From 50b4c66f1f9751c6cb57e17224db28ce817af3a5 Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Fri, 21 May 2021 02:29:23 +0000
Subject: [PATCH] Allow ptemcee initialization with array
---
bilby/core/sampler/ptemcee.py | 19 +++++++++++++++++--
1 file changed, 17 insertions(+), 2 deletions(-)
diff --git a/bilby/core/sampler/ptemcee.py b/bilby/core/sampler/ptemcee.py
index 471439a1d..d7f79d80c 100644
--- a/bilby/core/sampler/ptemcee.py
+++ b/bilby/core/sampler/ptemcee.py
@@ -92,13 +92,15 @@ class Ptemcee(MCMCSampler):
is not recommended for cases where tau is large.
ignore_keys_for_tau: str
A pattern used to ignore keys in estimating the autocorrelation time.
- pos0: str, list ("prior")
+ pos0: str, list, np.ndarray
If a string, one of "prior" or "minimize". For "prior", the initial
positions of the sampler are drawn from the sampler. If "minimize",
a scipy.optimize step is applied to all parameters a number of times.
The walkers are then initialized from the range of values obtained.
If a list, for the keys in the list the optimization step is applied,
- otherwise the initial points are drawn from the prior.
+ otherwise the initial points are drawn from the prior. If a numpy array
+ the shape should be (ntemps, nwalkers, ndim).
+
niterations_per_check: int (5)
The number of iteration steps to take before checking ACT. This
effectively pre-thins the chains. Larger values reduce the per-eval
@@ -363,6 +365,17 @@ class Ptemcee(MCMCSampler):
)
return pos0
+ def get_pos0_from_array(self):
+ if self.pos0.shape != (self.ntemps, self.nwalkers, self.ndim):
+ raise ValueError(
+ "Shape of starting array should be (ntemps, nwalkers, ndim). "
+ "In this case that is ({}, {}, {}), got {}".format(
+ self.ntemps, self.nwalkers, self.ndim, self.pos0.shape
+ )
+ )
+ else:
+ return self.pos0
+
def setup_sampler(self):
""" Either initialize the sampler or read in the resume file """
import ptemcee
@@ -446,6 +459,8 @@ class Ptemcee(MCMCSampler):
return self.get_pos0_from_minimize()
elif isinstance(self.pos0, list):
return self.get_pos0_from_minimize(minimize_list=self.pos0)
+ elif isinstance(self.pos0, np.ndarray):
+ return self.get_pos0_from_array()
else:
raise SamplerError("pos0={} not implemented".format(self.pos0))
--
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