from __future__ import division, absolute_import, print_function import warnings import itertools import pytest import numpy as np import numpy.core._umath_tests as umt import numpy.linalg._umath_linalg as uml import numpy.core._operand_flag_tests as opflag_tests import numpy.core._rational_tests as _rational_tests from numpy.testing import ( assert_, assert_equal, assert_raises, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_no_warnings, assert_allclose, ) from numpy.compat import pickle class TestUfuncKwargs(object): def test_kwarg_exact(self): assert_raises(TypeError, np.add, 1, 2, castingx='safe') assert_raises(TypeError, np.add, 1, 2, dtypex=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=int) assert_raises(RuntimeError, np.add, 1, 2, signature='ii->i', dtype=int) def test_extobj_refcount(self): # Should not segfault with USE_DEBUG. assert_raises(TypeError, np.add, 1, 2, extobj=[4096], parrot=True) class TestUfuncGenericLoops(object): """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. """ np_dtypes = [ (np.single, np.single), (np.single, np.double), (np.csingle, np.csingle), (np.csingle, np.cdouble), (np.double, np.double), (np.longdouble, np.longdouble), (np.cdouble, np.cdouble), (np.clongdouble, np.clongdouble)] @pytest.mark.parametrize('input_dtype,output_dtype', np_dtypes) def test_unary_PyUFunc(self, input_dtype, output_dtype, f=np.exp, x=0, y=1): xs = np.full(10, input_dtype(x), dtype=output_dtype) ys = f(xs)[::2] assert_allclose(ys, y) assert_equal(ys.dtype, output_dtype) def f2(x, y): return x**y @pytest.mark.parametrize('input_dtype,output_dtype', np_dtypes) def test_binary_PyUFunc(self, input_dtype, output_dtype, f=f2, x=0, y=1): xs = np.full(10, input_dtype(x), dtype=output_dtype) ys = f(xs, xs)[::2] assert_allclose(ys, y) assert_equal(ys.dtype, output_dtype) # 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) def test_unary_PyUFunc_O_O(self): x = np.ones(10, dtype=object) assert_(np.all(np.abs(x) == 1)) def test_unary_PyUFunc_O_O_method(self, foo=foo): x = np.full(10, foo(), dtype=object) assert_(np.all(np.conjugate(x) == True)) def test_binary_PyUFunc_OO_O(self): x = np.ones(10, dtype=object) assert_(np.all(np.add(x, x) == 2)) def test_binary_PyUFunc_OO_O_method(self, foo=foo): x = np.full(10, foo(), dtype=object) assert_(np.all(np.logical_xor(x, x))) def test_binary_PyUFunc_On_Om_method(self, foo=foo): x = np.full((10, 2, 3), foo(), dtype=object) assert_(np.all(np.logical_xor(x, x))) class TestUfunc(object): def test_pickle(self): for proto in range(2, pickle.HIGHEST_PROTOCOL + 1): assert_(pickle.loads(pickle.dumps(np.sin, protocol=proto)) is np.sin) # Check that ufunc not defined in the top level numpy namespace # such as numpy.core._rational_tests.test_add can also be pickled res = pickle.loads(pickle.dumps(_rational_tests.test_add, protocol=proto)) assert_(res is _rational_tests.test_add) def test_pickle_withstring(self): 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_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 # from include/numpy/ufuncobject.h size_inferred = 2 can_ignore = 4 def test_signature0(self): # the arguments to test_signature are: nin, nout, core_signature enabled, num_dims, ixs, flags, sizes = umt.test_signature( 2, 1, "(i),(i)->()") assert_equal(enabled, 1) assert_equal(num_dims, (1, 1, 0)) assert_equal(ixs, (0, 0)) assert_equal(flags, (self.size_inferred,)) assert_equal(sizes, (-1,)) def test_signature1(self): # empty core signature; treat as plain ufunc (with trivial core) enabled, num_dims, ixs, flags, sizes = umt.test_signature( 2, 1, "(),()->()") assert_equal(enabled, 0) assert_equal(num_dims, (0, 0, 0)) assert_equal(ixs, ()) assert_equal(flags, ()) assert_equal(sizes, ()) def test_signature2(self): # more complicated names for variables enabled, num_dims, ixs, flags, sizes = umt.test_signature( 2, 1, "(i1,i2),(J_1)->(_kAB)") assert_equal(enabled, 1) assert_equal(num_dims, (2, 1, 1)) assert_equal(ixs, (0, 1, 2, 3)) assert_equal(flags, (self.size_inferred,)*4) assert_equal(sizes, (-1, -1, -1, -1)) def test_signature3(self): enabled, num_dims, ixs, flags, sizes = umt.test_signature( 2, 1, u"(i1, i12), (J_1)->(i12, i2)") assert_equal(enabled, 1) assert_equal(num_dims, (2, 1, 2)) assert_equal(ixs, (0, 1, 2, 1, 3)) assert_equal(flags, (self.size_inferred,)*4) assert_equal(sizes, (-1, -1, -1, -1)) def test_signature4(self): # matrix_multiply signature from _umath_tests enabled, num_dims, ixs, flags, sizes = umt.test_signature( 2, 1, "(n,k),(k,m)->(n,m)") assert_equal(enabled, 1) assert_equal(num_dims, (2, 2, 2)) assert_equal(ixs, (0, 1, 1, 2, 0, 2)) assert_equal(flags, (self.size_inferred,)*3) assert_equal(sizes, (-1, -1, -1)) def test_signature5(self): # matmul signature from _umath_tests enabled, num_dims, ixs, flags, sizes = umt.test_signature( 2, 1, "(n?,k),(k,m?)->(n?,m?)") assert_equal(enabled, 1) assert_equal(num_dims, (2, 2, 2)) assert_equal(ixs, (0, 1, 1, 2, 0, 2)) assert_equal(flags, (self.size_inferred | self.can_ignore, self.size_inferred, self.size_inferred | self.can_ignore)) assert_equal(sizes, (-1, -1, -1)) def test_signature6(self): enabled, num_dims, ixs, flags, sizes = umt.test_signature( 1, 1, "(3)->()") assert_equal(enabled, 1) assert_equal(num_dims, (1, 0)) assert_equal(ixs, (0,)) assert_equal(flags, (0,)) assert_equal(sizes, (3,)) def test_signature7(self): enabled, num_dims, ixs, flags, sizes = umt.test_signature( 3, 1, "(3),(03,3),(n)->(9)") assert_equal(enabled, 1) assert_equal(num_dims, (1, 2, 1, 1)) assert_equal(ixs, (0, 0, 0, 1, 2)) assert_equal(flags, (0, self.size_inferred, 0)) assert_equal(sizes, (3, -1, 9)) def test_signature8(self): enabled, num_dims, ixs, flags, sizes = umt.test_signature( 3, 1, "(3?),(3?,3?),(n)->(9)") assert_equal(enabled, 1) assert_equal(num_dims, (1, 2, 1, 1)) assert_equal(ixs, (0, 0, 0, 1, 2)) assert_equal(flags, (self.can_ignore, self.size_inferred, 0)) assert_equal(sizes, (3, -1, 9)) def test_signature_failure_extra_parenthesis(self): with assert_raises(ValueError): umt.test_signature(2, 1, "((i)),(i)->()") def test_signature_failure_mismatching_parenthesis(self): with assert_raises(ValueError): umt.test_signature(2, 1, "(i),)i(->()") def test_signature_failure_signature_missing_input_arg(self): with assert_raises(ValueError): umt.test_signature(2, 1, "(i),->()") def test_signature_failure_signature_missing_output_arg(self): with assert_raises(ValueError): umt.test_signature(2, 2, "(i),(i)->()") 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 (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) # warning if sum overflows, which it does in float16 overflow = not np.isfinite(tgt) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") assert_almost_equal(np.sum(d), tgt) assert_equal(len(w), 1 * overflow) assert_almost_equal(np.sum(d[::-1]), tgt) assert_equal(len(w), 2 * overflow) 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_sum_initial(self): # Integer, single axis assert_equal(np.sum([3], initial=2), 5) # Floating point assert_almost_equal(np.sum([0.2], initial=0.1), 0.3) # Multiple non-adjacent axes assert_equal(np.sum(np.ones((2, 3, 5), dtype=np.int64), axis=(0, 2), initial=2), [12, 12, 12]) def test_sum_where(self): # More extensive tests done in test_reduction_with_where. assert_equal(np.sum([[1., 2.], [3., 4.]], where=[True, False]), 4.) assert_equal(np.sum([[1., 2.], [3., 4.]], axis=0, initial=5., where=[True, False]), [9., 5.]) 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) # Writing to a broadcasted array with overlap should warn, gh-2705 a = np.arange(2) b = np.arange(4).reshape((2, 2)) u, v = np.broadcast_arrays(a, b) assert_equal(u.strides[0], 0) x = u + v with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") u += v assert_equal(len(w), 1) assert_(x[0,0] != u[0, 0]) 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='()' inner1d = umt.inner1d a = np.arange(27.).reshape((3, 3, 3)) b = np.arange(10., 19.).reshape((3, 1, 3)) # basic tests on inputs (outputs tested below with matrix_multiply). c = inner1d(a, b) assert_array_equal(c, (a * b).sum(-1)) # default c = inner1d(a, b, axes=[(-1,), (-1,), ()]) assert_array_equal(c, (a * b).sum(-1)) # integers ok for single axis. c = inner1d(a, b, axes=[-1, -1, ()]) assert_array_equal(c, (a * b).sum(-1)) # mix fine c = inner1d(a, b, axes=[(-1,), -1, ()]) assert_array_equal(c, (a * b).sum(-1)) # can omit last axis. c = inner1d(a, b, axes=[-1, -1]) assert_array_equal(c, (a * b).sum(-1)) # can pass in other types of integer (with __index__ protocol) c = inner1d(a, b, axes=[np.int8(-1), np.array(-1, dtype=np.int32)]) assert_array_equal(c, (a * b).sum(-1)) # swap some axes c = inner1d(a, b, axes=[0, 0]) assert_array_equal(c, (a * b).sum(0)) c = inner1d(a, b, axes=[0, 2]) assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1)) # Check errors for improperly constructed axes arguments. # should have list. assert_raises(TypeError, inner1d, a, b, axes=-1) # needs enough elements assert_raises(ValueError, inner1d, a, b, axes=[-1]) # should pass in indices. assert_raises(TypeError, inner1d, a, b, axes=[-1.0, -1.0]) assert_raises(TypeError, inner1d, a, b, axes=[(-1.0,), -1]) assert_raises(TypeError, inner1d, a, b, axes=[None, 1]) # cannot pass an index unless there is only one dimension # (output is wrong in this case) assert_raises(TypeError, inner1d, a, b, axes=[-1, -1, -1]) # or pass in generally the wrong number of axes assert_raises(ValueError, inner1d, a, b, axes=[-1, -1, (-1,)]) assert_raises(ValueError, inner1d, a, b, axes=[-1, (-2, -1), ()]) # axes need to have same length. assert_raises(ValueError, inner1d, a, b, axes=[0, 1]) # matrix_multiply signature: '(m,n),(n,p)->(m,p)' mm = umt.matrix_multiply a = np.arange(12).reshape((2, 3, 2)) b = np.arange(8).reshape((2, 2, 2, 1)) + 1 # Sanity check. c = mm(a, b) assert_array_equal(c, np.matmul(a, b)) # Default axes. c = mm(a, b, axes=[(-2, -1), (-2, -1), (-2, -1)]) assert_array_equal(c, np.matmul(a, b)) # Default with explicit axes. c = mm(a, b, axes=[(1, 2), (2, 3), (2, 3)]) assert_array_equal(c, np.matmul(a, b)) # swap some axes. c = mm(a, b, axes=[(0, -1), (1, 2), (-2, -1)]) assert_array_equal(c, np.matmul(a.transpose(1, 0, 2), b.transpose(0, 3, 1, 2))) # Default with output array. c = np.empty((2, 2, 3, 1)) d = mm(a, b, out=c, axes=[(1, 2), (2, 3), (2, 3)]) assert_(c is d) assert_array_equal(c, np.matmul(a, b)) # Transposed output array c = np.empty((1, 2, 2, 3)) d = mm(a, b, out=c, axes=[(-2, -1), (-2, -1), (3, 0)]) assert_(c is d) assert_array_equal(c, np.matmul(a, b).transpose(3, 0, 1, 2)) # Check errors for improperly constructed axes arguments. # wrong argument assert_raises(TypeError, mm, a, b, axis=1) # axes should be list assert_raises(TypeError, mm, a, b, axes=1) assert_raises(TypeError, mm, a, b, axes=((-2, -1), (-2, -1), (-2, -1))) # list needs to have right length assert_raises(ValueError, mm, a, b, axes=[]) assert_raises(ValueError, mm, a, b, axes=[(-2, -1)]) # list should contain tuples for multiple axes assert_raises(TypeError, mm, a, b, axes=[-1, -1, -1]) assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), -1]) assert_raises(TypeError, mm, a, b, axes=[[-2, -1], [-2, -1], [-2, -1]]) assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), [-2, -1]]) assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), None]) # tuples should not have duplicated values assert_raises(ValueError, mm, a, b, axes=[(-2, -1), (-2, -1), (-2, -2)]) # arrays should have enough axes. z = np.zeros((2, 2)) assert_raises(ValueError, mm, z, z[0]) assert_raises(ValueError, mm, z, z, out=z[:, 0]) assert_raises(ValueError, mm, z[1], z, axes=[0, 1]) assert_raises(ValueError, mm, z, z, out=z[0], axes=[0, 1]) # Regular ufuncs should not accept axes. assert_raises(TypeError, np.add, 1., 1., axes=[0]) # should be able to deal with bad unrelated kwargs. assert_raises(TypeError, mm, z, z, axes=[0, 1], parrot=True) def test_axis_argument(self): # inner1d signature: '(i),(i)->()' inner1d = umt.inner1d a = np.arange(27.).reshape((3, 3, 3)) b = np.arange(10., 19.).reshape((3, 1, 3)) c = inner1d(a, b) assert_array_equal(c, (a * b).sum(-1)) c = inner1d(a, b, axis=-1) assert_array_equal(c, (a * b).sum(-1)) out = np.zeros_like(c) d = inner1d(a, b, axis=-1, out=out) assert_(d is out) assert_array_equal(d, c) c = inner1d(a, b, axis=0) assert_array_equal(c, (a * b).sum(0)) # Sanity checks on innerwt and cumsum. 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, axis=0), np.sum(a * b * w, axis=0)) assert_array_equal(umt.cumsum(a, axis=0), np.cumsum(a, axis=0)) assert_array_equal(umt.cumsum(a, axis=-1), np.cumsum(a, axis=-1)) out = np.empty_like(a) b = umt.cumsum(a, out=out, axis=0) assert_(out is b) assert_array_equal(b, np.cumsum(a, axis=0)) b = umt.cumsum(a, out=out, axis=1) assert_(out is b) assert_array_equal(b, np.cumsum(a, axis=-1)) # Check errors. # Cannot pass in both axis and axes. assert_raises(TypeError, inner1d, a, b, axis=0, axes=[0, 0]) # Not an integer. assert_raises(TypeError, inner1d, a, b, axis=[0]) # more than 1 core dimensions. mm = umt.matrix_multiply assert_raises(TypeError, mm, a, b, axis=1) # Output wrong size in axis. out = np.empty((1, 2, 3), dtype=a.dtype) assert_raises(ValueError, umt.cumsum, a, out=out, axis=0) # Regular ufuncs should not accept axis. assert_raises(TypeError, np.add, 1., 1., axis=0) def test_keepdims_argument(self): # inner1d signature: '(i),(i)->()' inner1d = umt.inner1d a = np.arange(27.).reshape((3, 3, 3)) b = np.arange(10., 19.).reshape((3, 1, 3)) c = inner1d(a, b) assert_array_equal(c, (a * b).sum(-1)) c = inner1d(a, b, keepdims=False) assert_array_equal(c, (a * b).sum(-1)) c = inner1d(a, b, keepdims=True) assert_array_equal(c, (a * b).sum(-1, keepdims=True)) out = np.zeros_like(c) d = inner1d(a, b, keepdims=True, out=out) assert_(d is out) assert_array_equal(d, c) # Now combined with axis and axes. c = inner1d(a, b, axis=-1, keepdims=False) assert_array_equal(c, (a * b).sum(-1, keepdims=False)) c = inner1d(a, b, axis=-1, keepdims=True) assert_array_equal(c, (a * b).sum(-1, keepdims=True)) c = inner1d(a, b, axis=0, keepdims=False) assert_array_equal(c, (a * b).sum(0, keepdims=False)) c = inner1d(a, b, axis=0, keepdims=True) assert_array_equal(c, (a * b).sum(0, keepdims=True)) c = inner1d(a, b, axes=[(-1,), (-1,), ()], keepdims=False) assert_array_equal(c, (a * b).sum(-1)) c = inner1d(a, b, axes=[(-1,), (-1,), (-1,)], keepdims=True) assert_array_equal(c, (a * b).sum(-1, keepdims=True)) c = inner1d(a, b, axes=[0, 0], keepdims=False) assert_array_equal(c, (a * b).sum(0)) c = inner1d(a, b, axes=[0, 0, 0], keepdims=True) assert_array_equal(c, (a * b).sum(0, keepdims=True)) c = inner1d(a, b, axes=[0, 2], keepdims=False) assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1)) c = inner1d(a, b, axes=[0, 2], keepdims=True) assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1, keepdims=True)) c = inner1d(a, b, axes=[0, 2, 2], keepdims=True) assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1, keepdims=True)) c = inner1d(a, b, axes=[0, 2, 0], keepdims=True) assert_array_equal(c, (a * b.transpose(2, 0, 1)).sum(0, keepdims=True)) # Hardly useful, but should work. c = inner1d(a, b, axes=[0, 2, 1], keepdims=True) assert_array_equal(c, (a.transpose(1, 0, 2) * b.transpose(0, 2, 1)) .sum(1, keepdims=True)) # Check with two core dimensions. a = np.eye(3) * np.arange(4.)[:, np.newaxis, np.newaxis] expected = uml.det(a) c = uml.det(a, keepdims=False) assert_array_equal(c, expected) c = uml.det(a, keepdims=True) assert_array_equal(c, expected[:, np.newaxis, np.newaxis]) a = np.eye(3) * np.arange(4.)[:, np.newaxis, np.newaxis] expected_s, expected_l = uml.slogdet(a) cs, cl = uml.slogdet(a, keepdims=False) assert_array_equal(cs, expected_s) assert_array_equal(cl, expected_l) cs, cl = uml.slogdet(a, keepdims=True) assert_array_equal(cs, expected_s[:, np.newaxis, np.newaxis]) assert_array_equal(cl, expected_l[:, np.newaxis, np.newaxis]) # Sanity check on innerwt. 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, keepdims=True), np.sum(a * b * w, axis=-1, keepdims=True)) assert_array_equal(umt.innerwt(a, b, w, axis=0, keepdims=True), np.sum(a * b * w, axis=0, keepdims=True)) # Check errors. # Not a boolean assert_raises(TypeError, inner1d, a, b, keepdims='true') # More than 1 core dimension, and core output dimensions. mm = umt.matrix_multiply assert_raises(TypeError, mm, a, b, keepdims=True) assert_raises(TypeError, mm, a, b, keepdims=False) # Regular ufuncs should not accept keepdims. assert_raises(TypeError, np.add, 1., 1., keepdims=False) 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_cross1d(self): """Test with fixed-sized signature.""" a = np.eye(3) assert_array_equal(umt.cross1d(a, a), np.zeros((3, 3))) out = np.zeros((3, 3)) result = umt.cross1d(a[0], a, out) assert_(result is out) assert_array_equal(result, np.vstack((np.zeros(3), a[2], -a[1]))) assert_raises(ValueError, umt.cross1d, np.eye(4), np.eye(4)) assert_raises(ValueError, umt.cross1d, a, np.arange(4.)) assert_raises(ValueError, umt.cross1d, a, np.arange(3.), np.zeros((3, 4))) def test_can_ignore_signature(self): # Comparing the effects of ? in signature: # matrix_multiply: (m,n),(n,p)->(m,p) # all must be there. # matmul: (m?,n),(n,p?)->(m?,p?) # allow missing m, p. mat = np.arange(12).reshape((2, 3, 2)) single_vec = np.arange(2) col_vec = single_vec[:, np.newaxis] col_vec_array = np.arange(8).reshape((2, 2, 2, 1)) + 1 # matrix @ single column vector with proper dimension mm_col_vec = umt.matrix_multiply(mat, col_vec) # matmul does the same thing matmul_col_vec = umt.matmul(mat, col_vec) assert_array_equal(matmul_col_vec, mm_col_vec) # matrix @ vector without dimension making it a column vector. # matrix multiply fails -> missing core dim. assert_raises(ValueError, umt.matrix_multiply, mat, single_vec) # matmul mimicker passes, and returns a vector. matmul_col = umt.matmul(mat, single_vec) assert_array_equal(matmul_col, mm_col_vec.squeeze()) # Now with a column array: same as for column vector, # broadcasting sensibly. mm_col_vec = umt.matrix_multiply(mat, col_vec_array) matmul_col_vec = umt.matmul(mat, col_vec_array) assert_array_equal(matmul_col_vec, mm_col_vec) # As above, but for row vector single_vec = np.arange(3) row_vec = single_vec[np.newaxis, :] row_vec_array = np.arange(24).reshape((4, 2, 1, 1, 3)) + 1 # row vector @ matrix mm_row_vec = umt.matrix_multiply(row_vec, mat) matmul_row_vec = umt.matmul(row_vec, mat) assert_array_equal(matmul_row_vec, mm_row_vec) # single row vector @ matrix assert_raises(ValueError, umt.matrix_multiply, single_vec, mat) matmul_row = umt.matmul(single_vec, mat) assert_array_equal(matmul_row, mm_row_vec.squeeze()) # row vector array @ matrix mm_row_vec = umt.matrix_multiply(row_vec_array, mat) matmul_row_vec = umt.matmul(row_vec_array, mat) assert_array_equal(matmul_row_vec, mm_row_vec) # Now for vector combinations # row vector @ column vector col_vec = row_vec.T col_vec_array = row_vec_array.swapaxes(-2, -1) mm_row_col_vec = umt.matrix_multiply(row_vec, col_vec) matmul_row_col_vec = umt.matmul(row_vec, col_vec) assert_array_equal(matmul_row_col_vec, mm_row_col_vec) # single row vector @ single col vector assert_raises(ValueError, umt.matrix_multiply, single_vec, single_vec) matmul_row_col = umt.matmul(single_vec, single_vec) assert_array_equal(matmul_row_col, mm_row_col_vec.squeeze()) # row vector array @ matrix mm_row_col_array = umt.matrix_multiply(row_vec_array, col_vec_array) matmul_row_col_array = umt.matmul(row_vec_array, col_vec_array) assert_array_equal(matmul_row_col_array, mm_row_col_array) # Finally, check that things are *not* squeezed if one gives an # output. out = np.zeros_like(mm_row_col_array) out = umt.matrix_multiply(row_vec_array, col_vec_array, out=out) assert_array_equal(out, mm_row_col_array) out[:] = 0 out = umt.matmul(row_vec_array, col_vec_array, out=out) assert_array_equal(out, mm_row_col_array) # And check one cannot put missing dimensions back. out = np.zeros_like(mm_row_col_vec) assert_raises(ValueError, umt.matrix_multiply, single_vec, single_vec, out) # But fine for matmul, since it is just a broadcast. out = umt.matmul(single_vec, single_vec, out) assert_array_equal(out, mm_row_col_vec.squeeze()) 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=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_cumsum(self): a = np.arange(10) result = umt.cumsum(a) assert_array_equal(result, a.cumsum()) 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_comparison(self): class HasComparisons(object): def __eq__(self, other): return '==' arr0d = np.array(HasComparisons()) assert_equal(arr0d == arr0d, True) assert_equal(np.equal(arr0d, arr0d), True) # normal behavior is a cast assert_equal(np.equal(arr0d, arr0d, dtype=object), '==') arr1d = np.array([HasComparisons()]) assert_equal(arr1d == arr1d, np.array([True])) assert_equal(np.equal(arr1d, arr1d), np.array([True])) # normal behavior is a cast assert_equal(np.equal(arr1d, arr1d, dtype=object), np.array(['=='])) 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]) assert_equal(np.array([1], dtype=object).sum(initial=1), 2) assert_equal(np.array([[1], [2, 3]], dtype=object) .sum(initial=[0], where=[False, True]), [0, 2, 3]) 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_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 an 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_initial_reduction(self): # np.minimum.reduce is an identityless reduction # For cases like np.maximum(np.abs(...), initial=0) # More generally, a supremum over non-negative numbers. assert_equal(np.maximum.reduce([], initial=0), 0) # For cases like reduction of an empty array over the reals. assert_equal(np.minimum.reduce([], initial=np.inf), np.inf) assert_equal(np.maximum.reduce([], initial=-np.inf), -np.inf) # Random tests assert_equal(np.minimum.reduce([5], initial=4), 4) assert_equal(np.maximum.reduce([4], initial=5), 5) assert_equal(np.maximum.reduce([5], initial=4), 5) assert_equal(np.minimum.reduce([4], initial=5), 4) # Check initial=None raises ValueError for both types of ufunc reductions assert_raises(ValueError, np.minimum.reduce, [], initial=None) assert_raises(ValueError, np.add.reduce, [], initial=None) # Check that np._NoValue gives default behavior. assert_equal(np.add.reduce([], initial=np._NoValue), 0) # Check that initial kwarg behaves as intended for dtype=object a = np.array([10], dtype=object) res = np.add.reduce(a, initial=5) assert_equal(res, 15) @pytest.mark.parametrize('axis', (0, 1, None)) @pytest.mark.parametrize('where', (np.array([False, True, True]), np.array([[True], [False], [True]]), np.array([[True, False, False], [False, True, False], [False, True, True]]))) def test_reduction_with_where(self, axis, where): a = np.arange(9.).reshape(3, 3) a_copy = a.copy() a_check = np.zeros_like(a) np.positive(a, out=a_check, where=where) res = np.add.reduce(a, axis=axis, where=where) check = a_check.sum(axis) assert_equal(res, check) # Check we do not overwrite elements of a internally. assert_array_equal(a, a_copy) @pytest.mark.parametrize(('axis', 'where'), ((0, np.array([True, False, True])), (1, [True, True, False]), (None, True))) @pytest.mark.parametrize('initial', (-np.inf, 5.)) def test_reduction_with_where_and_initial(self, axis, where, initial): a = np.arange(9.).reshape(3, 3) a_copy = a.copy() a_check = np.full(a.shape, -np.inf) np.positive(a, out=a_check, where=where) res = np.maximum.reduce(a, axis=axis, where=where, initial=initial) check = a_check.max(axis, initial=initial) assert_equal(res, check) def test_reduction_where_initial_needed(self): a = np.arange(9.).reshape(3, 3) m = [False, True, False] assert_raises(ValueError, np.maximum.reduce, a, where=m) 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_tests.rational) # Output must be specified so numpy knows what # ufunc signature to look for result = _rational_tests.test_add(a, b, c) target = np.array([0, 2, 4], dtype=_rational_tests.rational) assert_equal(result, target) # no output type should raise TypeError with assert_raises(TypeError): _rational_tests.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_tests 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')) assert_raises(RuntimeError, struct_ufunc.register_fail) def test_custom_ufunc(self): a = np.array( [_rational_tests.rational(1, 2), _rational_tests.rational(1, 3), _rational_tests.rational(1, 4)], dtype=_rational_tests.rational) b = np.array( [_rational_tests.rational(1, 2), _rational_tests.rational(1, 3), _rational_tests.rational(1, 4)], dtype=_rational_tests.rational) result = _rational_tests.test_add_rationals(a, b) expected = np.array( [_rational_tests.rational(1), _rational_tests.rational(2, 3), _rational_tests.rational(1, 2)], dtype=_rational_tests.rational) assert_equal(result, expected) def test_custom_ufunc_forced_sig(self): # gh-9351 - looking for a non-first userloop would previously hang with assert_raises(TypeError): np.multiply(_rational_tests.rational(1), 1, signature=(_rational_tests.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=object) assert_raises(TypeError, np.add.at, values, [0, 1], 1) assert_array_equal(values, np.array(['a', 1], dtype=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)) assert_equal(f(d, 0, None, None, False, 0), r) assert_equal(f(d, 0, None, None, False, initial=0), r) assert_equal(f(d, 0, None, None, False, 0, True), r) assert_equal(f(d, 0, None, None, False, 0, where=True), r) # 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) assert_equal(f(d, 0, None, out=None, keepdims=False, initial=0, where=True), r) # too little assert_raises(TypeError, f) # too much assert_raises(TypeError, f, d, 0, None, None, False, 0, True, 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_scalar_equal(self): # Scalar comparisons should always work, without deprecation warnings. # even when the ufunc fails. a = np.array(0.) b = np.array('a') assert_(a != b) assert_(b != a) assert_(not (a == b)) assert_(not (b == a)) 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, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a) 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__) def test_invalid_args(self): # gh-7961 exc = pytest.raises(TypeError, np.sqrt, None) # minimally check the exception text assert exc.match('loop of ufunc does not support') @pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')]) def test_nat_is_not_finite(self, nat): try: assert not np.isfinite(nat) except TypeError: pass # ok, just not implemented @pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')]) def test_nat_is_nan(self, nat): try: assert np.isnan(nat) except TypeError: pass # ok, just not implemented @pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')]) def test_nat_is_not_inf(self, nat): try: assert not np.isinf(nat) except TypeError: pass # ok, just not implemented @pytest.mark.parametrize('ufunc', [getattr(np, x) for x in dir(np) if isinstance(getattr(np, x), np.ufunc)]) def test_ufunc_types(ufunc): ''' Check all ufuncs that the correct type is returned. Avoid object and boolean types since many operations are not defined for for them. Choose the shape so even dot and matmul will succeed ''' for typ in ufunc.types: # types is a list of strings like ii->i if 'O' in typ or '?' in typ: continue inp, out = typ.split('->') args = [np.ones((3, 3), t) for t in inp] with warnings.catch_warnings(record=True): warnings.filterwarnings("always") res = ufunc(*args) if isinstance(res, tuple): outs = tuple(out) assert len(res) == len(outs) for r, t in zip(res, outs): assert r.dtype == np.dtype(t) else: assert res.dtype == np.dtype(out) @pytest.mark.parametrize('ufunc', [getattr(np, x) for x in dir(np) if isinstance(getattr(np, x), np.ufunc)]) def test_ufunc_noncontiguous(ufunc): ''' Check that contiguous and non-contiguous calls to ufuncs have the same results for values in range(9) ''' for typ in ufunc.types: # types is a list of strings like ii->i if any(set('O?mM') & set(typ)): # bool, object, datetime are too irregular for this simple test continue inp, out = typ.split('->') args_c = [np.empty(6, t) for t in inp] args_n = [np.empty(18, t)[::3] for t in inp] for a in args_c: a.flat = range(1,7) for a in args_n: a.flat = range(1,7) with warnings.catch_warnings(record=True): warnings.filterwarnings("always") res_c = ufunc(*args_c) res_n = ufunc(*args_n) if len(out) == 1: res_c = (res_c,) res_n = (res_n,) for c_ar, n_ar in zip(res_c, res_n): dt = c_ar.dtype if np.issubdtype(dt, np.floating): # for floating point results allow a small fuss in comparisons # since different algorithms (libm vs. intrinsics) can be used # for different input strides res_eps = np.finfo(dt).eps tol = 2*res_eps assert_allclose(res_c, res_n, atol=tol, rtol=tol) else: assert_equal(c_ar, n_ar)