# -------------------------------
alpha_cell_size_curve = []
beta_cell_size_curve = []
volPM_typical = 2 * 30E-6**2 + 4 * 30E-6 * 8E-6
volCP_typical = 8E-6 * 30E-6**2
radii = list(np.logspace(np.log10(30E-7), np.log10(30E-5)))
for radius in radii:
volPM = 2 * radius**2 + 4 * radius * 8E-6
volCP = 8E-6 * radius**2
# Alpha
response = Mixed_Model.timecourse(
list(np.linspace(0, 60)),
'TotalpSTAT',
parameters={
'Ia': 10E-12 * 6.022E23 * 1E-5,
'Ib': 0,
'R1': (volPM / volPM_typical) * 1200,
'R2': (volPM / volPM_typical) * 4920,
'S': (volCP / volCP_typical) * 1E4
},
return_type='list',
scale_factor=scale_factor)['TotalpSTAT'][-1]
normalized_response = response / ((volCP / volCP_typical) * 1E4)
alpha_cell_size_curve.append(normalized_response)
# Beta
response = Mixed_Model.timecourse(
list(np.linspace(0, 60)),
'TotalpSTAT',
parameters={
'Ib': 10 * 1E-12 * 6.022E23 * 1E-5,
'Ia': 0,
'R1': (volPM / volPM_typical) * 1200,
dr_plot_mean_fit.show_figure(save_flag=False)
# -------------------------------
# Plot time course paper figure
# -------------------------------
alpha_palette = sns.color_palette("Reds", 8)
beta_palette = sns.color_palette("Greens", 8)
# Simulate time courses
alpha_time_courses = []
for d in doses_alpha:
alpha_time_courses.append(
Mixed_Model.timecourse(list(np.linspace(0, 60, 30)),
'TotalpSTAT', {
'Ia': d * 6.022E23 * 1E-5 * 1E-12,
'Ib': 0
},
return_type='dataframe',
dataframe_labels=['Alpha', d]))
beta_time_courses = []
for d in doses_beta:
beta_time_courses.append(
Mixed_Model.timecourse(list(np.linspace(0, 60, 30)),
'TotalpSTAT', {
'Ib': d * 6.022E23 * 1E-5 * 1E-12,
'Ia': 0
},
return_type='dataframe',
dataframe_labels=['Beta', d]))
# Scale simulations
for i in range(30):
if t not in beta_mask:
new_fit.add_trajectory(Sagar_data, t, 'errorbar', beta_palette[idx], (0, 1), 'Beta', dn=1)
new_fit.add_trajectory(Sagar_data, t, 'scatter', 'go', (0, 1), 'Beta', dn=1, color=beta_palette[idx], label='Beta ' + str(t))
new_fit.show_figure(save_flag=False)
print(Mixed_Model.parameters)
# ----------------------------------
# Time course plot
# ----------------------------------
# Simulate time courses
alpha_time_courses = []
for d in [10, 90, 600, 4000, 8000]:
alpha_time_courses.append(Mixed_Model.timecourse(list(linspace(0, 60, 25)), 'TotalpSTAT',
{'Ia': d * 6.022E23 * 1E-5 * 1E-12, 'Ib': 0},
return_type='dataframe', dataframe_labels=['Alpha', d]))
beta_time_courses = []
for d in [10, 90, 600, 2000, 11000]:
beta_time_courses.append(Mixed_Model.timecourse(list(linspace(0, 60, 25)), 'TotalpSTAT',
{'Ib': d * 6.022E23 * 1E-5 * 1E-12, 'Ia': 0},
return_type='dataframe', dataframe_labels=['Beta', d]))
# Scale simulations
for i in range(25):
for j in range(5):
alpha_time_courses[j].loc['Alpha'].iloc[:, i] = alpha_time_courses[j].loc['Alpha'].iloc[:, i].apply(scale_data)
beta_time_courses[j].loc['Beta'].iloc[:, i] = beta_time_courses[j].loc['Beta'].iloc[:, i].apply(scale_data)
# Turn into IfnData objects
alpha_IfnData_objects = []
beta_IfnData_objects = []
for j in range(5):