def repeat(space, w_arr, repeats, w_axis):
arr = convert_to_array(space, w_arr)
if space.is_none(w_axis):
arr = arr.descr_flatten(space)
orig_size = arr.get_shape()[0]
shape = [arr.get_shape()[0] * repeats]
w_res = W_NDimArray.from_shape(space,
shape,
arr.get_dtype(),
w_instance=arr)
for i in range(repeats):
chunks = [Chunk(i, shape[0] - repeats + i, repeats, orig_size)]
view = new_view(space, w_res, chunks)
view.implementation.setslice(space, arr)
else:
axis = space.int_w(w_axis)
shape = arr.get_shape()[:]
chunks = [Chunk(0, i, 1, i) for i in shape]
orig_size = shape[axis]
shape[axis] *= repeats
w_res = W_NDimArray.from_shape(space,
shape,
arr.get_dtype(),
w_instance=arr)
for i in range(repeats):
chunks[axis] = Chunk(i, shape[axis] - repeats + i, repeats,
orig_size)
view = new_view(space, w_res, chunks)
view.implementation.setslice(space, arr)
return w_res
def concatenate(space, w_args, w_axis=None):
args_w = space.listview(w_args)
if len(args_w) == 0:
raise oefmt(space.w_ValueError,
"need at least one array to concatenate")
args_w = [convert_to_array(space, w_arg) for w_arg in args_w]
if w_axis is None:
w_axis = space.wrap(0)
if space.is_none(w_axis):
args_w = [
w_arg.reshape(space, space.newlist([w_arg.descr_get_size(space)]))
for w_arg in args_w
]
w_axis = space.wrap(0)
dtype = args_w[0].get_dtype()
shape = args_w[0].get_shape()[:]
ndim = len(shape)
if ndim == 0:
raise oefmt(space.w_ValueError,
"zero-dimensional arrays cannot be concatenated")
axis = space.int_w(w_axis)
orig_axis = axis
if axis < 0:
axis = ndim + axis
if ndim == 1 and axis != 0:
axis = 0
if axis < 0 or axis >= ndim:
raise oefmt(space.w_IndexError, "axis %d out of bounds [0, %d)",
orig_axis, ndim)
for arr in args_w[1:]:
if len(arr.get_shape()) != ndim:
raise OperationError(
space.w_ValueError,
space.wrap(
"all the input arrays must have same number of dimensions")
)
for i, axis_size in enumerate(arr.get_shape()):
if i == axis:
shape[i] += axis_size
elif axis_size != shape[i]:
raise OperationError(
space.w_ValueError,
space.wrap("all the input array dimensions except for the "
"concatenation axis must match exactly"))
dtype = find_result_type(space, args_w, [])
# concatenate does not handle ndarray subtypes, it always returns a ndarray
res = W_NDimArray.from_shape(space, shape, dtype, 'C')
chunks = [Chunk(0, i, 1, i) for i in shape]
axis_start = 0
for arr in args_w:
if arr.get_shape()[axis] == 0:
continue
chunks[axis] = Chunk(axis_start, axis_start + arr.get_shape()[axis], 1,
arr.get_shape()[axis])
view = new_view(space, res, chunks)
view.implementation.setslice(space, arr)
axis_start += arr.get_shape()[axis]
return res
def descr_getitem(self, space, orig_arr, w_index):
try:
item = self._single_item_index(space, w_index)
return self.getitem(item)
except IndexError:
# not a single result
chunks = self._prepare_slice_args(space, w_index)
return new_view(space, orig_arr, chunks)
def descr_getitem(self, space, orig_arr, w_index):
try:
item = self._single_item_index(space, w_index)
return self.getitem(item)
except IndexError:
# not a single result
chunks = self._prepare_slice_args(space, w_index)
return new_view(space, orig_arr, chunks)
def descr_setitem(self, space, orig_arr, w_index, w_value):
try:
item = self._single_item_index(space, w_index)
self.setitem(item, self.dtype.coerce(space, w_value))
except IndexError:
w_value = convert_to_array(space, w_value)
chunks = self._prepare_slice_args(space, w_index)
view = new_view(space, orig_arr, chunks)
view.implementation.setslice(space, w_value)
def descr_setitem(self, space, orig_arr, w_index, w_value):
try:
item = self._single_item_index(space, w_index)
self.setitem(item, self.dtype.coerce(space, w_value))
except IndexError:
w_value = convert_to_array(space, w_value)
chunks = self._prepare_slice_args(space, w_index)
view = new_view(space, orig_arr, chunks)
view.implementation.setslice(space, w_value)
def concatenate(space, w_args, w_axis=None):
args_w = space.listview(w_args)
if len(args_w) == 0:
raise oefmt(space.w_ValueError, "need at least one array to concatenate")
args_w = [convert_to_array(space, w_arg) for w_arg in args_w]
if w_axis is None:
w_axis = space.wrap(0)
if space.is_none(w_axis):
args_w = [w_arg.reshape(space,
space.newlist([w_arg.descr_get_size(space)]),
w_arg.get_order())
for w_arg in args_w]
w_axis = space.wrap(0)
dtype = args_w[0].get_dtype()
shape = args_w[0].get_shape()[:]
ndim = len(shape)
if ndim == 0:
raise oefmt(space.w_ValueError,
"zero-dimensional arrays cannot be concatenated")
axis = space.int_w(w_axis)
orig_axis = axis
if axis < 0:
axis = ndim + axis
if ndim == 1 and axis != 0:
axis = 0
if axis < 0 or axis >= ndim:
raise oefmt(space.w_IndexError, "axis %d out of bounds [0, %d)",
orig_axis, ndim)
for arr in args_w[1:]:
if len(arr.get_shape()) != ndim:
raise oefmt(space.w_ValueError,
"all the input arrays must have same number of "
"dimensions")
for i, axis_size in enumerate(arr.get_shape()):
if i == axis:
shape[i] += axis_size
elif axis_size != shape[i]:
raise oefmt(space.w_ValueError,
"all the input array dimensions except for the "
"concatenation axis must match exactly")
dtype = find_result_type(space, args_w, [])
# concatenate does not handle ndarray subtypes, it always returns a ndarray
res = W_NDimArray.from_shape(space, shape, dtype, NPY.CORDER)
chunks = [Chunk(0, i, 1, i) for i in shape]
axis_start = 0
for arr in args_w:
if arr.get_shape()[axis] == 0:
continue
chunks[axis] = Chunk(axis_start, axis_start + arr.get_shape()[axis], 1,
arr.get_shape()[axis])
view = new_view(space, res, chunks)
view.implementation.setslice(space, arr)
axis_start += arr.get_shape()[axis]
return res
def descr_getitem(self, space, orig_arr, w_index):
try:
item = self._single_item_index(space, w_index)
return self.getitem(item)
except IndexError:
# not a single result
chunks = self._prepare_slice_args(space, w_index)
copy = False
if isinstance(chunks[0], BooleanChunk):
# numpy compatibility
copy = True
w_ret = new_view(space, orig_arr, chunks)
if copy:
w_ret = w_ret.descr_copy(space, space.newint(w_ret.get_order()))
return w_ret
def repeat(space, w_arr, repeats, w_axis):
arr = convert_to_array(space, w_arr)
if space.is_none(w_axis):
arr = arr.descr_flatten(space)
orig_size = arr.get_shape()[0]
shape = [arr.get_shape()[0] * repeats]
w_res = W_NDimArray.from_shape(space, shape, arr.get_dtype(), w_instance=arr)
for i in range(repeats):
chunks = [Chunk(i, shape[0] - repeats + i, repeats, orig_size)]
view = new_view(space, w_res, chunks)
view.implementation.setslice(space, arr)
else:
axis = space.int_w(w_axis)
shape = arr.get_shape()[:]
chunks = [Chunk(0, i, 1, i) for i in shape]
orig_size = shape[axis]
shape[axis] *= repeats
w_res = W_NDimArray.from_shape(space, shape, arr.get_dtype(), w_instance=arr)
for i in range(repeats):
chunks[axis] = Chunk(i, shape[axis] - repeats + i, repeats,
orig_size)
view = new_view(space, w_res, chunks)
view.implementation.setslice(space, arr)
return w_res
def descr_getitem(self, space, orig_arr, w_index):
try:
item = self._single_item_index(space, w_index)
return self.getitem(item)
except IndexError:
# not a single result
chunks = self._prepare_slice_args(space, w_index)
copy = False
if isinstance(chunks[0], BooleanChunk):
# numpy compatibility
copy = True
w_ret = new_view(space, orig_arr, chunks)
if copy:
w_ret = w_ret.descr_copy(space, space.wrap(w_ret.get_order()))
return w_ret
