def test_using_list(self):
data = self.data.tolist()
c = ChainConsumer()
c.add_chain(data)
summary = c.get_summary()
actual = np.array(list(summary.values())[0])
assert np.abs(actual[1] - 5.0) < 0.1
def test_using_dict(self):
data = {"x": self.data, "y": self.data2}
c = ChainConsumer()
c.add_chain(data)
summary = c.get_summary()
print(c.chains[0].shape)
deviations = np.abs([summary["x"][1] - 5, summary["y"][1] - 3])
assert np.all(deviations < 0.1)
def is_unconstrained(chain, param):
c = ChainConsumer()
c.add_chain(chain, parameters=param)
constraints = c.get_summary()[0]
for key in constraints:
val = constraints[key]
if val[0] is None or val[2] is None:
return True
return False
def test_summary1(self):
tolerance = 5e-2
consumer = ChainConsumer()
consumer.add_chain(self.data)
consumer.configure_general(bins=1.6)
summary = consumer.get_summary()
actual = np.array(list(summary.values())[0])
expected = np.array([3.5, 5.0, 6.5])
diff = np.abs(expected - actual)
assert np.all(diff < tolerance)
def test_convergence_failure(self):
data = np.concatenate(
(np.random.normal(loc=0.0,
size=10000), np.random.normal(loc=4.0,
size=10000)))
consumer = ChainConsumer()
consumer.add_chain(data)
summary = consumer.get_summary()
actual = np.array(list(summary.values())[0])
print(actual)
assert actual[0] is None and actual[2] is None
def test_file_loading2(self):
data = self.data[:1000]
directory = tempfile._get_default_tempdir()
filename = next(tempfile._get_candidate_names())
filename = directory + os.sep + filename + ".npy"
np.save(filename, data)
consumer = ChainConsumer()
consumer.add_chain(filename)
summary = consumer.get_summary()
actual = np.array(list(summary.values())[0])
assert np.abs(actual[1] - 5.0) < 0.5
def test_summary2(self):
tolerance = 5e-2
consumer = ChainConsumer()
consumer.add_chain(self.data_combined,
parameters=["a", "b"],
name="chain1")
consumer.add_chain(self.data_combined, name="chain2")
summary = consumer.get_summary()
k1 = list(summary[0].keys())
k2 = list(summary[1].keys())
assert len(k1) == 2
assert "a" in k1
assert "b" in k1
assert len(k2) == 2
assert "a" in k2
assert "b" in k2
expected1 = np.array([3.5, 5.0, 6.5])
expected2 = np.array([2.0, 3.0, 4.0])
diff1 = np.abs(expected1 - np.array(list(summary[0]["a"])))
diff2 = np.abs(expected2 - np.array(list(summary[0]["b"])))
assert np.all(diff1 < tolerance)
assert np.all(diff2 < tolerance)
lnprob,
args=(zhel, zcmb, mb, x1, color, thirdvar,
Cmu, blind_values),
threads=4)
# run the sampler
# how many steps (will have nSteps*nwalkers of samples)
sampler.run_mcmc(pos, nSteps)
if options.plot:
burnin = 0 # change
data = np.genfromtxt(
options.chains +
'/my_params_JLA_FlatwCDM_CPL_uncorrected_PV_blind_%s.txt' % date,
delimiter=',')
data = data[burnin:]
c = ChainConsumer()
c.add_chain(data,
parameters=[
r'$\Omega_m$', r'$w_0$', r'$\\alpha$', r'$\beta$',
r'$M_B$', r'$\Delta_M$'
])
params = c.get_summary()
print params.keys()
figw = c.plot_walks()
figw.show()
fig = c.plot()
figw.savefig(options.chains + '/walks.png')
fig.savefig(options.chains + '/marginals.png')
fig.show()
plt.plot(out_absc[20:], out_lines[20:], '-', color='k')
plt.axhline(1.01)
plt.savefig(dir_output + 'GRtrace_pam_' + repr(nd) + '.png', bbox_inches='tight')
plt.close()
print
print '*************************************************************'
print
if args.cc != 'False':
cc = ChainConsumer()
for nd in xrange(0, mc.ndim): # (0,ndim):
cc.add_chain(chain[:, :, nd].flatten(), walkers=mc.nwalkers)
#print(cc.get_latex_table())
print cc.get_summary()
print cc.diagnostic_gelman_rubin(threshold=0.05)
print cc.diagnostic_geweke()
print
print '*************************************************************'
print
x0 = 1. / 150
M_star1_rand = np.random.normal(M_star1, M_star1_err, n_kept)
if 'kepler' in mc.model_list:
if args.p != 'False' or args.v != 'False':
plt.axhline(1.01)
plt.savefig(dir_output + 'GRtrace_pam_' + repr(nd) + '.png',
bbox_inches='tight')
plt.close()
print
print '*************************************************************'
print
if args.cc != 'False':
cc = ChainConsumer()
for nd in xrange(0, mc.ndim): # (0,ndim):
cc.add_chain(chain[:, :, nd].flatten(), walkers=mc.nwalkers)
#print(cc.get_latex_table())
print cc.get_summary()
print cc.diagnostic_gelman_rubin(threshold=0.05)
print cc.diagnostic_geweke()
print
print '*************************************************************'
print
x0 = 1. / 150
M_star1_rand = np.random.normal(M_star1, M_star1_err, n_kept)
if 'kepler' in mc.model_list:
if args.p != 'False' or args.v != 'False':
def load_stan_from_folder(folder, replace=True, merge=True, cut=False, num=None):
vals = get_truths_labels_significance()
full_params = [[k[2]] if not isinstance(k[2], list) else k[2] for k in vals if k[2] is not None]
params = [[k[2]] if not isinstance(k[2], list) else k[2] for k in vals if
k[3] and k[2] is not None]
full_params = list(itertools.chain.from_iterable(full_params))
full_params.remove("$\\rho$")
params = list(itertools.chain.from_iterable(params))
name_map = {k[0]: k[2] for k in vals}
truths = {k[2]: k[1] for k in vals if not isinstance(k[2], list)}
is_array = [k[0] for k in vals if not isinstance(k[1], float) and not isinstance(k[1], int)]
cs = {}
fs = sorted([f for f in os.listdir(folder) if f.startswith("stan") and f.endswith(".pkl")])
if num is not None:
filter = "_%d_" % num
fs = [f for f in fs if filter in f]
for f in fs:
splits = f.split("_")
c = splits[1]
t = os.path.abspath(folder + os.sep + f)
if cs.get(c) is None:
cs[c] = []
cs[c].append(get_chain(t, name_map, replace=replace))
assert len(cs.keys()) > 0, "No results found"
result = []
good_ks = []
for k in sorted(list(cs.keys())):
chains = cs[k]
chain = chains[0]
for c in chains[1:]:
for key in chain.keys():
chain[key] = np.concatenate((chain[key], c[key]))
posterior = chain["Posterior"]
del chain["Posterior"]
if "weight" in chain.keys():
weights = chain["weight"]
del chain["weight"]
else:
weights = np.ones(posterior.shape)
if "old\\_weight" in chain.keys():
ow = chain["old\\_weight"]
# ow -= ow.min()
# ow = np.exp(ow)
del chain["old\\_weight"]
elif "old_weight" in chain.keys():
ow = chain["old_weight"]
del chain["old_weight"]
else:
ow = np.ones(posterior.shape)
print(chain.keys())
for param in is_array:
latex = name_map[param]
truth_val = truths[latex]
shape = truth_val.shape
if not replace:
del chain[param]
if len(shape) > 1 or latex not in chain: continue # Dont do 2D parameters
for i in range(shape[0]):
column = chain[latex][:, i]
chain[latex % i] = column
truths[latex % i] = truth_val[i]
del chain[latex]
c = ChainConsumer()
c.add_chain(chain, weights=weights)
summary = c.get_summary()
num_failed = sum([1 if summary[k][0] is None else 0 for k in summary.keys()])
num_param = len(list(summary.keys()))
if not cut or num_failed < 4:
print("Chain %s good" % k)
good_ks.append(k)
result.append((chain, posterior, truths, params, full_params, len(chains), weights, ow))
else:
print("Chain %s is bad" % k)
if merge:
rr = list(result[0])
for r in result[1:]:
for key in rr[0].keys():
rr[0][key] = np.concatenate((rr[0][key], r[0][key]))
rr[1] = np.concatenate((rr[1], r[1]))
rr[6] = np.concatenate((rr[6], r[6]))
rr[7] = np.concatenate((rr[7], r[7]))
rr[5] += r[5]
return tuple(rr)
else:
return result
def test_output_text(self):
consumer = ChainConsumer()
consumer.add_chain(self.data, parameters=["a"])
vals = consumer.get_summary()["a"]
text = consumer.get_parameter_text(*vals)
assert text == r"5.0\pm 1.5"
def test_squeeze_doesnt_squeeze_multi(self):
c = ChainConsumer()
c.add_chain(self.data).add_chain(self.data)
sum = c.get_summary()
assert isinstance(sum, list)
assert len(sum) == 2
def test_summary_when_no_parameter_names(self):
c = ChainConsumer()
c.add_chain(self.data)
summary = c.get_summary()
assert list(summary.keys()) == [0]