mantidimaging.gui.windows.recon.model module

class mantidimaging.gui.windows.recon.model.ReconstructWindowModel(data_model: CorTiltPointQtModel)

Bases: object

auto_find_correlation(progress: Progress) → Tuple[ScalarCoR, Degrees]

auto_find_minimisation_sqsum(slices: List[int], recon_params: ReconstructionParameters, initial_cor: List[float], progress: Progress) → List[float]

Parameters:

• slices – Slice indices to be reconstructed

• recon_params – Reconstruction parameters

• initial_cor – Initial COR for the slices. Will be used as the start for the minimisation. If a float is passed it will be used for all slices. If a list is passed, the COR will be retrieved for each slice.

• progress – Progress reporter

property cors: ndarray

do_fit() → bool

find_initial_cor() → Tuple[int, ScalarCoR]

static get_allowed_filters(alg_name: str) → list

get_cor_for_slice_from_regression() → ScalarCoR

get_me_a_cor(cor: ScalarCoR | None = None) → ScalarCoR

get_results() → Tuple[ScalarCoR, Degrees, Slope]

get_slice_indices(num_cors: int) → Tuple[int, ndarray | Tuple[ndarray, float | None]]

property has_results: bool

property images

initial_select_data(images: ImageStack | None)

is_current_stack(uuid: uuid.UUID) → bool

property last_cor: ScalarCoR

static load_allowed_recon_kwargs() → dict[str, Any]

property num_points: int

property preview_projection_idx: int

property preview_slice_idx: int

static proj_180_degree_shape_matches_images(images)

reset_cor_model() → None

reset_selected_row() → None

run_full_recon(recon_params: ReconstructionParameters, progress: Progress) → ImageStack | None

run_preview_recon(slice_idx: int, cor: ScalarCoR, recon_params: ReconstructionParameters, progress: Progress | None = None) → ImageStack | None

property selected_row: int

set_precalculated(cor: ScalarCoR, tilt: Degrees) → None

property slices: ndarray

stack_contains_nans() → bool

stack_contains_negative_values() → bool

stack_contains_zeroes() → bool

property stack_id

property tilt_angle: Degrees | None