Merge pull request #95 from sasaray/master

Updating feature optimization (ignoring some AP1 features, changing p…

Author: sasaray

optimizer/fitnessFunctions.py

@@ -878,18 +878,19 @@ def FFun_for_Features(self, mod_t, features_data, feature, k, args={}):
         exp_mean = features_data[feature]["mean"][k]
         exp_std = features_data[feature]["std"][k]
         mod_result=traces_results[0][feature]
-        #print feature
 
         #print "exp mean", exp_mean
         #print "exp std", exp_std
         #print "model", mod_result
 
-        if mod_result is not None and mod_result.size > 1:
+        if mod_result is not None and mod_result.size > 1 and (feature == 'AP_rise_time' or feature == 'AP_amplitude' or feature == 'AP_duration_half_width' or feature == 'AP_begin_voltage' or feature == 'AP_rise_rate'):
+            mod_result = scipy.mean(mod_result[1:])
+        elif mod_result is not None and mod_result.size > 1:
             mod_result = scipy.mean(mod_result)      #for features (AHP_depth, AP_duration_half_width) that gives a list as a result, the mean of the results is used
         elif mod_result is not None and mod_result.size!=0:                    # mod_result is a numpy array with one element, 0 element for AP1_amp or APlast_amp if no AP generated by the model
             mod_result=mod_result[0]
 
-        if (mod_result == None or mod_result == 0 or mod_result.size==0) and (exp_mean != None and exp_std != None):
+        if (mod_result == None or mod_result.size==0) and (exp_mean != None and exp_std != None):
             temp_fit=250
         elif exp_mean == None and exp_std == None :
             temp_fit=0
@@ -919,6 +920,7 @@ def combineFeatures(self, candidates, args={}):
         :return: the ``list`` of fitness values corresponding to the parameter sets
 
         """
+
         self.fitnes = []
         features = self.option.feats
 
@@ -979,6 +981,7 @@ def combineFeatures(self, candidates, args={}):
                 print "current fitness: ",temp_fit
             temp_fit = 0
 
+
         return self.fitnes
 
     def getErrorComponents(self, index_of_trace, model_output):

optimizer/graphic.py

@@ -1611,7 +1611,7 @@ def Next(self, e):
                                 })
             self.kwargs.update({"weights" : [float(w.GetValue()) for w in filter(lambda x: x.IsEnabled(), self.weights)]})
         else:
-            #self.my_list=self.core.data_handler.features_data.keys()[3:]
+            #self.my_list=self.core.data_handler.features_data.keys()[3:-1]
             self.kwargs.update({"feat":
                                 [tmp_dict,
                                  [self.my_list[fun[0]] for fun in filter(lambda x: x[1].IsEnabled(), enumerate(self.weights))]]