def set_shape(self, space, orig_array, new_shape):
if len(self.get_shape()) < 2 or self.size == 0:
# TODO: this code could be refactored into calc_strides
# but then calc_strides would have to accept a stepping factor
strides = []
backstrides = []
dtype = self.dtype
try:
s = self.get_strides()[0] // dtype.elsize
except IndexError:
s = 1
if self.order == 'C':
new_shape.reverse()
for sh in new_shape:
strides.append(s * dtype.elsize)
backstrides.append(s * (sh - 1) * dtype.elsize)
s *= max(1, sh)
if self.order == 'C':
strides.reverse()
backstrides.reverse()
new_shape.reverse()
return self.__class__(self.start, strides, backstrides, new_shape,
self, orig_array)
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(), self.order)
if new_strides is None or len(new_strides) != len(new_shape):
raise oefmt(space.w_AttributeError,
"incompatible shape for a non-contiguous array")
new_backstrides = [0] * len(new_shape)
for nd in range(len(new_shape)):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
return self.__class__(self.start, new_strides, new_backstrides,
new_shape, self, orig_array)
def set_shape(self, space, orig_array, new_shape):
if len(self.get_shape()) < 2 or self.size == 0:
# TODO: this code could be refactored into calc_strides
# but then calc_strides would have to accept a stepping factor
strides = []
backstrides = []
dtype = self.dtype
try:
s = self.get_strides()[0] // dtype.elsize
except IndexError:
s = 1
if self.order == 'C':
new_shape.reverse()
for sh in new_shape:
strides.append(s * dtype.elsize)
backstrides.append(s * (sh - 1) * dtype.elsize)
s *= max(1, sh)
if self.order == 'C':
strides.reverse()
backstrides.reverse()
new_shape.reverse()
return self.__class__(self.start, strides, backstrides, new_shape,
self, orig_array)
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(),
self.order)
if new_strides is None or len(new_strides) != len(new_shape):
raise oefmt(space.w_AttributeError,
"incompatible shape for a non-contiguous array")
new_backstrides = [0] * len(new_shape)
for nd in range(len(new_shape)):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
return self.__class__(self.start, new_strides, new_backstrides, new_shape,
self, orig_array)
def set_shape(self, space, orig_array, new_shape):
if len(self.get_shape()) < 2 or self.size == 0:
# TODO: this code could be refactored into calc_strides
# but then calc_strides would have to accept a stepping factor
strides = []
backstrides = []
dtype = self.dtype
s = self.get_strides()[0] // dtype.get_size()
if self.order == 'C':
new_shape.reverse()
for sh in new_shape:
strides.append(s * dtype.get_size())
backstrides.append(s * (sh - 1) * dtype.get_size())
s *= max(1, sh)
if self.order == 'C':
strides.reverse()
backstrides.reverse()
new_shape.reverse()
return SliceArray(self.start, strides, backstrides, new_shape,
self, orig_array)
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(), self.order)
if new_strides is None:
raise OperationError(
space.w_AttributeError,
space.wrap("incompatible shape for a non-contiguous array"))
new_backstrides = [0] * len(new_shape)
for nd in range(len(new_shape)):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
return SliceArray(self.start, new_strides, new_backstrides, new_shape,
self, orig_array)
def set_shape(self, space, orig_array, new_shape):
if len(self.get_shape()) < 2 or self.size == 0:
# TODO: this code could be refactored into calc_strides
# but then calc_strides would have to accept a stepping factor
strides = []
backstrides = []
dtype = self.dtype
s = self.get_strides()[0] // dtype.get_size()
if self.order == 'C':
new_shape.reverse()
for sh in new_shape:
strides.append(s * dtype.get_size())
backstrides.append(s * (sh - 1) * dtype.get_size())
s *= max(1, sh)
if self.order == 'C':
strides.reverse()
backstrides.reverse()
new_shape.reverse()
return SliceArray(self.start, strides, backstrides, new_shape,
self, orig_array)
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(),
self.order)
if new_strides is None:
raise OperationError(space.w_AttributeError, space.wrap(
"incompatible shape for a non-contiguous array"))
new_backstrides = [0] * len(new_shape)
for nd in range(len(new_shape)):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
return SliceArray(self.start, new_strides, new_backstrides, new_shape,
self, orig_array)
def reshape(self, orig_array, new_shape):
# Since we got to here, prod(new_shape) == self.size
new_strides = None
if self.size == 0:
new_strides, _ = calc_strides(new_shape, self.dtype, self.order)
else:
if len(self.get_shape()) == 0:
new_strides = [self.dtype.elsize] * len(new_shape)
else:
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(), self.order)
if new_strides is not None:
# We can create a view, strides somehow match up.
new_backstrides = calc_backstrides(new_strides, new_shape)
assert isinstance(orig_array, W_NDimArray) or orig_array is None
return SliceArray(self.start, new_strides, new_backstrides,
new_shape, self, orig_array)
def reshape(self, space, orig_array, new_shape):
# Since we got to here, prod(new_shape) == self.size
new_strides = None
if self.size > 0:
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(), self.order)
if new_strides:
# We can create a view, strides somehow match up.
ndims = len(new_shape)
new_backstrides = [0] * ndims
for nd in range(ndims):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
assert isinstance(orig_array, W_NDimArray) or orig_array is None
return SliceArray(self.start, new_strides, new_backstrides,
new_shape, self, orig_array)
else:
return None
def reshape(self, space, orig_array, new_shape):
# Since we got to here, prod(new_shape) == self.size
new_strides = None
if self.size > 0:
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(), self.order)
if new_strides:
# We can create a view, strides somehow match up.
ndims = len(new_shape)
new_backstrides = [0] * ndims
for nd in range(ndims):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
assert isinstance(orig_array, W_NDimArray) or orig_array is None
return SliceArray(self.start, new_strides, new_backstrides,
new_shape, self, orig_array)
else:
return None
def reshape(self, orig_array, new_shape):
# Since we got to here, prod(new_shape) == self.size
new_strides = None
if self.size == 0:
new_strides, _ = calc_strides(new_shape, self.dtype, self.order)
else:
if len(self.get_shape()) == 0:
new_strides = [self.dtype.elsize] * len(new_shape)
else:
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(), self.order)
if new_strides is not None:
# We can create a view, strides somehow match up.
new_backstrides = calc_backstrides(new_strides, new_shape)
assert isinstance(orig_array, W_NDimArray) or orig_array is None
return SliceArray(self.start, new_strides, new_backstrides,
new_shape, self, orig_array)
def set_shape(self, space, orig_array, new_shape):
if len(new_shape) > NPY.MAXDIMS:
raise oefmt(space.w_ValueError,
"sequence too large; cannot be greater than %d", NPY.MAXDIMS)
try:
ovfcheck(support.product_check(new_shape) * self.dtype.elsize)
except OverflowError as e:
raise oefmt(space.w_ValueError, "array is too big.")
if len(self.get_shape()) < 2 or self.size == 0:
# TODO: this code could be refactored into calc_strides
# but then calc_strides would have to accept a stepping factor
strides = []
backstrides = []
dtype = self.dtype
try:
s = self.get_strides()[0] // dtype.elsize
except IndexError:
s = 1
if self.order != NPY.FORTRANORDER:
new_shape.reverse()
for sh in new_shape:
strides.append(s * dtype.elsize)
backstrides.append(s * (sh - 1) * dtype.elsize)
s *= max(1, sh)
if self.order != NPY.FORTRANORDER:
strides.reverse()
backstrides.reverse()
new_shape.reverse()
return self.__class__(self.start, strides, backstrides, new_shape,
self, orig_array)
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(),
self.order)
if new_strides is None or len(new_strides) != len(new_shape):
raise oefmt(space.w_AttributeError,
"incompatible shape for a non-contiguous array")
new_backstrides = [0] * len(new_shape)
for nd in range(len(new_shape)):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
return self.__class__(self.start, new_strides, new_backstrides, new_shape,
self, orig_array)
def set_shape(self, space, orig_array, new_shape):
if len(new_shape) > NPY.MAXDIMS:
raise oefmt(space.w_ValueError,
"sequence too large; cannot be greater than %d", NPY.MAXDIMS)
try:
ovfcheck(support.product_check(new_shape) * self.dtype.elsize)
except OverflowError as e:
raise oefmt(space.w_ValueError, "array is too big.")
if len(self.get_shape()) < 2 or self.size == 0:
# TODO: this code could be refactored into calc_strides
# but then calc_strides would have to accept a stepping factor
strides = []
backstrides = []
dtype = self.dtype
try:
s = self.get_strides()[0] // dtype.elsize
except IndexError:
s = 1
if self.order != NPY.FORTRANORDER:
new_shape.reverse()
for sh in new_shape:
strides.append(s * dtype.elsize)
backstrides.append(s * (sh - 1) * dtype.elsize)
s *= max(1, sh)
if self.order != NPY.FORTRANORDER:
strides.reverse()
backstrides.reverse()
new_shape.reverse()
return self.__class__(self.start, strides, backstrides, new_shape,
self, orig_array)
new_strides = calc_new_strides(new_shape, self.get_shape(),
self.get_strides(),
self.order)
if new_strides is None or len(new_strides) != len(new_shape):
raise oefmt(space.w_AttributeError,
"incompatible shape for a non-contiguous array")
new_backstrides = [0] * len(new_shape)
for nd in range(len(new_shape)):
new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd]
return self.__class__(self.start, new_strides, new_backstrides, new_shape,
self, orig_array)
