from __future__ import division, absolute_import, print_function import sys import warnings import itertools import platform from decimal import Decimal import numpy as np from numpy.core import umath from numpy.random import rand, randint, randn from numpy.testing import ( TestCase, run_module_suite, assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal, dec ) class TestResize(TestCase): def test_copies(self): A = np.array([[1, 2], [3, 4]]) Ar1 = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) assert_equal(np.resize(A, (2, 4)), Ar1) Ar2 = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) assert_equal(np.resize(A, (4, 2)), Ar2) Ar3 = np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]]) assert_equal(np.resize(A, (4, 3)), Ar3) def test_zeroresize(self): A = np.array([[1, 2], [3, 4]]) Ar = np.resize(A, (0,)) assert_array_equal(Ar, np.array([])) assert_equal(A.dtype, Ar.dtype) def test_reshape_from_zero(self): # See also gh-6740 A = np.zeros(0, dtype=[('a', np.float32, 1)]) Ar = np.resize(A, (2, 1)) assert_array_equal(Ar, np.zeros((2, 1), Ar.dtype)) assert_equal(A.dtype, Ar.dtype) class TestNonarrayArgs(TestCase): # check that non-array arguments to functions wrap them in arrays def test_choose(self): choices = [[0, 1, 2], [3, 4, 5], [5, 6, 7]] tgt = [5, 1, 5] a = [2, 0, 1] out = np.choose(a, choices) assert_equal(out, tgt) def test_clip(self): arr = [-1, 5, 2, 3, 10, -4, -9] out = np.clip(arr, 2, 7) tgt = [2, 5, 2, 3, 7, 2, 2] assert_equal(out, tgt) def test_compress(self): arr = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] tgt = [[5, 6, 7, 8, 9]] out = np.compress([0, 1], arr, axis=0) assert_equal(out, tgt) def test_cumproduct(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.all(np.cumproduct(A) == np.array([1, 2, 6, 24, 120, 720]))) def test_diagonal(self): a = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] out = np.diagonal(a) tgt = [0, 5, 10] assert_equal(out, tgt) def test_mean(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.mean(A) == 3.5) assert_(np.all(np.mean(A, 0) == np.array([2.5, 3.5, 4.5]))) assert_(np.all(np.mean(A, 1) == np.array([2., 5.]))) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.mean([]))) assert_(w[0].category is RuntimeWarning) def test_ptp(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0) def test_prod(self): arr = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] tgt = [24, 1890, 600] assert_equal(np.prod(arr, axis=-1), tgt) def test_ravel(self): a = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] assert_equal(np.ravel(a), tgt) def test_repeat(self): a = [1, 2, 3] tgt = [1, 1, 2, 2, 3, 3] out = np.repeat(a, 2) assert_equal(out, tgt) def test_reshape(self): arr = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]] assert_equal(np.reshape(arr, (2, 6)), tgt) def test_round(self): arr = [1.56, 72.54, 6.35, 3.25] tgt = [1.6, 72.5, 6.4, 3.2] assert_equal(np.around(arr, decimals=1), tgt) def test_searchsorted(self): arr = [-8, -5, -1, 3, 6, 10] out = np.searchsorted(arr, 0) assert_equal(out, 3) def test_size(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.size(A) == 6) assert_(np.size(A, 0) == 2) assert_(np.size(A, 1) == 3) def test_squeeze(self): A = [[[1, 1, 1], [2, 2, 2], [3, 3, 3]]] assert_(np.squeeze(A).shape == (3, 3)) def test_std(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.std(A), 1.707825127659933) assert_almost_equal(np.std(A, 0), np.array([1.5, 1.5, 1.5])) assert_almost_equal(np.std(A, 1), np.array([0.81649658, 0.81649658])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.std([]))) assert_(w[0].category is RuntimeWarning) def test_swapaxes(self): tgt = [[[0, 4], [2, 6]], [[1, 5], [3, 7]]] a = [[[0, 1], [2, 3]], [[4, 5], [6, 7]]] out = np.swapaxes(a, 0, 2) assert_equal(out, tgt) def test_sum(self): m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] tgt = [[6], [15], [24]] out = np.sum(m, axis=1, keepdims=True) assert_equal(tgt, out) def test_take(self): tgt = [2, 3, 5] indices = [1, 2, 4] a = [1, 2, 3, 4, 5] out = np.take(a, indices) assert_equal(out, tgt) def test_trace(self): c = [[1, 2], [3, 4], [5, 6]] assert_equal(np.trace(c), 5) def test_transpose(self): arr = [[1, 2], [3, 4], [5, 6]] tgt = [[1, 3, 5], [2, 4, 6]] assert_equal(np.transpose(arr, (1, 0)), tgt) def test_var(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.var(A), 2.9166666666666665) assert_almost_equal(np.var(A, 0), np.array([2.25, 2.25, 2.25])) assert_almost_equal(np.var(A, 1), np.array([0.66666667, 0.66666667])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.var([]))) assert_(w[0].category is RuntimeWarning) class TestBoolScalar(TestCase): def test_logical(self): f = np.False_ t = np.True_ s = "xyz" self.assertTrue((t and s) is s) self.assertTrue((f and s) is f) def test_bitwise_or(self): f = np.False_ t = np.True_ self.assertTrue((t | t) is t) self.assertTrue((f | t) is t) self.assertTrue((t | f) is t) self.assertTrue((f | f) is f) def test_bitwise_and(self): f = np.False_ t = np.True_ self.assertTrue((t & t) is t) self.assertTrue((f & t) is f) self.assertTrue((t & f) is f) self.assertTrue((f & f) is f) def test_bitwise_xor(self): f = np.False_ t = np.True_ self.assertTrue((t ^ t) is f) self.assertTrue((f ^ t) is t) self.assertTrue((t ^ f) is t) self.assertTrue((f ^ f) is f) class TestBoolArray(TestCase): def setUp(self): # offset for simd tests self.t = np.array([True] * 41, dtype=np.bool)[1::] self.f = np.array([False] * 41, dtype=np.bool)[1::] self.o = np.array([False] * 42, dtype=np.bool)[2::] self.nm = self.f.copy() self.im = self.t.copy() self.nm[3] = True self.nm[-2] = True self.im[3] = False self.im[-2] = False def test_all_any(self): self.assertTrue(self.t.all()) self.assertTrue(self.t.any()) self.assertFalse(self.f.all()) self.assertFalse(self.f.any()) self.assertTrue(self.nm.any()) self.assertTrue(self.im.any()) self.assertFalse(self.nm.all()) self.assertFalse(self.im.all()) # check bad element in all positions for i in range(256 - 7): d = np.array([False] * 256, dtype=np.bool)[7::] d[i] = True self.assertTrue(np.any(d)) e = np.array([True] * 256, dtype=np.bool)[7::] e[i] = False self.assertFalse(np.all(e)) assert_array_equal(e, ~d) # big array test for blocked libc loops for i in list(range(9, 6000, 507)) + [7764, 90021, -10]: d = np.array([False] * 100043, dtype=np.bool) d[i] = True self.assertTrue(np.any(d), msg="%r" % i) e = np.array([True] * 100043, dtype=np.bool) e[i] = False self.assertFalse(np.all(e), msg="%r" % i) def test_logical_not_abs(self): assert_array_equal(~self.t, self.f) assert_array_equal(np.abs(~self.t), self.f) assert_array_equal(np.abs(~self.f), self.t) assert_array_equal(np.abs(self.f), self.f) assert_array_equal(~np.abs(self.f), self.t) assert_array_equal(~np.abs(self.t), self.f) assert_array_equal(np.abs(~self.nm), self.im) np.logical_not(self.t, out=self.o) assert_array_equal(self.o, self.f) np.abs(self.t, out=self.o) assert_array_equal(self.o, self.t) def test_logical_and_or_xor(self): assert_array_equal(self.t | self.t, self.t) assert_array_equal(self.f | self.f, self.f) assert_array_equal(self.t | self.f, self.t) assert_array_equal(self.f | self.t, self.t) np.logical_or(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t & self.t, self.t) assert_array_equal(self.f & self.f, self.f) assert_array_equal(self.t & self.f, self.f) assert_array_equal(self.f & self.t, self.f) np.logical_and(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t ^ self.t, self.f) assert_array_equal(self.f ^ self.f, self.f) assert_array_equal(self.t ^ self.f, self.t) assert_array_equal(self.f ^ self.t, self.t) np.logical_xor(self.t, self.t, out=self.o) assert_array_equal(self.o, self.f) assert_array_equal(self.nm & self.t, self.nm) assert_array_equal(self.im & self.f, False) assert_array_equal(self.nm & True, self.nm) assert_array_equal(self.im & False, self.f) assert_array_equal(self.nm | self.t, self.t) assert_array_equal(self.im | self.f, self.im) assert_array_equal(self.nm | True, self.t) assert_array_equal(self.im | False, self.im) assert_array_equal(self.nm ^ self.t, self.im) assert_array_equal(self.im ^ self.f, self.im) assert_array_equal(self.nm ^ True, self.im) assert_array_equal(self.im ^ False, self.im) class TestBoolCmp(TestCase): def setUp(self): self.f = np.ones(256, dtype=np.float32) self.ef = np.ones(self.f.size, dtype=np.bool) self.d = np.ones(128, dtype=np.float64) self.ed = np.ones(self.d.size, dtype=np.bool) # generate values for all permutation of 256bit simd vectors s = 0 for i in range(32): self.f[s:s+8] = [i & 2**x for x in range(8)] self.ef[s:s+8] = [(i & 2**x) != 0 for x in range(8)] s += 8 s = 0 for i in range(16): self.d[s:s+4] = [i & 2**x for x in range(4)] self.ed[s:s+4] = [(i & 2**x) != 0 for x in range(4)] s += 4 self.nf = self.f.copy() self.nd = self.d.copy() self.nf[self.ef] = np.nan self.nd[self.ed] = np.nan def test_float(self): # offset for alignment test for i in range(4): assert_array_equal(self.f[i:] > 0, self.ef[i:]) assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:]) assert_array_equal(self.f[i:] == 0, ~self.ef[i:]) assert_array_equal(-self.f[i:] < 0, self.ef[i:]) assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:]) r = self.f[i:] != 0 assert_array_equal(r, self.ef[i:]) r2 = self.f[i:] != np.zeros_like(self.f[i:]) r3 = 0 != self.f[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:]) def test_double(self): # offset for alignment test for i in range(2): assert_array_equal(self.d[i:] > 0, self.ed[i:]) assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:]) assert_array_equal(self.d[i:] == 0, ~self.ed[i:]) assert_array_equal(-self.d[i:] < 0, self.ed[i:]) assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:]) r = self.d[i:] != 0 assert_array_equal(r, self.ed[i:]) r2 = self.d[i:] != np.zeros_like(self.d[i:]) r3 = 0 != self.d[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:]) class TestSeterr(TestCase): def test_default(self): err = np.geterr() self.assertEqual(err, dict( divide='warn', invalid='warn', over='warn', under='ignore', )) def test_set(self): with np.errstate(): err = np.seterr() old = np.seterr(divide='print') self.assertTrue(err == old) new = np.seterr() self.assertTrue(new['divide'] == 'print') np.seterr(over='raise') self.assertTrue(np.geterr()['over'] == 'raise') self.assertTrue(new['divide'] == 'print') np.seterr(**old) self.assertTrue(np.geterr() == old) @dec.skipif(platform.machine() == "armv5tel", "See gh-413.") def test_divide_err(self): with np.errstate(divide='raise'): try: np.array([1.]) / np.array([0.]) except FloatingPointError: pass else: self.fail() np.seterr(divide='ignore') np.array([1.]) / np.array([0.]) def test_errobj(self): olderrobj = np.geterrobj() self.called = 0 try: with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") with np.errstate(divide='warn'): np.seterrobj([20000, 1, None]) np.array([1.]) / np.array([0.]) self.assertEqual(len(w), 1) def log_err(*args): self.called += 1 extobj_err = args assert_(len(extobj_err) == 2) assert_("divide" in extobj_err[0]) with np.errstate(divide='ignore'): np.seterrobj([20000, 3, log_err]) np.array([1.]) / np.array([0.]) self.assertEqual(self.called, 1) np.seterrobj(olderrobj) with np.errstate(divide='ignore'): np.divide(1., 0., extobj=[20000, 3, log_err]) self.assertEqual(self.called, 2) finally: np.seterrobj(olderrobj) del self.called def test_errobj_noerrmask(self): # errmask = 0 has a special code path for the default olderrobj = np.geterrobj() try: # set errobj to something non default np.seterrobj([umath.UFUNC_BUFSIZE_DEFAULT, umath.ERR_DEFAULT + 1, None]) # call a ufunc np.isnan(np.array([6])) # same with the default, lots of times to get rid of possible # pre-existing stack in the code for i in range(10000): np.seterrobj([umath.UFUNC_BUFSIZE_DEFAULT, umath.ERR_DEFAULT, None]) np.isnan(np.array([6])) finally: np.seterrobj(olderrobj) class TestFloatExceptions(TestCase): def assert_raises_fpe(self, fpeerr, flop, x, y): ftype = type(x) try: flop(x, y) assert_(False, "Type %s did not raise fpe error '%s'." % (ftype, fpeerr)) except FloatingPointError as exc: assert_(str(exc).find(fpeerr) >= 0, "Type %s raised wrong fpe error '%s'." % (ftype, exc)) def assert_op_raises_fpe(self, fpeerr, flop, sc1, sc2): # Check that fpe exception is raised. # # Given a floating operation `flop` and two scalar values, check that # the operation raises the floating point exception specified by # `fpeerr`. Tests all variants with 0-d array scalars as well. self.assert_raises_fpe(fpeerr, flop, sc1, sc2) self.assert_raises_fpe(fpeerr, flop, sc1[()], sc2) self.assert_raises_fpe(fpeerr, flop, sc1, sc2[()]) self.assert_raises_fpe(fpeerr, flop, sc1[()], sc2[()]) @dec.knownfailureif(True, "See ticket #2350") def test_floating_exceptions(self): # Test basic arithmetic function errors with np.errstate(all='raise'): # Test for all real and complex float types for typecode in np.typecodes['AllFloat']: ftype = np.obj2sctype(typecode) if np.dtype(ftype).kind == 'f': # Get some extreme values for the type fi = np.finfo(ftype) ft_tiny = fi.tiny ft_max = fi.max ft_eps = fi.eps underflow = 'underflow' divbyzero = 'divide by zero' else: # 'c', complex, corresponding real dtype rtype = type(ftype(0).real) fi = np.finfo(rtype) ft_tiny = ftype(fi.tiny) ft_max = ftype(fi.max) ft_eps = ftype(fi.eps) # The complex types raise different exceptions underflow = '' divbyzero = '' overflow = 'overflow' invalid = 'invalid' self.assert_raises_fpe(underflow, lambda a, b: a/b, ft_tiny, ft_max) self.assert_raises_fpe(underflow, lambda a, b: a*b, ft_tiny, ft_tiny) self.assert_raises_fpe(overflow, lambda a, b: a*b, ft_max, ftype(2)) self.assert_raises_fpe(overflow, lambda a, b: a/b, ft_max, ftype(0.5)) self.assert_raises_fpe(overflow, lambda a, b: a+b, ft_max, ft_max*ft_eps) self.assert_raises_fpe(overflow, lambda a, b: a-b, -ft_max, ft_max*ft_eps) self.assert_raises_fpe(overflow, np.power, ftype(2), ftype(2**fi.nexp)) self.assert_raises_fpe(divbyzero, lambda a, b: a/b, ftype(1), ftype(0)) self.assert_raises_fpe(invalid, lambda a, b: a/b, ftype(np.inf), ftype(np.inf)) self.assert_raises_fpe(invalid, lambda a, b: a/b, ftype(0), ftype(0)) self.assert_raises_fpe(invalid, lambda a, b: a-b, ftype(np.inf), ftype(np.inf)) self.assert_raises_fpe(invalid, lambda a, b: a+b, ftype(np.inf), ftype(-np.inf)) self.assert_raises_fpe(invalid, lambda a, b: a*b, ftype(0), ftype(np.inf)) def test_warnings(self): # test warning code path with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") with np.errstate(all="warn"): np.divide(1, 0.) self.assertEqual(len(w), 1) self.assertTrue("divide by zero" in str(w[0].message)) np.array(1e300) * np.array(1e300) self.assertEqual(len(w), 2) self.assertTrue("overflow" in str(w[-1].message)) np.array(np.inf) - np.array(np.inf) self.assertEqual(len(w), 3) self.assertTrue("invalid value" in str(w[-1].message)) np.array(1e-300) * np.array(1e-300) self.assertEqual(len(w), 4) self.assertTrue("underflow" in str(w[-1].message)) class TestTypes(TestCase): def check_promotion_cases(self, promote_func): # tests that the scalars get coerced correctly. b = np.bool_(0) i8, i16, i32, i64 = np.int8(0), np.int16(0), np.int32(0), np.int64(0) u8, u16, u32, u64 = np.uint8(0), np.uint16(0), np.uint32(0), np.uint64(0) f32, f64, fld = np.float32(0), np.float64(0), np.longdouble(0) c64, c128, cld = np.complex64(0), np.complex128(0), np.clongdouble(0) # coercion within the same kind assert_equal(promote_func(i8, i16), np.dtype(np.int16)) assert_equal(promote_func(i32, i8), np.dtype(np.int32)) assert_equal(promote_func(i16, i64), np.dtype(np.int64)) assert_equal(promote_func(u8, u32), np.dtype(np.uint32)) assert_equal(promote_func(f32, f64), np.dtype(np.float64)) assert_equal(promote_func(fld, f32), np.dtype(np.longdouble)) assert_equal(promote_func(f64, fld), np.dtype(np.longdouble)) assert_equal(promote_func(c128, c64), np.dtype(np.complex128)) assert_equal(promote_func(cld, c128), np.dtype(np.clongdouble)) assert_equal(promote_func(c64, fld), np.dtype(np.clongdouble)) # coercion between kinds assert_equal(promote_func(b, i32), np.dtype(np.int32)) assert_equal(promote_func(b, u8), np.dtype(np.uint8)) assert_equal(promote_func(i8, u8), np.dtype(np.int16)) assert_equal(promote_func(u8, i32), np.dtype(np.int32)) assert_equal(promote_func(i64, u32), np.dtype(np.int64)) assert_equal(promote_func(u64, i32), np.dtype(np.float64)) assert_equal(promote_func(i32, f32), np.dtype(np.float64)) assert_equal(promote_func(i64, f32), np.dtype(np.float64)) assert_equal(promote_func(f32, i16), np.dtype(np.float32)) assert_equal(promote_func(f32, u32), np.dtype(np.float64)) assert_equal(promote_func(f32, c64), np.dtype(np.complex64)) assert_equal(promote_func(c128, f32), np.dtype(np.complex128)) assert_equal(promote_func(cld, f64), np.dtype(np.clongdouble)) # coercion between scalars and 1-D arrays assert_equal(promote_func(np.array([b]), i8), np.dtype(np.int8)) assert_equal(promote_func(np.array([b]), u8), np.dtype(np.uint8)) assert_equal(promote_func(np.array([b]), i32), np.dtype(np.int32)) assert_equal(promote_func(np.array([b]), u32), np.dtype(np.uint32)) assert_equal(promote_func(np.array([i8]), i64), np.dtype(np.int8)) assert_equal(promote_func(u64, np.array([i32])), np.dtype(np.int32)) assert_equal(promote_func(i64, np.array([u32])), np.dtype(np.uint32)) assert_equal(promote_func(np.int32(-1), np.array([u64])), np.dtype(np.float64)) assert_equal(promote_func(f64, np.array([f32])), np.dtype(np.float32)) assert_equal(promote_func(fld, np.array([f32])), np.dtype(np.float32)) assert_equal(promote_func(np.array([f64]), fld), np.dtype(np.float64)) assert_equal(promote_func(fld, np.array([c64])), np.dtype(np.complex64)) assert_equal(promote_func(c64, np.array([f64])), np.dtype(np.complex128)) assert_equal(promote_func(np.complex64(3j), np.array([f64])), np.dtype(np.complex128)) # coercion between scalars and 1-D arrays, where # the scalar has greater kind than the array assert_equal(promote_func(np.array([b]), f64), np.dtype(np.float64)) assert_equal(promote_func(np.array([b]), i64), np.dtype(np.int64)) assert_equal(promote_func(np.array([b]), u64), np.dtype(np.uint64)) assert_equal(promote_func(np.array([i8]), f64), np.dtype(np.float64)) assert_equal(promote_func(np.array([u16]), f64), np.dtype(np.float64)) # uint and int are treated as the same "kind" for # the purposes of array-scalar promotion. assert_equal(promote_func(np.array([u16]), i32), np.dtype(np.uint16)) # float and complex are treated as the same "kind" for # the purposes of array-scalar promotion, so that you can do # (0j + float32array) to get a complex64 array instead of # a complex128 array. assert_equal(promote_func(np.array([f32]), c128), np.dtype(np.complex64)) def test_coercion(self): def res_type(a, b): return np.add(a, b).dtype self.check_promotion_cases(res_type) # Use-case: float/complex scalar * bool/int8 array # shouldn't narrow the float/complex type for a in [np.array([True, False]), np.array([-3, 12], dtype=np.int8)]: b = 1.234 * a assert_equal(b.dtype, np.dtype('f8'), "array type %s" % a.dtype) b = np.longdouble(1.234) * a assert_equal(b.dtype, np.dtype(np.longdouble), "array type %s" % a.dtype) b = np.float64(1.234) * a assert_equal(b.dtype, np.dtype('f8'), "array type %s" % a.dtype) b = np.float32(1.234) * a assert_equal(b.dtype, np.dtype('f4'), "array type %s" % a.dtype) b = np.float16(1.234) * a assert_equal(b.dtype, np.dtype('f2'), "array type %s" % a.dtype) b = 1.234j * a assert_equal(b.dtype, np.dtype('c16'), "array type %s" % a.dtype) b = np.clongdouble(1.234j) * a assert_equal(b.dtype, np.dtype(np.clongdouble), "array type %s" % a.dtype) b = np.complex128(1.234j) * a assert_equal(b.dtype, np.dtype('c16'), "array type %s" % a.dtype) b = np.complex64(1.234j) * a assert_equal(b.dtype, np.dtype('c8'), "array type %s" % a.dtype) # The following use-case is problematic, and to resolve its # tricky side-effects requires more changes. # # Use-case: (1-t)*a, where 't' is a boolean array and 'a' is # a float32, shouldn't promote to float64 # # a = np.array([1.0, 1.5], dtype=np.float32) # t = np.array([True, False]) # b = t*a # assert_equal(b, [1.0, 0.0]) # assert_equal(b.dtype, np.dtype('f4')) # b = (1-t)*a # assert_equal(b, [0.0, 1.5]) # assert_equal(b.dtype, np.dtype('f4')) # # Probably ~t (bitwise negation) is more proper to use here, # but this is arguably less intuitive to understand at a glance, and # would fail if 't' is actually an integer array instead of boolean: # # b = (~t)*a # assert_equal(b, [0.0, 1.5]) # assert_equal(b.dtype, np.dtype('f4')) def test_result_type(self): self.check_promotion_cases(np.result_type) assert_(np.result_type(None) == np.dtype(None)) def test_promote_types_endian(self): # promote_types should always return native-endian types assert_equal(np.promote_types('i8', '>i8'), np.dtype('i8')) assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21')) assert_equal(np.promote_types('U16', '>i8'), np.dtype('U21')) assert_equal(np.promote_types('S5', '>U8'), np.dtype('U8')) assert_equal(np.promote_types('U8', '>S5'), np.dtype('U8')) assert_equal(np.promote_types('U8', '>U5'), np.dtype('U8')) assert_equal(np.promote_types('M8', '>M8'), np.dtype('M8')) assert_equal(np.promote_types('m8', '>m8'), np.dtype('m8')) def test_promote_types_strings(self): assert_equal(np.promote_types('bool', 'S'), np.dtype('S5')) assert_equal(np.promote_types('b', 'S'), np.dtype('S4')) assert_equal(np.promote_types('u1', 'S'), np.dtype('S3')) assert_equal(np.promote_types('u2', 'S'), np.dtype('S5')) assert_equal(np.promote_types('u4', 'S'), np.dtype('S10')) assert_equal(np.promote_types('u8', 'S'), np.dtype('S20')) assert_equal(np.promote_types('i1', 'S'), np.dtype('S4')) assert_equal(np.promote_types('i2', 'S'), np.dtype('S6')) assert_equal(np.promote_types('i4', 'S'), np.dtype('S11')) assert_equal(np.promote_types('i8', 'S'), np.dtype('S21')) assert_equal(np.promote_types('bool', 'U'), np.dtype('U5')) assert_equal(np.promote_types('b', 'U'), np.dtype('U4')) assert_equal(np.promote_types('u1', 'U'), np.dtype('U3')) assert_equal(np.promote_types('u2', 'U'), np.dtype('U5')) assert_equal(np.promote_types('u4', 'U'), np.dtype('U10')) assert_equal(np.promote_types('u8', 'U'), np.dtype('U20')) assert_equal(np.promote_types('i1', 'U'), np.dtype('U4')) assert_equal(np.promote_types('i2', 'U'), np.dtype('U6')) assert_equal(np.promote_types('i4', 'U'), np.dtype('U11')) assert_equal(np.promote_types('i8', 'U'), np.dtype('U21')) assert_equal(np.promote_types('bool', 'S1'), np.dtype('S5')) assert_equal(np.promote_types('bool', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('b', 'S1'), np.dtype('S4')) assert_equal(np.promote_types('b', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u1', 'S1'), np.dtype('S3')) assert_equal(np.promote_types('u1', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u2', 'S1'), np.dtype('S5')) assert_equal(np.promote_types('u2', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u4', 'S1'), np.dtype('S10')) assert_equal(np.promote_types('u4', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u8', 'S1'), np.dtype('S20')) assert_equal(np.promote_types('u8', 'S30'), np.dtype('S30')) def test_can_cast(self): assert_(np.can_cast(np.int32, np.int64)) assert_(np.can_cast(np.float64, np.complex)) assert_(not np.can_cast(np.complex, np.float)) assert_(np.can_cast('i8', 'f8')) assert_(not np.can_cast('i8', 'f4')) assert_(np.can_cast('i4', 'S11')) assert_(np.can_cast('i8', 'i8', 'no')) assert_(not np.can_cast('i8', 'no')) assert_(np.can_cast('i8', 'equiv')) assert_(not np.can_cast('i8', 'equiv')) assert_(np.can_cast('i8', 'safe')) assert_(not np.can_cast('i4', 'safe')) assert_(np.can_cast('i4', 'same_kind')) assert_(not np.can_cast('u4', 'same_kind')) assert_(np.can_cast('u4', 'unsafe')) assert_(np.can_cast('bool', 'S5')) assert_(not np.can_cast('bool', 'S4')) assert_(np.can_cast('b', 'S4')) assert_(not np.can_cast('b', 'S3')) assert_(np.can_cast('u1', 'S3')) assert_(not np.can_cast('u1', 'S2')) assert_(np.can_cast('u2', 'S5')) assert_(not np.can_cast('u2', 'S4')) assert_(np.can_cast('u4', 'S10')) assert_(not np.can_cast('u4', 'S9')) assert_(np.can_cast('u8', 'S20')) assert_(not np.can_cast('u8', 'S19')) assert_(np.can_cast('i1', 'S4')) assert_(not np.can_cast('i1', 'S3')) assert_(np.can_cast('i2', 'S6')) assert_(not np.can_cast('i2', 'S5')) assert_(np.can_cast('i4', 'S11')) assert_(not np.can_cast('i4', 'S10')) assert_(np.can_cast('i8', 'S21')) assert_(not np.can_cast('i8', 'S20')) assert_(np.can_cast('bool', 'S5')) assert_(not np.can_cast('bool', 'S4')) assert_(np.can_cast('b', 'U4')) assert_(not np.can_cast('b', 'U3')) assert_(np.can_cast('u1', 'U3')) assert_(not np.can_cast('u1', 'U2')) assert_(np.can_cast('u2', 'U5')) assert_(not np.can_cast('u2', 'U4')) assert_(np.can_cast('u4', 'U10')) assert_(not np.can_cast('u4', 'U9')) assert_(np.can_cast('u8', 'U20')) assert_(not np.can_cast('u8', 'U19')) assert_(np.can_cast('i1', 'U4')) assert_(not np.can_cast('i1', 'U3')) assert_(np.can_cast('i2', 'U6')) assert_(not np.can_cast('i2', 'U5')) assert_(np.can_cast('i4', 'U11')) assert_(not np.can_cast('i4', 'U10')) assert_(np.can_cast('i8', 'U21')) assert_(not np.can_cast('i8', 'U20')) assert_raises(TypeError, np.can_cast, 'i4', None) assert_raises(TypeError, np.can_cast, None, 'i4') # Custom exception class to test exception propagation in fromiter class NIterError(Exception): pass class TestFromiter(TestCase): def makegen(self): for x in range(24): yield x**2 def test_types(self): ai32 = np.fromiter(self.makegen(), np.int32) ai64 = np.fromiter(self.makegen(), np.int64) af = np.fromiter(self.makegen(), float) self.assertTrue(ai32.dtype == np.dtype(np.int32)) self.assertTrue(ai64.dtype == np.dtype(np.int64)) self.assertTrue(af.dtype == np.dtype(float)) def test_lengths(self): expected = np.array(list(self.makegen())) a = np.fromiter(self.makegen(), int) a20 = np.fromiter(self.makegen(), int, 20) self.assertTrue(len(a) == len(expected)) self.assertTrue(len(a20) == 20) self.assertRaises(ValueError, np.fromiter, self.makegen(), int, len(expected) + 10) def test_values(self): expected = np.array(list(self.makegen())) a = np.fromiter(self.makegen(), int) a20 = np.fromiter(self.makegen(), int, 20) self.assertTrue(np.alltrue(a == expected, axis=0)) self.assertTrue(np.alltrue(a20 == expected[:20], axis=0)) def load_data(self, n, eindex): # Utility method for the issue 2592 tests. # Raise an exception at the desired index in the iterator. for e in range(n): if e == eindex: raise NIterError('error at index %s' % eindex) yield e def test_2592(self): # Test iteration exceptions are correctly raised. count, eindex = 10, 5 self.assertRaises(NIterError, np.fromiter, self.load_data(count, eindex), dtype=int, count=count) def test_2592_edge(self): # Test iter. exceptions, edge case (exception at end of iterator). count = 10 eindex = count-1 self.assertRaises(NIterError, np.fromiter, self.load_data(count, eindex), dtype=int, count=count) class TestNonzero(TestCase): def test_nonzero_trivial(self): assert_equal(np.count_nonzero(np.array([])), 0) assert_equal(np.count_nonzero(np.array([], dtype='?')), 0) assert_equal(np.nonzero(np.array([])), ([],)) assert_equal(np.count_nonzero(np.array(0)), 0) assert_equal(np.count_nonzero(np.array(0, dtype='?')), 0) assert_equal(np.nonzero(np.array(0)), ([],)) assert_equal(np.count_nonzero(np.array(1)), 1) assert_equal(np.count_nonzero(np.array(1, dtype='?')), 1) assert_equal(np.nonzero(np.array(1)), ([0],)) def test_nonzero_onedim(self): x = np.array([1, 0, 2, -1, 0, 0, 8]) assert_equal(np.count_nonzero(x), 4) assert_equal(np.count_nonzero(x), 4) assert_equal(np.nonzero(x), ([0, 2, 3, 6],)) x = np.array([(1, 2), (0, 0), (1, 1), (-1, 3), (0, 7)], dtype=[('a', 'i4'), ('b', 'i2')]) assert_equal(np.count_nonzero(x['a']), 3) assert_equal(np.count_nonzero(x['b']), 4) assert_equal(np.nonzero(x['a']), ([0, 2, 3],)) assert_equal(np.nonzero(x['b']), ([0, 2, 3, 4],)) def test_nonzero_twodim(self): x = np.array([[0, 1, 0], [2, 0, 3]]) assert_equal(np.count_nonzero(x), 3) assert_equal(np.nonzero(x), ([0, 1, 1], [1, 0, 2])) x = np.eye(3) assert_equal(np.count_nonzero(x), 3) assert_equal(np.nonzero(x), ([0, 1, 2], [0, 1, 2])) x = np.array([[(0, 1), (0, 0), (1, 11)], [(1, 1), (1, 0), (0, 0)], [(0, 0), (1, 5), (0, 1)]], dtype=[('a', 'f4'), ('b', 'u1')]) assert_equal(np.count_nonzero(x['a']), 4) assert_equal(np.count_nonzero(x['b']), 5) assert_equal(np.nonzero(x['a']), ([0, 1, 1, 2], [2, 0, 1, 1])) assert_equal(np.nonzero(x['b']), ([0, 0, 1, 2, 2], [0, 2, 0, 1, 2])) assert_(not x['a'].T.flags.aligned) assert_equal(np.count_nonzero(x['a'].T), 4) assert_equal(np.count_nonzero(x['b'].T), 5) assert_equal(np.nonzero(x['a'].T), ([0, 1, 1, 2], [1, 1, 2, 0])) assert_equal(np.nonzero(x['b'].T), ([0, 0, 1, 2, 2], [0, 1, 2, 0, 2])) def test_sparse(self): # test special sparse condition boolean code path for i in range(20): c = np.zeros(200, dtype=np.bool) c[i::20] = True assert_equal(np.nonzero(c)[0], np.arange(i, 200 + i, 20)) c = np.zeros(400, dtype=np.bool) c[10 + i:20 + i] = True c[20 + i*2] = True assert_equal(np.nonzero(c)[0], np.concatenate((np.arange(10 + i, 20 + i), [20 + i*2]))) def test_return_type(self): class C(np.ndarray): pass for view in (C, np.ndarray): for nd in range(1, 4): shape = tuple(range(2, 2+nd)) x = np.arange(np.prod(shape)).reshape(shape).view(view) for nzx in (np.nonzero(x), x.nonzero()): for nzx_i in nzx: assert_(type(nzx_i) is np.ndarray) assert_(nzx_i.flags.writeable) # Tests that the array method # call works def test_array_method(self): m = np.array([[1, 0, 0], [4, 0, 6]]) tgt = [[0, 1, 1], [0, 0, 2]] assert_equal(m.nonzero(), tgt) class TestIndex(TestCase): def test_boolean(self): a = rand(3, 5, 8) V = rand(5, 8) g1 = randint(0, 5, size=15) g2 = randint(0, 8, size=15) V[g1, g2] = -V[g1, g2] assert_((np.array([a[0][V > 0], a[1][V > 0], a[2][V > 0]]) == a[:, V > 0]).all()) def test_boolean_edgecase(self): a = np.array([], dtype='int32') b = np.array([], dtype='bool') c = a[b] assert_equal(c, []) assert_equal(c.dtype, np.dtype('int32')) class TestBinaryRepr(TestCase): def test_zero(self): assert_equal(np.binary_repr(0), '0') def test_large(self): assert_equal(np.binary_repr(10736848), '101000111101010011010000') def test_negative(self): assert_equal(np.binary_repr(-1), '-1') assert_equal(np.binary_repr(-1, width=8), '11111111') class TestBaseRepr(TestCase): def test_base3(self): assert_equal(np.base_repr(3**5, 3), '100000') def test_positive(self): assert_equal(np.base_repr(12, 10), '12') assert_equal(np.base_repr(12, 10, 4), '000012') assert_equal(np.base_repr(12, 4), '30') assert_equal(np.base_repr(3731624803700888, 36), '10QR0ROFCEW') def test_negative(self): assert_equal(np.base_repr(-12, 10), '-12') assert_equal(np.base_repr(-12, 10, 4), '-000012') assert_equal(np.base_repr(-12, 4), '-30') class TestArrayComparisons(TestCase): def test_array_equal(self): res = np.array_equal(np.array([1, 2]), np.array([1, 2])) assert_(res) assert_(type(res) is bool) res = np.array_equal(np.array([1, 2]), np.array([1, 2, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equal(np.array([1, 2]), np.array([3, 4])) assert_(not res) assert_(type(res) is bool) res = np.array_equal(np.array([1, 2]), np.array([1, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equal(np.array(['a'], dtype='S1'), np.array(['a'], dtype='S1')) assert_(res) assert_(type(res) is bool) res = np.array_equal(np.array([('a', 1)], dtype='S1,u4'), np.array([('a', 1)], dtype='S1,u4')) assert_(res) assert_(type(res) is bool) def test_array_equiv(self): res = np.array_equiv(np.array([1, 2]), np.array([1, 2])) assert_(res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([1, 2, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([3, 4])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([1, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 1]), np.array([1])) assert_(res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 1]), np.array([[1], [1]])) assert_(res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([2])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([[1], [2]])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])) assert_(not res) assert_(type(res) is bool) def assert_array_strict_equal(x, y): assert_array_equal(x, y) # Check flags, 32 bit arches typically don't provide 16 byte alignment if ((x.dtype.alignment <= 8 or np.intp().dtype.itemsize != 4) and sys.platform != 'win32'): assert_(x.flags == y.flags) else: assert_(x.flags.owndata == y.flags.owndata) assert_(x.flags.writeable == y.flags.writeable) assert_(x.flags.c_contiguous == y.flags.c_contiguous) assert_(x.flags.f_contiguous == y.flags.f_contiguous) assert_(x.flags.updateifcopy == y.flags.updateifcopy) # check endianness assert_(x.dtype.isnative == y.dtype.isnative) class TestClip(TestCase): def setUp(self): self.nr = 5 self.nc = 3 def fastclip(self, a, m, M, out=None): if out is None: return a.clip(m, M) else: return a.clip(m, M, out) def clip(self, a, m, M, out=None): # use slow-clip selector = np.less(a, m) + 2*np.greater(a, M) return selector.choose((a, m, M), out=out) # Handy functions def _generate_data(self, n, m): return randn(n, m) def _generate_data_complex(self, n, m): return randn(n, m) + 1.j * rand(n, m) def _generate_flt_data(self, n, m): return (randn(n, m)).astype(np.float32) def _neg_byteorder(self, a): a = np.asarray(a) if sys.byteorder == 'little': a = a.astype(a.dtype.newbyteorder('>')) else: a = a.astype(a.dtype.newbyteorder('<')) return a def _generate_non_native_data(self, n, m): data = randn(n, m) data = self._neg_byteorder(data) assert_(not data.dtype.isnative) return data def _generate_int_data(self, n, m): return (10 * rand(n, m)).astype(np.int64) def _generate_int32_data(self, n, m): return (10 * rand(n, m)).astype(np.int32) # Now the real test cases def test_simple_double(self): # Test native double input with scalar min/max. a = self._generate_data(self.nr, self.nc) m = 0.1 M = 0.6 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_simple_int(self): # Test native int input with scalar min/max. a = self._generate_int_data(self.nr, self.nc) a = a.astype(int) m = -2 M = 4 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_array_double(self): # Test native double input with array min/max. a = self._generate_data(self.nr, self.nc) m = np.zeros(a.shape) M = m + 0.5 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_simple_nonnative(self): # Test non native double input with scalar min/max. # Test native double input with non native double scalar min/max. a = self._generate_non_native_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_equal(ac, act) # Test native double input with non native double scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = self._neg_byteorder(0.6) assert_(not M.dtype.isnative) ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_equal(ac, act) def test_simple_complex(self): # Test native complex input with native double scalar min/max. # Test native input with complex double scalar min/max. a = 3 * self._generate_data_complex(self.nr, self.nc) m = -0.5 M = 1. ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) # Test native input with complex double scalar min/max. a = 3 * self._generate_data(self.nr, self.nc) m = -0.5 + 1.j M = 1. + 2.j ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_clip_complex(self): # Address Issue gh-5354 for clipping complex arrays # Test native complex input without explicit min/max # ie, either min=None or max=None a = np.ones(10, dtype=np.complex) m = a.min() M = a.max() am = self.fastclip(a, m, None) aM = self.fastclip(a, None, M) assert_array_strict_equal(am, a) assert_array_strict_equal(aM, a) def test_clip_non_contig(self): # Test clip for non contiguous native input and native scalar min/max. a = self._generate_data(self.nr * 2, self.nc * 3) a = a[::2, ::3] assert_(not a.flags['F_CONTIGUOUS']) assert_(not a.flags['C_CONTIGUOUS']) ac = self.fastclip(a, -1.6, 1.7) act = self.clip(a, -1.6, 1.7) assert_array_strict_equal(ac, act) def test_simple_out(self): # Test native double input with scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = np.zeros(a.shape) act = np.zeros(a.shape) self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int32_inout(self): # Test native int32 input with double min/max and int32 out. a = self._generate_int32_data(self.nr, self.nc) m = np.float64(0) M = np.float64(2) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int64_out(self): # Test native int32 input with int32 scalar min/max and int64 out. a = self._generate_int32_data(self.nr, self.nc) m = np.int32(-1) M = np.int32(1) ac = np.zeros(a.shape, dtype=np.int64) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int64_inout(self): # Test native int32 input with double array min/max and int32 out. a = self._generate_int32_data(self.nr, self.nc) m = np.zeros(a.shape, np.float64) M = np.float64(1) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int32_out(self): # Test native double input with scalar min/max and int out. a = self._generate_data(self.nr, self.nc) m = -1.0 M = 2.0 ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_inplace_01(self): # Test native double input with array min/max in-place. a = self._generate_data(self.nr, self.nc) ac = a.copy() m = np.zeros(a.shape) M = 1.0 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_simple_inplace_02(self): # Test native double input with scalar min/max in-place. a = self._generate_data(self.nr, self.nc) ac = a.copy() m = -0.5 M = 0.6 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_noncontig_inplace(self): # Test non contiguous double input with double scalar min/max in-place. a = self._generate_data(self.nr * 2, self.nc * 3) a = a[::2, ::3] assert_(not a.flags['F_CONTIGUOUS']) assert_(not a.flags['C_CONTIGUOUS']) ac = a.copy() m = -0.5 M = 0.6 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_equal(a, ac) def test_type_cast_01(self): # Test native double input with scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_type_cast_02(self): # Test native int32 input with int32 scalar min/max. a = self._generate_int_data(self.nr, self.nc) a = a.astype(np.int32) m = -2 M = 4 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_type_cast_03(self): # Test native int32 input with float64 scalar min/max. a = self._generate_int32_data(self.nr, self.nc) m = -2 M = 4 ac = self.fastclip(a, np.float64(m), np.float64(M)) act = self.clip(a, np.float64(m), np.float64(M)) assert_array_strict_equal(ac, act) def test_type_cast_04(self): # Test native int32 input with float32 scalar min/max. a = self._generate_int32_data(self.nr, self.nc) m = np.float32(-2) M = np.float32(4) act = self.fastclip(a, m, M) ac = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_type_cast_05(self): # Test native int32 with double arrays min/max. a = self._generate_int_data(self.nr, self.nc) m = -0.5 M = 1. ac = self.fastclip(a, m * np.zeros(a.shape), M) act = self.clip(a, m * np.zeros(a.shape), M) assert_array_strict_equal(ac, act) def test_type_cast_06(self): # Test native with NON native scalar min/max. a = self._generate_data(self.nr, self.nc) m = 0.5 m_s = self._neg_byteorder(m) M = 1. act = self.clip(a, m_s, M) ac = self.fastclip(a, m_s, M) assert_array_strict_equal(ac, act) def test_type_cast_07(self): # Test NON native with native array min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 * np.ones(a.shape) M = 1. a_s = self._neg_byteorder(a) assert_(not a_s.dtype.isnative) act = a_s.clip(m, M) ac = self.fastclip(a_s, m, M) assert_array_strict_equal(ac, act) def test_type_cast_08(self): # Test NON native with native scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = 1. a_s = self._neg_byteorder(a) assert_(not a_s.dtype.isnative) ac = self.fastclip(a_s, m, M) act = a_s.clip(m, M) assert_array_strict_equal(ac, act) def test_type_cast_09(self): # Test native with NON native array min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 * np.ones(a.shape) M = 1. m_s = self._neg_byteorder(m) assert_(not m_s.dtype.isnative) ac = self.fastclip(a, m_s, M) act = self.clip(a, m_s, M) assert_array_strict_equal(ac, act) def test_type_cast_10(self): # Test native int32 with float min/max and float out for output argument. a = self._generate_int_data(self.nr, self.nc) b = np.zeros(a.shape, dtype=np.float32) m = np.float32(-0.5) M = np.float32(1) act = self.clip(a, m, M, out=b) ac = self.fastclip(a, m, M, out=b) assert_array_strict_equal(ac, act) def test_type_cast_11(self): # Test non native with native scalar, min/max, out non native a = self._generate_non_native_data(self.nr, self.nc) b = a.copy() b = b.astype(b.dtype.newbyteorder('>')) bt = b.copy() m = -0.5 M = 1. self.fastclip(a, m, M, out=b) self.clip(a, m, M, out=bt) assert_array_strict_equal(b, bt) def test_type_cast_12(self): # Test native int32 input and min/max and float out a = self._generate_int_data(self.nr, self.nc) b = np.zeros(a.shape, dtype=np.float32) m = np.int32(0) M = np.int32(1) act = self.clip(a, m, M, out=b) ac = self.fastclip(a, m, M, out=b) assert_array_strict_equal(ac, act) def test_clip_with_out_simple(self): # Test native double input with scalar min/max a = self._generate_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = np.zeros(a.shape) act = np.zeros(a.shape) self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_simple2(self): # Test native int32 input with double min/max and int32 out a = self._generate_int32_data(self.nr, self.nc) m = np.float64(0) M = np.float64(2) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_simple_int32(self): # Test native int32 input with int32 scalar min/max and int64 out a = self._generate_int32_data(self.nr, self.nc) m = np.int32(-1) M = np.int32(1) ac = np.zeros(a.shape, dtype=np.int64) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_array_int32(self): # Test native int32 input with double array min/max and int32 out a = self._generate_int32_data(self.nr, self.nc) m = np.zeros(a.shape, np.float64) M = np.float64(1) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_array_outint32(self): # Test native double input with scalar min/max and int out a = self._generate_data(self.nr, self.nc) m = -1.0 M = 2.0 ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_inplace_array(self): # Test native double input with array min/max a = self._generate_data(self.nr, self.nc) ac = a.copy() m = np.zeros(a.shape) M = 1.0 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_clip_inplace_simple(self): # Test native double input with scalar min/max a = self._generate_data(self.nr, self.nc) ac = a.copy() m = -0.5 M = 0.6 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_clip_func_takes_out(self): # Ensure that the clip() function takes an out=argument. a = self._generate_data(self.nr, self.nc) ac = a.copy() m = -0.5 M = 0.6 a2 = np.clip(a, m, M, out=a) self.clip(a, m, M, ac) assert_array_strict_equal(a2, ac) self.assertTrue(a2 is a) def test_clip_nan(self): d = np.arange(7.) assert_equal(d.clip(min=np.nan), d) assert_equal(d.clip(max=np.nan), d) assert_equal(d.clip(min=np.nan, max=np.nan), d) assert_equal(d.clip(min=-2, max=np.nan), d) assert_equal(d.clip(min=np.nan, max=10), d) class TestAllclose(object): rtol = 1e-5 atol = 1e-8 def setUp(self): self.olderr = np.seterr(invalid='ignore') def tearDown(self): np.seterr(**self.olderr) def tst_allclose(self, x, y): assert_(np.allclose(x, y), "%s and %s not close" % (x, y)) def tst_not_allclose(self, x, y): assert_(not np.allclose(x, y), "%s and %s shouldn't be close" % (x, y)) def test_ip_allclose(self): # Parametric test factory. arr = np.array([100, 1000]) aran = np.arange(125).reshape((5, 5, 5)) atol = self.atol rtol = self.rtol data = [([1, 0], [1, 0]), ([atol], [0]), ([1], [1+rtol+atol]), (arr, arr + arr*rtol), (arr, arr + arr*rtol + atol*2), (aran, aran + aran*rtol), (np.inf, np.inf), (np.inf, [np.inf])] for (x, y) in data: yield (self.tst_allclose, x, y) def test_ip_not_allclose(self): # Parametric test factory. aran = np.arange(125).reshape((5, 5, 5)) atol = self.atol rtol = self.rtol data = [([np.inf, 0], [1, np.inf]), ([np.inf, 0], [1, 0]), ([np.inf, np.inf], [1, np.inf]), ([np.inf, np.inf], [1, 0]), ([-np.inf, 0], [np.inf, 0]), ([np.nan, 0], [np.nan, 0]), ([atol*2], [0]), ([1], [1+rtol+atol*2]), (aran, aran + aran*atol + atol*2), (np.array([np.inf, 1]), np.array([0, np.inf]))] for (x, y) in data: yield (self.tst_not_allclose, x, y) def test_no_parameter_modification(self): x = np.array([np.inf, 1]) y = np.array([0, np.inf]) np.allclose(x, y) assert_array_equal(x, np.array([np.inf, 1])) assert_array_equal(y, np.array([0, np.inf])) def test_min_int(self): # Could make problems because of abs(min_int) == min_int min_int = np.iinfo(np.int_).min a = np.array([min_int], dtype=np.int_) assert_(np.allclose(a, a)) def test_equalnan(self): x = np.array([1.0, np.nan]) assert_(np.allclose(x, x, equal_nan=True)) def test_return_class_is_ndarray(self): # Issue gh-6475 # Check that allclose does not preserve subtypes class Foo(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(cls) a = Foo([1]) assert_(type(np.allclose(a, a)) is bool) class TestIsclose(object): rtol = 1e-5 atol = 1e-8 def setup(self): atol = self.atol rtol = self.rtol arr = np.array([100, 1000]) aran = np.arange(125).reshape((5, 5, 5)) self.all_close_tests = [ ([1, 0], [1, 0]), ([atol], [0]), ([1], [1 + rtol + atol]), (arr, arr + arr*rtol), (arr, arr + arr*rtol + atol), (aran, aran + aran*rtol), (np.inf, np.inf), (np.inf, [np.inf]), ([np.inf, -np.inf], [np.inf, -np.inf]), ] self.none_close_tests = [ ([np.inf, 0], [1, np.inf]), ([np.inf, -np.inf], [1, 0]), ([np.inf, np.inf], [1, -np.inf]), ([np.inf, np.inf], [1, 0]), ([np.nan, 0], [np.nan, -np.inf]), ([atol*2], [0]), ([1], [1 + rtol + atol*2]), (aran, aran + rtol*1.1*aran + atol*1.1), (np.array([np.inf, 1]), np.array([0, np.inf])), ] self.some_close_tests = [ ([np.inf, 0], [np.inf, atol*2]), ([atol, 1, 1e6*(1 + 2*rtol) + atol], [0, np.nan, 1e6]), (np.arange(3), [0, 1, 2.1]), (np.nan, [np.nan, np.nan, np.nan]), ([0], [atol, np.inf, -np.inf, np.nan]), (0, [atol, np.inf, -np.inf, np.nan]), ] self.some_close_results = [ [True, False], [True, False, False], [True, True, False], [False, False, False], [True, False, False, False], [True, False, False, False], ] def test_ip_isclose(self): self.setup() tests = self.some_close_tests results = self.some_close_results for (x, y), result in zip(tests, results): yield (assert_array_equal, np.isclose(x, y), result) def tst_all_isclose(self, x, y): assert_(np.all(np.isclose(x, y)), "%s and %s not close" % (x, y)) def tst_none_isclose(self, x, y): msg = "%s and %s shouldn't be close" assert_(not np.any(np.isclose(x, y)), msg % (x, y)) def tst_isclose_allclose(self, x, y): msg = "isclose.all() and allclose aren't same for %s and %s" msg2 = "isclose and allclose aren't same for %s and %s" if np.isscalar(x) and np.isscalar(y): assert_(np.isclose(x, y) == np.allclose(x, y), msg=msg2 % (x, y)) else: assert_array_equal(np.isclose(x, y).all(), np.allclose(x, y), msg % (x, y)) def test_ip_all_isclose(self): self.setup() for (x, y) in self.all_close_tests: yield (self.tst_all_isclose, x, y) def test_ip_none_isclose(self): self.setup() for (x, y) in self.none_close_tests: yield (self.tst_none_isclose, x, y) def test_ip_isclose_allclose(self): self.setup() tests = (self.all_close_tests + self.none_close_tests + self.some_close_tests) for (x, y) in tests: yield (self.tst_isclose_allclose, x, y) def test_equal_nan(self): assert_array_equal(np.isclose(np.nan, np.nan, equal_nan=True), [True]) arr = np.array([1.0, np.nan]) assert_array_equal(np.isclose(arr, arr, equal_nan=True), [True, True]) def test_masked_arrays(self): # Make sure to test the output type when arguments are interchanged. x = np.ma.masked_where([True, True, False], np.arange(3)) assert_(type(x) is type(np.isclose(2, x))) assert_(type(x) is type(np.isclose(x, 2))) x = np.ma.masked_where([True, True, False], [np.nan, np.inf, np.nan]) assert_(type(x) is type(np.isclose(np.inf, x))) assert_(type(x) is type(np.isclose(x, np.inf))) x = np.ma.masked_where([True, True, False], [np.nan, np.nan, np.nan]) y = np.isclose(np.nan, x, equal_nan=True) assert_(type(x) is type(y)) # Ensure that the mask isn't modified... assert_array_equal([True, True, False], y.mask) y = np.isclose(x, np.nan, equal_nan=True) assert_(type(x) is type(y)) # Ensure that the mask isn't modified... assert_array_equal([True, True, False], y.mask) x = np.ma.masked_where([True, True, False], [np.nan, np.nan, np.nan]) y = np.isclose(x, x, equal_nan=True) assert_(type(x) is type(y)) # Ensure that the mask isn't modified... assert_array_equal([True, True, False], y.mask) def test_scalar_return(self): assert_(np.isscalar(np.isclose(1, 1))) def test_no_parameter_modification(self): x = np.array([np.inf, 1]) y = np.array([0, np.inf]) np.isclose(x, y) assert_array_equal(x, np.array([np.inf, 1])) assert_array_equal(y, np.array([0, np.inf])) def test_non_finite_scalar(self): # GH7014, when two scalars are compared the output should also be a # scalar assert_(np.isclose(np.inf, -np.inf) is False) assert_(np.isclose(0, np.inf) is False) assert_(type(np.isclose(0, np.inf)) is bool) class TestStdVar(TestCase): def setUp(self): self.A = np.array([1, -1, 1, -1]) self.real_var = 1 def test_basic(self): assert_almost_equal(np.var(self.A), self.real_var) assert_almost_equal(np.std(self.A)**2, self.real_var) def test_scalars(self): assert_equal(np.var(1), 0) assert_equal(np.std(1), 0) def test_ddof1(self): assert_almost_equal(np.var(self.A, ddof=1), self.real_var*len(self.A)/float(len(self.A)-1)) assert_almost_equal(np.std(self.A, ddof=1)**2, self.real_var*len(self.A)/float(len(self.A)-1)) def test_ddof2(self): assert_almost_equal(np.var(self.A, ddof=2), self.real_var*len(self.A)/float(len(self.A)-2)) assert_almost_equal(np.std(self.A, ddof=2)**2, self.real_var*len(self.A)/float(len(self.A)-2)) def test_out_scalar(self): d = np.arange(10) out = np.array(0.) r = np.std(d, out=out) assert_(r is out) assert_array_equal(r, out) r = np.var(d, out=out) assert_(r is out) assert_array_equal(r, out) r = np.mean(d, out=out) assert_(r is out) assert_array_equal(r, out) class TestStdVarComplex(TestCase): def test_basic(self): A = np.array([1, 1.j, -1, -1.j]) real_var = 1 assert_almost_equal(np.var(A), real_var) assert_almost_equal(np.std(A)**2, real_var) def test_scalars(self): assert_equal(np.var(1j), 0) assert_equal(np.std(1j), 0) class TestCreationFuncs(TestCase): # Test ones, zeros, empty and filled def setUp(self): self.dtypes = ('b', 'i', 'u', 'f', 'c', 'S', 'a', 'U', 'V') self.orders = {'C': 'c_contiguous', 'F': 'f_contiguous'} self.ndims = 10 def check_function(self, func, fill_value=None): par = ( (0, 1, 2), range(self.ndims), self.orders, self.dtypes, 2**np.arange(9) ) fill_kwarg = {} if fill_value is not None: fill_kwarg = {'fill_value': fill_value} with warnings.catch_warnings(): warnings.simplefilter('ignore', DeprecationWarning) for size, ndims, order, type, bytes in itertools.product(*par): shape = ndims * [size] try: dtype = np.dtype('{0}{1}'.format(type, bytes)) except TypeError: # dtype combination does not exist continue else: # do not fill void type if fill_value is not None and type in 'V': continue arr = func(shape, order=order, dtype=dtype, **fill_kwarg) assert_(arr.dtype == dtype) assert_(getattr(arr.flags, self.orders[order])) if fill_value is not None: if dtype.str.startswith('|S'): val = str(fill_value) else: val = fill_value assert_equal(arr, dtype.type(val)) def test_zeros(self): self.check_function(np.zeros) def test_ones(self): self.check_function(np.zeros) def test_empty(self): self.check_function(np.empty) def test_filled(self): self.check_function(np.full, 0) self.check_function(np.full, 1) def test_for_reference_leak(self): # Make sure we have an object for reference dim = 1 beg = sys.getrefcount(dim) np.zeros([dim]*10) assert_(sys.getrefcount(dim) == beg) np.ones([dim]*10) assert_(sys.getrefcount(dim) == beg) np.empty([dim]*10) assert_(sys.getrefcount(dim) == beg) np.full([dim]*10, 0) assert_(sys.getrefcount(dim) == beg) class TestLikeFuncs(TestCase): '''Test ones_like, zeros_like, empty_like and full_like''' def setUp(self): self.data = [ # Array scalars (np.array(3.), None), (np.array(3), 'f8'), # 1D arrays (np.arange(6, dtype='f4'), None), (np.arange(6), 'c16'), # 2D C-layout arrays (np.arange(6).reshape(2, 3), None), (np.arange(6).reshape(3, 2), 'i1'), # 2D F-layout arrays (np.arange(6).reshape((2, 3), order='F'), None), (np.arange(6).reshape((3, 2), order='F'), 'i1'), # 3D C-layout arrays (np.arange(24).reshape(2, 3, 4), None), (np.arange(24).reshape(4, 3, 2), 'f4'), # 3D F-layout arrays (np.arange(24).reshape((2, 3, 4), order='F'), None), (np.arange(24).reshape((4, 3, 2), order='F'), 'f4'), # 3D non-C/F-layout arrays (np.arange(24).reshape(2, 3, 4).swapaxes(0, 1), None), (np.arange(24).reshape(4, 3, 2).swapaxes(0, 1), '?'), ] def compare_array_value(self, dz, value, fill_value): if value is not None: if fill_value: try: z = dz.dtype.type(value) except OverflowError: pass else: assert_(np.all(dz == z)) else: assert_(np.all(dz == value)) def check_like_function(self, like_function, value, fill_value=False): if fill_value: fill_kwarg = {'fill_value': value} else: fill_kwarg = {} for d, dtype in self.data: # default (K) order, dtype dz = like_function(d, dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) assert_equal(np.array(dz.strides)*d.dtype.itemsize, np.array(d.strides)*dz.dtype.itemsize) assert_equal(d.flags.c_contiguous, dz.flags.c_contiguous) assert_equal(d.flags.f_contiguous, dz.flags.f_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # C order, default dtype dz = like_function(d, order='C', dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) assert_(dz.flags.c_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # F order, default dtype dz = like_function(d, order='F', dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) assert_(dz.flags.f_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # A order dz = like_function(d, order='A', dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) if d.flags.f_contiguous: assert_(dz.flags.f_contiguous) else: assert_(dz.flags.c_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # Test the 'subok' parameter a = np.matrix([[1, 2], [3, 4]]) b = like_function(a, **fill_kwarg) assert_(type(b) is np.matrix) b = like_function(a, subok=False, **fill_kwarg) assert_(type(b) is not np.matrix) def test_ones_like(self): self.check_like_function(np.ones_like, 1) def test_zeros_like(self): self.check_like_function(np.zeros_like, 0) def test_empty_like(self): self.check_like_function(np.empty_like, None) def test_filled_like(self): self.check_like_function(np.full_like, 0, True) self.check_like_function(np.full_like, 1, True) self.check_like_function(np.full_like, 1000, True) self.check_like_function(np.full_like, 123.456, True) self.check_like_function(np.full_like, np.inf, True) class TestCorrelate(TestCase): def _setup(self, dt): self.x = np.array([1, 2, 3, 4, 5], dtype=dt) self.xs = np.arange(1, 20)[::3] self.y = np.array([-1, -2, -3], dtype=dt) self.z1 = np.array([ -3., -8., -14., -20., -26., -14., -5.], dtype=dt) self.z1_4 = np.array([-2., -5., -8., -11., -14., -5.], dtype=dt) self.z1r = np.array([-15., -22., -22., -16., -10., -4., -1.], dtype=dt) self.z2 = np.array([-5., -14., -26., -20., -14., -8., -3.], dtype=dt) self.z2r = np.array([-1., -4., -10., -16., -22., -22., -15.], dtype=dt) self.zs = np.array([-3., -14., -30., -48., -66., -84., -102., -54., -19.], dtype=dt) def test_float(self): self._setup(np.float) z = np.correlate(self.x, self.y, 'full') assert_array_almost_equal(z, self.z1) z = np.correlate(self.x, self.y[:-1], 'full') assert_array_almost_equal(z, self.z1_4) z = np.correlate(self.y, self.x, 'full') assert_array_almost_equal(z, self.z2) z = np.correlate(self.x[::-1], self.y, 'full') assert_array_almost_equal(z, self.z1r) z = np.correlate(self.y, self.x[::-1], 'full') assert_array_almost_equal(z, self.z2r) z = np.correlate(self.xs, self.y, 'full') assert_array_almost_equal(z, self.zs) def test_object(self): self._setup(Decimal) z = np.correlate(self.x, self.y, 'full') assert_array_almost_equal(z, self.z1) z = np.correlate(self.y, self.x, 'full') assert_array_almost_equal(z, self.z2) def test_no_overwrite(self): d = np.ones(100) k = np.ones(3) np.correlate(d, k) assert_array_equal(d, np.ones(100)) assert_array_equal(k, np.ones(3)) def test_complex(self): x = np.array([1, 2, 3, 4+1j], dtype=np.complex) y = np.array([-1, -2j, 3+1j], dtype=np.complex) r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=np.complex) r_z = r_z[::-1].conjugate() z = np.correlate(y, x, mode='full') assert_array_almost_equal(z, r_z) class TestConvolve(TestCase): def test_object(self): d = [1.] * 100 k = [1.] * 3 assert_array_almost_equal(np.convolve(d, k)[2:-2], np.full(98, 3)) def test_no_overwrite(self): d = np.ones(100) k = np.ones(3) np.convolve(d, k) assert_array_equal(d, np.ones(100)) assert_array_equal(k, np.ones(3)) class TestArgwhere(object): def test_2D(self): x = np.arange(6).reshape((2, 3)) assert_array_equal(np.argwhere(x > 1), [[0, 2], [1, 0], [1, 1], [1, 2]]) def test_list(self): assert_equal(np.argwhere([4, 0, 2, 1, 3]), [[0], [2], [3], [4]]) class TestStringFunction(object): def test_set_string_function(self): a = np.array([1]) np.set_string_function(lambda x: "FOO", repr=True) assert_equal(repr(a), "FOO") np.set_string_function(None, repr=True) assert_equal(repr(a), "array([1])") np.set_string_function(lambda x: "FOO", repr=False) assert_equal(str(a), "FOO") np.set_string_function(None, repr=False) assert_equal(str(a), "[1]") class TestRoll(TestCase): def test_roll1d(self): x = np.arange(10) xr = np.roll(x, 2) assert_equal(xr, np.array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])) def test_roll2d(self): x2 = np.reshape(np.arange(10), (2, 5)) x2r = np.roll(x2, 1) assert_equal(x2r, np.array([[9, 0, 1, 2, 3], [4, 5, 6, 7, 8]])) x2r = np.roll(x2, 1, axis=0) assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]])) x2r = np.roll(x2, 1, axis=1) assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]])) def test_roll_empty(self): x = np.array([]) assert_equal(np.roll(x, 1), np.array([])) class TestRollaxis(TestCase): # expected shape indexed by (axis, start) for array of # shape (1, 2, 3, 4) tgtshape = {(0, 0): (1, 2, 3, 4), (0, 1): (1, 2, 3, 4), (0, 2): (2, 1, 3, 4), (0, 3): (2, 3, 1, 4), (0, 4): (2, 3, 4, 1), (1, 0): (2, 1, 3, 4), (1, 1): (1, 2, 3, 4), (1, 2): (1, 2, 3, 4), (1, 3): (1, 3, 2, 4), (1, 4): (1, 3, 4, 2), (2, 0): (3, 1, 2, 4), (2, 1): (1, 3, 2, 4), (2, 2): (1, 2, 3, 4), (2, 3): (1, 2, 3, 4), (2, 4): (1, 2, 4, 3), (3, 0): (4, 1, 2, 3), (3, 1): (1, 4, 2, 3), (3, 2): (1, 2, 4, 3), (3, 3): (1, 2, 3, 4), (3, 4): (1, 2, 3, 4)} def test_exceptions(self): a = np.arange(1*2*3*4).reshape(1, 2, 3, 4) assert_raises(ValueError, np.rollaxis, a, -5, 0) assert_raises(ValueError, np.rollaxis, a, 0, -5) assert_raises(ValueError, np.rollaxis, a, 4, 0) assert_raises(ValueError, np.rollaxis, a, 0, 5) def test_results(self): a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy() aind = np.indices(a.shape) assert_(a.flags['OWNDATA']) for (i, j) in self.tgtshape: # positive axis, positive start res = np.rollaxis(a, axis=i, start=j) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(i, j)], str((i,j))) assert_(not res.flags['OWNDATA']) # negative axis, positive start ip = i + 1 res = np.rollaxis(a, axis=-ip, start=j) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(4 - ip, j)]) assert_(not res.flags['OWNDATA']) # positive axis, negative start jp = j + 1 if j < 4 else j res = np.rollaxis(a, axis=i, start=-jp) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(i, 4 - jp)]) assert_(not res.flags['OWNDATA']) # negative axis, negative start ip = i + 1 jp = j + 1 if j < 4 else j res = np.rollaxis(a, axis=-ip, start=-jp) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(4 - ip, 4 - jp)]) assert_(not res.flags['OWNDATA']) class TestMoveaxis(TestCase): def test_move_to_end(self): x = np.random.randn(5, 6, 7) for source, expected in [(0, (6, 7, 5)), (1, (5, 7, 6)), (2, (5, 6, 7)), (-1, (5, 6, 7))]: actual = np.moveaxis(x, source, -1).shape assert_(actual, expected) def test_move_new_position(self): x = np.random.randn(1, 2, 3, 4) for source, destination, expected in [ (0, 1, (2, 1, 3, 4)), (1, 2, (1, 3, 2, 4)), (1, -1, (1, 3, 4, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected) def test_preserve_order(self): x = np.zeros((1, 2, 3, 4)) for source, destination in [ (0, 0), (3, -1), (-1, 3), ([0, -1], [0, -1]), ([2, 0], [2, 0]), (range(4), range(4)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, (1, 2, 3, 4)) def test_move_multiples(self): x = np.zeros((0, 1, 2, 3)) for source, destination, expected in [ ([0, 1], [2, 3], (2, 3, 0, 1)), ([2, 3], [0, 1], (2, 3, 0, 1)), ([0, 1, 2], [2, 3, 0], (2, 3, 0, 1)), ([3, 0], [1, 0], (0, 3, 1, 2)), ([0, 3], [0, 1], (0, 3, 1, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected) def test_errors(self): x = np.random.randn(1, 2, 3) assert_raises_regex(ValueError, 'invalid axis .* `source`', np.moveaxis, x, 3, 0) assert_raises_regex(ValueError, 'invalid axis .* `source`', np.moveaxis, x, -4, 0) assert_raises_regex(ValueError, 'invalid axis .* `destination`', np.moveaxis, x, 0, 5) assert_raises_regex(ValueError, 'repeated axis in `source`', np.moveaxis, x, [0, 0], [0, 1]) assert_raises_regex(ValueError, 'repeated axis in `destination`', np.moveaxis, x, [0, 1], [1, 1]) assert_raises_regex(ValueError, 'must have the same number', np.moveaxis, x, 0, [0, 1]) assert_raises_regex(ValueError, 'must have the same number', np.moveaxis, x, [0, 1], [0]) def test_array_likes(self): x = np.ma.zeros((1, 2, 3)) result = np.moveaxis(x, 0, 0) assert_(x.shape, result.shape) assert_(isinstance(result, np.ma.MaskedArray)) x = [1, 2, 3] result = np.moveaxis(x, 0, 0) assert_(x, list(result)) assert_(isinstance(result, np.ndarray)) class TestCross(TestCase): def test_2x2(self): u = [1, 2] v = [3, 4] z = -2 cp = np.cross(u, v) assert_equal(cp, z) cp = np.cross(v, u) assert_equal(cp, -z) def test_2x3(self): u = [1, 2] v = [3, 4, 5] z = np.array([10, -5, -2]) cp = np.cross(u, v) assert_equal(cp, z) cp = np.cross(v, u) assert_equal(cp, -z) def test_3x3(self): u = [1, 2, 3] v = [4, 5, 6] z = np.array([-3, 6, -3]) cp = np.cross(u, v) assert_equal(cp, z) cp = np.cross(v, u) assert_equal(cp, -z) def test_broadcasting(self): # Ticket #2624 (Trac #2032) u = np.tile([1, 2], (11, 1)) v = np.tile([3, 4], (11, 1)) z = -2 assert_equal(np.cross(u, v), z) assert_equal(np.cross(v, u), -z) assert_equal(np.cross(u, u), 0) u = np.tile([1, 2], (11, 1)).T v = np.tile([3, 4, 5], (11, 1)) z = np.tile([10, -5, -2], (11, 1)) assert_equal(np.cross(u, v, axisa=0), z) assert_equal(np.cross(v, u.T), -z) assert_equal(np.cross(v, v), 0) u = np.tile([1, 2, 3], (11, 1)).T v = np.tile([3, 4], (11, 1)).T z = np.tile([-12, 9, -2], (11, 1)) assert_equal(np.cross(u, v, axisa=0, axisb=0), z) assert_equal(np.cross(v.T, u.T), -z) assert_equal(np.cross(u.T, u.T), 0) u = np.tile([1, 2, 3], (5, 1)) v = np.tile([4, 5, 6], (5, 1)).T z = np.tile([-3, 6, -3], (5, 1)) assert_equal(np.cross(u, v, axisb=0), z) assert_equal(np.cross(v.T, u), -z) assert_equal(np.cross(u, u), 0) def test_broadcasting_shapes(self): u = np.ones((2, 1, 3)) v = np.ones((5, 3)) assert_equal(np.cross(u, v).shape, (2, 5, 3)) u = np.ones((10, 3, 5)) v = np.ones((2, 5)) assert_equal(np.cross(u, v, axisa=1, axisb=0).shape, (10, 5, 3)) assert_raises(ValueError, np.cross, u, v, axisa=1, axisb=2) assert_raises(ValueError, np.cross, u, v, axisa=3, axisb=0) u = np.ones((10, 3, 5, 7)) v = np.ones((5, 7, 2)) assert_equal(np.cross(u, v, axisa=1, axisc=2).shape, (10, 5, 3, 7)) assert_raises(ValueError, np.cross, u, v, axisa=-5, axisb=2) assert_raises(ValueError, np.cross, u, v, axisa=1, axisb=-4) # gh-5885 u = np.ones((3, 4, 2)) for axisc in range(-2, 2): assert_equal(np.cross(u, u, axisc=axisc).shape, (3, 4)) def test_outer_out_param(): arr1 = np.ones((5,)) arr2 = np.ones((2,)) arr3 = np.linspace(-2, 2, 5) out1 = np.ndarray(shape=(5,5)) out2 = np.ndarray(shape=(2, 5)) res1 = np.outer(arr1, arr3, out1) assert_equal(res1, out1) assert_equal(np.outer(arr2, arr3, out2), out2) class TestRequire(object): flag_names = ['C', 'C_CONTIGUOUS', 'CONTIGUOUS', 'F', 'F_CONTIGUOUS', 'FORTRAN', 'A', 'ALIGNED', 'W', 'WRITEABLE', 'O', 'OWNDATA'] def generate_all_false(self, dtype): arr = np.zeros((2, 2), [('junk', 'i1'), ('a', dtype)]) arr.setflags(write=False) a = arr['a'] assert_(not a.flags['C']) assert_(not a.flags['F']) assert_(not a.flags['O']) assert_(not a.flags['W']) assert_(not a.flags['A']) return a def set_and_check_flag(self, flag, dtype, arr): if dtype is None: dtype = arr.dtype b = np.require(arr, dtype, [flag]) assert_(b.flags[flag]) assert_(b.dtype == dtype) # a further call to np.require ought to return the same array # unless OWNDATA is specified. c = np.require(b, None, [flag]) if flag[0] != 'O': assert_(c is b) else: assert_(c.flags[flag]) def test_require_each(self): id = ['f8', 'i4'] fd = [None, 'f8', 'c16'] for idtype, fdtype, flag in itertools.product(id, fd, self.flag_names): a = self.generate_all_false(idtype) yield self.set_and_check_flag, flag, fdtype, a def test_unknown_requirement(self): a = self.generate_all_false('f8') assert_raises(KeyError, np.require, a, None, 'Q') def test_non_array_input(self): a = np.require([1, 2, 3, 4], 'i4', ['C', 'A', 'O']) assert_(a.flags['O']) assert_(a.flags['C']) assert_(a.flags['A']) assert_(a.dtype == 'i4') assert_equal(a, [1, 2, 3, 4]) def test_C_and_F_simul(self): a = self.generate_all_false('f8') assert_raises(ValueError, np.require, a, None, ['C', 'F']) def test_ensure_array(self): class ArraySubclass(np.ndarray): pass a = ArraySubclass((2, 2)) b = np.require(a, None, ['E']) assert_(type(b) is np.ndarray) def test_preserve_subtype(self): class ArraySubclass(np.ndarray): pass for flag in self.flag_names: a = ArraySubclass((2, 2)) yield self.set_and_check_flag, flag, None, a class TestBroadcast(TestCase): def test_broadcast_in_args(self): # gh-5881 arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)), np.empty((5, 1, 7))] mits = [np.broadcast(*arrs), np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])), np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])] for mit in mits: assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 4) for a, ia in zip(arrs, mit.iters): assert_(a is ia.base) def test_broadcast_single_arg(self): # gh-6899 arrs = [np.empty((5, 6, 7))] mit = np.broadcast(*arrs) assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 1) assert_(arrs[0] is mit.iters[0].base) def test_number_of_arguments(self): arr = np.empty((5,)) for j in range(35): arrs = [arr] * j if j < 1 or j > 32: assert_raises(ValueError, np.broadcast, *arrs) else: mit = np.broadcast(*arrs) assert_equal(mit.numiter, j) class TestKeepdims(TestCase): class sub_array(np.ndarray): def sum(self, axis=None, dtype=None, out=None): return np.ndarray.sum(self, axis, dtype, out, keepdims=True) def test_raise(self): sub_class = self.sub_array x = np.arange(30).view(sub_class) assert_raises(TypeError, np.sum, x, keepdims=True) if __name__ == "__main__": run_module_suite()