From 7abed404b478c91d9a201ae805039dca3d7ea1b7 Mon Sep 17 00:00:00 2001
From: Wouter Peters <wouter.peters@wur.nl>
Date: Fri, 6 Aug 2010 09:28:32 +0000
Subject: [PATCH] changed the CtMember and StateVector object so that there is
no longer a separate 'mean' in the statevector, it is simply member 0
---
da/ct/statevector.py | 31 +++++++++++++------------------
1 file changed, 13 insertions(+), 18 deletions(-)
diff --git a/da/ct/statevector.py b/da/ct/statevector.py
index 16ce6348..373df692 100755
--- a/da/ct/statevector.py
+++ b/da/ct/statevector.py
@@ -26,7 +26,7 @@ class CtMember(object):
def __init__(self, membernumber):
self.membernumber = membernumber # the member number
- self.MeanDeviations = None # Parameter values of this member
+ self.ParameterValues = None # Parameter values of this member
self.ModelSample = None # Model Sampled Parameter values of this member
self.ParameterOutputFile = None # File to which the member info should be written
@@ -40,9 +40,9 @@ class CtMember(object):
filename = os.path.join(outdir,'parameters.%03d.nc'% self.membernumber)
f = CT_CDF(filename,'create')
- dimparams = f.AddParamsDim(len(self.MeanDeviations))
+ dimparams = f.AddParamsDim(len(self.ParameterValues))
- data = self.MeanDeviations
+ data = self.ParameterValues
savedict = std_savedict.copy()
savedict['name'] = "parametervalues"
@@ -75,9 +75,8 @@ class CtStateVector(object):
self.isOptimized = False
# These list objects hold the data for each time step of lag in the system. Note that the ensembles for each time step consist
- # of lists of CtMember objects, whereas the MeanValues will simply be a list of array values.
+ # of lists of CtMember objects, we define member 0 as the mean of the distribution and n=1,...,nmembers as the spread.
- self.MeanValues = range(self.nlag)
self.EnsembleMembers = range(self.nlag)
for n in range(self.nlag):
@@ -158,15 +157,14 @@ class CtStateVector(object):
plt.close('all')
- # Create mean values for the new time step (default = 1.0) and add to the MeanValues list
+ # Create mean values for the new time step (default = 1.0)
NewMean = np.ones(self.nparams,float)
- self.MeanValues[lag-1] = NewMean
# Create the first ensemble member with a deviation of 0.0 and add to list
NewMember = CtMember(0)
- NewMember.MeanDeviations = np.zeros((self.nparams),float)
+ NewMember.ParameterValues = np.zeros((self.nparams),float) + NewMean
dummy = self.EnsembleMembers[lag-1].append(NewMember)
# Create members 1:nmembers and add to EnsembleMembers list
@@ -176,7 +174,7 @@ class CtStateVector(object):
rands = np.random.randn(self.nparams)
NewMember = CtMember(member)
- NewMember.MeanDeviations = np.dot(C,rands)
+ NewMember.ParameterValues = np.dot(C,rands) + NewMean
dummy = self.EnsembleMembers[lag-1].append(NewMember)
msg = '%d new ensemble members were added to the state vector # %d'%(self.nmembers,lag) ; logging.debug(msg)
@@ -193,10 +191,8 @@ class CtStateVector(object):
# Remove State Vector n=1 by simply "popping" it from the list and appending a new empty list at the front. This empty list will
# hold the new ensemble for the new cycle
- dummy = self.MeanValues.pop(0)
dummy = self.EnsembleMembers.pop(0)
- dummy = self.MeanValues.append([])
dummy = self.EnsembleMembers.append([])
# And now create a new week of mean + members for n=nlag
@@ -227,17 +223,18 @@ class CtStateVector(object):
for n in range(self.nlag):
- data = self.MeanValues[n]
+ members = self.EnsembleMembers[n]
+ MeanState = members[0].ParameterValues
savedict = f.StandardVar(varname='meanstate')
savedict['dims'] = dimlag+dimparams
- savedict['values'] = data
+ savedict['values'] = MeanState
savedict['count'] = n
savedict['comment'] = 'this represents the mean of the ensemble'
dummy = f.AddData(savedict)
members = self.EnsembleMembers[n]
- data = np.array([m.MeanDeviations for m in members])
+ data = np.array([m.ParameterValues for m in members])- MeanState
savedict = f.StandardVar(varname='ensemblestate')
savedict['dims'] = dimlag+dimmembers+dimparams
@@ -261,11 +258,9 @@ class CtStateVector(object):
dummy = f.close()
for n in range(self.nlag):
-
- self.MeanValues[n] = MeanState[n,:]
for m in range(self.nmembers):
NewMember = CtMember(m)
- NewMember.MeanDeviations = EnsembleMembers[n,m,:]
+ NewMember.ParameterValues = EnsembleMembers[n,m,:] + MeanState # add the mean to the deviations to hold the full parameter values
dummy = self.EnsembleMembers[n].append(NewMember)
msg = 'Successfully read the State Vector from file (%s) ' % (filename,) ; logging.info(msg)
@@ -361,7 +356,7 @@ if __name__ == "__main__":
members[0].WriteToFile(DaCycle.da_settings['dir.input'])
- data = np.array([m.MeanDeviations for m in members])
+ data = np.array([m.ParameterValues for m in members])
StateVector.WriteToFile()
--
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