Adding efel.reset()

hippounit/tests/test_BackpropagatingAPTest.py

@@ -310,7 +310,7 @@ def find_current_amp(self, model, delay, dur, section_stim, loc_stim, section_re
                         amplitude = binsearch_result[1]
                         spikecount = binsearch_result[2]
                 elif i==0:  #spikecount[i-1]  is the last element here
-                    if spikecounts[i] >= 10 and spikecounts[i] <= 20 and spikecounts[i+1] >= spikecounts[i]: 
+                    if spikecounts[i] >= 10 and spikecounts[i] <= 20 and spikecounts[i+1] >= spikecounts[i]:
                         amplitudes.append(amps[i])
                         _spikecounts.append(spikecounts[i])
                     elif spikecounts[i] < 10 and spikecounts[i+1] > 20 and spikecounts[i+1] >= spikecounts[i]:
@@ -697,6 +697,8 @@ def validate_observation(self, observation):
     def generate_prediction(self, model, verbose=False):
         """Implementation of sciunit.Test.generate_prediction."""
 
+        efel.reset()
+
         if self.base_directory:
             self.path_results = self.base_directory + 'results/' + 'backpropagating_AP/' + model.name + '/'
         else:
@@ -806,6 +808,8 @@ def generate_prediction(self, model, verbose=False):
 
         self.plot_features(model, features, actual_distances)
 
+        efel.reset()
+
         return prediction
 
     def compute_score(self, observation, prediction, verbose=False):

hippounit/tests/test_DepolarizationBlockTest.py

@@ -539,6 +539,8 @@ def validate_observation(self, observation):
 	def generate_prediction(self, model, verbose=False):
 		"""Implementation of sciunit.Test.generate_prediction."""
 
+		efel.reset()
+
 		pool = multiprocessing.Pool(self.npool, maxtasksperchild=1)
 		#amps = numpy.arange(0,3.55,0.05)
 		amps = numpy.arange(0,1.65,0.05)
@@ -557,6 +559,8 @@ def generate_prediction(self, model, verbose=False):
 
 		prediction = {'model_I_maxNumAP':float(I_maxNumAP)*nA, 'model_I_below_depol_block':float(I_below_depol_block)*nA, 'model_Veq': float(Veq)*mV}
 
+		efel.reset()
+
 		return prediction
 
 	def compute_score(self, observation, prediction, verbose=False):

hippounit/tests/test_ObliqueIntegrationTest.py

@@ -1637,6 +1637,8 @@ def validate_observation(self, observation):
     def generate_prediction(self, model, verbose=False):
         """Implementation of sciunit.Test.generate_prediction."""
 
+        efel.reset()
+
         if self.base_directory:
             self.path_results = self.base_directory + 'results/' + 'oblique_integration/' + model.name + '/'
         else:
@@ -1878,6 +1880,8 @@ def generate_prediction(self, model, verbose=False):
 
         print "Results are saved in the directory: ", self.path_results
 
+        efel.reset()
+
         return prediction
 
     def compute_score(self, observation, prediction, verbose=False):

hippounit/tests/test_PSPAttenuationTest.py

@@ -430,6 +430,8 @@ def validate_observation(self, observation):
     def generate_prediction(self, model, verbose=False):
         """Implementation of sciunit.Test.generate_prediction."""
 
+        efel.reset()
+
         if self.base_directory:
             self.path_results = self.base_directory + 'results/' + 'PSP_attenuation/' + model.name + '/'
         else:
@@ -534,6 +536,8 @@ def generate_prediction(self, model, verbose=False):
         file_name_mean_features = self.path_results + 'PSP_attenuation_mean_model_features.json'
         json.dump(prediction, open(file_name_mean_features, "wb"), indent=4)
 
+        efel.reset()
+
         return prediction
 
     def compute_score(self, observation, prediction, verbose=False):

hippounit/tests/test_SomaticFeaturesTest.py

@@ -221,8 +221,9 @@ def analyse_traces(self, stimuli_list, traces_results, features_list):
 	            stim_name=key
 	        if stim_name == stimulus:
 
-	            trace['T'] = traces_results[i][key][0]
-	            trace['V'] = traces_results[i][key][1]
+	            trace['T'] = traces_results[i][stim_name][0]
+	            trace['V'] = traces_results[i][stim_name][1]
+
 
 	    for i in range (0, len(stimuli_list)):
 	        if stimuli_list[i][0]==stimulus:
@@ -360,6 +361,8 @@ def create_figs(self, model, traces_results, features_names, feature_results_dic
 	def generate_prediction(self, model, verbose=False):
 		"""Implementation of sciunit.Test.generate_prediction."""
 
+		efel.reset()
+
 		self.observation = collections.OrderedDict(sorted(self.observation.items()))
 
 		global model_name_soma
@@ -369,7 +372,6 @@ def generate_prediction(self, model, verbose=False):
 
 		stimuli_list=self.create_stimuli_list()
 
-
 		run_stim_ = functools.partial(self.run_stim, model)
 		traces_results = pool.map(run_stim_, stimuli_list, chunksize=1)
 		#traces_results = traces_result.get()
@@ -440,6 +442,8 @@ def generate_prediction(self, model, verbose=False):
 
 		prediction=soma_features
 
+		efel.reset()
+
 		return prediction
 
 	def compute_score(self, observation, prediction, verbose=False):

setup.py

@@ -17,7 +17,7 @@ def package_files(directory):
 
 setup(
     name='hippounit',
-    version='1.2.3',
+    version='1.2.4',
     author='Sara Saray, Szabolcs Kali, Christian Rossert, Andrew Davison, Shailesh Appukuttan',
     author_email='[email protected], [email protected], [email protected], [email protected], [email protected]',
     packages=['hippounit', 'hippounit.tests', 'hippounit.capabilities', 'hippounit.scores'],