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Commit 02cd2c40 authored by frcojimenez's avatar frcojimenez
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python notebook

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code_new/NR_dynesty_t0_loop.py
+ 41
− 35
View file @ 02cd2c40
...@@ -82,7 +82,7 @@ try: ...@@ -82,7 +82,7 @@ try:
parser.sections() parser.sections()
except SystemExit: except SystemExit:
parser = ConfigParser() parser = ConfigParser()
parser.read('config_n2_q10.ini') parser.read('config_fixed_n1_m_af.ini')
parser.sections() parser.sections()
pass pass
...@@ -331,26 +331,26 @@ def QNM_Berti(mf,af,l,m): ...@@ -331,26 +331,26 @@ def QNM_Berti(mf,af,l,m):
tau_ma_a=[None]*(nmax+1) tau_ma_a=[None]*(nmax+1)
for i in range(nmax+1): for i in range(nmax+1):
qnm=rdowndata[0,1:3,position] qnm=rdowndata[i,1:3,position]
w_m_a[i] = qnm[0]/mf w_m_a[i] = qnm[0]/mf
tau_ma_a[i] = -1/(qnm[1])*mf tau_ma_a[i] = -1/(qnm[1])*mf
return w_m_a, tau_ma_a return w_m_a, tau_ma_a
# In[14]: # In[15]:
np.sqrt(12/2*1/tauRD_to_t_NR(1.3*10**-47,70)*(5*10**(-21))**2) np.sqrt(12/2*1/tauRD_to_t_NR(1.3*10**-47,70)*(5*10**(-21))**2)
# In[15]: # In[16]:
np.sqrt(0.004/2*1/(1.3*10**-47)*(5*10**(-21))**2) np.sqrt(0.004/2*1/(1.3*10**-47)*(5*10**(-21))**2)
# In[16]: # In[17]:
gw = {} gw = {}
...@@ -406,7 +406,7 @@ elif nr_code=='LaZeV': ...@@ -406,7 +406,7 @@ elif nr_code=='LaZeV':
times=times_1 times=times_1
# In[17]: # In[18]:
if nr_code=='SXS': if nr_code=='SXS':
...@@ -436,7 +436,7 @@ tmax5=FindTmaximum(gw5_sxs_bbh_0305[round(len(gw_sxs_bbh_0305)/2):]) ...@@ -436,7 +436,7 @@ tmax5=FindTmaximum(gw5_sxs_bbh_0305[round(len(gw_sxs_bbh_0305)/2):])
times5 = times5 - tmax5 times5 = times5 - tmax5
# In[18]: # In[19]:
if parser.has_option('setup','qnm_model'): if parser.has_option('setup','qnm_model'):
...@@ -449,7 +449,7 @@ else: ...@@ -449,7 +449,7 @@ else:
w , tau = QNM_spectrum(mf,af,2,2) w , tau = QNM_spectrum(mf,af,2,2)
# In[19]: # In[20]:
# loading priors # loading priors
...@@ -509,7 +509,7 @@ elif model == 'w-tau-fixed-m-af': ...@@ -509,7 +509,7 @@ elif model == 'w-tau-fixed-m-af':
priors=np.column_stack((priors_min,priors_max)) priors=np.column_stack((priors_min,priors_max))
# In[20]: # In[21]:
#Select the data from 0 onwards #Select the data from 0 onwards
...@@ -521,7 +521,7 @@ timesrd=gw_sxs_bbh_0305[position:-1][:,0][:]-tmax ...@@ -521,7 +521,7 @@ timesrd=gw_sxs_bbh_0305[position:-1][:,0][:]-tmax
timesrd5=gw5_sxs_bbh_0305[position5:-1][:,0][:]-tmax5 timesrd5=gw5_sxs_bbh_0305[position5:-1][:,0][:]-tmax5
# In[21]: # In[22]:
#Test plot real part (data was picked in the last cell). Aligning in time #Test plot real part (data was picked in the last cell). Aligning in time
...@@ -533,13 +533,13 @@ plt.plot(timesrd5, np.sqrt(gw_sxs_bbh_0305rd5[:,1]**2+gw_sxs_bbh_0305rd5[:,2]**2 ...@@ -533,13 +533,13 @@ plt.plot(timesrd5, np.sqrt(gw_sxs_bbh_0305rd5[:,1]**2+gw_sxs_bbh_0305rd5[:,2]**2
plt.legend() plt.legend()
# In[22]: # In[23]:
#[plt.errorbar(csv_data_fixed[i]['t_shift'], -csv_data_fixed[i]['dlogz'], yerr=csv_data_fixed[i]['dlogz_err'], fmt='o',color=colors[i],label =tags_fixed[i]) for i in range(len(csv_data_fixed))] #[plt.errorbar(csv_data_fixed[i]['t_shift'], -csv_data_fixed[i]['dlogz'], yerr=csv_data_fixed[i]['dlogz_err'], fmt='o',color=colors[i],label =tags_fixed[i]) for i in range(len(csv_data_fixed))]
# In[23]: # In[24]:
gwnew_re = interpolate.interp1d(timesrd, gw_sxs_bbh_0305rd[:,1], kind = 'cubic') gwnew_re = interpolate.interp1d(timesrd, gw_sxs_bbh_0305rd[:,1], kind = 'cubic')
...@@ -549,7 +549,7 @@ gwnew_re5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,1], kind = 'cubi ...@@ -549,7 +549,7 @@ gwnew_re5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,1], kind = 'cubi
gwnew_im5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,2], kind = 'cubic') gwnew_im5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,2], kind = 'cubic')
# In[24]: # In[25]:
if timesrd5[-1]>= timesrd[-1]: if timesrd5[-1]>= timesrd[-1]:
...@@ -566,7 +566,7 @@ gwdatanew = gwdatanew_re - 1j*gwdatanew_im ...@@ -566,7 +566,7 @@ gwdatanew = gwdatanew_re - 1j*gwdatanew_im
gwdatanew5 = gwdatanew_re5- 1j*gwdatanew_im5 gwdatanew5 = gwdatanew_re5- 1j*gwdatanew_im5
# In[25]: # In[26]:
#taus, corr= twopoint_autocovariance(timesrd,gwdatanew-gwdatanew5) #taus, corr= twopoint_autocovariance(timesrd,gwdatanew-gwdatanew5)
...@@ -578,7 +578,7 @@ gwdatanew5 = gwdatanew_re5- 1j*gwdatanew_im5 ...@@ -578,7 +578,7 @@ gwdatanew5 = gwdatanew_re5- 1j*gwdatanew_im5
#taus[index] #taus[index]
# In[26]: # In[27]:
mismatch=1-EasyMatchT(timesrd_final,gwdatanew,gwdatanew5,0,0+90) mismatch=1-EasyMatchT(timesrd_final,gwdatanew,gwdatanew5,0,0+90)
...@@ -588,7 +588,7 @@ print('mismatch:', mismatch) ...@@ -588,7 +588,7 @@ print('mismatch:', mismatch)
print('snr:', EasySNRT(timesrd_final,gwdatanew,gwdatanew,0,0+90)/error**2) print('snr:', EasySNRT(timesrd_final,gwdatanew,gwdatanew,0,0+90)/error**2)
# In[27]: # In[28]:
if error_str and error_val==0: if error_str and error_val==0:
...@@ -601,7 +601,7 @@ if error_str and error_val==0: ...@@ -601,7 +601,7 @@ if error_str and error_val==0:
plt.legend() plt.legend()
# In[28]: # In[29]:
if parser.has_option('rd-model','phase_alignment'): if parser.has_option('rd-model','phase_alignment'):
...@@ -610,7 +610,7 @@ else: ...@@ -610,7 +610,7 @@ else:
phase_alignment=False phase_alignment=False
# In[29]: # In[30]:
# Phase alignement # Phase alignement
...@@ -637,7 +637,7 @@ if phase_alignment: ...@@ -637,7 +637,7 @@ if phase_alignment:
EasySNRT(timesrd_final,gwdatanew,gwdatanew5/error,0,0+90) EasySNRT(timesrd_final,gwdatanew,gwdatanew5/error,0,0+90)
# In[30]: # In[31]:
#Test the new interpolated data #Test the new interpolated data
...@@ -649,7 +649,7 @@ if error_str and error_val==0: ...@@ -649,7 +649,7 @@ if error_str and error_val==0:
plt.legend() plt.legend()
# In[31]: # In[32]:
#Test the error data #Test the error data
...@@ -659,7 +659,7 @@ if error_str and error_val==0: ...@@ -659,7 +659,7 @@ if error_str and error_val==0:
plt.legend() plt.legend()
# In[32]: # In[33]:
#Test the error data #Test the error data
...@@ -671,7 +671,7 @@ if error_str and error_val==0: ...@@ -671,7 +671,7 @@ if error_str and error_val==0:
plt.legend() plt.legend()
# In[33]: # In[34]:
#Take the piece of waveform you want #Take the piece of waveform you want
...@@ -690,7 +690,7 @@ else: ...@@ -690,7 +690,7 @@ else:
error_tsh=1 error_tsh=1
# In[34]: # In[35]:
...@@ -699,7 +699,7 @@ plt.xlabel(r'$t/M$') ...@@ -699,7 +699,7 @@ plt.xlabel(r'$t/M$')
plt.ylabel(r'$r \, h_+$') plt.ylabel(r'$r \, h_+$')
# In[35]: # In[36]:
#Fitting #Fitting
...@@ -799,7 +799,7 @@ dict = {'w-tau': model_dv_tau , 'w-q': model_dv_q, 'w-tau-fixed': model_dv,'w-ta ...@@ -799,7 +799,7 @@ dict = {'w-tau': model_dv_tau , 'w-q': model_dv_q, 'w-tau-fixed': model_dv,'w-ta
dict_omega = {'berti': QNM_Berti , 'qnm': QNM_spectrum} dict_omega = {'berti': QNM_Berti , 'qnm': QNM_spectrum}
# In[36]: # In[37]:
nll = lambda *args: -log_likelihood(*args) nll = lambda *args: -log_likelihood(*args)
...@@ -817,7 +817,7 @@ else: ...@@ -817,7 +817,7 @@ else:
vars_ml=soln.x vars_ml=soln.x
# In[37]: # In[38]:
mypool = Pool(nbcores) mypool = Pool(nbcores)
...@@ -1007,39 +1007,45 @@ fg, ax = dyplot.cornerplot(res, color='blue', ...@@ -1007,39 +1007,45 @@ fg, ax = dyplot.cornerplot(res, color='blue',
) )
# In[52]: # In[93]:
if not eval(overwrite): if not eval(overwrite):
fg.savefig(corner_plot, format = 'png', bbox_inches = 'tight') fg.savefig(corner_plot, format = 'png', bbox_inches = 'tight')
if model == 'w-tau-fixed-m-af': if model == 'w-tau-fixed-m-af':
truths=np.concatenate((w,tau))
fmass_spin=(samps.T)[-2:].T
fmass_spin_dist=[None]*len(fmass_spin)
labels_mf = np.concatenate((w_lab,tau_lab))
for i in range(len(fmass_spin)):
fmass_spin_dist[i]=np.concatenate(dict_omega[qnm_model](fmass_spin[i,0],fmass_spin[i,1],2,2))
figure = corner.corner(fmass_spin_dist,truths=truths,quantiles=[0.05,0.95],labels=labels_mf,smooth=True,color='b',truth_color='r')
figure.savefig(corner_plot_extra, format = 'png', bbox_inches = 'tight') figure.savefig(corner_plot_extra, format = 'png', bbox_inches = 'tight')
# In[64]: # In[94]:
from dynesty import plotting as dyplot
lnz_truth = ndim * -np.log(2 * 10.) # analytic evidence solution lnz_truth = ndim * -np.log(2 * 10.) # analytic evidence solution
fig, axes = dyplot.runplot(res, lnz_truth=lnz_truth) fig, axes = dyplot.runplot(res, lnz_truth=lnz_truth)
fig.tight_layout() fig.tight_layout()
# In[54]: # In[95]:
if not eval(overwrite): if not eval(overwrite):
fig.savefig(diagnosis_plot, format = 'png', dpi = 384, bbox_inches = 'tight') fig.savefig(diagnosis_plot, format = 'png', dpi = 384, bbox_inches = 'tight')
# In[55]: # In[101]:
figband = plt.figure(figsize = (12, 9)) 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,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'$fit$') plt.plot(timesrd_final_tsh,dict[model](vars_ml).real,'bo', alpha=0.9, lw=3, label=r'$fit$')
onesig_bounds = np.array([np.percentile(samps[:, i], [16, 84]) for i in range(len(samps[0]))]).T onesig_bounds = np.array([np.percentile(samps[:, i], [5, 95]) for i in range(len(samps[0]))]).T
samples_1sigma = filter(lambda sample: np.all(onesig_bounds[0] <= sample) and np.all(sample <= onesig_bounds[1]), samps) 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] samples_1sigma_down = list(samples_1sigma)[::downfactor]
for sample in samples_1sigma_down: for sample in samples_1sigma_down:
...@@ -1051,14 +1057,14 @@ plt.ylabel('h') ...@@ -1051,14 +1057,14 @@ plt.ylabel('h')
plt.show() plt.show()
# In[56]: # In[99]:
if not eval(overwrite): if not eval(overwrite):
figband.savefig(fit_plot) figband.savefig(fit_plot)
# In[57]: # In[100]:
if not eval(overwrite): if not eval(overwrite):
......
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