swyft.utils

swyft.utils.array_to_tensor(array, dtype=None, device=None)

Converts np.ndarray and torch.Tensor to torch.Tensor with dtype and on device. When dtype is None, unsafe casts all float-type arrays to torch.float32 and all int-type arrays to torch.int64

Parameters:

• array (Union[ndarray, Tensor]) –

• dtype (Optional[dtype]) –

• device (Optional[Union[device, str]]) –

Return type:

Tensor

swyft.utils.get_corner_marginal_indices(n_parameters)

produce the marginals for a corner plot

Parameters:

n_parameters (int) –

Returns:

marginal_indices_1d, marginal_indices_2d

Return type:

Tuple[Tuple[Tuple[int, …], …], Tuple[Tuple[int, …], …]]

swyft.utils.tupleize_marginal_indices(marginal_indices)

Reformat input marginal_indices into sorted and hashable standard form: tuples of tuples.

a lone input tuple will be respected as coming from the same marginal lists will assumed to be collections of different marginals

Parameters:

marginal_indices (Union[int, Sequence[int], Sequence[Sequence[int]]]) –

Return type:

Tuple[Tuple[int, …], …]