final['CO'] = hh[1]['CO'][:]
hh.close()
else:
binSizeP = (final['RPMAX'] - final['RPMIN']) / final['NP']
binSizeT = (final['RTMAX'] - 0.) / final['NT']
if not args.do_not_smooth_cov:
print('INFO: The covariance will be smoothed')
final['CO'] = smooth_cov(final['DA'],
final['WE'],
final['RP'],
final['RT'],
drt=binSizeT,
drp=binSizeP)
else:
print('INFO: The covariance will not be smoothed')
final['CO'] = cov(final['DA'], final['WE'])
### Test covariance matrix
try:
scipy.linalg.cholesky(final['CO'])
except scipy.linalg.LinAlgError:
print('WARNING: Matrix is not positive definite')
### Measurement
final['DA'] = (final['DA'] * final['WE']).sum(axis=0)
final['WE'] = final['WE'].sum(axis=0)
w = final['WE'] > 0.
final['DA'][w] /= final['WE'][w]
### Distortion matrix
if args.dmat is not None:
we = data['DD']['WE']
else:
dd = data['xDD']['WE']
rr = data['xRR']['WE']
d1r2 = data['xD1R2']['WE']
d2r1 = data['xD2R1']['WE']
w = rr>0.
da = sp.zeros(dd.shape)
da[w] = (dd[w]+rr[w]-d1r2[w]-d2r1[w])/rr[w]
we = data['xDD']['WE']
data['HLP_DA'] = da
data['HLP_WE'] = we
if args.do_not_smooth_cov:
print('INFO: The covariance will not be smoothed')
co = cov(da,we)
else:
print('INFO: The covariance will be smoothed')
binSizeRP = (data['RPMAX']-data['RPMIN']) / data['NP']
binSizeRT = (data['RTMAX']-0.) / data['NT']
co = smooth_cov(da,we,data['RP'],data['RT'],drp=binSizeRP,drt=binSizeRT)
data['CO'] = co
try:
scipy.linalg.cholesky(data['CO'])
except scipy.linalg.LinAlgError:
print('WARNING: Matrix is not positive definite')
### Distortion matrix
data['DM'] = sp.eye(data['DA'].size)