From 0ccf0c918d27281ee392546bc794ee31d2199b2b Mon Sep 17 00:00:00 2001 From: Michael Jasper Martins Date: Wed, 13 Nov 2024 18:35:10 +0100 Subject: [PATCH] Updated method documentation --- bilby/core/prior/dict.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/bilby/core/prior/dict.py b/bilby/core/prior/dict.py index 20fc3f3d..ab642e24 100644 --- a/bilby/core/prior/dict.py +++ b/bilby/core/prior/dict.py @@ -490,7 +490,7 @@ def normalize_constraint_factor(self, keys, nrepeats=10, sampling_chunk=10000, r Parameters ========== - keys : list, tuple + keys: list, tuple The set of keys in the prior dict to perform the integration for. Must contain all keys that the constraint depends on. For joint priors, the full distribution is sampled. Joint prior keys not present in 'keys' are marginalized. @@ -521,11 +521,11 @@ def normalize_constraint_factor(self, keys, nrepeats=10, sampling_chunk=10000, r Notes ===== .. seealso:: - Scipy's :scipy:stats:qmc:`Halton` class - Documentation of the quasi-random number generator :scipy:stats:qmc:`Halton` used for the quasi-monte - carlo integration of the normalization factor. - Scipy's :scipy:integrate:`qmc_quad` method - Documentation of the quasi-monte carlo integration scheme native to scipy. + :py:class:`scipy.stats.qmc.Halton` + Documentation of the quasi-random number generator used for the quasi-monte carlo-based method + to integrate the normalization factor. + :py:func:`scipy.integrate.qmc_quad` + Documentation of the scipy-native quasi-monte carlo integration scheme. The implementation, particularly the error estimate, motivates this implementation. (The error estimate was re-implemented to also apply for the 'from_samples' method.)