Hello Eric!
On Thursday 03.02.2011 08:05:13 Eric Lofgren wrote: > def eq_system(PopIn,x): > '''Defining SIR System of Equations''' > #Creating an array of equations > Eqs= np.zeros((3)) > Eqs[0]= -beta * PopIn[0]*PopIn[1] > Eqs[1]= beta * PopIn[0]*PopIn[1] - gamma*PopIn[1] > Eqs[2]= gamma*PopIn[1] > return Eqs > > SIR = spi.odeint(eq_system, PopIn, t_interval) > print SIR > The part that is confusing me is defining the function. Namely, it seems to > need two arguments - the first needs to be the initial states of the > population being modeled, but the second...it doesn't seem to matter what > it is. Originally, I didn't include it at all, and the program was very, > very unhappy - looking back at the example, it had a second argument, so I > put one in, and magically, it worked. But it can be x, it can be t, it can > be anything. The meaning of the arguments of the system function (`eq_system` in your code) is as follows: 1. (`PopIn` in your code) the current state of the system 2. (`x` in your code) the current time The arguments can have any namem, you are free to choose what you think is most appropriate. The documentation is here: http://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.odeint.html In your system function the time is not used because the system is time invariant. But in a more advanced model you might use the time too, for example the virus particles might survive longer outside the human body in winter (`beta` a function of time). I think a good way to write system functions is like this: def sir_system(states, time): '''Defining SIR System of Equations''' s, i, r = states ds_dt = -beta * s * i di_dt = beta * s * i - gamma * i dr_dt = gamma * i return np.array([ds_dt, di_dt, dr_dt]) Eike. _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
