def zeros(space, w_shape, w_dtype=None, w_order=None):
dtype = space.interp_w(descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
shape = shape_converter(space, w_shape, dtype)
return W_NDimArray.from_shape(space, shape, dtype=dtype)
def _zeros_or_empty(space, w_shape, w_dtype, w_order, zero):
dtype = space.interp_w(descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
shape = shape_converter(space, w_shape, dtype)
for dim in shape:
if dim < 0:
raise OperationError(space.w_ValueError, space.wrap(
"negative dimensions are not allowed"))
try:
support.product_check(shape)
except OverflowError:
raise oefmt(space.w_ValueError, "array is too big.")
return W_NDimArray.from_shape(space, shape, dtype=dtype, zero=zero)
def _zeros_or_empty(space, w_shape, w_dtype, w_order, zero):
dtype = space.interp_w(descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
shape = shape_converter(space, w_shape, dtype)
for dim in shape:
if dim < 0:
raise OperationError(space.w_ValueError, space.wrap(
"negative dimensions are not allowed"))
try:
support.product(shape)
except OverflowError:
raise OperationError(space.w_ValueError, space.wrap(
"array is too big."))
return W_NDimArray.from_shape(space, shape, dtype=dtype, zero=zero)
def _zeros_or_empty(space, w_shape, w_dtype, w_order, zero):
# w_order can be None, str, or boolean
order = order_converter(space, w_order, NPY.CORDER)
dtype = space.interp_w(
descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
shape = shape_converter(space, w_shape, dtype)
for dim in shape:
if dim < 0:
raise oefmt(space.w_ValueError,
"negative dimensions are not allowed")
try:
support.product_check(shape)
except OverflowError:
raise oefmt(space.w_ValueError, "array is too big.")
return W_NDimArray.from_shape(space, shape, dtype, order, zero=zero)
