本文整理匯總了Python中JLA_library.computeOffsets方法的典型用法代碼示例。如果您正苦於以下問題：Python JLA_library.computeOffsets方法的具體用法？Python JLA_library.computeOffsets怎麽用？Python JLA_library.computeOffsets使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類JLA_library的用法示例。
在下文中一共展示了JLA_library.computeOffsets方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: compute_Ccal
# 需要導入模塊: import JLA_library [as 別名]
# 或者: from JLA_library import computeOffsets [as 別名]
#.........這裏部分代碼省略.........
# ------------- Create an area to work in -----------------------
workArea = JLA.get_full_path(options.workArea)
try:
os.mkdir(workArea)
except:
pass
# ----------- The lightcurve fitting --------------------------
firstSN=True
log=open('log.txt','w')
for i,SN in enumerate(SNeList):
J=[]
try:
os.mkdir(workArea+'/'+SN['id'])
except:
pass
#firstModel=True
print 'Examining SN #%d %s' % (i+1,SN['id'])
# Set up the number of processes
pool = mp.Pool(processes=int(options.processes))
# runSALT is the program that does the lightcurve fitting
results = [pool.apply(runSALT, args=(SALTpath,
SALTmodel,
salt_prefix,
SN['lc'],
SN['id'])) for SALTmodel in SALTmodels]
for result in results[1:]:
# The first model is the unperturbed model
dM,dX,dC=JLA.computeOffsets(results[0],result)
J.extend([dM,dX,dC])
pool.close() # This prevents to many open files
if firstSN:
J_new=numpy.array(J).reshape(nSALTmodels,3).T
firstSN=False
else:
J_new=numpy.concatenate((J_new,numpy.array(J).reshape(nSALTmodels,3).T),axis=0)
log.write('%d rows %d columns\n' % (J_new.shape[0],J_new.shape[1]))
log.close()
# Compute the new covariance matrix J . Cal . J.T produces a 3 * n_SN by 3 * n_SN matrix
# J=jacobian
J_smoothed=numpy.array(J_new)*0.0
J=J_new
# We need to concatenate the different samples ...
if options.Plot:
try:
os.mkdir('figures')
except:
pass
nPoints={'SNLS':11,'SDSS':11,'nearby':11,'high-z':11,'DES':11}
#sampleList=['nearby','DES']
sampleList=params['smoothList'].split(',')
if options.smoothed:
# We smooth the Jacobian