name : test_umath_complex.py import sys import platform import pytest import numpy as np # import the c-extension module directly since _arg is not exported via umath import numpy.core._multiarray_umath as ncu from numpy.testing import ( assert_raises, assert_equal, assert_array_equal, assert_almost_equal, assert_array_max_ulp ) # TODO: branch cuts (use Pauli code) # TODO: conj 'symmetry' # TODO: FPU exceptions # At least on Windows the results of many complex functions are not conforming # to the C99 standard. See ticket 1574. # Ditto for Solaris (ticket 1642) and OS X on PowerPC. #FIXME: this will probably change when we require full C99 campatibility with np.errstate(all='ignore'): functions_seem_flaky = ((np.exp(complex(np.inf, 0)).imag != 0) or (np.log(complex(np.NZERO, 0)).imag != np.pi)) # TODO: replace with a check on whether platform-provided C99 funcs are used xfail_complex_tests = (not sys.platform.startswith('linux') or functions_seem_flaky) # TODO This can be xfail when the generator functions are got rid of. platform_skip = pytest.mark.skipif(xfail_complex_tests, reason="Inadequate C99 complex support") class TestCexp: def test_simple(self): check = check_complex_value f = np.exp check(f, 1, 0, np.exp(1), 0, False) check(f, 0, 1, np.cos(1), np.sin(1), False) ref = np.exp(1) * complex(np.cos(1), np.sin(1)) check(f, 1, 1, ref.real, ref.imag, False) @platform_skip def test_special_values(self): # C99: Section G 6.3.1 check = check_complex_value f = np.exp # cexp(+-0 + 0i) is 1 + 0i check(f, np.PZERO, 0, 1, 0, False) check(f, np.NZERO, 0, 1, 0, False) # cexp(x + infi) is nan + nani for finite x and raises 'invalid' FPU # exception check(f, 1, np.inf, np.nan, np.nan) check(f, -1, np.inf, np.nan, np.nan) check(f, 0, np.inf, np.nan, np.nan) # cexp(inf + 0i) is inf + 0i check(f, np.inf, 0, np.inf, 0) # cexp(-inf + yi) is +0 * (cos(y) + i sin(y)) for finite y check(f, -np.inf, 1, np.PZERO, np.PZERO) check(f, -np.inf, 0.75 * np.pi, np.NZERO, np.PZERO) # cexp(inf + yi) is +inf * (cos(y) + i sin(y)) for finite y check(f, np.inf, 1, np.inf, np.inf) check(f, np.inf, 0.75 * np.pi, -np.inf, np.inf) # cexp(-inf + inf i) is +-0 +- 0i (signs unspecified) def _check_ninf_inf(dummy): msgform = "cexp(-inf, inf) is (%f, %f), expected (+-0, +-0)" with np.errstate(invalid='ignore'): z = f(np.array(complex(-np.inf, np.inf))) if z.real != 0 or z.imag != 0: raise AssertionError(msgform % (z.real, z.imag)) _check_ninf_inf(None) # cexp(inf + inf i) is +-inf + NaNi and raised invalid FPU ex. def _check_inf_inf(dummy): msgform = "cexp(inf, inf) is (%f, %f), expected (+-inf, nan)" with np.errstate(invalid='ignore'): z = f(np.array(complex(np.inf, np.inf))) if not np.isinf(z.real) or not np.isnan(z.imag): raise AssertionError(msgform % (z.real, z.imag)) _check_inf_inf(None) # cexp(-inf + nan i) is +-0 +- 0i def _check_ninf_nan(dummy): msgform = "cexp(-inf, nan) is (%f, %f), expected (+-0, +-0)" with np.errstate(invalid='ignore'): z = f(np.array(complex(-np.inf, np.nan))) if z.real != 0 or z.imag != 0: raise AssertionError(msgform % (z.real, z.imag)) _check_ninf_nan(None) # cexp(inf + nan i) is +-inf + nan def _check_inf_nan(dummy): msgform = "cexp(-inf, nan) is (%f, %f), expected (+-inf, nan)" with np.errstate(invalid='ignore'): z = f(np.array(complex(np.inf, np.nan))) if not np.isinf(z.real) or not np.isnan(z.imag): raise AssertionError(msgform % (z.real, z.imag)) _check_inf_nan(None) # cexp(nan + yi) is nan + nani for y != 0 (optional: raises invalid FPU # ex) check(f, np.nan, 1, np.nan, np.nan) check(f, np.nan, -1, np.nan, np.nan) check(f, np.nan, np.inf, np.nan, np.nan) check(f, np.nan, -np.inf, np.nan, np.nan) # cexp(nan + nani) is nan + nani check(f, np.nan, np.nan, np.nan, np.nan) # TODO This can be xfail when the generator functions are got rid of. @pytest.mark.skip(reason="cexp(nan + 0I) is wrong on most platforms") def test_special_values2(self): # XXX: most implementations get it wrong here (including glibc <= 2.10) # cexp(nan + 0i) is nan + 0i check = check_complex_value f = np.exp check(f, np.nan, 0, np.nan, 0) class TestClog: def test_simple(self): x = np.array([1+0j, 1+2j]) y_r = np.log(np.abs(x)) + 1j * np.angle(x) y = np.log(x) assert_almost_equal(y, y_r) @platform_skip @pytest.mark.skipif(platform.machine() == "armv5tel", reason="See gh-413.") def test_special_values(self): xl = [] yl = [] # From C99 std (Sec 6.3.2) # XXX: check exceptions raised # --- raise for invalid fails. # clog(-0 + i0) returns -inf + i pi and raises the 'divide-by-zero' # floating-point exception. with np.errstate(divide='raise'): x = np.array([np.NZERO], dtype=complex) y = complex(-np.inf, np.pi) assert_raises(FloatingPointError, np.log, x) with np.errstate(divide='ignore'): assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(+0 + i0) returns -inf + i0 and raises the 'divide-by-zero' # floating-point exception. with np.errstate(divide='raise'): x = np.array([0], dtype=complex) y = complex(-np.inf, 0) assert_raises(FloatingPointError, np.log, x) with np.errstate(divide='ignore'): assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(x + i inf returns +inf + i pi /2, for finite x. x = np.array([complex(1, np.inf)], dtype=complex) y = complex(np.inf, 0.5 * np.pi) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) x = np.array([complex(-1, np.inf)], dtype=complex) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(x + iNaN) returns NaN + iNaN and optionally raises the # 'invalid' floating- point exception, for finite x. with np.errstate(invalid='raise'): x = np.array([complex(1., np.nan)], dtype=complex) y = complex(np.nan, np.nan) #assert_raises(FloatingPointError, np.log, x) with np.errstate(invalid='ignore'): assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) with np.errstate(invalid='raise'): x = np.array([np.inf + 1j * np.nan], dtype=complex) #assert_raises(FloatingPointError, np.log, x) with np.errstate(invalid='ignore'): assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(- inf + iy) returns +inf + ipi , for finite positive-signed y. x = np.array([-np.inf + 1j], dtype=complex) y = complex(np.inf, np.pi) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(+ inf + iy) returns +inf + i0, for finite positive-signed y. x = np.array([np.inf + 1j], dtype=complex) y = complex(np.inf, 0) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(- inf + i inf) returns +inf + i3pi /4. x = np.array([complex(-np.inf, np.inf)], dtype=complex) y = complex(np.inf, 0.75 * np.pi) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(+ inf + i inf) returns +inf + ipi /4. x = np.array([complex(np.inf, np.inf)], dtype=complex) y = complex(np.inf, 0.25 * np.pi) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(+/- inf + iNaN) returns +inf + iNaN. x = np.array([complex(np.inf, np.nan)], dtype=complex) y = complex(np.inf, np.nan) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) x = np.array([complex(-np.inf, np.nan)], dtype=complex) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(NaN + iy) returns NaN + iNaN and optionally raises the # 'invalid' floating-point exception, for finite y. x = np.array([complex(np.nan, 1)], dtype=complex) y = complex(np.nan, np.nan) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(NaN + i inf) returns +inf + iNaN. x = np.array([complex(np.nan, np.inf)], dtype=complex) y = complex(np.inf, np.nan) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(NaN + iNaN) returns NaN + iNaN. x = np.array([complex(np.nan, np.nan)], dtype=complex) y = complex(np.nan, np.nan) assert_almost_equal(np.log(x), y) xl.append(x) yl.append(y) # clog(conj(z)) = conj(clog(z)). xa = np.array(xl, dtype=complex) ya = np.array(yl, dtype=complex) with np.errstate(divide='ignore'): for i in range(len(xa)): assert_almost_equal(np.log(xa[i].conj()), ya[i].conj()) class TestCsqrt: def test_simple(self): # sqrt(1) check_complex_value(np.sqrt, 1, 0, 1, 0) # sqrt(1i) rres = 0.5*np.sqrt(2) ires = rres check_complex_value(np.sqrt, 0, 1, rres, ires, False) # sqrt(-1) check_complex_value(np.sqrt, -1, 0, 0, 1) def test_simple_conjugate(self): ref = np.conj(np.sqrt(complex(1, 1))) def f(z): return np.sqrt(np.conj(z)) check_complex_value(f, 1, 1, ref.real, ref.imag, False) #def test_branch_cut(self): # _check_branch_cut(f, -1, 0, 1, -1) @platform_skip def test_special_values(self): # C99: Sec G 6.4.2 check = check_complex_value f = np.sqrt # csqrt(+-0 + 0i) is 0 + 0i check(f, np.PZERO, 0, 0, 0) check(f, np.NZERO, 0, 0, 0) # csqrt(x + infi) is inf + infi for any x (including NaN) check(f, 1, np.inf, np.inf, np.inf) check(f, -1, np.inf, np.inf, np.inf) check(f, np.PZERO, np.inf, np.inf, np.inf) check(f, np.NZERO, np.inf, np.inf, np.inf) check(f, np.inf, np.inf, np.inf, np.inf) check(f, -np.inf, np.inf, np.inf, np.inf) check(f, -np.nan, np.inf, np.inf, np.inf) # csqrt(x + nani) is nan + nani for any finite x check(f, 1, np.nan, np.nan, np.nan) check(f, -1, np.nan, np.nan, np.nan) check(f, 0, np.nan, np.nan, np.nan) # csqrt(-inf + yi) is +0 + infi for any finite y > 0 check(f, -np.inf, 1, np.PZERO, np.inf) # csqrt(inf + yi) is +inf + 0i for any finite y > 0 check(f, np.inf, 1, np.inf, np.PZERO) # csqrt(-inf + nani) is nan +- infi (both +i infi are valid) def _check_ninf_nan(dummy): msgform = "csqrt(-inf, nan) is (%f, %f), expected (nan, +-inf)" z = np.sqrt(np.array(complex(-np.inf, np.nan))) #Fixme: ugly workaround for isinf bug. with np.errstate(invalid='ignore'): if not (np.isnan(z.real) and np.isinf(z.imag)): raise AssertionError(msgform % (z.real, z.imag)) _check_ninf_nan(None) # csqrt(+inf + nani) is inf + nani check(f, np.inf, np.nan, np.inf, np.nan) # csqrt(nan + yi) is nan + nani for any finite y (infinite handled in x # + nani) check(f, np.nan, 0, np.nan, np.nan) check(f, np.nan, 1, np.nan, np.nan) check(f, np.nan, np.nan, np.nan, np.nan) # XXX: check for conj(csqrt(z)) == csqrt(conj(z)) (need to fix branch # cuts first) class TestCpow: def setup_method(self): self.olderr = np.seterr(invalid='ignore') def teardown_method(self): np.seterr(**self.olderr) def test_simple(self): x = np.array([1+1j, 0+2j, 1+2j, np.inf, np.nan]) y_r = x ** 2 y = np.power(x, 2) assert_almost_equal(y, y_r) def test_scalar(self): x = np.array([1, 1j, 2, 2.5+.37j, np.inf, np.nan]) y = np.array([1, 1j, -0.5+1.5j, -0.5+1.5j, 2, 3]) lx = list(range(len(x))) # Hardcode the expected `builtins.complex` values, # as complex exponentiation is broken as of bpo-44698 p_r = [ 1+0j, 0.20787957635076193+0j, 0.35812203996480685+0.6097119028618724j, 0.12659112128185032+0.48847676699581527j, complex(np.inf, np.nan), complex(np.nan, np.nan), ] n_r = [x[i] ** y[i] for i in lx] for i in lx: assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i) def test_array(self): x = np.array([1, 1j, 2, 2.5+.37j, np.inf, np.nan]) y = np.array([1, 1j, -0.5+1.5j, -0.5+1.5j, 2, 3]) lx = list(range(len(x))) # Hardcode the expected `builtins.complex` values, # as complex exponentiation is broken as of bpo-44698 p_r = [ 1+0j, 0.20787957635076193+0j, 0.35812203996480685+0.6097119028618724j, 0.12659112128185032+0.48847676699581527j, complex(np.inf, np.nan), complex(np.nan, np.nan), ] n_r = x ** y for i in lx: assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i) class TestCabs: def setup_method(self): self.olderr = np.seterr(invalid='ignore') def teardown_method(self): np.seterr(**self.olderr) def test_simple(self): x = np.array([1+1j, 0+2j, 1+2j, np.inf, np.nan]) y_r = np.array([np.sqrt(2.), 2, np.sqrt(5), np.inf, np.nan]) y = np.abs(x) assert_almost_equal(y, y_r) def test_fabs(self): # Test that np.abs(x +- 0j) == np.abs(x) (as mandated by C99 for cabs) x = np.array([1+0j], dtype=complex) assert_array_equal(np.abs(x), np.real(x)) x = np.array([complex(1, np.NZERO)], dtype=complex) assert_array_equal(np.abs(x), np.real(x)) x = np.array([complex(np.inf, np.NZERO)], dtype=complex) assert_array_equal(np.abs(x), np.real(x)) x = np.array([complex(np.nan, np.NZERO)], dtype=complex) assert_array_equal(np.abs(x), np.real(x)) def test_cabs_inf_nan(self): x, y = [], [] # cabs(+-nan + nani) returns nan x.append(np.nan) y.append(np.nan) check_real_value(np.abs, np.nan, np.nan, np.nan) x.append(np.nan) y.append(-np.nan) check_real_value(np.abs, -np.nan, np.nan, np.nan) # According to C99 standard, if exactly one of the real/part is inf and # the other nan, then cabs should return inf x.append(np.inf) y.append(np.nan) check_real_value(np.abs, np.inf, np.nan, np.inf) x.append(-np.inf) y.append(np.nan) check_real_value(np.abs, -np.inf, np.nan, np.inf) # cabs(conj(z)) == conj(cabs(z)) (= cabs(z)) def f(a): return np.abs(np.conj(a)) def g(a, b): return np.abs(complex(a, b)) xa = np.array(x, dtype=complex) assert len(xa) == len(x) == len(y) for xi, yi in zip(x, y): ref = g(xi, yi) check_real_value(f, xi, yi, ref) class TestCarg: def test_simple(self): check_real_value(ncu._arg, 1, 0, 0, False) check_real_value(ncu._arg, 0, 1, 0.5*np.pi, False) check_real_value(ncu._arg, 1, 1, 0.25*np.pi, False) check_real_value(ncu._arg, np.PZERO, np.PZERO, np.PZERO) # TODO This can be xfail when the generator functions are got rid of. @pytest.mark.skip( reason="Complex arithmetic with signed zero fails on most platforms") def test_zero(self): # carg(-0 +- 0i) returns +- pi check_real_value(ncu._arg, np.NZERO, np.PZERO, np.pi, False) check_real_value(ncu._arg, np.NZERO, np.NZERO, -np.pi, False) # carg(+0 +- 0i) returns +- 0 check_real_value(ncu._arg, np.PZERO, np.PZERO, np.PZERO) check_real_value(ncu._arg, np.PZERO, np.NZERO, np.NZERO) # carg(x +- 0i) returns +- 0 for x > 0 check_real_value(ncu._arg, 1, np.PZERO, np.PZERO, False) check_real_value(ncu._arg, 1, np.NZERO, np.NZERO, False) # carg(x +- 0i) returns +- pi for x < 0 check_real_value(ncu._arg, -1, np.PZERO, np.pi, False) check_real_value(ncu._arg, -1, np.NZERO, -np.pi, False) # carg(+- 0 + yi) returns pi/2 for y > 0 check_real_value(ncu._arg, np.PZERO, 1, 0.5 * np.pi, False) check_real_value(ncu._arg, np.NZERO, 1, 0.5 * np.pi, False) # carg(+- 0 + yi) returns -pi/2 for y < 0 check_real_value(ncu._arg, np.PZERO, -1, 0.5 * np.pi, False) check_real_value(ncu._arg, np.NZERO, -1, -0.5 * np.pi, False) #def test_branch_cuts(self): # _check_branch_cut(ncu._arg, -1, 1j, -1, 1) def test_special_values(self): # carg(-np.inf +- yi) returns +-pi for finite y > 0 check_real_value(ncu._arg, -np.inf, 1, np.pi, False) check_real_value(ncu._arg, -np.inf, -1, -np.pi, False) # carg(np.inf +- yi) returns +-0 for finite y > 0 check_real_value(ncu._arg, np.inf, 1, np.PZERO, False) check_real_value(ncu._arg, np.inf, -1, np.NZERO, False) # carg(x +- np.infi) returns +-pi/2 for finite x check_real_value(ncu._arg, 1, np.inf, 0.5 * np.pi, False) check_real_value(ncu._arg, 1, -np.inf, -0.5 * np.pi, False) # carg(-np.inf +- np.infi) returns +-3pi/4 check_real_value(ncu._arg, -np.inf, np.inf, 0.75 * np.pi, False) check_real_value(ncu._arg, -np.inf, -np.inf, -0.75 * np.pi, False) # carg(np.inf +- np.infi) returns +-pi/4 check_real_value(ncu._arg, np.inf, np.inf, 0.25 * np.pi, False) check_real_value(ncu._arg, np.inf, -np.inf, -0.25 * np.pi, False) # carg(x + yi) returns np.nan if x or y is nan check_real_value(ncu._arg, np.nan, 0, np.nan, False) check_real_value(ncu._arg, 0, np.nan, np.nan, False) check_real_value(ncu._arg, np.nan, np.inf, np.nan, False) check_real_value(ncu._arg, np.inf, np.nan, np.nan, False) def check_real_value(f, x1, y1, x, exact=True): z1 = np.array([complex(x1, y1)]) if exact: assert_equal(f(z1), x) else: assert_almost_equal(f(z1), x) def check_complex_value(f, x1, y1, x2, y2, exact=True): z1 = np.array([complex(x1, y1)]) z2 = complex(x2, y2) with np.errstate(invalid='ignore'): if exact: assert_equal(f(z1), z2) else: assert_almost_equal(f(z1), z2) class TestSpecialComplexAVX: @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4]) @pytest.mark.parametrize("astype", [np.complex64, np.complex128]) def test_array(self, stride, astype): arr = np.array([complex(np.nan , np.nan), complex(np.nan , np.inf), complex(np.inf , np.nan), complex(np.inf , np.inf), complex(0. , np.inf), complex(np.inf , 0.), complex(0. , 0.), complex(0. , np.nan), complex(np.nan , 0.)], dtype=astype) abs_true = np.array([np.nan, np.inf, np.inf, np.inf, np.inf, np.inf, 0., np.nan, np.nan], dtype=arr.real.dtype) sq_true = np.array([complex(np.nan, np.nan), complex(np.nan, np.nan), complex(np.nan, np.nan), complex(np.nan, np.inf), complex(-np.inf, np.nan), complex(np.inf, np.nan), complex(0., 0.), complex(np.nan, np.nan), complex(np.nan, np.nan)], dtype=astype) assert_equal(np.abs(arr[::stride]), abs_true[::stride]) with np.errstate(invalid='ignore'): assert_equal(np.square(arr[::stride]), sq_true[::stride]) class TestComplexAbsoluteAVX: @pytest.mark.parametrize("arraysize", [1,2,3,4,5,6,7,8,9,10,11,13,15,17,18,19]) @pytest.mark.parametrize("stride", [-4,-3,-2,-1,1,2,3,4]) @pytest.mark.parametrize("astype", [np.complex64, np.complex128]) # test to ensure masking and strides work as intended in the AVX implementation def test_array(self, arraysize, stride, astype): arr = np.ones(arraysize, dtype=astype) abs_true = np.ones(arraysize, dtype=arr.real.dtype) assert_equal(np.abs(arr[::stride]), abs_true[::stride]) # Testcase taken as is from https://github.com/numpy/numpy/issues/16660 class TestComplexAbsoluteMixedDTypes: @pytest.mark.parametrize("stride", [-4,-3,-2,-1,1,2,3,4]) @pytest.mark.parametrize("astype", [np.complex64, np.complex128]) @pytest.mark.parametrize("func", ['abs', 'square', 'conjugate']) def test_array(self, stride, astype, func): dtype = [('template_id', '<i8'), ('bank_chisq','<f4'), ('bank_chisq_dof','<i8'), ('chisq', '<f4'), ('chisq_dof','<i8'), ('cont_chisq', '<f4'), ('psd_var_val', '<f4'), ('sg_chisq','<f4'), ('mycomplex', astype), ('time_index', '<i8')] vec = np.array([ (0, 0., 0, -31.666483, 200, 0., 0., 1. , 3.0+4.0j , 613090), (1, 0., 0, 260.91525 , 42, 0., 0., 1. , 5.0+12.0j , 787315), (1, 0., 0, 52.15155 , 42, 0., 0., 1. , 8.0+15.0j , 806641), (1, 0., 0, 52.430195, 42, 0., 0., 1. , 7.0+24.0j , 1363540), (2, 0., 0, 304.43646 , 58, 0., 0., 1. , 20.0+21.0j , 787323), (3, 0., 0, 299.42108 , 52, 0., 0., 1. , 12.0+35.0j , 787332), (4, 0., 0, 39.4836 , 28, 0., 0., 9.182192, 9.0+40.0j , 787304), (4, 0., 0, 76.83787 , 28, 0., 0., 1. , 28.0+45.0j, 1321869), (5, 0., 0, 143.26366 , 24, 0., 0., 10.996129, 11.0+60.0j , 787299)], dtype=dtype) myfunc = getattr(np, func) a = vec['mycomplex'] g = myfunc(a[::stride]) b = vec['mycomplex'].copy() h = myfunc(b[::stride]) assert_array_max_ulp(h.real, g.real, 1) assert_array_max_ulp(h.imag, g.imag, 1)