mantidimaging.core.rotation.data_model module#

class mantidimaging.core.rotation.data_model.CorTiltDataModel[source]#

Bases: object

Model for finding COR/Tilt from (slice index, centre of rotation) data points

add_point(idx: int | None = None, slice_idx: int = 0, cor: float = 0.0) → None[source]#

property angle_in_degrees: Degrees#

clear_points() → None[source]#

clear_results() → None[source]#

property cor: ScalarCoR#

property cors: list[float]#

property empty: bool#

get_all_cors_from_regression(image_height: int) → list[ScalarCoR][source]#

Parameters:: image_height – How many cors will be generated, this should be equal to the image height (i.e. number of sinograms that will be reconstructed)
Returns:: List of cors for every slice of the image height

get_cor_from_regression(slice_idx: int) → float[source]#

property gradient: Slope#

property has_results: bool#

iter_points() → Iterator[Point][source]#

linear_regression() → None[source]#

property num_points: int#

point(idx: int) → Point | None[source]#

populate_slice_indices(begin, end, count, cor=0.0) → None[source]#

remove_point(idx: int) → None[source]#

set_cor_at_slice(slice_idx: int, cor: float) → None[source]#

set_point(idx: int, slice_idx: int | None = None, cor: float | None = None, reset_results: bool = True) → None[source]#

set_precalculated(cor: ScalarCoR, tilt: Degrees) → None[source]#

property slices: list[int]#

sort_points() → None[source]#

property stack_properties: dict#

class mantidimaging.core.rotation.data_model.Point(slice_index, cor)#

Bases: NamedTuple

cor: float#: Alias for field number 1

slice_index: int#: Alias for field number 0