def test_type_resolver(self, space):
c128_dtype = get_dtype_cache(space).w_complex128dtype
c64_dtype = get_dtype_cache(space).w_complex64dtype
f64_dtype = get_dtype_cache(space).w_float64dtype
f32_dtype = get_dtype_cache(space).w_float32dtype
u32_dtype = get_dtype_cache(space).w_uint32dtype
b_dtype = get_dtype_cache(space).w_booldtype
ufunc = W_UfuncGeneric(space, [None, None, None], 'eigenvals', None, 1, 1,
[f32_dtype, c64_dtype,
f64_dtype, c128_dtype,
c128_dtype, c128_dtype],
'')
f32_array = W_NDimArray(VoidBoxStorage(0, f32_dtype))
index, dtypes = ufunc.type_resolver(space, [f32_array], [None],
'd->D', ufunc.dtypes)
#needs to cast input type, create output type
assert index == 1
assert dtypes == [f64_dtype, c128_dtype]
index, dtypes = ufunc.type_resolver(space, [f32_array], [None],
'', ufunc.dtypes)
assert index == 0
assert dtypes == [f32_dtype, c64_dtype]
raises(OperationError, ufunc.type_resolver, space, [f32_array], [None],
'u->u', ufunc.dtypes)
exc = raises(OperationError, ufunc.type_resolver, space, [f32_array], [None],
'i->i', ufunc.dtypes)
def array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False,
ndmin=0):
w_res = _array(space, w_object, w_dtype, copy, w_order, subok)
shape = w_res.get_shape()
if len(shape) < ndmin:
shape = [1] * (ndmin - len(shape)) + shape
impl = w_res.implementation.set_shape(space, w_res, shape)
if w_res is w_object:
return W_NDimArray(impl)
else:
w_res.implementation = impl
return w_res
def getitem(self, it, st):
w_res = W_NDimArray(it.getoperand(st))
return w_res
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 _get_item(self, it, st):
return W_NDimArray(it.getoperand(st))
def getitem(self, it, st):
res = it.getoperand(st)
return W_NDimArray(res)
def __init__(self,
space,
w_seq,
w_flags,
w_op_flags,
w_op_dtypes,
w_casting,
w_op_axes,
w_itershape,
buffersize=0,
order='K'):
self.order = order
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 = []
# 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)]
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 ooutput cannot be broadcast
shape_agreement(space, self.shape, self.seq[i], False)
if self.tracked_index != "":
if self.order == "K":
self.order = self.seq[0].implementation.order
if self.tracked_index == "multi":
backward = False
else:
backward = self.order != self.tracked_index
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)):
selfd = self.dtypes[i]
seq_d = self.seq[i].get_dtype()
if not selfd:
self.dtypes[i] = seq_d
elif selfd != seq_d:
if not 'r' in self.op_flags[i].tmp_copy:
raise oefmt(
space.w_TypeError,
"Iterator operand required copying or "
"buffering for operand %d", i)
impl = self.seq[i].implementation
new_impl = impl.astype(space, selfd)
self.seq[i] = W_NDimArray(new_impl)
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 = get_iter(space, self.order, self.seq[i], self.shape,
self.dtypes[i], self.op_flags[i], self)
it.contiguous = False
self.iters.append((it, it.reset()))
if self.external_loop:
coalesce_axes(self, space)
def try_interface_method(space, w_object, copy):
try:
w_interface = space.getattr(w_object,
space.newtext("__array_interface__"))
if w_interface is None:
return None, False
version_w = space.finditem(w_interface, space.newtext("version"))
if version_w is None:
raise oefmt(space.w_ValueError, "__array_interface__ found without"
" 'version' key")
if not space.isinstance_w(version_w, space.w_int):
raise oefmt(
space.w_ValueError, "__array_interface__ found with"
" non-int 'version' key")
version = space.int_w(version_w)
if version < 3:
raise oefmt(space.w_ValueError,
"__array_interface__ version %d not supported",
version)
# make a view into the data
w_shape = space.finditem(w_interface, space.newtext('shape'))
w_dtype = space.finditem(w_interface, space.newtext('typestr'))
w_descr = space.finditem(w_interface, space.newtext('descr'))
w_data = space.finditem(w_interface, space.newtext('data'))
w_strides = space.finditem(w_interface, space.newtext('strides'))
if w_shape is None or w_dtype is None:
raise oefmt(
space.w_ValueError,
"__array_interface__ missing one or more required keys: shape, typestr"
)
if w_descr is not None:
raise oefmt(space.w_NotImplementedError,
"__array_interface__ descr not supported yet")
if w_strides is None or space.is_w(w_strides, space.w_None):
strides = None
else:
strides = [space.int_w(i) for i in space.listview(w_strides)]
shape = [space.int_w(i) for i in space.listview(w_shape)]
dtype = descriptor.decode_w_dtype(space, w_dtype)
if dtype is None:
raise oefmt(space.w_ValueError,
"__array_interface__ could not decode dtype %R",
w_dtype)
if w_data is not None and (space.isinstance_w(w_data, space.w_tuple) or
space.isinstance_w(w_data, space.w_list)):
data_w = space.listview(w_data)
w_data = rffi.cast(RAW_STORAGE_PTR, space.int_w(data_w[0]))
read_only = space.is_true(data_w[1]) or copy
offset = 0
w_base = w_object
if read_only:
w_base = None
return W_NDimArray.from_shape_and_storage(space,
shape,
w_data,
dtype,
w_base=w_base,
strides=strides,
start=offset), read_only
if w_data is None:
w_data = w_object
w_offset = space.finditem(w_interface, space.newtext('offset'))
if w_offset is None:
offset = 0
else:
offset = space.int_w(w_offset)
#print 'create view from shape',shape,'dtype',dtype,'data',data
if strides is not None:
raise oefmt(space.w_NotImplementedError,
"__array_interface__ strides not fully supported yet")
arr = frombuffer(space, w_data, dtype, support.product(shape), offset)
new_impl = arr.implementation.reshape(arr, shape)
return W_NDimArray(new_impl), False
except OperationError as e:
if e.match(space, space.w_AttributeError):
return None, False
raise
