Skip to content
Snippets Groups Projects
Commit fe0a08c3 authored by frcojimenez's avatar frcojimenez
Browse files

added the cte error option

parent 15981225
No related branches found
No related tags found
No related merge requests found
Expand all Show whitespace changes
Inline Side-by-side
code_new/NR_dynesty_t0_loop.ipynb
+ 326
− 137
View file @ fe0a08c3
This diff is collapsed.
code_new/NR_dynesty_t0_loop.py 100755 → 100644
+ 114
− 218
View file @ fe0a08c3
#!/usr/bin/env python
# coding: utf-8
# In[48]:
#Import relevant modules, import data and all that
import numpy as np
from scipy import interpolate
......@@ -44,14 +38,10 @@ try:
parser.sections()
except SystemExit:
parser = ConfigParser()
parser.read('config.ini')
parser.read('config_n1.ini')
parser.sections()
pass
# In[49]:
# path
rootpath=parser.get('nr-paths','rootpath')
......@@ -66,18 +56,10 @@ overwrite = parser.get('setup','overwrite')
downfactor = np.int(parser.get('setup','plot_down_factor'))
sampler = parser.get('setup','sampler')
# In[50]:
if not os.path.exists(output_folder):
os.mkdir(output_folder)
print("Directory " , output_folder , " Created ")
# In[51]:
# time config
tshift=parser.get('time-setup','tshift')
......@@ -89,10 +71,6 @@ tend = np.float(tend)
t_align=parser.get('time-setup','t_align')
t_align = np.float(t_align)
# In[52]:
# n-tones & nlive
nmax=parser.get('n-tones','nmax')
......@@ -101,13 +79,17 @@ nmax = np.int(nmax)
npoints=parser.get('n-live-points','npoints')
npoints = np.int(npoints)
# In[81]:
# model
model=parser.get('rd-model','model')
error_str = eval(parser.get('rd-model','error_str'))
if error_str:
error_val=np.float(parser.get('rd-model','error_val'))
if error_val==0:
error_type='NR_estimate'
else:
error_type=error_val
if model == 'w-tau':
tau_var_str='tau'
elif model == 'w-q':
......@@ -119,35 +101,26 @@ print('model:',model)
print('nmax:',nmax)
print('tshift:',tshift)
print('error:', error_str)
print('error value:',error_type)
# In[83]:
if error_str:
output_folder_1=output_folder+'/'+model+'-nmax'+str(nmax)+'_'+str(error_str)+'_'+str(error_type)
else:
output_folder_1=output_folder+'/'+model+'-nmax'+str(nmax)+'_'+str(error_str)
if not os.path.exists(output_folder_1):
os.mkdir(output_folder_1)
print("Directory " , output_folder_1 , " Created ")
# In[84]:
corner_plot=output_folder_1+'/Dynesty_'+str(simulation_number)+'_'+model+'_nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'corner_plot.png'
diagnosis_plot=output_folder_1+'/Dynesty_diagnosis'+str(simulation_number)+'_'+model+'_nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'.png'
fit_plot=output_folder_1+'/Fit_results_'+str(simulation_number)+'tshift_'+str(tshift)+'_'+model+'_nmax_'+str(nmax)+'.png'
# In[85]:
samples_file=output_folder_1+'/posterior_samples-'+str(simulation_number)+'tshift_'+str(tshift)+'_'+model+'_nmax_'+str(nmax)+'.csv'
sumary_data = output_folder_1+'/summary'+str(simulation_number)+'_'+model+'_nmax_'+str(nmax)+'.csv'
best_data=output_folder_1+'/best_values_'+str(simulation_number)+'_'+model+'_nmax_'+str(nmax)+'.csv'
# In[86]:
# loading priors
w_mins=np.empty(nmax+1)
w_maxs=np.empty(nmax+1)
......@@ -193,10 +166,6 @@ if model == 'w-tau-fixed':
priors_max = np.concatenate((a_maxs,ph_maxs))
prior_dim = len(priors_min)
# In[87]:
vary_fund = True
#sampler parameters
......@@ -222,8 +191,8 @@ def EasyMatchT(t,h1,h2,tmin,tmax):
#Computes the match for complex waveforms
pos = np.argmax(t >= (tmin));
h1red=h1[pos:-1];
h2red=h2[pos:-1];
h1red=h1[pos:];
h2red=h2[pos:];
norm1=np.sum(np.abs(h1red)**2)
norm2=np.sum(np.abs(h2red)**2)
......@@ -233,6 +202,18 @@ def EasyMatchT(t,h1,h2,tmin,tmax):
return res
def EasySNRT(t,h1,h2,tmin,tmax):
#Computes the match for complex waveforms
pos = np.argmax(t >= (tmin));
h1red=h1[pos:];
h2red=h2[pos:];
myTable=h1red*np.conjugate(h2red)
res=2*np.sqrt((np.sum(myTable)).real)
return res
def wRD_to_f_Phys(f,M):
c=2.99792458*10**8;G=6.67259*10**(-11);MS=1.9885*10**30;
return (c**3/(M*MS*G*2*np.pi))*f
......@@ -242,8 +223,16 @@ def tauRD_to_t_Phys(tau,M):
return ((M*MS*G)/c**3)*tau
# In[88]:
def twopoint_autocovariance(t,n):
#Computes the match for complex waveforms
dt=t[1]-t[0]
res = np.zeros(len(n))
taus = np.zeros(len(n))
for tau in range(0,int(len(n)/2)):
ntau=np.roll(n, tau)
taus[tau] = t[tau]
res[tau]=np.sum(n*ntau).real
return (taus[:int(len(n)/2)],res[:int(len(n)/2)])
#This loads the 22 mode data
gw = {}
......@@ -273,21 +262,13 @@ times5 = gw5_sxs_bbh_0305[:,0]
tmax5=FindTmaximum(gw5_sxs_bbh_0305)
times5 = times5 - tmax5
# In[89]:
#Select the data from 0 onwards
position = np.argmax(times >= (t_align))
position5 = np.argmax(times5 >= (t_align))
gw_sxs_bbh_0305rd=gw_sxs_bbh_0305[position+1:-1]
gw_sxs_bbh_0305rd5=gw5_sxs_bbh_0305[position5+1:-1]
timesrd=gw_sxs_bbh_0305[position:-1][:,0][:-1]-tmax
timesrd5=gw5_sxs_bbh_0305[position5:-1][:,0][:-1]-tmax5
# In[90]:
gw_sxs_bbh_0305rd=gw_sxs_bbh_0305[position+1:]
gw_sxs_bbh_0305rd5=gw5_sxs_bbh_0305[position5+1:]
timesrd=gw_sxs_bbh_0305[position:-1][:,0][:]-tmax
timesrd5=gw5_sxs_bbh_0305[position5:-1][:,0][:]-tmax5
#Test plot real part (data was picked in the last cell). Aligning in time
plt.figure(figsize = (12, 8))
......@@ -297,10 +278,6 @@ plt.plot(timesrd5, gw_sxs_bbh_0305rd5[:,1], "b", alpha=0.3, lw=3, label=r'$Lev5:
plt.plot(timesrd5, np.sqrt(gw_sxs_bbh_0305rd5[:,1]**2+gw_sxs_bbh_0305rd5[:,2]**2), "b", alpha=0.3, lw=3, label=r'$Lev5\,amp$')
plt.legend()
# In[91]:
#Test plot im part (data was picked in the last cell). Aligning in time
plt.figure(figsize = (12, 8))
plt.plot(timesrd, gw_sxs_bbh_0305rd[:,2], "r", alpha=0.3, lw=3, label=r'$Lev6: imag$')
......@@ -309,29 +286,17 @@ plt.plot(timesrd5, gw_sxs_bbh_0305rd5[:,2], "b", alpha=0.3, lw=3, label=r'$Lev5:
plt.plot(timesrd5, np.sqrt(gw_sxs_bbh_0305rd5[:,1]**2+gw_sxs_bbh_0305rd5[:,2]**2), "b", alpha=0.3, lw=3, label=r'$Lev5\,amp$')
plt.legend()
# In[92]:
# Depending on nmax, you load nmax number of freqs. and damping times from the qnm package
omegas = [qnm.modes_cache(s=-2,l=2,m=2,n=i)(a=af)[0] for i in range (0,dim)]
w = (np.real(omegas))/mf
tau=-1/(np.imag(omegas))*mf
# In[93]:
gwnew_re = interpolate.interp1d(timesrd, gw_sxs_bbh_0305rd[:,1], kind = 'cubic')
gwnew_im = interpolate.interp1d(timesrd, gw_sxs_bbh_0305rd[:,2], kind = 'cubic')
gwnew_re5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,1], kind = 'cubic')
gwnew_im5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,2], kind = 'cubic')
# In[94]:
if timesrd5[-1]>= timesrd[-1]:
timesrd_final = timesrd
else:
......@@ -345,17 +310,36 @@ gwdatanew_im5 = gwnew_im5(timesrd_final)
gwdatanew = gwdatanew_re - 1j*gwdatanew_im
gwdatanew5 = gwdatanew_re5- 1j*gwdatanew_im5
# In[95]:
taus, corr= twopoint_autocovariance(timesrd,gwdatanew-gwdatanew5)
plt.figure(figsize = (12, 8))
plt.plot(taus, corr,'ro')
plt.show()
vmax=np.max(corr)
index = np.argmax(corr == vmax)
taus[index]
mismatch=1-EasyMatchT(timesrd_final,gwdatanew,gwdatanew5,0,0+90)
error=np.sqrt(2*mismatch)
print(mismatch)
print('error estimate:',error)
print('mismatch:', mismatch)
print('snr:', EasySNRT(timesrd_final,gwdatanew,gwdatanew,0,0+90)/error**2)
# In[96]:
if error_str and error_val==0:
error = np.sqrt(gwdatanew*gwdatanew-2*gwdatanew*gwdatanew5+gwdatanew5*gwdatanew5)
error_est=np.sqrt(error.imag**2+error.real**2)
plt.figure(figsize = (12, 8))
plt.plot(timesrd_final, gwdatanew.real, "r", alpha=0.3, lw=2, label='Lev6')
plt.plot(timesrd_final, gwdatanew5.real, "b", alpha=0.3, lw=2, label='Lev5')
plt.plot(timesrd_final, error_est, "b", alpha=0.3, lw=2, label='error')
plt.legend()
if error_str and error_val==0:
error = np.sqrt(gwdatanew*gwdatanew-2*gwdatanew*gwdatanew5+gwdatanew5*gwdatanew5)
error_est=np.sqrt(error.imag**2+error.real**2)
plt.figure(figsize = (12, 8))
plt.xlim(0,80)
plt.plot(timesrd_final, np.abs(gwdatanew)/error_est, "r", alpha=0.3, lw=2, label='Lev6')
plt.legend()
# Phase alignement
phas = np.angle(gwdatanew)
......@@ -365,10 +349,6 @@ phas5 = np.unwrap(phas5)
plt.plot(timesrd_final, phas, "r", alpha=0.3, lw=3, label=r'$phase$')
plt.plot(timesrd_final, phas5, "blue", alpha=0.3, lw=3, label=r'$phase$')
# In[97]:
position = np.argmax(timesrd_final >= (t_align))
dphase = phas5[position]-phas[position]
print(dphase)
......@@ -384,44 +364,40 @@ phas5 = np.unwrap(phas5)
plt.plot(timesrd_final, phas, "r", alpha=0.3, lw=3, label=r'$phase$')
plt.plot(timesrd_final, phas5, "blue", alpha=0.3, lw=3, label=r'$phase$')
# In[98]:
mismatch=1-EasyMatchT(timesrd_final,gwdatanew,gwdatanew5,0,+90)
print(mismatch)
error=np.sqrt(2*mismatch)
print('error estimate:',error)
print('mismatch:', mismatch)
print('snr:', EasySNRT(timesrd_final,gwdatanew,gwdatanew5,0,0+90)/error)
if error_str:
error = np.sqrt(gwdatanew*gwdatanew-2*gwdatanew*gwdatanew5+gwdatanew5*gwdatanew5)
error_est=np.sqrt(error.imag**2+error.real**2)
else :
error = 1
# In[100]:
EasySNRT(timesrd_final,gwdatanew,gwdatanew5/error,0,0+90)
#Test the new interpolated data
if error_str:
if error_str and error_val==0:
plt.figure(figsize = (12, 8))
plt.plot(timesrd_final, gwdatanew.real, "r", alpha=0.3, lw=2, label='Lev6')
plt.plot(timesrd_final, gwdatanew5.real, "b", alpha=0.3, lw=2, label='Lev5')
plt.plot(timesrd_final, error.real, "b", alpha=0.3, lw=2, label='error')
plt.legend()
# In[101]:
#Test the error data
if error_str:
if error_str and error_val==0:
plt.figure(figsize = (12, 8))
plt.plot(timesrd_final, error.real, "b", alpha=0.3, lw=2, label='error real')
plt.plot(timesrd_final, error.imag, "r", alpha=0.3, lw=2, label='error imag')
plt.plot(timesrd_final, np.sqrt(error.imag**2+error.real**2), "r", alpha=0.3, lw=2, label='all error')
plt.legend()
# In[103]:
#Test the error data
if error_str and error_val==0:
plt.figure(figsize = (12, 8))
plt.xlim(1,40)
plt.ylim(-300,300)
plt.plot(timesrd_final,gwdatanew.real/np.sqrt(error.imag**2+error.real**2), "r", alpha=0.3, lw=2, label='all error')
plt.legend()
#Take the piece of waveform you want
position_in = np.argmax(timesrd_final >= tshift)
......@@ -429,14 +405,10 @@ position_end = np.argmax(timesrd_final >= tend)
timesrd_final_tsh = timesrd_final[position_in:position_end]
gwdatanew_re_tsh = gwdatanew_re[position_in:position_end]
gwdatanew_im_tsh = gwdatanew_im[position_in:position_end]
if error_str:
if error_str and error_val==0:
error_tsh=error[position_in:position_end]
else:
error_tsh=1
# In[104]:
error_tsh=error_val
#Fitting
#RD model for nmax tones. Amplitudes are in (xn*Exp[i yn]) version. Used here.
......@@ -509,16 +481,8 @@ def log_probability(theta):
return -np.inf
return lp + log_likelihood(theta)
# In[105]:
dict = {'w-tau': model_dv_tau , 'w-q': model_dv_q, 'w-tau-fixed': model_dv}
# In[106]:
#I need to provid an initial guess for 4*(nmax+1) the parameters
np.random.seed(42)
nll = lambda *args: -log_likelihood(*args)
......@@ -529,16 +493,8 @@ print("Maximum likelihood estimates:")
vars_ml=soln.x
print(vars_ml)
# In[107]:
f2=dynesty.NestedSampler(log_likelihood, prior_transform, prior_dim, nlive=npoints,sample=sampler)
f2.run_nested()
# In[108]:
f2.run_nested(dlogz=0.01)
wstr = r'$\omega_'
......@@ -568,10 +524,6 @@ labels = np.concatenate((w_lab,tau_lab,amp_lab,pha_lab))
if model=='w-tau-fixed':
labels = np.concatenate((amp_lab,pha_lab))
# In[109]:
if model=='w-tau-fixed':
rg = (nmax+1)
else:
......@@ -591,32 +543,17 @@ else:
amps_aux = samps_tr[i][half_points:-1]
npamps[i] = np.quantile(amps_aux, 0.5)
# In[110]:
res = f2.results
res.samples_u.shape
res.summary()
samps=f2.results.samples
# In[111]:
evidence = res.logz[-1]
evidence_error = res.logzerr[-1]
# In[112]:
summary_titles=['n','id','t_shift','dlogz','dlogz_err']
# In[113]:
if not eval(overwrite):
if os.path.exists(sumary_data):
outvalues = np.array([[nmax, simulation_number, tshift, evidence,evidence_error]])
else:
......@@ -629,49 +566,30 @@ with open(sumary_data, 'a') as file:
else:
writer.writerow(outvalues[0])
# In[114]:
samps=f2.results.samples
samps_tr=np.transpose(samps)
# In[115]:
sigma_vars_m = np.empty(prior_dim)
sigma_vars_p = np.empty(prior_dim)
sigma_vars = np.empty(prior_dim)
for i in range(prior_dim):
amps_aux = samps_tr[i][half_points:-1]
amps_aux = samps_tr[i][half_points:]
sigma_vars_m[i] = np.quantile(amps_aux, 0.1)
sigma_vars[i] = np.quantile(amps_aux, 0.5)
sigma_vars_p[i] = np.quantile(amps_aux, 0.9)
# In[116]:
sigma_vars_all = [sigma_vars,sigma_vars_m,sigma_vars_p]
sigma_vars_all=np.stack([sigma_vars,sigma_vars_m,sigma_vars_p], axis=0)
# In[117]:
key =['max val','lower bound','higher bound']
dfslist = [pd.DataFrame(np.concatenate(([tshift],sigma_vars_all[i])).reshape((-1,prior_dim+1)), columns=np.concatenate((['tshift'],labels)), index = [key[i]]) for i in range(3)]
df2 = pd.concat(dfslist)
if not eval(overwrite):
if os.path.exists(best_data):
df2.to_csv(best_data, mode='a', header=False,index = True)
else:
df2.to_csv(best_data,index = True)
# In[118]:
if model == 'w-q':
tau_val = np.pi*w*tau
truths = np.concatenate((w,tau_val,npamps))
......@@ -681,10 +599,6 @@ elif model == 'w-tau':
elif model == 'w-tau-fixed':
truths = npamps
# In[119]:
fg, ax = dyplot.cornerplot(res, color='blue',
show_titles=True,
labels=labels,
......@@ -693,32 +607,18 @@ fg, ax = dyplot.cornerplot(res, color='blue',
truth_color='red',
)
# In[121]:
if not eval(overwrite):
fg.savefig(corner_plot, format = 'png', bbox_inches = 'tight')
# In[122]:
from dynesty import plotting as dyplot
lnz_truth = ndim * -np.log(2 * 10.) # analytic evidence solution
fig, axes = dyplot.runplot(res, lnz_truth=lnz_truth)
fig.tight_layout()
# In[123]:
if not eval(overwrite):
fig.savefig(diagnosis_plot, format = 'png', dpi = 384, bbox_inches = 'tight')
# In[124]:
figband = plt.figure(figsize = (12, 9))
plt.plot(timesrd_final_tsh,gwdatanew_re_tsh, "green", alpha=0.9, lw=3, label=r'$res_{240}$')
plt.plot(timesrd_final_tsh,dict[model](vars_ml).real,'bo', alpha=0.9, lw=3, label=r'$fit$')
......@@ -726,22 +626,18 @@ onesig_bounds = np.array([np.percentile(samps[:, i], [16, 84]) for i in range(le
samples_1sigma = filter(lambda sample: np.all(onesig_bounds[0] <= sample) and np.all(sample <= onesig_bounds[1]), samps)
samples_1sigma_down = list(samples_1sigma)[::downfactor]
for sample in samples_1sigma_down:
plt.plot(timesrd_final_tsh, dict[model](sample).real, "r-", alpha=0.04, lw=3)
plt.plot(timesrd_final_tsh, dict[model](sample).real, "r-", alpha=0.1, lw=1)
plt.title(r'Comparison of the MC fit data and the $1-\sigma$ error band')
plt.legend()
plt.xlabel('t')
plt.ylabel('h')
plt.show()
# In[125]:
if not eval(overwrite):
figband.savefig(fit_plot)
# In[ ]:
if not eval(overwrite):
with open(samples_file,'w') as file:
writer = csv.writer(file)
writer.writerow(labels)
writer.writerows(samps[::downfactor])
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment

AEI Privacy Policy Imprint