From 33cc86a7e5b9d06f653cdde2b10200763a719374 Mon Sep 17 00:00:00 2001
From: Rayne Liu <rayne.liu@atlas1>
Date: Wed, 14 Oct 2020 18:08:39 +0000
Subject: [PATCH] Atlas probably should not run the waveform band plot together
---
code/Mock_Interpolate-0001_t_10M_wandt.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/code/Mock_Interpolate-0001_t_10M_wandt.py b/code/Mock_Interpolate-0001_t_10M_wandt.py
index c6801a9..365a5a6 100755
--- a/code/Mock_Interpolate-0001_t_10M_wandt.py
+++ b/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')
-
+"""
--
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