control.control — Control Network

The control.control module encapsulates a control network class that can be used to represent a graph in a single data structure. This class is an integral part of the conversion from a CandidateGraph to an ISIS control network

New in version 0.1.0.

autocnet.control.control.compute_covariance(df, latsigma, lonsigma, radsigma, radius)

Compute the covariance matrices for constrained or fixed points.

Parameters

• df (pd.DataFrame) – with columns pointtype, adjustedY, and adjustedX

• latsigma (int/float) – The estimated sigma (error) in the latitude direction

• lonsigma (int/float) – The estimated sigma (error) in the longitude direction

• radsigma (int/float) – The estimated sigma (error) in the radius direction

• radius (int/float) – The body semimajor radius

autocnet.control.control.identify_potential_overlaps(cg, cn, overlap=True)

Identify those points that could have additional measures

Parameters

overlap (boolean) – If True, apply aprint(g)n additional point in polygon check, where the polygon is the footprint intersection between images and the point is a keypoint projected into lat/lon space. Note that the projection can be inaccurate if the method used estimates the transformation.

Returns

candidate_cliques – with the index as the point id (in the data attribute) and the value as an iterable of image ids to search for a new point.

Return type

DataFrame