# logging.warning('Using only the diagonal values of the covariancematrix')
dims=covariancematrix.shape[0]
ifdims!=self.nparams:
logging.error("The total dimension of the covariance matrices passed (%d) does not add up to the prescribed nparams (%d), exiting..."%(dims,self.nparams))
raiseValueError
#if dims != self.nparams:
# logging.error("The total dimension of the covariance matrices passed (%d) does not add up to the prescribed nparams (%d), exiting..." % (dims, self.nparams))
# raise ValueError
# Make a cholesky decomposition of the covariance matrix
...
...
@@ -239,7 +239,7 @@ class CO2StateVector(StateVector):
logging.info('Appr. degrees of freedom in covariance matrix is %s'%(int(dof)))
# Create mean values
self.prmval=prmval
newmean=np.ones(self.nparams,float)*prmval# standard value for a new time step is 1.0
# If this is not the start of the filter, average previous two optimized steps into the mix