mpciROIs.uuids (#663)

* mpciROIs.uuids
* Added tests

brainbox/behavior/training.py

@@ -83,15 +83,16 @@ class TrainingStatus(IntFlag):
     ... assert TrainingStatus[status.upper()] in ~TrainingStatus.FAILED, 'Subject untrained'
     ... assert TrainingStatus[status.upper()] in TrainingStatus.TRAINED ^ TrainingStatus.READY
 
-    # Get the next training status
+    Get the next training status
+
     >>> next(member for member in sorted(TrainingStatus) if member > TrainingStatus[status.upper()])
     <TrainingStatus.READY4RECORDING: 128>
 
     Notes
     -----
     - ~TrainingStatus.TRAINED means any status but trained 1a or trained 1b.
     - A subject may acheive both TRAINED_1A and TRAINED_1B within a single session, therefore it
-     is possible to have skipped the TRAINED_1A session status.
+      is possible to have skipped the TRAINED_1A session status.
     """
     UNTRAINABLE = auto()
     UNBIASABLE = auto()

ibllib/io/extractors/mesoscope.py

@@ -280,6 +280,7 @@ def get_wheel_positions(self, ticks=WHEEL_TICKS, radius=WHEEL_RADIUS_CM, coding=
         moves = extract_wheel_moves(wheel['timestamps'], wheel['position'])
 
         if display:
+            assert self.bpod_trials, 'no bpod trials to compare'
             fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
             bpod_ts = self.bpod_trials['wheel_timestamps']
             bpod_pos = self.bpod_trials['wheel_position']

ibllib/pipes/mesoscope_tasks.py

@@ -216,6 +216,7 @@ def signature(self):
                              ('mpciROIs.stackPos.npy', 'alf/FOV*', True),
                              ('mpciROIs.mpciROITypes.npy', 'alf/FOV*', True),
                              ('mpciROIs.cellClassifier.npy', 'alf/FOV*', True),
+                             ('mpciROIs.uuids.csv', 'alf/FOV*', True),
                              ('mpciROITypes.names.tsv', 'alf/FOV*', True),
                              ('mpciROIs.masks.npy', 'alf/FOV*', True),
                              ('mpciROIs.neuropilMasks.npy', 'alf/FOV*', True),
@@ -328,6 +329,11 @@ def _rename_outputs(self, suite2p_dir, frameQC_names, frameQC, rename_dict=None)
                 np.save(fov_dir.joinpath('mpciROIs.stackPos.npy'), np.asarray([(*s['med'], 0) for s in stat], dtype=int))
                 np.save(fov_dir.joinpath('mpciROIs.cellClassifier.npy'), np.asarray(iscell[:, 1], dtype=float))
                 np.save(fov_dir.joinpath('mpciROIs.mpciROITypes.npy'), np.asarray(iscell[:, 0], dtype=np.int16))
+                # clusters uuids
+                uuid_list = ['uuids'] + list(map(str, [uuid.uuid4() for _ in range(len(iscell))]))
+                with open(fov_dir.joinpath('mpciROIs.uuids.csv'), 'w+') as fid:
+                    fid.write('\n'.join(uuid_list))
+
                 pd.DataFrame([(0, 'no cell'), (1, 'cell')], columns=['roi_values', 'roi_labels']
                              ).to_csv(fov_dir.joinpath('mpciROITypes.names.tsv'), sep='\t', index=False)
                 # ROI and neuropil masks

ibllib/tests/extractors/test_ephys_trials.py

@@ -90,6 +90,10 @@ def test_align_to_trial(self):
         desired_out = np.array([4, 13, np.nan, 33, np.nan])
         self.assertTrue(np.allclose(desired_out, t_event_nans, equal_nan=True, atol=0, rtol=0))
 
+        # test errors
+        self.assertRaises(ValueError, ephys_fpga._assign_events_to_trial, np.array([0., 2., 1.]), t_event)
+        self.assertRaises(ValueError, ephys_fpga._assign_events_to_trial, t_trial_start, np.array([0., 2., 1.]))
+
     def test_wheel_trace_from_sync(self):
         pos_ = - np.array([-1, 0, -1, -2, -1, -2]) * (np.pi / ephys_fpga.WHEEL_TICKS)
         ta = np.array([1, 2, 3, 4, 5, 6])

ibllib/tests/test_mesoscope.py

@@ -4,13 +4,15 @@
 from unittest import mock
 import tempfile
 import json
+from itertools import chain
 from pathlib import Path
 
 from one.api import ONE
 import numpy as np
 
 from ibllib.pipes.mesoscope_tasks import MesoscopePreprocess, MesoscopeFOV, \
     find_triangle, surface_normal, _nearest_neighbour_1d
+from ibllib.io.extractors import mesoscope
 from ibllib.tests import TEST_DB
 
 # Mock suit2p which is imported in MesoscopePreprocess
@@ -248,3 +250,21 @@ def tearDown(self) -> None:
         Here we return the mode back to the default after testing behaviour in offline mode.
         """
         self.one.mode = 'auto'
+
+
+class TestImagingMeta(unittest.TestCase):
+    """Test raw imaging metadata versioning."""
+    def test_patch_imaging_meta(self):
+        """Test for ibllib.io.extractors.mesoscope.patch_imaging_meta function."""
+        meta = {'version': '0.1.0', 'FOV': [{'roiUuid': None}, {'roiUUID': None}]}
+        new_meta = mesoscope.patch_imaging_meta(meta)
+        self.assertEqual(set(chain(*map(dict.keys, new_meta['FOV']))), {'roiUUID'})
+        meta = {'FOV': [
+            dict.fromkeys(['topLeftDeg', 'topRightDeg', 'bottomLeftDeg', 'bottomRightDeg']),
+            dict.fromkeys(['topLeftMM', 'topRightMM', 'bottomLeftMM', 'bottomRightMM'])
+        ]}
+        new_meta = mesoscope.patch_imaging_meta(meta)
+        self.assertIn('channelSaved', new_meta)
+        self.assertCountEqual(new_meta['FOV'][0], ('Deg', 'MM'))
+        expected = ('topLeft', 'topRight', 'bottomLeft', 'bottomRight')
+        self.assertCountEqual(new_meta['FOV'][0]['MM'], expected)