add test and fix for pool_spiketrains with list of spiketrains

Author: Moritz-Alexander-Kern

elephant/statistics.py

@@ -1031,7 +1031,7 @@ def optimal_kernel(st):
                              sigma=str(kernel.sigma),
                              invert=kernel.invert)
 
-    if isinstance(spiketrains, neo.core.spiketrainlist.SpikeTrainList) and (
+    if isinstance(spiketrains, (neo.core.spiketrainlist.SpikeTrainList, list, tuple)) and (
                   pool_spike_trains):
         rate = np.mean(rate, axis=1)
 

elephant/test/test_statistics.py

@@ -1025,7 +1025,7 @@ def test_instantaneous_rate_trials_pool_spiketrains_false_pool_trials_false(self
         self.assertEqual(len(rate), self.trial_object.n_trials)
         self.assertEqual(rate[0].shape[1], self.trial_object.n_spiketrains_trial_by_trial[0])
 
-    def test_instantaneous_rate_list_pool_spike_trains(self):
+    def test_instantaneous_rate_spiketrainlist_pool_spike_trains(self):
         kernel = kernels.GaussianKernel(sigma=500 * pq.ms)
 
         rate = statistics.instantaneous_rate(
@@ -1037,6 +1037,18 @@ def test_instantaneous_rate_list_pool_spike_trains(self):
         self.assertIsInstance(rate, neo.core.AnalogSignal)
         self.assertEqual(rate.shape[1], 1)
 
+    def test_instantaneous_rate_list_pool_spike_trains(self):
+        kernel = kernels.GaussianKernel(sigma=500 * pq.ms)
+
+        rate = statistics.instantaneous_rate(
+            list(self.trial_object.get_spiketrains_from_trial_as_list(0)),
+            sampling_period=0.1 * pq.ms,
+            kernel=kernel,
+            pool_spike_trains=True,
+            pool_trials=False)
+        self.assertIsInstance(rate, neo.core.AnalogSignal)
+        self.assertEqual(rate.shape[1], 1)
+
     def test_instantaneous_rate_list_of_spike_trains(self):
         kernel = kernels.GaussianKernel(sigma=500 * pq.ms)
         rate = statistics.instantaneous_rate(