Modeling

Mostly based on an older version of this paper that comes with its own modsim library.

• A model depicts a system.
• The state of the system attributes is stored in a Pandas#Series
• Results of how the system attributes change over time are stored in Pandas Series too (1 Series per attribute).
• Parameters that determine how the system attributes change are also stored in a pandas series.
• The functions that change the system attributes take the system parameters as parameter. This way you can play with the parameter values to get the best result.
  • Numpy#linspace provides an equally distributed range of values so you can play easily between the limits you set.
• The quality of the model can be judged by calculating the relative error of the Approximation

Other remarks

• If you use randomness in a function, get the mean values of several runs.

Example code

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)

Store results in a Pandas Series

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

Naming conventions

system

Pandas#Series to store parameters of how the model behaves

system.alpha

Variable to store the net change.

system.t_0

First time-stamp

system.p_0

State of the system at t_0 (a Pandas series)

system.t_end

Last time-stamp