My own Python class for making Pyplot figures compatible with Latex documents. These functions are used in my most recent publications, as well as in:
The Python code to generate the figure:
# Initialise figure myplt = PltTools() myplt.prepare_figure(fig_size = (18,12), dark_mode = dark_mode, transparency = True, n_colone = 2) # Panel 1 myplt.add_panel(panel_position = [0, 0]) myplt.add_plot(x = x1, y = y1/2, marker = "o", data_color = 2, markersize = 6, data_label = r"$\textrm{data}~\alpha$") myplt.add_plot(x = x3, y = y3, marker = "o", data_color = 0, markersize = 10, data_label = r"$\textrm{data}~\beta$") myplt.add_plot(x = x2, y = y2, marker = "-", data_color = "autogray", data_label = r"$\textrm{linear fit}$") myplt.complete_panel(xlabel = r"$x ~ \textrm{[\AA{}]}$", ylabel = r"$t ~ \textrm{[ps]}$", panel_title = r"$\textrm{Time vs distance}$", xpad = 10) myplt.set_boundaries(y_boundaries = (0, 5)) # Panel 2 myplt.add_panel(type = 'loglog', panel_position = [0, 1]) myplt.add_plot(x = x4, y = y4, marker = "o", data_color = 0, markersize = 16) myplt.add_plot(x = x5, y = y5, marker = "p", data_color = 0, markersize = 16) myplt.add_plot(x = x6, y = y6, marker = "^", data_color = 0, markersize = 16) myplt.complete_panel(xlabel = r"$E ~ \textrm{[J]}$", ylabel = r"$N_\textrm{p} ~ [0]$", panel_title = r"$\textrm{Population size vs energy}$", xpad = 10) myplt.set_boundaries(x_boundaries = (0.01, 1000), y_boundaries = (0.01, 1000)) # Finish figure myplt.add_subplotlabels(type_label_panel = "a") myplt.save_figure(filename = "bi-panel", saving_path = 'examples/', show = False)See also the examples notebook.
- For web integration, use either dark or light mode with a transparent background, as done for instance in lammps tutorials.
- For scientific publication, use the light mode without transparent background.