get_operator#
- mrinufft.get_operator(backend_name: Literal['stacked'], wrt_data: bool = False, wrt_traj: bool = False, paired_batch: bool = False) partial[MRIStackedNUFFT][source]#
- mrinufft.get_operator(backend_name: str, wrt_data: Literal[False] = False, wrt_traj: Literal[False] = False, paired_batch: bool = False) type[FourierOperatorBase]
- mrinufft.get_operator(backend_name: str, wrt_data: Literal[False] = False, wrt_traj: Literal[False] = False, paired_batch: bool = False, *args: Any, **kwargs: Any) FourierOperatorBase
- mrinufft.get_operator(backend_name: str, wrt_data: Literal[True] = True, wrt_traj: bool = False, paired_batch: bool = False) partial[MRINufftAutoGrad]
- mrinufft.get_operator(backend_name: str, wrt_data: bool = False, wrt_traj: Literal[True] = True, paired_batch: bool = False) partial[MRINufftAutoGrad]
- mrinufft.get_operator(backend_name: str, wrt_data: Literal[True] = True, wrt_traj: bool = False, paired_batch: bool = False, *args: Any, **kwargs: Any) MRINufftAutoGrad
Return an MRI Fourier operator interface using the correct backend.
Tip
Don’t be scared of by the huge type signature, it is here to help IDEs and linters to understand the return type of this function.
- Parameters:
backend_name (str) – Backend name
wrt_data (bool, default False) – if set gradients wrt to data and images will be available.
wrt_traj (bool, default False) – if set gradients wrt to trajectory will be available.
paired_batch (bool, default False) – if set, the autograd will be done with paired batchs of data and smaps.
*args – Arguments to pass to the operator constructor.
**kwargs – Arguments to pass to the operator constructor.
- Returns:
class or instance of class if args or kwargs are given.
- Return type:
FourierOperator
- Raises:
ValueError if the backend is not available. –
Examples
>>> from mrinufft import get_operator # Return a constructor for the finufft backend with autograd support for data. >>> nufftKlass = get_operator("finufft", wrt_data=True, wrt_traj=False) # create an instance of this operator with the given samples and shape >>> nufft = nufftKlass(samples, shape, density=True)
Alternatively, you can create an instance directly by passing the arguments to
get_operator:>>> nufft = get_operator("finufft", wrt_data=True, wrt_traj=False, samples=samples, shape=shape, density=True. ...)
