0x1998 - MANAGER

Düzenlenen Dosya: test_ufunc.py

from __future__ import division, absolute_import, print_function import warnings import itertools import numpy as np import numpy.core.umath_tests as umt import numpy.core.operand_flag_tests as opflag_tests from numpy.core.test_rational import rational, test_add, test_add_rationals from numpy.testing import ( TestCase, run_module_suite, assert_, assert_equal, assert_raises, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_no_warnings, assert_allclose, ) class TestUfuncKwargs(TestCase): def test_kwarg_exact(self): assert_raises(TypeError, np.add, 1, 2, castingx='safe') assert_raises(TypeError, np.add, 1, 2, dtypex=np.int) assert_raises(TypeError, np.add, 1, 2, extobjx=[4096]) assert_raises(TypeError, np.add, 1, 2, outx=None) assert_raises(TypeError, np.add, 1, 2, sigx='ii->i') assert_raises(TypeError, np.add, 1, 2, signaturex='ii->i') assert_raises(TypeError, np.add, 1, 2, subokx=False) assert_raises(TypeError, np.add, 1, 2, wherex=[True]) def test_sig_signature(self): assert_raises(ValueError, np.add, 1, 2, sig='ii->i', signature='ii->i') def test_sig_dtype(self): assert_raises(RuntimeError, np.add, 1, 2, sig='ii->i', dtype=np.int) assert_raises(RuntimeError, np.add, 1, 2, signature='ii->i', dtype=np.int) class TestUfunc(TestCase): def test_pickle(self): import pickle assert_(pickle.loads(pickle.dumps(np.sin)) is np.sin) # Check that ufunc not defined in the top level numpy namespace such as # numpy.core.test_rational.test_add can also be pickled assert_(pickle.loads(pickle.dumps(test_add)) is test_add) def test_pickle_withstring(self): import pickle astring = (b"cnumpy.core\n_ufunc_reconstruct\np0\n" b"(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.") assert_(pickle.loads(astring) is np.cos) def test_reduceat_shifting_sum(self): L = 6 x = np.arange(L) idx = np.array(list(zip(np.arange(L - 2), np.arange(L - 2) + 2))).ravel() assert_array_equal(np.add.reduceat(x, idx)[::2], [1, 3, 5, 7]) def test_generic_loops(self): """Test generic loops. The loops to be tested are: PyUFunc_ff_f_As_dd_d PyUFunc_ff_f PyUFunc_dd_d PyUFunc_gg_g PyUFunc_FF_F_As_DD_D PyUFunc_DD_D PyUFunc_FF_F PyUFunc_GG_G PyUFunc_OO_O PyUFunc_OO_O_method PyUFunc_f_f_As_d_d PyUFunc_d_d PyUFunc_f_f PyUFunc_g_g PyUFunc_F_F_As_D_D PyUFunc_F_F PyUFunc_D_D PyUFunc_G_G PyUFunc_O_O PyUFunc_O_O_method PyUFunc_On_Om Where: f -- float d -- double g -- long double F -- complex float D -- complex double G -- complex long double O -- python object It is difficult to assure that each of these loops is entered from the Python level as the special cased loops are a moving target and the corresponding types are architecture dependent. We probably need to define C level testing ufuncs to get at them. For the time being, I've just looked at the signatures registered in the build directory to find relevant functions. Fixme, currently untested: PyUFunc_ff_f_As_dd_d PyUFunc_FF_F_As_DD_D PyUFunc_f_f_As_d_d PyUFunc_F_F_As_D_D PyUFunc_On_Om """ fone = np.exp ftwo = lambda x, y: x**y fone_val = 1 ftwo_val = 1 # check unary PyUFunc_f_f. msg = "PyUFunc_f_f" x = np.zeros(10, dtype=np.single)[0::2] assert_almost_equal(fone(x), fone_val, err_msg=msg) # check unary PyUFunc_d_d. msg = "PyUFunc_d_d" x = np.zeros(10, dtype=np.double)[0::2] assert_almost_equal(fone(x), fone_val, err_msg=msg) # check unary PyUFunc_g_g. msg = "PyUFunc_g_g" x = np.zeros(10, dtype=np.longdouble)[0::2] assert_almost_equal(fone(x), fone_val, err_msg=msg) # check unary PyUFunc_F_F. msg = "PyUFunc_F_F" x = np.zeros(10, dtype=np.csingle)[0::2] assert_almost_equal(fone(x), fone_val, err_msg=msg) # check unary PyUFunc_D_D. msg = "PyUFunc_D_D" x = np.zeros(10, dtype=np.cdouble)[0::2] assert_almost_equal(fone(x), fone_val, err_msg=msg) # check unary PyUFunc_G_G. msg = "PyUFunc_G_G" x = np.zeros(10, dtype=np.clongdouble)[0::2] assert_almost_equal(fone(x), fone_val, err_msg=msg) # check binary PyUFunc_ff_f. msg = "PyUFunc_ff_f" x = np.ones(10, dtype=np.single)[0::2] assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg) # check binary PyUFunc_dd_d. msg = "PyUFunc_dd_d" x = np.ones(10, dtype=np.double)[0::2] assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg) # check binary PyUFunc_gg_g. msg = "PyUFunc_gg_g" x = np.ones(10, dtype=np.longdouble)[0::2] assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg) # check binary PyUFunc_FF_F. msg = "PyUFunc_FF_F" x = np.ones(10, dtype=np.csingle)[0::2] assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg) # check binary PyUFunc_DD_D. msg = "PyUFunc_DD_D" x = np.ones(10, dtype=np.cdouble)[0::2] assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg) # check binary PyUFunc_GG_G. msg = "PyUFunc_GG_G" x = np.ones(10, dtype=np.clongdouble)[0::2] assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg) # class to use in testing object method loops class foo(object): def conjugate(self): return np.bool_(1) def logical_xor(self, obj): return np.bool_(1) # check unary PyUFunc_O_O msg = "PyUFunc_O_O" x = np.ones(10, dtype=np.object)[0::2] assert_(np.all(np.abs(x) == 1), msg) # check unary PyUFunc_O_O_method msg = "PyUFunc_O_O_method" x = np.zeros(10, dtype=np.object)[0::2] for i in range(len(x)): x[i] = foo() assert_(np.all(np.conjugate(x) == True), msg) # check binary PyUFunc_OO_O msg = "PyUFunc_OO_O" x = np.ones(10, dtype=np.object)[0::2] assert_(np.all(np.add(x, x) == 2), msg) # check binary PyUFunc_OO_O_method msg = "PyUFunc_OO_O_method" x = np.zeros(10, dtype=np.object)[0::2] for i in range(len(x)): x[i] = foo() assert_(np.all(np.logical_xor(x, x)), msg) # check PyUFunc_On_Om # fixme -- I don't know how to do this yet def test_all_ufunc(self): """Try to check presence and results of all ufuncs. The list of ufuncs comes from generate_umath.py and is as follows: ===== ==== ============= =============== ======================== done args function types notes ===== ==== ============= =============== ======================== n 1 conjugate nums + O n 1 absolute nums + O complex -> real n 1 negative nums + O n 1 sign nums + O -> int n 1 invert bool + ints + O flts raise an error n 1 degrees real + M cmplx raise an error n 1 radians real + M cmplx raise an error n 1 arccos flts + M n 1 arccosh flts + M n 1 arcsin flts + M n 1 arcsinh flts + M n 1 arctan flts + M n 1 arctanh flts + M n 1 cos flts + M n 1 sin flts + M n 1 tan flts + M n 1 cosh flts + M n 1 sinh flts + M n 1 tanh flts + M n 1 exp flts + M n 1 expm1 flts + M n 1 log flts + M n 1 log10 flts + M n 1 log1p flts + M n 1 sqrt flts + M real x < 0 raises error n 1 ceil real + M n 1 trunc real + M n 1 floor real + M n 1 fabs real + M n 1 rint flts + M n 1 isnan flts -> bool n 1 isinf flts -> bool n 1 isfinite flts -> bool n 1 signbit real -> bool n 1 modf real -> (frac, int) n 1 logical_not bool + nums + M -> bool n 2 left_shift ints + O flts raise an error n 2 right_shift ints + O flts raise an error n 2 add bool + nums + O boolean + is || n 2 subtract bool + nums + O boolean - is ^ n 2 multiply bool + nums + O boolean * is & n 2 divide nums + O n 2 floor_divide nums + O n 2 true_divide nums + O bBhH -> f, iIlLqQ -> d n 2 fmod nums + M n 2 power nums + O n 2 greater bool + nums + O -> bool n 2 greater_equal bool + nums + O -> bool n 2 less bool + nums + O -> bool n 2 less_equal bool + nums + O -> bool n 2 equal bool + nums + O -> bool n 2 not_equal bool + nums + O -> bool n 2 logical_and bool + nums + M -> bool n 2 logical_or bool + nums + M -> bool n 2 logical_xor bool + nums + M -> bool n 2 maximum bool + nums + O n 2 minimum bool + nums + O n 2 bitwise_and bool + ints + O flts raise an error n 2 bitwise_or bool + ints + O flts raise an error n 2 bitwise_xor bool + ints + O flts raise an error n 2 arctan2 real + M n 2 remainder ints + real + O n 2 hypot real + M ===== ==== ============= =============== ======================== Types other than those listed will be accepted, but they are cast to the smallest compatible type for which the function is defined. The casting rules are: bool -> int8 -> float32 ints -> double """ pass def test_signature(self): # the arguments to test_signature are: nin, nout, core_signature # pass assert_equal(umt.test_signature(2, 1, "(i),(i)->()"), 1) # pass. empty core signature; treat as plain ufunc (with trivial core) assert_equal(umt.test_signature(2, 1, "(),()->()"), 0) # in the following calls, a ValueError should be raised because # of error in core signature # FIXME These should be using assert_raises # error: extra parenthesis msg = "core_sig: extra parenthesis" try: ret = umt.test_signature(2, 1, "((i)),(i)->()") assert_equal(ret, None, err_msg=msg) except ValueError: pass # error: parenthesis matching msg = "core_sig: parenthesis matching" try: ret = umt.test_signature(2, 1, "(i),)i(->()") assert_equal(ret, None, err_msg=msg) except ValueError: pass # error: incomplete signature. letters outside of parenthesis are ignored msg = "core_sig: incomplete signature" try: ret = umt.test_signature(2, 1, "(i),->()") assert_equal(ret, None, err_msg=msg) except ValueError: pass # error: incomplete signature. 2 output arguments are specified msg = "core_sig: incomplete signature" try: ret = umt.test_signature(2, 2, "(i),(i)->()") assert_equal(ret, None, err_msg=msg) except ValueError: pass # more complicated names for variables assert_equal(umt.test_signature(2, 1, "(i1,i2),(J_1)->(_kAB)"), 1) def test_get_signature(self): assert_equal(umt.inner1d.signature, "(i),(i)->()") def test_forced_sig(self): a = 0.5*np.arange(3, dtype='f8') assert_equal(np.add(a, 0.5), [0.5, 1, 1.5]) assert_equal(np.add(a, 0.5, sig='i', casting='unsafe'), [0, 0, 1]) assert_equal(np.add(a, 0.5, sig='ii->i', casting='unsafe'), [0, 0, 1]) assert_equal(np.add(a, 0.5, sig=('i4',), casting='unsafe'), [0, 0, 1]) assert_equal(np.add(a, 0.5, sig=('i4', 'i4', 'i4'), casting='unsafe'), [0, 0, 1]) b = np.zeros((3,), dtype='f8') np.add(a, 0.5, out=b) assert_equal(b, [0.5, 1, 1.5]) b[:] = 0 np.add(a, 0.5, sig='i', out=b, casting='unsafe') assert_equal(b, [0, 0, 1]) b[:] = 0 np.add(a, 0.5, sig='ii->i', out=b, casting='unsafe') assert_equal(b, [0, 0, 1]) b[:] = 0 np.add(a, 0.5, sig=('i4',), out=b, casting='unsafe') assert_equal(b, [0, 0, 1]) b[:] = 0 np.add(a, 0.5, sig=('i4', 'i4', 'i4'), out=b, casting='unsafe') assert_equal(b, [0, 0, 1]) def test_true_divide(self): a = np.array(10) b = np.array(20) tgt = np.array(0.5) for tc in 'bhilqBHILQefdgFDG': dt = np.dtype(tc) aa = a.astype(dt) bb = b.astype(dt) # Check result value and dtype. for x, y in itertools.product([aa, -aa], [bb, -bb]): # Check with no output type specified if tc in 'FDG': tgt = complex(x)/complex(y) else: tgt = float(x)/float(y) res = np.true_divide(x, y) rtol = max(np.finfo(res).resolution, 1e-15) assert_allclose(res, tgt, rtol=rtol) if tc in 'bhilqBHILQ': assert_(res.dtype.name == 'float64') else: assert_(res.dtype.name == dt.name ) # Check with output type specified. This also checks for the # incorrect casts in issue gh-3484 because the unary '-' does # not change types, even for unsigned types, Hence casts in the # ufunc from signed to unsigned and vice versa will lead to # errors in the values. for tcout in 'bhilqBHILQ': dtout = np.dtype(tcout) assert_raises(TypeError, np.true_divide, x, y, dtype=dtout) for tcout in 'efdg': dtout = np.dtype(tcout) if tc in 'FDG': # Casting complex to float is not allowed assert_raises(TypeError, np.true_divide, x, y, dtype=dtout) else: tgt = float(x)/float(y) rtol = max(np.finfo(dtout).resolution, 1e-15) atol = max(np.finfo(dtout).tiny, 3e-308) # Some test values result in invalid for float16. with np.errstate(invalid='ignore'): res = np.true_divide(x, y, dtype=dtout) if not np.isfinite(res) and tcout == 'e': continue assert_allclose(res, tgt, rtol=rtol, atol=atol) assert_(res.dtype.name == dtout.name) for tcout in 'FDG': dtout = np.dtype(tcout) tgt = complex(x)/complex(y) rtol = max(np.finfo(dtout).resolution, 1e-15) atol = max(np.finfo(dtout).tiny, 3e-308) res = np.true_divide(x, y, dtype=dtout) if not np.isfinite(res): continue assert_allclose(res, tgt, rtol=rtol, atol=atol) assert_(res.dtype.name == dtout.name) # Check booleans a = np.ones((), dtype=np.bool_) res = np.true_divide(a, a) assert_(res == 1.0) assert_(res.dtype.name == 'float64') res = np.true_divide(~a, a) assert_(res == 0.0) assert_(res.dtype.name == 'float64') def test_sum_stability(self): a = np.ones(500, dtype=np.float32) assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 4) a = np.ones(500, dtype=np.float64) assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 13) def test_sum(self): for dt in (np.int, np.float16, np.float32, np.float64, np.longdouble): for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127, 128, 1024, 1235): tgt = dt(v * (v + 1) / 2) d = np.arange(1, v + 1, dtype=dt) assert_almost_equal(np.sum(d), tgt) assert_almost_equal(np.sum(d[::-1]), tgt) d = np.ones(500, dtype=dt) assert_almost_equal(np.sum(d[::2]), 250.) assert_almost_equal(np.sum(d[1::2]), 250.) assert_almost_equal(np.sum(d[::3]), 167.) assert_almost_equal(np.sum(d[1::3]), 167.) assert_almost_equal(np.sum(d[::-2]), 250.) assert_almost_equal(np.sum(d[-1::-2]), 250.) assert_almost_equal(np.sum(d[::-3]), 167.) assert_almost_equal(np.sum(d[-1::-3]), 167.) # sum with first reduction entry != 0 d = np.ones((1,), dtype=dt) d += d assert_almost_equal(d, 2.) def test_sum_complex(self): for dt in (np.complex64, np.complex128, np.clongdouble): for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127, 128, 1024, 1235): tgt = dt(v * (v + 1) / 2) - dt((v * (v + 1) / 2) * 1j) d = np.empty(v, dtype=dt) d.real = np.arange(1, v + 1) d.imag = -np.arange(1, v + 1) assert_almost_equal(np.sum(d), tgt) assert_almost_equal(np.sum(d[::-1]), tgt) d = np.ones(500, dtype=dt) + 1j assert_almost_equal(np.sum(d[::2]), 250. + 250j) assert_almost_equal(np.sum(d[1::2]), 250. + 250j) assert_almost_equal(np.sum(d[::3]), 167. + 167j) assert_almost_equal(np.sum(d[1::3]), 167. + 167j) assert_almost_equal(np.sum(d[::-2]), 250. + 250j) assert_almost_equal(np.sum(d[-1::-2]), 250. + 250j) assert_almost_equal(np.sum(d[::-3]), 167. + 167j) assert_almost_equal(np.sum(d[-1::-3]), 167. + 167j) # sum with first reduction entry != 0 d = np.ones((1,), dtype=dt) + 1j d += d assert_almost_equal(d, 2. + 2j) def test_inner1d(self): a = np.arange(6).reshape((2, 3)) assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1)) a = np.arange(6) assert_array_equal(umt.inner1d(a, a), np.sum(a*a)) def test_broadcast(self): msg = "broadcast" a = np.arange(4).reshape((2, 1, 2)) b = np.arange(4).reshape((1, 2, 2)) assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg) msg = "extend & broadcast loop dimensions" b = np.arange(4).reshape((2, 2)) assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg) # Broadcast in core dimensions should fail a = np.arange(8).reshape((4, 2)) b = np.arange(4).reshape((4, 1)) assert_raises(ValueError, umt.inner1d, a, b) # Extend core dimensions should fail a = np.arange(8).reshape((4, 2)) b = np.array(7) assert_raises(ValueError, umt.inner1d, a, b) # Broadcast should fail a = np.arange(2).reshape((2, 1, 1)) b = np.arange(3).reshape((3, 1, 1)) assert_raises(ValueError, umt.inner1d, a, b) def test_type_cast(self): msg = "type cast" a = np.arange(6, dtype='short').reshape((2, 3)) assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1), err_msg=msg) msg = "type cast on one argument" a = np.arange(6).reshape((2, 3)) b = a + 0.1 assert_array_almost_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg) def test_endian(self): msg = "big endian" a = np.arange(6, dtype='>i4').reshape((2, 3)) assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1), err_msg=msg) msg = "little endian" a = np.arange(6, dtype='<i4').reshape((2, 3)) assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1), err_msg=msg) # Output should always be native-endian Ba = np.arange(1, dtype='>f8') La = np.arange(1, dtype='<f8') assert_equal((Ba+Ba).dtype, np.dtype('f8')) assert_equal((Ba+La).dtype, np.dtype('f8')) assert_equal((La+Ba).dtype, np.dtype('f8')) assert_equal((La+La).dtype, np.dtype('f8')) assert_equal(np.absolute(La).dtype, np.dtype('f8')) assert_equal(np.absolute(Ba).dtype, np.dtype('f8')) assert_equal(np.negative(La).dtype, np.dtype('f8')) assert_equal(np.negative(Ba).dtype, np.dtype('f8')) def test_incontiguous_array(self): msg = "incontiguous memory layout of array" x = np.arange(64).reshape((2, 2, 2, 2, 2, 2)) a = x[:, 0,:, 0,:, 0] b = x[:, 1,:, 1,:, 1] a[0, 0, 0] = -1 msg2 = "make sure it references to the original array" assert_equal(x[0, 0, 0, 0, 0, 0], -1, err_msg=msg2) assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg) x = np.arange(24).reshape(2, 3, 4) a = x.T b = x.T a[0, 0, 0] = -1 assert_equal(x[0, 0, 0], -1, err_msg=msg2) assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg) def test_output_argument(self): msg = "output argument" a = np.arange(12).reshape((2, 3, 2)) b = np.arange(4).reshape((2, 1, 2)) + 1 c = np.zeros((2, 3), dtype='int') umt.inner1d(a, b, c) assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg) c[:] = -1 umt.inner1d(a, b, out=c) assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg) msg = "output argument with type cast" c = np.zeros((2, 3), dtype='int16') umt.inner1d(a, b, c) assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg) c[:] = -1 umt.inner1d(a, b, out=c) assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg) msg = "output argument with incontiguous layout" c = np.zeros((2, 3, 4), dtype='int16') umt.inner1d(a, b, c[..., 0]) assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg) c[:] = -1 umt.inner1d(a, b, out=c[..., 0]) assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg) def test_innerwt(self): a = np.arange(6).reshape((2, 3)) b = np.arange(10, 16).reshape((2, 3)) w = np.arange(20, 26).reshape((2, 3)) assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1)) a = np.arange(100, 124).reshape((2, 3, 4)) b = np.arange(200, 224).reshape((2, 3, 4)) w = np.arange(300, 324).reshape((2, 3, 4)) assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1)) def test_innerwt_empty(self): """Test generalized ufunc with zero-sized operands""" a = np.array([], dtype='f8') b = np.array([], dtype='f8') w = np.array([], dtype='f8') assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1)) def test_matrix_multiply(self): self.compare_matrix_multiply_results(np.long) self.compare_matrix_multiply_results(np.double) def test_matrix_multiply_umath_empty(self): res = umt.matrix_multiply(np.ones((0, 10)), np.ones((10, 0))) assert_array_equal(res, np.zeros((0, 0))) res = umt.matrix_multiply(np.ones((10, 0)), np.ones((0, 10))) assert_array_equal(res, np.zeros((10, 10))) def compare_matrix_multiply_results(self, tp): d1 = np.array(np.random.rand(2, 3, 4), dtype=tp) d2 = np.array(np.random.rand(2, 3, 4), dtype=tp) msg = "matrix multiply on type %s" % d1.dtype.name def permute_n(n): if n == 1: return ([0],) ret = () base = permute_n(n-1) for perm in base: for i in range(n): new = perm + [n-1] new[n-1] = new[i] new[i] = n-1 ret += (new,) return ret def slice_n(n): if n == 0: return ((),) ret = () base = slice_n(n-1) for sl in base: ret += (sl+(slice(None),),) ret += (sl+(slice(0, 1),),) return ret def broadcastable(s1, s2): return s1 == s2 or s1 == 1 or s2 == 1 permute_3 = permute_n(3) slice_3 = slice_n(3) + ((slice(None, None, -1),)*3,) ref = True for p1 in permute_3: for p2 in permute_3: for s1 in slice_3: for s2 in slice_3: a1 = d1.transpose(p1)[s1] a2 = d2.transpose(p2)[s2] ref = ref and a1.base is not None ref = ref and a2.base is not None if (a1.shape[-1] == a2.shape[-2] and broadcastable(a1.shape[0], a2.shape[0])): assert_array_almost_equal( umt.matrix_multiply(a1, a2), np.sum(a2[..., np.newaxis].swapaxes(-3, -1) * a1[..., np.newaxis,:], axis=-1), err_msg=msg + ' %s %s' % (str(a1.shape), str(a2.shape))) assert_equal(ref, True, err_msg="reference check") def test_euclidean_pdist(self): a = np.arange(12, dtype=np.float).reshape(4, 3) out = np.empty((a.shape[0] * (a.shape[0] - 1) // 2,), dtype=a.dtype) umt.euclidean_pdist(a, out) b = np.sqrt(np.sum((a[:, None] - a)**2, axis=-1)) b = b[~np.tri(a.shape[0], dtype=bool)] assert_almost_equal(out, b) # An output array is required to determine p with signature (n,d)->(p) assert_raises(ValueError, umt.euclidean_pdist, a) def test_object_logical(self): a = np.array([3, None, True, False, "test", ""], dtype=object) assert_equal(np.logical_or(a, None), np.array([x or None for x in a], dtype=object)) assert_equal(np.logical_or(a, True), np.array([x or True for x in a], dtype=object)) assert_equal(np.logical_or(a, 12), np.array([x or 12 for x in a], dtype=object)) assert_equal(np.logical_or(a, "blah"), np.array([x or "blah" for x in a], dtype=object)) assert_equal(np.logical_and(a, None), np.array([x and None for x in a], dtype=object)) assert_equal(np.logical_and(a, True), np.array([x and True for x in a], dtype=object)) assert_equal(np.logical_and(a, 12), np.array([x and 12 for x in a], dtype=object)) assert_equal(np.logical_and(a, "blah"), np.array([x and "blah" for x in a], dtype=object)) assert_equal(np.logical_not(a), np.array([not x for x in a], dtype=object)) assert_equal(np.logical_or.reduce(a), 3) assert_equal(np.logical_and.reduce(a), None) def test_object_array_reduction(self): # Reductions on object arrays a = np.array(['a', 'b', 'c'], dtype=object) assert_equal(np.sum(a), 'abc') assert_equal(np.max(a), 'c') assert_equal(np.min(a), 'a') a = np.array([True, False, True], dtype=object) assert_equal(np.sum(a), 2) assert_equal(np.prod(a), 0) assert_equal(np.any(a), True) assert_equal(np.all(a), False) assert_equal(np.max(a), True) assert_equal(np.min(a), False) assert_equal(np.array([[1]], dtype=object).sum(), 1) assert_equal(np.array([[[1, 2]]], dtype=object).sum((0, 1)), [1, 2]) def test_object_array_accumulate_inplace(self): # Checks that in-place accumulates work, see also gh-7402 arr = np.ones(4, dtype=object) arr[:] = [[1] for i in range(4)] # Twice reproduced also for tuples: np.add.accumulate(arr, out=arr) np.add.accumulate(arr, out=arr) assert_array_equal(arr, np.array([[1]*i for i in [1, 3, 6, 10]])) # And the same if the axis argument is used arr = np.ones((2, 4), dtype=object) arr[0, :] = [[2] for i in range(4)] np.add.accumulate(arr, out=arr, axis=-1) np.add.accumulate(arr, out=arr, axis=-1) assert_array_equal(arr[0, :], np.array([[2]*i for i in [1, 3, 6, 10]])) def test_object_array_reduceat_inplace(self): # Checks that in-place reduceats work, see also gh-7465 arr = np.empty(4, dtype=object) arr[:] = [[1] for i in range(4)] out = np.empty(4, dtype=object) out[:] = [[1] for i in range(4)] np.add.reduceat(arr, np.arange(4), out=arr) np.add.reduceat(arr, np.arange(4), out=arr) assert_array_equal(arr, out) # And the same if the axis argument is used arr = np.ones((2, 4), dtype=object) arr[0, :] = [[2] for i in range(4)] out = np.ones((2, 4), dtype=object) out[0, :] = [[2] for i in range(4)] np.add.reduceat(arr, np.arange(4), out=arr, axis=-1) np.add.reduceat(arr, np.arange(4), out=arr, axis=-1) assert_array_equal(arr, out) def test_object_scalar_multiply(self): # Tickets #2469 and #4482 arr = np.matrix([1, 2], dtype=object) desired = np.matrix([[3, 6]], dtype=object) assert_equal(np.multiply(arr, 3), desired) assert_equal(np.multiply(3, arr), desired) def test_zerosize_reduction(self): # Test with default dtype and object dtype for a in [[], np.array([], dtype=object)]: assert_equal(np.sum(a), 0) assert_equal(np.prod(a), 1) assert_equal(np.any(a), False) assert_equal(np.all(a), True) assert_raises(ValueError, np.max, a) assert_raises(ValueError, np.min, a) def test_axis_out_of_bounds(self): a = np.array([False, False]) assert_raises(np.AxisError, a.all, axis=1) a = np.array([False, False]) assert_raises(np.AxisError, a.all, axis=-2) a = np.array([False, False]) assert_raises(np.AxisError, a.any, axis=1) a = np.array([False, False]) assert_raises(np.AxisError, a.any, axis=-2) def test_scalar_reduction(self): # The functions 'sum', 'prod', etc allow specifying axis=0 # even for scalars assert_equal(np.sum(3, axis=0), 3) assert_equal(np.prod(3.5, axis=0), 3.5) assert_equal(np.any(True, axis=0), True) assert_equal(np.all(False, axis=0), False) assert_equal(np.max(3, axis=0), 3) assert_equal(np.min(2.5, axis=0), 2.5) # Check scalar behaviour for ufuncs without an identity assert_equal(np.power.reduce(3), 3) # Make sure that scalars are coming out from this operation assert_(type(np.prod(np.float32(2.5), axis=0)) is np.float32) assert_(type(np.sum(np.float32(2.5), axis=0)) is np.float32) assert_(type(np.max(np.float32(2.5), axis=0)) is np.float32) assert_(type(np.min(np.float32(2.5), axis=0)) is np.float32) # check if scalars/0-d arrays get cast assert_(type(np.any(0, axis=0)) is np.bool_) # assert that 0-d arrays get wrapped class MyArray(np.ndarray): pass a = np.array(1).view(MyArray) assert_(type(np.any(a)) is MyArray) def test_casting_out_param(self): # Test that it's possible to do casts on output a = np.ones((200, 100), np.int64) b = np.ones((200, 100), np.int64) c = np.ones((200, 100), np.float64) np.add(a, b, out=c) assert_equal(c, 2) a = np.zeros(65536) b = np.zeros(65536, dtype=np.float32) np.subtract(a, 0, out=b) assert_equal(b, 0) def test_where_param(self): # Test that the where= ufunc parameter works with regular arrays a = np.arange(7) b = np.ones(7) c = np.zeros(7) np.add(a, b, out=c, where=(a % 2 == 1)) assert_equal(c, [0, 2, 0, 4, 0, 6, 0]) a = np.arange(4).reshape(2, 2) + 2 np.power(a, [2, 3], out=a, where=[[0, 1], [1, 0]]) assert_equal(a, [[2, 27], [16, 5]]) # Broadcasting the where= parameter np.subtract(a, 2, out=a, where=[True, False]) assert_equal(a, [[0, 27], [14, 5]]) def test_where_param_buffer_output(self): # This test is temporarily skipped because it requires # adding masking features to the nditer to work properly # With casting on output a = np.ones(10, np.int64) b = np.ones(10, np.int64) c = 1.5 * np.ones(10, np.float64) np.add(a, b, out=c, where=[1, 0, 0, 1, 0, 0, 1, 1, 1, 0]) assert_equal(c, [2, 1.5, 1.5, 2, 1.5, 1.5, 2, 2, 2, 1.5]) def test_where_param_alloc(self): # With casting and allocated output a = np.array([1], dtype=np.int64) m = np.array([True], dtype=bool) assert_equal(np.sqrt(a, where=m), [1]) # No casting and allocated output a = np.array([1], dtype=np.float64) m = np.array([True], dtype=bool) assert_equal(np.sqrt(a, where=m), [1]) def check_identityless_reduction(self, a): # np.minimum.reduce is a identityless reduction # Verify that it sees the zero at various positions a[...] = 1 a[1, 0, 0] = 0 assert_equal(np.minimum.reduce(a, axis=None), 0) assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1]) assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1]) assert_equal(np.minimum.reduce(a, axis=(1, 2)), [1, 0]) assert_equal(np.minimum.reduce(a, axis=0), [[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=1), [[1, 1, 1, 1], [0, 1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=2), [[1, 1, 1], [0, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=()), a) a[...] = 1 a[0, 1, 0] = 0 assert_equal(np.minimum.reduce(a, axis=None), 0) assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1]) assert_equal(np.minimum.reduce(a, axis=(0, 2)), [1, 0, 1]) assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1]) assert_equal(np.minimum.reduce(a, axis=0), [[1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=1), [[0, 1, 1, 1], [1, 1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=2), [[1, 0, 1], [1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=()), a) a[...] = 1 a[0, 0, 1] = 0 assert_equal(np.minimum.reduce(a, axis=None), 0) assert_equal(np.minimum.reduce(a, axis=(0, 1)), [1, 0, 1, 1]) assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1]) assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1]) assert_equal(np.minimum.reduce(a, axis=0), [[1, 0, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=1), [[1, 0, 1, 1], [1, 1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=2), [[0, 1, 1], [1, 1, 1]]) assert_equal(np.minimum.reduce(a, axis=()), a) def test_identityless_reduction_corder(self): a = np.empty((2, 3, 4), order='C') self.check_identityless_reduction(a) def test_identityless_reduction_forder(self): a = np.empty((2, 3, 4), order='F') self.check_identityless_reduction(a) def test_identityless_reduction_otherorder(self): a = np.empty((2, 4, 3), order='C').swapaxes(1, 2) self.check_identityless_reduction(a) def test_identityless_reduction_noncontig(self): a = np.empty((3, 5, 4), order='C').swapaxes(1, 2) a = a[1:, 1:, 1:] self.check_identityless_reduction(a) def test_identityless_reduction_noncontig_unaligned(self): a = np.empty((3*4*5*8 + 1,), dtype='i1') a = a[1:].view(dtype='f8') a.shape = (3, 4, 5) a = a[1:, 1:, 1:] self.check_identityless_reduction(a) def test_identityless_reduction_nonreorderable(self): a = np.array([[8.0, 2.0, 2.0], [1.0, 0.5, 0.25]]) res = np.divide.reduce(a, axis=0) assert_equal(res, [8.0, 4.0, 8.0]) res = np.divide.reduce(a, axis=1) assert_equal(res, [2.0, 8.0]) res = np.divide.reduce(a, axis=()) assert_equal(res, a) assert_raises(ValueError, np.divide.reduce, a, axis=(0, 1)) def test_reduce_zero_axis(self): # If we have a n x m array and do a reduction with axis=1, then we are # doing n reductions, and each reduction takes an m-element array. For # a reduction operation without an identity, then: # n > 0, m > 0: fine # n = 0, m > 0: fine, doing 0 reductions of m-element arrays # n > 0, m = 0: can't reduce a 0-element array, ValueError # n = 0, m = 0: can't reduce a 0-element array, ValueError (for # consistency with the above case) # This test doesn't actually look at return values, it just checks to # make sure that error we get an error in exactly those cases where we # expect one, and assumes the calculations themselves are done # correctly. def ok(f, *args, **kwargs): f(*args, **kwargs) def err(f, *args, **kwargs): assert_raises(ValueError, f, *args, **kwargs) def t(expect, func, n, m): expect(func, np.zeros((n, m)), axis=1) expect(func, np.zeros((m, n)), axis=0) expect(func, np.zeros((n // 2, n // 2, m)), axis=2) expect(func, np.zeros((n // 2, m, n // 2)), axis=1) expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2)) expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2)) expect(func, np.zeros((m // 3, m // 3, m // 3, n // 2, n // 2)), axis=(0, 1, 2)) # Check what happens if the inner (resp. outer) dimensions are a # mix of zero and non-zero: expect(func, np.zeros((10, m, n)), axis=(0, 1)) expect(func, np.zeros((10, n, m)), axis=(0, 2)) expect(func, np.zeros((m, 10, n)), axis=0) expect(func, np.zeros((10, m, n)), axis=1) expect(func, np.zeros((10, n, m)), axis=2) # np.maximum is just an arbitrary ufunc with no reduction identity assert_equal(np.maximum.identity, None) t(ok, np.maximum.reduce, 30, 30) t(ok, np.maximum.reduce, 0, 30) t(err, np.maximum.reduce, 30, 0) t(err, np.maximum.reduce, 0, 0) err(np.maximum.reduce, []) np.maximum.reduce(np.zeros((0, 0)), axis=()) # all of the combinations are fine for a reduction that has an # identity t(ok, np.add.reduce, 30, 30) t(ok, np.add.reduce, 0, 30) t(ok, np.add.reduce, 30, 0) t(ok, np.add.reduce, 0, 0) np.add.reduce([]) np.add.reduce(np.zeros((0, 0)), axis=()) # OTOH, accumulate always makes sense for any combination of n and m, # because it maps an m-element array to an m-element array. These # tests are simpler because accumulate doesn't accept multiple axes. for uf in (np.maximum, np.add): uf.accumulate(np.zeros((30, 0)), axis=0) uf.accumulate(np.zeros((0, 30)), axis=0) uf.accumulate(np.zeros((30, 30)), axis=0) uf.accumulate(np.zeros((0, 0)), axis=0) def test_safe_casting(self): # In old versions of numpy, in-place operations used the 'unsafe' # casting rules. In versions >= 1.10, 'same_kind' is the # default and an exception is raised instead of a warning. # when 'same_kind' is not satisfied. a = np.array([1, 2, 3], dtype=int) # Non-in-place addition is fine assert_array_equal(assert_no_warnings(np.add, a, 1.1), [2.1, 3.1, 4.1]) assert_raises(TypeError, np.add, a, 1.1, out=a) def add_inplace(a, b): a += b assert_raises(TypeError, add_inplace, a, 1.1) # Make sure that explicitly overriding the exception is allowed: assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe") assert_array_equal(a, [2, 3, 4]) def test_ufunc_custom_out(self): # Test ufunc with built in input types and custom output type a = np.array([0, 1, 2], dtype='i8') b = np.array([0, 1, 2], dtype='i8') c = np.empty(3, dtype=rational) # Output must be specified so numpy knows what # ufunc signature to look for result = test_add(a, b, c) assert_equal(result, np.array([0, 2, 4], dtype=rational)) # no output type should raise TypeError assert_raises(TypeError, test_add, a, b) def test_operand_flags(self): a = np.arange(16, dtype='l').reshape(4, 4) b = np.arange(9, dtype='l').reshape(3, 3) opflag_tests.inplace_add(a[:-1, :-1], b) assert_equal(a, np.array([[0, 2, 4, 3], [7, 9, 11, 7], [14, 16, 18, 11], [12, 13, 14, 15]], dtype='l')) a = np.array(0) opflag_tests.inplace_add(a, 3) assert_equal(a, 3) opflag_tests.inplace_add(a, [3, 4]) assert_equal(a, 10) def test_struct_ufunc(self): import numpy.core.struct_ufunc_test as struct_ufunc a = np.array([(1, 2, 3)], dtype='u8,u8,u8') b = np.array([(1, 2, 3)], dtype='u8,u8,u8') result = struct_ufunc.add_triplet(a, b) assert_equal(result, np.array([(2, 4, 6)], dtype='u8,u8,u8')) def test_custom_ufunc(self): a = np.array([rational(1, 2), rational(1, 3), rational(1, 4)], dtype=rational) b = np.array([rational(1, 2), rational(1, 3), rational(1, 4)], dtype=rational) result = test_add_rationals(a, b) expected = np.array([rational(1), rational(2, 3), rational(1, 2)], dtype=rational) assert_equal(result, expected) def test_custom_ufunc_forced_sig(self): # gh-9351 - looking for a non-first userloop would previously hang assert_raises(TypeError, np.multiply, rational(1), 1, signature=(rational, int, None)) def test_custom_array_like(self): class MyThing(object): __array_priority__ = 1000 rmul_count = 0 getitem_count = 0 def __init__(self, shape): self.shape = shape def __len__(self): return self.shape[0] def __getitem__(self, i): MyThing.getitem_count += 1 if not isinstance(i, tuple): i = (i,) if len(i) > self.ndim: raise IndexError("boo") return MyThing(self.shape[len(i):]) def __rmul__(self, other): MyThing.rmul_count += 1 return self np.float64(5)*MyThing((3, 3)) assert_(MyThing.rmul_count == 1, MyThing.rmul_count) assert_(MyThing.getitem_count <= 2, MyThing.getitem_count) def test_inplace_fancy_indexing(self): a = np.arange(10) np.add.at(a, [2, 5, 2], 1) assert_equal(a, [0, 1, 4, 3, 4, 6, 6, 7, 8, 9]) a = np.arange(10) b = np.array([100, 100, 100]) np.add.at(a, [2, 5, 2], b) assert_equal(a, [0, 1, 202, 3, 4, 105, 6, 7, 8, 9]) a = np.arange(9).reshape(3, 3) b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]]) np.add.at(a, (slice(None), [1, 2, 1]), b) assert_equal(a, [[0, 201, 102], [3, 404, 205], [6, 607, 308]]) a = np.arange(27).reshape(3, 3, 3) b = np.array([100, 200, 300]) np.add.at(a, (slice(None), slice(None), [1, 2, 1]), b) assert_equal(a, [[[0, 401, 202], [3, 404, 205], [6, 407, 208]], [[9, 410, 211], [12, 413, 214], [15, 416, 217]], [[18, 419, 220], [21, 422, 223], [24, 425, 226]]]) a = np.arange(9).reshape(3, 3) b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]]) np.add.at(a, ([1, 2, 1], slice(None)), b) assert_equal(a, [[0, 1, 2], [403, 404, 405], [206, 207, 208]]) a = np.arange(27).reshape(3, 3, 3) b = np.array([100, 200, 300]) np.add.at(a, (slice(None), [1, 2, 1], slice(None)), b) assert_equal(a, [[[0, 1, 2], [203, 404, 605], [106, 207, 308]], [[9, 10, 11], [212, 413, 614], [115, 216, 317]], [[18, 19, 20], [221, 422, 623], [124, 225, 326]]]) a = np.arange(9).reshape(3, 3) b = np.array([100, 200, 300]) np.add.at(a, (0, [1, 2, 1]), b) assert_equal(a, [[0, 401, 202], [3, 4, 5], [6, 7, 8]]) a = np.arange(27).reshape(3, 3, 3) b = np.array([100, 200, 300]) np.add.at(a, ([1, 2, 1], 0, slice(None)), b) assert_equal(a, [[[0, 1, 2], [3, 4, 5], [6, 7, 8]], [[209, 410, 611], [12, 13, 14], [15, 16, 17]], [[118, 219, 320], [21, 22, 23], [24, 25, 26]]]) a = np.arange(27).reshape(3, 3, 3) b = np.array([100, 200, 300]) np.add.at(a, (slice(None), slice(None), slice(None)), b) assert_equal(a, [[[100, 201, 302], [103, 204, 305], [106, 207, 308]], [[109, 210, 311], [112, 213, 314], [115, 216, 317]], [[118, 219, 320], [121, 222, 323], [124, 225, 326]]]) a = np.arange(10) np.negative.at(a, [2, 5, 2]) assert_equal(a, [0, 1, 2, 3, 4, -5, 6, 7, 8, 9]) # Test 0-dim array a = np.array(0) np.add.at(a, (), 1) assert_equal(a, 1) assert_raises(IndexError, np.add.at, a, 0, 1) assert_raises(IndexError, np.add.at, a, [], 1) # Test mixed dtypes a = np.arange(10) np.power.at(a, [1, 2, 3, 2], 3.5) assert_equal(a, np.array([0, 1, 4414, 46, 4, 5, 6, 7, 8, 9])) # Test boolean indexing and boolean ufuncs a = np.arange(10) index = a % 2 == 0 np.equal.at(a, index, [0, 2, 4, 6, 8]) assert_equal(a, [1, 1, 1, 3, 1, 5, 1, 7, 1, 9]) # Test unary operator a = np.arange(10, dtype='u4') np.invert.at(a, [2, 5, 2]) assert_equal(a, [0, 1, 2, 3, 4, 5 ^ 0xffffffff, 6, 7, 8, 9]) # Test empty subspace orig = np.arange(4) a = orig[:, None][:, 0:0] np.add.at(a, [0, 1], 3) assert_array_equal(orig, np.arange(4)) # Test with swapped byte order index = np.array([1, 2, 1], np.dtype('i').newbyteorder()) values = np.array([1, 2, 3, 4], np.dtype('f').newbyteorder()) np.add.at(values, index, 3) assert_array_equal(values, [1, 8, 6, 4]) # Test exception thrown values = np.array(['a', 1], dtype=np.object) self.assertRaises(TypeError, np.add.at, values, [0, 1], 1) assert_array_equal(values, np.array(['a', 1], dtype=np.object)) # Test multiple output ufuncs raise error, gh-5665 assert_raises(ValueError, np.modf.at, np.arange(10), [1]) def test_reduce_arguments(self): f = np.add.reduce d = np.ones((5,2), dtype=int) o = np.ones((2,), dtype=d.dtype) r = o * 5 assert_equal(f(d), r) # a, axis=0, dtype=None, out=None, keepdims=False assert_equal(f(d, axis=0), r) assert_equal(f(d, 0), r) assert_equal(f(d, 0, dtype=None), r) assert_equal(f(d, 0, dtype='i'), r) assert_equal(f(d, 0, 'i'), r) assert_equal(f(d, 0, None), r) assert_equal(f(d, 0, None, out=None), r) assert_equal(f(d, 0, None, out=o), r) assert_equal(f(d, 0, None, o), r) assert_equal(f(d, 0, None, None), r) assert_equal(f(d, 0, None, None, keepdims=False), r) assert_equal(f(d, 0, None, None, True), r.reshape((1,) + r.shape)) # multiple keywords assert_equal(f(d, axis=0, dtype=None, out=None, keepdims=False), r) assert_equal(f(d, 0, dtype=None, out=None, keepdims=False), r) assert_equal(f(d, 0, None, out=None, keepdims=False), r) # too little assert_raises(TypeError, f) # too much assert_raises(TypeError, f, d, 0, None, None, False, 1) # invalid axis assert_raises(TypeError, f, d, "invalid") assert_raises(TypeError, f, d, axis="invalid") assert_raises(TypeError, f, d, axis="invalid", dtype=None, keepdims=True) # invalid dtype assert_raises(TypeError, f, d, 0, "invalid") assert_raises(TypeError, f, d, dtype="invalid") assert_raises(TypeError, f, d, dtype="invalid", out=None) # invalid out assert_raises(TypeError, f, d, 0, None, "invalid") assert_raises(TypeError, f, d, out="invalid") assert_raises(TypeError, f, d, out="invalid", dtype=None) # keepdims boolean, no invalid value # assert_raises(TypeError, f, d, 0, None, None, "invalid") # assert_raises(TypeError, f, d, keepdims="invalid", axis=0, dtype=None) # invalid mix assert_raises(TypeError, f, d, 0, keepdims="invalid", dtype="invalid", out=None) # invalid keyord assert_raises(TypeError, f, d, axis=0, dtype=None, invalid=0) assert_raises(TypeError, f, d, invalid=0) assert_raises(TypeError, f, d, 0, keepdims=True, invalid="invalid", out=None) assert_raises(TypeError, f, d, axis=0, dtype=None, keepdims=True, out=None, invalid=0) assert_raises(TypeError, f, d, axis=0, dtype=None, out=None, invalid=0) def test_structured_equal(self): # https://github.com/numpy/numpy/issues/4855 class MyA(np.ndarray): def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): return getattr(ufunc, method)(*(input.view(np.ndarray) for input in inputs), **kwargs) a = np.arange(12.).reshape(4,3) ra = a.view(dtype=('f8,f8,f8')).squeeze() mra = ra.view(MyA) target = np.array([ True, False, False, False], dtype=bool) assert_equal(np.all(target == (mra == ra[0])), True) def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b) def test_reduce_noncontig_output(self): # Check that reduction deals with non-contiguous output arrays # appropriately. # # gh-8036 x = np.arange(7*13*8, dtype=np.int16).reshape(7, 13, 8) x = x[4:6,1:11:6,1:5].transpose(1, 2, 0) y_base = np.arange(4*4, dtype=np.int16).reshape(4, 4) y = y_base[::2,:] y_base_copy = y_base.copy() r0 = np.add.reduce(x, out=y.copy(), axis=2) r1 = np.add.reduce(x, out=y, axis=2) # The results should match, and y_base shouldn't get clobbered assert_equal(r0, r1) assert_equal(y_base[1,:], y_base_copy[1,:]) assert_equal(y_base[3,:], y_base_copy[3,:]) def test_no_doc_string(self): # gh-9337 assert_('\n' not in umt.inner1d_no_doc.__doc__) if __name__ == "__main__": run_module_suite()
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