add type check for pool parameters

Author: Moritz-Alexander-Kern

elephant/statistics.py

@@ -364,23 +364,31 @@ def _compute_fano(spiketrains: neo.SpikeTrain) -> float:
         return fano
 
     if isinstance(spiketrains, elephant.trials.Trials):
+        # Check if parameters are of the correct type
+        if not isinstance(pool_trials, bool):
+            raise TypeError(f"'pool_trials' must be of type bool, but got {type(pool_trials)}")
+        elif not isinstance(pool_spike_trains, bool):
+            raise TypeError(f"'pool_spike_trains' must be of type bool, but got {type(pool_spike_trains)}")
         if not pool_trials and not pool_spike_trains:
             return [[_compute_fano([spiketrain]) for spiketrain in spiketrains.get_spiketrains_from_trial_as_list(idx)]
                     for idx in range(spiketrains.n_trials)]
-        if not pool_trials and pool_spike_trains:
+        elif not pool_trials and pool_spike_trains:
             return [_compute_fano(spiketrains.get_spiketrains_from_trial_as_list(idx))
                     for idx in range(spiketrains.n_trials)]
-        if pool_trials and not pool_spike_trains:
+        elif pool_trials and not pool_spike_trains:
             list_of_lists_of_spiketrains = [
                 spiketrains.get_spiketrains_from_trial_as_list(trial_id=trial_no)
                 for trial_no in range(spiketrains.n_trials)]
             return [_compute_fano([list_of_lists_of_spiketrains[trial_no][st_no]
                                    for trial_no in range(len(list_of_lists_of_spiketrains))])
                     for st_no in range(len(list_of_lists_of_spiketrains[0]))]
-        if pool_trials and pool_spike_trains:
+        elif pool_trials and pool_spike_trains:
             return [_compute_fano(
                 [spiketrain for trial_no in range(spiketrains.n_trials)
                     for spiketrain in spiketrains.get_spiketrains_from_trial_as_list(trial_id=trial_no)])]
+        else:
+            raise TypeError(f"pool_spiketrains and pool_trials must be of type: bool, but are "
+                            f"{type(pool_spike_trains)} and {type(pool_trials)}")
     else:  # Legacy behavior
         return _compute_fano(spiketrains)
 

elephant/test/test_statistics.py

@@ -375,6 +375,12 @@ def test_fanofactor_trials_pool_trials_false_pool_spiketrains_false(self):
         for result in results:
             self.assertEqual(len(result), self.test_trials.n_spiketrains_trial_by_trial[0])
 
+    def test_fanofactor_trials_pool_spike_trains_wrong_type(self):
+        self.assertRaises(TypeError, statistics.fanofactor, self.test_trials, pool_spike_trains="Wrong Type")
+        self.assertRaises(TypeError, statistics.fanofactor, self.test_trials, pool_spike_trials="Wrong Type")
+        self.assertRaises(TypeError, statistics.fanofactor, self.test_trials, pool_spike_trials="Wrong Type",
+                          pool_spike_trains="Wrong Type")
+
 
 class LVTestCase(unittest.TestCase):
     def setUp(self):