API Reference

This detailed reference lists all the classes and functions contained in the package. If you are just looking to get started, read the Tutorial first.

File(filename)

A convenient HDF5 file wrapper for reading data exported from Bluelake

channel.Slice(data_source[, labels, calibration])

A lazily evaluated slice of a timeline/HDF5 channel

fdcurve.FdCurve(file, start, stop, name[, ...])

An FD curve exported from Bluelake

kymo.Kymo(name, file, start, stop, metadata)

A Kymograph exported from Bluelake

scan.Scan(name, file, start, stop, metadata)

A confocal scan exported from Bluelake

point_scan.PointScan(name, file, start, ...)

A confocal point scan exported from Bluelake

correlated_stack.CorrelatedStack(*image_names)

CorrelatedStack acquired with Bluelake.

ColorAdjustment(minimum, maximum[, mode, gamma])

Utility class to adjust the min/max values of image colormaps.

Force calibration

PassiveCalibrationModel(bead_diameter[, ...])

Model to fit data acquired during passive calibration.

ActiveCalibrationModel(driving_data, ...[, ...])

Model to fit data acquired during active calibration.

force_calibration.power_spectrum.PowerSpectrum(...)

Power spectrum data for a time series.

force_calibration.power_spectrum_calibration.CalibrationResults(...)

Power spectrum calibration results.

calibrate_force(force_voltage_data, ...[, ...])

Determine force calibration factors.

calculate_power_spectrum(data, sample_rate)

Compute power spectrum and return it as a PowerSpectrum.

fit_power_spectrum(power_spectrum, model[, ...])

Power Spectrum Calibration

viscosity_of_water(temperature)

Computes the viscosity of water in [Pa*s] at a particular temperature.

FD Fitting

fitting.model.Model(name, model_function[, ...])

FdFit(*models)

Object which is used for fitting.

parameter_trace(model, params, ...)

Invert a model with respect to one parameter.

Available models

force_offset(name)

Offset on the the model output.

distance_offset(name)

Offset on the the model output.

wlc_marko_siggia_force(name)

Marko Siggia's Worm-like Chain model.

wlc_marko_siggia_distance(name)

Marko Siggia's Worm-like Chain model.

ewlc_marko_siggia_force(name)

Marko Siggia's Worm-like Chain model with force as dependent parameter.

ewlc_marko_siggia_distance(name)

Marko Siggia's Worm-like Chain model with distance as dependent parameter

ewlc_odijk_force(name)

Odijk's Extensible Worm-Like Chain model with force as dependent variable

ewlc_odijk_distance(name)

Odijk's Extensible Worm-Like Chain model with distance as dependent variable

twlc_force(name)

Twistable Worm-like Chain model with force as the dependent variable.

twlc_distance(name)

Twistable Worm-like Chain model with distance as dependent variable.

efjc_force(name)

Extensible Freely-Jointed Chain model with force as the dependent parameter.

efjc_distance(name)

Extensible Freely-Jointed Chain with distance as dependent parameter.

Kymotracking

kymotracker.kymotrack.KymoTrack(time_idx, ...)

A tracked particle on a kymograph.

kymotracker.kymotrack.KymoTrackGroup(kymo_tracks)

Tracks on a kymograph.

kymotracker.detail.msd_estimation.EnsembleMSD(...)

Ensemble MSD result

track_greedy(kymograph, channel[, ...])

Track particles on an image using a greedy algorithm.

track_lines(kymograph, channel, line_width, ...)

Track particles on an image using an algorithm that looks for line-like structures.

filter_tracks(tracks, minimum_length)

Remove tracks shorter than a minimum number of time points from the list.

refine_tracks_centroid(tracks[, track_width])

Refine the tracks based on the brightness-weighted centroid.

refine_tracks_gaussian(tracks, window, ...)

Refine the tracks by gaussian peak MLE.

Notebook widgets

FdRangeSelector(fd_curves)

FdDistanceRangeSelector(fd_curves[, max_gap])

Population Dynamics

GaussianMixtureModel(data, n_states, ...)

A wrapper around scikit-learn's GMM.

DwelltimeModel(dwelltimes[, n_components, ...])

Exponential mixture model optimization for dwelltime analysis.

population.dwelltime.DwelltimeBootstrap(_samples)

Bootstrap distributions for a dwelltime model.

Piezo tracking

DistanceCalibration(trap_position, ...[, degree])

Class to calibrate trap position to camera distance

ForceBaseLine(model, trap_data, force)

A force baseline as a function of trap position

PiezoTrackingCalibration(trap_calibration[, ...])

Class to handle piezo tracking calibration

PiezoForceDistance(trap_calibration[, ...])

Class to determine both piezo distance and baseline corrected force