drb25df.loc['Beta'].iloc[:, i] = drb25df.loc['Beta'].iloc[:, i].apply(scale_data)
# Make the DR curves into IfnData objects, for easy plotting
dra25 = IfnData('custom', df=dra25df, conditions={'Alpha': {'Ib': 0}})
drb25 = IfnData('custom', df=drb25df, conditions={'Beta': {'Ia': 0}})
# Generate figure by adding simulations, then data
results_plot = DoseresponsePlot((1, 2))
for idx, t in enumerate(['5', '10', '20', '60']):
results_plot.add_trajectory(dra25, t, 'plot', alpha_palette[idx], (0, 0), 'Alpha')
results_plot.add_trajectory(drb25, t, 'plot', beta_palette[idx], (0,1), 'Beta')
for idx, t in enumerate([5, 10, 20, 60]):
results_plot.add_trajectory(newdata, t, 'scatter', alpha_palette[idx], (0, 0), 'Alpha', dn=1)
results_plot.add_trajectory(newdata, t, 'scatter', beta_palette[idx], (0, 1), 'Beta', dn=1)
results_plot.save_figure()
# Now try to fit the 60 minute data starting from the 2.5 minute results
# ----------------------------------------------------------------------
stepfit60 = StepwiseFit(stepfit25.model, smooth60IfnData,
{'kSOCSon': (1E-8, 1E-7), 'kd4': (0.03, 0.1), 'k_d4': (0.03, 0.1),
'krec_a2': (0.0005, 0.05), 'krec_b2': (0.01, 0.0001),
'krec_a1': (0.00003, 0.003), 'krec_b1': (0.001, 0.00001)}, n=2)
best_parameters, best_scale_factor60 = stepfit60.fit()
scale_data = lambda q: (best_scale_factor60*q[0], best_scale_factor60*q[1])
with open('stepwisefitmodel.p','wb') as f:
pickle.dump(stepfit60.model.__dict__,f,2)
with open('stepwisefitobject.p','wb') as f:
pickle.dump(stepfit60.__dict__,f,2)
print(stepfit60.model.parameters)
scale_factor=scale_factor)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
dra60_int = IfnData('custom', df=dradf_int, conditions={'Alpha': {'Ib': 0}})
drb60_int = IfnData('custom', df=drbdf_int, conditions={'Beta': {'Ia': 0}})
dra60_rec = IfnData('custom', df=dradf_rec, conditions={'Alpha': {'Ib': 0}})
drb60_rec = IfnData('custom', df=drbdf_rec, conditions={'Beta': {'Ia': 0}})
dr_plot = DoseresponsePlot((1, 1))
alpha_mask = []
beta_mask = []
# Add fits
for idx, t in enumerate([el for el in times]):
if t not in alpha_mask:
dr_plot.add_trajectory(dra60, t, 'plot', alpha_palette[5], (0, 0), 'Alpha', label='Alpha - No Feedback', linewidth=2.0)
dr_plot.add_trajectory(dra60_int, t, 'plot', '--', (0, 0), 'Alpha', color=alpha_palette[3],
label='Internalization only', linewidth=2.0, )
dr_plot.add_trajectory(dra60_rec, t, 'plot', ':', (0, 0), 'Alpha', color=alpha_palette[1], label='SOCS only', linewidth=2.0)
if t not in beta_mask:
dr_plot.add_trajectory(drb60, t, 'plot', beta_palette[5], (0, 0), 'Beta', label='Beta - No Feedback', linewidth=2.0)
dr_plot.add_trajectory(drb60_int, t, 'plot', '--', (0, 0), 'Beta', color=beta_palette[3],
label='Internalization only', linewidth=2.0)
dr_plot.add_trajectory(drb60_rec, t, 'plot', ':', (0, 0), 'Beta', color=beta_palette[1], label='SOCS only', linewidth=2.0)
dr_plot.fig.suptitle('Internalization vs SOCS')
dir = os.path.join(os.getcwd(), 'results', 'Figures', 'Figure_5')
if not os.path.exists(dir):
os.makedirs(dir)
dr_plot.save_figure(save_dir=os.path.join(dir, 'Figure_5.pdf'))
figure=dr_plot.fig)
plt.scatter([], [],
color=beta_palette[5],
label=r'IFN$\beta$',
figure=dr_plot.fig)
plt.plot([], [],
c='grey',
label='No Feedback',
linewidth=2.0,
figure=dr_plot.fig)
plt.plot([], [],
'--',
c='grey',
label='+ Internalization',
linewidth=2.0,
figure=dr_plot.fig)
plt.plot([], [],
':',
c='grey',
label='+ SOCS',
linewidth=2.0,
figure=dr_plot.fig)
# Plot formatting
dr_plot.fig.set_size_inches((5, 4))
dr_plot.axes.set_title('Effects of Negative Feedback')
dr_plot.axes.set_ylabel('pSTAT')
dr_plot.axes.spines['top'].set_visible(False)
dr_plot.axes.spines['right'].set_visible(False)
dr_plot.save_figure(save_dir=fname, tight=True)
