def GxETrain(x,mu,sigma, A, tau):
#This model convolves a pulsetrain of length 20 with a broadening function defined over several P's (lf*P)
#It extracts one of the last convolved profiles, subtracts the climbed baseline and then adds noise to it
bins, profile = psr.makeprofile(nbins = P, ncomps = 1, amps = A, means = mu, sigmas = sigma)
binstau = np.linspace(1,P,P) #Tested: having a longer exp here makes no difference
scat = psr.psrscatter(psr.broadfunc(binstau,tau),psr.pulsetrain(3, bins, profile))
climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 2, P)
return observed_nonoise
def GxETrain(x,mu,sigma, A, tau):
#This model convolves a pulsetrain with a broadening function
#It extracts one of the last convolved profiles, subtracts the climbed baseline and then adds noise to it
bins, profile = psr.makeprofile(nbins = P, ncomps = 1, amps = A, means = mu, sigmas = sigma)
binstau = np.linspace(1,P,P)
scat = psr.psrscatter(psr.broadfunc(binstau,tau),psr.pulsetrain(3, bins, profile))
# plt.figure()
# plt.plot(scat,'r')
climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 2, P)
return observed_nonoise
observednonoise = []
observedflux = []
observedfluxfixed = []
scatt = []
#scintt = []
results = []
xsec = np.linspace(0,pulseperiod,P)
#np.savetxt('Pythondata/photonsfreq.txt',photonsfreq)
for i in range(len(nurange)):
scat = psr.psrscatter(probfreq[i],psr.pulsetrain(3, bins, profile_intr_norm[i]))
scatt.append(scat)
# climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 2, P)
climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 0, P)
observedflux.append(flux)
observednonoise.append(observed_nonoise)
#sys.exit()
# the flux as produced by psr.extractpulse is normalised such that the lowest frequency has flux 1
# Profiles:
plt.figure()
for i in range(len(nurange)):
observedfluxfix = observednonoise[i]*fluxtr[i]
# plt.figure()
## plt.plot(xsec,observedfluxfix/np.max(observedfluxfix),'b',linewidth = 4.0)
## plt.plot(scatt[i]/np.max(scatt[i]),'m',linewidth = 4.0)
observedflux = []
observedfluxfixed = []
scatt = []
#scintt = []
xsec = np.linspace(0,pulseperiod,P)
#np.savetxt('Pythondata/photonsfreq.txt',photonsfreq)
for i in range(len(nurange)):
# scat = psr.psrscatter(probfreq[i],psr.pulsetrain(3, bins, profile_intr_norm[i]))
# scat = psr.psrscatter(photonsinterpp[i],epn_norm[i])
scat = psr.psrscatter(probfreq[i],epn_norm_interpp[i])
scatt.append(scat)
# climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 2, P)
climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 0, P)
# observedflux.append(flux)
observednonoise.append(observed_nonoise)
np.savetxt("Pythondata/EPN123725/Profiles_120_220.txt",observednonoise)
#sys.exit()
# the flux as produced by psr.extractpulse is normalised such that the lowest frequency has flux 1
# Profiles:
#plt.figure()
for i in range(len(nurange)):
# observedfluxfix = observednonoise[i]*fluxtr[i]
plt.figure()
# obtainedtau2=[]
# obtainedtaustd2=[]
obtainedchecktau = []
obtainedchecktaustd = []
# plt.figure(1)
for k in range(0,len(taubins)):
trainlength = int(10*taubins[k]/P)
if trainlength < 5:
trainlength = 5
print ''
print '%.3f GHz' % nurange[k]
print 'trainlength: '+str(trainlength)
# scat = psr.psrscatter(psr.broadfunc(xaxlong,taubins[k]),psr.pulsetrain(trainlength, bins, profile_intr_norm[k]))
scat = psr.psrscatter(psr.broadfunc2(xax,taubins[k],taubins2[k]),psr.pulsetrain(3, bins, profile_intr_norm[k]))
scatt.append(scat)
climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 2, P)
peak = np.max(observed_nonoise)
noise = np.random.normal(0,peak/snr,P)
observedadd = climb + noise
checkfloor = observed_nonoise + noise
observed.append(observedadd)
observedflux.append(flux)
observednonoise.append(observed_nonoise)
checkfloors.append(checkfloor)
## Method 1: Create a median filter around the absolute minimum, to find minimum that represents the underlying noiseless minimum well.
# idmin = np.argmin(observedadd)
