"""
mean = np.log(parameter_ic.value)
cv = cv
if parameter_ic.name == 'C3_0':
cv = 0.282
elif parameter_ic.name == 'XIAP_0' or parameter_ic == 'Bid_0':
cv = 0.288
elif parameter_ic.name == 'Bax_0':
cv = 0.271
else:
parameter_ic.name == 'Bcl2_0'
sd = cv
return lognormal(mean, sd, size)
parameters_ic = {idx: p for idx, p in enumerate(model.parameters) if p in model.parameters_initial_conditions()[1:]}
samples = 10
f = open('/home/oscar/Documents/tropical_project/parameters_5000/pars_embedded_5400.txt')
data = csv.reader(f)
parames = [float(i[1]) for i in data]
# parames[0] = 10
param_values = np.array(parames).reshape(1, len(parames))
repeated_parameter_values = np.repeat(param_values, samples, axis=0)
for idx, par in parameters_ic.items():
repeated_parameter_values[:, idx] = sample_lognormal(par, size=samples)
tspan = np.linspace(0, 20000, 100)
# # extracted with a slice expression instead of requiring interpolation. # tspan = np.linspace(0.0, exp_data['Time'][-1], (ntimes-1) * tmul + 1) # #tspan = np.linspace(exp_data['Time'][0], exp_data['Time'][-1], # # (ntimes-1) * tmul + 1) tspan = exp_data['Time'] # Initialize solver object #solver = Solver(model, tspan, integrator='lsoda', rtol=1e-5, atol=1e-5, nsteps=20000) solver = Solver(model, tspan, integrator='vode', with_jacobian=True, atol=1e-5, rtol=1e-5, nsteps=20000) # Determine IDs for rate parameters, original values for all parameters to overlay, and reference values for scaling. k_ids = [i for i,p in enumerate(model.parameters) if p in model.parameters_initial_conditions()] #k_ids_names = [p for i,p in enumerate(model.parameters) if p in model.parameters_rules()] k_ids_names = [p for i,p in enumerate(model.parameters) if p in model.parameters_initial_conditions()] par_vals = np.array([p.value for p in model.parameters]) ref = np.array([p.value for p in model.parameters_initial_conditions()]) #k_ids = [i for i,p in enumerate(model.parameters) if p in model.parameters_rules()] #k_ids_names = [p.name for i,p in enumerate(model.parameters) if p in model.parameters_rules()] #par_vals = np.array([p.value for p in model.parameters]) #ref = np.array([p.value for p in model.parameters_rules()]) # List of model observables and corresponding data file columns for point-by-point fitting obs_names = ['mBid', 'cPARP'] data_names = ['norm_ICRP', 'norm_ECRP']
