# Path: pyglm/inference/gibbs.py
# def initialize_updates(population):
#     """ Compute the set of updates required for the given population.
#         TODO: Figure out how to do this in a really principled way.
#     """
#     serial_updates = []
#     parallel_updates = []
# 
#     print "Initializing latent variable samplers"
# 
#     print "Ignoring shared tuning curve update"
#     tc_sampler = SharedTuningCurveUpdate()
#     tc_sampler.preprocess(population)
#     serial_updates.append(tc_sampler)
# 
# 
#     loc_sampler = LatentLocationUpdate()
#     loc_sampler.preprocess(population)
#     serial_updates.append(loc_sampler)
# 
#     type_sampler = LatentTypeUpdate()
#     type_sampler.preprocess(population)
#     serial_updates.append(type_sampler)
# 
#     # All populations have a parallel GLM sampler
#     print "Initializing GLM samplers"
#     # glm_sampler = HmcGlmUpdate()
#     # glm_sampler.preprocess(population)
#     # parallel_updates.append(glm_sampler)
# 
#     bias_sampler = HmcBiasUpdate()
#     bias_sampler.preprocess(population)
#     parallel_updates.append(bias_sampler)
# 
#     bkgd_sampler = HmcBkgdUpdate()
#     bkgd_sampler.preprocess(population)
#     parallel_updates.append(bkgd_sampler)
# 
#     from pyglm.components.impulse import DirichletImpulses
#     if isinstance(population.glm.imp_model, DirichletImpulses):
#         imp_sampler = HmcDirichletImpulseUpdate()
#     else:
#         imp_sampler = HmcImpulseUpdate()
#     imp_sampler.preprocess(population)
#     parallel_updates.append(imp_sampler)
# 
#     # All populations have a network sampler
#     print "Initializing network sampler"
#     # net_sampler = GibbsNetworkColumnUpdate()
#     net_sampler = CollapsedGibbsNetworkColumnUpdate()
#     net_sampler.preprocess(population)
#     parallel_updates.append(net_sampler)
# 
#     # If the graph model is a latent distance model, add its update
#     # from components.graph import LatentDistanceGraphModel
#     # if isinstance(population.network.graph, LatentDistanceGraphModel):
#     #     print "Initializing latent location sampler"
#     #     loc_sampler = LatentLocationUpdate()
#     #     loc_sampler.preprocess(population)
#     #     serial_updates.append(loc_sampler)
# 
#     return serial_updates, parallel_updates


import cPickle
import os
import matplotlib.pyplot as plt
    import cProfile, pstats, StringIO
    import time
    import matplotlib.mlab as mlab
from pyglm.models.model_factory import *
from pyglm.inference.gibbs import initialize_updates
from population import Population
    from optparse import OptionParser
    from scipy.stats import norm
    from scipy.stats import gamma
# Run as script using 'python -m test.synth'




def geweke_test(population,
                data,
                N_samples=1000):
    """
    Sample the posterior distribution over parameters using MCMC.
    """
    N = population.model['N']

    # Draw initial state from prior
    x0 = population.sample()

    # Create updates for this population

# Path: pyglm/inference/gibbs.py
# def initialize_updates(population):
#     """ Compute the set of updates required for the given population.
#         TODO: Figure out how to do this in a really principled way.
#     """
#     serial_updates = []
#     parallel_updates = []
# 
#     print "Initializing latent variable samplers"
# 
#     print "Ignoring shared tuning curve update"
#     tc_sampler = SharedTuningCurveUpdate()
#     tc_sampler.preprocess(population)
#     serial_updates.append(tc_sampler)
# 
# 
#     loc_sampler = LatentLocationUpdate()
#     loc_sampler.preprocess(population)
#     serial_updates.append(loc_sampler)
# 
#     type_sampler = LatentTypeUpdate()
#     type_sampler.preprocess(population)
#     serial_updates.append(type_sampler)
# 
#     # All populations have a parallel GLM sampler
#     print "Initializing GLM samplers"
#     # glm_sampler = HmcGlmUpdate()
#     # glm_sampler.preprocess(population)
#     # parallel_updates.append(glm_sampler)
# 
#     bias_sampler = HmcBiasUpdate()
#     bias_sampler.preprocess(population)
#     parallel_updates.append(bias_sampler)
# 
#     bkgd_sampler = HmcBkgdUpdate()
#     bkgd_sampler.preprocess(population)
#     parallel_updates.append(bkgd_sampler)
# 
#     from pyglm.components.impulse import DirichletImpulses
#     if isinstance(population.glm.imp_model, DirichletImpulses):
#         imp_sampler = HmcDirichletImpulseUpdate()
#     else:
#         imp_sampler = HmcImpulseUpdate()
#     imp_sampler.preprocess(population)
#     parallel_updates.append(imp_sampler)
# 
#     # All populations have a network sampler
#     print "Initializing network sampler"
#     # net_sampler = GibbsNetworkColumnUpdate()
#     net_sampler = CollapsedGibbsNetworkColumnUpdate()
#     net_sampler.preprocess(population)
#     parallel_updates.append(net_sampler)
# 
#     # If the graph model is a latent distance model, add its update
#     # from components.graph import LatentDistanceGraphModel
#     # if isinstance(population.network.graph, LatentDistanceGraphModel):
#     #     print "Initializing latent location sampler"
#     #     loc_sampler = LatentLocationUpdate()
#     #     loc_sampler.preprocess(population)
#     #     serial_updates.append(loc_sampler)
# 
#     return serial_updates, parallel_updates


# Path: test/synth_geweke.py
import cPickle
import os
import matplotlib.pyplot as plt
    import cProfile, pstats, StringIO
    import time
    import matplotlib.mlab as mlab
from pyglm.models.model_factory import *
from pyglm.inference.gibbs import initialize_updates
from population import Population
    from optparse import OptionParser
    from scipy.stats import norm
    from scipy.stats import gamma

# Run as script using 'python -m test.synth'




def geweke_test(population,
                data,
                N_samples=1000):
    """
    Sample the posterior distribution over parameters using MCMC.
    """
    N = population.model['N']

    # Draw initial state from prior
    x0 = population.sample()

    # Create updates for this population

    serial_updates, parallel_updates = initialize_updates(population)