observed = []
observednonoise = []
observedflux = []
scatt = []
#
# plt.subplot(2,2,j+1)
## plt.subplot(2,1,j+1)
# plt.subplots_adjust(hspace=.2)
#
for i in range(0, len(taubins)):
scat = psr.psrscatter(
bins, psr.broadfunc(bins, taubins[i]), psr.pulsetrain(trainlength, bins, profile_intr_norm[i]), taubins[i]
)
scatt.append(scat)
climb, observed_nonoise, flux = psr.extractpulse(scat, 2, P)
peak = np.max(observed_nonoise)
noise = np.random.normal(0, peak / snr, P)
observedadd = observed_nonoise + noise
observed.append(observedadd)
observedflux.append(flux)
observednonoise.append(observed_nonoise)
# # plt.plot(nurange,observedflux,linewidth=2.0,label=r'$\sigma_{x,y} = %d$ mas. $\tau_{max} = %.1f$' % (k1, tauval[0]))
## plt.plot(1000*nurange,observedflux,linewidth=2.0,label=r'$D_s/D = %.1f$, $\tau_{max} = %.1f$' % (Dsval/Dval,tauval[0]))
# plt.plot(1000*nurange,observedflux,linewidth=2.0,label=r'$D_s/D = %.1f$' % (Dsval/Dval))
# plt.title('D = %.0f kpc' %Dval)
# plt.legend(loc = 'best',fontsize=14)
## plt.xlabel(r'$\nu$ (MHz)',fontsize=16)
# plt.ylabel('normalized flux',fontsize=16)
# plt.xticks(fontsize=14)
# plt.yticks(fontsize=14)
##Create observed pulses by scattering a pulsetrain with an exponential broadening function
observed = []
observednonoise = []
observedflux = []
scatt = []
# plt.subplot(2,2,j+1)
plt.subplot(2,1,j+1)
plt.subplots_adjust(hspace=.2)
#
for i in range(0,len(taubins)):
scat = psr.psrscatter(bins, psr.broadfunc(bins,taubins[i]),psr.pulsetrain(trainlength, bins, profile_intr_norm[i]),taubins[i])
scatt.append(scat)
climb, observed_nonoise, flux = psr.extractpulse(scat, 2, P)
peak = np.max(observed_nonoise)
noise = np.random.normal(0,peak/snr,P)
observedadd = observed_nonoise + noise
observed.append(observedadd)
observedflux.append(flux)
observednonoise.append(observed_nonoise)
# plt.plot(nurange,observedflux,linewidth=2.0,label=r'$\sigma_{x,y} = %d$ mas. $\tau_{max} = %.1f$' % (k1, tauval[0]))
# plt.plot(1000*nurange,observedflux,linewidth=2.0,label=r'$D_s/D = %.1f$, $\tau_{max} = %.1f$' % (Dsval/Dval,tauval[0]))
plt.plot(1000*nurange,observedflux,linewidth=2.0,label=r'$D_s/D = %.1f$' % (Dsval/Dval))
plt.title('D = %.0f kpc' %Dval, fontsize=20)
plt.legend(loc = 'best',fontsize=16)
# plt.xlabel(r'$\nu$ (MHz)',fontsize=16)
plt.ylabel('normalized flux',fontsize=20)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)