trackpy.refine.least_squares

Functions

dimer(dist[, ndim])

Constraint setting clusters of 2 at a fixed distance.

dimer_global(mpp[, ndim])

Constrain clusters of 2 to a constant, unknown distance.

disc_fun(r2, p, ndim)

dr2_anisotropic_2d(mesh, p)

dr2_anisotropic_3d(mesh, p)

dr2_isotropic_2d(mesh, p)

dr2_isotropic_3d(mesh, p)

gauss_dfun(r2, p, ndim)

gauss_fun(r2, p, ndim)

ignore_clip_warnings(func)

inv_series_fun(r2, p, ndim)

p is a vector of arguments [mult, a, b, c, ...], defining the series: signal_mult / (1 + a r^2 + b r^4 + c r^6 + ...)

prepare_subimage(coords, image, radius)

prepare_subimages(coords, groups, frame_nos, ...)

r2_anisotropic_2d(mesh, p)

r2_anisotropic_2d_safe(mesh, p)

r2_anisotropic_3d(mesh, p)

r2_anisotropic_3d_safe(mesh, p)

r2_isotropic_2d(mesh, p)

r2_isotropic_2d_safe(mesh, p)

r2_isotropic_3d(mesh, p)

r2_isotropic_3d_safe(mesh, p)

refine_leastsq(f, reader, diameter[, ...])

Refines overlapping feature coordinates by least-squares fitting to radial model functions.

ring_dfun(r2, p, ndim)

ring_fun(r2, p, ndim)

tetramer(dist[, ndim])

Constraint setting clusters of 4 at a fixed distance from each other.

train_leastsq(f, reader, diameter, ...[, ...])

Obtain fit parameters from an image of well-separated features with known location, in order to be able to use them in refine_leastsq.

trimer(dist[, ndim])

Constraint setting clusters of 3 at a fixed distance from each other.

vect_from_params(params, modes[, groups, ...])

Convert an array of per-feature parameters into a vector for least squares optimization

vect_to_params(vect, params, modes[, groups])

Convert a vector from least squares optimization to an array of per-feature parameters.

Classes

FitFunctions([fit_function, ndim, ...])

Helper class maintaining fit functions and bounds.

Exceptions

RefineException