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, json[, ...])	A Kymograph exported from Bluelake
scan.Scan(name, file, start, stop, json)	A confocal scan exported from Bluelake
point_scan.PointScan(name, file, start, ...)	A confocal point scan exported from Bluelake
correlated_stack.CorrelatedStack(image_name)	CorrelatedStack acquired with Bluelake.
ColorAdjustment(minimum, maximum[, mode, gamma])	Color adjustment for plotting

Force calibration

calibrate_force(force_voltage_data, ...[, ...])	Determine force calibration factors.
calculate_power_spectrum(data, sample_rate)	Compute power spectrum and returns it as a PowerSpectrum.
fit_power_spectrum(power_spectrum, model[, ...])	Power Spectrum 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.
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.
marko_siggia_ewlc_force(name)	Marko Siggia's Worm-like Chain model with force as dependent parameter.
marko_siggia_ewlc_distance(name)	Marko Siggia's Worm-like Chain model with distance as dependent parameter.
marko_siggia_simplified(name)	Marko Siggia's Worm-like Chain model based on only entropic contributions (valid for F << 10 pN).
inverted_marko_siggia_simplified(name)	Marko Siggia's Worm-like Chain model based on only entropic contributions (valid for F << 10 pN).
odijk(name)	Odijk's Extensible Worm-Like Chain model with distance as dependent variable (useful for 10 pN < F < 30 pN).
inverted_odijk(name)	Odijk's Extensible Worm-Like Chain model with force as dependent variable (useful for 10 pN < F < 30 pN).
freely_jointed_chain(name)	Freely-Jointed Chain with distance as dependent parameter.
inverted_freely_jointed_chain(name)	Inverted Freely-Jointed Chain with force as dependent parameter.
twistable_wlc(name)	Twistable Worm-like Chain model.
inverted_twistable_wlc(name)	Twistable Worm-like Chain model.

Kymotracking

kymotracker.kymoline.KymoLine(time_idx, ...)	A line on a kymograph
track_greedy(kymograph, channel, line_width, ...)	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_lines(lines, minimum_length)	Remove lines below a specific minimum number of points from the list.
refine_lines_centroid(lines, line_width)	Refine the lines based on the brightness-weighted centroid.
refine_lines_gaussian(lines, window, ...[, ...])	Refine the lines 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.