import platform import warnings import fnmatch import itertools import pytest import sys import os import operator from fractions import Fraction from functools import reduce from collections import namedtuple import numpy.core.umath as ncu from numpy.core import _umath_tests as ncu_tests import numpy as np from numpy.testing import ( assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_array_max_ulp, assert_allclose, assert_no_warnings, suppress_warnings, _gen_alignment_data, assert_array_almost_equal_nulp, IS_WASM, IS_MUSL ) from numpy.testing._private.utils import _glibc_older_than UFUNCS = [obj for obj in np.core.umath.__dict__.values() if isinstance(obj, np.ufunc)] UFUNCS_UNARY = [ uf for uf in UFUNCS if uf.nin == 1 ] UFUNCS_UNARY_FP = [ uf for uf in UFUNCS_UNARY if 'f->f' in uf.types ] UFUNCS_BINARY = [ uf for uf in UFUNCS if uf.nin == 2 ] UFUNCS_BINARY_ACC = [ uf for uf in UFUNCS_BINARY if hasattr(uf, "accumulate") and uf.nout == 1 ] def interesting_binop_operands(val1, val2, dtype): """ Helper to create "interesting" operands to cover common code paths: * scalar inputs * only first "values" is an array (e.g. scalar division fast-paths) * Longer array (SIMD) placing the value of interest at different positions * Oddly strided arrays which may not be SIMD compatible It does not attempt to cover unaligned access or mixed dtypes. These are normally handled by the casting/buffering machinery. This is not a fixture (currently), since I believe a fixture normally only yields once? """ fill_value = 1 # could be a parameter, but maybe not an optional one? arr1 = np.full(10003, dtype=dtype, fill_value=fill_value) arr2 = np.full(10003, dtype=dtype, fill_value=fill_value) arr1[0] = val1 arr2[0] = val2 extractor = lambda res: res yield arr1[0], arr2[0], extractor, "scalars" extractor = lambda res: res yield arr1[0, ...], arr2[0, ...], extractor, "scalar-arrays" # reset array values to fill_value: arr1[0] = fill_value arr2[0] = fill_value for pos in [0, 1, 2, 3, 4, 5, -1, -2, -3, -4]: arr1[pos] = val1 arr2[pos] = val2 extractor = lambda res: res[pos] yield arr1, arr2, extractor, f"off-{pos}" yield arr1, arr2[pos], extractor, f"off-{pos}-with-scalar" arr1[pos] = fill_value arr2[pos] = fill_value for stride in [-1, 113]: op1 = arr1[::stride] op2 = arr2[::stride] op1[10] = val1 op2[10] = val2 extractor = lambda res: res[10] yield op1, op2, extractor, f"stride-{stride}" op1[10] = fill_value op2[10] = fill_value def on_powerpc(): """ True if we are running on a Power PC platform.""" return platform.processor() == 'powerpc' or \ platform.machine().startswith('ppc') def bad_arcsinh(): """The blocklisted trig functions are not accurate on aarch64/PPC for complex256. Rather than dig through the actual problem skip the test. This should be fixed when we can move past glibc2.17 which is the version in manylinux2014 """ if platform.machine() == 'aarch64': x = 1.78e-10 elif on_powerpc(): x = 2.16e-10 else: return False v1 = np.arcsinh(np.float128(x)) v2 = np.arcsinh(np.complex256(x)).real # The eps for float128 is 1-e33, so this is way bigger return abs((v1 / v2) - 1.0) > 1e-23 class _FilterInvalids: def setup_method(self): self.olderr = np.seterr(invalid='ignore') def teardown_method(self): np.seterr(**self.olderr) class TestConstants: def test_pi(self): assert_allclose(ncu.pi, 3.141592653589793, 1e-15) def test_e(self): assert_allclose(ncu.e, 2.718281828459045, 1e-15) def test_euler_gamma(self): assert_allclose(ncu.euler_gamma, 0.5772156649015329, 1e-15) class TestOut: def test_out_subok(self): for subok in (True, False): a = np.array(0.5) o = np.empty(()) r = np.add(a, 2, o, subok=subok) assert_(r is o) r = np.add(a, 2, out=o, subok=subok) assert_(r is o) r = np.add(a, 2, out=(o,), subok=subok) assert_(r is o) d = np.array(5.7) o1 = np.empty(()) o2 = np.empty((), dtype=np.int32) r1, r2 = np.frexp(d, o1, None, subok=subok) assert_(r1 is o1) r1, r2 = np.frexp(d, None, o2, subok=subok) assert_(r2 is o2) r1, r2 = np.frexp(d, o1, o2, subok=subok) assert_(r1 is o1) assert_(r2 is o2) r1, r2 = np.frexp(d, out=(o1, None), subok=subok) assert_(r1 is o1) r1, r2 = np.frexp(d, out=(None, o2), subok=subok) assert_(r2 is o2) r1, r2 = np.frexp(d, out=(o1, o2), subok=subok) assert_(r1 is o1) assert_(r2 is o2) with assert_raises(TypeError): # Out argument must be tuple, since there are multiple outputs. r1, r2 = np.frexp(d, out=o1, subok=subok) assert_raises(TypeError, np.add, a, 2, o, o, subok=subok) assert_raises(TypeError, np.add, a, 2, o, out=o, subok=subok) assert_raises(TypeError, np.add, a, 2, None, out=o, subok=subok) assert_raises(ValueError, np.add, a, 2, out=(o, o), subok=subok) assert_raises(ValueError, np.add, a, 2, out=(), subok=subok) assert_raises(TypeError, np.add, a, 2, [], subok=subok) assert_raises(TypeError, np.add, a, 2, out=[], subok=subok) assert_raises(TypeError, np.add, a, 2, out=([],), subok=subok) o.flags.writeable = False assert_raises(ValueError, np.add, a, 2, o, subok=subok) assert_raises(ValueError, np.add, a, 2, out=o, subok=subok) assert_raises(ValueError, np.add, a, 2, out=(o,), subok=subok) def test_out_wrap_subok(self): class ArrayWrap(np.ndarray): __array_priority__ = 10 def __new__(cls, arr): return np.asarray(arr).view(cls).copy() def __array_wrap__(self, arr, context): return arr.view(type(self)) for subok in (True, False): a = ArrayWrap([0.5]) r = np.add(a, 2, subok=subok) if subok: assert_(isinstance(r, ArrayWrap)) else: assert_(type(r) == np.ndarray) r = np.add(a, 2, None, subok=subok) if subok: assert_(isinstance(r, ArrayWrap)) else: assert_(type(r) == np.ndarray) r = np.add(a, 2, out=None, subok=subok) if subok: assert_(isinstance(r, ArrayWrap)) else: assert_(type(r) == np.ndarray) r = np.add(a, 2, out=(None,), subok=subok) if subok: assert_(isinstance(r, ArrayWrap)) else: assert_(type(r) == np.ndarray) d = ArrayWrap([5.7]) o1 = np.empty((1,)) o2 = np.empty((1,), dtype=np.int32) r1, r2 = np.frexp(d, o1, subok=subok) if subok: assert_(isinstance(r2, ArrayWrap)) else: assert_(type(r2) == np.ndarray) r1, r2 = np.frexp(d, o1, None, subok=subok) if subok: assert_(isinstance(r2, ArrayWrap)) else: assert_(type(r2) == np.ndarray) r1, r2 = np.frexp(d, None, o2, subok=subok) if subok: assert_(isinstance(r1, ArrayWrap)) else: assert_(type(r1) == np.ndarray) r1, r2 = np.frexp(d, out=(o1, None), subok=subok) if subok: assert_(isinstance(r2, ArrayWrap)) else: assert_(type(r2) == np.ndarray) r1, r2 = np.frexp(d, out=(None, o2), subok=subok) if subok: assert_(isinstance(r1, ArrayWrap)) else: assert_(type(r1) == np.ndarray) with assert_raises(TypeError): # Out argument must be tuple, since there are multiple outputs. r1, r2 = np.frexp(d, out=o1, subok=subok) class TestComparisons: import operator @pytest.mark.parametrize('dtype', np.sctypes['uint'] + np.sctypes['int'] + np.sctypes['float'] + [np.bool_]) @pytest.mark.parametrize('py_comp,np_comp', [ (operator.lt, np.less), (operator.le, np.less_equal), (operator.gt, np.greater), (operator.ge, np.greater_equal), (operator.eq, np.equal), (operator.ne, np.not_equal) ]) def test_comparison_functions(self, dtype, py_comp, np_comp): # Initialize input arrays if dtype == np.bool_: a = np.random.choice(a=[False, True], size=1000) b = np.random.choice(a=[False, True], size=1000) scalar = True else: a = np.random.randint(low=1, high=10, size=1000).astype(dtype) b = np.random.randint(low=1, high=10, size=1000).astype(dtype) scalar = 5 np_scalar = np.dtype(dtype).type(scalar) a_lst = a.tolist() b_lst = b.tolist() # (Binary) Comparison (x1=array, x2=array) comp_b = np_comp(a, b).view(np.uint8) comp_b_list = [int(py_comp(x, y)) for x, y in zip(a_lst, b_lst)] # (Scalar1) Comparison (x1=scalar, x2=array) comp_s1 = np_comp(np_scalar, b).view(np.uint8) comp_s1_list = [int(py_comp(scalar, x)) for x in b_lst] # (Scalar2) Comparison (x1=array, x2=scalar) comp_s2 = np_comp(a, np_scalar).view(np.uint8) comp_s2_list = [int(py_comp(x, scalar)) for x in a_lst] # Sequence: Binary, Scalar1 and Scalar2 assert_(comp_b.tolist() == comp_b_list, f"Failed comparison ({py_comp.__name__})") assert_(comp_s1.tolist() == comp_s1_list, f"Failed comparison ({py_comp.__name__})") assert_(comp_s2.tolist() == comp_s2_list, f"Failed comparison ({py_comp.__name__})") def test_ignore_object_identity_in_equal(self): # Check comparing identical objects whose comparison # is not a simple boolean, e.g., arrays that are compared elementwise. a = np.array([np.array([1, 2, 3]), None], dtype=object) assert_raises(ValueError, np.equal, a, a) # Check error raised when comparing identical non-comparable objects. class FunkyType: def __eq__(self, other): raise TypeError("I won't compare") a = np.array([FunkyType()]) assert_raises(TypeError, np.equal, a, a) # Check identity doesn't override comparison mismatch. a = np.array([np.nan], dtype=object) assert_equal(np.equal(a, a), [False]) def test_ignore_object_identity_in_not_equal(self): # Check comparing identical objects whose comparison # is not a simple boolean, e.g., arrays that are compared elementwise. a = np.array([np.array([1, 2, 3]), None], dtype=object) assert_raises(ValueError, np.not_equal, a, a) # Check error raised when comparing identical non-comparable objects. class FunkyType: def __ne__(self, other): raise TypeError("I won't compare") a = np.array([FunkyType()]) assert_raises(TypeError, np.not_equal, a, a) # Check identity doesn't override comparison mismatch. a = np.array([np.nan], dtype=object) assert_equal(np.not_equal(a, a), [True]) def test_error_in_equal_reduce(self): # gh-20929 # make sure np.equal.reduce raises a TypeError if an array is passed # without specifying the dtype a = np.array([0, 0]) assert_equal(np.equal.reduce(a, dtype=bool), True) assert_raises(TypeError, np.equal.reduce, a) def test_object_dtype(self): assert np.equal(1, [1], dtype=object).dtype == object assert np.equal(1, [1], signature=(None, None, "O")).dtype == object def test_object_nonbool_dtype_error(self): # bool output dtype is fine of course: assert np.equal(1, [1], dtype=bool).dtype == bool # but the following are examples do not have a loop: with pytest.raises(TypeError, match="No loop matching"): np.equal(1, 1, dtype=np.int64) with pytest.raises(TypeError, match="No loop matching"): np.equal(1, 1, sig=(None, None, "l")) @pytest.mark.parametrize("dtypes", ["qQ", "Qq"]) @pytest.mark.parametrize('py_comp, np_comp', [ (operator.lt, np.less), (operator.le, np.less_equal), (operator.gt, np.greater), (operator.ge, np.greater_equal), (operator.eq, np.equal), (operator.ne, np.not_equal) ]) @pytest.mark.parametrize("vals", [(2**60, 2**60+1), (2**60+1, 2**60)]) def test_large_integer_direct_comparison( self, dtypes, py_comp, np_comp, vals): # Note that float(2**60) + 1 == float(2**60). a1 = np.array([2**60], dtype=dtypes[0]) a2 = np.array([2**60 + 1], dtype=dtypes[1]) expected = py_comp(2**60, 2**60+1) assert py_comp(a1, a2) == expected assert np_comp(a1, a2) == expected # Also check the scalars: s1 = a1[0] s2 = a2[0] assert isinstance(s1, np.integer) assert isinstance(s2, np.integer) # The Python operator here is mainly interesting: assert py_comp(s1, s2) == expected assert np_comp(s1, s2) == expected @pytest.mark.parametrize("dtype", np.typecodes['UnsignedInteger']) @pytest.mark.parametrize('py_comp_func, np_comp_func', [ (operator.lt, np.less), (operator.le, np.less_equal), (operator.gt, np.greater), (operator.ge, np.greater_equal), (operator.eq, np.equal), (operator.ne, np.not_equal) ]) @pytest.mark.parametrize("flip", [True, False]) def test_unsigned_signed_direct_comparison( self, dtype, py_comp_func, np_comp_func, flip): if flip: py_comp = lambda x, y: py_comp_func(y, x) np_comp = lambda x, y: np_comp_func(y, x) else: py_comp = py_comp_func np_comp = np_comp_func arr = np.array([np.iinfo(dtype).max], dtype=dtype) expected = py_comp(int(arr[0]), -1) assert py_comp(arr, -1) == expected assert np_comp(arr, -1) == expected scalar = arr[0] assert isinstance(scalar, np.integer) # The Python operator here is mainly interesting: assert py_comp(scalar, -1) == expected assert np_comp(scalar, -1) == expected class TestAdd: def test_reduce_alignment(self): # gh-9876 # make sure arrays with weird strides work with the optimizations in # pairwise_sum_@TYPE@. On x86, the 'b' field will count as aligned at a # 4 byte offset, even though its itemsize is 8. a = np.zeros(2, dtype=[('a', np.int32), ('b', np.float64)]) a['a'] = -1 assert_equal(a['b'].sum(), 0) class TestDivision: def test_division_int(self): # int division should follow Python x = np.array([5, 10, 90, 100, -5, -10, -90, -100, -120]) if 5 / 10 == 0.5: assert_equal(x / 100, [0.05, 0.1, 0.9, 1, -0.05, -0.1, -0.9, -1, -1.2]) else: assert_equal(x / 100, [0, 0, 0, 1, -1, -1, -1, -1, -2]) assert_equal(x // 100, [0, 0, 0, 1, -1, -1, -1, -1, -2]) assert_equal(x % 100, [5, 10, 90, 0, 95, 90, 10, 0, 80]) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize("dtype,ex_val", itertools.product( np.sctypes['int'] + np.sctypes['uint'], ( ( # dividend "np.array(range(fo.max-lsize, fo.max)).astype(dtype)," # divisors "np.arange(lsize).astype(dtype)," # scalar divisors "range(15)" ), ( # dividend "np.arange(fo.min, fo.min+lsize).astype(dtype)," # divisors "np.arange(lsize//-2, lsize//2).astype(dtype)," # scalar divisors "range(fo.min, fo.min + 15)" ), ( # dividend "np.array(range(fo.max-lsize, fo.max)).astype(dtype)," # divisors "np.arange(lsize).astype(dtype)," # scalar divisors "[1,3,9,13,neg, fo.min+1, fo.min//2, fo.max//3, fo.max//4]" ) ) )) def test_division_int_boundary(self, dtype, ex_val): fo = np.iinfo(dtype) neg = -1 if fo.min < 0 else 1 # Large enough to test SIMD loops and remainder elements lsize = 512 + 7 a, b, divisors = eval(ex_val) a_lst, b_lst = a.tolist(), b.tolist() c_div = lambda n, d: ( 0 if d == 0 else ( fo.min if (n and n == fo.min and d == -1) else n//d ) ) with np.errstate(divide='ignore'): ac = a.copy() ac //= b div_ab = a // b div_lst = [c_div(x, y) for x, y in zip(a_lst, b_lst)] msg = "Integer arrays floor division check (//)" assert all(div_ab == div_lst), msg msg_eq = "Integer arrays floor division check (//=)" assert all(ac == div_lst), msg_eq for divisor in divisors: ac = a.copy() with np.errstate(divide='ignore', over='ignore'): div_a = a // divisor ac //= divisor div_lst = [c_div(i, divisor) for i in a_lst] assert all(div_a == div_lst), msg assert all(ac == div_lst), msg_eq with np.errstate(divide='raise', over='raise'): if 0 in b: # Verify overflow case with pytest.raises(FloatingPointError, match="divide by zero encountered in floor_divide"): a // b else: a // b if fo.min and fo.min in a: with pytest.raises(FloatingPointError, match='overflow encountered in floor_divide'): a // -1 elif fo.min: a // -1 with pytest.raises(FloatingPointError, match="divide by zero encountered in floor_divide"): a // 0 with pytest.raises(FloatingPointError, match="divide by zero encountered in floor_divide"): ac = a.copy() ac //= 0 np.array([], dtype=dtype) // 0 @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize("dtype,ex_val", itertools.product( np.sctypes['int'] + np.sctypes['uint'], ( "np.array([fo.max, 1, 2, 1, 1, 2, 3], dtype=dtype)", "np.array([fo.min, 1, -2, 1, 1, 2, -3]).astype(dtype)", "np.arange(fo.min, fo.min+(100*10), 10, dtype=dtype)", "np.array(range(fo.max-(100*7), fo.max, 7)).astype(dtype)", ) )) def test_division_int_reduce(self, dtype, ex_val): fo = np.iinfo(dtype) a = eval(ex_val) lst = a.tolist() c_div = lambda n, d: ( 0 if d == 0 or (n and n == fo.min and d == -1) else n//d ) with np.errstate(divide='ignore'): div_a = np.floor_divide.reduce(a) div_lst = reduce(c_div, lst) msg = "Reduce floor integer division check" assert div_a == div_lst, msg with np.errstate(divide='raise', over='raise'): with pytest.raises(FloatingPointError, match="divide by zero encountered in reduce"): np.floor_divide.reduce(np.arange(-100, 100).astype(dtype)) if fo.min: with pytest.raises(FloatingPointError, match='overflow encountered in reduce'): np.floor_divide.reduce( np.array([fo.min, 1, -1], dtype=dtype) ) @pytest.mark.parametrize( "dividend,divisor,quotient", [(np.timedelta64(2,'Y'), np.timedelta64(2,'M'), 12), (np.timedelta64(2,'Y'), np.timedelta64(-2,'M'), -12), (np.timedelta64(-2,'Y'), np.timedelta64(2,'M'), -12), (np.timedelta64(-2,'Y'), np.timedelta64(-2,'M'), 12), (np.timedelta64(2,'M'), np.timedelta64(-2,'Y'), -1), (np.timedelta64(2,'Y'), np.timedelta64(0,'M'), 0), (np.timedelta64(2,'Y'), 2, np.timedelta64(1,'Y')), (np.timedelta64(2,'Y'), -2, np.timedelta64(-1,'Y')), (np.timedelta64(-2,'Y'), 2, np.timedelta64(-1,'Y')), (np.timedelta64(-2,'Y'), -2, np.timedelta64(1,'Y')), (np.timedelta64(-2,'Y'), -2, np.timedelta64(1,'Y')), (np.timedelta64(-2,'Y'), -3, np.timedelta64(0,'Y')), (np.timedelta64(-2,'Y'), 0, np.timedelta64('Nat','Y')), ]) def test_division_int_timedelta(self, dividend, divisor, quotient): # If either divisor is 0 or quotient is Nat, check for division by 0 if divisor and (isinstance(quotient, int) or not np.isnat(quotient)): msg = "Timedelta floor division check" assert dividend // divisor == quotient, msg # Test for arrays as well msg = "Timedelta arrays floor division check" dividend_array = np.array([dividend]*5) quotient_array = np.array([quotient]*5) assert all(dividend_array // divisor == quotient_array), msg else: if IS_WASM: pytest.skip("fp errors don't work in wasm") with np.errstate(divide='raise', invalid='raise'): with pytest.raises(FloatingPointError): dividend // divisor def test_division_complex(self): # check that implementation is correct msg = "Complex division implementation check" x = np.array([1. + 1.*1j, 1. + .5*1j, 1. + 2.*1j], dtype=np.complex128) assert_almost_equal(x**2/x, x, err_msg=msg) # check overflow, underflow msg = "Complex division overflow/underflow check" x = np.array([1.e+110, 1.e-110], dtype=np.complex128) y = x**2/x assert_almost_equal(y/x, [1, 1], err_msg=msg) def test_zero_division_complex(self): with np.errstate(invalid="ignore", divide="ignore"): x = np.array([0.0], dtype=np.complex128) y = 1.0/x assert_(np.isinf(y)[0]) y = complex(np.inf, np.nan)/x assert_(np.isinf(y)[0]) y = complex(np.nan, np.inf)/x assert_(np.isinf(y)[0]) y = complex(np.inf, np.inf)/x assert_(np.isinf(y)[0]) y = 0.0/x assert_(np.isnan(y)[0]) def test_floor_division_complex(self): # check that floor division, divmod and remainder raises type errors x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128) with pytest.raises(TypeError): x // 7 with pytest.raises(TypeError): np.divmod(x, 7) with pytest.raises(TypeError): np.remainder(x, 7) def test_floor_division_signed_zero(self): # Check that the sign bit is correctly set when dividing positive and # negative zero by one. x = np.zeros(10) assert_equal(np.signbit(x//1), 0) assert_equal(np.signbit((-x)//1), 1) @pytest.mark.skipif(hasattr(np.__config__, "blas_ssl2_info"), reason="gh-22982") @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize('dtype', np.typecodes['Float']) def test_floor_division_errors(self, dtype): fnan = np.array(np.nan, dtype=dtype) fone = np.array(1.0, dtype=dtype) fzer = np.array(0.0, dtype=dtype) finf = np.array(np.inf, dtype=dtype) # divide by zero error check with np.errstate(divide='raise', invalid='ignore'): assert_raises(FloatingPointError, np.floor_divide, fone, fzer) with np.errstate(divide='ignore', invalid='raise'): np.floor_divide(fone, fzer) # The following already contain a NaN and should not warn with np.errstate(all='raise'): np.floor_divide(fnan, fone) np.floor_divide(fone, fnan) np.floor_divide(fnan, fzer) np.floor_divide(fzer, fnan) @pytest.mark.parametrize('dtype', np.typecodes['Float']) def test_floor_division_corner_cases(self, dtype): # test corner cases like 1.0//0.0 for errors and return vals x = np.zeros(10, dtype=dtype) y = np.ones(10, dtype=dtype) fnan = np.array(np.nan, dtype=dtype) fone = np.array(1.0, dtype=dtype) fzer = np.array(0.0, dtype=dtype) finf = np.array(np.inf, dtype=dtype) with suppress_warnings() as sup: sup.filter(RuntimeWarning, "invalid value encountered in floor_divide") div = np.floor_divide(fnan, fone) assert(np.isnan(div)), "dt: %s, div: %s" % (dt, div) div = np.floor_divide(fone, fnan) assert(np.isnan(div)), "dt: %s, div: %s" % (dt, div) div = np.floor_divide(fnan, fzer) assert(np.isnan(div)), "dt: %s, div: %s" % (dt, div) # verify 1.0//0.0 computations return inf with np.errstate(divide='ignore'): z = np.floor_divide(y, x) assert_(np.isinf(z).all()) def floor_divide_and_remainder(x, y): return (np.floor_divide(x, y), np.remainder(x, y)) def _signs(dt): if dt in np.typecodes['UnsignedInteger']: return (+1,) else: return (+1, -1) class TestRemainder: def test_remainder_basic(self): dt = np.typecodes['AllInteger'] + np.typecodes['Float'] for op in [floor_divide_and_remainder, np.divmod]: for dt1, dt2 in itertools.product(dt, dt): for sg1, sg2 in itertools.product(_signs(dt1), _signs(dt2)): fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s' msg = fmt % (op.__name__, dt1, dt2, sg1, sg2) a = np.array(sg1*71, dtype=dt1) b = np.array(sg2*19, dtype=dt2) div, rem = op(a, b) assert_equal(div*b + rem, a, err_msg=msg) if sg2 == -1: assert_(b < rem <= 0, msg) else: assert_(b > rem >= 0, msg) def test_float_remainder_exact(self): # test that float results are exact for small integers. This also # holds for the same integers scaled by powers of two. nlst = list(range(-127, 0)) plst = list(range(1, 128)) dividend = nlst + [0] + plst divisor = nlst + plst arg = list(itertools.product(dividend, divisor)) tgt = list(divmod(*t) for t in arg) a, b = np.array(arg, dtype=int).T # convert exact integer results from Python to float so that # signed zero can be used, it is checked. tgtdiv, tgtrem = np.array(tgt, dtype=float).T tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv) tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem) for op in [floor_divide_and_remainder, np.divmod]: for dt in np.typecodes['Float']: msg = 'op: %s, dtype: %s' % (op.__name__, dt) fa = a.astype(dt) fb = b.astype(dt) div, rem = op(fa, fb) assert_equal(div, tgtdiv, err_msg=msg) assert_equal(rem, tgtrem, err_msg=msg) def test_float_remainder_roundoff(self): # gh-6127 dt = np.typecodes['Float'] for op in [floor_divide_and_remainder, np.divmod]: for dt1, dt2 in itertools.product(dt, dt): for sg1, sg2 in itertools.product((+1, -1), (+1, -1)): fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s' msg = fmt % (op.__name__, dt1, dt2, sg1, sg2) a = np.array(sg1*78*6e-8, dtype=dt1) b = np.array(sg2*6e-8, dtype=dt2) div, rem = op(a, b) # Equal assertion should hold when fmod is used assert_equal(div*b + rem, a, err_msg=msg) if sg2 == -1: assert_(b < rem <= 0, msg) else: assert_(b > rem >= 0, msg) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.xfail(sys.platform.startswith("darwin"), reason="MacOS seems to not give the correct 'invalid' warning for " "`fmod`. Hopefully, others always do.") @pytest.mark.parametrize('dtype', np.typecodes['Float']) def test_float_divmod_errors(self, dtype): # Check valid errors raised for divmod and remainder fzero = np.array(0.0, dtype=dtype) fone = np.array(1.0, dtype=dtype) finf = np.array(np.inf, dtype=dtype) fnan = np.array(np.nan, dtype=dtype) # since divmod is combination of both remainder and divide # ops it will set both dividebyzero and invalid flags with np.errstate(divide='raise', invalid='ignore'): assert_raises(FloatingPointError, np.divmod, fone, fzero) with np.errstate(divide='ignore', invalid='raise'): assert_raises(FloatingPointError, np.divmod, fone, fzero) with np.errstate(invalid='raise'): assert_raises(FloatingPointError, np.divmod, fzero, fzero) with np.errstate(invalid='raise'): assert_raises(FloatingPointError, np.divmod, finf, finf) with np.errstate(divide='ignore', invalid='raise'): assert_raises(FloatingPointError, np.divmod, finf, fzero) with np.errstate(divide='raise', invalid='ignore'): # inf / 0 does not set any flags, only the modulo creates a NaN np.divmod(finf, fzero) @pytest.mark.skipif(hasattr(np.__config__, "blas_ssl2_info"), reason="gh-22982") @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.xfail(sys.platform.startswith("darwin"), reason="MacOS seems to not give the correct 'invalid' warning for " "`fmod`. Hopefully, others always do.") @pytest.mark.parametrize('dtype', np.typecodes['Float']) @pytest.mark.parametrize('fn', [np.fmod, np.remainder]) def test_float_remainder_errors(self, dtype, fn): fzero = np.array(0.0, dtype=dtype) fone = np.array(1.0, dtype=dtype) finf = np.array(np.inf, dtype=dtype) fnan = np.array(np.nan, dtype=dtype) # The following already contain a NaN and should not warn. with np.errstate(all='raise'): with pytest.raises(FloatingPointError, match="invalid value"): fn(fone, fzero) fn(fnan, fzero) fn(fzero, fnan) fn(fone, fnan) fn(fnan, fone) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_float_remainder_overflow(self): a = np.finfo(np.float64).tiny with np.errstate(over='ignore', invalid='ignore'): div, mod = np.divmod(4, a) np.isinf(div) assert_(mod == 0) with np.errstate(over='raise', invalid='ignore'): assert_raises(FloatingPointError, np.divmod, 4, a) with np.errstate(invalid='raise', over='ignore'): assert_raises(FloatingPointError, np.divmod, 4, a) def test_float_divmod_corner_cases(self): # check nan cases for dt in np.typecodes['Float']: fnan = np.array(np.nan, dtype=dt) fone = np.array(1.0, dtype=dt) fzer = np.array(0.0, dtype=dt) finf = np.array(np.inf, dtype=dt) with suppress_warnings() as sup: sup.filter(RuntimeWarning, "invalid value encountered in divmod") sup.filter(RuntimeWarning, "divide by zero encountered in divmod") div, rem = np.divmod(fone, fzer) assert(np.isinf(div)), 'dt: %s, div: %s' % (dt, rem) assert(np.isnan(rem)), 'dt: %s, rem: %s' % (dt, rem) div, rem = np.divmod(fzer, fzer) assert(np.isnan(rem)), 'dt: %s, rem: %s' % (dt, rem) assert_(np.isnan(div)), 'dt: %s, rem: %s' % (dt, rem) div, rem = np.divmod(finf, finf) assert(np.isnan(div)), 'dt: %s, rem: %s' % (dt, rem) assert(np.isnan(rem)), 'dt: %s, rem: %s' % (dt, rem) div, rem = np.divmod(finf, fzer) assert(np.isinf(div)), 'dt: %s, rem: %s' % (dt, rem) assert(np.isnan(rem)), 'dt: %s, rem: %s' % (dt, rem) div, rem = np.divmod(fnan, fone) assert(np.isnan(rem)), "dt: %s, rem: %s" % (dt, rem) assert(np.isnan(div)), "dt: %s, rem: %s" % (dt, rem) div, rem = np.divmod(fone, fnan) assert(np.isnan(rem)), "dt: %s, rem: %s" % (dt, rem) assert(np.isnan(div)), "dt: %s, rem: %s" % (dt, rem) div, rem = np.divmod(fnan, fzer) assert(np.isnan(rem)), "dt: %s, rem: %s" % (dt, rem) assert(np.isnan(div)), "dt: %s, rem: %s" % (dt, rem) def test_float_remainder_corner_cases(self): # Check remainder magnitude. for dt in np.typecodes['Float']: fone = np.array(1.0, dtype=dt) fzer = np.array(0.0, dtype=dt) fnan = np.array(np.nan, dtype=dt) b = np.array(1.0, dtype=dt) a = np.nextafter(np.array(0.0, dtype=dt), -b) rem = np.remainder(a, b) assert_(rem <= b, 'dt: %s' % dt) rem = np.remainder(-a, -b) assert_(rem >= -b, 'dt: %s' % dt) # Check nans, inf with suppress_warnings() as sup: sup.filter(RuntimeWarning, "invalid value encountered in remainder") sup.filter(RuntimeWarning, "invalid value encountered in fmod") for dt in np.typecodes['Float']: fone = np.array(1.0, dtype=dt) fzer = np.array(0.0, dtype=dt) finf = np.array(np.inf, dtype=dt) fnan = np.array(np.nan, dtype=dt) rem = np.remainder(fone, fzer) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) # MSVC 2008 returns NaN here, so disable the check. #rem = np.remainder(fone, finf) #assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem)) rem = np.remainder(finf, fone) fmod = np.fmod(finf, fone) assert_(np.isnan(fmod), 'dt: %s, fmod: %s' % (dt, fmod)) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) rem = np.remainder(finf, finf) fmod = np.fmod(finf, fone) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) assert_(np.isnan(fmod), 'dt: %s, fmod: %s' % (dt, fmod)) rem = np.remainder(finf, fzer) fmod = np.fmod(finf, fzer) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) assert_(np.isnan(fmod), 'dt: %s, fmod: %s' % (dt, fmod)) rem = np.remainder(fone, fnan) fmod = np.fmod(fone, fnan) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) assert_(np.isnan(fmod), 'dt: %s, fmod: %s' % (dt, fmod)) rem = np.remainder(fnan, fzer) fmod = np.fmod(fnan, fzer) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) assert_(np.isnan(fmod), 'dt: %s, fmod: %s' % (dt, rem)) rem = np.remainder(fnan, fone) fmod = np.fmod(fnan, fone) assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem)) assert_(np.isnan(fmod), 'dt: %s, fmod: %s' % (dt, rem)) class TestDivisionIntegerOverflowsAndDivideByZero: result_type = namedtuple('result_type', ['nocast', 'casted']) helper_lambdas = { 'zero': lambda dtype: 0, 'min': lambda dtype: np.iinfo(dtype).min, 'neg_min': lambda dtype: -np.iinfo(dtype).min, 'min-zero': lambda dtype: (np.iinfo(dtype).min, 0), 'neg_min-zero': lambda dtype: (-np.iinfo(dtype).min, 0), } overflow_results = { np.remainder: result_type( helper_lambdas['zero'], helper_lambdas['zero']), np.fmod: result_type( helper_lambdas['zero'], helper_lambdas['zero']), operator.mod: result_type( helper_lambdas['zero'], helper_lambdas['zero']), operator.floordiv: result_type( helper_lambdas['min'], helper_lambdas['neg_min']), np.floor_divide: result_type( helper_lambdas['min'], helper_lambdas['neg_min']), np.divmod: result_type( helper_lambdas['min-zero'], helper_lambdas['neg_min-zero']) } @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize("dtype", np.typecodes["Integer"]) def test_signed_division_overflow(self, dtype): to_check = interesting_binop_operands(np.iinfo(dtype).min, -1, dtype) for op1, op2, extractor, operand_identifier in to_check: with pytest.warns(RuntimeWarning, match="overflow encountered"): res = op1 // op2 assert res.dtype == op1.dtype assert extractor(res) == np.iinfo(op1.dtype).min # Remainder is well defined though, and does not warn: res = op1 % op2 assert res.dtype == op1.dtype assert extractor(res) == 0 # Check fmod as well: res = np.fmod(op1, op2) assert extractor(res) == 0 # Divmod warns for the division part: with pytest.warns(RuntimeWarning, match="overflow encountered"): res1, res2 = np.divmod(op1, op2) assert res1.dtype == res2.dtype == op1.dtype assert extractor(res1) == np.iinfo(op1.dtype).min assert extractor(res2) == 0 @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) def test_divide_by_zero(self, dtype): # Note that the return value cannot be well defined here, but NumPy # currently uses 0 consistently. This could be changed. to_check = interesting_binop_operands(1, 0, dtype) for op1, op2, extractor, operand_identifier in to_check: with pytest.warns(RuntimeWarning, match="divide by zero"): res = op1 // op2 assert res.dtype == op1.dtype assert extractor(res) == 0 with pytest.warns(RuntimeWarning, match="divide by zero"): res1, res2 = np.divmod(op1, op2) assert res1.dtype == res2.dtype == op1.dtype assert extractor(res1) == 0 assert extractor(res2) == 0 @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize("dividend_dtype", np.sctypes['int']) @pytest.mark.parametrize("divisor_dtype", np.sctypes['int']) @pytest.mark.parametrize("operation", [np.remainder, np.fmod, np.divmod, np.floor_divide, operator.mod, operator.floordiv]) @np.errstate(divide='warn', over='warn') def test_overflows(self, dividend_dtype, divisor_dtype, operation): # SIMD tries to perform the operation on as many elements as possible # that is a multiple of the register's size. We resort to the # default implementation for the leftover elements. # We try to cover all paths here. arrays = [np.array([np.iinfo(dividend_dtype).min]*i, dtype=dividend_dtype) for i in range(1, 129)] divisor = np.array([-1], dtype=divisor_dtype) # If dividend is a larger type than the divisor (`else` case), # then, result will be a larger type than dividend and will not # result in an overflow for `divmod` and `floor_divide`. if np.dtype(dividend_dtype).itemsize >= np.dtype( divisor_dtype).itemsize and operation in ( np.divmod, np.floor_divide, operator.floordiv): with pytest.warns( RuntimeWarning, match="overflow encountered in"): result = operation( dividend_dtype(np.iinfo(dividend_dtype).min), divisor_dtype(-1) ) assert result == self.overflow_results[operation].nocast( dividend_dtype) # Arrays for a in arrays: # In case of divmod, we need to flatten the result # column first as we get a column vector of quotient and # remainder and a normal flatten of the expected result. with pytest.warns( RuntimeWarning, match="overflow encountered in"): result = np.array(operation(a, divisor)).flatten('f') expected_array = np.array( [self.overflow_results[operation].nocast( dividend_dtype)]*len(a)).flatten() assert_array_equal(result, expected_array) else: # Scalars result = operation( dividend_dtype(np.iinfo(dividend_dtype).min), divisor_dtype(-1) ) assert result == self.overflow_results[operation].casted( dividend_dtype) # Arrays for a in arrays: # See above comment on flatten result = np.array(operation(a, divisor)).flatten('f') expected_array = np.array( [self.overflow_results[operation].casted( dividend_dtype)]*len(a)).flatten() assert_array_equal(result, expected_array) class TestCbrt: def test_cbrt_scalar(self): assert_almost_equal((np.cbrt(np.float32(-2.5)**3)), -2.5) def test_cbrt(self): x = np.array([1., 2., -3., np.inf, -np.inf]) assert_almost_equal(np.cbrt(x**3), x) assert_(np.isnan(np.cbrt(np.nan))) assert_equal(np.cbrt(np.inf), np.inf) assert_equal(np.cbrt(-np.inf), -np.inf) class TestPower: def test_power_float(self): x = np.array([1., 2., 3.]) assert_equal(x**0, [1., 1., 1.]) assert_equal(x**1, x) assert_equal(x**2, [1., 4., 9.]) y = x.copy() y **= 2 assert_equal(y, [1., 4., 9.]) assert_almost_equal(x**(-1), [1., 0.5, 1./3]) assert_almost_equal(x**(0.5), [1., ncu.sqrt(2), ncu.sqrt(3)]) for out, inp, msg in _gen_alignment_data(dtype=np.float32, type='unary', max_size=11): exp = [ncu.sqrt(i) for i in inp] assert_almost_equal(inp**(0.5), exp, err_msg=msg) np.sqrt(inp, out=out) assert_equal(out, exp, err_msg=msg) for out, inp, msg in _gen_alignment_data(dtype=np.float64, type='unary', max_size=7): exp = [ncu.sqrt(i) for i in inp] assert_almost_equal(inp**(0.5), exp, err_msg=msg) np.sqrt(inp, out=out) assert_equal(out, exp, err_msg=msg) def test_power_complex(self): x = np.array([1+2j, 2+3j, 3+4j]) assert_equal(x**0, [1., 1., 1.]) assert_equal(x**1, x) assert_almost_equal(x**2, [-3+4j, -5+12j, -7+24j]) assert_almost_equal(x**3, [(1+2j)**3, (2+3j)**3, (3+4j)**3]) assert_almost_equal(x**4, [(1+2j)**4, (2+3j)**4, (3+4j)**4]) assert_almost_equal(x**(-1), [1/(1+2j), 1/(2+3j), 1/(3+4j)]) assert_almost_equal(x**(-2), [1/(1+2j)**2, 1/(2+3j)**2, 1/(3+4j)**2]) assert_almost_equal(x**(-3), [(-11+2j)/125, (-46-9j)/2197, (-117-44j)/15625]) assert_almost_equal(x**(0.5), [ncu.sqrt(1+2j), ncu.sqrt(2+3j), ncu.sqrt(3+4j)]) norm = 1./((x**14)[0]) assert_almost_equal(x**14 * norm, [i * norm for i in [-76443+16124j, 23161315+58317492j, 5583548873 + 2465133864j]]) # Ticket #836 def assert_complex_equal(x, y): assert_array_equal(x.real, y.real) assert_array_equal(x.imag, y.imag) for z in [complex(0, np.inf), complex(1, np.inf)]: z = np.array([z], dtype=np.complex_) with np.errstate(invalid="ignore"): assert_complex_equal(z**1, z) assert_complex_equal(z**2, z*z) assert_complex_equal(z**3, z*z*z) def test_power_zero(self): # ticket #1271 zero = np.array([0j]) one = np.array([1+0j]) cnan = np.array([complex(np.nan, np.nan)]) # FIXME cinf not tested. #cinf = np.array([complex(np.inf, 0)]) def assert_complex_equal(x, y): x, y = np.asarray(x), np.asarray(y) assert_array_equal(x.real, y.real) assert_array_equal(x.imag, y.imag) # positive powers for p in [0.33, 0.5, 1, 1.5, 2, 3, 4, 5, 6.6]: assert_complex_equal(np.power(zero, p), zero) # zero power assert_complex_equal(np.power(zero, 0), one) with np.errstate(invalid="ignore"): assert_complex_equal(np.power(zero, 0+1j), cnan) # negative power for p in [0.33, 0.5, 1, 1.5, 2, 3, 4, 5, 6.6]: assert_complex_equal(np.power(zero, -p), cnan) assert_complex_equal(np.power(zero, -1+0.2j), cnan) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_zero_power_nonzero(self): # Testing 0^{Non-zero} issue 18378 zero = np.array([0.0+0.0j]) cnan = np.array([complex(np.nan, np.nan)]) def assert_complex_equal(x, y): assert_array_equal(x.real, y.real) assert_array_equal(x.imag, y.imag) #Complex powers with positive real part will not generate a warning assert_complex_equal(np.power(zero, 1+4j), zero) assert_complex_equal(np.power(zero, 2-3j), zero) #Testing zero values when real part is greater than zero assert_complex_equal(np.power(zero, 1+1j), zero) assert_complex_equal(np.power(zero, 1+0j), zero) assert_complex_equal(np.power(zero, 1-1j), zero) #Complex powers will negative real part or 0 (provided imaginary # part is not zero) will generate a NAN and hence a RUNTIME warning with pytest.warns(expected_warning=RuntimeWarning) as r: assert_complex_equal(np.power(zero, -1+1j), cnan) assert_complex_equal(np.power(zero, -2-3j), cnan) assert_complex_equal(np.power(zero, -7+0j), cnan) assert_complex_equal(np.power(zero, 0+1j), cnan) assert_complex_equal(np.power(zero, 0-1j), cnan) assert len(r) == 5 def test_fast_power(self): x = np.array([1, 2, 3], np.int16) res = x**2.0 assert_((x**2.00001).dtype is res.dtype) assert_array_equal(res, [1, 4, 9]) # check the inplace operation on the casted copy doesn't mess with x assert_(not np.may_share_memory(res, x)) assert_array_equal(x, [1, 2, 3]) # Check that the fast path ignores 1-element not 0-d arrays res = x ** np.array([[[2]]]) assert_equal(res.shape, (1, 1, 3)) def test_integer_power(self): a = np.array([15, 15], 'i8') b = np.power(a, a) assert_equal(b, [437893890380859375, 437893890380859375]) def test_integer_power_with_integer_zero_exponent(self): dtypes = np.typecodes['Integer'] for dt in dtypes: arr = np.arange(-10, 10, dtype=dt) assert_equal(np.power(arr, 0), np.ones_like(arr)) dtypes = np.typecodes['UnsignedInteger'] for dt in dtypes: arr = np.arange(10, dtype=dt) assert_equal(np.power(arr, 0), np.ones_like(arr)) def test_integer_power_of_1(self): dtypes = np.typecodes['AllInteger'] for dt in dtypes: arr = np.arange(10, dtype=dt) assert_equal(np.power(1, arr), np.ones_like(arr)) def test_integer_power_of_zero(self): dtypes = np.typecodes['AllInteger'] for dt in dtypes: arr = np.arange(1, 10, dtype=dt) assert_equal(np.power(0, arr), np.zeros_like(arr)) def test_integer_to_negative_power(self): dtypes = np.typecodes['Integer'] for dt in dtypes: a = np.array([0, 1, 2, 3], dtype=dt) b = np.array([0, 1, 2, -3], dtype=dt) one = np.array(1, dtype=dt) minusone = np.array(-1, dtype=dt) assert_raises(ValueError, np.power, a, b) assert_raises(ValueError, np.power, a, minusone) assert_raises(ValueError, np.power, one, b) assert_raises(ValueError, np.power, one, minusone) def test_float_to_inf_power(self): for dt in [np.float32, np.float64]: a = np.array([1, 1, 2, 2, -2, -2, np.inf, -np.inf], dt) b = np.array([np.inf, -np.inf, np.inf, -np.inf, np.inf, -np.inf, np.inf, -np.inf], dt) r = np.array([1, 1, np.inf, 0, np.inf, 0, np.inf, 0], dt) assert_equal(np.power(a, b), r) class TestFloat_power: def test_type_conversion(self): arg_type = '?bhilBHILefdgFDG' res_type = 'ddddddddddddgDDG' for dtin, dtout in zip(arg_type, res_type): msg = "dtin: %s, dtout: %s" % (dtin, dtout) arg = np.ones(1, dtype=dtin) res = np.float_power(arg, arg) assert_(res.dtype.name == np.dtype(dtout).name, msg) class TestLog2: @pytest.mark.parametrize('dt', ['f', 'd', 'g']) def test_log2_values(self, dt): x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_almost_equal(np.log2(xf), yf) @pytest.mark.parametrize("i", range(1, 65)) def test_log2_ints(self, i): # a good log2 implementation should provide this, # might fail on OS with bad libm v = np.log2(2.**i) assert_equal(v, float(i), err_msg='at exponent %d' % i) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_log2_special(self): assert_equal(np.log2(1.), 0.) assert_equal(np.log2(np.inf), np.inf) assert_(np.isnan(np.log2(np.nan))) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.log2(-1.))) assert_(np.isnan(np.log2(-np.inf))) assert_equal(np.log2(0.), -np.inf) assert_(w[0].category is RuntimeWarning) assert_(w[1].category is RuntimeWarning) assert_(w[2].category is RuntimeWarning) class TestExp2: def test_exp2_values(self): x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] for dt in ['f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_almost_equal(np.exp2(yf), xf) class TestLogAddExp2(_FilterInvalids): # Need test for intermediate precisions def test_logaddexp2_values(self): x = [1, 2, 3, 4, 5] y = [5, 4, 3, 2, 1] z = [6, 6, 6, 6, 6] for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]): xf = np.log2(np.array(x, dtype=dt)) yf = np.log2(np.array(y, dtype=dt)) zf = np.log2(np.array(z, dtype=dt)) assert_almost_equal(np.logaddexp2(xf, yf), zf, decimal=dec_) def test_logaddexp2_range(self): x = [1000000, -1000000, 1000200, -1000200] y = [1000200, -1000200, 1000000, -1000000] z = [1000200, -1000000, 1000200, -1000000] for dt in ['f', 'd', 'g']: logxf = np.array(x, dtype=dt) logyf = np.array(y, dtype=dt) logzf = np.array(z, dtype=dt) assert_almost_equal(np.logaddexp2(logxf, logyf), logzf) def test_inf(self): inf = np.inf x = [inf, -inf, inf, -inf, inf, 1, -inf, 1] y = [inf, inf, -inf, -inf, 1, inf, 1, -inf] z = [inf, inf, inf, -inf, inf, inf, 1, 1] with np.errstate(invalid='raise'): for dt in ['f', 'd', 'g']: logxf = np.array(x, dtype=dt) logyf = np.array(y, dtype=dt) logzf = np.array(z, dtype=dt) assert_equal(np.logaddexp2(logxf, logyf), logzf) def test_nan(self): assert_(np.isnan(np.logaddexp2(np.nan, np.inf))) assert_(np.isnan(np.logaddexp2(np.inf, np.nan))) assert_(np.isnan(np.logaddexp2(np.nan, 0))) assert_(np.isnan(np.logaddexp2(0, np.nan))) assert_(np.isnan(np.logaddexp2(np.nan, np.nan))) def test_reduce(self): assert_equal(np.logaddexp2.identity, -np.inf) assert_equal(np.logaddexp2.reduce([]), -np.inf) assert_equal(np.logaddexp2.reduce([-np.inf]), -np.inf) assert_equal(np.logaddexp2.reduce([-np.inf, 0]), 0) class TestLog: def test_log_values(self): x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] for dt in ['f', 'd', 'g']: log2_ = 0.69314718055994530943 xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt)*log2_ assert_almost_equal(np.log(xf), yf) # test aliasing(issue #17761) x = np.array([2, 0.937500, 3, 0.947500, 1.054697]) xf = np.log(x) assert_almost_equal(np.log(x, out=x), xf) # test log() of max for dtype does not raise for dt in ['f', 'd', 'g']: try: with np.errstate(all='raise'): x = np.finfo(dt).max np.log(x) except FloatingPointError as exc: if dt == 'g' and IS_MUSL: # FloatingPointError is known to occur on longdouble # for musllinux_x86_64 x is very large pytest.skip( "Overflow has occurred for" " np.log(np.finfo(np.longdouble).max)" ) else: raise exc def test_log_strides(self): np.random.seed(42) strides = np.array([-4,-3,-2,-1,1,2,3,4]) sizes = np.arange(2,100) for ii in sizes: x_f64 = np.float64(np.random.uniform(low=0.01, high=100.0,size=ii)) x_special = x_f64.copy() x_special[3:-1:4] = 1.0 y_true = np.log(x_f64) y_special = np.log(x_special) for jj in strides: assert_array_almost_equal_nulp(np.log(x_f64[::jj]), y_true[::jj], nulp=2) assert_array_almost_equal_nulp(np.log(x_special[::jj]), y_special[::jj], nulp=2) class TestExp: def test_exp_values(self): x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] for dt in ['f', 'd', 'g']: log2_ = 0.69314718055994530943 xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt)*log2_ assert_almost_equal(np.exp(yf), xf) def test_exp_strides(self): np.random.seed(42) strides = np.array([-4,-3,-2,-1,1,2,3,4]) sizes = np.arange(2,100) for ii in sizes: x_f64 = np.float64(np.random.uniform(low=0.01, high=709.1,size=ii)) y_true = np.exp(x_f64) for jj in strides: assert_array_almost_equal_nulp(np.exp(x_f64[::jj]), y_true[::jj], nulp=2) class TestSpecialFloats: def test_exp_values(self): with np.errstate(under='raise', over='raise'): x = [np.nan, np.nan, np.inf, 0.] y = [np.nan, -np.nan, np.inf, -np.inf] for dt in ['e', 'f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_equal(np.exp(yf), xf) # See: https://github.com/numpy/numpy/issues/19192 @pytest.mark.xfail( _glibc_older_than("2.17"), reason="Older glibc versions may not raise appropriate FP exceptions" ) def test_exp_exceptions(self): with np.errstate(over='raise'): assert_raises(FloatingPointError, np.exp, np.float16(11.0899)) assert_raises(FloatingPointError, np.exp, np.float32(100.)) assert_raises(FloatingPointError, np.exp, np.float32(1E19)) assert_raises(FloatingPointError, np.exp, np.float64(800.)) assert_raises(FloatingPointError, np.exp, np.float64(1E19)) with np.errstate(under='raise'): assert_raises(FloatingPointError, np.exp, np.float16(-17.5)) assert_raises(FloatingPointError, np.exp, np.float32(-1000.)) assert_raises(FloatingPointError, np.exp, np.float32(-1E19)) assert_raises(FloatingPointError, np.exp, np.float64(-1000.)) assert_raises(FloatingPointError, np.exp, np.float64(-1E19)) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_log_values(self): with np.errstate(all='ignore'): x = [np.nan, np.nan, np.inf, np.nan, -np.inf, np.nan] y = [np.nan, -np.nan, np.inf, -np.inf, 0.0, -1.0] y1p = [np.nan, -np.nan, np.inf, -np.inf, -1.0, -2.0] for dt in ['e', 'f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) yf1p = np.array(y1p, dtype=dt) assert_equal(np.log(yf), xf) assert_equal(np.log2(yf), xf) assert_equal(np.log10(yf), xf) assert_equal(np.log1p(yf1p), xf) with np.errstate(divide='raise'): for dt in ['e', 'f', 'd']: assert_raises(FloatingPointError, np.log, np.array(0.0, dtype=dt)) assert_raises(FloatingPointError, np.log2, np.array(0.0, dtype=dt)) assert_raises(FloatingPointError, np.log10, np.array(0.0, dtype=dt)) assert_raises(FloatingPointError, np.log1p, np.array(-1.0, dtype=dt)) with np.errstate(invalid='raise'): for dt in ['e', 'f', 'd']: assert_raises(FloatingPointError, np.log, np.array(-np.inf, dtype=dt)) assert_raises(FloatingPointError, np.log, np.array(-1.0, dtype=dt)) assert_raises(FloatingPointError, np.log2, np.array(-np.inf, dtype=dt)) assert_raises(FloatingPointError, np.log2, np.array(-1.0, dtype=dt)) assert_raises(FloatingPointError, np.log10, np.array(-np.inf, dtype=dt)) assert_raises(FloatingPointError, np.log10, np.array(-1.0, dtype=dt)) assert_raises(FloatingPointError, np.log1p, np.array(-np.inf, dtype=dt)) assert_raises(FloatingPointError, np.log1p, np.array(-2.0, dtype=dt)) # See https://github.com/numpy/numpy/issues/18005 with assert_no_warnings(): a = np.array(1e9, dtype='float32') np.log(a) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize('dtype', ['e', 'f', 'd', 'g']) def test_sincos_values(self, dtype): with np.errstate(all='ignore'): x = [np.nan, np.nan, np.nan, np.nan] y = [np.nan, -np.nan, np.inf, -np.inf] xf = np.array(x, dtype=dtype) yf = np.array(y, dtype=dtype) assert_equal(np.sin(yf), xf) assert_equal(np.cos(yf), xf) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.xfail( sys.platform.startswith("darwin"), reason="underflow is triggered for scalar 'sin'" ) def test_sincos_underflow(self): with np.errstate(under='raise'): underflow_trigger = np.array( float.fromhex("0x1.f37f47a03f82ap-511"), dtype=np.float64 ) np.sin(underflow_trigger) np.cos(underflow_trigger) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize('callable', [np.sin, np.cos]) @pytest.mark.parametrize('dtype', ['e', 'f', 'd']) @pytest.mark.parametrize('value', [np.inf, -np.inf]) def test_sincos_errors(self, callable, dtype, value): with np.errstate(invalid='raise'): assert_raises(FloatingPointError, callable, np.array([value], dtype=dtype)) @pytest.mark.parametrize('callable', [np.sin, np.cos]) @pytest.mark.parametrize('dtype', ['f', 'd']) @pytest.mark.parametrize('stride', [-1, 1, 2, 4, 5]) def test_sincos_overlaps(self, callable, dtype, stride): N = 100 M = N // abs(stride) rng = np.random.default_rng(42) x = rng.standard_normal(N, dtype) y = callable(x[::stride]) callable(x[::stride], out=x[:M]) assert_equal(x[:M], y) @pytest.mark.parametrize('dt', ['e', 'f', 'd', 'g']) def test_sqrt_values(self, dt): with np.errstate(all='ignore'): x = [np.nan, np.nan, np.inf, np.nan, 0.] y = [np.nan, -np.nan, np.inf, -np.inf, 0.] xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_equal(np.sqrt(yf), xf) # with np.errstate(invalid='raise'): # assert_raises( # FloatingPointError, np.sqrt, np.array(-100., dtype=dt) # ) def test_abs_values(self): x = [np.nan, np.nan, np.inf, np.inf, 0., 0., 1.0, 1.0] y = [np.nan, -np.nan, np.inf, -np.inf, 0., -0., -1.0, 1.0] for dt in ['e', 'f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_equal(np.abs(yf), xf) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_square_values(self): x = [np.nan, np.nan, np.inf, np.inf] y = [np.nan, -np.nan, np.inf, -np.inf] with np.errstate(all='ignore'): for dt in ['e', 'f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_equal(np.square(yf), xf) with np.errstate(over='raise'): assert_raises(FloatingPointError, np.square, np.array(1E3, dtype='e')) assert_raises(FloatingPointError, np.square, np.array(1E32, dtype='f')) assert_raises(FloatingPointError, np.square, np.array(1E200, dtype='d')) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_reciprocal_values(self): with np.errstate(all='ignore'): x = [np.nan, np.nan, 0.0, -0.0, np.inf, -np.inf] y = [np.nan, -np.nan, np.inf, -np.inf, 0., -0.] for dt in ['e', 'f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_equal(np.reciprocal(yf), xf) with np.errstate(divide='raise'): for dt in ['e', 'f', 'd', 'g']: assert_raises(FloatingPointError, np.reciprocal, np.array(-0.0, dtype=dt)) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_tan(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan, 0.0, -0.0, np.inf, -np.inf] out = [np.nan, np.nan, 0.0, -0.0, np.nan, np.nan] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.tan(in_arr), out_arr) with np.errstate(invalid='raise'): for dt in ['e', 'f', 'd']: assert_raises(FloatingPointError, np.tan, np.array(np.inf, dtype=dt)) assert_raises(FloatingPointError, np.tan, np.array(-np.inf, dtype=dt)) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_arcsincos(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, np.nan, np.nan] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.arcsin(in_arr), out_arr) assert_equal(np.arccos(in_arr), out_arr) for callable in [np.arcsin, np.arccos]: for value in [np.inf, -np.inf, 2.0, -2.0]: for dt in ['e', 'f', 'd']: with np.errstate(invalid='raise'): assert_raises(FloatingPointError, callable, np.array(value, dtype=dt)) def test_arctan(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan] out = [np.nan, np.nan] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.arctan(in_arr), out_arr) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_sinh(self): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, np.inf, -np.inf] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.sinh(in_arr), out_arr) with np.errstate(over='raise'): assert_raises(FloatingPointError, np.sinh, np.array(12.0, dtype='e')) assert_raises(FloatingPointError, np.sinh, np.array(120.0, dtype='f')) assert_raises(FloatingPointError, np.sinh, np.array(1200.0, dtype='d')) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_cosh(self): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, np.inf, np.inf] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.cosh(in_arr), out_arr) with np.errstate(over='raise'): assert_raises(FloatingPointError, np.cosh, np.array(12.0, dtype='e')) assert_raises(FloatingPointError, np.cosh, np.array(120.0, dtype='f')) assert_raises(FloatingPointError, np.cosh, np.array(1200.0, dtype='d')) def test_tanh(self): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, 1.0, -1.0] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.tanh(in_arr), out_arr) def test_arcsinh(self): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, np.inf, -np.inf] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.arcsinh(in_arr), out_arr) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_arccosh(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan, np.inf, -np.inf, 1.0, 0.0] out = [np.nan, np.nan, np.inf, np.nan, 0.0, np.nan] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.arccosh(in_arr), out_arr) for value in [0.0, -np.inf]: with np.errstate(invalid='raise'): for dt in ['e', 'f', 'd']: assert_raises(FloatingPointError, np.arccosh, np.array(value, dtype=dt)) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_arctanh(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan, np.inf, -np.inf, 1.0, -1.0, 2.0] out = [np.nan, np.nan, np.nan, np.nan, np.inf, -np.inf, np.nan] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.arctanh(in_arr), out_arr) for value in [1.01, np.inf, -np.inf, 1.0, -1.0]: with np.errstate(invalid='raise', divide='raise'): for dt in ['e', 'f', 'd']: assert_raises(FloatingPointError, np.arctanh, np.array(value, dtype=dt)) # See: https://github.com/numpy/numpy/issues/20448 @pytest.mark.xfail( _glibc_older_than("2.17"), reason="Older glibc versions may not raise appropriate FP exceptions" ) def test_exp2(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, np.inf, 0.0] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.exp2(in_arr), out_arr) for value in [2000.0, -2000.0]: with np.errstate(over='raise', under='raise'): for dt in ['e', 'f', 'd']: assert_raises(FloatingPointError, np.exp2, np.array(value, dtype=dt)) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") def test_expm1(self): with np.errstate(all='ignore'): in_ = [np.nan, -np.nan, np.inf, -np.inf] out = [np.nan, np.nan, np.inf, -1.0] for dt in ['e', 'f', 'd']: in_arr = np.array(in_, dtype=dt) out_arr = np.array(out, dtype=dt) assert_equal(np.expm1(in_arr), out_arr) for value in [200.0, 2000.0]: with np.errstate(over='raise'): for dt in ['e', 'f']: assert_raises(FloatingPointError, np.expm1, np.array(value, dtype=dt)) # test to ensure no spurious FP exceptions are raised due to SIMD INF_INVALID_ERR = [ np.cos, np.sin, np.tan, np.arccos, np.arcsin, np.spacing, np.arctanh ] NEG_INVALID_ERR = [ np.log, np.log2, np.log10, np.log1p, np.sqrt, np.arccosh, np.arctanh ] ONE_INVALID_ERR = [ np.arctanh, ] LTONE_INVALID_ERR = [ np.arccosh, ] BYZERO_ERR = [ np.log, np.log2, np.log10, np.reciprocal, np.arccosh ] @pytest.mark.parametrize("ufunc", UFUNCS_UNARY_FP) @pytest.mark.parametrize("dtype", ('e', 'f', 'd')) @pytest.mark.parametrize("data, escape", ( ([0.03], LTONE_INVALID_ERR), ([0.03]*32, LTONE_INVALID_ERR), # neg ([-1.0], NEG_INVALID_ERR), ([-1.0]*32, NEG_INVALID_ERR), # flat ([1.0], ONE_INVALID_ERR), ([1.0]*32, ONE_INVALID_ERR), # zero ([0.0], BYZERO_ERR), ([0.0]*32, BYZERO_ERR), ([-0.0], BYZERO_ERR), ([-0.0]*32, BYZERO_ERR), # nan ([0.5, 0.5, 0.5, np.nan], LTONE_INVALID_ERR), ([0.5, 0.5, 0.5, np.nan]*32, LTONE_INVALID_ERR), ([np.nan, 1.0, 1.0, 1.0], ONE_INVALID_ERR), ([np.nan, 1.0, 1.0, 1.0]*32, ONE_INVALID_ERR), ([np.nan], []), ([np.nan]*32, []), # inf ([0.5, 0.5, 0.5, np.inf], INF_INVALID_ERR + LTONE_INVALID_ERR), ([0.5, 0.5, 0.5, np.inf]*32, INF_INVALID_ERR + LTONE_INVALID_ERR), ([np.inf, 1.0, 1.0, 1.0], INF_INVALID_ERR), ([np.inf, 1.0, 1.0, 1.0]*32, INF_INVALID_ERR), ([np.inf], INF_INVALID_ERR), ([np.inf]*32, INF_INVALID_ERR), # ninf ([0.5, 0.5, 0.5, -np.inf], NEG_INVALID_ERR + INF_INVALID_ERR + LTONE_INVALID_ERR), ([0.5, 0.5, 0.5, -np.inf]*32, NEG_INVALID_ERR + INF_INVALID_ERR + LTONE_INVALID_ERR), ([-np.inf, 1.0, 1.0, 1.0], NEG_INVALID_ERR + INF_INVALID_ERR), ([-np.inf, 1.0, 1.0, 1.0]*32, NEG_INVALID_ERR + INF_INVALID_ERR), ([-np.inf], NEG_INVALID_ERR + INF_INVALID_ERR), ([-np.inf]*32, NEG_INVALID_ERR + INF_INVALID_ERR), )) def test_unary_spurious_fpexception(self, ufunc, dtype, data, escape): if escape and ufunc in escape: return # FIXME: NAN raises FP invalid exception: # - ceil/float16 on MSVC:32-bit # - spacing/float16 on almost all platforms if ufunc in (np.spacing, np.ceil) and dtype == 'e': return array = np.array(data, dtype=dtype) with assert_no_warnings(): ufunc(array) class TestFPClass: @pytest.mark.parametrize("stride", [-5, -4, -3, -2, -1, 1, 2, 4, 5, 6, 7, 8, 9, 10]) def test_fpclass(self, stride): arr_f64 = np.array([np.nan, -np.nan, np.inf, -np.inf, -1.0, 1.0, -0.0, 0.0, 2.2251e-308, -2.2251e-308], dtype='d') arr_f32 = np.array([np.nan, -np.nan, np.inf, -np.inf, -1.0, 1.0, -0.0, 0.0, 1.4013e-045, -1.4013e-045], dtype='f') nan = np.array([True, True, False, False, False, False, False, False, False, False]) inf = np.array([False, False, True, True, False, False, False, False, False, False]) sign = np.array([False, True, False, True, True, False, True, False, False, True]) finite = np.array([False, False, False, False, True, True, True, True, True, True]) assert_equal(np.isnan(arr_f32[::stride]), nan[::stride]) assert_equal(np.isnan(arr_f64[::stride]), nan[::stride]) assert_equal(np.isinf(arr_f32[::stride]), inf[::stride]) assert_equal(np.isinf(arr_f64[::stride]), inf[::stride]) assert_equal(np.signbit(arr_f32[::stride]), sign[::stride]) assert_equal(np.signbit(arr_f64[::stride]), sign[::stride]) assert_equal(np.isfinite(arr_f32[::stride]), finite[::stride]) assert_equal(np.isfinite(arr_f64[::stride]), finite[::stride]) @pytest.mark.parametrize("dtype", ['d', 'f']) def test_fp_noncontiguous(self, dtype): data = np.array([np.nan, -np.nan, np.inf, -np.inf, -1.0, 1.0, -0.0, 0.0, 2.2251e-308, -2.2251e-308], dtype=dtype) nan = np.array([True, True, False, False, False, False, False, False, False, False]) inf = np.array([False, False, True, True, False, False, False, False, False, False]) sign = np.array([False, True, False, True, True, False, True, False, False, True]) finite = np.array([False, False, False, False, True, True, True, True, True, True]) out = np.ndarray(data.shape, dtype='bool') ncontig_in = data[1::3] ncontig_out = out[1::3] contig_in = np.array(ncontig_in) assert_equal(ncontig_in.flags.c_contiguous, False) assert_equal(ncontig_out.flags.c_contiguous, False) assert_equal(contig_in.flags.c_contiguous, True) # ncontig in, ncontig out assert_equal(np.isnan(ncontig_in, out=ncontig_out), nan[1::3]) assert_equal(np.isinf(ncontig_in, out=ncontig_out), inf[1::3]) assert_equal(np.signbit(ncontig_in, out=ncontig_out), sign[1::3]) assert_equal(np.isfinite(ncontig_in, out=ncontig_out), finite[1::3]) # contig in, ncontig out assert_equal(np.isnan(contig_in, out=ncontig_out), nan[1::3]) assert_equal(np.isinf(contig_in, out=ncontig_out), inf[1::3]) assert_equal(np.signbit(contig_in, out=ncontig_out), sign[1::3]) assert_equal(np.isfinite(contig_in, out=ncontig_out), finite[1::3]) # ncontig in, contig out assert_equal(np.isnan(ncontig_in), nan[1::3]) assert_equal(np.isinf(ncontig_in), inf[1::3]) assert_equal(np.signbit(ncontig_in), sign[1::3]) assert_equal(np.isfinite(ncontig_in), finite[1::3]) # contig in, contig out, nd stride data_split = np.array(np.array_split(data, 2)) nan_split = np.array(np.array_split(nan, 2)) inf_split = np.array(np.array_split(inf, 2)) sign_split = np.array(np.array_split(sign, 2)) finite_split = np.array(np.array_split(finite, 2)) assert_equal(np.isnan(data_split), nan_split) assert_equal(np.isinf(data_split), inf_split) assert_equal(np.signbit(data_split), sign_split) assert_equal(np.isfinite(data_split), finite_split) class TestLDExp: @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4]) @pytest.mark.parametrize("dtype", ['f', 'd']) def test_ldexp(self, dtype, stride): mant = np.array([0.125, 0.25, 0.5, 1., 1., 2., 4., 8.], dtype=dtype) exp = np.array([3, 2, 1, 0, 0, -1, -2, -3], dtype='i') out = np.zeros(8, dtype=dtype) assert_equal(np.ldexp(mant[::stride], exp[::stride], out=out[::stride]), np.ones(8, dtype=dtype)[::stride]) assert_equal(out[::stride], np.ones(8, dtype=dtype)[::stride]) class TestFRExp: @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4]) @pytest.mark.parametrize("dtype", ['f', 'd']) @pytest.mark.xfail(IS_MUSL, reason="gh23048") @pytest.mark.skipif(not sys.platform.startswith('linux'), reason="np.frexp gives different answers for NAN/INF on windows and linux") def test_frexp(self, dtype, stride): arr = np.array([np.nan, np.nan, np.inf, -np.inf, 0.0, -0.0, 1.0, -1.0], dtype=dtype) mant_true = np.array([np.nan, np.nan, np.inf, -np.inf, 0.0, -0.0, 0.5, -0.5], dtype=dtype) exp_true = np.array([0, 0, 0, 0, 0, 0, 1, 1], dtype='i') out_mant = np.ones(8, dtype=dtype) out_exp = 2*np.ones(8, dtype='i') mant, exp = np.frexp(arr[::stride], out=(out_mant[::stride], out_exp[::stride])) assert_equal(mant_true[::stride], mant) assert_equal(exp_true[::stride], exp) assert_equal(out_mant[::stride], mant_true[::stride]) assert_equal(out_exp[::stride], exp_true[::stride]) # func : [maxulperror, low, high] avx_ufuncs = {'sqrt' :[1, 0., 100.], 'absolute' :[0, -100., 100.], 'reciprocal' :[1, 1., 100.], 'square' :[1, -100., 100.], 'rint' :[0, -100., 100.], 'floor' :[0, -100., 100.], 'ceil' :[0, -100., 100.], 'trunc' :[0, -100., 100.]} class TestAVXUfuncs: def test_avx_based_ufunc(self): strides = np.array([-4,-3,-2,-1,1,2,3,4]) np.random.seed(42) for func, prop in avx_ufuncs.items(): maxulperr = prop[0] minval = prop[1] maxval = prop[2] # various array sizes to ensure masking in AVX is tested for size in range(1,32): myfunc = getattr(np, func) x_f32 = np.float32(np.random.uniform(low=minval, high=maxval, size=size)) x_f64 = np.float64(x_f32) x_f128 = np.longdouble(x_f32) y_true128 = myfunc(x_f128) if maxulperr == 0: assert_equal(myfunc(x_f32), np.float32(y_true128)) assert_equal(myfunc(x_f64), np.float64(y_true128)) else: assert_array_max_ulp(myfunc(x_f32), np.float32(y_true128), maxulp=maxulperr) assert_array_max_ulp(myfunc(x_f64), np.float64(y_true128), maxulp=maxulperr) # various strides to test gather instruction if size > 1: y_true32 = myfunc(x_f32) y_true64 = myfunc(x_f64) for jj in strides: assert_equal(myfunc(x_f64[::jj]), y_true64[::jj]) assert_equal(myfunc(x_f32[::jj]), y_true32[::jj]) class TestAVXFloat32Transcendental: def test_exp_float32(self): np.random.seed(42) x_f32 = np.float32(np.random.uniform(low=0.0,high=88.1,size=1000000)) x_f64 = np.float64(x_f32) assert_array_max_ulp(np.exp(x_f32), np.float32(np.exp(x_f64)), maxulp=3) def test_log_float32(self): np.random.seed(42) x_f32 = np.float32(np.random.uniform(low=0.0,high=1000,size=1000000)) x_f64 = np.float64(x_f32) assert_array_max_ulp(np.log(x_f32), np.float32(np.log(x_f64)), maxulp=4) def test_sincos_float32(self): np.random.seed(42) N = 1000000 M = np.int_(N/20) index = np.random.randint(low=0, high=N, size=M) x_f32 = np.float32(np.random.uniform(low=-100.,high=100.,size=N)) if not _glibc_older_than("2.17"): # test coverage for elements > 117435.992f for which glibc is used # this is known to be problematic on old glibc, so skip it there x_f32[index] = np.float32(10E+10*np.random.rand(M)) x_f64 = np.float64(x_f32) assert_array_max_ulp(np.sin(x_f32), np.float32(np.sin(x_f64)), maxulp=2) assert_array_max_ulp(np.cos(x_f32), np.float32(np.cos(x_f64)), maxulp=2) # test aliasing(issue #17761) tx_f32 = x_f32.copy() assert_array_max_ulp(np.sin(x_f32, out=x_f32), np.float32(np.sin(x_f64)), maxulp=2) assert_array_max_ulp(np.cos(tx_f32, out=tx_f32), np.float32(np.cos(x_f64)), maxulp=2) def test_strided_float32(self): np.random.seed(42) strides = np.array([-4,-3,-2,-1,1,2,3,4]) sizes = np.arange(2,100) for ii in sizes: x_f32 = np.float32(np.random.uniform(low=0.01,high=88.1,size=ii)) x_f32_large = x_f32.copy() x_f32_large[3:-1:4] = 120000.0 exp_true = np.exp(x_f32) log_true = np.log(x_f32) sin_true = np.sin(x_f32_large) cos_true = np.cos(x_f32_large) for jj in strides: assert_array_almost_equal_nulp(np.exp(x_f32[::jj]), exp_true[::jj], nulp=2) assert_array_almost_equal_nulp(np.log(x_f32[::jj]), log_true[::jj], nulp=2) assert_array_almost_equal_nulp(np.sin(x_f32_large[::jj]), sin_true[::jj], nulp=2) assert_array_almost_equal_nulp(np.cos(x_f32_large[::jj]), cos_true[::jj], nulp=2) class TestLogAddExp(_FilterInvalids): def test_logaddexp_values(self): x = [1, 2, 3, 4, 5] y = [5, 4, 3, 2, 1] z = [6, 6, 6, 6, 6] for dt, dec_ in zip(['f', 'd', 'g'], [6, 15, 15]): xf = np.log(np.array(x, dtype=dt)) yf = np.log(np.array(y, dtype=dt)) zf = np.log(np.array(z, dtype=dt)) assert_almost_equal(np.logaddexp(xf, yf), zf, decimal=dec_) def test_logaddexp_range(self): x = [1000000, -1000000, 1000200, -1000200] y = [1000200, -1000200, 1000000, -1000000] z = [1000200, -1000000, 1000200, -1000000] for dt in ['f', 'd', 'g']: logxf = np.array(x, dtype=dt) logyf = np.array(y, dtype=dt) logzf = np.array(z, dtype=dt) assert_almost_equal(np.logaddexp(logxf, logyf), logzf) def test_inf(self): inf = np.inf x = [inf, -inf, inf, -inf, inf, 1, -inf, 1] y = [inf, inf, -inf, -inf, 1, inf, 1, -inf] z = [inf, inf, inf, -inf, inf, inf, 1, 1] with np.errstate(invalid='raise'): for dt in ['f', 'd', 'g']: logxf = np.array(x, dtype=dt) logyf = np.array(y, dtype=dt) logzf = np.array(z, dtype=dt) assert_equal(np.logaddexp(logxf, logyf), logzf) def test_nan(self): assert_(np.isnan(np.logaddexp(np.nan, np.inf))) assert_(np.isnan(np.logaddexp(np.inf, np.nan))) assert_(np.isnan(np.logaddexp(np.nan, 0))) assert_(np.isnan(np.logaddexp(0, np.nan))) assert_(np.isnan(np.logaddexp(np.nan, np.nan))) def test_reduce(self): assert_equal(np.logaddexp.identity, -np.inf) assert_equal(np.logaddexp.reduce([]), -np.inf) class TestLog1p: def test_log1p(self): assert_almost_equal(ncu.log1p(0.2), ncu.log(1.2)) assert_almost_equal(ncu.log1p(1e-6), ncu.log(1+1e-6)) def test_special(self): with np.errstate(invalid="ignore", divide="ignore"): assert_equal(ncu.log1p(np.nan), np.nan) assert_equal(ncu.log1p(np.inf), np.inf) assert_equal(ncu.log1p(-1.), -np.inf) assert_equal(ncu.log1p(-2.), np.nan) assert_equal(ncu.log1p(-np.inf), np.nan) class TestExpm1: def test_expm1(self): assert_almost_equal(ncu.expm1(0.2), ncu.exp(0.2)-1) assert_almost_equal(ncu.expm1(1e-6), ncu.exp(1e-6)-1) def test_special(self): assert_equal(ncu.expm1(np.inf), np.inf) assert_equal(ncu.expm1(0.), 0.) assert_equal(ncu.expm1(-0.), -0.) assert_equal(ncu.expm1(np.inf), np.inf) assert_equal(ncu.expm1(-np.inf), -1.) def test_complex(self): x = np.asarray(1e-12) assert_allclose(x, ncu.expm1(x)) x = x.astype(np.complex128) assert_allclose(x, ncu.expm1(x)) class TestHypot: def test_simple(self): assert_almost_equal(ncu.hypot(1, 1), ncu.sqrt(2)) assert_almost_equal(ncu.hypot(0, 0), 0) def test_reduce(self): assert_almost_equal(ncu.hypot.reduce([3.0, 4.0]), 5.0) assert_almost_equal(ncu.hypot.reduce([3.0, 4.0, 0]), 5.0) assert_almost_equal(ncu.hypot.reduce([9.0, 12.0, 20.0]), 25.0) assert_equal(ncu.hypot.reduce([]), 0.0) def assert_hypot_isnan(x, y): with np.errstate(invalid='ignore'): assert_(np.isnan(ncu.hypot(x, y)), "hypot(%s, %s) is %s, not nan" % (x, y, ncu.hypot(x, y))) def assert_hypot_isinf(x, y): with np.errstate(invalid='ignore'): assert_(np.isinf(ncu.hypot(x, y)), "hypot(%s, %s) is %s, not inf" % (x, y, ncu.hypot(x, y))) class TestHypotSpecialValues: def test_nan_outputs(self): assert_hypot_isnan(np.nan, np.nan) assert_hypot_isnan(np.nan, 1) def test_nan_outputs2(self): assert_hypot_isinf(np.nan, np.inf) assert_hypot_isinf(np.inf, np.nan) assert_hypot_isinf(np.inf, 0) assert_hypot_isinf(0, np.inf) assert_hypot_isinf(np.inf, np.inf) assert_hypot_isinf(np.inf, 23.0) def test_no_fpe(self): assert_no_warnings(ncu.hypot, np.inf, 0) def assert_arctan2_isnan(x, y): assert_(np.isnan(ncu.arctan2(x, y)), "arctan(%s, %s) is %s, not nan" % (x, y, ncu.arctan2(x, y))) def assert_arctan2_ispinf(x, y): assert_((np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) > 0), "arctan(%s, %s) is %s, not +inf" % (x, y, ncu.arctan2(x, y))) def assert_arctan2_isninf(x, y): assert_((np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0), "arctan(%s, %s) is %s, not -inf" % (x, y, ncu.arctan2(x, y))) def assert_arctan2_ispzero(x, y): assert_((ncu.arctan2(x, y) == 0 and not np.signbit(ncu.arctan2(x, y))), "arctan(%s, %s) is %s, not +0" % (x, y, ncu.arctan2(x, y))) def assert_arctan2_isnzero(x, y): assert_((ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))), "arctan(%s, %s) is %s, not -0" % (x, y, ncu.arctan2(x, y))) class TestArctan2SpecialValues: def test_one_one(self): # atan2(1, 1) returns pi/4. assert_almost_equal(ncu.arctan2(1, 1), 0.25 * np.pi) assert_almost_equal(ncu.arctan2(-1, 1), -0.25 * np.pi) assert_almost_equal(ncu.arctan2(1, -1), 0.75 * np.pi) def test_zero_nzero(self): # atan2(+-0, -0) returns +-pi. assert_almost_equal(ncu.arctan2(np.PZERO, np.NZERO), np.pi) assert_almost_equal(ncu.arctan2(np.NZERO, np.NZERO), -np.pi) def test_zero_pzero(self): # atan2(+-0, +0) returns +-0. assert_arctan2_ispzero(np.PZERO, np.PZERO) assert_arctan2_isnzero(np.NZERO, np.PZERO) def test_zero_negative(self): # atan2(+-0, x) returns +-pi for x < 0. assert_almost_equal(ncu.arctan2(np.PZERO, -1), np.pi) assert_almost_equal(ncu.arctan2(np.NZERO, -1), -np.pi) def test_zero_positive(self): # atan2(+-0, x) returns +-0 for x > 0. assert_arctan2_ispzero(np.PZERO, 1) assert_arctan2_isnzero(np.NZERO, 1) def test_positive_zero(self): # atan2(y, +-0) returns +pi/2 for y > 0. assert_almost_equal(ncu.arctan2(1, np.PZERO), 0.5 * np.pi) assert_almost_equal(ncu.arctan2(1, np.NZERO), 0.5 * np.pi) def test_negative_zero(self): # atan2(y, +-0) returns -pi/2 for y < 0. assert_almost_equal(ncu.arctan2(-1, np.PZERO), -0.5 * np.pi) assert_almost_equal(ncu.arctan2(-1, np.NZERO), -0.5 * np.pi) def test_any_ninf(self): # atan2(+-y, -infinity) returns +-pi for finite y > 0. assert_almost_equal(ncu.arctan2(1, np.NINF), np.pi) assert_almost_equal(ncu.arctan2(-1, np.NINF), -np.pi) def test_any_pinf(self): # atan2(+-y, +infinity) returns +-0 for finite y > 0. assert_arctan2_ispzero(1, np.inf) assert_arctan2_isnzero(-1, np.inf) def test_inf_any(self): # atan2(+-infinity, x) returns +-pi/2 for finite x. assert_almost_equal(ncu.arctan2( np.inf, 1), 0.5 * np.pi) assert_almost_equal(ncu.arctan2(-np.inf, 1), -0.5 * np.pi) def test_inf_ninf(self): # atan2(+-infinity, -infinity) returns +-3*pi/4. assert_almost_equal(ncu.arctan2( np.inf, -np.inf), 0.75 * np.pi) assert_almost_equal(ncu.arctan2(-np.inf, -np.inf), -0.75 * np.pi) def test_inf_pinf(self): # atan2(+-infinity, +infinity) returns +-pi/4. assert_almost_equal(ncu.arctan2( np.inf, np.inf), 0.25 * np.pi) assert_almost_equal(ncu.arctan2(-np.inf, np.inf), -0.25 * np.pi) def test_nan_any(self): # atan2(nan, x) returns nan for any x, including inf assert_arctan2_isnan(np.nan, np.inf) assert_arctan2_isnan(np.inf, np.nan) assert_arctan2_isnan(np.nan, np.nan) class TestLdexp: def _check_ldexp(self, tp): assert_almost_equal(ncu.ldexp(np.array(2., np.float32), np.array(3, tp)), 16.) assert_almost_equal(ncu.ldexp(np.array(2., np.float64), np.array(3, tp)), 16.) assert_almost_equal(ncu.ldexp(np.array(2., np.longdouble), np.array(3, tp)), 16.) def test_ldexp(self): # The default Python int type should work assert_almost_equal(ncu.ldexp(2., 3), 16.) # The following int types should all be accepted self._check_ldexp(np.int8) self._check_ldexp(np.int16) self._check_ldexp(np.int32) self._check_ldexp('i') self._check_ldexp('l') def test_ldexp_overflow(self): # silence warning emitted on overflow with np.errstate(over="ignore"): imax = np.iinfo(np.dtype('l')).max imin = np.iinfo(np.dtype('l')).min assert_equal(ncu.ldexp(2., imax), np.inf) assert_equal(ncu.ldexp(2., imin), 0) class TestMaximum(_FilterInvalids): def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.maximum.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), np.nan) assert_equal(func(tmp2), np.nan) def test_reduce_complex(self): assert_equal(np.maximum.reduce([1, 2j]), 1) assert_equal(np.maximum.reduce([1+3j, 2j]), 1+3j) def test_float_nans(self): nan = np.nan arg1 = np.array([0, nan, nan]) arg2 = np.array([nan, 0, nan]) out = np.array([nan, nan, nan]) assert_equal(np.maximum(arg1, arg2), out) def test_object_nans(self): # Multiple checks to give this a chance to # fail if cmp is used instead of rich compare. # Failure cannot be guaranteed. for i in range(1): x = np.array(float('nan'), object) y = 1.0 z = np.array(float('nan'), object) assert_(np.maximum(x, y) == 1.0) assert_(np.maximum(z, y) == 1.0) def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]: arg1 = np.array([0, cnan, cnan], dtype=complex) arg2 = np.array([cnan, 0, cnan], dtype=complex) out = np.array([nan, nan, nan], dtype=complex) assert_equal(np.maximum(arg1, arg2), out) def test_object_array(self): arg1 = np.arange(5, dtype=object) arg2 = arg1 + 1 assert_equal(np.maximum(arg1, arg2), arg2) def test_strided_array(self): arr1 = np.array([-4.0, 1.0, 10.0, 0.0, np.nan, -np.nan, np.inf, -np.inf]) arr2 = np.array([-2.0,-1.0, np.nan, 1.0, 0.0, np.nan, 1.0, -3.0]) maxtrue = np.array([-2.0, 1.0, np.nan, 1.0, np.nan, np.nan, np.inf, -3.0]) out = np.ones(8) out_maxtrue = np.array([-2.0, 1.0, 1.0, 10.0, 1.0, 1.0, np.nan, 1.0]) assert_equal(np.maximum(arr1,arr2), maxtrue) assert_equal(np.maximum(arr1[::2],arr2[::2]), maxtrue[::2]) assert_equal(np.maximum(arr1[:4:], arr2[::2]), np.array([-2.0, np.nan, 10.0, 1.0])) assert_equal(np.maximum(arr1[::3], arr2[:3:]), np.array([-2.0, 0.0, np.nan])) assert_equal(np.maximum(arr1[:6:2], arr2[::3], out=out[::3]), np.array([-2.0, 10., np.nan])) assert_equal(out, out_maxtrue) def test_precision(self): dtypes = [np.float16, np.float32, np.float64, np.longdouble] for dt in dtypes: dtmin = np.finfo(dt).min dtmax = np.finfo(dt).max d1 = dt(0.1) d1_next = np.nextafter(d1, np.inf) test_cases = [ # v1 v2 expected (dtmin, -np.inf, dtmin), (dtmax, -np.inf, dtmax), (d1, d1_next, d1_next), (dtmax, np.nan, np.nan), ] for v1, v2, expected in test_cases: assert_equal(np.maximum([v1], [v2]), [expected]) assert_equal(np.maximum.reduce([v1, v2]), expected) class TestMinimum(_FilterInvalids): def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.minimum.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 0) assert_equal(func(tmp2), 0) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 0) assert_equal(func(tmp2), 0) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), np.nan) assert_equal(func(tmp2), np.nan) def test_reduce_complex(self): assert_equal(np.minimum.reduce([1, 2j]), 2j) assert_equal(np.minimum.reduce([1+3j, 2j]), 2j) def test_float_nans(self): nan = np.nan arg1 = np.array([0, nan, nan]) arg2 = np.array([nan, 0, nan]) out = np.array([nan, nan, nan]) assert_equal(np.minimum(arg1, arg2), out) def test_object_nans(self): # Multiple checks to give this a chance to # fail if cmp is used instead of rich compare. # Failure cannot be guaranteed. for i in range(1): x = np.array(float('nan'), object) y = 1.0 z = np.array(float('nan'), object) assert_(np.minimum(x, y) == 1.0) assert_(np.minimum(z, y) == 1.0) def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]: arg1 = np.array([0, cnan, cnan], dtype=complex) arg2 = np.array([cnan, 0, cnan], dtype=complex) out = np.array([nan, nan, nan], dtype=complex) assert_equal(np.minimum(arg1, arg2), out) def test_object_array(self): arg1 = np.arange(5, dtype=object) arg2 = arg1 + 1 assert_equal(np.minimum(arg1, arg2), arg1) def test_strided_array(self): arr1 = np.array([-4.0, 1.0, 10.0, 0.0, np.nan, -np.nan, np.inf, -np.inf]) arr2 = np.array([-2.0,-1.0, np.nan, 1.0, 0.0, np.nan, 1.0, -3.0]) mintrue = np.array([-4.0, -1.0, np.nan, 0.0, np.nan, np.nan, 1.0, -np.inf]) out = np.ones(8) out_mintrue = np.array([-4.0, 1.0, 1.0, 1.0, 1.0, 1.0, np.nan, 1.0]) assert_equal(np.minimum(arr1,arr2), mintrue) assert_equal(np.minimum(arr1[::2],arr2[::2]), mintrue[::2]) assert_equal(np.minimum(arr1[:4:], arr2[::2]), np.array([-4.0, np.nan, 0.0, 0.0])) assert_equal(np.minimum(arr1[::3], arr2[:3:]), np.array([-4.0, -1.0, np.nan])) assert_equal(np.minimum(arr1[:6:2], arr2[::3], out=out[::3]), np.array([-4.0, 1.0, np.nan])) assert_equal(out, out_mintrue) def test_precision(self): dtypes = [np.float16, np.float32, np.float64, np.longdouble] for dt in dtypes: dtmin = np.finfo(dt).min dtmax = np.finfo(dt).max d1 = dt(0.1) d1_next = np.nextafter(d1, np.inf) test_cases = [ # v1 v2 expected (dtmin, np.inf, dtmin), (dtmax, np.inf, dtmax), (d1, d1_next, d1), (dtmin, np.nan, np.nan), ] for v1, v2, expected in test_cases: assert_equal(np.minimum([v1], [v2]), [expected]) assert_equal(np.minimum.reduce([v1, v2]), expected) class TestFmax(_FilterInvalids): def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9) def test_reduce_complex(self): assert_equal(np.fmax.reduce([1, 2j]), 1) assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j) def test_float_nans(self): nan = np.nan arg1 = np.array([0, nan, nan]) arg2 = np.array([nan, 0, nan]) out = np.array([0, 0, nan]) assert_equal(np.fmax(arg1, arg2), out) def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]: arg1 = np.array([0, cnan, cnan], dtype=complex) arg2 = np.array([cnan, 0, cnan], dtype=complex) out = np.array([0, 0, nan], dtype=complex) assert_equal(np.fmax(arg1, arg2), out) def test_precision(self): dtypes = [np.float16, np.float32, np.float64, np.longdouble] for dt in dtypes: dtmin = np.finfo(dt).min dtmax = np.finfo(dt).max d1 = dt(0.1) d1_next = np.nextafter(d1, np.inf) test_cases = [ # v1 v2 expected (dtmin, -np.inf, dtmin), (dtmax, -np.inf, dtmax), (d1, d1_next, d1_next), (dtmax, np.nan, dtmax), ] for v1, v2, expected in test_cases: assert_equal(np.fmax([v1], [v2]), [expected]) assert_equal(np.fmax.reduce([v1, v2]), expected) class TestFmin(_FilterInvalids): def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmin.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 0) assert_equal(func(tmp2), 0) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 0) assert_equal(func(tmp2), 0) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 1) assert_equal(func(tmp2), 1) def test_reduce_complex(self): assert_equal(np.fmin.reduce([1, 2j]), 2j) assert_equal(np.fmin.reduce([1+3j, 2j]), 2j) def test_float_nans(self): nan = np.nan arg1 = np.array([0, nan, nan]) arg2 = np.array([nan, 0, nan]) out = np.array([0, 0, nan]) assert_equal(np.fmin(arg1, arg2), out) def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]: arg1 = np.array([0, cnan, cnan], dtype=complex) arg2 = np.array([cnan, 0, cnan], dtype=complex) out = np.array([0, 0, nan], dtype=complex) assert_equal(np.fmin(arg1, arg2), out) def test_precision(self): dtypes = [np.float16, np.float32, np.float64, np.longdouble] for dt in dtypes: dtmin = np.finfo(dt).min dtmax = np.finfo(dt).max d1 = dt(0.1) d1_next = np.nextafter(d1, np.inf) test_cases = [ # v1 v2 expected (dtmin, np.inf, dtmin), (dtmax, np.inf, dtmax), (d1, d1_next, d1), (dtmin, np.nan, dtmin), ] for v1, v2, expected in test_cases: assert_equal(np.fmin([v1], [v2]), [expected]) assert_equal(np.fmin.reduce([v1, v2]), expected) class TestBool: def test_exceptions(self): a = np.ones(1, dtype=np.bool_) assert_raises(TypeError, np.negative, a) assert_raises(TypeError, np.positive, a) assert_raises(TypeError, np.subtract, a, a) def test_truth_table_logical(self): # 2, 3 and 4 serves as true values input1 = [0, 0, 3, 2] input2 = [0, 4, 0, 2] typecodes = (np.typecodes['AllFloat'] + np.typecodes['AllInteger'] + '?') # boolean for dtype in map(np.dtype, typecodes): arg1 = np.asarray(input1, dtype=dtype) arg2 = np.asarray(input2, dtype=dtype) # OR out = [False, True, True, True] for func in (np.logical_or, np.maximum): assert_equal(func(arg1, arg2).astype(bool), out) # AND out = [False, False, False, True] for func in (np.logical_and, np.minimum): assert_equal(func(arg1, arg2).astype(bool), out) # XOR out = [False, True, True, False] for func in (np.logical_xor, np.not_equal): assert_equal(func(arg1, arg2).astype(bool), out) def test_truth_table_bitwise(self): arg1 = [False, False, True, True] arg2 = [False, True, False, True] out = [False, True, True, True] assert_equal(np.bitwise_or(arg1, arg2), out) out = [False, False, False, True] assert_equal(np.bitwise_and(arg1, arg2), out) out = [False, True, True, False] assert_equal(np.bitwise_xor(arg1, arg2), out) def test_reduce(self): none = np.array([0, 0, 0, 0], bool) some = np.array([1, 0, 1, 1], bool) every = np.array([1, 1, 1, 1], bool) empty = np.array([], bool) arrs = [none, some, every, empty] for arr in arrs: assert_equal(np.logical_and.reduce(arr), all(arr)) for arr in arrs: assert_equal(np.logical_or.reduce(arr), any(arr)) for arr in arrs: assert_equal(np.logical_xor.reduce(arr), arr.sum() % 2 == 1) class TestBitwiseUFuncs: bitwise_types = [np.dtype(c) for c in '?' + 'bBhHiIlLqQ' + 'O'] def test_values(self): for dt in self.bitwise_types: zeros = np.array([0], dtype=dt) ones = np.array([-1]).astype(dt) msg = "dt = '%s'" % dt.char assert_equal(np.bitwise_not(zeros), ones, err_msg=msg) assert_equal(np.bitwise_not(ones), zeros, err_msg=msg) assert_equal(np.bitwise_or(zeros, zeros), zeros, err_msg=msg) assert_equal(np.bitwise_or(zeros, ones), ones, err_msg=msg) assert_equal(np.bitwise_or(ones, zeros), ones, err_msg=msg) assert_equal(np.bitwise_or(ones, ones), ones, err_msg=msg) assert_equal(np.bitwise_xor(zeros, zeros), zeros, err_msg=msg) assert_equal(np.bitwise_xor(zeros, ones), ones, err_msg=msg) assert_equal(np.bitwise_xor(ones, zeros), ones, err_msg=msg) assert_equal(np.bitwise_xor(ones, ones), zeros, err_msg=msg) assert_equal(np.bitwise_and(zeros, zeros), zeros, err_msg=msg) assert_equal(np.bitwise_and(zeros, ones), zeros, err_msg=msg) assert_equal(np.bitwise_and(ones, zeros), zeros, err_msg=msg) assert_equal(np.bitwise_and(ones, ones), ones, err_msg=msg) def test_types(self): for dt in self.bitwise_types: zeros = np.array([0], dtype=dt) ones = np.array([-1]).astype(dt) msg = "dt = '%s'" % dt.char assert_(np.bitwise_not(zeros).dtype == dt, msg) assert_(np.bitwise_or(zeros, zeros).dtype == dt, msg) assert_(np.bitwise_xor(zeros, zeros).dtype == dt, msg) assert_(np.bitwise_and(zeros, zeros).dtype == dt, msg) def test_identity(self): assert_(np.bitwise_or.identity == 0, 'bitwise_or') assert_(np.bitwise_xor.identity == 0, 'bitwise_xor') assert_(np.bitwise_and.identity == -1, 'bitwise_and') def test_reduction(self): binary_funcs = (np.bitwise_or, np.bitwise_xor, np.bitwise_and) for dt in self.bitwise_types: zeros = np.array([0], dtype=dt) ones = np.array([-1]).astype(dt) for f in binary_funcs: msg = "dt: '%s', f: '%s'" % (dt, f) assert_equal(f.reduce(zeros), zeros, err_msg=msg) assert_equal(f.reduce(ones), ones, err_msg=msg) # Test empty reduction, no object dtype for dt in self.bitwise_types[:-1]: # No object array types empty = np.array([], dtype=dt) for f in binary_funcs: msg = "dt: '%s', f: '%s'" % (dt, f) tgt = np.array(f.identity).astype(dt) res = f.reduce(empty) assert_equal(res, tgt, err_msg=msg) assert_(res.dtype == tgt.dtype, msg) # Empty object arrays use the identity. Note that the types may # differ, the actual type used is determined by the assign_identity # function and is not the same as the type returned by the identity # method. for f in binary_funcs: msg = "dt: '%s'" % (f,) empty = np.array([], dtype=object) tgt = f.identity res = f.reduce(empty) assert_equal(res, tgt, err_msg=msg) # Non-empty object arrays do not use the identity for f in binary_funcs: msg = "dt: '%s'" % (f,) btype = np.array([True], dtype=object) assert_(type(f.reduce(btype)) is bool, msg) class TestInt: def test_logical_not(self): x = np.ones(10, dtype=np.int16) o = np.ones(10 * 2, dtype=bool) tgt = o.copy() tgt[::2] = False os = o[::2] assert_array_equal(np.logical_not(x, out=os), False) assert_array_equal(o, tgt) class TestFloatingPoint: def test_floating_point(self): assert_equal(ncu.FLOATING_POINT_SUPPORT, 1) class TestDegrees: def test_degrees(self): assert_almost_equal(ncu.degrees(np.pi), 180.0) assert_almost_equal(ncu.degrees(-0.5*np.pi), -90.0) class TestRadians: def test_radians(self): assert_almost_equal(ncu.radians(180.0), np.pi) assert_almost_equal(ncu.radians(-90.0), -0.5*np.pi) class TestHeavside: def test_heaviside(self): x = np.array([[-30.0, -0.1, 0.0, 0.2], [7.5, np.nan, np.inf, -np.inf]]) expectedhalf = np.array([[0.0, 0.0, 0.5, 1.0], [1.0, np.nan, 1.0, 0.0]]) expected1 = expectedhalf.copy() expected1[0, 2] = 1 h = ncu.heaviside(x, 0.5) assert_equal(h, expectedhalf) h = ncu.heaviside(x, 1.0) assert_equal(h, expected1) x = x.astype(np.float32) h = ncu.heaviside(x, np.float32(0.5)) assert_equal(h, expectedhalf.astype(np.float32)) h = ncu.heaviside(x, np.float32(1.0)) assert_equal(h, expected1.astype(np.float32)) class TestSign: def test_sign(self): a = np.array([np.inf, -np.inf, np.nan, 0.0, 3.0, -3.0]) out = np.zeros(a.shape) tgt = np.array([1., -1., np.nan, 0.0, 1.0, -1.0]) with np.errstate(invalid='ignore'): res = ncu.sign(a) assert_equal(res, tgt) res = ncu.sign(a, out) assert_equal(res, tgt) assert_equal(out, tgt) def test_sign_dtype_object(self): # In reference to github issue #6229 foo = np.array([-.1, 0, .1]) a = np.sign(foo.astype(object)) b = np.sign(foo) assert_array_equal(a, b) def test_sign_dtype_nan_object(self): # In reference to github issue #6229 def test_nan(): foo = np.array([np.nan]) # FIXME: a not used a = np.sign(foo.astype(object)) assert_raises(TypeError, test_nan) class TestMinMax: def test_minmax_blocked(self): # simd tests on max/min, test all alignments, slow but important # for 2 * vz + 2 * (vs - 1) + 1 (unrolled once) for dt, sz in [(np.float32, 15), (np.float64, 7)]: for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary', max_size=sz): for i in range(inp.size): inp[:] = np.arange(inp.size, dtype=dt) inp[i] = np.nan emsg = lambda: '%r\n%s' % (inp, msg) with suppress_warnings() as sup: sup.filter(RuntimeWarning, "invalid value encountered in reduce") assert_(np.isnan(inp.max()), msg=emsg) assert_(np.isnan(inp.min()), msg=emsg) inp[i] = 1e10 assert_equal(inp.max(), 1e10, err_msg=msg) inp[i] = -1e10 assert_equal(inp.min(), -1e10, err_msg=msg) def test_lower_align(self): # check data that is not aligned to element size # i.e doubles are aligned to 4 bytes on i386 d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64) assert_equal(d.max(), d[0]) assert_equal(d.min(), d[0]) def test_reduce_reorder(self): # gh 10370, 11029 Some compilers reorder the call to npy_getfloatstatus # and put it before the call to an intrisic function that causes # invalid status to be set. Also make sure warnings are not emitted for n in (2, 4, 8, 16, 32): for dt in (np.float32, np.float16, np.complex64): for r in np.diagflat(np.array([np.nan] * n, dtype=dt)): assert_equal(np.min(r), np.nan) def test_minimize_no_warns(self): a = np.minimum(np.nan, 1) assert_equal(a, np.nan) class TestAbsoluteNegative: def test_abs_neg_blocked(self): # simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1 for dt, sz in [(np.float32, 11), (np.float64, 5)]: for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary', max_size=sz): tgt = [ncu.absolute(i) for i in inp] np.absolute(inp, out=out) assert_equal(out, tgt, err_msg=msg) assert_((out >= 0).all()) tgt = [-1*(i) for i in inp] np.negative(inp, out=out) assert_equal(out, tgt, err_msg=msg) for v in [np.nan, -np.inf, np.inf]: for i in range(inp.size): d = np.arange(inp.size, dtype=dt) inp[:] = -d inp[i] = v d[i] = -v if v == -np.inf else v assert_array_equal(np.abs(inp), d, err_msg=msg) np.abs(inp, out=out) assert_array_equal(out, d, err_msg=msg) assert_array_equal(-inp, -1*inp, err_msg=msg) d = -1 * inp np.negative(inp, out=out) assert_array_equal(out, d, err_msg=msg) def test_lower_align(self): # check data that is not aligned to element size # i.e doubles are aligned to 4 bytes on i386 d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64) assert_equal(np.abs(d), d) assert_equal(np.negative(d), -d) np.negative(d, out=d) np.negative(np.ones_like(d), out=d) np.abs(d, out=d) np.abs(np.ones_like(d), out=d) @pytest.mark.parametrize("dtype", ['d', 'f', 'int32', 'int64']) @pytest.mark.parametrize("big", [True, False]) def test_noncontiguous(self, dtype, big): data = np.array([-1.0, 1.0, -0.0, 0.0, 2.2251e-308, -2.5, 2.5, -6, 6, -2.2251e-308, -8, 10], dtype=dtype) expect = np.array([1.0, -1.0, 0.0, -0.0, -2.2251e-308, 2.5, -2.5, 6, -6, 2.2251e-308, 8, -10], dtype=dtype) if big: data = np.repeat(data, 10) expect = np.repeat(expect, 10) out = np.ndarray(data.shape, dtype=dtype) ncontig_in = data[1::2] ncontig_out = out[1::2] contig_in = np.array(ncontig_in) # contig in, contig out assert_array_equal(np.negative(contig_in), expect[1::2]) # contig in, ncontig out assert_array_equal(np.negative(contig_in, out=ncontig_out), expect[1::2]) # ncontig in, contig out assert_array_equal(np.negative(ncontig_in), expect[1::2]) # ncontig in, ncontig out assert_array_equal(np.negative(ncontig_in, out=ncontig_out), expect[1::2]) # contig in, contig out, nd stride data_split = np.array(np.array_split(data, 2)) expect_split = np.array(np.array_split(expect, 2)) assert_equal(np.negative(data_split), expect_split) class TestPositive: def test_valid(self): valid_dtypes = [int, float, complex, object] for dtype in valid_dtypes: x = np.arange(5, dtype=dtype) result = np.positive(x) assert_equal(x, result, err_msg=str(dtype)) def test_invalid(self): with assert_raises(TypeError): np.positive(True) with assert_raises(TypeError): np.positive(np.datetime64('2000-01-01')) with assert_raises(TypeError): np.positive(np.array(['foo'], dtype=str)) with assert_raises(TypeError): np.positive(np.array(['bar'], dtype=object)) class TestSpecialMethods: def test_wrap(self): class with_wrap: def __array__(self): return np.zeros(1) def __array_wrap__(self, arr, context): r = with_wrap() r.arr = arr r.context = context return r a = with_wrap() x = ncu.minimum(a, a) assert_equal(x.arr, np.zeros(1)) func, args, i = x.context assert_(func is ncu.minimum) assert_equal(len(args), 2) assert_equal(args[0], a) assert_equal(args[1], a) assert_equal(i, 0) def test_wrap_and_prepare_out(self): # Calling convention for out should not affect how special methods are # called class StoreArrayPrepareWrap(np.ndarray): _wrap_args = None _prepare_args = None def __new__(cls): return np.zeros(()).view(cls) def __array_wrap__(self, obj, context): self._wrap_args = context[1] return obj def __array_prepare__(self, obj, context): self._prepare_args = context[1] return obj @property def args(self): # We need to ensure these are fetched at the same time, before # any other ufuncs are called by the assertions return (self._prepare_args, self._wrap_args) def __repr__(self): return "a" # for short test output def do_test(f_call, f_expected): a = StoreArrayPrepareWrap() f_call(a) p, w = a.args expected = f_expected(a) try: assert_equal(p, expected) assert_equal(w, expected) except AssertionError as e: # assert_equal produces truly useless error messages raise AssertionError("\n".join([ "Bad arguments passed in ufunc call", " expected: {}".format(expected), " __array_prepare__ got: {}".format(p), " __array_wrap__ got: {}".format(w) ])) # method not on the out argument do_test(lambda a: np.add(a, 0), lambda a: (a, 0)) do_test(lambda a: np.add(a, 0, None), lambda a: (a, 0)) do_test(lambda a: np.add(a, 0, out=None), lambda a: (a, 0)) do_test(lambda a: np.add(a, 0, out=(None,)), lambda a: (a, 0)) # method on the out argument do_test(lambda a: np.add(0, 0, a), lambda a: (0, 0, a)) do_test(lambda a: np.add(0, 0, out=a), lambda a: (0, 0, a)) do_test(lambda a: np.add(0, 0, out=(a,)), lambda a: (0, 0, a)) # Also check the where mask handling: do_test(lambda a: np.add(a, 0, where=False), lambda a: (a, 0)) do_test(lambda a: np.add(0, 0, a, where=False), lambda a: (0, 0, a)) def test_wrap_with_iterable(self): # test fix for bug #1026: class with_wrap(np.ndarray): __array_priority__ = 10 def __new__(cls): return np.asarray(1).view(cls).copy() def __array_wrap__(self, arr, context): return arr.view(type(self)) a = with_wrap() x = ncu.multiply(a, (1, 2, 3)) assert_(isinstance(x, with_wrap)) assert_array_equal(x, np.array((1, 2, 3))) def test_priority_with_scalar(self): # test fix for bug #826: class A(np.ndarray): __array_priority__ = 10 def __new__(cls): return np.asarray(1.0, 'float64').view(cls).copy() a = A() x = np.float64(1)*a assert_(isinstance(x, A)) assert_array_equal(x, np.array(1)) def test_old_wrap(self): class with_wrap: def __array__(self): return np.zeros(1) def __array_wrap__(self, arr): r = with_wrap() r.arr = arr return r a = with_wrap() x = ncu.minimum(a, a) assert_equal(x.arr, np.zeros(1)) def test_priority(self): class A: def __array__(self): return np.zeros(1) def __array_wrap__(self, arr, context): r = type(self)() r.arr = arr r.context = context return r class B(A): __array_priority__ = 20. class C(A): __array_priority__ = 40. x = np.zeros(1) a = A() b = B() c = C() f = ncu.minimum assert_(type(f(x, x)) is np.ndarray) assert_(type(f(x, a)) is A) assert_(type(f(x, b)) is B) assert_(type(f(x, c)) is C) assert_(type(f(a, x)) is A) assert_(type(f(b, x)) is B) assert_(type(f(c, x)) is C) assert_(type(f(a, a)) is A) assert_(type(f(a, b)) is B) assert_(type(f(b, a)) is B) assert_(type(f(b, b)) is B) assert_(type(f(b, c)) is C) assert_(type(f(c, b)) is C) assert_(type(f(c, c)) is C) assert_(type(ncu.exp(a) is A)) assert_(type(ncu.exp(b) is B)) assert_(type(ncu.exp(c) is C)) def test_failing_wrap(self): class A: def __array__(self): return np.zeros(2) def __array_wrap__(self, arr, context): raise RuntimeError a = A() assert_raises(RuntimeError, ncu.maximum, a, a) assert_raises(RuntimeError, ncu.maximum.reduce, a) def test_failing_out_wrap(self): singleton = np.array([1.0]) class Ok(np.ndarray): def __array_wrap__(self, obj): return singleton class Bad(np.ndarray): def __array_wrap__(self, obj): raise RuntimeError ok = np.empty(1).view(Ok) bad = np.empty(1).view(Bad) # double-free (segfault) of "ok" if "bad" raises an exception for i in range(10): assert_raises(RuntimeError, ncu.frexp, 1, ok, bad) def test_none_wrap(self): # Tests that issue #8507 is resolved. Previously, this would segfault class A: def __array__(self): return np.zeros(1) def __array_wrap__(self, arr, context=None): return None a = A() assert_equal(ncu.maximum(a, a), None) def test_default_prepare(self): class with_wrap: __array_priority__ = 10 def __array__(self): return np.zeros(1) def __array_wrap__(self, arr, context): return arr a = with_wrap() x = ncu.minimum(a, a) assert_equal(x, np.zeros(1)) assert_equal(type(x), np.ndarray) @pytest.mark.parametrize("use_where", [True, False]) def test_prepare(self, use_where): class with_prepare(np.ndarray): __array_priority__ = 10 def __array_prepare__(self, arr, context): # make sure we can return a new return np.array(arr).view(type=with_prepare) a = np.array(1).view(type=with_prepare) if use_where: x = np.add(a, a, where=np.array(True)) else: x = np.add(a, a) assert_equal(x, np.array(2)) assert_equal(type(x), with_prepare) @pytest.mark.parametrize("use_where", [True, False]) def test_prepare_out(self, use_where): class with_prepare(np.ndarray): __array_priority__ = 10 def __array_prepare__(self, arr, context): return np.array(arr).view(type=with_prepare) a = np.array([1]).view(type=with_prepare) if use_where: x = np.add(a, a, a, where=[True]) else: x = np.add(a, a, a) # Returned array is new, because of the strange # __array_prepare__ above assert_(not np.shares_memory(x, a)) assert_equal(x, np.array([2])) assert_equal(type(x), with_prepare) def test_failing_prepare(self): class A: def __array__(self): return np.zeros(1) def __array_prepare__(self, arr, context=None): raise RuntimeError a = A() assert_raises(RuntimeError, ncu.maximum, a, a) assert_raises(RuntimeError, ncu.maximum, a, a, where=False) def test_array_too_many_args(self): class A: def __array__(self, dtype, context): return np.zeros(1) a = A() assert_raises_regex(TypeError, '2 required positional', np.sum, a) def test_ufunc_override(self): # check override works even with instance with high priority. class A: def __array_ufunc__(self, func, method, *inputs, **kwargs): return self, func, method, inputs, kwargs class MyNDArray(np.ndarray): __array_priority__ = 100 a = A() b = np.array([1]).view(MyNDArray) res0 = np.multiply(a, b) res1 = np.multiply(b, b, out=a) # self assert_equal(res0[0], a) assert_equal(res1[0], a) assert_equal(res0[1], np.multiply) assert_equal(res1[1], np.multiply) assert_equal(res0[2], '__call__') assert_equal(res1[2], '__call__') assert_equal(res0[3], (a, b)) assert_equal(res1[3], (b, b)) assert_equal(res0[4], {}) assert_equal(res1[4], {'out': (a,)}) def test_ufunc_override_mro(self): # Some multi arg functions for testing. def tres_mul(a, b, c): return a * b * c def quatro_mul(a, b, c, d): return a * b * c * d # Make these into ufuncs. three_mul_ufunc = np.frompyfunc(tres_mul, 3, 1) four_mul_ufunc = np.frompyfunc(quatro_mul, 4, 1) class A: def __array_ufunc__(self, func, method, *inputs, **kwargs): return "A" class ASub(A): def __array_ufunc__(self, func, method, *inputs, **kwargs): return "ASub" class B: def __array_ufunc__(self, func, method, *inputs, **kwargs): return "B" class C: def __init__(self): self.count = 0 def __array_ufunc__(self, func, method, *inputs, **kwargs): self.count += 1 return NotImplemented class CSub(C): def __array_ufunc__(self, func, method, *inputs, **kwargs): self.count += 1 return NotImplemented a = A() a_sub = ASub() b = B() c = C() # Standard res = np.multiply(a, a_sub) assert_equal(res, "ASub") res = np.multiply(a_sub, b) assert_equal(res, "ASub") # With 1 NotImplemented res = np.multiply(c, a) assert_equal(res, "A") assert_equal(c.count, 1) # Check our counter works, so we can trust tests below. res = np.multiply(c, a) assert_equal(c.count, 2) # Both NotImplemented. c = C() c_sub = CSub() assert_raises(TypeError, np.multiply, c, c_sub) assert_equal(c.count, 1) assert_equal(c_sub.count, 1) c.count = c_sub.count = 0 assert_raises(TypeError, np.multiply, c_sub, c) assert_equal(c.count, 1) assert_equal(c_sub.count, 1) c.count = 0 assert_raises(TypeError, np.multiply, c, c) assert_equal(c.count, 1) c.count = 0 assert_raises(TypeError, np.multiply, 2, c) assert_equal(c.count, 1) # Ternary testing. assert_equal(three_mul_ufunc(a, 1, 2), "A") assert_equal(three_mul_ufunc(1, a, 2), "A") assert_equal(three_mul_ufunc(1, 2, a), "A") assert_equal(three_mul_ufunc(a, a, 6), "A") assert_equal(three_mul_ufunc(a, 2, a), "A") assert_equal(three_mul_ufunc(a, 2, b), "A") assert_equal(three_mul_ufunc(a, 2, a_sub), "ASub") assert_equal(three_mul_ufunc(a, a_sub, 3), "ASub") c.count = 0 assert_equal(three_mul_ufunc(c, a_sub, 3), "ASub") assert_equal(c.count, 1) c.count = 0 assert_equal(three_mul_ufunc(1, a_sub, c), "ASub") assert_equal(c.count, 0) c.count = 0 assert_equal(three_mul_ufunc(a, b, c), "A") assert_equal(c.count, 0) c_sub.count = 0 assert_equal(three_mul_ufunc(a, b, c_sub), "A") assert_equal(c_sub.count, 0) assert_equal(three_mul_ufunc(1, 2, b), "B") assert_raises(TypeError, three_mul_ufunc, 1, 2, c) assert_raises(TypeError, three_mul_ufunc, c_sub, 2, c) assert_raises(TypeError, three_mul_ufunc, c_sub, 2, 3) # Quaternary testing. assert_equal(four_mul_ufunc(a, 1, 2, 3), "A") assert_equal(four_mul_ufunc(1, a, 2, 3), "A") assert_equal(four_mul_ufunc(1, 1, a, 3), "A") assert_equal(four_mul_ufunc(1, 1, 2, a), "A") assert_equal(four_mul_ufunc(a, b, 2, 3), "A") assert_equal(four_mul_ufunc(1, a, 2, b), "A") assert_equal(four_mul_ufunc(b, 1, a, 3), "B") assert_equal(four_mul_ufunc(a_sub, 1, 2, a), "ASub") assert_equal(four_mul_ufunc(a, 1, 2, a_sub), "ASub") c = C() c_sub = CSub() assert_raises(TypeError, four_mul_ufunc, 1, 2, 3, c) assert_equal(c.count, 1) c.count = 0 assert_raises(TypeError, four_mul_ufunc, 1, 2, c_sub, c) assert_equal(c_sub.count, 1) assert_equal(c.count, 1) c2 = C() c.count = c_sub.count = 0 assert_raises(TypeError, four_mul_ufunc, 1, c, c_sub, c2) assert_equal(c_sub.count, 1) assert_equal(c.count, 1) assert_equal(c2.count, 0) c.count = c2.count = c_sub.count = 0 assert_raises(TypeError, four_mul_ufunc, c2, c, c_sub, c) assert_equal(c_sub.count, 1) assert_equal(c.count, 0) assert_equal(c2.count, 1) def test_ufunc_override_methods(self): class A: def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): return self, ufunc, method, inputs, kwargs # __call__ a = A() with assert_raises(TypeError): np.multiply.__call__(1, a, foo='bar', answer=42) res = np.multiply.__call__(1, a, subok='bar', where=42) assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], '__call__') assert_equal(res[3], (1, a)) assert_equal(res[4], {'subok': 'bar', 'where': 42}) # __call__, wrong args assert_raises(TypeError, np.multiply, a) assert_raises(TypeError, np.multiply, a, a, a, a) assert_raises(TypeError, np.multiply, a, a, sig='a', signature='a') assert_raises(TypeError, ncu_tests.inner1d, a, a, axis=0, axes=[0, 0]) # reduce, positional args res = np.multiply.reduce(a, 'axis0', 'dtype0', 'out0', 'keep0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'reduce') assert_equal(res[3], (a,)) assert_equal(res[4], {'dtype':'dtype0', 'out': ('out0',), 'keepdims': 'keep0', 'axis': 'axis0'}) # reduce, kwargs res = np.multiply.reduce(a, axis='axis0', dtype='dtype0', out='out0', keepdims='keep0', initial='init0', where='where0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'reduce') assert_equal(res[3], (a,)) assert_equal(res[4], {'dtype':'dtype0', 'out': ('out0',), 'keepdims': 'keep0', 'axis': 'axis0', 'initial': 'init0', 'where': 'where0'}) # reduce, output equal to None removed, but not other explicit ones, # even if they are at their default value. res = np.multiply.reduce(a, 0, None, None, False) assert_equal(res[4], {'axis': 0, 'dtype': None, 'keepdims': False}) res = np.multiply.reduce(a, out=None, axis=0, keepdims=True) assert_equal(res[4], {'axis': 0, 'keepdims': True}) res = np.multiply.reduce(a, None, out=(None,), dtype=None) assert_equal(res[4], {'axis': None, 'dtype': None}) res = np.multiply.reduce(a, 0, None, None, False, 2, True) assert_equal(res[4], {'axis': 0, 'dtype': None, 'keepdims': False, 'initial': 2, 'where': True}) # np._NoValue ignored for initial res = np.multiply.reduce(a, 0, None, None, False, np._NoValue, True) assert_equal(res[4], {'axis': 0, 'dtype': None, 'keepdims': False, 'where': True}) # None kept for initial, True for where. res = np.multiply.reduce(a, 0, None, None, False, None, True) assert_equal(res[4], {'axis': 0, 'dtype': None, 'keepdims': False, 'initial': None, 'where': True}) # reduce, wrong args assert_raises(ValueError, np.multiply.reduce, a, out=()) assert_raises(ValueError, np.multiply.reduce, a, out=('out0', 'out1')) assert_raises(TypeError, np.multiply.reduce, a, 'axis0', axis='axis0') # accumulate, pos args res = np.multiply.accumulate(a, 'axis0', 'dtype0', 'out0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'accumulate') assert_equal(res[3], (a,)) assert_equal(res[4], {'dtype':'dtype0', 'out': ('out0',), 'axis': 'axis0'}) # accumulate, kwargs res = np.multiply.accumulate(a, axis='axis0', dtype='dtype0', out='out0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'accumulate') assert_equal(res[3], (a,)) assert_equal(res[4], {'dtype':'dtype0', 'out': ('out0',), 'axis': 'axis0'}) # accumulate, output equal to None removed. res = np.multiply.accumulate(a, 0, None, None) assert_equal(res[4], {'axis': 0, 'dtype': None}) res = np.multiply.accumulate(a, out=None, axis=0, dtype='dtype1') assert_equal(res[4], {'axis': 0, 'dtype': 'dtype1'}) res = np.multiply.accumulate(a, None, out=(None,), dtype=None) assert_equal(res[4], {'axis': None, 'dtype': None}) # accumulate, wrong args assert_raises(ValueError, np.multiply.accumulate, a, out=()) assert_raises(ValueError, np.multiply.accumulate, a, out=('out0', 'out1')) assert_raises(TypeError, np.multiply.accumulate, a, 'axis0', axis='axis0') # reduceat, pos args res = np.multiply.reduceat(a, [4, 2], 'axis0', 'dtype0', 'out0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'reduceat') assert_equal(res[3], (a, [4, 2])) assert_equal(res[4], {'dtype':'dtype0', 'out': ('out0',), 'axis': 'axis0'}) # reduceat, kwargs res = np.multiply.reduceat(a, [4, 2], axis='axis0', dtype='dtype0', out='out0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'reduceat') assert_equal(res[3], (a, [4, 2])) assert_equal(res[4], {'dtype':'dtype0', 'out': ('out0',), 'axis': 'axis0'}) # reduceat, output equal to None removed. res = np.multiply.reduceat(a, [4, 2], 0, None, None) assert_equal(res[4], {'axis': 0, 'dtype': None}) res = np.multiply.reduceat(a, [4, 2], axis=None, out=None, dtype='dt') assert_equal(res[4], {'axis': None, 'dtype': 'dt'}) res = np.multiply.reduceat(a, [4, 2], None, None, out=(None,)) assert_equal(res[4], {'axis': None, 'dtype': None}) # reduceat, wrong args assert_raises(ValueError, np.multiply.reduce, a, [4, 2], out=()) assert_raises(ValueError, np.multiply.reduce, a, [4, 2], out=('out0', 'out1')) assert_raises(TypeError, np.multiply.reduce, a, [4, 2], 'axis0', axis='axis0') # outer res = np.multiply.outer(a, 42) assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'outer') assert_equal(res[3], (a, 42)) assert_equal(res[4], {}) # outer, wrong args assert_raises(TypeError, np.multiply.outer, a) assert_raises(TypeError, np.multiply.outer, a, a, a, a) assert_raises(TypeError, np.multiply.outer, a, a, sig='a', signature='a') # at res = np.multiply.at(a, [4, 2], 'b0') assert_equal(res[0], a) assert_equal(res[1], np.multiply) assert_equal(res[2], 'at') assert_equal(res[3], (a, [4, 2], 'b0')) # at, wrong args assert_raises(TypeError, np.multiply.at, a) assert_raises(TypeError, np.multiply.at, a, a, a, a) def test_ufunc_override_out(self): class A: def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): return kwargs class B: def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): return kwargs a = A() b = B() res0 = np.multiply(a, b, 'out_arg') res1 = np.multiply(a, b, out='out_arg') res2 = np.multiply(2, b, 'out_arg') res3 = np.multiply(3, b, out='out_arg') res4 = np.multiply(a, 4, 'out_arg') res5 = np.multiply(a, 5, out='out_arg') assert_equal(res0['out'][0], 'out_arg') assert_equal(res1['out'][0], 'out_arg') assert_equal(res2['out'][0], 'out_arg') assert_equal(res3['out'][0], 'out_arg') assert_equal(res4['out'][0], 'out_arg') assert_equal(res5['out'][0], 'out_arg') # ufuncs with multiple output modf and frexp. res6 = np.modf(a, 'out0', 'out1') res7 = np.frexp(a, 'out0', 'out1') assert_equal(res6['out'][0], 'out0') assert_equal(res6['out'][1], 'out1') assert_equal(res7['out'][0], 'out0') assert_equal(res7['out'][1], 'out1') # While we're at it, check that default output is never passed on. assert_(np.sin(a, None) == {}) assert_(np.sin(a, out=None) == {}) assert_(np.sin(a, out=(None,)) == {}) assert_(np.modf(a, None) == {}) assert_(np.modf(a, None, None) == {}) assert_(np.modf(a, out=(None, None)) == {}) with assert_raises(TypeError): # Out argument must be tuple, since there are multiple outputs. np.modf(a, out=None) # don't give positional and output argument, or too many arguments. # wrong number of arguments in the tuple is an error too. assert_raises(TypeError, np.multiply, a, b, 'one', out='two') assert_raises(TypeError, np.multiply, a, b, 'one', 'two') assert_raises(ValueError, np.multiply, a, b, out=('one', 'two')) assert_raises(TypeError, np.multiply, a, out=()) assert_raises(TypeError, np.modf, a, 'one', out=('two', 'three')) assert_raises(TypeError, np.modf, a, 'one', 'two', 'three') assert_raises(ValueError, np.modf, a, out=('one', 'two', 'three')) assert_raises(ValueError, np.modf, a, out=('one',)) def test_ufunc_override_where(self): class OverriddenArrayOld(np.ndarray): def _unwrap(self, objs): cls = type(self) result = [] for obj in objs: if isinstance(obj, cls): obj = np.array(obj) elif type(obj) != np.ndarray: return NotImplemented result.append(obj) return result def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): inputs = self._unwrap(inputs) if inputs is NotImplemented: return NotImplemented kwargs = kwargs.copy() if "out" in kwargs: kwargs["out"] = self._unwrap(kwargs["out"]) if kwargs["out"] is NotImplemented: return NotImplemented r = super().__array_ufunc__(ufunc, method, *inputs, **kwargs) if r is not NotImplemented: r = r.view(type(self)) return r class OverriddenArrayNew(OverriddenArrayOld): def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): kwargs = kwargs.copy() if "where" in kwargs: kwargs["where"] = self._unwrap((kwargs["where"], )) if kwargs["where"] is NotImplemented: return NotImplemented else: kwargs["where"] = kwargs["where"][0] r = super().__array_ufunc__(ufunc, method, *inputs, **kwargs) if r is not NotImplemented: r = r.view(type(self)) return r ufunc = np.negative array = np.array([1, 2, 3]) where = np.array([True, False, True]) expected = ufunc(array, where=where) with pytest.raises(TypeError): ufunc(array, where=where.view(OverriddenArrayOld)) result_1 = ufunc( array, where=where.view(OverriddenArrayNew) ) assert isinstance(result_1, OverriddenArrayNew) assert np.all(np.array(result_1) == expected, where=where) result_2 = ufunc( array.view(OverriddenArrayNew), where=where.view(OverriddenArrayNew) ) assert isinstance(result_2, OverriddenArrayNew) assert np.all(np.array(result_2) == expected, where=where) def test_ufunc_override_exception(self): class A: def __array_ufunc__(self, *a, **kwargs): raise ValueError("oops") a = A() assert_raises(ValueError, np.negative, 1, out=a) assert_raises(ValueError, np.negative, a) assert_raises(ValueError, np.divide, 1., a) def test_ufunc_override_not_implemented(self): class A: def __array_ufunc__(self, *args, **kwargs): return NotImplemented msg = ("operand type(s) all returned NotImplemented from " "__array_ufunc__(, '__call__', <*>): 'A'") with assert_raises_regex(TypeError, fnmatch.translate(msg)): np.negative(A()) msg = ("operand type(s) all returned NotImplemented from " "__array_ufunc__(, '__call__', <*>,