Use 1 core first

code/NR_Interpolate-0001_t_10M.py

@@ -33,18 +33,18 @@ import random
 #tshift: time shift after the strain peak
 #vary_fund: whether you vary the fundamental frequency. Works in the model_dv function.
 
-rootpath= "/Users/RayneLiu/git/rdstackingproject"#"/work/rayne.liu/git/rdstackingproject"
+rootpath= "/work/rayne.liu/git/rdstackingproject"#"/Users/RayneLiu/git/rdstackingproject"
 nmax=1
 tshift=10
 vary_fund = True
 
 #sampler parameters
-npoints = 100
-nwalkers = 50
+npoints = 10
+nwalkers = 20
 ntemps=16
 dim = nmax+1
 ndim = 4*dim
-burnin = 50  #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 5  #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! I usually use half the points.
 numbins = 42 #corner plot parameter - how many bins you want
 datacolor = '#105670' #'#4fa3a7'
@@ -204,10 +204,10 @@ def log_probability(theta):
 
 #This cell uses the tshift=10 results
 #Set the number of cores of your processors
-pool = choose_pool(8)
-pool.size = 8
+pool = choose_pool(1)
+pool.size = 1
 np.random.seed(42)
-pos = np.array([random.uniform(-0.05,0.05), random.uniform(-0.25,-0.15), random.uniform(0.,0.8),                 random.uniform(0.5,1.), random.uniform(0.4,0.8), random.uniform(0.5, 1.), random.uniform(0.5, 0.6),                 random.uniform(0.5, 0.6)])
+pos = np.array([random.uniform(-0.02,0.02), random.uniform(-0.1,0.15), random.uniform(-0.2.,0.08),                 random.uniform(0.,1.), random.uniform(0.4,0.8), random.uniform(0.5, 1.), random.uniform(0.5, 0.6),                 random.uniform(0.5, 0.6)])
 pos = list(pos)
 pos += 1e-5 * np.random.randn(ntemps, nwalkers, ndim)
 sampler = ptemcee.Sampler(nwalkers, ndim, log_likelihood, log_prior, ntemps=ntemps, pool=pool)