Global Optimization

class pysmFISH.stitching_package.GlobalOptimization.GlobalOptimization

Use linear regression to find the global transitions that fit the pairwise transitions best

self.logger – logger instance self.global_trans – 2D numpy array containg the global

translation for each tile. (Or empty list before running performOptimization())

performOptimization(tile_set, contig_tuples, P, covs, nr_tiles, nr_dim)

Use linear regression to find the global transition

Fills up self.global_trans; a numpy array with shape (nr_tiles, nr_dim).

tile_set: np.array

Array filled with ones that has the same shape as tile set

contig_tuples: list

List of tuples denoting which tiles are contingent to each other.

P: np.array

1D numpy array containing pairwise alignment y and x coordinates (and z-coordinates when applicable) for each neighbouring pair of tiles, array should be 2 * len(contig_typles) f for 2D data or 3 * len(contig_typles) for 3D data.

covs: np.array

Covariance for each pairwise alignment in P, array should be len(contig_typles).

nr_tiles: int

The number of tiles in the dataset

nr_dim: int

The number of dimensions the image.