ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Questions
1 
import pymc3 as pm
import numpy as np
import scipy.integrate as integrate
data= np.loadtxt('sn_union2.txt',comments="#")
x=data[:,0]
y=data[:,1]
yerr=data[:,2]
c=3e5
mul=[]
def Or(h):
    Neff= 3.04
    Orad= 2.469*10**(-5)*h**(-2)*(1+0.2271* Neff)
    return Orad
def H(z,Om,Orc,h):
    Orad= Or(h)
    H0= 100*h
    E=np.sqrt((1-(np.sqrt(Orc)+np.sqrt(Orc+Om))**2)*(1+z)**2+(np.sqrt(Orc)+np.sqrt(Orc+Om*(1+z)**3))**2)
    return E
def Hinv(z,Om,Orc,h):
    return 1.0/H(z,Om,Orc,h)         
def dl(z,Om,Orc,h):
    int=integrate.quad(Hinv,0,z,args=(Om,Orc,h))[0]
    return int
def mu(z,Om,Orc,h, mu0):
    H0=100.0*h
    smu=5.0*np.log10((c/H0)*(1.0+z)*dl(z,Om,Orc,h)) + mu0
    return smu
with pm.Model() as model:
    Orc=pm.Uniform('Orc', lower=0, upper=0.25, transform=None)
    Om=pm.Uniform('Om', lower=0, upper=0.5, transform=None)
    mu0= pm.Uniform('mu0', lower=0, upper=100,	 transform=None)
    h=pm.Normal('h',mu=0.7324, sigma=0.0174)
    mu=mu(x,Om,Orc,h,mu0)
    likelihood=pm.Normal('Y', mu=mu,sd=yerr, observed=y)
with model:
    step = pm.Metropolis ()
    trace = pm.sample (2000, tune=100, init=None, step=step, cores=8)
x=trace.get_values('Orc',burn=100, combine=True)
y=trace.get_values('Om',burn=100, combine=True)
z=trace.get_values('h',burn=100, combine=True)
mu0=trace.get_values('mu0',burn=100, combine=True)
t=list(zip(x,y,z,mu0))
np.savetxt('SNIaDGP.txt',t)
2

This error message is telling you that the boolean value of the entire array is unclear because it has multiple elements.

To resolve this ambiguity, NumPy provides two methods that can be used to determine the truth value of an array with multiple elements: a.any() and a.all().

  • a.any() : returns True if at least one element in the array a is True.
  • a.all() : returns True only if all the elements in the array a are True.

So, instead of using the array itself as a condition, you should use one of these methods to determine its truth value.