# ----------------------------------------
# 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,