# ---------------------------------------- # Finally, plot both models in comparison # ---------------------------------------- fig = plt.figure(figsize=(6.4 * 2.5, 4.8)) gs = gridspec.GridSpec(nrows=1, ncols=5) panelA = fig.add_subplot(gs[0, 0:2]) panelB = fig.add_subplot(gs[0, 2:4]) for ax in [panelA, panelB]: ax.set(xscale='log', yscale='linear') ax.set_xlabel('Dose (pM)') ax.set_ylabel('Response') legend_panel = fig.add_subplot(gs[0, 4]) new_fit = DoseresponsePlot((1, 2)) new_fit.fig = fig new_fit.axes = [panelA, panelB, legend_panel] alpha_palette = sns.color_palette("rocket_r", 6) beta_palette = sns.color_palette("rocket_r", 6) t_mask = [2.5, 7.5, 20.] # Add fits for idx, t in enumerate(times): if t not in t_mask: new_fit.add_trajectory(dra_s, t, 'plot', alpha_palette[idx], (0, 0), 'Alpha', linewidth=2.0) new_fit.add_trajectory(dra_d, t, 'plot', '--', (0, 0), 'Alpha', color=alpha_palette[idx], linewidth=2.0) new_fit.add_trajectory(drb_s, t, 'plot', beta_palette[idx], (0, 1), 'Beta', linewidth=2.0) new_fit.add_trajectory(drb_d, t, 'plot', '--', (0, 1), 'Beta', color=beta_palette[idx], linewidth=2.0) new_fit.show_figure(show_flag=False, save_flag=False)
# ------------------------------- alpha_palette = sns.color_palette("rocket_r", 6) beta_palette = sns.color_palette("rocket_r", 6) new_fit = DoseresponsePlot((1, 2)) new_fit.axes = [ Figure_2.add_subplot(gs[0, 0:2]), Figure_2.add_subplot(gs[0, 2:4]) ] new_fit.axes[0].set_xscale('log') new_fit.axes[0].set_xlabel('Dose (pM)') new_fit.axes[0].set_ylabel('pSTAT (MFI)') new_fit.axes[1].set_xscale('log') new_fit.axes[1].set_xlabel('Dose (pM)') new_fit.axes[1].set_ylabel('pSTAT (MFI)') new_fit.fig = Figure_2 alpha_mask = [7.5, 10.0] beta_mask = [7.5, 10.0] # Add fits for idx, t in enumerate(times): if t not in alpha_mask: new_fit.add_trajectory(dra60, t, PLOT_KWARGS['line_type'], alpha_palette[idx], (0, 0), 'Alpha', label=str(t) + ' min', linewidth=2, alpha=PLOT_KWARGS['alpha']) new_fit.add_trajectory(mean_data,