def _compute_nlive(self, logL_birth):
if is_int(logL_birth):
nlive = logL_birth
self['nlive'] = nlive
descending = np.arange(nlive, 0, -1)
self.loc[len(self) - nlive:, 'nlive'] = descending
else:
self['logL_birth'] = logL_birth
self.tex['logL_birth'] = r'$\log\mathcal{L}_{\rm birth}$'
self['nlive'] = compute_nlive(self.logL, self.logL_birth)
self.tex['nlive'] = r'$n_{\rm live}$'
self.beta = self._beta
def recompute(self, logL_birth=None, inplace=False):
"""Re-calculate the nested sampling contours and live points.
Parameters
----------
logL_birth: array-like or int, optional
array-like: the birth contours.
int: the number of live points.
default: use the existing birth contours to compute nlive
inplace: bool, optional
Indicates whether to modify the existing array, or return a new
frame with contours resorted and nlive recomputed
default: False
"""
samples = self.sort_values('logL').reset_index(drop=True)
if is_int(logL_birth):
nlive = logL_birth
samples['nlive'] = nlive
descending = np.arange(nlive, 0, -1)
samples.loc[len(samples) - nlive:, 'nlive'] = descending
else:
if logL_birth is not None:
samples['logL_birth'] = logL_birth
samples.tex['logL_birth'] = r'$\log\mathcal{L}_{\rm birth}$'
if 'logL_birth' not in samples:
raise RuntimeError("Cannot recompute run without "
"birth contours logL_birth.")
invalid = samples.logL <= samples.logL_birth
n_bad = invalid.sum()
n_equal = (samples.logL == samples.logL_birth).sum()
if n_bad:
warnings.warn(
"%i out of %i samples have logL <= logL_birth,"
"\n%i of which have logL == logL_birth."
"\nThis may just indicate numerical rounding "
"errors at the peak of the likelihood, but "
"further investigation of the chains files is "
"recommended."
"\nDropping the invalid samples." %
(n_bad, len(samples), n_equal), RuntimeWarning)
samples = samples[~invalid].reset_index(drop=True)
samples['nlive'] = compute_nlive(samples.logL, samples.logL_birth)
samples.tex['nlive'] = r'$n_{\rm live}$'
samples.beta = samples._beta
return modify_inplace(self, samples, inplace)
def recompute(self, logL_birth=None, inplace=False):
"""Re-calculate the nested sampling contours and live points.
Parameters
----------
logL_birth, array-like or int, optional
array-like: the birth contours.
int: the number of live points.
default: use the existing birth contours to compute nlive
inplace: bool, optional
Indicates whether to modify the existing array, or return a new
frame with contours resorted and nlive recomputed
default: False
"""
samples = self.sort_values('logL').reset_index(drop=True)
if is_int(logL_birth):
nlive = logL_birth
samples['nlive'] = nlive
descending = np.arange(nlive, 0, -1)
samples.loc[len(samples) - nlive:, 'nlive'] = descending
else:
if logL_birth is not None:
samples['logL_birth'] = logL_birth
samples.tex['logL_birth'] = r'$\log\mathcal{L}_{\rm birth}$'
if 'logL_birth' not in samples:
raise RuntimeError("Cannot recompute run without "
"birth contours logL_birth.")
if (samples.logL <= samples.logL_birth).any():
raise RuntimeError("Not a valid nested sampling run. "
"Require logL > logL_birth.")
samples['nlive'] = compute_nlive(samples.logL, samples.logL_birth)
samples.tex['nlive'] = r'$n_{\rm live}$'
samples.beta = samples._beta
return modify_inplace(self, samples, inplace)
def test_compute_nlive():
# Generate a 'pure' nested sampling run
np.random.seed(0)
nlive = 500
ncompress = 100
logL = np.cumsum(np.random.rand(nlive, ncompress), axis=1)
logL_birth = np.concatenate((np.ones((nlive, 1)) * -1e30, logL[:, :-1]),
axis=1)
i = np.argsort(logL.flatten())
logL = logL.flatten()[i]
logL_birth = logL_birth.flatten()[i]
# Compute nlive
nlives = compute_nlive(logL, logL_birth)
# Check the first half are constant
assert_array_equal(nlives[:len(nlives) // 2], nlive)
# Check one point at the end
assert (nlives[-1] == 1)
# Check never more than nlive
assert (nlives.max() <= nlive)