@deterministic
def y_plus_gen(gamma=gamma, deltaB=deltaB, rB=rB):
return commons.y_plus(gamma, deltaB, rB)
@deterministic
def x_minus_gen(gamma=gamma, deltaB=deltaB, rB=rB):
return commons.x_minus(gamma, deltaB, rB)
@deterministic
def y_minus_gen(gamma=gamma, deltaB=deltaB, rB=rB):
return commons.y_minus(gamma, deltaB, rB)
#Averaged experiments
@stochastic
def vars(x_plus_gen=x_plus_gen, y_plus_gen=y_plus_gen, x_minus_gen=x_minus_gen, y_minus_gen=y_minus_gen, value=0):
return pymc.mv_normal_like([x_plus_gen, y_plus_gen, x_minus_gen, y_minus_gen], experiment['y_exp'], experiment['y_covar_inv'])
# Model
if load_last_model:
mcmc = pymc.database.pickle.load('mcmc-{}.pickle'.format(name))
else:
mcmc = MCMC([vars, gamma, deltaB, rB],
db='pickle',
dbmode='w',
dbname='mcmc-{}.pickle'.format(name))
mcmc.sample(iter = N, burn = min(5000, int(N/10)), thin = 1)
for v in var_list:
commons.plot(mcmc.trace(v.__name__)[:], v.__doc__, "pics/{}_{}.png".format(name, v.__name__))
for x,y in combinations(var_list, 2):
commons.plot_2d_hist(mcmc.trace(x.__name__)[:], mcmc.trace(y.__name__)[:],
"pics/{}_{}-{}_hist.png".format(name, x.__name__, y.__name__))
#Averaged experiments
@stochastic
def vars(x_plus_gen=x_plus_gen,
y_plus_gen=y_plus_gen,
x_minus_gen=x_minus_gen,
y_minus_gen=y_minus_gen,
value=0):
return pymc.mv_normal_like(
[x_plus_gen, y_plus_gen, x_minus_gen, y_minus_gen],
experiment['y_exp'], experiment['y_covar_inv'])
# Model
if load_last_model:
mcmc = pymc.database.pickle.load('mcmc-{}.pickle'.format(name))
else:
mcmc = MCMC([vars, gamma, deltaB, rB],
db='pickle',
dbmode='w',
dbname='mcmc-{}.pickle'.format(name))
mcmc.sample(iter=N, burn=min(5000, int(N / 10)), thin=1)
for v in var_list:
commons.plot(
mcmc.trace(v.__name__)[:], v.__doc__,
"pics/{}_{}.png".format(name, v.__name__))
for x, y in combinations(var_list, 2):
commons.plot_2d_hist(
mcmc.trace(x.__name__)[:],
mcmc.trace(y.__name__)[:],
"pics/{}_{}-{}_hist.png".format(name, x.__name__, y.__name__))
data_to_save = {}
for v in data:
data_to_save[v] = np.histogram(data[v], bins=edges[v])
pickle.dump({'data': data_to_save}, file, protocol=2)
else:
with gzip.open('output/raw.dat.gz', 'w') as file:
pickle.dump({'data': data}, file, protocol=2)
else: #not MCMC
var_dict = {}
weights = []
data = {v: [] for v in desired_variables}
for i in range(N):
for i, p in enumerate(desired_variables):
rval = np.random.uniform(lower_bounds[i], upper_bounds[i])
data[p].append(rval)
parameters.setRealValue(p, rval)
weights.append(pdf.getVal())
if bins:
with gzip.open('output/bins.dat.gz', 'w') as file:
data_to_save = {}
for v in data:
data_to_save[v] = np.histogram(data[v], bins=edges[v], weights=weights)
pickle.dump({'data': data_to_save}, file, protocol=2)
else:
with gzip.open('output/raw.dat.gz', 'w') as file:
pickle.dump({'data': data, 'weights': weights}, file, protocol=2)
if plot_graph:
for v in desired_variables:
commons.plot(np.array(data[v]), combination, v, weights=weights)
print("Total time: {}".format(time.time() - start_time))