def saveFitRes(ftype, xi, eff, nsig, fres):
""" """
np.savez(fitresfile(ftype, xi, eff),
status=np.array([[key, fres[0][key]] for key in fres[0]]),
fitres=np.array([[p.name, [p.value, p.error * np.sqrt(nsig)]]
for p in fres[1]]),
corr=np.array(fres[2]),
nsig=nsig)
def getConc(N):
""" Concentration matrix for Lambda form-factors """
print('MULT = {}'.format(MULT))
data = np.load(fitresfile('upol', 0., False) + '.npz')
corr = data['corr']
print(corr)
errs = [x[1] for x in data['fitres'][:,1]] / np.sqrt(N*MULT)
print(errs)
covs = corr * np.outer(errs, errs)
return np.linalg.inv(covs)
def readFitRes(ftype, xi, eff):
""" """
assert (ftype in FTYPES)
data = np.load(fitresfile(ftype, xi, eff) + '.npz')
status = OrderedDict(
[x, y] for x, y, in zip(data['status'][:, 0], data['status'][:, 1]))
fitres = OrderedDict(
[x, y] for x, y, in zip(data['fitres'][:, 0], data['fitres'][:, 1]))
return {
'status': status['is_valid'],
'fitres': fitres,
'fitcor': data['corr']
}
def xiErr(key, xi):
return np.load(fitresfile(key, xi, False) + '.npz')['fitres'][-1][1][1]
def allErrs(key, xi=0.):
""" """
return [
x[1][1] for x in np.load(fitresfile(key, xi, False) + '.npz')['fitres']
]