passed array is not modified. | |
| ndat = _ndat.copy() | |
| for f in self.nanfuncs: | |
| f(ndat) | |
| assert_equal(ndat, _ndat) | |
| def test_keepdims(self): | |
| mat = np.eye(3) | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| for axis in [None, 0, 1]: | |
| tgt = rf(mat, axis=axis, keepdims=True) | |
| res = nf(mat, axis=axis, keepdims=True) | |
| assert_(res.ndim == tgt.ndim) | |
| def test_out(self): | |
| mat = np.eye(3) | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| resout = np.zeros(3) | |
| tgt = rf(mat, axis=1) | |
| res = nf(mat, axis=1, out=resout) | |
| assert_almost_equal(res, resout) | |
| assert_almost_equal(res, tgt) | |
| def test_dtype_from_input(self): | |
| codes = 'efdgFDG' | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| for c in codes: | |
| mat = np.eye(3, dtype=c) | |
| tgt = rf(mat, axis=1).dtype.type | |
| res = nf(mat, axis=1).dtype.type | |
| assert_(res is tgt) | |
| # scalar case | |
| tgt = rf(mat, axis=None).dtype.type | |
| res = nf(mat, axis=None).dtype.type | |
| assert_(res is tgt) | |
| def test_result_values(self): | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| tgt = [rf(d) for d in _rdat] | |
| res = nf(_ndat, axis=1) | |
| assert_almost_equal(res, tgt) | |
| def test_allnans(self): | |
| mat = np.array([np.nan]*9).reshape(3, 3) | |
| for f in self.nanfuncs: | |
| for axis in [None, 0, 1]: | |
| 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 [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)) | |
| # 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): | |
| nanfuncs = [np.nanargmin, np.nanargmax] | |
| def test_mutation(self): | |
| # Check that passed array is not modified. | |
| ndat = _ndat.copy() | |
| for f in self.nanfuncs: | |
| f(ndat) | |
| assert_equal(ndat, _ndat) | |
| 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') | |
| ind = f(row) | |
| val = row[ind] | |
| # comparing with NaN is tricky as the result | |
| # is always false except for NaN != NaN | |
| assert_(not np.isnan(val)) | |
| assert_(not fcmp(val, row).any()) | |
| assert_(not np.equal(val, row[:ind]).any()) | |
| def test_allnans(self): | |
| mat = np.array([np.nan]*9).reshape(3, 3) | |
| for f in self.nanfuncs: | |
| for axis in [None, 0, 1]: | |
| assert_raises(ValueError, f, mat, axis=axis) | |
| assert_raises(ValueError, f, np.nan) | |
| def test_empty(self): | |
| mat = np.zeros((0, 3)) | |
| for f in self.nanfuncs: | |
| for axis in [0, None]: | |
| assert_raises(ValueError, f, mat, axis=axis) | |
| for axis in [1]: | |
| 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) | |
| 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): | |
| tgt = np.min(self.mat) | |
| for mat in self.integer_arrays(): | |
| assert_equal(np.nanmin(mat), tgt) | |
| def test_nanmax(self): | |
| tgt = np.max(self.mat) | |
| for mat in self.integer_arrays(): | |
| assert_equal(np.nanmax(mat), tgt) | |
| def test_nanargmin(self): | |
| tgt = np.argmin(self.mat) | |
| for mat in self.integer_arrays(): | |
| assert_equal(np.nanargmin(mat), tgt) | |
| def test_nanargmax(self): | |
| tgt = np.argmax(self.mat) | |
| for mat in self.integer_arrays(): | |
| 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): | |
| def test_mutation(self): | |
| # Check that passed array is not modified. | |
| ndat = _ndat.copy() | |
| 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 = 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 = np.nansum(mat, axis=1, out=resout) | |
| assert_almost_equal(res, resout) | |
| assert_almost_equal(res, tgt) | |
| def test_dtype_from_dtype(self): | |
| mat = np.eye(3) | |
| codes = 'efdgFDG' | |
| for c in codes: | |
| tgt = np.sum(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 = np.nansum(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 c in codes: | |
| tgt = np.sum(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 = np.nansum(mat, dtype=c, axis=None).dtype.type | |
| assert_(res is tgt) | |
| def test_dtype_from_input(self): | |
| codes = 'efdgFDG' | |
| for c in codes: | |
| mat = np.eye(3, dtype=c) | |
| tgt = np.sum(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 = 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 = np.nansum(_ndat, axis=1) | |
| assert_almost_equal(res, tgt) | |
| def test_allnans(self): | |
| # Check for FutureWarning | |
| with warnings.catch_warnings(record=True) as w: | |
| warnings.simplefilter('always') | |
| 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 = np.nansum(mat, axis=0) | |
| assert_equal(res, tgt) | |
| tgt = [] | |
| res = np.nansum(mat, axis=1) | |
| assert_equal(res, tgt) | |
| tgt = 0 | |
| 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 = [np.nanmean, np.nanvar, np.nanstd] | |
| stdfuncs = [np.mean, np.var, np.std] | |
| def test_mutation(self): | |
| # Check that passed array is not modified. | |
| ndat = _ndat.copy() | |
| for f in self.nanfuncs: | |
| f(ndat) | |
| assert_equal(ndat, _ndat) | |
| 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) | |
| 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) | |
| def test_keepdims(self): | |
| mat = np.eye(3) | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| for axis in [None, 0, 1]: | |
| tgt = rf(mat, axis=axis, keepdims=True) | |
| res = nf(mat, axis=axis, keepdims=True) | |
| assert_(res.ndim == tgt.ndim) | |
| def test_out(self): | |
| mat = np.eye(3) | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| resout = np.zeros(3) | |
| tgt = rf(mat, axis=1) | |
| res = nf(mat, axis=1, out=resout) | |
| assert_almost_equal(res, resout) | |
| assert_almost_equal(res, tgt) | |
| def test_dtype_from_dtype(self): | |
| mat = np.eye(3) | |
| 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) | |
| 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) | |
| def test_dtype_from_input(self): | |
| codes = 'efdgFDG' | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| for c in codes: | |
| mat = np.eye(3, dtype=c) | |
| tgt = rf(mat, axis=1).dtype.type | |
| res = nf(mat, axis=1).dtype.type | |
| 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 = [np.nanvar, np.nanstd] | |
| stdfuncs = [np.var, np.std] | |
| for nf, rf in zip(nanfuncs, stdfuncs): | |
| for ddof in [0, 1]: | |
| tgt = [rf(d, ddof=ddof) for d in _rdat] | |
| res = nf(_ndat, axis=1, ddof=ddof) | |
| assert_almost_equal(res, tgt) | |
| def test_ddof_too_big(self): | |
| nanfuncs = [np.nanvar, np.nanstd] | |
| stdfuncs = [np.var, np.std] | |
| 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') | |
| 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)) | |
| else: | |
| assert_(len(w) == 0) | |
| def test_result_values(self): | |
| for nf, rf in zip(self.nanfuncs, self.stdfuncs): | |
| tgt = [rf(d) for d in _rdat] | |
| res = nf(_ndat, axis=1) | |
| assert_almost_equal(res, tgt) | |
| def test_allnans(self): | |
| mat = np.array([np.nan]*9).reshape(3, 3) | |
| for f in self.nanfuncs: | |
| for axis in [None, 0, 1]: | |
| with warnings.catch_warnings(record=True) as w: | |
| warnings.simplefilter('always') | |
| assert_(np.isnan(f(mat, axis=axis)).all()) | |
| assert_(len(w) == 1) | |
| 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)) | |
| for f in self.nanfuncs: | |
| for axis in [0, None]: | |
| with warnings.catch_warnings(record=True) as w: | |
| warnings.simplefilter('always') | |
| assert_(np.isnan(f(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(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)) | |
| 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() | |