From 2d71127695fb08e9b71963f36f8e3a5332285dc5 Mon Sep 17 00:00:00 2001 From: Rayne Liu <rayne.liu@atlas1> Date: Fri, 14 Aug 2020 13:45:09 +0000 Subject: [PATCH] Most recent pyscript --- code/RDGW150914_ptemcee4.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/code/RDGW150914_ptemcee4.py b/code/RDGW150914_ptemcee4.py index f0a99d1..848d6a7 100755 --- a/code/RDGW150914_ptemcee4.py +++ b/code/RDGW150914_ptemcee4.py @@ -33,17 +33,17 @@ from scipy.optimize import minimize #tshift: time shift after the strain peak #vary_fund: whether you vary the fundamental frequency. Works in the model_dv function. -rootpath= "/Users/RayneLiu"#"/work/rayne.liu" +rootpath="/work/rayne.liu"# "/Users/RayneLiu" nmax=1 tshift=19 vary_fund = False #sampler parameters -npoints=1002 +npoints=1000000 nwalkers = 42 ntemps=12 ndim = int(4*(nmax+1)) -burnin = 200 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit. +burnin = 420000 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit. #This is trivial but often forgotten: this cannot be more than npoints! Usually 1/5~1/4 npoints is what I observe. numbins = 42 #corner plot parameter - how many bins you want datacolor = '#105670' #'#4fa3a7' @@ -172,8 +172,8 @@ def log_probability(theta): #Fit with ptemcee #Set the number of cores of your processors -pool = choose_pool(6) -pool.size = 6 +pool = choose_pool(12) +pool.size = 12 vary_param = float(vary_fund) pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]]) for i in range (1,nmax+1): -- GitLab