Changeset 13093

Timestamp:

08/20/12 10:55:23 (13 years ago)

Author:

jschierm

Message:

CHG: Added call to setdefaultparameters in inversion subclass.

File:

• issm/trunk-jpl/src/m/classes/inversion.py (modified) (5 diffs)

issm/trunk-jpl/src/m/classes/inversion.py

@@ -37 +37 @@
                 self.vel_obs                     = float('NaN')
                 self.thickness_obs               = float('NaN')
+
+                #set defaults
+                self.setdefaultparameters()
+
                 #}}}
         def __repr__(self):
@@ -78 +82 @@
                 #parameter to be inferred by control methods (only
                 #drag and B are supported yet)
-                self.control_parameters=['FrictionCoefficient']
+                self.control_parameters='FrictionCoefficient'
 
                 #number of steps in the control methods
@@ -85 +89 @@
                 #maximum number of iteration in the optimization algorithm for
                 #each step
-                self.maxiter_per_step=20*ones(self.nsteps)
+                self.maxiter_per_step=20*numpy.ones(self.nsteps)
 
                 #the inversed parameter is updated as follows:
@@ -92 +96 @@
                 #inversed parameter (10^8 for B, 50 for drag) and can be decreased
                 #after the first iterations
-                self.gradient_scaling=50*ones(self.nsteps)
+                self.gradient_scaling=50*numpy.ones(self.nsteps)
 
                 #several responses can be used:
-                self.cost_functions=101*ones(self.nsteps)
+                self.cost_functions=101*numpy.ones(self.nsteps)
 
                 #step_threshold is used to speed up control method. When
                 #misfit(1)/misfit(0) < self.step_threshold, we go directly to
                 #the next step
-                self.step_threshold=.7*ones(self.nsteps) #30 per cent decrement
+                self.step_threshold=.7*numpy.ones(self.nsteps) #30 per cent decrement
 
                 #stop control solution at the gradient computation and return it? 
@@ -108 +112 @@
                 #if J[n]-J[n-1]/J[n] < criteria, the control run stops
                 #NaN if not applied
-                self.cost_function_threshold=NaN #not activated 
+                self.cost_function_threshold=float('NaN')    #not activated 
 
                 return self