changed initialize to setup

gridded/da/baseclasses/obs.py

@@ -9,7 +9,7 @@ Revision History:
 File created on 28 Jul 2010.
 
 .. autoclass:: da.baseclasses.obs.Observations 
-   :members: initialize, Validate, add_observations, add_simulations, add_model_data_mismatch, write_sample_coords  
+   :members: setup, Validate, add_observations, add_simulations, add_model_data_mismatch, write_sample_coords  
 
 .. autoclass:: da.baseclasses.obs.ObservationList 
    :members: __init__
@@ -61,7 +61,7 @@ class Observations(object):
     def getlength(self):
         return len(self.datalist)
 
-    def initialize(self, cycleparams):
+    def setup(self, cycleparams):
         """ Perform all steps needed to start working with observational data, this can include moving data, concatenating files,
             selecting datasets, etc.
         """

gridded/da/baseclasses/observationoperator.py

@@ -52,7 +52,7 @@ class ObservationOperator(object):
     def get_initial_data(self):
         """ This method places all initial data needed by an ObservationOperator in the proper folder for the model """
 
-    def initialize(self):
+    def setup(self):
         """ Perform all steps necessary to start the observation operator through a simple Run() call """
 
     def validate_input(self):

gridded/da/baseclasses/optimizer.py

@@ -34,7 +34,7 @@ class Optimizer(object):
 
         logging.info('Optimizer object initialized: %s' % self.ID)
 
-    def initialize(self, dims):
+    def setup(self, dims):
         self.nlag = dims[0]
         self.nmembers = dims[1]
         self.nparams = dims[2]

gridded/da/baseclasses/statevector.py

@@ -80,7 +80,7 @@ class StateVector(object):
     modified to be used in other parts of the pipeline:
         * nobs
 
-    These values are set as soon as the :meth:`~da.baseclasses.statevector.StateVector.initialize` is called from the :ref:`pipeline`. 
+    These values are set as soon as the :meth:`~da.baseclasses.statevector.StateVector.setup` is called from the :ref:`pipeline`. 
     Additionally, the value of attribute `isOptimized` is set to `False` indicating that the StateVector holds a-priori values 
     and has not been modified by the :ref:`optimizer`.
 
@@ -98,7 +98,7 @@ class StateVector(object):
 
     The methods are described below:
 
-    .. automethod:: da.baseclasses.statevector.StateVector.initialize 
+    .. automethod:: da.baseclasses.statevector.StateVector.setup 
     .. automethod:: da.baseclasses.statevector.StateVector.read_from_file
     .. automethod:: da.baseclasses.statevector.StateVector.write_to_file
     .. automethod:: da.baseclasses.statevector.StateVector.make_new_ensemble
@@ -123,9 +123,9 @@ class StateVector(object):
 
         logging.info('Statevector object initialized: %s' % self.ID)
 
-    def initialize(self, dacycle):
+    def setup(self, dacycle):
         """
-        initialize the object by specifying the dimensions. 
+        setup the object by specifying the dimensions. 
         There are two major requirements for each statvector that you want to build:
         
             (1) is that the statevector can map itself onto a regular grid

gridded/da/carbondioxide/obs.py

@@ -31,7 +31,7 @@ import da.tools.rc as rc
 class CO2Observations(Observations):
     """ an object that holds data + methods and attributes needed to manipulate mixing ratio values """
 
-    def initialize(self, dacycle):
+    def setup(self, dacycle):
         self.startdate = dacycle['time.sample.start']
         self.enddate = dacycle['time.sample.end']
         

gridded/da/carbondioxide/obspack.py

@@ -33,7 +33,7 @@ version = '0.0'
 class ObsPackObservations(Observations):
     """ an object that holds data + methods and attributes needed to manipulate mixing ratio values """
 
-    def initialize(self, dacycle):
+    def setup(self, dacycle):
         self.startdate = dacycle['time.sample.start']
         self.enddate = dacycle['time.sample.end']
 

gridded/da/tm5/observationoperator.py

@@ -58,7 +58,7 @@ class TM5ObservationOperator(ObservationOperator):
            []> tm.WriteRunRc()
            []> tm.run()
 
-        To use this class inside a data assimilation cycle, a stand-alone method "initialize()" is included which modifies the TM5
+        To use this class inside a data assimilation cycle, a stand-alone method "setup()" is included which modifies the TM5
         settings according to an external dictionary of values to overwrite, and then runs the TM5 model.
 
     
@@ -78,7 +78,7 @@ class TM5ObservationOperator(ObservationOperator):
 
         logging.info('Observation Operator initialized: %s (%s)' % (self.ID, self.version))
         
-    def initialize(self, dacycle):
+    def setup(self, dacycle):
         """ 
            Execute all steps needed to prepare the ObsOperator for use inside CTDAS, only done at the very first cycle normally
     

gridded/da/tools/initexit.py

@@ -40,7 +40,7 @@ like this:::
 The most important method of the CycleControl object are listed below:
 
 .. autoclass:: da.tools.initexit.CycleControl 
-   :members: initialize, finalize,  collect_restart_data, move_restart_data, 
+   :members: setup, finalize,  collect_restart_data, move_restart_data, 
              submit_next_cycle, setup_file_structure, recover_run, random_seed
 
 Two important attributes of the CycleControl object are:
@@ -263,7 +263,7 @@ class CycleControl(dict):
         logging.info("Retrieved the random seed generator values of last cycle from file")
 
 
-    def initialize(self):
+    def setup(self):
         """ 
         This method determines how to proceed with the cycle. Three options are implemented:
 

gridded/da/tools/pipeline.py

@@ -120,12 +120,12 @@ def start_job(dacycle, dasystem, platform, statevector, samples, obsoperator):
     dasystem.validate()                               
     dacycle.dasystem = dasystem                       
     dacycle.daplatform = platform                    
-    dacycle.initialize()                              
+    dacycle.setup()                              
     #statevector.dacycle = dacycle # also embed object in statevector so it can access cycle information for I/O etc   
     #samples.dacycle = dacycle # also embed object in samples object so it can access cycle information for I/O etc    
     #obsoperator.dacycle = dacycle # also embed object in obsoperator object so it can access cycle information for I/O etc
-    obsoperator.initialize(dacycle)  # Setup Observation Operator                                                          
-    statevector.initialize(dacycle)   
+    obsoperator.setup(dacycle)  # Setup Observation Operator                                                          
+    statevector.setup(dacycle)   
 
 def prepare_state(dacycle, statevector):
     """ Set up the input data for the forward model: obs and parameters/fluxes"""
@@ -214,7 +214,7 @@ def sample_step(dacycle, samples, statevector, obsoperator, lag, advance=False):
 
     statevector.write_members_to_file(lag, dacycle['dir.input']) 
 
-    samples.initialize(dacycle)       
+    samples.setup(dacycle)       
     samples.add_observations() 
 
     # Add model-data mismatch to all samples, this *might* use output from the ensemble in the future??
@@ -280,7 +280,7 @@ def invert(dacycle, statevector, optimizer):
         logging.info("Using the bulk minimum least squares algorithm to solve ENKF equations")
         optimizer.set_algorithm('Bulk')
     
-    optimizer.initialize(dims)
+    optimizer.setup(dims)
     optimizer.state_to_matrix(statevector)    
     
     diagnostics_file = os.path.join(dacycle['dir.output'], 'optimizer.%s.nc' % dacycle['time.start'].strftime('%Y%m%d'))