Changeset 852354c8 in sasview

Timestamp:

Mar 29, 2011 9:45:24 AM (14 years ago)

Author:

Jae Cho <jhjcho@…>

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:

9466f2d6

Parents:

4b5bd73

Message:

added constraint bounding check

File:

• park_integration/ScipyFitting.py (modified) (5 diffs)

park_integration/ScipyFitting.py

@@ -14 +14 @@
 from sans.fit.AbstractFitEngine import SansAssembly
 from sans.fit.AbstractFitEngine import FitAbort
-
 
 class fitresult(object):
@@ -53 +52 @@
         self.iterations += 1
         result_param = zip(xrange(n), self.model.parameterset)
-        msg = [" [Iteration #: %s] | P%-3d  %s......|.....%s" % \
-               (self.iterations, p[0], p[1], p[1].value)\
+        msg1 = ["[Iteration #: %s ]" % self.iterations]
+        msg2 = ["P%-3d  %s......|.....%s" % \
+                (p[0], p[1], p[1].value)\
               for p in result_param if p[1].name in self.param_list]
-        msg.append("=== goodness of fit: %s" % (str(self.fitness)))
+        
+        msg3 = ["=== goodness of fit: %s ===" % (str(self.fitness))]
+        msg =  msg1 + msg3 + msg2
         return "\n".join(msg)
     
@@ -100 +102 @@
         self.param_list = []
         self.curr_thread = None
-        self.result = None
     #def fit(self, *args, **kw):
     #    return profile(self._fit, *args, **kw)
@@ -124 +125 @@
         # Concatenate dList set (contains one or more data)before fitting
         data = listdata
+       
         self.curr_thread = curr_thread
-        self.result = fitresult(model=model, param_list=self.param_list)
-        self.handler = handler
-        if self.handler is not None:
-            self.handler.set_result(result=self.result)
+        ftol = curr_thread.ftol
+        
+        # Check the initial value if it is within range
+        self._check_param_range(model)
+        
+        result = fitresult(model=model, param_list=self.param_list)
+        if handler is not None:
+            handler.set_result(result=result)
         #try:
-        functor = SansAssembly(self.param_list, model, data, handler=self.handler,
-                         fitresult=self.result, curr_thread= self.curr_thread)
-    
+        functor = SansAssembly(self.param_list, model, data, handler=handler,
+                         fitresult=result, curr_thread= self.curr_thread)
         try:
-            out, cov_x, _, mesg, success = optimize.leastsq(functor,
+                out, cov_x, _, mesg, success = optimize.leastsq(functor,
                                             model.get_params(self.param_list),
-                                                    ftol = 0.001,
+                                                    ftol=ftol,
                                                     full_output=1,
                                                     warning=True)
         except:
             if hasattr(sys, 'last_type') and sys.last_type == FitAbort:
-                if self.handler is not None:
+                if handler is not None:
                     msg = "Fit Stop!"
                     #self.handler.error(msg)
-                    self.result = self.handler.get_result()
-                    return self.result
+                    result = handler.get_result()
+                    return result
             else:
                 raise 
@@ -154 +159 @@
         else:
             stderr = None
-        
-        if (out is not None) and not (numpy.isnan(out).any()) \
-            and (cov_x != None):
-            self.result.fitness = chisqr
-            self.result.stderr  = stderr
-            self.result.pvec = out
-            self.result.success = success
-        else:  
-            msg = "SVD did not converge " + str(mesg)
-            #handler.error(msg)
-        return self.result
-
-       
-    
-
-
+
+        if not (numpy.isnan(out).any()) and (cov_x != None):
+            result.fitness = chisqr
+            result.stderr  = stderr
+            result.pvec = out
+            result.success = success
+            if q is not None:
+                q.put(result)
+                return q
+            return result
+        
+        # Error will be present to the client, not here 
+        #else:  
+        #    raise ValueError, "SVD did not converge" + str(mesg)
+        
+    def _check_param_range(self, model):
+        """
+        Check parameter range and set the initial value inside 
+        if it is out of range.
+        
+        : model: park model object
+        """
+        is_outofbound = False
+        # loop through parameterset
+        for p in model.parameterset:        
+            param_name = p.get_name()
+            # proceed only if the parameter name is in the list of fitting
+            if param_name in self.param_list:
+                # if the range was defined, check the range
+                if numpy.isfinite(p.range[0]):
+                    if p.value <= p.range[0]: 
+                        # 10 % backing up from the border if not zero
+                        # for Scipy engine to work properly.
+                        shift = self._get_zero_shift(p.range[0])
+                        new_value = p.range[0] + shift
+                        p.value =  new_value
+                        is_outofbound = True
+                if numpy.isfinite(p.range[1]):
+                    if p.value >= p.range[1]:
+                        shift = self._get_zero_shift(p.range[1])
+                        # 10 % backing up from the border if not zero
+                        # for Scipy engine to work properly.
+                        new_value = p.range[1] - shift
+                        # Check one more time if the new value goes below
+                        # the low bound, If so, re-evaluate the value 
+                        # with the mean of the range.
+                        if numpy.isfinite(p.range[0]):
+                            if new_value < p.range[0]:
+                                new_value = (p.range[0] + p.range[1]) / 2.0
+                        # Todo: 
+                        # Need to think about when both min and max are same.
+                        p.value =  new_value
+                        is_outofbound = True
+                        
+        return is_outofbound
+    
+    def _get_zero_shift(self, range):
+        """
+        Get 10% shift of the param value = 0 based on the range value
+        
+        : param range: min or max value of the bounds
+        """
+        if range == 0:
+            shift = 0.1
+        else:
+            shift = 0.1 * range
+            
+        return shift
+    
+    
 #def profile(fn, *args, **kw):
 #    import cProfile, pstats, os