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_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_allclose, assert_array_max_ulp, assert_warns, assert_raises_regex ) from numpy.random import rand from numpy.lib import * from numpy.compat import long class TestAny(TestCase): def test_basic(self): y1 = [0, 0, 1, 0] y2 = [0, 0, 0, 0] y3 = [1, 0, 1, 0] assert_(np.any(y1)) assert_(np.any(y3)) assert_(not np.any(y2)) def test_nd(self): y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]] assert_(np.any(y1)) assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0]) assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1]) class TestAll(TestCase): def test_basic(self): y1 = [0, 1, 1, 0] y2 = [0, 0, 0, 0] y3 = [1, 1, 1, 1] assert_(not np.all(y1)) assert_(np.all(y3)) assert_(not np.all(y2)) assert_(np.all(~np.array(y2))) def test_nd(self): y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]] assert_(not np.all(y1)) assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1]) assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1]) class TestCopy(TestCase): def test_basic(self): a = np.array([[1, 2], [3, 4]]) a_copy = np.copy(a) assert_array_equal(a, a_copy) a_copy[0, 0] = 10 assert_equal(a[0, 0], 1) assert_equal(a_copy[0, 0], 10) def test_order(self): # It turns out that people rely on np.copy() preserving order by # default; changing this broke scikit-learn: # https://github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783 a = np.array([[1, 2], [3, 4]]) assert_(a.flags.c_contiguous) assert_(not a.flags.f_contiguous) a_fort = np.array([[1, 2], [3, 4]], order="F") assert_(not a_fort.flags.c_contiguous) assert_(a_fort.flags.f_contiguous) a_copy = np.copy(a) assert_(a_copy.flags.c_contiguous) assert_(not a_copy.flags.f_contiguous) a_fort_copy = np.copy(a_fort) assert_(not a_fort_copy.flags.c_contiguous) assert_(a_fort_copy.flags.f_contiguous) class TestAverage(TestCase): def test_basic(self): y1 = np.array([1, 2, 3]) assert_(average(y1, axis=0) == 2.) y2 = np.array([1., 2., 3.]) assert_(average(y2, axis=0) == 2.) y3 = [0., 0., 0.] assert_(average(y3, axis=0) == 0.) y4 = np.ones((4, 4)) y4[0, 1] = 0 y4[1, 0] = 2 assert_almost_equal(y4.mean(0), average(y4, 0)) assert_almost_equal(y4.mean(1), average(y4, 1)) y5 = rand(5, 5) assert_almost_equal(y5.mean(0), average(y5, 0)) assert_almost_equal(y5.mean(1), average(y5, 1)) y6 = np.matrix(rand(5, 5)) assert_array_equal(y6.mean(0), average(y6, 0)) def test_weights(self): y = np.arange(10) w = np.arange(10) actual = average(y, weights=w) desired = (np.arange(10) ** 2).sum()*1. / np.arange(10).sum() assert_almost_equal(actual, desired) y1 = np.array([[1, 2, 3], [4, 5, 6]]) w0 = [1, 2] actual = average(y1, weights=w0, axis=0) desired = np.array([3., 4., 5.]) assert_almost_equal(actual, desired) w1 = [0, 0, 1] actual = average(y1, weights=w1, axis=1) desired = np.array([3., 6.]) assert_almost_equal(actual, desired) # This should raise an error. Can we test for that ? # assert_equal(average(y1, weights=w1), 9./2.) # 2D Case w2 = [[0, 0, 1], [0, 0, 2]] desired = np.array([3., 6.]) assert_array_equal(average(y1, weights=w2, axis=1), desired) assert_equal(average(y1, weights=w2), 5.) def test_returned(self): y = np.array([[1, 2, 3], [4, 5, 6]]) # No weights avg, scl = average(y, returned=True) assert_equal(scl, 6.) avg, scl = average(y, 0, returned=True) assert_array_equal(scl, np.array([2., 2., 2.])) avg, scl = average(y, 1, returned=True) assert_array_equal(scl, np.array([3., 3.])) # With weights w0 = [1, 2] avg, scl = average(y, weights=w0, axis=0, returned=True) assert_array_equal(scl, np.array([3., 3., 3.])) w1 = [1, 2, 3] avg, scl = average(y, weights=w1, axis=1, returned=True) assert_array_equal(scl, np.array([6., 6.])) w2 = [[0, 0, 1], [1, 2, 3]] avg, scl = average(y, weights=w2, axis=1, returned=True) assert_array_equal(scl, np.array([1., 6.])) class TestSelect(TestCase): choices = [np.array([1, 2, 3]), np.array([4, 5, 6]), np.array([7, 8, 9])] conditions = [np.array([False, False, False]), np.array([False, True, False]), np.array([False, False, True])] def _select(self, cond, values, default=0): output = [] for m in range(len(cond)): output += [V[m] for V, C in zip(values, cond) if C[m]] or [default] return output def test_basic(self): choices = self.choices conditions = self.conditions assert_array_equal(select(conditions, choices, default=15), self._select(conditions, choices, default=15)) assert_equal(len(choices), 3) assert_equal(len(conditions), 3) def test_broadcasting(self): conditions = [np.array(True), np.array([False, True, False])] choices = [1, np.arange(12).reshape(4, 3)] assert_array_equal(select(conditions, choices), np.ones((4, 3))) # default can broadcast too: assert_equal(select([True], [0], default=[0]).shape, (1,)) def test_return_dtype(self): assert_equal(select(self.conditions, self.choices, 1j).dtype, np.complex_) # But the conditions need to be stronger then the scalar default # if it is scalar. choices = [choice.astype(np.int8) for choice in self.choices] assert_equal(select(self.conditions, choices).dtype, np.int8) d = np.array([1, 2, 3, np.nan, 5, 7]) m = np.isnan(d) assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0]) def test_deprecated_empty(self): with warnings.catch_warnings(record=True): warnings.simplefilter("always") assert_equal(select([], [], 3j), 3j) with warnings.catch_warnings(): warnings.simplefilter("always") assert_warns(DeprecationWarning, select, [], []) warnings.simplefilter("error") assert_raises(DeprecationWarning, select, [], []) def test_non_bool_deprecation(self): choices = self.choices conditions = self.conditions[:] with warnings.catch_warnings(): warnings.filterwarnings("always") conditions[0] = conditions[0].astype(np.int_) assert_warns(DeprecationWarning, select, conditions, choices) conditions[0] = conditions[0].astype(np.uint8) assert_warns(DeprecationWarning, select, conditions, choices) warnings.filterwarnings("error") assert_raises(DeprecationWarning, select, conditions, choices) def test_many_arguments(self): # This used to be limited by NPY_MAXARGS == 32 conditions = [np.array([False])] * 100 choices = [np.array([1])] * 100 select(conditions, choices) class TestInsert(TestCase): def test_basic(self): a = [1, 2, 3] assert_equal(insert(a, 0, 1), [1, 1, 2, 3]) assert_equal(insert(a, 3, 1), [1, 2, 3, 1]) assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3]) assert_equal(insert(a, 1, [1, 2, 3]), [1, 1, 2, 3, 2, 3]) assert_equal(insert(a, [1, -1, 3], 9), [1, 9, 2, 9, 3, 9]) assert_equal(insert(a, slice(-1, None, -1), 9), [9, 1, 9, 2, 9, 3]) assert_equal(insert(a, [-1, 1, 3], [7, 8, 9]), [1, 8, 2, 7, 3, 9]) b = np.array([0, 1], dtype=np.float64) assert_equal(insert(b, 0, b[0]), [0., 0., 1.]) assert_equal(insert(b, [], []), b) # Bools will be treated differently in the future: #assert_equal(insert(a, np.array([True]*4), 9), [9,1,9,2,9,3,9]) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', FutureWarning) assert_equal( insert(a, np.array([True]*4), 9), [1, 9, 9, 9, 9, 2, 3]) assert_(w[0].category is FutureWarning) def test_multidim(self): a = [[1, 1, 1]] r = [[2, 2, 2], [1, 1, 1]] assert_equal(insert(a, 0, [1]), [1, 1, 1, 1]) assert_equal(insert(a, 0, [2, 2, 2], axis=0), r) assert_equal(insert(a, 0, 2, axis=0), r) assert_equal(insert(a, 2, 2, axis=1), [[1, 1, 2, 1]]) a = np.array([[1, 1], [2, 2], [3, 3]]) b = np.arange(1, 4).repeat(3).reshape(3, 3) c = np.concatenate( (a[:, 0:1], np.arange(1, 4).repeat(3).reshape(3, 3).T, a[:, 1:2]), axis=1) assert_equal(insert(a, [1], [[1], [2], [3]], axis=1), b) assert_equal(insert(a, [1], [1, 2, 3], axis=1), c) # scalars behave differently, in this case exactly opposite: assert_equal(insert(a, 1, [1, 2, 3], axis=1), b) assert_equal(insert(a, 1, [[1], [2], [3]], axis=1), c) a = np.arange(4).reshape(2, 2) assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a) assert_equal(insert(a[:1, :], 1, a[1, :], axis=0), a) # negative axis value a = np.arange(24).reshape((2, 3, 4)) assert_equal(insert(a, 1, a[:, :, 3], axis=-1), insert(a, 1, a[:, :, 3], axis=2)) assert_equal(insert(a, 1, a[:, 2, :], axis=-2), insert(a, 1, a[:, 2, :], axis=1)) # invalid axis value assert_raises(IndexError, insert, a, 1, a[:, 2, :], axis=3) assert_raises(IndexError, insert, a, 1, a[:, 2, :], axis=-4) # negative axis value a = np.arange(24).reshape((2,3,4)) assert_equal(insert(a, 1, a[:,:,3], axis=-1), insert(a, 1, a[:,:,3], axis=2)) assert_equal(insert(a, 1, a[:,2,:], axis=-2), insert(a, 1, a[:,2,:], axis=1)) def test_0d(self): # This is an error in the future a = np.array(1) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', DeprecationWarning) assert_equal(insert(a, [], 2, axis=0), np.array(2)) assert_(w[0].category is DeprecationWarning) def test_subclass(self): class SubClass(np.ndarray): pass a = np.arange(10).view(SubClass) assert_(isinstance(np.insert(a, 0, [0]), SubClass)) assert_(isinstance(np.insert(a, [], []), SubClass)) assert_(isinstance(np.insert(a, [0, 1], [1, 2]), SubClass)) assert_(isinstance(np.insert(a, slice(1, 2), [1, 2]), SubClass)) assert_(isinstance(np.insert(a, slice(1, -2, -1), []), SubClass)) # This is an error in the future: a = np.array(1).view(SubClass) assert_(isinstance(np.insert(a, 0, [0]), SubClass)) def test_index_array_copied(self): x = np.array([1, 1, 1]) np.insert([0, 1, 2], x, [3, 4, 5]) assert_equal(x, np.array([1, 1, 1])) def test_structured_array(self): a = np.array([(1, 'a'), (2, 'b'), (3, 'c')], dtype=[('foo', 'i'), ('bar', 'a1')]) val = (4, 'd') b = np.insert(a, 0, val) assert_array_equal(b[0], np.array(val, dtype=b.dtype)) val = [(4, 'd')] * 2 b = np.insert(a, [0, 2], val) assert_array_equal(b[[0, 3]], np.array(val, dtype=b.dtype)) class TestAmax(TestCase): def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.amax(a), 10.0) b = [[3, 6.0, 9.0], [4, 10.0, 5.0], [8, 3.0, 2.0]] assert_equal(np.amax(b, axis=0), [8.0, 10.0, 9.0]) assert_equal(np.amax(b, axis=1), [9.0, 10.0, 8.0]) class TestAmin(TestCase): def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.amin(a), -5.0) b = [[3, 6.0, 9.0], [4, 10.0, 5.0], [8, 3.0, 2.0]] assert_equal(np.amin(b, axis=0), [3.0, 3.0, 2.0]) assert_equal(np.amin(b, axis=1), [3.0, 4.0, 2.0]) class TestPtp(TestCase): def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0) b = [[3, 6.0, 9.0], [4, 10.0, 5.0], [8, 3.0, 2.0]] assert_equal(np.ptp(b, axis=0), [5.0, 7.0, 7.0]) assert_equal(np.ptp(b, axis=-1), [6.0, 6.0, 6.0]) class TestCumsum(TestCase): def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.float32, np.float64, np.complex64, np.complex128]: a = np.array(ba, ctype) a2 = np.array(ba2, ctype) tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype) assert_array_equal(np.cumsum(a, axis=0), tgt) tgt = np.array( [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype) assert_array_equal(np.cumsum(a2, axis=0), tgt) tgt = np.array( [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype) assert_array_equal(np.cumsum(a2, axis=1), tgt) class TestProd(TestCase): def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] for ctype in [np.int16, np.uint16, np.int32, np.uint32, np.float32, np.float64, np.complex64, np.complex128]: a = np.array(ba, ctype) a2 = np.array(ba2, ctype) if ctype in ['1', 'b']: self.assertRaises(ArithmeticError, prod, a) self.assertRaises(ArithmeticError, prod, a2, 1) self.assertRaises(ArithmeticError, prod, a) else: assert_equal(np.prod(a, axis=0), 26400) assert_array_equal(np.prod(a2, axis=0), np.array([50, 36, 84, 180], ctype)) assert_array_equal(np.prod(a2, axis=-1), np.array([24, 1890, 600], ctype)) class TestCumprod(TestCase): def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] for ctype in [np.int16, np.uint16, np.int32, np.uint32, np.float32, np.float64, np.complex64, np.complex128]: a = np.array(ba, ctype) a2 = np.array(ba2, ctype) if ctype in ['1', 'b']: self.assertRaises(ArithmeticError, cumprod, a) self.assertRaises(ArithmeticError, cumprod, a2, 1) self.assertRaises(ArithmeticError, cumprod, a) else: assert_array_equal(np.cumprod(a, axis=-1), np.array([1, 2, 20, 220, 1320, 6600, 26400], ctype)) assert_array_equal(np.cumprod(a2, axis=0), np.array([[1, 2, 3, 4], [5, 12, 21, 36], [50, 36, 84, 180]], ctype)) assert_array_equal(np.cumprod(a2, axis=-1), np.array([[1, 2, 6, 24], [5, 30, 210, 1890], [10, 30, 120, 600]], ctype)) class TestDiff(TestCase): def test_basic(self): x = [1, 4, 6, 7, 12] out = np.array([3, 2, 1, 5]) out2 = np.array([-1, -1, 4]) out3 = np.array([0, 5]) assert_array_equal(diff(x), out) assert_array_equal(diff(x, n=2), out2) assert_array_equal(diff(x, n=3), out3) def test_nd(self): x = 20 * rand(10, 20, 30) out1 = x[:, :, 1:] - x[:, :, :-1] out2 = out1[:, :, 1:] - out1[:, :, :-1] out3 = x[1:, :, :] - x[:-1, :, :] out4 = out3[1:, :, :] - out3[:-1, :, :] assert_array_equal(diff(x), out1) assert_array_equal(diff(x, n=2), out2) assert_array_equal(diff(x, axis=0), out3) assert_array_equal(diff(x, n=2, axis=0), out4) class TestDelete(TestCase): def setUp(self): self.a = np.arange(5) self.nd_a = np.arange(5).repeat(2).reshape(1, 5, 2) def _check_inverse_of_slicing(self, indices): a_del = delete(self.a, indices) nd_a_del = delete(self.nd_a, indices, axis=1) msg = 'Delete failed for obj: %r' % indices # NOTE: The cast should be removed after warning phase for bools if not isinstance(indices, (slice, int, long, np.integer)): indices = np.asarray(indices, dtype=np.intp) indices = indices[(indices >= 0) & (indices < 5)] assert_array_equal(setxor1d(a_del, self.a[indices, ]), self.a, err_msg=msg) xor = setxor1d(nd_a_del[0, :, 0], self.nd_a[0, indices, 0]) assert_array_equal(xor, self.nd_a[0, :, 0], err_msg=msg) def test_slices(self): lims = [-6, -2, 0, 1, 2, 4, 5] steps = [-3, -1, 1, 3] for start in lims: for stop in lims: for step in steps: s = slice(start, stop, step) self._check_inverse_of_slicing(s) def test_fancy(self): # Deprecation/FutureWarning tests should be kept after change. self._check_inverse_of_slicing(np.array([[0, 1], [2, 1]])) with warnings.catch_warnings(): warnings.filterwarnings('error', category=DeprecationWarning) assert_raises(DeprecationWarning, delete, self.a, [100]) assert_raises(DeprecationWarning, delete, self.a, [-100]) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', category=FutureWarning) self._check_inverse_of_slicing([0, -1, 2, 2]) obj = np.array([True, False, False], dtype=bool) self._check_inverse_of_slicing(obj) assert_(w[0].category is FutureWarning) assert_(w[1].category is FutureWarning) def test_single(self): self._check_inverse_of_slicing(0) self._check_inverse_of_slicing(-4) def test_0d(self): a = np.array(1) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', DeprecationWarning) assert_equal(delete(a, [], axis=0), a) assert_(w[0].category is DeprecationWarning) def test_subclass(self): class SubClass(np.ndarray): pass a = self.a.view(SubClass) assert_(isinstance(delete(a, 0), SubClass)) assert_(isinstance(delete(a, []), SubClass)) assert_(isinstance(delete(a, [0, 1]), SubClass)) assert_(isinstance(delete(a, slice(1, 2)), SubClass)) assert_(isinstance(delete(a, slice(1, -2)), SubClass)) class TestGradient(TestCase): def test_basic(self): v = [[1, 1], [3, 4]] x = np.array(v) dx = [np.array([[2., 3.], [2., 3.]]), np.array([[0., 0.], [1., 1.]])] assert_array_equal(gradient(x), dx) assert_array_equal(gradient(v), dx) def test_badargs(self): # for 2D array, gradient can take 0, 1, or 2 extra args x = np.array([[1, 1], [3, 4]]) assert_raises(SyntaxError, gradient, x, np.array([1., 1.]), np.array([1., 1.]), np.array([1., 1.])) def test_masked(self): # Make sure that gradient supports subclasses like masked arrays x = np.ma.array([[1, 1], [3, 4]]) assert_equal(type(gradient(x)[0]), type(x)) def test_datetime64(self): # Make sure gradient() can handle special types like datetime64 x = np.array( ['1910-08-16', '1910-08-11', '1910-08-10', '1910-08-12', '1910-10-12', '1910-12-12', '1912-12-12'], dtype='datetime64[D]') dx = np.array( [-7, -3, 0, 31, 61, 396, 1066], dtype='timedelta64[D]') assert_array_equal(gradient(x), dx) assert_(dx.dtype == np.dtype('timedelta64[D]')) def test_timedelta64(self): # Make sure gradient() can handle special types like timedelta64 x = np.array( [-5, -3, 10, 12, 61, 321, 300], dtype='timedelta64[D]') dx = np.array( [-3, 7, 7, 25, 154, 119, -161], dtype='timedelta64[D]') assert_array_equal(gradient(x), dx) assert_(dx.dtype == np.dtype('timedelta64[D]')) def test_second_order_accurate(self): # Testing that the relative numerical error is less that 3% for # this example problem. This corresponds to second order # accurate finite differences for all interior and boundary # points. x = np.linspace(0, 1, 10) dx = x[1] - x[0] y = 2 * x ** 3 + 4 * x ** 2 + 2 * x analytical = 6 * x ** 2 + 8 * x + 2 num_error = np.abs((np.gradient(y, dx) / analytical) - 1) assert_(np.all(num_error < 0.03) == True) class TestAngle(TestCase): def test_basic(self): x = [1 + 3j, np.sqrt(2) / 2.0 + 1j * np.sqrt(2) / 2, 1, 1j, -1, -1j, 1 - 3j, -1 + 3j] y = angle(x) yo = [ np.arctan(3.0 / 1.0), np.arctan(1.0), 0, np.pi / 2, np.pi, -np.pi / 2.0, -np.arctan(3.0 / 1.0), np.pi - np.arctan(3.0 / 1.0)] z = angle(x, deg=1) zo = np.array(yo) * 180 / np.pi assert_array_almost_equal(y, yo, 11) assert_array_almost_equal(z, zo, 11) class TestTrimZeros(TestCase): """ only testing for integer splits. """ def test_basic(self): a = np.array([0, 0, 1, 2, 3, 4, 0]) res = trim_zeros(a) assert_array_equal(res, np.array([1, 2, 3, 4])) def test_leading_skip(self): a = np.array([0, 0, 1, 0, 2, 3, 4, 0]) res = trim_zeros(a) assert_array_equal(res, np.array([1, 0, 2, 3, 4])) def test_trailing_skip(self): a = np.array([0, 0, 1, 0, 2, 3, 0, 4, 0]) res = trim_zeros(a) assert_array_equal(res, np.array([1, 0, 2, 3, 0, 4])) class TestExtins(TestCase): def test_basic(self): a = np.array([1, 3, 2, 1, 2, 3, 3]) b = extract(a > 1, a) assert_array_equal(b, [3, 2, 2, 3, 3]) def test_place(self): a = np.array([1, 4, 3, 2, 5, 8, 7]) place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6]) assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7]) def test_both(self): a = rand(10) mask = a > 0.5 ac = a.copy() c = extract(mask, a) place(a, mask, 0) place(a, mask, c) assert_array_equal(a, ac) class TestVectorize(TestCase): def test_simple(self): def addsubtract(a, b): if a > b: return a - b else: return a + b f = vectorize(addsubtract) r = f([0, 3, 6, 9], [1, 3, 5, 7]) assert_array_equal(r, [1, 6, 1, 2]) def test_scalar(self): def addsubtract(a, b): if a > b: return a - b else: return a + b f = vectorize(addsubtract) r = f([0, 3, 6, 9], 5) assert_array_equal(r, [5, 8, 1, 4]) def test_large(self): x = np.linspace(-3, 2, 10000) f = vectorize(lambda x: x) y = f(x) assert_array_equal(y, x) def test_ufunc(self): import math f = vectorize(math.cos) args = np.array([0, 0.5*np.pi, np.pi, 1.5*np.pi, 2*np.pi]) r1 = f(args) r2 = np.cos(args) assert_array_equal(r1, r2) def test_keywords(self): import math def foo(a, b=1): return a + b f = vectorize(foo) args = np.array([1, 2, 3]) r1 = f(args) r2 = np.array([2, 3, 4]) assert_array_equal(r1, r2) r1 = f(args, 2) r2 = np.array([3, 4, 5]) assert_array_equal(r1, r2) def test_keywords_no_func_code(self): # This needs to test a function that has keywords but # no func_code attribute, since otherwise vectorize will # inspect the func_code. import random try: f = vectorize(random.randrange) except: raise AssertionError() def test_keywords2_ticket_2100(self): r"""Test kwarg support: enhancement ticket 2100""" import math def foo(a, b=1): return a + b f = vectorize(foo) args = np.array([1, 2, 3]) r1 = f(a=args) r2 = np.array([2, 3, 4]) assert_array_equal(r1, r2) r1 = f(b=1, a=args) assert_array_equal(r1, r2) r1 = f(args, b=2) r2 = np.array([3, 4, 5]) assert_array_equal(r1, r2) def test_keywords3_ticket_2100(self): """Test excluded with mixed positional and kwargs: ticket 2100""" def mypolyval(x, p): _p = list(p) res = _p.pop(0) while _p: res = res*x + _p.pop(0) return res vpolyval = np.vectorize(mypolyval, excluded=['p', 1]) ans = [3, 6] assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3])) assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3])) assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3])) def test_keywords4_ticket_2100(self): """Test vectorizing function with no positional args.""" @vectorize def f(**kw): res = 1.0 for _k in kw: res *= kw[_k] return res assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8]) def test_keywords5_ticket_2100(self): """Test vectorizing function with no kwargs args.""" @vectorize def f(*v): return np.prod(v) assert_array_equal(f([1, 2], [3, 4]), [3, 8]) def test_coverage1_ticket_2100(self): def foo(): return 1 f = vectorize(foo) assert_array_equal(f(), 1) def test_assigning_docstring(self): def foo(x): return x doc = "Provided documentation" f = vectorize(foo, doc=doc) assert_equal(f.__doc__, doc) def test_UnboundMethod_ticket_1156(self): """Regression test for issue 1156""" class Foo: b = 2 def bar(self, a): return a**self.b assert_array_equal(vectorize(Foo().bar)(np.arange(9)), np.arange(9)**2) assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)), np.arange(9)**2) def test_execution_order_ticket_1487(self): """Regression test for dependence on execution order: issue 1487""" f1 = vectorize(lambda x: x) res1a = f1(np.arange(3)) res1b = f1(np.arange(0.1, 3)) f2 = vectorize(lambda x: x) res2b = f2(np.arange(0.1, 3)) res2a = f2(np.arange(3)) assert_equal(res1a, res2a) assert_equal(res1b, res2b) def test_string_ticket_1892(self): """Test vectorization over strings: issue 1892.""" f = np.vectorize(lambda x: x) s = '0123456789'*10 assert_equal(s, f(s)) #z = f(np.array([s,s])) #assert_array_equal([s,s], f(s)) def test_cache(self): """Ensure that vectorized func called exactly once per argument.""" _calls = [0] @vectorize def f(x): _calls[0] += 1 return x**2 f.cache = True x = np.arange(5) assert_array_equal(f(x), x*x) assert_equal(_calls[0], len(x)) def test_otypes(self): f = np.vectorize(lambda x: x) f.otypes = 'i' x = np.arange(5) assert_array_equal(f(x), x) class TestDigitize(TestCase): def test_forward(self): x = np.arange(-6, 5) bins = np.arange(-5, 5) assert_array_equal(digitize(x, bins), np.arange(11)) def test_reverse(self): x = np.arange(5, -6, -1) bins = np.arange(5, -5, -1) assert_array_equal(digitize(x, bins), np.arange(11)) def test_random(self): x = rand(10) bin = np.linspace(x.min(), x.max(), 10) assert_(np.all(digitize(x, bin) != 0)) def test_right_basic(self): x = [1, 5, 4, 10, 8, 11, 0] bins = [1, 5, 10] default_answer = [1, 2, 1, 3, 2, 3, 0] assert_array_equal(digitize(x, bins), default_answer) right_answer = [0, 1, 1, 2, 2, 3, 0] assert_array_equal(digitize(x, bins, True), right_answer) def test_right_open(self): x = np.arange(-6, 5) bins = np.arange(-6, 4) assert_array_equal(digitize(x, bins, True), np.arange(11)) def test_right_open_reverse(self): x = np.arange(5, -6, -1) bins = np.arange(4, -6, -1) assert_array_equal(digitize(x, bins, True), np.arange(11)) def test_right_open_random(self): x = rand(10) bins = np.linspace(x.min(), x.max(), 10) assert_(np.all(digitize(x, bins, True) != 10)) def test_monotonic(self): x = [-1, 0, 1, 2] bins = [0, 0, 1] assert_array_equal(digitize(x, bins, False), [0, 2, 3, 3]) assert_array_equal(digitize(x, bins, True), [0, 0, 2, 3]) bins = [1, 1, 0] assert_array_equal(digitize(x, bins, False), [3, 2, 0, 0]) assert_array_equal(digitize(x, bins, True), [3, 3, 2, 0]) bins = [1, 1, 1, 1] assert_array_equal(digitize(x, bins, False), [0, 0, 4, 4]) assert_array_equal(digitize(x, bins, True), [0, 0, 0, 4]) bins = [0, 0, 1, 0] assert_raises(ValueError, digitize, x, bins) bins = [1, 1, 0, 1] assert_raises(ValueError, digitize, x, bins) class TestUnwrap(TestCase): def test_simple(self): #check that unwrap removes jumps greather that 2*pi assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1]) #check that unwrap maintans continuity assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi)) class TestFilterwindows(TestCase): def test_hanning(self): #check symmetry w = hanning(10) assert_array_almost_equal(w, flipud(w), 7) #check known value assert_almost_equal(np.sum(w, axis=0), 4.500, 4) def test_hamming(self): #check symmetry w = hamming(10) assert_array_almost_equal(w, flipud(w), 7) #check known value assert_almost_equal(np.sum(w, axis=0), 4.9400, 4) def test_bartlett(self): #check symmetry w = bartlett(10) assert_array_almost_equal(w, flipud(w), 7) #check known value assert_almost_equal(np.sum(w, axis=0), 4.4444, 4) def test_blackman(self): #check symmetry w = blackman(10) assert_array_almost_equal(w, flipud(w), 7) #check known value assert_almost_equal(np.sum(w, axis=0), 3.7800, 4) class TestTrapz(TestCase): def test_simple(self): x = np.arange(-10, 10, .1) r = trapz(np.exp(-.5*x**2) / np.sqrt(2*np.pi), dx=0.1) #check integral of normal equals 1 assert_almost_equal(r, 1, 7) def test_ndim(self): x = np.linspace(0, 1, 3) y = np.linspace(0, 2, 8) z = np.linspace(0, 3, 13) wx = np.ones_like(x) * (x[1] - x[0]) wx[0] /= 2 wx[-1] /= 2 wy = np.ones_like(y) * (y[1] - y[0]) wy[0] /= 2 wy[-1] /= 2 wz = np.ones_like(z) * (z[1] - z[0]) wz[0] /= 2 wz[-1] /= 2 q = x[:, None, None] + y[None, :, None] + z[None, None, :] qx = (q * wx[:, None, None]).sum(axis=0) qy = (q * wy[None, :, None]).sum(axis=1) qz = (q * wz[None, None, :]).sum(axis=2) # n-d `x` r = trapz(q, x=x[:, None, None], axis=0) assert_almost_equal(r, qx) r = trapz(q, x=y[None, :, None], axis=1) assert_almost_equal(r, qy) r = trapz(q, x=z[None, None, :], axis=2) assert_almost_equal(r, qz) # 1-d `x` r = trapz(q, x=x, axis=0) assert_almost_equal(r, qx) r = trapz(q, x=y, axis=1) assert_almost_equal(r, qy) r = trapz(q, x=z, axis=2) assert_almost_equal(r, qz) def test_masked(self): #Testing that masked arrays behave as if the function is 0 where #masked x = np.arange(5) y = x * x mask = x == 2 ym = np.ma.array(y, mask=mask) r = 13.0 # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16)) assert_almost_equal(trapz(ym, x), r) xm = np.ma.array(x, mask=mask) assert_almost_equal(trapz(ym, xm), r) xm = np.ma.array(x, mask=mask) assert_almost_equal(trapz(y, xm), r) def test_matrix(self): #Test to make sure matrices give the same answer as ndarrays x = np.linspace(0, 5) y = x * x r = trapz(y, x) mx = np.matrix(x) my = np.matrix(y) mr = trapz(my, mx) assert_almost_equal(mr, r) class TestSinc(TestCase): def test_simple(self): assert_(sinc(0) == 1) w = sinc(np.linspace(-1, 1, 100)) #check symmetry assert_array_almost_equal(w, flipud(w), 7) def test_array_like(self): x = [0, 0.5] y1 = sinc(np.array(x)) y2 = sinc(list(x)) y3 = sinc(tuple(x)) assert_array_equal(y1, y2) assert_array_equal(y1, y3) class TestHistogram(TestCase): def setUp(self): pass def tearDown(self): pass def test_simple(self): n = 100 v = rand(n) (a, b) = histogram(v) #check if the sum of the bins equals the number of samples assert_equal(np.sum(a, axis=0), n) #check that the bin counts are evenly spaced when the data is from a # linear function (a, b) = histogram(np.linspace(0, 10, 100)) assert_array_equal(a, 10) def test_one_bin(self): # Ticket 632 hist, edges = histogram([1, 2, 3, 4], [1, 2]) assert_array_equal(hist, [2, ]) assert_array_equal(edges, [1, 2]) assert_raises(ValueError, histogram, [1, 2], bins=0) h, e = histogram([1, 2], bins=1) assert_equal(h, np.array([2])) assert_allclose(e, np.array([1., 2.])) def test_normed(self): # Check that the integral of the density equals 1. n = 100 v = rand(n) a, b = histogram(v, normed=True) area = np.sum(a * diff(b)) assert_almost_equal(area, 1) # Check with non-constant bin widths (buggy but backwards compatible) v = np.arange(10) bins = [0, 1, 5, 9, 10] a, b = histogram(v, bins, normed=True) area = np.sum(a * diff(b)) assert_almost_equal(area, 1) def test_density(self): # Check that the integral of the density equals 1. n = 100 v = rand(n) a, b = histogram(v, density=True) area = np.sum(a * diff(b)) assert_almost_equal(area, 1) # Check with non-constant bin widths v = np.arange(10) bins = [0, 1, 3, 6, 10] a, b = histogram(v, bins, density=True) assert_array_equal(a, .1) assert_equal(np.sum(a*diff(b)), 1) # Variale bin widths are especially useful to deal with # infinities. v = np.arange(10) bins = [0, 1, 3, 6, np.inf] a, b = histogram(v, bins, density=True) assert_array_equal(a, [.1, .1, .1, 0.]) # Taken from a bug report from N. Becker on the numpy-discussion # mailing list Aug. 6, 2010. counts, dmy = np.histogram( [1, 2, 3, 4], [0.5, 1.5, np.inf], density=True) assert_equal(counts, [.25, 0]) def test_outliers(self): # Check that outliers are not tallied a = np.arange(10) + .5 # Lower outliers h, b = histogram(a, range=[0, 9]) assert_equal(h.sum(), 9) # Upper outliers h, b = histogram(a, range=[1, 10]) assert_equal(h.sum(), 9) # Normalization h, b = histogram(a, range=[1, 9], normed=True) assert_almost_equal((h * diff(b)).sum(), 1, decimal=15) # Weights w = np.arange(10) + .5 h, b = histogram(a, range=[1, 9], weights=w, normed=True) assert_equal((h * diff(b)).sum(), 1) h, b = histogram(a, bins=8, range=[1, 9], weights=w) assert_equal(h, w[1:-1]) def test_type(self): # Check the type of the returned histogram a = np.arange(10) + .5 h, b = histogram(a) assert_(issubdtype(h.dtype, int)) h, b = histogram(a, normed=True) assert_(issubdtype(h.dtype, float)) h, b = histogram(a, weights=np.ones(10, int)) assert_(issubdtype(h.dtype, int)) h, b = histogram(a, weights=np.ones(10, float)) assert_(issubdtype(h.dtype, float)) def test_f32_rounding(self): # gh-4799, check that the rounding of the edges works with float32 x = np.array([276.318359 , -69.593948 , 21.329449], dtype=np.float32) y = np.array([5005.689453, 4481.327637, 6010.369629], dtype=np.float32) counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100) assert_equal(counts_hist.sum(), 3.) def test_weights(self): v = rand(100) w = np.ones(100) * 5 a, b = histogram(v) na, nb = histogram(v, normed=True) wa, wb = histogram(v, weights=w) nwa, nwb = histogram(v, weights=w, normed=True) assert_array_almost_equal(a * 5, wa) assert_array_almost_equal(na, nwa) # Check weights are properly applied. v = np.linspace(0, 10, 10) w = np.concatenate((np.zeros(5), np.ones(5))) wa, wb = histogram(v, bins=np.arange(11), weights=w) assert_array_almost_equal(wa, w) # Check with integer weights wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1]) assert_array_equal(wa, [4, 5, 0, 1]) wa, wb = histogram( [1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True) assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4) # Check weights with non-uniform bin widths a, b = histogram( np.arange(9), [0, 1, 3, 6, 10], weights=[2, 1, 1, 1, 1, 1, 1, 1, 1], density=True) assert_almost_equal(a, [.2, .1, .1, .075]) def test_empty(self): a, b = histogram([], bins=([0, 1])) assert_array_equal(a, np.array([0])) assert_array_equal(b, np.array([0, 1])) class TestHistogramdd(TestCase): def test_simple(self): x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], [.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]]) H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]]) answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]], [[0, 1, 0], [0, 0, 1], [0, 0, 1]]]) assert_array_equal(H, answer) # Check normalization ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]] H, edges = histogramdd(x, bins=ed, normed=True) assert_(np.all(H == answer / 12.)) # Check that H has the correct shape. H, edges = histogramdd(x, (2, 3, 4), range=[[-1, 1], [0, 3], [0, 4]], normed=True) answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]]) assert_array_almost_equal(H, answer / 6., 4) # Check that a sequence of arrays is accepted and H has the correct # shape. z = [np.squeeze(y) for y in split(x, 3, axis=1)] H, edges = histogramdd( z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]]) answer = np.array([[[0, 0], [0, 0], [0, 0]], [[0, 1], [0, 0], [1, 0]], [[0, 1], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]]]) assert_array_equal(H, answer) Z = np.zeros((5, 5, 5)) Z[list(range(5)), list(range(5)), list(range(5))] = 1. H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5) assert_array_equal(H, Z) def test_shape_3d(self): # All possible permutations for bins of different lengths in 3D. bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4), (4, 5, 6)) r = rand(10, 3) for b in bins: H, edges = histogramdd(r, b) assert_(H.shape == b) def test_shape_4d(self): # All possible permutations for bins of different lengths in 4D. bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4), (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6), (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7), (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5), (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5), (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4)) r = rand(10, 4) for b in bins: H, edges = histogramdd(r, b) assert_(H.shape == b) def test_weights(self): v = rand(100, 2) hist, edges = histogramdd(v) n_hist, edges = histogramdd(v, normed=True) w_hist, edges = histogramdd(v, weights=np.ones(100)) assert_array_equal(w_hist, hist) w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, normed=True) assert_array_equal(w_hist, n_hist) w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2) assert_array_equal(w_hist, 2 * hist) def test_identical_samples(self): x = np.zeros((10, 2), int) hist, edges = histogramdd(x, bins=2) assert_array_equal(edges[0], np.array([-0.5, 0., 0.5])) def test_empty(self): a, b = histogramdd([[], []], bins=([0, 1], [0, 1])) assert_array_max_ulp(a, np.array([[0.]])) a, b = np.histogramdd([[], [], []], bins=2) assert_array_max_ulp(a, np.zeros((2, 2, 2))) def test_bins_errors(self): """There are two ways to specify bins. Check for the right errors when mixing those.""" x = np.arange(8).reshape(2, 4) assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5]) assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1]) assert_raises( ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]]) assert_raises( ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]]) assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]])) def test_inf_edges(self): """Test using +/-inf bin edges works. See #1788.""" with np.errstate(invalid='ignore'): x = np.arange(6).reshape(3, 2) expected = np.array([[1, 0], [0, 1], [0, 1]]) h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]]) assert_allclose(h, expected) h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])]) assert_allclose(h, expected) h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]]) assert_allclose(h, expected) def test_rightmost_binedge(self): """Test event very close to rightmost binedge. See Github issue #4266""" x = [0.9999999995] bins = [[0.,0.5,1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0] bins = [[0.,0.5,1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0000000001] bins = [[0.,0.5,1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0001] bins = [[0.,0.5,1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 0.0) class TestUnique(TestCase): def test_simple(self): x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0]) assert_(np.all(unique(x) == [0, 1, 2, 3, 4])) assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1])) x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham'] assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget'])) x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j]) assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10])) class TestCheckFinite(TestCase): def test_simple(self): a = [1, 2, 3] b = [1, 2, np.inf] c = [1, 2, np.nan] np.lib.asarray_chkfinite(a) assert_raises(ValueError, np.lib.asarray_chkfinite, b) assert_raises(ValueError, np.lib.asarray_chkfinite, c) def test_dtype_order(self): """Regression test for missing dtype and order arguments""" a = [1, 2, 3] a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64) assert_(a.dtype == np.float64) class TestCorrCoef(TestCase): A = np.array( [[0.15391142, 0.18045767, 0.14197213], [0.70461506, 0.96474128, 0.27906989], [0.9297531, 0.32296769, 0.19267156]]) B = np.array( [[0.10377691, 0.5417086, 0.49807457], [0.82872117, 0.77801674, 0.39226705], [0.9314666, 0.66800209, 0.03538394]]) res1 = np.array( [[1., 0.9379533, -0.04931983], [0.9379533, 1., 0.30007991], [-0.04931983, 0.30007991, 1.]]) res2 = np.array( [[1., 0.9379533, -0.04931983, 0.30151751, 0.66318558, 0.51532523], [0.9379533, 1., 0.30007991, -0.04781421, 0.88157256, 0.78052386], [-0.04931983, 0.30007991, 1., -0.96717111, 0.71483595, 0.83053601], [0.30151751, -0.04781421, -0.96717111, 1., -0.51366032, -0.66173113], [0.66318558, 0.88157256, 0.71483595, -0.51366032, 1., 0.98317823], [0.51532523, 0.78052386, 0.83053601, -0.66173113, 0.98317823, 1.]]) def test_non_array(self): assert_almost_equal(np.corrcoef([0, 1, 0], [1, 0, 1]), [[1., -1.], [-1., 1.]]) def test_simple(self): assert_almost_equal(corrcoef(self.A), self.res1) assert_almost_equal(corrcoef(self.A, self.B), self.res2) def test_ddof(self): assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1) assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2) def test_complex(self): x = np.array([[1, 2, 3], [1j, 2j, 3j]]) assert_allclose(corrcoef(x), np.array([[1., -1.j], [1.j, 1.]])) def test_xy(self): x = np.array([[1, 2, 3]]) y = np.array([[1j, 2j, 3j]]) assert_allclose(np.corrcoef(x, y), np.array([[1., -1.j], [1.j, 1.]])) def test_empty(self): with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(corrcoef(np.array([])), np.nan) assert_array_equal(corrcoef(np.array([]).reshape(0, 2)), np.array([]).reshape(0, 0)) assert_array_equal(corrcoef(np.array([]).reshape(2, 0)), np.array([[np.nan, np.nan], [np.nan, np.nan]])) def test_wrong_ddof(self): x = np.array([[0, 2], [1, 1], [2, 0]]).T with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(corrcoef(x, ddof=5), np.array([[np.nan, np.nan], [np.nan, np.nan]])) class TestCov(TestCase): def test_basic(self): x = np.array([[0, 2], [1, 1], [2, 0]]).T assert_allclose(cov(x), np.array([[1., -1.], [-1., 1.]])) def test_complex(self): x = np.array([[1, 2, 3], [1j, 2j, 3j]]) assert_allclose(cov(x), np.array([[1., -1.j], [1.j, 1.]])) def test_xy(self): x = np.array([[1, 2, 3]]) y = np.array([[1j, 2j, 3j]]) assert_allclose(cov(x, y), np.array([[1., -1.j], [1.j, 1.]])) def test_empty(self): with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(cov(np.array([])), np.nan) assert_array_equal(cov(np.array([]).reshape(0, 2)), np.array([]).reshape(0, 0)) assert_array_equal(cov(np.array([]).reshape(2, 0)), np.array([[np.nan, np.nan], [np.nan, np.nan]])) def test_wrong_ddof(self): x = np.array([[0, 2], [1, 1], [2, 0]]).T with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(cov(x, ddof=5), np.array([[np.inf, -np.inf], [-np.inf, np.inf]])) class Test_I0(TestCase): def test_simple(self): assert_almost_equal( i0(0.5), np.array(1.0634833707413234)) A = np.array([0.49842636, 0.6969809, 0.22011976, 0.0155549]) assert_almost_equal( i0(A), np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049])) B = np.array([[0.827002, 0.99959078], [0.89694769, 0.39298162], [0.37954418, 0.05206293], [0.36465447, 0.72446427], [0.48164949, 0.50324519]]) assert_almost_equal( i0(B), np.array([[1.17843223, 1.26583466], [1.21147086, 1.03898290], [1.03633899, 1.00067775], [1.03352052, 1.13557954], [1.05884290, 1.06432317]])) class TestKaiser(TestCase): def test_simple(self): assert_(np.isfinite(kaiser(1, 1.0))) assert_almost_equal(kaiser(0, 1.0), np.array([])) assert_almost_equal(kaiser(2, 1.0), np.array([0.78984831, 0.78984831])) assert_almost_equal(kaiser(5, 1.0), np.array([0.78984831, 0.94503323, 1., 0.94503323, 0.78984831])) assert_almost_equal(kaiser(5, 1.56789), np.array([0.58285404, 0.88409679, 1., 0.88409679, 0.58285404])) def test_int_beta(self): kaiser(3, 4) class TestMsort(TestCase): def test_simple(self): A = np.array([[0.44567325, 0.79115165, 0.54900530], [0.36844147, 0.37325583, 0.96098397], [0.64864341, 0.52929049, 0.39172155]]) assert_almost_equal( msort(A), np.array([[0.36844147, 0.37325583, 0.39172155], [0.44567325, 0.52929049, 0.54900530], [0.64864341, 0.79115165, 0.96098397]])) class TestMeshgrid(TestCase): def test_simple(self): [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7]) assert_array_equal(X, np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])) assert_array_equal(Y, np.array([[4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]])) def test_single_input(self): [X] = meshgrid([1, 2, 3, 4]) assert_array_equal(X, np.array([1, 2, 3, 4])) def test_no_input(self): args = [] assert_array_equal([], meshgrid(*args)) def test_indexing(self): x = [1, 2, 3] y = [4, 5, 6, 7] [X, Y] = meshgrid(x, y, indexing='ij') assert_array_equal(X, np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]])) assert_array_equal(Y, np.array([[4, 5, 6, 7], [4, 5, 6, 7], [4, 5, 6, 7]])) # Test expected shapes: z = [8, 9] assert_(meshgrid(x, y)[0].shape == (4, 3)) assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4)) assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2)) assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2)) assert_raises(ValueError, meshgrid, x, y, indexing='notvalid') def test_sparse(self): [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True) assert_array_equal(X, np.array([[1, 2, 3]])) assert_array_equal(Y, np.array([[4], [5], [6], [7]])) def test_invalid_arguments(self): # Test that meshgrid complains about invalid arguments # Regression test for issue #4755: # https://github.com/numpy/numpy/issues/4755 assert_raises(TypeError, meshgrid, [1, 2, 3], [4, 5, 6, 7], indices='ij') class TestPiecewise(TestCase): def test_simple(self): # Condition is single bool list x = piecewise([0, 0], [True, False], [1]) assert_array_equal(x, [1, 0]) # List of conditions: single bool list x = piecewise([0, 0], [[True, False]], [1]) assert_array_equal(x, [1, 0]) # Conditions is single bool array x = piecewise([0, 0], np.array([True, False]), [1]) assert_array_equal(x, [1, 0]) # Condition is single int array x = piecewise([0, 0], np.array([1, 0]), [1]) assert_array_equal(x, [1, 0]) # List of conditions: int array x = piecewise([0, 0], [np.array([1, 0])], [1]) assert_array_equal(x, [1, 0]) x = piecewise([0, 0], [[False, True]], [lambda x:-1]) assert_array_equal(x, [0, -1]) def test_two_conditions(self): x = piecewise([1, 2], [[True, False], [False, True]], [3, 4]) assert_array_equal(x, [3, 4]) def test_default(self): # No value specified for x[1], should be 0 x = piecewise([1, 2], [True, False], [2]) assert_array_equal(x, [2, 0]) # Should set x[1] to 3 x = piecewise([1, 2], [True, False], [2, 3]) assert_array_equal(x, [2, 3]) def test_0d(self): x = np.array(3) y = piecewise(x, x > 3, [4, 0]) assert_(y.ndim == 0) assert_(y == 0) x = 5 y = piecewise(x, [[True], [False]], [1, 0]) assert_(y.ndim == 0) assert_(y == 1) def test_0d_comparison(self): x = 3 y = piecewise(x, [x <= 3, x > 3], [4, 0]) class TestBincount(TestCase): def test_simple(self): y = np.bincount(np.arange(4)) assert_array_equal(y, np.ones(4)) def test_simple2(self): y = np.bincount(np.array([1, 5, 2, 4, 1])) assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1])) def test_simple_weight(self): x = np.arange(4) w = np.array([0.2, 0.3, 0.5, 0.1]) y = np.bincount(x, w) assert_array_equal(y, w) def test_simple_weight2(self): x = np.array([1, 2, 4, 5, 2]) w = np.array([0.2, 0.3, 0.5, 0.1, 0.2]) y = np.bincount(x, w) assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1])) def test_with_minlength(self): x = np.array([0, 1, 0, 1, 1]) y = np.bincount(x, minlength=3) assert_array_equal(y, np.array([2, 3, 0])) def test_with_minlength_smaller_than_maxvalue(self): x = np.array([0, 1, 1, 2, 2, 3, 3]) y = np.bincount(x, minlength=2) assert_array_equal(y, np.array([1, 2, 2, 2])) def test_with_minlength_and_weights(self): x = np.array([1, 2, 4, 5, 2]) w = np.array([0.2, 0.3, 0.5, 0.1, 0.2]) y = np.bincount(x, w, 8) assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0])) def test_empty(self): x = np.array([], dtype=int) y = np.bincount(x) assert_array_equal(x, y) def test_empty_with_minlength(self): x = np.array([], dtype=int) y = np.bincount(x, minlength=5) assert_array_equal(y, np.zeros(5, dtype=int)) def test_with_incorrect_minlength(self): x = np.array([], dtype=int) assert_raises_regex(TypeError, "an integer is required", lambda: np.bincount(x, minlength="foobar")) assert_raises_regex(ValueError, "must be positive", lambda: np.bincount(x, minlength=-1)) assert_raises_regex(ValueError, "must be positive", lambda: np.bincount(x, minlength=0)) x = np.arange(5) assert_raises_regex(TypeError, "an integer is required", lambda: np.bincount(x, minlength="foobar")) assert_raises_regex(ValueError, "minlength must be positive", lambda: np.bincount(x, minlength=-1)) assert_raises_regex(ValueError, "minlength must be positive", lambda: np.bincount(x, minlength=0)) class TestInterp(TestCase): def test_exceptions(self): assert_raises(ValueError, interp, 0, [], []) assert_raises(ValueError, interp, 0, [0], [1, 2]) def test_basic(self): x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) x0 = np.linspace(0, 1, 50) assert_almost_equal(np.interp(x0, x, y), x0) def test_right_left_behavior(self): assert_equal(interp([-1, 0, 1], [0], [1]), [1, 1, 1]) assert_equal(interp([-1, 0, 1], [0], [1], left=0), [0, 1, 1]) assert_equal(interp([-1, 0, 1], [0], [1], right=0), [1, 1, 0]) assert_equal(interp([-1, 0, 1], [0], [1], left=0, right=0), [0, 1, 0]) def test_scalar_interpolation_point(self): x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) x0 = 0 assert_almost_equal(np.interp(x0, x, y), x0) x0 = .3 assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.float32(.3) assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.float64(.3) assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.nan assert_almost_equal(np.interp(x0, x, y), x0) def test_zero_dimensional_interpolation_point(self): x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) x0 = np.array(.3) assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.array(.3, dtype=object) assert_almost_equal(np.interp(x0, x, y), .3) def test_if_len_x_is_small(self): xp = np.arange(0, 10, 0.0001) fp = np.sin(xp) assert_almost_equal(np.interp(np.pi, xp, fp), 0.0) def compare_results(res, desired): for i in range(len(desired)): assert_array_equal(res[i], desired[i]) class TestScoreatpercentile(TestCase): def test_basic(self): x = np.arange(8) * 0.5 assert_equal(np.percentile(x, 0), 0.) assert_equal(np.percentile(x, 100), 3.5) assert_equal(np.percentile(x, 50), 1.75) def test_api(self): d = np.ones(5) np.percentile(d, 5, None, None, False) np.percentile(d, 5, None, None, False, 'linear') o = np.ones((1,)) np.percentile(d, 5, None, o, False, 'linear') def test_2D(self): x = np.array([[1, 1, 1], [1, 1, 1], [4, 4, 3], [1, 1, 1], [1, 1, 1]]) assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1]) def test_linear(self): # Test defaults assert_equal(np.percentile(range(10), 50), 4.5) # explicitly specify interpolation_method 'fraction' (the default) assert_equal(np.percentile(range(10), 50, interpolation='linear'), 4.5) def test_lower_higher(self): # interpolation_method 'lower'/'higher' assert_equal(np.percentile(range(10), 50, interpolation='lower'), 4) assert_equal(np.percentile(range(10), 50, interpolation='higher'), 5) def test_midpoint(self): assert_equal(np.percentile(range(10), 51, interpolation='midpoint'), 4.5) def test_nearest(self): assert_equal(np.percentile(range(10), 51, interpolation='nearest'), 5) assert_equal(np.percentile(range(10), 49, interpolation='nearest'), 4) def test_sequence(self): x = np.arange(8) * 0.5 assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75]) def test_axis(self): x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, (25, 50, 100)), [2.75, 5.5, 11.0]) r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]] assert_equal(np.percentile(x, (25, 50, 100), axis=0), r0) r1 = [[0.75, 1.5, 3], [4.75, 5.5, 7], [8.75, 9.5, 11]] assert_equal(np.percentile(x, (25, 50, 100), axis=1), np.array(r1).T) # ensure qth axis is always first as with np.array(old_percentile(..)) x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6) assert_equal(np.percentile(x, (25, 50)).shape, (2,)) assert_equal(np.percentile(x, (25, 50, 75)).shape, (3,)) assert_equal(np.percentile(x, (25, 50), axis=0).shape, (2, 4, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6)) assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5)) assert_equal(np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), interpolation="higher").shape, (2,)) assert_equal(np.percentile(x, (25, 50, 75), interpolation="higher").shape, (3,)) assert_equal(np.percentile(x, (25, 50), axis=0, interpolation="higher").shape, (2, 4, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=1, interpolation="higher").shape, (2, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=2, interpolation="higher").shape, (2, 3, 4, 6)) assert_equal(np.percentile(x, (25, 50), axis=3, interpolation="higher").shape, (2, 3, 4, 5)) assert_equal(np.percentile(x, (25, 50, 75), axis=1, interpolation="higher").shape, (3, 3, 5, 6)) def test_scalar_q(self): # test for no empty dimensions for compatiblity with old percentile x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, 50), 5.5) self.assertTrue(np.isscalar(np.percentile(x, 50))) r0 = np.array([ 4., 5., 6., 7.]) assert_equal(np.percentile(x, 50, axis=0), r0) assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape) r1 = np.array([ 1.5, 5.5, 9.5]) assert_almost_equal(np.percentile(x, 50, axis=1), r1) assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape) out = np.empty(1) assert_equal(np.percentile(x, 50, out=out), 5.5) assert_equal(out, 5.5) out = np.empty(4) assert_equal(np.percentile(x, 50, axis=0, out=out), r0) assert_equal(out, r0) out = np.empty(3) assert_equal(np.percentile(x, 50, axis=1, out=out), r1) assert_equal(out, r1) # test for no empty dimensions for compatiblity with old percentile x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, 50, interpolation='lower'), 5.) self.assertTrue(np.isscalar(np.percentile(x, 50))) r0 = np.array([ 4., 5., 6., 7.]) c0 = np.percentile(x, 50, interpolation='lower', axis=0) assert_equal(c0, r0) assert_equal(c0.shape, r0.shape) r1 = np.array([ 1., 5., 9.]) c1 = np.percentile(x, 50, interpolation='lower', axis=1) assert_almost_equal(c1, r1) assert_equal(c1.shape, r1.shape) out = np.empty((), dtype=x.dtype) c = np.percentile(x, 50, interpolation='lower', out=out) assert_equal(c, 5) assert_equal(out, 5) out = np.empty(4, dtype=x.dtype) c = np.percentile(x, 50, interpolation='lower', axis=0, out=out) assert_equal(c, r0) assert_equal(out, r0) out = np.empty(3, dtype=x.dtype) c = np.percentile(x, 50, interpolation='lower', axis=1, out=out) assert_equal(c, r1) assert_equal(out, r1) def test_exception(self): assert_raises(ValueError, np.percentile, [1, 2], 56, interpolation='foobar') assert_raises(ValueError, np.percentile, [1], 101) assert_raises(ValueError, np.percentile, [1], -1) assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101]) assert_raises(ValueError, np.percentile, [1], list(range(50)) + [-0.1]) def test_percentile_list(self): assert_equal(np.percentile([1, 2, 3], 0), 1) def test_percentile_out(self): x = np.array([1, 2, 3]) y = np.zeros((3,)) p = (1, 2, 3) np.percentile(x, p, out=y) assert_equal(y, np.percentile(x, p)) x = np.array([[1, 2, 3], [4, 5, 6]]) y = np.zeros((3, 3)) np.percentile(x, p, axis=0, out=y) assert_equal(y, np.percentile(x, p, axis=0)) y = np.zeros((3, 2)) np.percentile(x, p, axis=1, out=y) assert_equal(y, np.percentile(x, p, axis=1)) x = np.arange(12).reshape(3, 4) # q.dim > 1, float r0 = np.array([[2., 3., 4., 5.], [4., 5., 6., 7.]]) out = np.empty((2, 4)) assert_equal(np.percentile(x, (25, 50), axis=0, out=out), r0) assert_equal(out, r0) r1 = np.array([[0.75, 4.75, 8.75], [1.5, 5.5, 9.5]]) out = np.empty((2, 3)) assert_equal(np.percentile(x, (25, 50), axis=1, out=out), r1) assert_equal(out, r1) # q.dim > 1, int r0 = np.array([[0, 1, 2, 3], [4, 5, 6, 7]]) out = np.empty((2, 4), dtype=x.dtype) c = np.percentile(x, (25, 50), interpolation='lower', axis=0, out=out) assert_equal(c, r0) assert_equal(out, r0) r1 = np.array([[0, 4, 8], [1, 5, 9]]) out = np.empty((2, 3), dtype=x.dtype) c = np.percentile(x, (25, 50), interpolation='lower', axis=1, out=out) assert_equal(c, r1) assert_equal(out, r1) def test_percentile_empty_dim(self): # empty dims are preserved d = np.arange(11*2).reshape(11, 1, 2, 1) assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=3).shape, (11, 1, 2)) assert_array_equal(np.percentile(d, 50, axis=-1).shape, (11, 1, 2)) assert_array_equal(np.percentile(d, 50, axis=-2).shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=-3).shape, (11, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=2, interpolation='midpoint').shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=-2, interpolation='midpoint').shape, (11, 1, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape, (2, 1, 2, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=1)).shape, (2, 11, 2, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=2)).shape, (2, 11, 1, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape, (2, 11, 1, 2)) def test_percentile_no_overwrite(self): a = np.array([2, 3, 4, 1]) np.percentile(a, [50], overwrite_input=False) assert_equal(a, np.array([2, 3, 4, 1])) a = np.array([2, 3, 4, 1]) np.percentile(a, [50]) assert_equal(a, np.array([2, 3, 4, 1])) def test_no_p_overwrite(self): p = np.linspace(0., 100., num=5) np.percentile(np.arange(100.), p, interpolation="midpoint") assert_array_equal(p, np.linspace(0., 100., num=5)) p = np.linspace(0., 100., num=5).tolist() np.percentile(np.arange(100.), p, interpolation="midpoint") assert_array_equal(p, np.linspace(0., 100., num=5).tolist()) def test_percentile_overwrite(self): a = np.array([2, 3, 4, 1]) b = np.percentile(a, [50], overwrite_input=True) assert_equal(b, np.array([2.5])) b = np.percentile([2, 3, 4, 1], [50], overwrite_input=True) assert_equal(b, np.array([2.5])) def test_extended_axis(self): o = np.random.normal(size=(71, 23)) x = np.dstack([o] * 10) assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30)) x = np.rollaxis(x, -1, 0) assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30)) x = x.swapaxes(0, 1).copy() assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30)) x = x.swapaxes(0, 1).copy() assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)), np.percentile(x, [25, 60], axis=None)) assert_equal(np.percentile(x, [25, 60], axis=(0,)), np.percentile(x, [25, 60], axis=0)) d = np.arange(3 * 5 * 7 * 11).reshape(3, 5, 7, 11) np.random.shuffle(d) assert_equal(np.percentile(d, 25, axis=(0, 1, 2))[0], np.percentile(d[:, :, :, 0].flatten(), 25)) assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1], np.percentile(d[:, :, 1, :].flatten(), [10, 90])) assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2], np.percentile(d[:, :, 2, :].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2], np.percentile(d[2, :, :, :].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1], np.percentile(d[2, 1, :, :].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1], np.percentile(d[2, :, :, 1].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2], np.percentile(d[2, :, 2, :].flatten(), 25)) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) assert_raises(IndexError, np.percentile, d, axis=-5, q=25) assert_raises(IndexError, np.percentile, d, axis=(0, -5), q=25) assert_raises(IndexError, np.percentile, d, axis=4, q=25) assert_raises(IndexError, np.percentile, d, axis=(0, 4), q=25) assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25) def test_keepdims(self): d = np.ones((3, 5, 7, 11)) assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape, (1, 1, 7, 11)) assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape, (1, 5, 7, 1)) assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape, (3, 1, 7, 11)) assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape, (1, 1, 7, 1)) assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3), keepdims=True).shape, (2, 1, 1, 7, 1)) assert_equal(np.percentile(d, [1, 7], axis=(0, 3), keepdims=True).shape, (2, 1, 5, 7, 1)) class TestMedian(TestCase): def test_basic(self): a0 = np.array(1) a1 = np.arange(2) a2 = np.arange(6).reshape(2, 3) assert_equal(np.median(a0), 1) assert_allclose(np.median(a1), 0.5) assert_allclose(np.median(a2), 2.5) assert_allclose(np.median(a2, axis=0), [1.5, 2.5, 3.5]) assert_equal(np.median(a2, axis=1), [1, 4]) assert_allclose(np.median(a2, axis=None), 2.5) a = np.array([0.0444502, 0.0463301, 0.141249, 0.0606775]) assert_almost_equal((a[1] + a[3]) / 2., np.median(a)) a = np.array([0.0463301, 0.0444502, 0.141249]) assert_equal(a[0], np.median(a)) a = np.array([0.0444502, 0.141249, 0.0463301]) assert_equal(a[-1], np.median(a)) # check array scalar result assert_equal(np.median(a).ndim, 0) a[1] = np.nan assert_equal(np.median(a).ndim, 0) def test_axis_keyword(self): a3 = np.array([[2, 3], [0, 1], [6, 7], [4, 5]]) for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]: orig = a.copy() np.median(a, axis=None) for ax in range(a.ndim): np.median(a, axis=ax) assert_array_equal(a, orig) assert_allclose(np.median(a3, axis=0), [3, 4]) assert_allclose(np.median(a3.T, axis=1), [3, 4]) assert_allclose(np.median(a3), 3.5) assert_allclose(np.median(a3, axis=None), 3.5) assert_allclose(np.median(a3.T), 3.5) def test_overwrite_keyword(self): a3 = np.array([[2, 3], [0, 1], [6, 7], [4, 5]]) a0 = np.array(1) a1 = np.arange(2) a2 = np.arange(6).reshape(2, 3) assert_allclose(np.median(a0.copy(), overwrite_input=True), 1) assert_allclose(np.median(a1.copy(), overwrite_input=True), 0.5) assert_allclose(np.median(a2.copy(), overwrite_input=True), 2.5) assert_allclose(np.median(a2.copy(), overwrite_input=True, axis=0), [1.5, 2.5, 3.5]) assert_allclose( np.median(a2.copy(), overwrite_input=True, axis=1), [1, 4]) assert_allclose( np.median(a2.copy(), overwrite_input=True, axis=None), 2.5) assert_allclose( np.median(a3.copy(), overwrite_input=True, axis=0), [3, 4]) assert_allclose(np.median(a3.T.copy(), overwrite_input=True, axis=1), [3, 4]) a4 = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5)) map(np.random.shuffle, a4) assert_allclose(np.median(a4, axis=None), np.median(a4.copy(), axis=None, overwrite_input=True)) assert_allclose(np.median(a4, axis=0), np.median(a4.copy(), axis=0, overwrite_input=True)) assert_allclose(np.median(a4, axis=1), np.median(a4.copy(), axis=1, overwrite_input=True)) assert_allclose(np.median(a4, axis=2), np.median(a4.copy(), axis=2, overwrite_input=True)) def test_array_like(self): x = [1, 2, 3] assert_almost_equal(np.median(x), 2) x2 = [x] assert_almost_equal(np.median(x2), 2) assert_allclose(np.median(x2, axis=0), x) def test_subclass(self): # gh-3846 class MySubClass(np.ndarray): def __new__(cls, input_array, info=None): obj = np.asarray(input_array).view(cls) obj.info = info return obj def mean(self, axis=None, dtype=None, out=None): return -7 a = MySubClass([1,2,3]) assert_equal(np.median(a), -7) def test_object(self): o = np.arange(7.); assert_(type(np.median(o.astype(object))), float) o[2] = np.nan assert_(type(np.median(o.astype(object))), float) def test_extended_axis(self): o = np.random.normal(size=(71, 23)) x = np.dstack([o] * 10) assert_equal(np.median(x, axis=(0, 1)), np.median(o)) x = np.rollaxis(x, -1, 0) assert_equal(np.median(x, axis=(-2, -1)), np.median(o)) x = x.swapaxes(0, 1).copy() assert_equal(np.median(x, axis=(0, -1)), np.median(o)) assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None)) assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0)) assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1)) d = np.arange(3 * 5 * 7 * 11).reshape(3, 5, 7, 11) np.random.shuffle(d) assert_equal(np.median(d, axis=(0, 1, 2))[0], np.median(d[:, :, :, 0].flatten())) assert_equal(np.median(d, axis=(0, 1, 3))[1], np.median(d[:, :, 1, :].flatten())) assert_equal(np.median(d, axis=(3, 1, -4))[2], np.median(d[:, :, 2, :].flatten())) assert_equal(np.median(d, axis=(3, 1, 2))[2], np.median(d[2, :, :, :].flatten())) assert_equal(np.median(d, axis=(3, 2))[2, 1], np.median(d[2, 1, :, :].flatten())) assert_equal(np.median(d, axis=(1, -2))[2, 1], np.median(d[2, :, :, 1].flatten())) assert_equal(np.median(d, axis=(1, 3))[2, 2], np.median(d[2, :, 2, :].flatten())) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) assert_raises(IndexError, np.median, d, axis=-5) assert_raises(IndexError, np.median, d, axis=(0, -5)) assert_raises(IndexError, np.median, d, axis=4) assert_raises(IndexError, np.median, d, axis=(0, 4)) assert_raises(ValueError, np.median, d, axis=(1, 1)) def test_keepdims(self): d = np.ones((3, 5, 7, 11)) assert_equal(np.median(d, axis=None, keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.median(d, axis=(0, 1), keepdims=True).shape, (1, 1, 7, 11)) assert_equal(np.median(d, axis=(0, 3), keepdims=True).shape, (1, 5, 7, 1)) assert_equal(np.median(d, axis=(1,), keepdims=True).shape, (3, 1, 7, 11)) assert_equal(np.median(d, axis=(0, 1, 2, 3), keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.median(d, axis=(0, 1, 3), keepdims=True).shape, (1, 1, 7, 1)) class TestAdd_newdoc_ufunc(TestCase): def test_ufunc_arg(self): assert_raises(TypeError, add_newdoc_ufunc, 2, "blah") assert_raises(ValueError, add_newdoc_ufunc, np.add, "blah") def test_string_arg(self): assert_raises(TypeError, add_newdoc_ufunc, np.add, 3) class TestAdd_newdoc(TestCase): def test_add_doc(self): # test np.add_newdoc tgt = "Current flat index into the array." self.assertEqual(np.core.flatiter.index.__doc__[:len(tgt)], tgt) self.assertTrue(len(np.core.ufunc.identity.__doc__) > 300) self.assertTrue(len(np.lib.index_tricks.mgrid.__doc__) > 300) if __name__ == "__main__": run_module_suite()