Plotting Data

This module defines all the plotting functions for the fretbursts.burstlib.Data object.

The main plot function is dplot() that takes, as parameters, a Data() object and a 1-ch-plot-function and creates a subplot for each channel.

The 1-ch plot functions are usually called through dplot but can also be called directly to make a single channel plot.

The 1-ch plot functions names all start with the plot type (timetrace, ratetrace, hist or scatter).

Example 1 - Plot the timetrace for all ch:

dplot(d, timetrace, scroll=True)

Example 2 - Plot a FRET histogramm for each ch with a fit overlay:

dplot(d, hist_fret, show_model=True)

For more examples refer to FRETBurst notebooks.

Timetrace and ratetrace plots

fretbursts.burst_plot.timetrace(d, i=0, binwidth=0.001, bins=None, tmin=0, tmax=200, bursts=False, burst_picker=True, scroll=False, show_rate_th=True, F=None, rate_th_style={'label': None}, show_aa=True, legend=False, set_ax_limits=True, burst_color='#BBBBBB', plot_style=None)

Plot the timetraces (histogram) of photon timestamps.

Parameters:
  • d (Data object) – the measurement’s data to plot.
  • i (int) – the channel to plot. Default 0.
  • binwidth (float) – the bin width (seconds) of the timetrace histogram.
  • bins (array or None) – If not None, defines the bin edges for the timetrace (overriding binwidth). If None, binwidth is use to generate uniform bins.
  • tmin, tmax (float) – min and max time (seconds) to include in the timetrace. Note that a long time range and a small binwidth can require a significant amount of memory.
  • bursts (bool) – if True, plot the burst start-stop times.
  • burst_picker (bool) – if True, enable the ability to click on bursts to obtain burst info. This function requires the matplotlib’s QT backend.
  • scroll (bool) – if True, activate a scrolling bar to quickly scroll through the timetrace. This function requires the matplotlib’s QT backend.
  • show_rate_th (bool) – if True, plot the burst search threshold rate.
  • F (bool) – if show_rate is True, show a rate F times larger than the background rate.
  • rate_th_style (dict) – matplotlib style for the rate line.
  • show_aa (bool) – if True, plot a timetrace for the AexAem photons. If False (default), plot timetraces only for DexDem and DexAem streams.
  • legend (bool) – whether to show the legend or not.
  • set_ax_limits (bool) – if True, set the xlim to zoom on a small portion of timetrace. If False, do not set the xlim, display the full timetrace.
  • burst_color (string) – string containing the the HEX RGB color to use to highlight the burst regions.
  • plot_style (dict) – matplotlib’s style for the timetrace lines.
fretbursts.burst_plot.timetrace_single(d, i=0, binwidth=0.001, bins=None, tmin=0, tmax=200, ph_sel=Ph_sel(Dex='DAem', Aex='DAem'), invert=False, bursts=False, burst_picker=True, scroll=False, cache_bins=True, plot_style=None, show_rate_th=True, F=None, rate_th_style={}, set_ax_limits=True, burst_color='#BBBBBB')

Plot the timetrace (histogram) of timestamps for a photon selection.

See timetrace() to plot multiple photon selections (i.e. Donor and Acceptor photons) in one step.

fretbursts.burst_plot.ratetrace(d, i=0, m=None, max_num_ph=1000000.0, tmin=0, tmax=200, bursts=False, burst_picker=True, scroll=False, show_rate_th=True, F=None, rate_th_style={'label': None}, show_aa=True, legend=False, set_ax_limits=True, burst_color='#BBBBBB')

Plot the rate timetraces of photon timestamps.

Parameters:
  • d (Data object) – the measurement’s data to plot.
  • i (int) – the channel to plot. Default 0.
  • max_num_ph (int) – Clip the rate timetrace after the max number of photons max_num_ph is reached.
  • tmin, tmax (float) – min and max time (seconds) to include in the timetrace. Note that a long time range and a small binwidth can require a significant amount of memory.
  • bursts (bool) – if True, plot the burst start-stop times.
  • burst_picker (bool) – if True, enable the ability to click on bursts to obtain burst info. This function requires the matplotlib’s QT backend.
  • scroll (bool) – if True, activate a scrolling bar to quickly scroll through the timetrace. This function requires the matplotlib’s QT backend.
  • show_rate_th (bool) – if True, plot the burst search threshold rate.
  • F (bool) – if show_rate is True, show a rate F times larger than the background rate.
  • rate_th_style (dict) – matplotlib style for the rate line.
  • show_aa (bool) – if True, plot a timetrace for the AexAem photons. If False (default), plot timetraces only for DexDem and DexAem streams.
  • legend (bool) – whether to show the legend or not.
  • set_ax_limits (bool) – if True, set the xlim to zoom on a small portion of timetrace. If False, do not set the xlim, display the full timetrace.
  • burst_color (string) – string containing the the HEX RGB color to use to highlight the burst regions.
fretbursts.burst_plot.ratetrace_single(d, i=0, m=None, max_num_ph=1000000.0, tmin=0, tmax=200, ph_sel=Ph_sel(Dex='DAem', Aex='DAem'), invert=False, bursts=False, burst_picker=True, scroll=False, plot_style={}, show_rate_th=True, F=None, rate_th_style={}, set_ax_limits=True, burst_color='#BBBBBB')

Plot the ratetrace of timestamps for a photon selection.

See ratetrace() to plot multiple photon selections (i.e. Donor and Acceptor photons) in one step.

fretbursts.burst_plot.timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False)

Timetrace of background rates.

fretbursts.burst_plot.timetrace_b_rate(d, i=0)

Timetrace of bursts-per-second in each period.

1D Histograms

Bursts: ratiometric quantities

fretbursts.burst_plot.hist_fret(d, i=0, ax=None, binwidth=0.03, bins=None, pdf=True, hist_style='bar', weights=None, gamma=1.0, add_naa=False, show_fit_stats=False, show_fit_value=False, fit_from='kde', show_kde=False, bandwidth=0.03, show_kde_peak=False, show_model=False, show_model_peaks=True, hist_bar_style=None, hist_plot_style=None, model_plot_style=None, kde_plot_style=None, verbose=False)

Plot FRET histogram and KDE.

The most used argument is binwidth that sets the histogram bin width.

For detailed documentation see hist_burst_data().

fretbursts.burst_plot.hist_S(d, i=0, ax=None, binwidth=0.03, bins=None, pdf=True, hist_style='bar', weights=None, gamma=1.0, add_naa=False, show_fit_stats=False, show_fit_value=False, fit_from='kde', show_kde=False, bandwidth=0.03, show_kde_peak=False, show_model=False, show_model_peaks=True, hist_bar_style=None, hist_plot_style=None, model_plot_style=None, kde_plot_style=None, verbose=False)

Plot S histogram and KDE.

The most used argument is binwidth that sets the histogram bin width.

For detailed documentation see hist_burst_data().

fretbursts.burst_plot.hist_burst_data(d, i=0, data_name='E', ax=None, binwidth=0.03, bins=None, vertical=False, pdf=False, hist_style='bar', weights=None, gamma=1.0, add_naa=False, show_fit_stats=False, show_fit_value=False, fit_from='kde', show_kde=False, bandwidth=0.03, show_kde_peak=False, show_model=False, show_model_peaks=True, hist_bar_style=None, hist_plot_style=None, model_plot_style=None, kde_plot_style=None, verbose=False)

Plot burst_data (i.e. E, S, etc...) histogram and KDE.

This a generic function to plot histograms for any burst data. In particular this function is called by hist_fret() and hist_S() to make E and S histograms respectively.

Histograms and KDE can be plotted on any Data variable after burst search. To show a model, a model must be fitted first by calling d.E_fitter.fit_histogram(). To show the KDE peaks position, they must be computed first with d.E_fitter.find_kde_max().

The arguments are shown below grouped in logical sections.

Generic arguments

Parameters:
  • data_name (string) – name of the burst data (i.e. ‘E’ or ‘S’)
  • ax (None or matplotlib axis) – optional axis instance to plot in.
  • vertical (bool) – if True the x axis is oriented vertically.
  • verbose (bool) – if False, suppress any printed output.

Histogram arguments: control the histogram appearance

Parameters:
  • hist_style (string) – if ‘bar’ use a classical bar histogram, otherwise do a normal line plot of bin counts vs bin centers
  • bins (None or array) – if None the bins are computed according to binwidth. If not None contains the arrays of bin edges and overrides binwidth.
  • binwidth (float) – bin width for the histogram.
  • pdf (bool) – if True, normalize the histogram to obtain a PDF.
  • hist_bar_style (dict) – style dict for the histogram when hist_style == 'bar'.
  • hist_plot_style (dict) – style dict for the histogram when hist_style != 'bar'.

Model arguments: control the model plot

Parameters:
  • show_model (bool) – if True shows the model fitted to the histogram
  • model (lmfit.Model object or None) – lmfit Model used for histogram fitting. If None the histogram is not fitted.
  • show_model_peaks (bool) – if True marks the position of model peaks
  • model_plot_style (dict) – style dict for the model plot

KDE arguments: control the KDE plot

Parameters:
  • show_kde (bool) – if True shows the KDE curve
  • show_kde_peak (bool) – if True marks the position of the KDE peak
  • bandwidth (float or None) – bandwidth used to compute the KDE If None the KDE is not computed.
  • kde_plot_style (dict) – style dict for the KDE curve

Weights arguments (weights are used to weight bursts according to their size, affecting histograms and KDEs).

Parameters:
  • weights (string or None) – kind of burst-size weights. See fretbursts.fret_fit.get_weights().
  • gamma (float) – gamma factor passed to get_weights().
  • add_naa (bool) – if True adds naa to the burst size.

Fit text arguments: control how to print annotation with fit information.

Parameters:
  • fit_from (string) – determines how to obtain the fit value. If ‘kde’ the fit value is the KDE peak. Otherwise it must be the name of a model parameter that will be used as fit value.
  • show_fit_value (bool) – if True annotate the plot with fit value.
  • show_fit_stats (bool) – if True annotate the figure with mean fit value and max deviation across the channels (for multi-spot).

Bursts: tail distributions

fretbursts.burst_plot.hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None, yscale='log', gamma=1, add_naa=False, beta=1, donor_ref=True, add_aex=True, A_laser_weight=1, vline=None, label_prefix=None, legend=True, color=None, plot_style=None)

Plot histogram of burst sizes.

Parameters:
  • d (Data) – Data object
  • i (int) – channel index
  • bins (array or None) – array of bin edges. If len(bins) == 3 then is interpreted as (start, stop, step) values.
  • which (string) – what photons to include in “size”. Valid values are ‘all’, ‘nd’, ‘na’, ‘naa’. When ‘all’, sizes are computed with d.burst_sizes() (by default nd + na); ‘nd’, ‘na’, ‘naa’ get counts from d.nd, d.na, d.naa (respectively Dex-Dem, Dex-Aem, Aex-Aem).
  • gamma, beta (floats) – factors used to compute the corrected burst size. Ignored when which != ‘all’. See fretbursts.burstlib.Data.burst_sizes_ich().
  • add_naa (bool) – if True, include naa to the total burst size.
  • donor_ref (bool) – convention used for corrected burst size computation. See fretbursts.burstlib.Data.burst_sizes_ich() for details.
  • add_aex (bool) – PAX-only. Whether to add signal from Aex laser period to the burst size. Default True. See fretbursts.burstlib.Data.burst_sizes_pax_ich().
  • A_laser_weight (scalar) – PAX-only. Weight of A-ch photons during Aex period (AexAem) due to the A laser. See fretbursts.burstlib.Data.burst_sizes_pax_ich().
  • label_prefix (string or None) – a custom prefix for the legend label.
  • color (string or tuple or None) – matplotlib color used for the plot.
  • pdf (bool) – if True, normalize the histogram to obtain a PDF.
  • yscale (string) – ‘log’ or ‘linear’, sets the plot y scale.
  • legend (bool) – if True add legend to plot
  • plot_style (dict) – dict of matplotlib line style passed to plot.
  • vline (float) – If not None, plot vertical line at the specified x position.
fretbursts.burst_plot.hist_size_all(d, i=0, **kwargs)

Plot burst sizes for all the combinations of photons.

Calls hist_size() multiple times with different which parameters.

fretbursts.burst_plot.hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None, yscale='log', color=None, plot_style=None, vline=None)

Plot histogram of burst durations.

Parameters:
  • d (Data) – Data object
  • i (int) – channel index
  • bins (array or None) – array of bin edges. If len(bins) == 3 then is interpreted as (start, stop, step) values.
  • pdf (bool) – if True, normalize the histogram to obtain a PDF.
  • color (string or tuple or None) – matplotlib color used for the plot.
  • yscale (string) – ‘log’ or ‘linear’, sets the plot y scale.
  • plot_style (dict) – dict of matplotlib line style passed to plot.
  • vline (float) – If not None, plot vertical line at the specified x position.
fretbursts.burst_plot.hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None, yscale='log', gamma=1, add_naa=False, beta=1.0, donor_ref=True, add_aex=True, A_laser_weight=1, label_prefix=None, color=None, plot_style=None, vline=None)

Plot histogram of burst brightness, i.e. burst size / duration.

Parameters:
  • d (Data) – Data object
  • i (int) – channel index
  • bins (array or None) – array of bin edges. If len(bins) == 3 then is interpreted as (start, stop, step) values.
  • gamma, beta (floats) – factors used to compute the corrected burst size. See fretbursts.burstlib.Data.burst_sizes_ich().
  • add_naa (bool) – if True, include naa to the total burst size.
  • donor_ref (bool) – convention used for corrected burst size computation. See fretbursts.burstlib.Data.burst_sizes_ich() for details.
  • add_aex (bool) – PAX-only. Whether to add signal from Aex laser period to the burst size. Default True. See fretbursts.burstlib.Data.burst_sizes_pax_ich().
  • A_laser_weight (scalar) – PAX-only. Weight of A-ch photons during Aex period (AexAem) due to the A laser. See fretbursts.burstlib.Data.burst_sizes_pax_ich().
  • label_prefix (string or None) – a custom prefix for the legend label.
  • color (string or tuple or None) – matplotlib color used for the plot.
  • pdf (bool) – if True, normalize the histogram to obtain a PDF.
  • yscale (string) – ‘log’ or ‘linear’, sets the plot y scale.
  • plot_style (dict) – dict of matplotlib line style passed to plot.
  • vline (float) – If not None, plot vertical line at the specified x position.
fretbursts.burst_plot.hist_sbr(d, i=0, bins=(0, 30, 1), pdf=True, weights=None, color=None, plot_style=None)

Histogram of per-burst Signal-to-Background Ratio (SBR).

fretbursts.burst_plot.hist_burst_phrate(d, i=0, bins=(0, 1000, 20), pdf=True, weights=None, color=None, plot_style=None, vline=None)

Histogram of max photon rate in each burst.

Others

fretbursts.burst_plot.hist_interphoton_single(d, i=0, binwidth=0.0001, tmax=None, bins=None, ph_sel=Ph_sel(Dex='DAem', Aex='DAem'), period=None, yscale='log', xscale='linear', xunit='ms', plot_style=None)

Plot histogram of interphoton delays for a single photon streams.

Parameters:
  • d (Data object) – the input data.
  • i (int) – the channel for which the plot must be done. Default is 0. For single-spot data the only valid value is 0.
  • binwidth (float) – histogram bin width in seconds.
  • tmax (float or None) – max timestamp delay in the histogram (seconds). If None (default), uses the the max timestamp delay in the stream. If not None, the plotted histogram may be further trimmed to the smallest delay with counts > 0 if this delay happens to be smaller than tmax.
  • bins (array or None) – specifies the bin edged (in seconds). When bins is not None then the arguments binwidth and tmax are ignored. When bins is None, the bin edges are computed from the binwidth and tmax arguments.
  • ph_sel (Ph_sel object) – photon stream for which plotting the histogram
  • period (int) – the background period to use for plotting the histogram. The background period is a time-slice of the measurement from which timestamps are taken. If period is None (default) the time-windows is the full measurement.
  • yscale (string) – scale for the y-axis. Valid values include ‘log’ and ‘linear’. Default ‘log’.
  • xscale (string) – scale for the x-axis. Valid values include ‘log’ and ‘linear’. Default ‘linear’.
  • xunit (string) – unit used for the x-axis. Valid values are ‘s’, ‘ms’, ‘us’, ‘ns’. Default ‘ms’.
  • plot_style (dict) – keyword arguments to be passed to matplotlib’s plot function. Used to customize the plot style.
fretbursts.burst_plot.hist_interphoton(d, i=0, binwidth=0.0001, tmax=None, bins=None, period=None, yscale='log', xscale='linear', xunit='ms', plot_style=None, show_da=False, legend=True)

Plot histogram of photon interval for different photon streams.

Parameters:
  • d (Data object) – the input data.
  • i (int) – the channel for which the plot must be done. Default is 0. For single-spot data the only valid value is 0.
  • binwidth (float) – histogram bin width in seconds.
  • tmax (float or None) – max timestamp delay in the histogram (seconds). If None (default), uses the the max timestamp delay in the stream. If not None, the plotted histogram may be further trimmed to the smallest delay with counts > 0 if this delay happens to be smaller than tmax.
  • bins (array or None) – specifies the bin edged (in seconds). When bins is not None then the arguments binwidth and tmax are ignored. When bins is None, the bin edges are computed from the binwidth and tmax arguments.
  • period (int) – the background period to use for plotting the histogram. The background period is a time-slice of the measurement from which timestamps are taken. If period is None (default) the time-windows is the full measurement.
  • yscale (string) – scale for the y-axis. Valid values include ‘log’ and ‘linear’. Default ‘log’.
  • xscale (string) – scale for the x-axis. Valid values include ‘log’ and ‘linear’. Default ‘linear’.
  • xunit (string) – unit used for the x-axis. Valid values are ‘s’, ‘ms’, ‘us’, ‘ns’. Default ‘ms’.
  • plot_style (dict) – keyword arguments to be passed to matplotlib’s plot function. Used to customize the plot style.
  • show_da (bool) – If False (default) do not plot the AexDem photon stream. Ignored when the measurement is not ALEX.
  • legend (bool) – If True (default) plot a legend.
fretbursts.burst_plot.hist_bg_single(d, i=0, binwidth=0.0001, tmax=0.01, bins=None, ph_sel=Ph_sel(Dex='DAem', Aex='DAem'), period=0, yscale='log', xscale='linear', xunit='ms', plot_style=None, show_fit=True, fit_style=None, manual_rate=None)

Plot histogram of photon interval for a single photon streams.

Optionally plots the fitted background as an exponential curve. Most arguments are described in hist_interphoton_single(). In the following we document only the additional arguments.

Parameters:
  • show_fit (bool) – If True shows the fitted background rate as an exponential distribution.
  • manual_rate (float or None) – When not None use this value as background rate (ignoring the value saved in Data).
  • fit_style (dict) – arguments passed to matplotlib’s plot for for plotting the exponential curve.

For a description of all the other arguments see hist_interphoton_single().

fretbursts.burst_plot.hist_bg(d, i=0, binwidth=0.0001, tmax=0.01, bins=None, period=0, yscale='log', xscale='linear', xunit='ms', plot_style=None, show_da=False, legend=True, show_fit=True, fit_style=None)

Plot histogram of photon interval for different photon streams.

Optionally plots the fitted background. Most arguments are described in hist_interphoton(). In the following we document only the additional arguments.

Parameters:
  • show_fit (bool) – If True shows the fitted background rate as an exponential distribution.
  • fit_style (dict) – arguments passed to matplotlib’s plot for for plotting the exponential curve.

For a description of all the other arguments see hist_interphoton().

fretbursts.burst_plot.hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None, color=None, plot_style=None)

Histogram of waiting times between bursts.

fretbursts.burst_plot.hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=<function median>)

ALEX plots

fretbursts.burst_plot.alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex', vmax_fret=True, vmax_threshold=10, vmin_default=0, vmin=None, cmap_compensate=False, joint_kws=None, marginal_kws=None, histcolor_id=0, rightside_text=False, E_name='E', S_name='S')

Plot an ALEX join plot: an E-S 2D histograms with marginal E and S.

This function plots a jointplot: a main 2D histogram (hexbin plot) for E-S and the marginal histograms for E and S separately. The 2D histogram is an hexbin plot, i.e. the bin shape is hexagonal that has the advantage to reduce artifacts due to discretization.

Parameters:
  • d (Data object) – the variable containing the bursts to plot
  • i (int) – the channel number. Default 0.
  • gridsize (int) – the grid size for the 2D histogram (hexbin)
  • C (1D array or None) – array of weights, it must have size equal to the number of bursts in channel i (d.num_bursts[i]). Passed to matplotlib hexbin().
  • cmap (string) – name of the colormap for the 2D histogram. In addition to matplotlib colormaps, FRETbursts defines these custom colormaps: ‘alex_light’, ‘alex_dark’ and ‘alex_lv’. Default ‘alex_light’.
  • kind (string) – kind of plot for the 2-D distribution. Valid values: ‘hex’ for hexbin plots, ‘kde’ for kernel density estimation, ‘scatter’ for scatter plot.
  • vmax_fret (bool) – if True, the colormap max value is equal to the max bin counts in the FRET region (S < 0.8). If False the colormap max is equal to the max bin counts.
  • joint_kws (dict) – keyword arguments passed to the function with plots the inner 2-D distribution (i.e matplotlib scatter or hexbin or seaborn kdeplot). and hence to matplolib hexbin to customize the plot style.
  • marginal_kws (dict) – keyword arguments passed to hist_burst_() through seaborn plot_marginals() to customize the marginals plot style.
  • histcolor_id (int) – the colormap passes as cmap is divided in 12 colors. histcolor_id is the index of the color to be used for the marginal 1D histogram. Default 1.
  • rightside_text (bool) – when True, print the measurement name on the right side of the figure. When False (default) no additional text is printed.
  • E_name, S_name (string) – name of the Data attribute to be used for E and S. The default is ‘E’ and ‘S’ respectively. These arguments are used when adding your own cutom E or S attributes to Data using Data.add. In this case, you can specify the name of these custom attributes so that they can be plotted as an E-S histogram.
fretbursts.burst_plot.hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8, interp='bicubic', cmap='hot', under_color='white', over_color='white', scatter=True, scatter_ms=3, scatter_color='orange', scatter_alpha=0.2, gui_sel=False, cbar_ax=None, grid_color='#D0D0D0')

Plot 2-D E-S ALEX histogram with a scatterplot overlay.

fretbursts.burst_plot.hexbin_alex(d, i=0, vmin=0, vmax=None, gridsize=80, cmap='Spectral_r', E_name='E', S_name='S', **hexbin_kwargs)

Plot an hexbin 2D histogram for E-S.

fretbursts.burst_plot.plot_ES_selection(ax, E1, E2, S1, S2, rect=True, **kwargs)

Plot an overlay ROI on top of an E-S plot (i.e. ALEX histogram).

This function plots a rectangle and inscribed ellipsis with x-axis limits (E1, E2) and y-axis limits (S1, S2).

Note that, a dict with keys (E1, E2, S1, S2, rect) can be also passed to fretbursts.select_bursts.ES() to apply a selection.

Parameters:
  • ax (matplotlib axis) – the axis where the rectangle is plotted. Typically you pass the axis of a previous E-S scatter plot or histogram.
  • E1, E2, S1, S2 (floats) – limits for E and S (X and Y axis respectively) used to plot the rectangle.
  • rect (bool) – if True, the rectangle is highlighted and the ellipsis is grey. The color are swapped otherwise.
  • **kwargs – other keywords passed to both matplotlib’s Rectangle and Ellipse.

See also

For selecting bursts according to (E1, E2, S1, S2, rect) see:

fretbursts.burst_plot.plot_alternation_hist(d, bins=None, ax=None, **kwargs)

Plot the ALEX alternation histogram for the variable d.

This function works both for us-ALEX and ns-ALEX data.

This function must be called on ALEX data before calling fretbursts.loader.alex_apply_period().

fretbursts.burst_plot.plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0, hist_style={}, span_style={})

Plot the ns-ALEX alternation histogram for the variable d.

This function must be called on ns-ALEX data before calling fretbursts.loader.alex_apply_period().

Scatter plots

fretbursts.burst_plot.scatter_width_size(d, i=0)

Scatterplot of burst width versus size.

fretbursts.burst_plot.scatter_da(d, i=0, alpha=0.3)

Scatterplot of donor vs acceptor photons (nd, vs na) in each burst.

fretbursts.burst_plot.scatter_rate_da(d, i=0)

Scatter of nd rate vs na rate (rates for each burst).

fretbursts.burst_plot.scatter_fret_size(d, i=0, which='all', gamma=1, add_naa=False, plot_style=None)

Scatterplot of FRET efficiency versus burst size.

fretbursts.burst_plot.scatter_fret_nd_na(d, i=0, show_fit=False, no_text=False, gamma=1.0, **kwargs)

Scatterplot of FRET versus gamma-corrected burst size.

fretbursts.burst_plot.scatter_fret_width(d, i=0)

Scatterplot of FRET versus burst width.

fretbursts.burst_plot.scatter_naa_nt(d, i=0, alpha=0.5)

Scatterplot of nt versus naa.

fretbursts.burst_plot.scatter_alex(d, i=0, **kwargs)

Scatterplot of E vs S. Keyword arguments passed to plot.