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
def GxESingleFold(x,mu,sigma,A,tau,trainlength):
#This model takes a single Guassian pulse with mean mu and sigma
#Convolves it with a broadening function
#It extracts one of the last convolved profiles subtracts the climbed baseline and then adds noise to it
observed_postfold = np.zeros(P)
bins, profile = psr.makeprofile(nbins = P, ncomps = 1, amps = A, means = mu, sigmas = sigma)
binstau = np.linspace(1,trainlength*P,trainlength*P)
scat = psr.psrscatterpostfold(psr.broadfunc(binstau,tau),psr.pulsetrain(1, bins, profile))
climb, observed_nonoise, rec, flux = psr.extractpulse(scat, 0, trainlength*P)
for i in range(trainlength*P):
observed_postfold[np.mod(i,P)] += observed_nonoise[i]
GxESingleFold = observed_postfold[x]-np.min(observed_postfold[0:P])
return GxESingleFold
P = int(pulseperiod/binstimeres) m = float(P/4) #Let the mean of the pulse be at a 1/4 overall bins w50 = float((dutycycle/100)*P) #FWHM s = w50/(2*np.sqrt(2*np.log(2))) #sigma calculated through the dutycycle a = 1 #amplitude. at some point this will be replaced by the intrinsic spectrum. trainlength = 20 ## Intrinsic profile bins, profile = psr.makeprofile(nbins = P, ncomps = 1, amps = a, means = m, sigmas = s) xaxlong = np.linspace(1,20*P,20*P) #xaxlong = np.linspace(1,100*P,100*P) spectralindex = 1.6 #Input spectral index as a postivie number, for nu^-alpha profile_intr = psr.profilespec(nurange,spectralindex,profile) profile_intr_norm = profile_intr/np.sum(profile_intr[0]) #Normalised such that the max intrinsic flux = 1.0. That is the intrinsice pulse at the lowest frequency has flux = 1 ############################################################################### ## ISOTROPIC ############################################################################### ## In the case of importing data/broadening functions these parameters will only play a roll in plotting the alpha = 4.0 spectrum to compare ## In the case where I simulate the data to fit here, these paramteres will dicatate the shape of the broadening function Dval, Dsval = float(DD),float(Dss)
