pyemma.plots.plot_implied_timescales

pyemma.plots.plot_implied_timescales(ITS, ax=None, outfile=None, xlog=False, ylog=True, confidence=0.95, refs=None, units='steps', dt=1.0, **kwargs)

Generate a pretty implied timescale plot

Parameters:
  • ITS (implied timescales object.) – Object whose data will be plotted. Must provide the functions: get_timescales() and get_timescales(i) where i is the the property samples_available
  • ax (matplotlib Axes object, optional, default = None) – the axes to plot to. When set to None the default Axes object will be used.
  • outfile (str, optional, default = None) – output file to write the figure to. When not given, the plot will be displayed
  • xlog (bool, optional, default = False) – Iff true, the x-Axis is logarithmized
  • ylog (bool, optional, default = True) – Iff true, the y-Axis is logarithmized
  • confidence (float, optional, default = 0.95) – The confidence interval for plotting error bars (if available)
  • refs (ndarray((m), dtype=float), optional, default = None) – Reference (exact solution or other reference) timescales if known. The number of timescales must match those in the ITS object
  • units (str, optional, default = 'steps') – Affects the labeling of the axes. Used with dt, allows for changing the physical units of the axes. Accepts simple LaTeX math strings, eg. ‘$mu$s’
  • dt (float, optional, default = 1.0) – Physical time between frames, expressed the units given in units. E.g, if you know that each frame corresponds to .010 ns, you can use the combination of parameters dt =0.01, units =’ns’ to display the implied timescales in ns (instead of frames)
  • **kwargs –

    See the doc of pyplot for more options. Most useful lineproperties like marker=’o’ and/or :markersize=5
Returns:

ax
Return type:

Axes object containing the plot