def _run_cupy(data: cupy.ndarray) -> cupy.ndarray:
data = data.astype(cupy.float32)
griddim, blockdim = cuda_args(data.shape)
out = cupy.empty(data.shape, dtype='f4')
out[:] = cupy.nan
_run_gpu[griddim, blockdim](data, out)
return out
def _run_cupy(data: cupy.ndarray, cellsize_x: Union[int, float],
cellsize_y: Union[int, float]) -> cupy.ndarray:
cellsize_x_arr = cupy.array([float(cellsize_x)], dtype='f4')
cellsize_y_arr = cupy.array([float(cellsize_y)], dtype='f4')
data = data.astype(cupy.float32)
griddim, blockdim = cuda_args(data.shape)
out = cupy.empty(data.shape, dtype='f4')
out[:] = cupy.nan
_run_gpu[griddim, blockdim](data, cellsize_x_arr, cellsize_y_arr, out)
return out
def _run_cupy(data: cupy.ndarray, cellsize: Union[int, float]) -> cupy.ndarray:
data = data.astype(cupy.float32)
cellsize_arr = cupy.array([float(cellsize)], dtype='f4')
# TODO: add padding
griddim, blockdim = cuda_args(data.shape)
out = cupy.empty(data.shape, dtype='f4')
out[:] = cupy.nan
_run_gpu[griddim, blockdim](data, cellsize_arr, out)
return out
def KHK(self, KHK: cp.ndarray):
self.__KHK = KHK.astype(cp.float64)
def solution(self, solution: cp.ndarray):
self.__solution = solution.astype(cp.float64)
