removed unneeded routines and tools that are now general

da/ct/statevector.py

@@ -83,69 +83,6 @@ class CtStateVector(StateVector):
 
 ################### End Class CtStateVector ###################
 
-################# Stand alone methods that manipulate the objects above  ##############
-
-def PrepareState(CycleInfo):
-    """ 
-    
-    Prepare the ensemble of parameters needed by the forecast model. There are two possible pathways:
-
-    (A) Construct a brand new ensemble from a covariance matrix
-
-    (B) Get an existing ensemble and propagate it
-
-    The steps for procedure A are:
-
-        (A1) Construct the covariance matrix
-        (A2) Make a Cholesky decomposition 
-        (A3) Prepare a set of [nmembers] input parameters for each forecast member
-
-    The steps for procedure B are:
-
-        (B1) Get existing parameter values for each member
-        (B2) Run them through a forecast model
-
-
-    Both procedures finish with writing the parameter values to a NetCDF file
-
-
-    """
-    dims        = ( int(CycleInfo.da_settings['time.nlag']),
-                  int(CycleInfo.da_settings['forecast.nmembers']),
-                  int(CycleInfo.DaSystem.da_settings['nparameters']),
-                  )
-    StateVector = CtStateVector(dims)
-
-    nlag        = dims[0]
-
-    # We now have an empty CtStateVector object that we need to populate with data. If this is a continuation from a previous cycle, we can read
-    # the previous StateVector values from a NetCDF file in the save/ directory. If this is the first cycle, we need to populate the CtStateVector
-    # with new values for each week. After we have constructed the StateVector, it will be propagated by one cycle length so it is ready to be used
-    # in the current cycle
-
-    if not CycleInfo.da_settings['time.restart']:
-
-        # Fill each week from n=1 to n=nlag with a new ensemble
-
-        for n in range(0,nlag):
-            cov   = StateVector.GetCovariance(CycleInfo.DaSystem)
-            dummy = StateVector.MakeNewEnsemble(n+1,cov)
-    else:
-
-        # Read the StateVector data from file
-
-        savedir         = CycleInfo.da_settings['dir.save']
-        filtertime      = CycleInfo.da_settings['time.start'].strftime('%Y%m%d')
-        filename        = os.path.join(savedir,'savestate.nc')
-
-        StateVector.ReadFromFile(filename)
-
-        # Now propagate the ensemble by one cycle to prepare for the current cycle
-
-        dummy = StateVector.Propagate()
-
-    return StateVector
-
 
 if __name__ == "__main__":
 

da/ct/tools.py

@@ -23,69 +23,6 @@ needed_rc_items = ['obs.input.dir',
                    'deltaco2.prefix',
                    'regtype']
 
-################### Begin Class DaInfo ###################
-
-class DaInfo(object):
-    """ Information on the data assimilation system used. For CarbonTracker, this is an rc-file with settings.
-        The settings list includes at least the values above, in needed_rc_items.
-    """
-
-    def __init__(self,rcfilename):
-        """
-        Initialization occurs from passed rc-file name
-        """
-
-        self.LoadRc(rcfilename)
-        self.ValidateRC()
-
-    def __str__(self):
-        """
-        String representation of a DaInfo object
-        """
-
-        msg = "==============================================================="    ; print msg
-        msg = "DA System Info rc-file is %s" % self.RcFileName                                ; print msg
-        msg = "==============================================================="    ; print msg
-
-        return ""
-
-
-    def LoadRc(self,RcFileName):
-        """ 
-        This method loads a DA System Info rc-file with settings for this simulation 
-        """
-        import da.tools.rc as rc 
-
-        self.da_settings    = rc.read(RcFileName)
-        self.RcFileName     = RcFileName
-        self.DaRcLoaded    = True
-
-        msg                 = 'DA System Info rc-file (%s) loaded successfully'%self.RcFileName ; logging.info(msg)
-
-        return True
-
-
-    def ValidateRC(self):
-        """ 
-        Validate the contents of the rc-file given a dictionary of required keys
-        """
-
-        for k,v in self.da_settings.iteritems():
-            if v == 'True' : self.da_settings[k] = True
-            if v == 'False': self.da_settings[k] = False
-
-        for key in needed_rc_items:
-
-            if not self.da_settings.has_key(key):
-                status,msg = ( False,'Missing a required value in rc-file : %s' % key)
-                logging.error(msg)
-                raise IOError,msg
-
-        status,msg = ( True,'DA System Info settings have been validated succesfully' )  ; logging.debug(msg)
-
-        return None
-################### End Class DaInfo ###################
-
 def StateToGrid(DaInfo,values,reverse=False,avg=False):
     """ 
     This method converts parameters from a CarbonTracker StateVector object to a gridded map of linear multiplication values. These