sarkas.tools.transport.ElectricalConductivity.plot
sarkas.tools.transport.ElectricalConductivity.plot#
- ElectricalConductivity.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
fig (fig_par, fig_perp) (
matplotlib.pyplot.Figure, tuple) – Matplotlib figure handle. If the system is magnetized then it return a tuple with the handles for the parallel (fig_par) and perpendicular (fig_perp) figures.(ax1, ax2, ax3, ax4), ((ax1_par, ax2_par, ax3_par, ax4_par), (ax1_perp, ax2_perp, ax3_perp, ax4_perp)) (tuple,
matplotlib.axes.Axes) – Tuple containing the axes handles for fig. ‘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 tuple of tuples whose elements are the axes handles of each figure.