def get_array_read(self, memtype=MEM_HOST):
"""Get read-only access to a Vector via the specified memory type.
Args:
**memtype: memory type of the array being passed, default CEED_MEM_HOST
Returns:
*array: Numpy or Numba array"""
# Retrieve the length of the array
length_pointer = ffi.new("CeedInt *")
err_code = lib.CeedVectorGetLength(self._pointer[0], length_pointer)
self._ceed._check_error(err_code)
# Setup the pointer's pointer
array_pointer = ffi.new("CeedScalar **")
# libCEED call
err_code = lib.CeedVectorGetArrayRead(self._pointer[0], memtype,
array_pointer)
self._ceed._check_error(err_code)
# Return array created from buffer
if memtype == MEM_HOST:
# Create buffer object from returned pointer
buff = ffi.buffer(array_pointer[0],
ffi.sizeof("CeedScalar") * length_pointer[0])
# return read only Numpy array
ret = np.frombuffer(buff, dtype="float64")
ret.flags['WRITEABLE'] = False
return ret
else:
# CUDA array interface
# https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
import numba.cuda as nbcuda
desc = {
'shape': (length_pointer[0]),
'typestr': '>f8',
'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
'version': 2
}
# return read only Numba array
return nbcuda.from_cuda_array_interface(desc)
def get_array_write(self, memtype=MEM_HOST):
"""Get write-only access to a Vector via the specified memory type.
All old values should be considered invalid.
Args:
**memtype: memory type of the array being passed, default CEED_MEM_HOST
Returns:
*array: Numpy or Numba array"""
# Retrieve the length of the array
length_pointer = ffi.new("CeedSize *")
err_code = lib.CeedVectorGetLength(self._pointer[0], length_pointer)
self._ceed._check_error(err_code)
# Setup the pointer's pointer
array_pointer = ffi.new("CeedScalar **")
# libCEED call
err_code = lib.CeedVectorGetArrayWrite(self._pointer[0], memtype,
array_pointer)
self._ceed._check_error(err_code)
# Return array created from buffer
if memtype == MEM_HOST:
# Create buffer object from returned pointer
buff = ffi.buffer(array_pointer[0],
ffi.sizeof("CeedScalar") * length_pointer[0])
# return Numpy array
return np.frombuffer(buff,
dtype=scalar_types[lib.CEED_SCALAR_TYPE])
else:
# CUDA array interface
# https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html
import numba.cuda as nbcuda
desc = {
'shape': (length_pointer[0]),
'typestr': '>f8',
'data': (int(ffi.cast("intptr_t", array_pointer[0])), False),
'version': 2
}
# return Numba array
return nbcuda.from_cuda_array_interface(desc)
