def descr_transpose(self, space, args_w):
if len(args_w) == 1 and space.isinstance_w(args_w[0], space.w_tuple):
args_w = space.fixedview(args_w[0])
if len(args_w) >= 1:
for w_arg in args_w:
try:
support.index_w(space, w_arg)
except OperationError:
raise oefmt(space.w_TypeError, "an integer is required")
raise oefmt(space.w_ValueError, "axes don't match array")
return self
def descr_transpose(self, space, args_w):
if len(args_w) == 1 and space.isinstance_w(args_w[0], space.w_tuple):
args_w = space.fixedview(args_w[0])
if len(args_w) >= 1:
for w_arg in args_w:
try:
support.index_w(space, w_arg)
except OperationError:
raise oefmt(space.w_TypeError, "an integer is required")
raise oefmt(space.w_ValueError, "axes don't match array")
return self
def choose(space, arr, choices, shape, dtype, out, mode):
shapelen = len(shape)
pairs = [a.create_iter(shape) for a in choices]
iterators = [i[0] for i in pairs]
states = [i[1] for i in pairs]
arr_iter, arr_state = arr.create_iter(shape)
out_iter, out_state = out.create_iter(shape)
while not arr_iter.done(arr_state):
choose_driver.jit_merge_point(shapelen=shapelen, dtype=dtype,
mode=mode)
index = support.index_w(space, arr_iter.getitem(arr_state))
if index < 0 or index >= len(iterators):
if mode == NPY.RAISE:
raise OperationError(space.w_ValueError, space.wrap(
"invalid entry in choice array"))
elif mode == NPY.WRAP:
index = index % (len(iterators))
else:
assert mode == NPY.CLIP
if index < 0:
index = 0
else:
index = len(iterators) - 1
val = iterators[index].getitem(states[index]).convert_to(space, dtype)
out_iter.setitem(out_state, val)
for i in range(len(iterators)):
states[i] = iterators[i].next(states[i])
out_state = out_iter.next(out_state)
arr_state = arr_iter.next(arr_state)
def choose(space, arr, choices, shape, dtype, out, mode):
shapelen = len(shape)
pairs = [a.create_iter(shape) for a in choices]
iterators = [i[0] for i in pairs]
states = [i[1] for i in pairs]
arr_iter, arr_state = arr.create_iter(shape)
out_iter, out_state = out.create_iter(shape)
while not arr_iter.done(arr_state):
choose_driver.jit_merge_point(shapelen=shapelen,
dtype=dtype,
mode=mode)
index = support.index_w(space, arr_iter.getitem(arr_state))
if index < 0 or index >= len(iterators):
if mode == NPY.RAISE:
raise OperationError(
space.w_ValueError,
space.wrap("invalid entry in choice array"))
elif mode == NPY.WRAP:
index = index % (len(iterators))
else:
assert mode == NPY.CLIP
if index < 0:
index = 0
else:
index = len(iterators) - 1
val = iterators[index].getitem(states[index]).convert_to(space, dtype)
out_iter.setitem(out_state, val)
for i in range(len(iterators)):
states[i] = iterators[i].next(states[i])
out_state = out_iter.next(out_state)
arr_state = arr_iter.next(arr_state)
def _single_item_index(self, space, w_idx):
""" Return an index of single item if possible, otherwise raises
IndexError
"""
if (space.isinstance_w(w_idx, space.w_text) or
space.isinstance_w(w_idx, space.w_slice) or
space.is_w(w_idx, space.w_None)):
raise IndexError
if isinstance(w_idx, W_NDimArray) and not w_idx.is_scalar():
raise ArrayArgumentException
shape = self.get_shape()
shape_len = len(shape)
view_w = None
if space.isinstance_w(w_idx, space.w_list):
raise ArrayArgumentException
if space.isinstance_w(w_idx, space.w_tuple):
view_w = space.fixedview(w_idx)
if len(view_w) != shape_len:
raise IndexError
# check for arrays
for w_item in view_w:
if (isinstance(w_item, W_NDimArray) or
space.isinstance_w(w_item, space.w_list)):
raise ArrayArgumentException
elif space.is_w(w_item, space.w_Ellipsis):
raise IndexError
return self._lookup_by_index(space, view_w)
if shape_len == 0:
raise oefmt(space.w_IndexError, "too many indices for array")
elif shape_len > 1:
raise IndexError
idx = support.index_w(space, w_idx)
return self._lookup_by_index(space, [space.newint(idx)])
def _single_item_index(self, space, w_idx):
""" Return an index of single item if possible, otherwise raises
IndexError
"""
if (space.isinstance_w(w_idx, space.w_str) or
space.isinstance_w(w_idx, space.w_slice) or
space.is_w(w_idx, space.w_None)):
raise IndexError
if isinstance(w_idx, W_NDimArray) and not w_idx.is_scalar():
raise ArrayArgumentException
shape = self.get_shape()
shape_len = len(shape)
view_w = None
if space.isinstance_w(w_idx, space.w_list):
raise ArrayArgumentException
if space.isinstance_w(w_idx, space.w_tuple):
view_w = space.fixedview(w_idx)
if len(view_w) != shape_len:
raise IndexError
# check for arrays
for w_item in view_w:
if (isinstance(w_item, W_NDimArray) or
space.isinstance_w(w_item, space.w_list)):
raise ArrayArgumentException
elif space.is_w(w_item, space.w_Ellipsis):
raise IndexError
return self._lookup_by_index(space, view_w)
if shape_len == 0:
raise oefmt(space.w_IndexError, "too many indices for array")
elif shape_len > 1:
raise IndexError
idx = support.index_w(space, w_idx)
return self._lookup_by_index(space, [space.wrap(idx)])
def put(space, w_arr, w_indices, w_values, w_mode):
from pypy.module.micronumpy.support import index_w
arr = convert_to_array(space, w_arr)
mode = clipmode_converter(space, w_mode)
if not w_indices:
raise OperationError(space.w_ValueError,
space.wrap("indice list cannot be empty"))
if not w_values:
raise OperationError(space.w_ValueError,
space.wrap("value list cannot be empty"))
dtype = arr.get_dtype()
if space.isinstance_w(w_indices, space.w_list):
indices = space.listview(w_indices)
else:
indices = [w_indices]
if space.isinstance_w(w_values, space.w_list):
values = space.listview(w_values)
else:
values = [w_values]
v_idx = 0
for idx in indices:
index = index_w(space, idx)
if index < 0 or index >= arr.get_size():
if mode == NPY_RAISE:
raise OperationError(
space.w_IndexError,
space.wrap(
"index %d is out of bounds for axis 0 with size %d" %
(index, arr.get_size())))
elif mode == NPY_WRAP:
index = index % arr.get_size()
elif mode == NPY_CLIP:
if index < 0:
index = 0
else:
index = arr.get_size() - 1
else:
assert False
value = values[v_idx]
if v_idx + 1 < len(values):
v_idx += 1
arr.setitem(space, [index], dtype.coerce(space, value))
def put(space, w_arr, w_indices, w_values, w_mode):
from pypy.module.micronumpy.support import index_w
arr = convert_to_array(space, w_arr)
mode = clipmode_converter(space, w_mode)
if not w_indices:
raise OperationError(space.w_ValueError, space.wrap("indice list cannot be empty"))
if not w_values:
raise OperationError(space.w_ValueError, space.wrap("value list cannot be empty"))
dtype = arr.get_dtype()
if space.isinstance_w(w_indices, space.w_list):
indices = space.listview(w_indices)
else:
indices = [w_indices]
if space.isinstance_w(w_values, space.w_list):
values = space.listview(w_values)
else:
values = [w_values]
v_idx = 0
for idx in indices:
index = index_w(space, idx)
if index < 0 or index >= arr.get_size():
if mode == NPY_RAISE:
raise OperationError(
space.w_IndexError,
space.wrap("index %d is out of bounds for axis 0 with size %d" % (index, arr.get_size())),
)
elif mode == NPY_WRAP:
index = index % arr.get_size()
elif mode == NPY_CLIP:
if index < 0:
index = 0
else:
index = arr.get_size() - 1
else:
assert False
value = values[v_idx]
if v_idx + 1 < len(values):
v_idx += 1
arr.setitem(space, [index], dtype.coerce(space, value))
def _lookup_by_index(self, space, view_w):
item = self.start
strides = self.get_strides()
for i, w_index in enumerate(view_w):
if space.isinstance_w(w_index, space.w_slice):
raise IndexError
idx = support.index_w(space, w_index)
if idx < 0:
idx = self.get_shape()[i] + idx
if idx < 0 or idx >= self.get_shape()[i]:
raise oefmt(space.w_IndexError,
"index %d is out of bounds for axis %d with size "
"%d", idx, i, self.get_shape()[i])
item += idx * strides[i]
return item
def _lookup_by_index(self, space, view_w):
item = self.start
strides = self.get_strides()
for i, w_index in enumerate(view_w):
if space.isinstance_w(w_index, space.w_slice):
raise IndexError
idx = support.index_w(space, w_index)
if idx < 0:
idx = self.get_shape()[i] + idx
if idx < 0 or idx >= self.get_shape()[i]:
raise oefmt(space.w_IndexError,
"index %d is out of bounds for axis %d with size "
"%d", idx, i, self.get_shape()[i])
item += idx * strides[i]
return item
def descr_item(self, space, args_w):
if len(args_w) == 1 and space.isinstance_w(args_w[0], space.w_tuple):
args_w = space.fixedview(args_w[0])
if len(args_w) > 1:
raise oefmt(space.w_ValueError,
"incorrect number of indices for array")
elif len(args_w) == 1:
try:
idx = support.index_w(space, args_w[0])
except OperationError:
raise oefmt(space.w_TypeError, "an integer is required")
if idx != 0:
raise oefmt(space.w_IndexError,
"index %d is out of bounds for size 1", idx)
return self.item(space)
def descr_item(self, space, args_w):
if len(args_w) == 1 and space.isinstance_w(args_w[0], space.w_tuple):
args_w = space.fixedview(args_w[0])
if len(args_w) > 1:
raise oefmt(space.w_ValueError,
"incorrect number of indices for array")
elif len(args_w) == 1:
try:
idx = support.index_w(space, args_w[0])
except OperationError:
raise oefmt(space.w_TypeError, "an integer is required")
if idx != 0:
raise oefmt(space.w_IndexError,
"index %d is out of bounds for size 1", idx)
return self.item(space)
def put(space, w_arr, w_indices, w_values, w_mode):
arr = convert_to_array(space, w_arr)
mode = clipmode_converter(space, w_mode)
if not w_indices:
raise oefmt(space.w_ValueError, "indices list cannot be empty")
if not w_values:
raise oefmt(space.w_ValueError, "value list cannot be empty")
dtype = arr.get_dtype()
if space.isinstance_w(w_indices, space.w_list):
indices = space.listview(w_indices)
else:
indices = [w_indices]
if space.isinstance_w(w_values, space.w_list):
values = space.listview(w_values)
else:
values = [w_values]
v_idx = 0
for idx in indices:
index = support.index_w(space, idx)
if index < 0 or index >= arr.get_size():
if mode == NPY.RAISE:
raise oefmt(
space.w_IndexError,
"index %d is out of bounds for axis 0 with size %d", index,
arr.get_size())
elif mode == NPY.WRAP:
index = index % arr.get_size()
elif mode == NPY.CLIP:
if index < 0:
index = 0
else:
index = arr.get_size() - 1
else:
assert False
value = values[v_idx]
if v_idx + 1 < len(values):
v_idx += 1
arr.setitem(space, [index], dtype.coerce(space, value))
def put(space, w_arr, w_indices, w_values, w_mode):
arr = convert_to_array(space, w_arr)
mode = clipmode_converter(space, w_mode)
if not w_indices:
raise oefmt(space.w_ValueError, "indices list cannot be empty")
if not w_values:
raise oefmt(space.w_ValueError, "value list cannot be empty")
dtype = arr.get_dtype()
if space.isinstance_w(w_indices, space.w_list):
indices = space.listview(w_indices)
else:
indices = [w_indices]
if space.isinstance_w(w_values, space.w_list):
values = space.listview(w_values)
else:
values = [w_values]
v_idx = 0
for idx in indices:
index = support.index_w(space, idx)
if index < 0 or index >= arr.get_size():
if mode == NPY.RAISE:
raise oefmt(space.w_IndexError,
"index %d is out of bounds for axis 0 with size %d",
index, arr.get_size())
elif mode == NPY.WRAP:
index = index % arr.get_size()
elif mode == NPY.CLIP:
if index < 0:
index = 0
else:
index = arr.get_size() - 1
else:
assert False
value = values[v_idx]
if v_idx + 1 < len(values):
v_idx += 1
arr.setitem(space, [index], dtype.coerce(space, value))
def _single_item_index(self, space, w_idx):
""" Return an index of single item if possible, otherwise raises
IndexError
"""
if (space.isinstance_w(w_idx, space.w_str)
or space.isinstance_w(w_idx, space.w_slice)
or space.is_w(w_idx, space.w_None)):
raise IndexError
if isinstance(w_idx, W_NDimArray) and not w_idx.is_scalar():
raise ArrayArgumentException
shape = self.get_shape()
shape_len = len(shape)
view_w = None
if space.isinstance_w(w_idx, space.w_list):
raise ArrayArgumentException
if space.isinstance_w(w_idx, space.w_tuple):
view_w = space.fixedview(w_idx)
if len(view_w) < shape_len:
raise IndexError
if len(view_w) > shape_len:
# we can allow for one extra None
count = len(view_w)
for w_item in view_w:
if space.is_w(w_item, space.w_None):
count -= 1
if count == shape_len:
raise IndexError # but it's still not a single item
raise oefmt(space.w_IndexError, "invalid index")
# check for arrays
for w_item in view_w:
if (isinstance(w_item, W_NDimArray)
or space.isinstance_w(w_item, space.w_list)):
raise ArrayArgumentException
return self._lookup_by_index(space, view_w)
if shape_len == 0:
raise oefmt(space.w_IndexError, "0-d arrays can't be indexed")
elif shape_len > 1:
raise IndexError
idx = support.index_w(space, w_idx)
return self._lookup_by_index(space, [space.wrap(idx)])
def _single_item_index(self, space, w_idx):
""" Return an index of single item if possible, otherwise raises
IndexError
"""
if (space.isinstance_w(w_idx, space.w_str) or
space.isinstance_w(w_idx, space.w_slice) or
space.is_w(w_idx, space.w_None)):
raise IndexError
if isinstance(w_idx, W_NDimArray) and not w_idx.is_scalar():
raise ArrayArgumentException
shape = self.get_shape()
shape_len = len(shape)
view_w = None
if space.isinstance_w(w_idx, space.w_list):
raise ArrayArgumentException
if space.isinstance_w(w_idx, space.w_tuple):
view_w = space.fixedview(w_idx)
if len(view_w) < shape_len:
raise IndexError
if len(view_w) > shape_len:
# we can allow for one extra None
count = len(view_w)
for w_item in view_w:
if space.is_w(w_item, space.w_None):
count -= 1
if count == shape_len:
raise IndexError # but it's still not a single item
raise oefmt(space.w_IndexError, "invalid index")
# check for arrays
for w_item in view_w:
if (isinstance(w_item, W_NDimArray) or
space.isinstance_w(w_item, space.w_list)):
raise ArrayArgumentException
return self._lookup_by_index(space, view_w)
if shape_len == 0:
raise oefmt(space.w_IndexError, "0-d arrays can't be indexed")
elif shape_len > 1:
raise IndexError
idx = support.index_w(space, w_idx)
return self._lookup_by_index(space, [space.wrap(idx)])
