comment='', iter=40000):

import model

reload(model)

data = pl.csv2rec(data_fname)

# FIXME: this makes a big difference, but I don't understand why it would (could be prior on gp amp)

data.x1 = (data.x1-1990.)/10. # crude normalization of year data

print 'generating model'

mod_mc = eval('model.%s(data)' % mod)

print 'fitting model with mcmc'

mod_mc.sample(iter, iter/2, iter/2000, verbose=1)

print 'summarizing results'

import graphics

reload(graphics)

pl.figure()

pl.clf()

graphics.plot_prediction_over_time('IRQ', data, mod_mc.predicted, age=-1, cmap=pl.cm.RdYlBu, connected=False, jittered_posterior=False)

graphics.plot_prediction_over_time('IRQ', data[:40], mod_mc.param_predicted, age=-1)

#pl.plot(data.year, data.y, zorder=0,

# linestyle='', marker='x', mew=3, color='r', ms=8, alpha=.5)

pl.title('IRQ')

pl.xlabel('Time (Years)')

pl.ylabel('$\log(_5q_0)$')

pl.axis([1945, 2030, -1.8, -.5])

pl.figtext(0, 1, '\n %s' % comment, va='top', ha='left')

t1 = time.time()

pl.savefig(image_fname%t1)

try:

for stoch in 'beta gamma sigma_f tau_f'.split(' '):

print '%s =\n %s\n' % (stoch, mean_w_ui(mod_mc.__getattribute__(stoch)))

except AttributeError:

pass

stats = stoch.stats()

val_list = []

for i, v in enumerate(stats['mean']):

val_list.append('%.2f (%.2f, %.2f) [%.2f], ' % (v, stats['95% HPD interval'][i][0], stats['95% HPD interval'][i][1], stats['mc error'][i]/abs(v)*100))