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. |
Force calibration¶
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. |
force_calibration.power_spectrum.PowerSpectrum(…) |
Power spectrum data for a time series. |
force_calibration.power_spectrum_calibration.CalibrationResults(…) |
Power spectrum calibration results. |
FD Fitting¶
fitting.model.Model(name, model_function[, …]) |
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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. |
kymotracker.detail.binding_times.BindingDwelltimes(…) |
Results of exponential mixture model optimization for binding dwelltimes. |
kymotracker.detail.binding_times.BindingDwelltimesBootstrap(…) |
Bootstrap distributions for a binding dwelltime model. |
Notebook widgets¶
FdRangeSelector(fd_curves) |
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FdDistanceRangeSelector(fd_curves[, max_gap]) |
Population Dynamics¶
GaussianMixtureModel(data, n_states, …) |
A wrapper around scikit-learn’s GMM. |