def _check_unary_with_out_no_where_no_dtype(
self, args, kw, impl, name_xp, name_in1, name_axis, name_indices,
name_out, op, minval, maxval, shape, order_x, order_out, dtype_x,
dtype_out, mode, is_broadcast, ufunc_name, axes, indices, keepdims):
if mode == 'array':
param = itertools.product(shape, order_x, order_out, dtype_x,
dtype_out, axes, indices)
elif mode == 'scalar':
param = itertools.product(shape, order_out, dtype_x, dtype_out)
else:
raise TypeError('unknown mode was detected.')
for p in param:
if mode == 'array':
in1 = ufunc._create_random_array(p[0], p[1], p[3], minval, maxval)
in1 = _recreate_array_or_scalar(op, in1, ufunc_name)
out = _create_out_array(p[0],
p[2],
p[4],
ufunc_name,
mode,
axis=p[5],
indices=p[6],
keepdims=keepdims,
is_broadcast=is_broadcast)
worst_dtype = ufunc._guess_worst_dtype((in1.dtype, out.dtype))
n_calc = _count_number_of_calculation(ufunc_name, p[0], p[5], p[6])
if ufunc_name in ('reduce', 'accumulate', 'reduceat'):
kw[name_axis] = p[5]
if ufunc_name == 'reduceat':
kw[name_indices] = p[6]
elif mode == 'scalar':
in1 = ufunc._create_random_scalar(p[2], minval, maxval)
in1 = _recreate_array_or_scalar(op, in1, ufunc_name)
out = _create_out_array(p[0],
p[1],
p[3],
ufunc_name,
mode,
is_broadcast=is_broadcast)
worst_dtype = ufunc._guess_worst_dtype(
(numpy.dtype(p[2]), out.dtype))
n_calc = 1
kw[name_in1] = in1
kw[name_out] = out
nlcpy_result, numpy_result = ufunc._precheck_func_for_ufunc(
self, args, kw, impl, name_xp, op, True, Exception)
# result check
if nlcpy_result is not None and numpy_result is not None:
for nlcpy_r, numpy_r in zip(nlcpy_result, numpy_result):
ufunc._check_ufunc_result(op,
worst_dtype,
nlcpy_r,
numpy_r,
in1=in1,
out=out,
ufunc_name=ufunc_name,
n_calc=n_calc)
def _check_unary_no_out_no_where_with_dtype(self, args, kw, impl, name_xp,
name_in1, name_axis, name_indices,
name_dtype, op, minval, maxval,
shape, order_x, dtype_x, dtype_arg,
mode, ufunc_name, axes, indices):
if mode == 'array':
param = itertools.product(shape, order_x, dtype_x, dtype_arg, axes,
indices)
elif mode == 'scalar':
param = itertools.product(dtype_x, dtype_arg)
else:
raise TypeError('unknown mode was detected.')
for p in param:
if mode == 'array':
in1 = ufunc._create_random_array(p[0], p[1], p[2], minval, maxval)
in1 = _recreate_array_or_scalar(op, in1, ufunc_name)
dtype = numpy.dtype(p[3])
if dtype == numpy.bool and op in float16_op:
worst_dtype = ufunc._guess_worst_dtype(
(in1.dtype, numpy.dtype('f4')))
else:
worst_dtype = ufunc._guess_worst_dtype((in1.dtype, dtype))
n_calc = _count_number_of_calculation(ufunc_name, p[0], p[4], p[5])
if ufunc_name in ('reduce', 'accumulate', 'reduceat'):
kw[name_axis] = p[4]
if ufunc_name == 'reduceat':
kw[name_indices] = p[5]
elif mode == 'scalar':
in1 = ufunc._create_random_scalar(p[0], minval, maxval)
in1 = _recreate_array_or_scalar(op, in1, ufunc_name)
dtype = numpy.dtype(p[1])
if dtype == numpy.bool and op in float16_op:
worst_dtype = ufunc._guess_worst_dtype(
(in1.dtype, numpy.dtype('f4')))
else:
worst_dtype = ufunc._guess_worst_dtype(
(numpy.dtype(p[0]), dtype))
n_calc = 1
kw[name_in1] = in1
kw[name_dtype] = dtype
nlcpy_result, numpy_result = ufunc._precheck_func_for_ufunc(
self, args, kw, impl, name_xp, op, True, Exception)
# result check
if nlcpy_result is not None and numpy_result is not None:
for nlcpy_r, numpy_r in zip(nlcpy_result, numpy_result):
ufunc._check_ufunc_result(op,
worst_dtype,
nlcpy_r,
numpy_r,
in1=in1,
dtype=dtype,
ufunc_name=ufunc_name,
n_calc=n_calc)
def _check_binary_no_out_no_where_no_dtype(self, args, kw, impl, name_xp,
name_in1, name_in2, name_order,
name_casting, op, minval, maxval,
shape, order_x, order_y, order_arg,
dtype_x, dtype_y, mode, ufunc_name,
casting):
if mode == 'array_array':
param = itertools.product(shape, order_x, order_y, dtype_x, dtype_y,
order_arg, casting)
elif mode == 'array_scalar':
param = itertools.product(shape, order_x, dtype_x, dtype_y, order_arg,
casting)
elif mode == 'scalar_scalar':
param = itertools.product(dtype_x, dtype_y, casting)
else:
raise TypeError('unknown mode was detected.')
for p in param:
if mode == 'array_array':
shape1 = p[0][0]
shape2 = p[0][1]
order = p[5]
casting = p[6]
in1 = ufunc._create_random_array(shape1, p[1], p[3], minval,
maxval)
in2 = ufunc._create_random_array(shape2, p[2], p[4], minval,
maxval)
in1, in2 = _recreate_array_or_scalar(op, in1, in2)
worst_dtype = ufunc._guess_worst_dtype((in1.dtype, in2.dtype))
elif mode == 'array_scalar':
shape1 = p[0][0]
order = p[4]
casting = p[5]
in1 = ufunc._create_random_array(shape1, p[1], p[2], minval,
maxval)
in2 = ufunc._create_random_scalar(p[3], minval, maxval)
in1, in2 = _recreate_array_or_scalar(op, in1, in2)
dt_in2 = numpy.dtype(p[3])
worst_dtype = ufunc._guess_worst_dtype((in1.dtype, dt_in2))
elif mode == 'scalar_scalar':
casting = p[2]
order = 'K'
in1 = ufunc._create_random_scalar(p[0], minval, maxval)
in2 = ufunc._create_random_scalar(p[1], minval, maxval)
in1, in2 = _recreate_array_or_scalar(op, in1, in2)
dt_in1 = numpy.dtype(p[0])
dt_in2 = numpy.dtype(p[1])
worst_dtype = ufunc._guess_worst_dtype((dt_in1, dt_in2))
kw[name_in1] = in1
kw[name_in2] = in2
if ufunc_name == 'outer':
kw[name_order] = order
kw[name_casting] = casting
nlcpy_result, numpy_result = ufunc._precheck_func_for_ufunc(
self, args, kw, impl, name_xp, op, True, Exception)
# result check
if nlcpy_result is not None and numpy_result is not None:
for nlcpy_r, numpy_r in zip(nlcpy_result, numpy_result):
ufunc._check_ufunc_result(op,
worst_dtype,
nlcpy_r,
numpy_r,
in1=in1,
in2=in2)
def _check_binary_with_out_with_where_no_dtype(
self, args, kw, impl, name_xp, name_in1, name_in2, name_order,
name_casting, name_out, name_where, op, minval, maxval, shape, order_x,
order_y, order_out, order_where, order_arg, dtype_x, dtype_y,
dtype_out, mode, is_broadcast, ufunc_name, casting):
if mode == 'array_array':
param = itertools.product(shape, order_x, order_y, order_out,
order_where, dtype_x, dtype_y, dtype_out,
order_arg, casting)
elif mode == 'array_scalar':
param = itertools.product(shape, order_x, order_out, order_where,
dtype_x, dtype_y, dtype_out, order_arg,
casting)
elif mode == 'scalar_scalar':
param = itertools.product(order_out, order_where, dtype_x, dtype_y,
dtype_out, casting)
else:
raise TypeError('unknown mode was detected.')
for p in param:
if mode == 'array_array':
shape1 = p[0][0]
shape2 = p[0][1]
order = p[8]
casting = p[9]
in1 = ufunc._create_random_array(shape1, p[1], p[5], minval,
maxval)
in2 = ufunc._create_random_array(shape2, p[2], p[6], minval,
maxval)
in1, in2 = _recreate_array_or_scalar(op, in1, in2)
out = _create_out_array(in1, in2, p[3], p[7], ufunc_name,
is_broadcast)
where = ufunc._create_random_array(out.shape, p[4], ufunc.DT_BOOL,
minval, maxval)
worst_dtype = ufunc._guess_worst_dtype(
(in1.dtype, in2.dtype, out.dtype))
elif mode == 'array_scalar':
shape1 = p[0][0]
order = p[7]
casting = p[8]
in1 = ufunc._create_random_array(shape1, p[1], p[4], minval,
maxval)
in2 = ufunc._create_random_scalar(p[5], minval, maxval)
in1, in2 = _recreate_array_or_scalar(op, in1, in2)
out = _create_out_array(in1, in2, p[2], p[6], ufunc_name,
is_broadcast)
where = ufunc._create_random_array(out.shape, p[3], ufunc.DT_BOOL,
minval, maxval)
dt_in2 = numpy.dtype(p[5])
worst_dtype = ufunc._guess_worst_dtype(
(in1.dtype, dt_in2, out.dtype))
elif mode == 'scalar_scalar':
order = 'K'
casting = p[5]
in1 = ufunc._create_random_scalar(p[2], minval, maxval)
in2 = ufunc._create_random_scalar(p[3], minval, maxval)
in1, in2 = _recreate_array_or_scalar(op, in1, in2)
out = _create_out_array(in1, in2, p[0], p[4], ufunc_name,
is_broadcast)
where = ufunc._create_random_array(out.shape, p[1], ufunc.DT_BOOL,
minval, maxval)
dt_in1 = numpy.dtype(p[2])
dt_in2 = numpy.dtype(p[3])
worst_dtype = ufunc._guess_worst_dtype((dt_in1, dt_in2, out.dtype))
# expand shape for broadcast
if is_broadcast:
where = numpy.resize(where, ((2, ) + where.shape))
kw[name_in1] = in1
kw[name_in2] = in2
kw[name_out] = out
kw[name_where] = where
if ufunc_name == 'outer':
kw[name_order] = order
kw[name_casting] = casting
nlcpy_result, numpy_result = ufunc._precheck_func_for_ufunc(
self, args, kw, impl, name_xp, op, True, Exception)
# result check
if nlcpy_result is not None and numpy_result is not None:
for nlcpy_r, numpy_r in zip(nlcpy_result, numpy_result):
ufunc._check_ufunc_result(op,
worst_dtype,
nlcpy_r,
numpy_r,
in1=in1,
in2=in2,
out=out,
where=where)
