sarkas.tools.transport.Diffusion.plot
sarkas.tools.transport.Diffusion.plot#
- Diffusion.plot(observable, display_plot=False)[source]#
Make a dual plot comparing the ACF and the Transport Coefficient by using the
plot_tc()method.- Parameters
observable (
sarkas.tools.observables.VelocityAutoCorrelationFunction) – Observable object containing the ACF whose time integral leads to the self diffusion coefficient.display_plot (bool, optional) – Flag for displaying the plot if using the IPython. Default = False.
- Returns
figs (dict) – Dictionary of matplotlib figure handles for each species. If the system is magnetized then it returns a nested dictionary. Each figs[species_name] is a dictionary with keys Parallel and Perpendicular.
axes (dict,) – Dictionary of tuples containing the axes handles for each element of figs. Each element of axes is a tuple of four axes handles. ‘ax1` and ax2 are the handles for the left and right plots respectively. ax3 and ax4 are the handles for the “Index” axes, created from ax1.twiny() and ax2.twiny() respectively.
If the system is magnetized, then it returns a nested dictionary. Each axes[species_name] is a dictionary with keys Parallel and Perpendicular each containing a tuple of four axes handles.