diff options
Diffstat (limited to 'utils/llh.py')
| -rw-r--r-- | utils/llh.py | 27 |
1 files changed, 15 insertions, 12 deletions
diff --git a/utils/llh.py b/utils/llh.py index f4404c4..0c1e97d 100644 --- a/utils/llh.py +++ b/utils/llh.py @@ -12,7 +12,8 @@ from __future__ import absolute_import, division from functools import partial import numpy as np -from scipy.stats import multivariate_normal, rv_continuous +import scipy +from scipy.stats import multivariate_normal, truncnorm from utils import fr as fr_utils from utils import gf as gf_utils @@ -20,10 +21,11 @@ from utils.enums import EnergyDependance, Likelihood, ParamTag, PriorsCateg from utils.misc import enum_parse, gen_identifier, parse_bool -class Gaussian(rv_continuous): - """Gaussian for one dimension.""" - def _pdf(self, x, mu, sig): - return (1./np.sqrt(2*np.pi*sig**2))*np.exp(-((x-mu)**2)/(2*sig**2)) +def GaussianBoundedRV(loc=0., sigma=1., lower=-np.inf, upper=np.inf): + """Normalised gaussian bounded between lower and upper values""" + low, up = (lower - loc) / sigma, (upper - loc) / sigma + g = scipy.stats.truncnorm(loc=loc, scale=sigma, a=low, b=up) + return g def multi_gaussian(fr, fr_bf, sigma, offset=-320): @@ -69,13 +71,14 @@ def lnprior(theta, paramset): prior = 0 for param in paramset: if param.prior is PriorsCateg.GAUSSIAN: - prior += Gaussian().logpdf( - param.nominal_value, param.value, param.std - ) - elif param.prior is PriorsCateg.HALFGAUSS: - prior += Gaussian().logpdf( - param.nominal_value, param.value, param.std - ) + Gaussian().logcdf(1, param.value, param.std) + prior += GaussianBoundedRV( + loc=param.nominal_value, sigma=param.std + ).logpdf(param.value) + elif param.prior is PriorsCateg.LIMITEDGAUSS: + prior += GaussianBoundedRV( + loc=param.nominal_value, sigma=param.std, + lower=param.ranges[0], upper=param.ranges[1] + ).logpdf(param.value) return prior |