def test_invalid(self):
"""Test the interval function catches invalid intervals"""
x = np.array([-5, -3, -2, -2, 100])
with self.assertRaises(ValueError):
npinterval.interval(x, 1.01)
with self.assertRaises(ValueError):
npinterval.interval(x, 0)
def test_single(self):
"""Test the interval warns if only 1 sample is included"""
# Don't know how to test warnings below python 3.2
if sys.version_info[0] + 0.1*sys.version_info[1] < 3.2:
return
x = np.array([-5, -3, -2, -2, 100])
with self.assertWarns(RuntimeWarning):
npinterval.interval(x, 1/5)
def test_intervals(self):
"""Test the interval function correctly computes valid intervals"""
x = np.array([-5, -3, -2, -2, 100])
self.assertEqual(
npinterval.interval(x, 2/5),
(-2, -2, 2, 4))
self.assertEqual(
npinterval.interval(x, 3/5),
(-3, -2, 1, 4))
self.assertEqual(
npinterval.interval(x, 4/5),
(-5, -2, 0, 4))
def run_mcmc(meas, x, nsamples, covm=None, scales=None):
"""
Sample the likelihood space with a Markov Chain Monte Carlo.
:param meas: TemplateMeasurement
measurement whose spectrum likelihood space is to be probe
:param x: [float]
parameter values where to start the chain
:param covm: [[float]]
covariance matrix values if sampling transformed space
:param scales: [float]
parameter scales if not sampling transformed space
:return: [float], [float], [float], pymcmc.MCMC
posterior mean, lower CI, upper CI for each parameter, and the MCMC
object used for sampling
"""
mcmc = MCMC(meas.spec.npars)
mcmc.set_values(x)
if covm is not None and scales is None:
mcmc.set_covm(covm)
elif scales is not None:
mcmc.set_scales(scales)
else:
raise ValueError("Must provide covariance OR scales")
mcmc.rescale = 2 # good starting point
mcmc.learn_scale(meas.spec.ll, 1000)
mcmc.run(meas.spec.ll, nsamples)
mean = list()
mean_down = list()
mean_up = list()
for ipar in range(meas.spec.npars):
mean.append(np.mean(mcmc.data[:, ipar]))
low, high, _, _ = npinterval.interval(mcmc.data[:, ipar], 0.6827)
mean_down.append(low-mean[-1])
mean_up.append(high-mean[-1])
return mean, mean_down, mean_up, mcmc
def run_mcmc(meas, x, nsamples, covm=None, scales=None):
"""
Sample the likelihood space with a Markov Chain Monte Carlo.
:param meas: TemplateMeasurement
measurement whose spectrum likelihood space is to be probe
:param x: [float]
parameter values where to start the chain
:param covm: [[float]]
covariance matrix values if sampling transformed space
:param scales: [float]
parameter scales if not sampling transformed space
:return: [float], [float], [float], pymcmc.MCMC
posterior mean, lower CI, upper CI for each parameter, and the MCMC
object used for sampling
"""
mcmc = MCMC(meas.spec.npars)
mcmc.set_values(x)
if covm is not None and scales is None:
mcmc.set_covm(covm)
elif scales is not None:
mcmc.set_scales(scales)
else:
raise ValueError("Must provide covariance OR scales")
mcmc.rescale = 2 # good starting point
mcmc.learn_scale(meas.spec.ll, 1000)
mcmc.run(meas.spec.ll, nsamples)
mean = list()
mean_down = list()
mean_up = list()
for ipar in range(meas.spec.npars):
mean.append(np.mean(mcmc.data[:, ipar]))
low, high, _, _ = npinterval.interval(mcmc.data[:, ipar], 0.6827)
mean_down.append(low - mean[-1])
mean_up.append(high - mean[-1])
return mean, mean_down, mean_up, mcmc
def test_full(self):
"""Test the interval function correctly finds the full interval"""
x = np.array([-5, -3, -2, -2, 100])
self.assertEqual(
npinterval.interval(x, 1),
(-5, 100, 0, 5))
