使用emcee时函数定义中的语法错误

Question

我试图使用emcee模块重新创建一个发行版。这是我的代码：

freq,asd = np.loadtxt('noise.csv',delimiter=',',unpack=True)


psd = asd**2

SNRth = 4.5

d = 600

dm = 0.9
#interpolate!

S = interpolate.interp1d(freq,psd)

def SNR2(chirp,f): 
    return 5*np.pi**(-4/3)*chirp**(5/3)/(96*d**2)*integrate.quad(lambda f: f**(-7/3)/S(f), 20, 1500, limit=1000)[0]

def mp(SNR2, x):
    return integrate.quad(lambda x: np.exp(-0.5*(x+SNR2))*special.iv(0,np.sqrt(x*SNR2)),0,SNRth**2)

def pp(SNR2, x):
    return 0.5*np.exp(-0.5*(SNRth**2+SNR2))*special.iv(0,np.sqrt(x*SNR2))

def mm(SNR2, x):
    return 1-np.exp(-SNRth**2/2)

def pm(SNR2, x):
    return (1/(2*dm**2))*np.exp(-SNRth**2/2)

#global variables for the sampler
nwalkers = 500
ndim = 2 
nmcmc = 500 #number of mcmc steps...make it a factor of 2

#variables for the population
mupop = 0.4 #mean
sigpop = 0.1 #standard deviation

#variables for individual events
Ntrig = 37 #number of individual events.
Nnoise = 11
Nsamp = 100 #number of sample points for each individual event
stdmin = 1e-50 #smallest possible standard deviation
stdmax = 0.2 #largest possible standard deviation

#enforce uniform prior on the mean and the variance between (0,1)
def lnprior(theta):
    mu, sig = theta
    if 0 < mu < 1 and 0 < sig < 1:
        return 0
    return -np.inf

#log likelihood for the population
#seps are single event posteriors
def lnlike(theta,*seps):
    mu, sig= theta
    seps = np.asarray(seps).reshape((Ntrig,Nsamp)) #TODO: find a better way to pass a variable number of arguments to emcee
return np.prod(pp(SNR2(chirp,f),seps)+pm(SNR2(chirp,f),seps))*np.prod(mp(SNR2(chirp,f),seps[:Nnoise])+mm(SNR2(chirp,f),seps[:Nnoise])

def lnpop(theta,*seps):
    lp = lnprior(theta)
    if not np.isfinite(lp):
        return -np.inf
    seps = np.asarray(seps).reshape((Ntrig,Nsamp)) #TODO: find a better way to pass a variable number of arguments to emcee
    return lp + lnlike(theta,seps)

当我执行lnpop时，它给了我一个语法错误。知道问题出在哪里了吗？我认为这是定义函数的正确方法.

Answer 1

在这一行中缺少一个关闭的)：

return np.prod(pp(SNR2(chirp,f),seps)+pm(SNR2(chirp,f),seps))*np.prod(mp(SNR2(chirp,f),seps[:Nnoise])+mm(SNR2(chirp,f),seps[:Nnoise])) # <-- missing here

通常，语法错误来自于在跟踪中显示的内容之前的行。