Changeset f24b8f4 in sasview

Timestamp:

May 23, 2008 9:10:39 AM (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:

a1718df

Parents:

dfb58f8

Message:

made some chagnges on the unit test

Location:

park_integration

Files:

• docs/FittingModule.py (moved) (moved from park_integration/test/FittingModule.py) (8 diffs)
• docs/Loader.py (moved) (moved from park_integration/test/Loader.py)
• docs/uml.bmp (added)
• test/testfitting.py (modified) (1 diff)
• test/uml.bmp (deleted)

park_integration/docs/FittingModule.py

@@ -8 +8 @@
     def __init__(self):
         """
+            Store a set of data for a given model to perform the Fit
             @param model: the model selected by the user
             @param Ldata: a list of data what the user want to fit
@@ -47 +48 @@
     """
     def __init__(self,data=[]):
-        #self.model is a list of all models to fit
-        #self.model={}
+        #this is a dictionary of FitArrange elements
         self.fitArrangeList={}
-        #the list of all data to fit 
-        self.data = data
-        #list of models parameters
-        self.parameters=[]
-        
+        #the constraint of the Fit
         self.constraint =None
+        #Specify the use of scipy or park fit
         self.fitType =None
         
@@ -73 +70 @@
         fitproblem=self.fitArrangeList.values()[0]
         listdata=[]
-        model =fitproblem.get_model()
-        listdata= fitproblem.get_data()
-        self.set_param(model, pars)
+        model = fitproblem.get_model()
+        listdata = fitproblem.get_data()
+        
+        parameters = self.set_param(model,pars)
         if listdata==[]:
             raise ValueError, " data list missing"
@@ -83 +81 @@
             ytemp=[]
             dytemp=[]
-            
-            
+           
             for data in listdata:
                 for i in range(len(data.x)):
@@ -99 +96 @@
             if qmax==None:
                 qmax= max(xtemp)  
-            chisqr, out, cov = fitHelper(model,self.parameters, xtemp,ytemp, dytemp ,qmin,qmax)
+            chisqr, out, cov = fitHelper(model,parameters, xtemp,ytemp, dytemp ,qmin,qmax)
             return chisqr, out, cov
     
     def set_model(self,model,Uid):
         """ Set model """
-        #self.model[Uid] = model
-        fitproblem= FitArrange()
-        fitproblem.set_model(model)
-        self.fitArrangeList[Uid]=fitproblem
+        if self.fitArrangeList.has_key(Uid):
+            self.fitArrangeList[Uid].set_model(model)
+        else:
+            fitproblem= FitArrange()
+            fitproblem.set_model(model)
+            self.fitArrangeList[Uid]=fitproblem
         
     def set_data(self,data,Uid):
         """ Receive plottable and create a list of data to fit"""
-        #self.data.append(data)
+        
         if self.fitArrangeList.has_key(Uid):
             self.fitArrangeList[Uid].add_data(data)
@@ -119 +118 @@
             self.fitArrangeList[Uid]=fitproblem
             
-    def get_data(self):
+    def get_model(self,Uid):
         """ return list of data"""
-        return self.data
+        return self.fitArrangeList[Uid]
     
     def set_param(self,model, pars):
         """ Recieve a dictionary of parameter and save it """
-        self.parameters=[]
+        parameters=[]
         if model==None:
             raise ValueError, "Cannot set parameters for empty model"
@@ -131 +130 @@
             #for key ,value in pars:
             for key, value in pars.iteritems():
-                print "this is the key",key
-                print "this is the value",value
                 param = Parameter(model, key, value)
-                self.parameters.append(param)
-            
-    def add_contraint(self, contraint):
+                parameters.append(param)
+        return parameters
+    
+    def add_constraint(self, constraint):
         """ User specify contraint to fit """
-        self.contraint = str(contraint)
-        
-    def get_contraint(self):
+        self.constraint = str(constraint)
+        
+    def get_constraint(self):
         """ return the contraint value """
-        return self.contraint
-
-
+        return self.constraint
+   
+    def set_constraint(self,constraint):
+        """ 
+            receive a string as a constraint
+            @param constraint: a string used to constraint some parameters to get a 
+                specific value
+        """
+        self.constraint= constraint
+    
+   
+                
 
 class Parameter:
@@ -235 +242 @@
     Fit.set_model(model,1)
     Fit.set_data(data1,1)
-    #default_A = model.getParam('A') 
-    #default_B = model.getParam('B') 
-    #cstA = Parameter(model, 'A', default_A)
-    #cstB  = Parameter(model, 'B', default_B)
-    
-    #chisqr, out, cov=Fit.fit([cstA,cstB],None,None)
+    
     chisqr, out, cov=Fit.fit({'A':2,'B':1},None,None)
     print"fit only one data",chisqr, out, cov 

park_integration/test/testfitting.py

@@ -52 +52 @@
             
            
-    def testfit(self):
-        """ test fitting"""
+    def testfit_1Data_1Model(self):
+        """ test fitting for one data and one model"""
+        #load data
         from Loader import Load
         load= Load()
         load.set_filename("testdata_line.txt")
         load.set_values()
-        x,y,dx,dy = load.get_values()
         data1 = Data1D(x=[], y=[],dx=None, dy=None)
         load.load_data(data1)
         
+        #Importing the Fit module
+        from FittingModule import Fitting
+        Fit= Fitting()
         # Receives the type of model for the fitting
         from sans.guitools.LineModel import LineModel
         model  = LineModel()
         
+        #Do the fit
+        Fit.set_data(data1,1)
+        Fit.set_model(model,1)
+        chisqr, out, cov=Fit.fit({'A':2,'B':1},None,None)
+        #print"fit only one data",chisqr, out, cov   
+        #Testing results
+        self.assertEqual(Fit.fit_engine("scipy"),True)
+        self.assert_(math.fabs(out[1]-2.5)/math.sqrt(cov[1][1]) < 2)
+        self.assert_(math.fabs(out[0]-4.0)/math.sqrt(cov[0][0]) < 2)
+        self.assert_(chisqr/len(data1.x) < 2)
+        
+        #print "chisqr",chisqr/len(data1.x)
+        #print "Error on A",math.fabs(out[1]-2.5)/math.sqrt(cov[1][1])
+        #print "Error on B",math.fabs(out[0]-4.0)/math.sqrt(cov[0][0])
+        
+    def testfit_2Data_1Model(self):
+        """ test fitting for two set of data data and one model"""
+        from Loader import Load
+        load= Load()
+        #Load the first set of data
+        load.set_filename("testdata1.txt")
+        load.set_values()
+        data1 = Data1D(x=[], y=[],dx=None, dy=None)
+        load.load_data(data1)
+        
+        #Load the second set of data
+        load.set_filename("testdata2.txt")
+        load.set_values()
+        data2 = Data1D(x=[], y=[],dx=None, dy=None)
+        load.load_data(data2)
+       
+        #Importing the Fit module
         from FittingModule import Fitting
         Fit= Fitting()
-        Fit.set_data(data1)
-        Fit.set_model(model)
+        # Receives the type of model for the fitting
+        from sans.guitools.LineModel import LineModel
+        model  = LineModel()
         
-        default_A = model.getParam('A') 
-        default_B = model.getParam('B') 
-        cstA = Parameter(model, 'A', default_A)
-        cstB  = Parameter(model, 'B', default_B)
-        chisqr, out, cov=Fit.fit([cstA,cstB],None,None)
-        print"fit only one data",chisqr, out, cov   
-       
-        self.assertEqual(Fit.fit_engine(),True)
+        #Do the fit
+        Fit.set_data(data1,1)
+        Fit.set_model(model,1)
         
+        Fit.set_data(data2,2)
+        Fit.set_model(model,2)
+        
+        chisqr, out, cov=Fit.fit({'A':2,'B':1},None,None)
+        #print"fit only one data",chisqr, out, cov
+        
+        #Testing results
+        self.assertEqual(Fit.fit_engine("scipy"),True)
         self.assert_(math.fabs(out[1]-2.5)/math.sqrt(cov[1][1]) < 2)
-        print "chisqr",chisqr/len(data1.x)
+        self.assert_(math.fabs(out[0]-4.0)/math.sqrt(cov[0][0]) < 2)
         self.assert_(chisqr/len(data1.x) < 2)
-#        self.assertAlmostEqual(out[1],2.5)
-        #self.assertAlmostEquals(out[0],4.0)
-        #self.assertAlmostEquals(out[1]+cov[1][1],2.5)
-        #self.assertAlmostEquals(out[0]+cov[0][0],4.0)