def __init__(self, dtype, shape=None, strides=None, offset=0, nbytes=None):
self.shape = tuple() if shape is None else wrap_in_tuple(shape)
self.size = product(self.shape)
self.dtype = dtypes.normalize_type(dtype)
self.ctype = dtypes.ctype_module(self.dtype)
default_strides = helpers.default_strides(self.shape,
self.dtype.itemsize)
if strides is None:
strides = default_strides
else:
strides = tuple(strides)
self._default_strides = strides == default_strides
self.strides = strides
default_nbytes = helpers.min_buffer_size(self.shape,
self.dtype.itemsize,
self.strides)
if nbytes is None:
nbytes = default_nbytes
self._default_nbytes = nbytes == default_nbytes
self.nbytes = nbytes
self.offset = offset
self._cast = dtypes.cast(self.dtype)
def array(self,
shape,
dtype,
strides=None,
offset=0,
nbytes=None,
allocator=None,
base=None,
base_data=None):
if allocator is None:
allocator = self.allocate
dtype = dtypes.normalize_type(dtype)
shape = wrap_in_tuple(shape)
if nbytes is None:
nbytes = min_buffer_size(shape,
dtype.itemsize,
strides=strides,
offset=offset)
if (offset != 0
or strides is not None) and base_data is None and base is None:
base_data = allocator(nbytes)
elif base is not None:
base_data = base.data
return Array(self,
shape,
dtype,
strides=strides,
offset=offset,
allocator=allocator,
base_data=base_data,
nbytes=nbytes)
def array(self,
shape,
dtype,
strides=None,
offset=0,
nbytes=None,
allocator=None,
base=None,
base_data=None):
# In PyCUDA, the default allocator is not None, but a default alloc object
if allocator is None:
allocator = cuda.mem_alloc
dtype = dtypes.normalize_type(dtype)
shape = wrap_in_tuple(shape)
if nbytes is None:
nbytes = int(
min_buffer_size(shape,
dtype.itemsize,
strides=strides,
offset=offset))
if (offset != 0
or strides is not None) and base_data is None and base is None:
base_data = allocator(nbytes)
elif base is not None:
if isinstance(base, Array):
base_data = base.base_data
else:
base_data = base.gpudata
return Array(self,
shape,
dtype,
strides=strides,
allocator=allocator,
offset=offset,
base_data=base_data,
nbytes=nbytes)
def __init__(self, dtype, shape=None, strides=None, offset=0, nbytes=None):
self.shape = tuple() if shape is None else wrap_in_tuple(shape)
self.size = product(self.shape)
self.dtype = dtypes.normalize_type(dtype)
self.ctype = dtypes.ctype_module(self.dtype)
default_strides = helpers.default_strides(self.shape, self.dtype.itemsize)
if strides is None:
strides = default_strides
else:
strides = tuple(strides)
self._default_strides = strides == default_strides
self.strides = strides
default_nbytes = helpers.min_buffer_size(self.shape, self.dtype.itemsize, self.strides)
if nbytes is None:
nbytes = default_nbytes
self._default_nbytes = nbytes == default_nbytes
self.nbytes = nbytes
self.offset = offset
self._cast = dtypes.cast(self.dtype)
def array(
self, shape, dtype, strides=None, offset=0, nbytes=None,
allocator=None, base=None, base_data=None):
# In PyCUDA, the default allocator is not None, but a default alloc object
if allocator is None:
allocator = cuda.mem_alloc
dtype = dtypes.normalize_type(dtype)
shape = wrap_in_tuple(shape)
if nbytes is None:
nbytes = int(min_buffer_size(shape, dtype.itemsize, strides=strides, offset=offset))
if (offset != 0 or strides is not None) and base_data is None and base is None:
base_data = allocator(nbytes)
elif base is not None:
if isinstance(base, Array):
base_data = base.base_data
else:
base_data = base.gpudata
return Array(
self, shape, dtype, strides=strides, allocator=allocator,
offset=offset, base_data=base_data, nbytes=nbytes)