print p,result.x[i] # For visualization
k_result.append(result.x[i])
# Store IC results in an empty list that is appended with an empty list for each iteration of ICs. The inner empty list is filled with the
# IC values from the previous simulation (or initial for first - index + IC list (1 list) * 3 (items in list) + 7 (after all rate parameters).
# The previous result is then overwritten with the new parameters (using same equation above) - values are printed to screen
ic_result = []
for n in range(len(ICs())):
ic_result.append([])
print ICs()[n]," = [",
for i,p in enumerate(model.parameters[:3]):
print str(result.x[i+n*3+7]) + ",",
ic_result[-1].append(result.x[i+n*3+7])
print "]"
### Plotting parameter optimized model output ###
# Append a numpy array of 0s with rate parameters (last 7) and ICs (first 3). The rate parameters are the same for all the model outputs,
# while the ICs change. In each iteration, the parameter values are saved, normalized, and plotted.
param_values = np.zeros(len(model.parameters))
param_values[3:] = k_result
for i, ic in enumerate(ICs()):
param_values[:3] = ic_result[i] # N lists that contain 3 numbers
solver.verbose = False
solver.run(param_values=param_values) # saved in array - species y, obs in yobs, ic is 3 element list
nl2_sim = np.log2(solver.yobs["Obs_All"]/solver.yobs["Obs_All"][0]) # making it nl2
plt.figure(i)
plt.plot(time, nl2_sim, 'go-', ms = 12, mfc = "None", mew = 2, mec = "g")
plt.xlabel('Time')
plt.ylabel('Population Doublings (nl2)')
plt.title('Parameter Estimation')
plt.show()
t90 = scipy.interpolate.sproot((st, sc - 0.90, sk))[0]
# Calculate Td as the mean of these times
td = (t10 + t90) / 2
# Calculate Ts as their difference
ts = t90 - t10
# Get yfinal, the last element from the trajectory
yfinal = ysim_momp[-1]
# Build a vector of the 3 variables to fit
momp_sim = [td, ts, yfinal]
# Perform chi-squared calculation against mean and variance vectors
e2 = np.sum((momp_data - momp_sim)**2 / (2 * momp_var)) / 3
return [emBid, ecPARP, e2]
N_SAMPLES = range(10, 100, 10) + range(100, 501, 50)
solver.verbose = False
for n_samples in N_SAMPLES:
sample = Sample(
len(model.parameters_rules()),
n_samples,
lambda x: scale.linear(x, lower_bound=0.1 * ref, upper_bound=10 * ref),
verbose=True)
objective = Objective(len(model.parameters_rules()),
n_samples,
sample,
objective_func,
verbose=True)
v = Varsens(objective, verbose=True)
