def astype(self, space, dtype, order, copy=True):
# copy the general pattern of the strides
# but make the array storage contiguous in memory
shape = self.get_shape()
strides = self.get_strides()
if order not in (NPY.KEEPORDER, NPY.FORTRANORDER, NPY.CORDER):
raise oefmt(space.w_ValueError, "Unknown order %d in astype", order)
if len(strides) == 0:
t_strides = []
backstrides = []
elif order in (NPY.FORTRANORDER, NPY.CORDER):
t_strides, backstrides = calc_strides(shape, dtype, order)
else:
indx_array = range(len(strides))
list_sorter = StrideSort(indx_array, strides, self.order)
list_sorter.sort()
t_elsize = dtype.elsize
t_strides = strides[:]
base = dtype.elsize
for i in indx_array:
t_strides[i] = base
base *= shape[i]
backstrides = calc_backstrides(t_strides, shape)
order = support.get_order_as_CF(self.order, order)
impl = ConcreteArray(shape, dtype, order, t_strides, backstrides)
if copy:
loop.setslice(space, impl.get_shape(), impl, self)
return impl
def astype(self, space, dtype, order, copy=True):
# copy the general pattern of the strides
# but make the array storage contiguous in memory
shape = self.get_shape()
strides = self.get_strides()
if order not in (NPY.KEEPORDER, NPY.FORTRANORDER, NPY.CORDER):
raise oefmt(space.w_ValueError, "Unknown order %d in astype", order)
if len(strides) == 0:
t_strides = []
backstrides = []
elif order in (NPY.FORTRANORDER, NPY.CORDER):
t_strides, backstrides = calc_strides(shape, dtype, order)
else:
indx_array = range(len(strides))
list_sorter = StrideSort(indx_array, strides, self.order)
list_sorter.sort()
t_elsize = dtype.elsize
t_strides = strides[:]
base = dtype.elsize
for i in indx_array:
t_strides[i] = base
base *= shape[i]
backstrides = calc_backstrides(t_strides, shape)
order = support.get_order_as_CF(self.order, order)
impl = ConcreteArray(shape, dtype, order, t_strides, backstrides)
if copy:
loop.setslice(space, impl.get_shape(), impl, self)
return impl
def reshape(self, orig_array, new_shape, order=NPY.ANYORDER):
# Since we got to here, prod(new_shape) == self.size
order = support.get_order_as_CF(self.order, order)
new_strides = None
if self.size == 0:
new_strides, _ = calc_strides(new_shape, self.dtype, 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(), order)
if new_strides is None or len(new_strides) != len(new_shape):
return None
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)
return None
def reshape(self, orig_array, new_shape, order=NPY.ANYORDER):
# Since we got to here, prod(new_shape) == self.size
order = support.get_order_as_CF(self.order, order)
new_strides = None
if self.size == 0:
new_strides, _ = calc_strides(new_shape, self.dtype, 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(), order)
if new_strides is None or len(new_strides) != len(new_shape):
return None
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)
return None
def __init__(self,
space,
w_seq,
w_flags,
w_op_flags,
w_op_dtypes,
w_casting,
w_op_axes,
w_itershape,
buffersize=0,
order=NPY.KEEPORDER,
allow_backward=True):
self.external_loop = False
self.buffered = False
self.tracked_index = ''
self.common_dtype = False
self.delay_bufalloc = False
self.grow_inner = False
self.ranged = False
self.refs_ok = False
self.reduce_ok = False
self.zerosize_ok = False
self.index_iter = None
self.done = False
self.first_next = True
self.op_axes = []
self.allow_backward = allow_backward
if not space.is_w(w_casting, space.w_None):
self.casting = space.str_w(w_casting)
else:
self.casting = 'safe'
# convert w_seq operands to a list of W_NDimArray
if space.isinstance_w(w_seq, space.w_tuple) or \
space.isinstance_w(w_seq, space.w_list):
w_seq_as_list = space.listview(w_seq)
self.seq = [
convert_to_array(space, w_elem)
if not space.is_none(w_elem) else None
for w_elem in w_seq_as_list
]
else:
self.seq = [convert_to_array(space, w_seq)]
if order == NPY.ANYORDER:
# 'A' means "'F' order if all the arrays are Fortran contiguous,
# 'C' order otherwise"
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_flags() & NPY.ARRAY_F_CONTIGUOUS):
break
else:
order = NPY.FORTRANORDER
elif order == NPY.KEEPORDER:
# 'K' means "as close to the order the array elements appear in
# memory as possible", so match self.order to seq.order
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_order() == NPY.FORTRANORDER):
break
else:
order = NPY.FORTRANORDER
self.order = order
parse_func_flags(space, self, w_flags)
self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags,
len(self.seq), parse_op_flag)
# handle w_op_axes
oa_ndim = -1
if not space.is_none(w_op_axes):
oa_ndim = self.set_op_axes(space, w_op_axes)
self.ndim = calculate_ndim(self.seq, oa_ndim)
# handle w_op_dtypes part 1: creating self.dtypes list from input
if not space.is_none(w_op_dtypes):
w_seq_as_list = space.listview(w_op_dtypes)
self.dtypes = [
decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list
]
if len(self.dtypes) != len(self.seq):
raise oefmt(
space.w_ValueError,
"op_dtypes must be a tuple/list matching the number of ops"
)
else:
self.dtypes = []
# handle None or writable operands, calculate my shape
outargs = [
i for i in range(len(self.seq))
if self.seq[i] is None or self.op_flags[i].rw == 'w'
]
if len(outargs) > 0:
out_shape = shape_agreement_multiple(
space, [self.seq[i] for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or \
space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
self.shape = shape_agreement_multiple(space,
self.seq,
shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
out_dtype = self.dtypes[outargs[0]]
else:
out_dtype = None
for i in range(len(self.seq)):
if self.seq[i] is None:
self.op_flags[i].allocate = True
continue
if self.op_flags[i].rw == 'w':
continue
out_dtype = find_binop_result_dtype(
space, self.seq[i].get_dtype(), out_dtype)
for i in outargs:
if self.seq[i] is None:
# XXX can we postpone allocation to later?
self.seq[i] = W_NDimArray.from_shape(
space, self.shape, out_dtype)
else:
if not self.op_flags[i].broadcast:
# Raises if output cannot be broadcast
try:
shape_agreement(space, self.shape, self.seq[i],
False)
except OperationError as e:
raise oefmt(
space.w_ValueError, "non-broadcastable"
" output operand with shape %s doesn't match "
"the broadcast shape %s",
str(self.seq[i].get_shape()), str(self.shape))
if self.tracked_index != "":
order = self.order
if order == NPY.KEEPORDER:
order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = ((order == NPY.CORDER and self.tracked_index != 'C')
or (order == NPY.FORTRANORDER
and self.tracked_index != 'F'))
self.index_iter = IndexIterator(self.shape, backward=backward)
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
self_d = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not self_d:
self.dtypes[i] = seq_d
elif self_d != seq_d:
impl = self.seq[i].implementation
if self.buffered or 'r' in self.op_flags[i].tmp_copy:
if not can_cast_array(space, self.seq[i], self_d,
self.casting):
raise oefmt(
space.w_TypeError, "Iterator operand %d"
" dtype could not be cast from %s to %s"
" according to the rule '%s'", i,
space.str_w(seq_d.descr_repr(space)),
space.str_w(self_d.descr_repr(space)),
self.casting)
order = support.get_order_as_CF(impl.order, self.order)
new_impl = impl.astype(space, self_d,
order).copy(space)
self.seq[i] = W_NDimArray(new_impl)
else:
raise oefmt(
space.w_TypeError, "Iterator "
"operand required copying or buffering, "
"but neither copying nor buffering was "
"enabled")
if 'w' in self.op_flags[i].rw:
if not can_cast_type(space, self_d, seq_d,
self.casting):
raise oefmt(
space.w_TypeError, "Iterator"
" requested dtype could not be cast from "
" %s to %s, the operand %d dtype, accord"
"ing to the rule '%s'",
space.str_w(self_d.descr_repr(space)),
space.str_w(seq_d.descr_repr(space)), i,
self.casting)
elif self.buffered and not (self.external_loop and len(self.seq) < 2):
for i in range(len(self.seq)):
if i not in outargs:
self.seq[i] = self.seq[i].descr_copy(space,
w_order=space.wrap(
self.order))
self.dtypes = [s.get_dtype() for s in self.seq]
else:
#copy them from seq
self.dtypes = [s.get_dtype() for s in self.seq]
# create an iterator for each operand
self.iters = []
for i in range(len(self.seq)):
it = self.get_iter(space, i)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
def __init__(
self,
space,
w_seq,
w_flags,
w_op_flags,
w_op_dtypes,
w_casting,
w_op_axes,
w_itershape,
buffersize=0,
order=NPY.KEEPORDER,
allow_backward=True,
):
self.external_loop = False
self.buffered = False
self.tracked_index = ""
self.common_dtype = False
self.delay_bufalloc = False
self.grow_inner = False
self.ranged = False
self.refs_ok = False
self.reduce_ok = False
self.zerosize_ok = False
self.index_iter = None
self.done = False
self.first_next = True
self.op_axes = []
self.allow_backward = allow_backward
if not space.is_w(w_casting, space.w_None):
self.casting = space.str_w(w_casting)
else:
self.casting = "safe"
# convert w_seq operands to a list of W_NDimArray
if space.isinstance_w(w_seq, space.w_tuple) or space.isinstance_w(w_seq, space.w_list):
w_seq_as_list = space.listview(w_seq)
self.seq = [
convert_to_array(space, w_elem) if not space.is_none(w_elem) else None for w_elem in w_seq_as_list
]
else:
self.seq = [convert_to_array(space, w_seq)]
if order == NPY.ANYORDER:
# 'A' means "'F' order if all the arrays are Fortran contiguous,
# 'C' order otherwise"
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_flags() & NPY.ARRAY_F_CONTIGUOUS):
break
else:
order = NPY.FORTRANORDER
elif order == NPY.KEEPORDER:
# 'K' means "as close to the order the array elements appear in
# memory as possible", so match self.order to seq.order
order = NPY.CORDER
for s in self.seq:
if s and not (s.get_order() == NPY.FORTRANORDER):
break
else:
order = NPY.FORTRANORDER
self.order = order
parse_func_flags(space, self, w_flags)
self.op_flags = parse_op_arg(space, "op_flags", w_op_flags, len(self.seq), parse_op_flag)
# handle w_op_axes
oa_ndim = -1
if not space.is_none(w_op_axes):
oa_ndim = self.set_op_axes(space, w_op_axes)
self.ndim = calculate_ndim(self.seq, oa_ndim)
# handle w_op_dtypes part 1: creating self.dtypes list from input
if not space.is_none(w_op_dtypes):
w_seq_as_list = space.listview(w_op_dtypes)
self.dtypes = [decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list]
if len(self.dtypes) != len(self.seq):
raise oefmt(space.w_ValueError, "op_dtypes must be a tuple/list matching the number of ops")
else:
self.dtypes = []
# handle None or writable operands, calculate my shape
outargs = [i for i in range(len(self.seq)) if self.seq[i] is None or self.op_flags[i].rw == "w"]
if len(outargs) > 0:
out_shape = shape_agreement_multiple(space, [self.seq[i] for i in outargs])
else:
out_shape = None
if space.isinstance_w(w_itershape, space.w_tuple) or space.isinstance_w(w_itershape, space.w_list):
self.shape = [space.int_w(i) for i in space.listview(w_itershape)]
else:
self.shape = shape_agreement_multiple(space, self.seq, shape=out_shape)
if len(outargs) > 0:
# Make None operands writeonly and flagged for allocation
if len(self.dtypes) > 0:
out_dtype = self.dtypes[outargs[0]]
else:
out_dtype = None
for i in range(len(self.seq)):
if self.seq[i] is None:
self.op_flags[i].allocate = True
continue
if self.op_flags[i].rw == "w":
continue
out_dtype = find_binop_result_dtype(space, self.seq[i].get_dtype(), out_dtype)
for i in outargs:
if self.seq[i] is None:
# XXX can we postpone allocation to later?
self.seq[i] = W_NDimArray.from_shape(space, self.shape, out_dtype)
else:
if not self.op_flags[i].broadcast:
# Raises if output cannot be broadcast
try:
shape_agreement(space, self.shape, self.seq[i], False)
except OperationError as e:
raise oefmt(
space.w_ValueError,
"non-broadcastable"
" output operand with shape %s doesn't match "
"the broadcast shape %s",
str(self.seq[i].get_shape()),
str(self.shape),
)
if self.tracked_index != "":
order = self.order
if order == NPY.KEEPORDER:
order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = (order == NPY.CORDER and self.tracked_index != "C") or (
order == NPY.FORTRANORDER and self.tracked_index != "F"
)
self.index_iter = IndexIterator(self.shape, backward=backward)
# handle w_op_dtypes part 2: copy where needed if possible
if len(self.dtypes) > 0:
for i in range(len(self.seq)):
self_d = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not self_d:
self.dtypes[i] = seq_d
elif self_d != seq_d:
impl = self.seq[i].implementation
if self.buffered or "r" in self.op_flags[i].tmp_copy:
if not can_cast_array(space, self.seq[i], self_d, self.casting):
raise oefmt(
space.w_TypeError,
"Iterator operand %d"
" dtype could not be cast from %s to %s"
" according to the rule '%s'",
i,
space.str_w(seq_d.descr_repr(space)),
space.str_w(self_d.descr_repr(space)),
self.casting,
)
order = support.get_order_as_CF(impl.order, self.order)
new_impl = impl.astype(space, self_d, order).copy(space)
self.seq[i] = W_NDimArray(new_impl)
else:
raise oefmt(
space.w_TypeError,
"Iterator "
"operand required copying or buffering, "
"but neither copying nor buffering was "
"enabled",
)
if "w" in self.op_flags[i].rw:
if not can_cast_type(space, self_d, seq_d, self.casting):
raise oefmt(
space.w_TypeError,
"Iterator"
" requested dtype could not be cast from "
" %s to %s, the operand %d dtype, accord"
"ing to the rule '%s'",
space.str_w(self_d.descr_repr(space)),
space.str_w(seq_d.descr_repr(space)),
i,
self.casting,
)
elif self.buffered and not (self.external_loop and len(self.seq) < 2):
for i in range(len(self.seq)):
if i not in outargs:
self.seq[i] = self.seq[i].descr_copy(space, w_order=space.wrap(self.order))
self.dtypes = [s.get_dtype() for s in self.seq]
else:
# copy them from seq
self.dtypes = [s.get_dtype() for s in self.seq]
# create an iterator for each operand
self.iters = []
for i in range(len(self.seq)):
it = self.get_iter(space, i)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
def _array(space,
w_object,
w_dtype=None,
copy=True,
w_order=None,
subok=False):
from pypy.module.micronumpy.boxes import W_GenericBox
# numpy testing calls array(type(array([]))) and expects a ValueError
if space.isinstance_w(w_object, space.w_type):
raise oefmt(space.w_ValueError,
"cannot create ndarray from type instance")
# for anything that isn't already an array, try __array__ method first
dtype = descriptor.decode_w_dtype(space, w_dtype)
if not isinstance(w_object, W_NDimArray):
w_array = try_array_method(space, w_object, w_dtype)
if w_array is None:
if (not space.isinstance_w(w_object, space.w_bytes)
and not space.isinstance_w(w_object, space.w_unicode)
and not isinstance(w_object, W_GenericBox)):
# use buffer interface
w_object = _array_from_buffer_3118(space, w_object, dtype)
else:
# continue with w_array, but do further operations in place
w_object = w_array
copy = False
dtype = w_object.get_dtype()
if not isinstance(w_object, W_NDimArray):
w_array, _copy = try_interface_method(space, w_object, copy)
if w_array is not None:
w_object = w_array
copy = _copy
dtype = w_object.get_dtype()
if isinstance(w_object, W_NDimArray):
npy_order = order_converter(space, w_order, NPY.ANYORDER)
if (dtype is None or w_object.get_dtype() is dtype) and (
subok or type(w_object) is W_NDimArray):
flags = w_object.get_flags()
must_copy = copy
must_copy |= (npy_order == NPY.CORDER
and not flags & NPY.ARRAY_C_CONTIGUOUS)
must_copy |= (npy_order == NPY.FORTRANORDER
and not flags & NPY.ARRAY_F_CONTIGUOUS)
if must_copy:
return w_object.descr_copy(space, space.newint(npy_order))
else:
return w_object
if subok and not type(w_object) is W_NDimArray:
raise oefmt(space.w_NotImplementedError,
"array(..., subok=True) only partially implemented")
# we have a ndarray, but need to copy or change dtype
if dtype is None:
dtype = w_object.get_dtype()
if dtype != w_object.get_dtype():
# silently reject the copy value
copy = True
if copy:
shape = w_object.get_shape()
order = support.get_order_as_CF(w_object.get_order(), npy_order)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
if support.product(shape) == 1:
w_arr.set_scalar_value(
dtype.coerce(space, w_object.implementation.getitem(0)))
else:
loop.setslice(space, shape, w_arr.implementation,
w_object.implementation)
return w_arr
else:
imp = w_object.implementation
w_base = w_object
sz = w_base.get_size() * dtype.elsize
if imp.base() is not None:
w_base = imp.base()
if type(w_base) is W_NDimArray:
sz = w_base.get_size() * dtype.elsize
else:
# this must succeed (mmap, buffer, ...)
sz = space.int_w(space.call_method(w_base, 'size'))
with imp as storage:
return W_NDimArray.from_shape_and_storage(space,
w_object.get_shape(),
storage,
dtype,
storage_bytes=sz,
w_base=w_base,
strides=imp.strides,
start=imp.start)
else:
# not an array
npy_order = order_converter(space, w_order, NPY.CORDER)
shape, elems_w = find_shape_and_elems(space, w_object, dtype)
if dtype is None and space.isinstance_w(w_object, space.w_buffer):
dtype = descriptor.get_dtype_cache(space).w_uint8dtype
if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
dtype = find_dtype_for_seq(space, elems_w, dtype)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=npy_order)
if support.product(
shape) == 1: # safe from overflow since from_shape checks
w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
else:
loop.assign(space, w_arr, elems_w)
return w_arr
def _array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False):
from pypy.module.micronumpy.boxes import W_GenericBox
# numpy testing calls array(type(array([]))) and expects a ValueError
if space.isinstance_w(w_object, space.w_type):
raise oefmt(space.w_ValueError, "cannot create ndarray from type instance")
# for anything that isn't already an array, try __array__ method first
dtype = descriptor.decode_w_dtype(space, w_dtype)
if not isinstance(w_object, W_NDimArray):
w_array = try_array_method(space, w_object, w_dtype)
if w_array is None:
if ( not space.isinstance_w(w_object, space.w_str) and
not space.isinstance_w(w_object, space.w_unicode) and
not isinstance(w_object, W_GenericBox)):
# use buffer interface
w_object = _array_from_buffer_3118(space, w_object, dtype)
else:
# continue with w_array, but do further operations in place
w_object = w_array
copy = False
dtype = w_object.get_dtype()
if not isinstance(w_object, W_NDimArray):
w_array, _copy = try_interface_method(space, w_object, copy)
if w_array is not None:
w_object = w_array
copy = _copy
dtype = w_object.get_dtype()
if isinstance(w_object, W_NDimArray):
npy_order = order_converter(space, w_order, NPY.ANYORDER)
if (dtype is None or w_object.get_dtype() is dtype) and (subok or
type(w_object) is W_NDimArray):
flags = w_object.get_flags()
must_copy = copy
must_copy |= (npy_order == NPY.CORDER and not flags & NPY.ARRAY_C_CONTIGUOUS)
must_copy |= (npy_order == NPY.FORTRANORDER and not flags & NPY.ARRAY_F_CONTIGUOUS)
if must_copy:
return w_object.descr_copy(space, space.wrap(npy_order))
else:
return w_object
if subok and not type(w_object) is W_NDimArray:
raise oefmt(space.w_NotImplementedError,
"array(..., subok=True) only partially implemented")
# we have a ndarray, but need to copy or change dtype
if dtype is None:
dtype = w_object.get_dtype()
if dtype != w_object.get_dtype():
# silently reject the copy value
copy = True
if copy:
shape = w_object.get_shape()
order = support.get_order_as_CF(w_object.get_order(), npy_order)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
if support.product(shape) == 1:
w_arr.set_scalar_value(dtype.coerce(space,
w_object.implementation.getitem(0)))
else:
loop.setslice(space, shape, w_arr.implementation, w_object.implementation)
return w_arr
else:
imp = w_object.implementation
w_base = w_object
sz = w_base.get_size() * dtype.elsize
if imp.base() is not None:
w_base = imp.base()
if type(w_base) is W_NDimArray:
sz = w_base.get_size() * dtype.elsize
else:
# this must succeed (mmap, buffer, ...)
sz = space.int_w(space.call_method(w_base, 'size'))
with imp as storage:
return W_NDimArray.from_shape_and_storage(space,
w_object.get_shape(), storage, dtype, storage_bytes=sz,
w_base=w_base, strides=imp.strides, start=imp.start)
else:
# not an array
npy_order = order_converter(space, w_order, NPY.CORDER)
shape, elems_w = find_shape_and_elems(space, w_object, dtype)
if dtype is None and space.isinstance_w(w_object, space.w_buffer):
dtype = descriptor.get_dtype_cache(space).w_uint8dtype
if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
dtype = find_dtype_for_seq(space, elems_w, dtype)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=npy_order)
if support.product(shape) == 1: # safe from overflow since from_shape checks
w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
else:
loop.assign(space, w_arr, elems_w)
return w_arr
