Modeling

Mostly based on this paper that comes with its own modsim library.

Eyeopeners

• Store results in a list

for a in range(100):
    funcAresults[a] = functionAcall(bla,bla)
    funcBresults[a] = functionBcall(bla,bla)

ModSimPy is using Series from Pandas to store results. This adds handy functions like.

• The state of the model is stored in a Pandas Series too.
• Put other interesting metrics in the state object too.
• If you use randomness in a function, get the mean values of several runs.
• Check the effect of different parameter values using Numpy linspace.

funcAresults = pd.Series([])
p1_array = np.linspace(0,1,12)
for p1 in p1_array:
    for a in range(50):
        funcAresults[a] = functionAcall(p1,p2)
    print(funcAresults)

• The Approximation error (benaderingsfout) is the difference between a calculated value and the real value. By dividing it by the real value you get the relative error. A bit relative error indicates bad calculation.

The largest relative error between two arrays as percentage:

max(abs(calculated - real) / real ) * 100