DisasterModel.py

__all__ = ['swichpoint','early_mean','late_mean','disasters']

from pymc import DiscreteUniform, Exponential, deterministic, Poisson, Uniform
import numpy as np

D_array = array([ 4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])

s = DiscreteUniform('s', lower=0, upper=110)

e = Exponential('e', beta=1)
l = Exponential('l', beta=1)

def r(s=s, e=e, l=l):
    #Create an empty array of size 111
    out = np.empty(111)
    #Assign e to the first s elements
    out[:s] = e
    # ... and 1 to the rest. 
    out[s:] = 1
    return out

D = Poisson('D', mu=r, value=D_array, isdata=True)

if __name__ == "__main__":
    x = r(s=s,e=e,l=l)
    print x