def set_data(self, data, memtype=MEM_HOST, cmode=COPY_VALUES):
"""Set the data used by a QFunction Context, freeing any previously allocated
data if applicable.
Args:
*data: Numpy or Numba array to be used
**memtype: memory type of the array being passed, default CEED_MEM_HOST
**cmode: copy mode for the array, default CEED_COPY_VALUES"""
# Store array reference if needed
if cmode == USE_POINTER:
self._array_reference = data
else:
self._array_reference = None
# Setup the numpy array for the libCEED call
data_pointer = ffi.new("CeedScalar *")
if memtype == MEM_HOST:
data_pointer = ffi.cast("void *",
data.__array_interface__['data'][0])
else:
array_pointer = ffi.cast("void *",
data.__cuda_array_interface__['data'][0])
# libCEED call
err_code = lib.CeedQFunctionContextSetData(
self._pointer[0], memtype, cmode,
len(data) * ffi.sizeof("CeedScalar"), data_pointer)
self._ceed._check_error(err_code)
def get_grad_1d(self):
"""Return 1D gradient matrix of a tensor product Basis.
Returns:
*array: Numpy array"""
# Compute the length of the array
nnodes_pointer = ffi.new("CeedInt *")
lib.CeedBasisGetNumNodes1D(self._pointer[0], nnodes_pointer)
nqpts_pointer = ffi.new("CeedInt *")
lib.CeedBasisGetNumQuadraturePoints1D(self._pointer[0], nqpts_pointer)
length = nnodes_pointer[0] * nqpts_pointer[0]
# Setup the pointer's pointer
array_pointer = ffi.new("CeedScalar **")
# libCEED call
lib.CeedBasisGetGrad1D(self._pointer[0], array_pointer)
# Return array created from buffer
# Create buffer object from returned pointer
buff = ffi.buffer(array_pointer[0], ffi.sizeof("CeedScalar") * length)
# return read only Numpy array
ret = np.frombuffer(buff, dtype="float64")
ret.flags['WRITEABLE'] = False
return ret
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 array"""
# Retrieve the length of the array
length_pointer = ffi.new("CeedInt *")
lib.CeedVectorGetLength(self._pointer[0], length_pointer)
# Setup the pointer's pointer
array_pointer = ffi.new("CeedScalar **")
# libCEED call
lib.CeedVectorGetArrayRead(self._pointer[0], memtype, array_pointer)
# Create buffer object from returned pointer
buff = ffi.buffer(array_pointer[0],
ffi.sizeof("CeedScalar") * length_pointer[0])
# Create numpy array from buffer
ret = np.frombuffer(buff, dtype="float64")
# Make the numpy array read-only
ret.flags['WRITEABLE'] = False
return ret
def get_data(self, memtype=MEM_HOST):
"""Get read/write access to a QFunction Context via the specified memory type.
Args:
**memtype: memory type of the array being passed, default CEED_MEM_HOST
Returns:
*data: Numpy or Numba array"""
# Retrieve the length of the array
size_pointer = ffi.new("size_t *")
err_code = lib.CeedQFunctionContextGetContextSize(
self._pointer[0], size_pointer)
self._ceed._check_error(err_code)
# Setup the pointer's pointer
data_pointer = ffi.new("CeedScalar **")
# libCEED call
err_code = lib.CeedQFunctionContextGetData(self._pointer[0], memtype,
data_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(data_pointer[0], size_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
if lib.CEED_SCALAR_TYPE == lib.CEED_SCALAR_FP32:
scalar_type_str = '>f4'
else:
scalar_type_str = '>f8'
desc = {
'shape': (size_pointer[0] / ffi.sizeof("CeedScalar")),
'typestr': scalar_type_str,
'data': (int(ffi.cast("intptr_t", data_pointer[0])), False),
'version': 2
}
# return Numba array
return nbcuda.from_cuda_array_interface(desc)
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)