def dtype_from_list(space, w_lst, simple):
lst_w = space.listview(w_lst)
fields = {}
offset = 0
names = []
for i in range(len(lst_w)):
w_elem = lst_w[i]
if simple:
subdtype = descr__new__(space, space.gettypefor(W_Dtype), w_elem)
fldname = 'f%d' % i
else:
w_shape = space.newtuple([])
if space.len_w(w_elem) == 3:
w_fldname, w_flddesc, w_shape = space.fixedview(w_elem)
if not support.issequence_w(space, w_shape):
w_shape = space.newtuple([w_shape])
else:
w_fldname, w_flddesc = space.fixedview(w_elem, 2)
subdtype = descr__new__(space, space.gettypefor(W_Dtype), w_flddesc, w_shape=w_shape)
fldname = space.str_w(w_fldname)
if fldname == '':
fldname = 'f%d' % i
if fldname in fields:
raise oefmt(space.w_ValueError, "two fields with the same name")
assert isinstance(subdtype, W_Dtype)
fields[fldname] = (offset, subdtype)
offset += subdtype.elsize
names.append(fldname)
return W_Dtype(types.RecordType(), NPY.VOID, NPY.VOIDLTR, NPY.VOIDLTR,
space.gettypefor(boxes.W_VoidBox),
names=names, fields=fields, elsize=offset)
def descr_setstate(self, space, w_data):
if self.fields is None: # if builtin dtype
return space.w_None
version = space.int_w(space.getitem(w_data, space.wrap(0)))
if version != 3:
raise oefmt(space.w_ValueError,
"can't handle version %d of numpy.dtype pickle",
version)
endian = space.str_w(space.getitem(w_data, space.wrap(1)))
if endian == NPY.NATBYTE:
endian = NPY.NATIVE
w_subarray = space.getitem(w_data, space.wrap(2))
w_names = space.getitem(w_data, space.wrap(3))
w_fields = space.getitem(w_data, space.wrap(4))
size = space.int_w(space.getitem(w_data, space.wrap(5)))
alignment = space.int_w(space.getitem(w_data, space.wrap(6)))
if (w_names == space.w_None) != (w_fields == space.w_None):
raise oefmt(space.w_ValueError, "inconsistent fields and names")
self.byteorder = endian
self.shape = []
self.subdtype = None
self.base = self
if w_subarray != space.w_None:
if not space.isinstance_w(w_subarray, space.w_tuple) or \
space.len_w(w_subarray) != 2:
raise oefmt(space.w_ValueError,
"incorrect subarray in __setstate__")
subdtype, w_shape = space.fixedview(w_subarray)
assert isinstance(subdtype, W_Dtype)
if not support.issequence_w(space, w_shape):
self.shape = [space.int_w(w_shape)]
else:
self.shape = [space.int_w(w_s) for w_s in space.fixedview(w_shape)]
self.subdtype = subdtype
self.base = subdtype.base
if w_names != space.w_None:
self.names = []
self.fields = {}
for w_name in space.fixedview(w_names):
name = space.str_w(w_name)
value = space.getitem(w_fields, w_name)
dtype = space.getitem(value, space.wrap(0))
assert isinstance(dtype, W_Dtype)
offset = space.int_w(space.getitem(value, space.wrap(1)))
self.names.append(name)
self.fields[name] = offset, dtype
self.itemtype = types.RecordType()
if self.is_flexible():
self.elsize = size
self.alignment = alignment
def find_shape_and_elems(space, w_iterable, dtype):
isstr = space.isinstance_w(w_iterable, space.w_str)
if not support.issequence_w(space, w_iterable) or isstr:
if dtype is None or dtype.char != NPY.CHARLTR:
return [], [w_iterable]
is_rec_type = dtype is not None and dtype.is_record()
if is_rec_type and is_single_elem(space, w_iterable, is_rec_type):
return [], [w_iterable]
if isinstance(w_iterable, W_NDimArray) and w_iterable.is_scalar():
return [], [w_iterable]
return _find_shape_and_elems(space, w_iterable, is_rec_type)
def find_shape_and_elems(space, w_iterable, dtype):
isstr = space.isinstance_w(w_iterable, space.w_str)
if not support.issequence_w(space, w_iterable) or isstr:
if dtype is None or dtype.char != NPY.CHARLTR:
return [], [w_iterable]
is_rec_type = dtype is not None and dtype.is_record()
if is_rec_type and is_single_elem(space, w_iterable, is_rec_type):
return [], [w_iterable]
if isinstance(w_iterable, W_NDimArray) and w_iterable.is_scalar():
return [], [w_iterable]
return _find_shape_and_elems(space, w_iterable, is_rec_type)
def is_scalar_like(space, w_obj, dtype):
isstr = space.isinstance_w(w_obj, space.w_bytes)
if not support.issequence_w(space, w_obj) or isstr:
if dtype is None or dtype.char != NPY.CHARLTR:
return True
is_rec_type = dtype is not None and dtype.is_record()
if is_rec_type and is_single_elem(space, w_obj, is_rec_type):
return True
if isinstance(w_obj, W_NDimArray) and w_obj.is_scalar():
return True
return False
def is_scalar_like(space, w_obj, dtype):
isstr = space.isinstance_w(w_obj, space.w_str)
if not support.issequence_w(space, w_obj) or isstr:
if dtype is None or dtype.char != NPY.CHARLTR:
return True
is_rec_type = dtype is not None and dtype.is_record()
if is_rec_type and is_single_elem(space, w_obj, is_rec_type):
return True
if isinstance(w_obj, W_NDimArray) and w_obj.is_scalar():
return True
return False
def dtype_from_list(space, w_lst, simple):
lst_w = space.listview(w_lst)
fields = {}
offset = 0
names = []
for i in range(len(lst_w)):
w_elem = lst_w[i]
if simple:
subdtype = descr__new__(space, space.gettypefor(W_Dtype), w_elem)
fldname = 'f%d' % i
else:
w_shape = space.newtuple([])
if space.len_w(w_elem) == 3:
w_fldname, w_flddesc, w_shape = space.fixedview(w_elem)
if not support.issequence_w(space, w_shape):
w_shape = space.newtuple([w_shape])
else:
w_fldname, w_flddesc = space.fixedview(w_elem, 2)
subdtype = descr__new__(space,
space.gettypefor(W_Dtype),
w_flddesc,
w_shape=w_shape)
fldname = space.str_w(w_fldname)
if fldname == '':
fldname = 'f%d' % i
if fldname in fields:
raise oefmt(space.w_ValueError,
"two fields with the same name")
assert isinstance(subdtype, W_Dtype)
fields[fldname] = (offset, subdtype)
offset += subdtype.elsize
names.append(fldname)
return W_Dtype(types.RecordType(),
NPY.VOID,
NPY.VOIDLTR,
NPY.VOIDLTR,
space.gettypefor(boxes.W_VoidBox),
names=names,
fields=fields,
elsize=offset)
def descr_setstate(self, space, w_data):
if self.fields is None: # if builtin dtype
return space.w_None
version = space.int_w(space.getitem(w_data, space.wrap(0)))
if version != 3:
raise oefmt(space.w_ValueError,
"can't handle version %d of numpy.dtype pickle",
version)
endian = space.str_w(space.getitem(w_data, space.wrap(1)))
if endian == NPY.NATBYTE:
endian = NPY.NATIVE
w_subarray = space.getitem(w_data, space.wrap(2))
w_names = space.getitem(w_data, space.wrap(3))
w_fields = space.getitem(w_data, space.wrap(4))
size = space.int_w(space.getitem(w_data, space.wrap(5)))
alignment = space.int_w(space.getitem(w_data, space.wrap(6)))
if (w_names == space.w_None) != (w_fields == space.w_None):
raise oefmt(
space.w_ValueError,
"inconsistent fields and names in Numpy dtype unpickling")
self.byteorder = endian
self.shape = []
self.subdtype = None
self.base = self
if w_subarray != space.w_None:
if not space.isinstance_w(w_subarray, space.w_tuple) or \
space.len_w(w_subarray) != 2:
raise oefmt(space.w_ValueError,
"incorrect subarray in __setstate__")
subdtype, w_shape = space.fixedview(w_subarray)
assert isinstance(subdtype, W_Dtype)
if not support.issequence_w(space, w_shape):
self.shape = [space.int_w(w_shape)]
else:
self.shape = [
space.int_w(w_s) for w_s in space.fixedview(w_shape)
]
self.subdtype = subdtype
self.base = subdtype.base
if w_names != space.w_None:
self.names = []
self.fields = {}
for w_name in space.fixedview(w_names):
name = space.str_w(w_name)
value = space.getitem(w_fields, w_name)
dtype = space.getitem(value, space.wrap(0))
assert isinstance(dtype, W_Dtype)
offset = space.int_w(space.getitem(value, space.wrap(1)))
self.names.append(name)
self.fields[name] = offset, dtype
self.itemtype = types.RecordType(space)
if self.is_flexible():
self.elsize = size
self.alignment = alignment
