!date
import matplotlib.pyplot as plt, numpy as np, seaborn as sns, pandas as pd
import pymc
%matplotlib inline
sns.set_context("poster")
sns.set_style('whitegrid')

x = np.ones(10)
f = np.ones(10)
f_error = np.ones(10)

# define the model/function to be fitted.
def model(x, f): 
    amp = pymc.Uniform('amp', 0.05, 0.4, value= 0.15)
    size = pymc.Uniform('size', 0.5, 2.5, value= 1.0)
    ps = pymc.Normal('ps', 0.13, 40, value=0.15)

    @pymc.deterministic(plot=False)
    def gauss(x=x, amp=amp, size=size, ps=ps):
        global calls_to_gauss
        calls_to_gauss += 1
        
        e = -1*(np.pi**2*size*x/(3600.*180.))**2/(4.*np.log(2.))
        return amp*np.exp(e)+ps
    y = pymc.Normal('y', mu=gauss, tau=1.0/f_error**2, value=f, observed=True)
    return locals()

calls_to_gauss = 0
MDL = pymc.MCMC(model(x,f))
MDL.sample(1e4)
print '\ncalls to gauss:', calls_to_gauss

calls_to_gauss = 0
MDL = pymc.MCMC(model(x,f))

# change step method, change number of calls to gauss (and acceptance rate...)
MDL.use_step_method(pymc.AdaptiveMetropolis, [MDL.amp, MDL.size, MDL.ps])

MDL.sample(1e4)
print '\ncalls to gauss:', calls_to_gauss

calls_to_gauss = 0
MDL = pymc.MCMC(model(x,f))

# change step method, change number of calls to gauss (and acceptance rate...)
MDL.use_step_method(pymc.AdaptiveMetropolis, [MDL.amp, MDL.size, MDL.ps])
MDL.use_step_method(pymc.AdaptiveMetropolis, [MDL.amp, MDL.size])
MDL.use_step_method(pymc.AdaptiveMetropolis, [MDL.amp, MDL.ps])
MDL.use_step_method(pymc.AdaptiveMetropolis, [MDL.size, MDL.ps])

MDL.sample(1e4)
print '\ncalls to gauss:', calls_to_gauss