Atlas probably should not run the waveform band plot together

code/Mock_Interpolate-0001_t_10M_wandt.py

@@ -42,12 +42,12 @@ tshift=10
 vary_fund = True
 
 #sampler parameters
-npoints = 220
-nwalkers = 100
+npoints = 400
+nwalkers = 240
 ntemps=12
 dim = nmax+1
 ndim = 4*dim
-burnin = 150  #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 280  #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 = 32 #corner plot parameter - how many bins you want
 datacolor = '#105670' #'#4fa3a7'
@@ -155,7 +155,7 @@ def log_prior(theta):
             return 0.0
     
     elif tshift == 10:
-        if all([0.52 <= omega0 <= 0.64, 0.43 <= omega1 <= 0.58, 4. <= tau0 <= 18., 0. <= tau1 <= 10., \
+        if all([0.53 <= omega0 <= 0.66, 0.43 <= omega1 <= 0.58, 9. <= tau0 <= 15., 0. <= tau1 <= 5.8, \
             0. <= xvar0 <= 1., 0. <= xvar1 <= 1.5, -np.pi <= yvar0 <= np.pi, -np.pi <= yvar1 <= np.pi]):        
             return 0.0
     
@@ -188,7 +188,7 @@ def log_probability(theta):
 #pool = choose_pool(1)
 #pool.size = 1
 np.random.seed(42)
-pos = np.array([random.uniform(0.57,0.63), random.uniform(0.5,0.54), random.uniform(8., 13.7),                 random.uniform(4.,7.), random.uniform(0.3,0.5), random.uniform(0.3, 0.5), random.uniform(-1., 1.),                 random.uniform(-1., 1.)])
+pos = np.array([random.uniform(0.59,0.64), random.uniform(0.5,0.54), random.uniform(10., 13.7),                 random.uniform(2.,5.), random.uniform(0.3,0.5), random.uniform(0.3, 0.5), random.uniform(-1., 1.),                 random.uniform(-1., 1.)])
 pos = list(pos)
 pos += 1e-5 * np.random.randn(ntemps, nwalkers, ndim)
 with Pool() as pool:
@@ -265,7 +265,7 @@ for yi in range(naxes):
 figcorn.savefig(rootpath + '/plotsmc/0001_10M_mockinterpolated_cornerplot_wandt_'+'nmax'+str(nmax)+'_tshift'+str(tshift)+'_'+str(nwalkers)+'walkers_'+str(npoints)+'pts.png', format='png', bbox_inches='tight', dpi=300)
 
 
-
+"""
 #Now plot the NR data against the mcmc fit data, together with the 1-sigma varying error data
 onesig_bounds = np.array([np.percentile(samples[:, i], [16, 84]) for i in range(len(samples[0]))]).T
 modelfitpk = model_dv(pk)
@@ -284,4 +284,4 @@ plt.xlabel("t")
 plt.ylabel("h")
 
 figband.savefig(rootpath + '/plotsmc/0001_10M_mockinterpolated_waveform_wandt_'+'nmax'+str(nmax)+'_tshift'+str(tshift)+'_'+str(nwalkers)+'walkers_'+str(npoints)+'pts.png', format = 'png', dpi = 384, bbox_inches = 'tight')
-
+"""