locan.analysis.drift

Drift analysis for localization coordinates.

This module provides functions for estimating spatial drift in localization data.

Software based drift correction using image correlation has been described in several publications . Methods employed for drift estimation comprise single molecule localization analysis (an iterative closest point (icp) algorithm as implemented in the open3d library [1], [2]) or image cross-correlation analysis [3], [4], [5].

Examples

Please use the following procedure to estimate and correct for spatial drift:

from lmfit import LinearModel
drift = Drift(chunk_size=1000, target='first').\
        compute(locdata).\
        fit_transformations(slice_data=slice(0, -1),
                            matrix_models=None,
                            offset_models=(LinearModel(), LinearModel())).\
        apply_correction()
locdata_corrected = drift.locdata_corrected

References

[1]

Qian-Yi Zhou, Jaesik Park, Vladlen Koltun, Open3D: A Modern Library for 3D Data Processing, arXiv 2018, 1801.09847

[2]

Rusinkiewicz and M. Levoy, Efficient variants of the ICP algorithm, In 3-D Digital Imaging and Modeling, 2001.

[3]

C. Geisler, Drift estimation for single marker switching based imaging schemes, Optics Express. 2012, 20(7):7274-89.

[4]

Yina Wang et al., Localization events-based sample drift correction for localization microscopy with redundant cross-correlation algorithm, Optics Express 2014, 22(13):15982-91.

[5]

Michael J. Mlodzianoski et al., Sample drift correction in 3D fluorescence photoactivation localization microscopy, Opt Express. 2011 Aug 1;19(16):15009-19.

Classes

Drift([meta, chunks, chunk_size, n_chunks, ...])	Estimate drift from localization coordinates by registering points in successive time-chunks of localization data using an iterative closest point algorithm (icp) or image cross-correlation algorithm (cc).
DriftComponent([type])	Class carrying model functions to describe drift over time (in unit of frames).
DriftModel(*args, **kwargs)