From 724d69503037eccabded3ad8a3d4bf66916aef19 Mon Sep 17 00:00:00 2001
From: "gregory.ashton" <gregory.ashton@ligo.org>
Date: Mon, 21 Aug 2017 09:34:06 +0200
Subject: [PATCH] Fixes plotting det stat histogram and update timing
coefficients
---
pyfstat/mcmc_based_searches.py | 38 +++++++++++++++++++++++-----------
1 file changed, 26 insertions(+), 12 deletions(-)
diff --git a/pyfstat/mcmc_based_searches.py b/pyfstat/mcmc_based_searches.py
index 13d08f6..f5a9f24 100644
--- a/pyfstat/mcmc_based_searches.py
+++ b/pyfstat/mcmc_based_searches.py
@@ -372,13 +372,25 @@ class MCMCSearch(core.BaseSearchClass):
return sampler
def _estimate_run_time(self):
- tau0S = 2.7e-8
- tau0LD = 1.6e-7
+ """ Print the estimated run time
+
+ Uses timing coefficients based on a Lenovo T460p Intel(R)
+ Core(TM) i5-6300HQ CPU @ 2.30GHz.
+
+ """
+ # Todo: add option to time on a machine, and move coefficients to
+ # ~/.pyfstat.conf
+ if (type(self.theta_prior['Alpha']) == dict or
+ type(self.theta_prior['Delta']) == dict):
+ tau0S = 7.3e-5
+ tau0LD = 4.2e-7
+ else:
+ tau0S = 5.0e-5
+ tau0LD = 6.2e-8
Nsfts = (self.maxStartTime - self.minStartTime) / 1800.
- average_numb_evals = np.sum(self.nsteps)*self.nwalkers*self.ntemps
- a = tau0S * Nsfts * average_numb_evals
- b = tau0LD * Nsfts * average_numb_evals
- print(a, b, Nsfts)
+ numb_evals = np.sum(self.nsteps)*self.nwalkers*self.ntemps
+ a = tau0S * numb_evals
+ b = tau0LD * Nsfts * numb_evals
logging.info('Estimated run-time = {} s = {:1.0f}:{:1.0f} m'.format(
a+b, *divmod(a+b, 60)))
@@ -993,18 +1005,20 @@ class MCMCSearch(core.BaseSearchClass):
if burnin_idx and add_det_stat_burnin:
burn_in_vals = lnl[:, :burnin_idx].flatten()
try:
- axes[-1].hist(burn_in_vals[~np.isnan(burn_in_vals)],
- bins=50, histtype='step', color='C3')
+ twoF_burnin = (burn_in_vals[~np.isnan(burn_in_vals)]
+ - self.likelihoodcoef)
+ axes[-1].hist(twoF_burnin, bins=50, histtype='step',
+ color='C3')
except ValueError:
logging.info('Det. Stat. hist failed, most likely all '
'values where the same')
pass
else:
- burn_in_vals = []
+ twoF_burnin = []
prod_vals = lnl[:, burnin_idx:].flatten()
try:
- axes[-1].hist(prod_vals[~np.isnan(prod_vals)], bins=50,
- histtype='step', color='k')
+ twoF = prod_vals[~np.isnan(prod_vals)]-self.likelihoodcoef
+ axes[-1].hist(twoF, bins=50, histtype='step', color='k')
except ValueError:
logging.info('Det. Stat. hist failed, most likely all '
'values where the same')
@@ -1014,7 +1028,7 @@ class MCMCSearch(core.BaseSearchClass):
else:
axes[-1].set_xlabel(r'$\widetilde{2\mathcal{F}}$')
axes[-1].set_ylabel(r'$\textrm{Counts}$')
- combined_vals = np.append(burn_in_vals, prod_vals)
+ combined_vals = np.append(twoF_burnin, twoF)
if len(combined_vals) > 0:
minv = np.min(combined_vals)
maxv = np.max(combined_vals)
--
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