Changeset 681f0dc in sasview

Timestamp:

Jan 15, 2009 5:35:45 PM (16 years ago)

Author:

Gervaise Alina <gervyh@…>

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.1.1, release-4.1.2, release-4.2.2, release_4.0.1, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

e9b4cc4

Parents:

4853c04

Message:

added flexibility on handler choice for fit (park). allow to print some update on the sansview

Location:

park_integration

Files:

• AbstractFitEngine.py (modified) (1 diff)
• Fitting.py (modified) (1 diff)
• ParkFitting.py (modified) (2 diffs)
• ScipyFitting.py (modified) (2 diffs)

park_integration/AbstractFitEngine.py

@@ -364 +364 @@
             @param params: value of parameters to fit
         """
+        #import thread
         self.model.setParams(self.paramlist,params)
         self.res= self.data.residuals(self.model.eval)
+        #print "residuals",thread.get_ident()
         return self.res
     

park_integration/Fitting.py

@@ -52 +52 @@
         return self._engine
     
-    def fit(self, qmin=None, qmax=None):
+    def fit(self,handler=None, qmin=None, qmax=None):
         """Perform the fit """
-        return self._engine.fit(qmin,qmax)
+    
+        return self._engine.fit(handler, qmin,qmax)
     
     def set_model(self,model,Uid,pars=[]):

park_integration/ParkFitting.py

@@ -83 +83 @@
         
     
-    def fit(self, qmin=None, qmax=None):
+    def fit(self,handler=None, qmin=None, qmax=None):
         """
             Performs fit with park.fit module.It can  perform fit with one model
@@ -103 +103 @@
         localfit = FitSimplex()
         localfit.ftol = 1e-8
-        # fitmc(fitness,localfit,n,handler):
-        #Run a monte carlo fit.
-        #This procedure maps a local optimizer across a set of n initial points.
-        #The initial parameter value defined by the fitness parameters defines
-        #one initial point.  The remainder are randomly generated within the
-        #bounds of the problem.
-        #localfit is the local optimizer to use.  It should be a bounded
-        #optimizer following the `park.fitmc.LocalFit` interface.
-        #handler accepts updates to the current best set of fit parameters.
+        
         # See `park.fitresult.FitHandler` for details.
         fitter = FitMC(localfit=localfit)
-        #result = fit.fit(self.problem,
-        #             fitter=fitter,
-        #            handler= GuiUpdate(window))
+        if handler == None:
+            print "no handler"
+            handler= fitresult.ConsoleUpdate(improvement_delta=0.1)
+        print "park handler", handler
         result = fit.fit(self.problem,
                          fitter=fitter,
-                         handler= fitresult.ConsoleUpdate(improvement_delta=0.1))
-        #handler = fitresult.ConsoleUpdate(improvement_delta=0.1)
-        #models=self.problem
-        #service=None
-        #if models is None: raise RuntimeError('fit expected a list of models')
-        #from park.fit import LocalQueue,FitJob
-        #if service is None: service = LocalQueue()
-        #if fitter is None: fitter = fitmc.FitMC()
-        #if handler is None: handler = fitresult.FitHandler()
-    
-        #objective = assembly.Assembly(models) if isinstance(models,list) else models
-        #job = FitJob(self.problem,fitter,handler)
-        #service.start(job)
-        #import wx
-        #while not self.job.handler.done:
-        #    time.sleep(interval)
-        #    wx.Yield()
-        #result=service.job.handler.result
+                         handler= handler)
+       
         
         if result !=None:

park_integration/ScipyFitting.py

@@ -59 +59 @@
         self.fitArrangeDict={}
         self.paramList=[]
-    def fit(self,qmin=None, qmax=None):
+    def fit(self ,handler=None, qmin=None, qmax=None):
         # Protect against simultanous fitting attempts
         #if len(self.fitArrangeDict)>1: 
@@ -65 +65 @@
         # fitproblem contains first fitArrange object(one model and a list of data)
         #list of fitproblem
+       
         fitproblem=[]
         for id ,fproblem in self.fitArrangeDict.iteritems():