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, *[, rgb_to_detectors])	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
fdensemble.FdEnsemble(fd_curves)	An ensemble of F,d curves 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
ImageStack(*image_names[, align])	Open a TIF file acquired with Bluelake.
ColorAdjustment(minimum, maximum[, mode, gamma])	Utility class to adjust the min/max values of image colormaps.
ScaleBar(size_x, size_y, label_x, label_y, ...)	Draws a scale legend for the current axis.
colormaps	Pylake custom colormaps.
download_from_doi(doi[, target_path, ...])	Download files from a Zenodo DOI (i.e.

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[, ...])	Fit a power spectrum.
viscosity_of_water(temperature[, ...])	Computes the viscosity of water in [Pa*s] at a particular temperature, molality of NaCl and pressure.
density_of_water(temperature, molarity[, ...])	Determine the density of water with NaCl.

FD Fitting

fitting.model.Model(name, model_function[, ...])
FdFit(*models)	Object which is used for fitting.
parameter_trace(model, params, ...)	Fit a model with respect to one parameter for each data point.

Available models

force_offset(name)	Offset on the model output.
distance_offset(name)	Offset on the model output.
wlc_marko_siggia_force(name)	Marko Siggia's Worm-like Chain (WLC) model.
wlc_marko_siggia_distance(name)	Marko Siggia's Worm-like Chain (WLC) model.
ewlc_marko_siggia_force(name)	Marko Siggia's Worm-like Chain model with force as the dependent variable.
ewlc_marko_siggia_distance(name)	Marko Siggia's Worm-like Chain model with distance as the dependent variable
ewlc_odijk_force(name)	Odijk's Extensible Worm-Like Chain model with force as the dependent variable
ewlc_odijk_distance(name)	Odijk's Extensible Worm-Like Chain model with distance as the dependent variable
dsdna_ewlc_odijk_distance(name, dna_length_kbp)	Model for dsDNA with distance as the 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 the dependent variable.
efjc_force(name)	Extensible Freely-Jointed Chain model with force as the dependent variable.
efjc_distance(name)	Extensible Freely-Jointed Chain with distance as the dependent variable.
ssdna_efjc_distance(name, dna_length_kb[, ...])	Model of ssDNA with distance as the dependent variable.

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.DiffusionEstimate(...)	Diffusion estimate
kymotracker.detail.msd_estimation.EnsembleMSD(...)	Ensemble MSD result
KymoWidgetGreedy(kymo, channel, *[, ...])	Create a widget for performing kymotracking.
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 specified criteria from the list.
refine_tracks_centroid(tracks[, ...])	Refine the tracks based on the brightness-weighted centroid.
refine_tracks_gaussian(tracks, window, ...)	Refine the tracks by gaussian peak MLE.

Notebook widgets

KymoWidgetGreedy(kymo, channel, *[, ...])	Create a widget for performing kymotracking.
lumicks.pylake.nb_widgets.range_selector.SliceRangeSelectorWidget(...)	Notebook widget for selecting data ranges by time.
FdRangeSelector(fd_curves)	Notebook widget for selecting data ranges by time.
FdDistanceRangeSelector(fd_curves[, max_gap])	Notebook widget for selecting data ranges by distance.
lumicks.pylake.nb_widgets.image_editing.ImageEditorWidget(image)	Open a widget to interactively edit the image stack using a tether.

Population Dynamics

HiddenMarkovModel(data, n_states, *[, tol, ...])	A Hidden Markov Model describing hidden state occupancy and state transitions of observed time series data (force, fluorescence, etc.)
GaussianMixtureModel(data, n_states[, ...])	A wrapper around sklearn.mixture.GaussianMixture.
DwelltimeModel(dwelltimes[, n_components, ...])	Exponential mixture model optimization for dwelltime analysis.
population.dwelltime.DwelltimeBootstrap(...)	Bootstrap distributions for a dwelltime model.
population.dwelltime.DwelltimeProfiles(...)	Profile likelihoods for a dwelltime model.
population.detail.fit_info.PopulationFitInfo(...)	Fitting information for a HiddenMarkovModel

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

Simulation

simulation.simulate_diffusive_tracks(...[, ...])

Generate a KymoTrackGroup of pure diffusive traces.