# max_response_axes[0].plot(time_list, # [avg_pSTAT_Max_Alpha for _ in time_list], # '--', color=alpha_palette[5], linewidth=2) # max_response_axes[1].plot(time_list, # [avg_pSTAT_Max_Beta for _ in time_list], # '--', color=alpha_palette[5], linewidth=2) # ------------------------------- # Plot model dose response curves # ------------------------------- 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,
# ---------------------------------------- # 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) # formatting and legend
large_alignment.get_scaled_data() mean_large_data = large_alignment.summarize_data() # ---------------------- # Set up Figure layout # ---------------------- Figure_3 = plt.figure(tight_layout=True) gs = gridspec.GridSpec(nrows=2, ncols=2, height_ratios=[1, 1]) Figure_3.align_labels() # same as fig.align_xlabels(); fig.align_ylabels() # Set up dose response figures new_fit = DoseresponsePlot((1, 2)) new_fit.fig = Figure_3 plt.figure(Figure_3.number) new_fit.axes = [ Figure_3.add_subplot(gs[0, 0]), Figure_3.add_subplot(gs[0, 1]) ] new_fit.axes[0].set_label(r'IFN$\alpha$') 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_label(r'IFN$\beta$') new_fit.axes[1].set_xscale('log') new_fit.axes[1].set_xlabel('Dose (pM)') new_fit.axes[1].set_ylabel('pSTAT (MFI)') # Plot Dose respsonse data times = [2.5, 5.0, 7.5, 10.0, 20.0, 60.0] alpha_palette = sns.color_palette("deep", 6) beta_palette = sns.color_palette("deep", 6) alpha_mask = [5.0, 7.5, 10.0]