Lightweight data structures for readable, maintainable models. Stocks are nouns and flows connect them. Wrapper around scipy.integrate.odeint, which is a wrapper around the Fortran library odepack using lsoda. The goal of this project is to provide an easy interface for describing stock/flow systems that will set up the ODEs automatically and solve them.
Create simulation
from stockflow import simulation
import numpy as np
tmax = 100
dt = 0.01
t = np.arange(0,tmax,dt)
s = simulation(t)Create stocks
# define names of states and their initial conditions
s.stocks({'A': 0, 'B': 10, 'C': 20})Define flows and their functions
# The flow called 'Q' moves (stuff) from stock 'A' to stock 'B'.
# Function f(t) is evaluated at each timestep.
s.flow('Q', start='A', end='B', f=lambda t: k*s.A - l*s.B)
# Flow 'P' starts outside the control volume (None) and ends at C.
s.flow('P', start=None, end='C', f=lambda t: m*s.C if s.B > 2 else 0)
# etc.Forcing flows that originate outside the system (control volume) will have start='None'. Flows that originate inside the system but leave will have end='None'. Flow functions f can depend on any stocks or flows that already exist, or will exist at runtime.
Flow functions can also depend on t, as will usually be the case with forcing functions. This can either be continuous (e.g. sin(t)) or discrete (P[t]). If you are using discrete flow functions, run the model with s.run(discrete=True). This allows t to be used as an array index in the forcing functions, which would cause an error if discrete=False.
Define parameters (global constants)
k = 0.2; l = 0.7; m = 1.2Run simulation and look at time series of stocks/flows
s.run() # use discrete=True for discrete flow functions
# now access/plot any of s.A, s.B, s.C, s.Q, s.P, which are all vectors of length len(t)####Examples For more examples check out these IPython notebooks.
####License Copyright (c) 2014 Jon Herman. Released under the MIT license.