Added waveform band plotting

code/NR_Interpolate-0001_t_10M_wandt.py

@@ -42,12 +42,12 @@ tshift=10
 vary_fund = True
 
 #sampler parameters
-npoints = 1200
-nwalkers = 256
+npoints = 100#1200
+nwalkers = 30#256
 ntemps=12
 dim = nmax+1
 ndim = 4*dim
-burnin = 600  #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 50#600  #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'
@@ -180,8 +180,8 @@ def log_prior(theta):
             return 0.0
     
     elif tshift == 10:
-        if all([0.54 <= omega0 <= 0.6, 0.4 <= omega1 <= 0.58, 8.8 <= tau0 <= 14.2, 3.7 <= tau1 <= 10., \
-            0. <= xvar0 <= 1.0, 0. <= xvar1 <= 1.2, 0 <= yvar0 <= 2*np.pi, -np.pi <= yvar1 <= np.pi]):        
+        if all([0.54 <= omega0 <= 0.6, 0.35 <= omega1 <= 0.64, 9.1 <= tau0 <= 15.7, 0. <= tau1 <= 9., \
+            0. <= xvar0 <= 1.0, 0. <= xvar1 <= 1.2, -np.pi <= yvar0 <= np.pi, -np.pi <= yvar1 <= np.pi]):        
             return 0.0
     
     return -np.inf
@@ -288,3 +288,25 @@ for yi in range(naxes):
         ax.axhline(median[yi], color=mediancolor)
         ax.plot(median[xi], median[yi], color = mediancolor, marker = 's')
 figcorn.savefig(rootpath + '/plotsmc/0001_10M_interpolated_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)
+figband = plt.figure(figsize = (12, 9))
+#Plot the 1-sigma_percentile
+for j in range(len(samples)):
+    sample = samples[j]
+    if np.all(onesig_bounds[0] <= sample) and np.all(sample <= onesig_bounds[1]):
+        plt.plot(timespan_new, model_dv(sample).real, "#79CAF2", alpha=0.3)
+    
+plt.plot(timespan_new, gwdatanew_re, "k", alpha=0.7, lw=2, label=r'NR_re')
+plt.plot(timespan_new, modelfitpk.real, "r", alpha=0.7, lw=2, label=r'FitMCmax_re')
+plt.title(r'Comparison of the MC fit data and the $1-\sigma$ error band')
+plt.legend()
+plt.xlabel("t")
+plt.ylabel("h")
+
+figband.savefig(rootpath + '/plotsmc/0001_10M_interpolated_waveform_wandt_'+'nmax'+str(nmax)+'_tshift'+str(tshift)+'_'+str(nwalkers)+'walkers_'+str(npoints)+'pts.png', format = 'png', dpi = 384, bbox_inches = 'tight')
+