Neuromodulator

ibllib/__init__.py

@@ -2,7 +2,7 @@
 import logging
 import warnings
 
-__version__ = '2.39.1'
+__version__ = '2.39.2'
 warnings.filterwarnings('always', category=DeprecationWarning, module='ibllib')
 
 # if this becomes a full-blown library we should let the logging configuration to the discretion of the dev

ibllib/io/extractors/bpod_trials.py

@@ -3,17 +3,14 @@
 This module will extract the Bpod trials and wheel data based on the task protocol,
 i.e. habituation, training or biased.
 """
-import logging
 import importlib
 
-from ibllib.io.extractors.base import get_bpod_extractor_class, protocol2extractor
+from ibllib.io.extractors.base import get_bpod_extractor_class, protocol2extractor, BaseExtractor
 from ibllib.io.extractors.habituation_trials import HabituationTrials
 from ibllib.io.extractors.training_trials import TrainingTrials
 from ibllib.io.extractors.biased_trials import BiasedTrials, EphysTrials
 from ibllib.io.extractors.base import BaseBpodTrialsExtractor
 
-_logger = logging.getLogger(__name__)
-
 
 def get_bpod_extractor(session_path, protocol=None, task_collection='raw_behavior_data') -> BaseBpodTrialsExtractor:
     """
@@ -39,20 +36,25 @@ def get_bpod_extractor(session_path, protocol=None, task_collection='raw_behavio
         'BiasedTrials': BiasedTrials,
         'EphysTrials': EphysTrials
     }
+
     if protocol:
-        class_name = protocol2extractor(protocol)
+        extractor_class_name = protocol2extractor(protocol)
     else:
-        class_name = get_bpod_extractor_class(session_path, task_collection=task_collection)
-    if class_name in builtins:
-        return builtins[class_name](session_path)
+        extractor_class_name = get_bpod_extractor_class(session_path, task_collection=task_collection)
+    if extractor_class_name in builtins:
+        return builtins[extractor_class_name](session_path)
 
     # look if there are custom extractor types in the personal projects repo
-    if not class_name.startswith('projects.'):
-        class_name = 'projects.' + class_name
-    module, class_name = class_name.rsplit('.', 1)
+    if not extractor_class_name.startswith('projects.'):
+        extractor_class_name = 'projects.' + extractor_class_name
+    module, extractor_class_name = extractor_class_name.rsplit('.', 1)
     mdl = importlib.import_module(module)
-    extractor_class = getattr(mdl, class_name, None)
+    extractor_class = getattr(mdl, extractor_class_name, None)
     if extractor_class:
-        return extractor_class(session_path)
+        my_extractor = extractor_class(session_path)
+        if not isinstance(my_extractor, BaseExtractor):
+            raise ValueError(
+                f"{my_extractor} should be an Extractor class inheriting from ibllib.io.extractors.base.BaseExtractor")
+        return my_extractor
     else:
-        raise ValueError(f'extractor {class_name} not found')
+        raise ValueError(f'extractor {extractor_class_name} not found')

ibllib/tests/extractors/test_extractors.py

@@ -3,13 +3,14 @@
 import shutil
 import tempfile
 import unittest
-import unittest.mock
+from unittest.mock import patch, Mock, MagicMock
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
 
 import one.alf.io as alfio
+from ibllib.io.extractors.bpod_trials import get_bpod_extractor
 from ibllib.io.extractors import training_trials, biased_trials, camera
 from ibllib.io import raw_data_loaders as raw
 from ibllib.io.extractors.base import BaseExtractor
@@ -530,13 +531,13 @@ def test_size_outputs(self):
             'peakVelocity_times': np.array([1, 1])}
         function_name = 'ibllib.io.extractors.training_wheel.extract_wheel_moves'
         # Training
-        with unittest.mock.patch(function_name, return_value=mock_data):
+        with patch(function_name, return_value=mock_data):
             task, = get_trials_tasks(self.training_lt5['path'])
             trials, _ = task.extract_behaviour(save=True)
         trials = alfio.load_object(self.training_lt5['path'] / 'alf', object='trials')
         self.assertTrue(alfio.check_dimensions(trials) == 0)
         # Biased
-        with unittest.mock.patch(function_name, return_value=mock_data):
+        with patch(function_name, return_value=mock_data):
             task, = get_trials_tasks(self.biased_lt5['path'])
             trials, _ = task.extract_behaviour(save=True)
         trials = alfio.load_object(self.biased_lt5['path'] / 'alf', object='trials')
@@ -753,5 +754,33 @@ def test_attribute_times(self, display=False):
             camera.attribute_times(tsa, tsb, injective=False, take='closest')
 
 
+class TestGetBpodExtractor(unittest.TestCase):
+
+    def test_get_bpod_extractor(self):
+        # un-existing extractor should raise a value error
+        with self.assertRaises(ValueError):
+            get_bpod_extractor('', protocol='sdf', task_collection='raw_behavior_data')
+        # in this case this returns an ibllib.io.extractors.training_trials.TrainingTrials instance
+        extractor = get_bpod_extractor(
+            '', protocol='_trainingChoiceWorld',
+            task_collection='raw_behavior_data'
+        )
+        self.assertTrue(isinstance(extractor, BaseExtractor))
+
+    def test_get_bpod_custom_extractor(self):
+        # here we'll mock a custom module with a custom extractor
+        DummyModule = MagicMock()
+        DummyExtractor = Mock(spec_set=BaseExtractor)
+        DummyModule.toto.return_value = DummyExtractor
+        base_module = 'ibllib.io.extractors.bpod_trials'
+        with patch(f'{base_module}.get_bpod_extractor_class', return_value='toto'), \
+                patch(f'{base_module}.importlib.import_module', return_value=DummyModule) as import_mock:
+            self.assertIs(get_bpod_extractor(''), DummyExtractor)
+            import_mock.assert_called_with('projects')
+            # Check raises when imported class not an extractor
+            DummyModule.toto.return_value = MagicMock(spec=dict)
+            self.assertRaisesRegex(ValueError, 'should be an Extractor class', get_bpod_extractor, '')
+
+
 if __name__ == '__main__':
     unittest.main(exit=False, verbosity=2)

release_notes.md

@@ -12,6 +12,10 @@
 
 #### 2.39.1
 - Bugfix: brainbox.metrics.single_unit.quick_unit_metrics fix for indexing of n_spike_below2
+- 
+#### 2.39.2
+- Bugfix: routing of protocol to extractor through the project repository checks that the
+target is indeed an extractor class.
 
 ## Release Note 2.38.0