# 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]