"""
A model for the disasters data with an linearly varying mean:

rate_of_mean ~ N(0,1)
amp_of_mean ~ Exp(3)
disasters[t] ~ Po(intercept_of_mean + slope_of_mean * t)
"""

from pymc import *
from numpy import *


disasters_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])

# Define data and stochastics

@stochastic
def params_of_mean(value=array([-.005, 1.]), tau=.1, rate = 4.):
    """
    Intercept and slope of rate stochastic of poisson distribution
    Rate stochastic must be positive for t in [0,T]

    p(intercept, slope|tau,rate) =
    N(slope|0,tau) Exp(intercept|rate) 1(intercept>0) 1(intercept + slope * T>0)
    """

    def logp(value, tau, rate):
        if value[1]>0 and value[1] + value[0] * 110 > 0:
            return normal_like(value[0],0.,tau) + exponential_like(value[1], rate)
        else:
            return -Inf

    def random(tau, rate):
        val = zeros(2)
        val[0] = rnormal(0., tau)
        val[1] = rexponential(rate)
        while val[1]<0 or val[1] + val[0] * 110 <= 0:
            val[0] = rnormal(0., tau)
            val[1] = rexponential(rate)
        return val


@stochastic(observed=True, dtype=int)
def disasters(value = disasters_array, params_of_mean = params_of_mean):
    """Annual occurences of coal mining disasters."""
    val = params_of_mean[1] + params_of_mean[0] * arange(111)
    return poisson_like(value,val)

