From 15725eac073dc7ec8256ec756f07d0416a5afca5 Mon Sep 17 00:00:00 2001
From: Rutger van Haasteren <vhaasteren@gmail.com>
Date: Thu, 8 Feb 2024 17:16:25 +0100
Subject: [PATCH] Changed definition of powerlaw_flat_tail
---
prior_wrapper.py | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/prior_wrapper.py b/prior_wrapper.py
index 6107d40..41b0d6c 100644
--- a/prior_wrapper.py
+++ b/prior_wrapper.py
@@ -70,6 +70,7 @@ import scipy.linalg as sl
import re
from enterprise.signals.parameter import function
import enterprise.constants as const
+import enterprise.signals.gp_priors as gp_priors
class kumaraswamy_distribution(sstats.rv_continuous):
"""Kumaraswamy distribution like for scipy"""
@@ -211,9 +212,9 @@ def ptapar_mapping(pta):
@function
def powerlaw_flat_tail(f, log10_A=-16, gamma=5, log10_kappa=-7, components=2):
df = np.diff(np.concatenate((np.array([0]), f[::components])))
- return (
- (10**log10_A) ** 2 / 12.0 / np.pi**2 * const.fyr ** (gamma - 3) * f ** (-gamma) * np.repeat(df, components) + 10 ** (2*log10_kappa)
- )
+ pl = (10**log10_A) ** 2 / 12.0 / np.pi**2 * const.fyr ** (gamma - 3) * f ** (-gamma) * np.repeat(df, components)
+ flat = 10 ** (2*log10_kappa)
+ return np.maximum(pl, flat)
class BoundedMvNormalPlHierarchicalPrior(object):
"""Class to represent a Bounded MvNormal hierarchical prior for Enterprise Powerlaw Signals"""
--
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