API reference

Core functionality

find_link() links and segments the images and refine_leastsq() refines the positions by fitting to sums of model functions.

Constraints

constraints.dimer(dist[, ndim])	Constrain clusters of 2 at given distance.
constraints.trimer(dist[, ndim])	Constrain clusters of 3 at given distance.
constraints.tetramer(dist[, ndim])	Constrain clusters of 4 at given distance.
constraints.dimer_global(mpp[, ndim])	Constrain clusters of 2 to a constant, unknown distance.

Model image creation

artificial.SimulatedImage(shape, size[, ...])	This class makes it easy to generate artificial pictures.
artificial.CoordinateReader(f, shape, size, ...)	Generate a FramesSquence that draws features at given coordinates
artificial.get_single(shape[, size, offset, ...])	Get image of a single feature
artificial.get_dimer(shape[, size, ...])	Get image of a two features at given separation distance
artificial.get_multiple(N[, signal_range, ...])	Get image of a N features on a grid with random subpx coordinate

Cluster motion analysis

motion.orientation_df(f[, cluster_size, ...])	Calculate the orientation of a dataframe of clusters, given by three orthonormal unit vectors.
motion.diffusion_tensor(positions, orientations)	Calculate the diffusion tensor.
motion.diffusion_tensor_ci(positions, ...[, ...])	Calculate the diffusion tensor and the confidence interval using bootstrap.
motion.friction_tensor(diff_tens)	Convert diffusion tensor to friction tensor.

Helper functions

find.find_clusters(f, separation[, ...])	Find clusters in a DataFrame of points from several frames.
fitfunc.FitFunctions([fit_function, ndim, ...])	Helper class maintaining fit functions and bounds.
fitfunc.vect_from_params(params, modes[, ...])	Convert an array of per-feature parameters into a vector
fitfunc.vect_to_params(vect, params, modes)	Convert a vector from leastsquares optimization to an array of per-feature parameters.
masks.slice_pad(image, center, radius)	Slice a single feature from an image, pad when appropriate.
masks.slices_multiple(coords, shape, radius)	Creates the smallest box so that every coord in coords is in the box up to radius from the coordinate.
masks.slice_image(coords, image, radius)	Creates the smallest box so that every coord in coords is in the box up to radius from the coordinate.
masks.mask_image(coords, image, radius[, ...])	Masks an image with elliptical masks with size radius.
masks.binary_mask_multiple(coords_rel, ...)	Creates multiple elliptical masks.