def test(challenge,lim=None):
meta,desi,times_f,resid_f,error_f = load(challenge,limit=int(lim))
alphaab = alphamat(meta)
print 'Working with {0} pulsars.'.format(len(meta))
with timing('GW covariance matrix [recurring]'):
cgw = Cgw_100ns(alphaab,times_f,-2.0/3.0,fL=1.0/500,approx_ksum=True)
cgw2 = cgw.copy()
with timing('Cgw interpolation'):
cgw3 = 0.2 * cgw + 0.8 * cgw2
with timing('PN covariance matrix'):
cpn = Cpn(error_f)
with timing('Design matrix'):
gmat = Gdesi2(desi,meta) # note this takes meta, not len(meta)
with timing('Reduced data'):
resid_f = N.dot(gmat.T,resid_f)
with timing('Reduced Cpn'):
cpn = blockmul(cpn,gmat,meta,blas=True)
with timing('Reduced Cgw [recurring]'):
cgw = blockmul(cgw,gmat,meta,blas=True)
cgw2 = cgw.copy()
with timing('Reduced-Cgw interpolation'):
cgw3 = 0.2 * cgw + 0.8 * cgw2
with timing('Likelihood [recurring]'):
logl = logL(resid_f,cgw,cpn)
def lnprob(x):
"""logL stub function for emcee. It needs to use global variables because otherwise
multiprocessing.map will need to transmit big arrays to the slave processes."""
global resid_f,cgw,cpn
return logL(resid_f,cgw,cpn,A=x[0],n=1,cgwunit='100ns')