photometry sync task

ibllib/__init__.py

@@ -2,7 +2,7 @@
 import logging
 import warnings
 
-__version__ = '2.40.1'
+__version__ = '2.40.2'
 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/oneibl/patcher.py

@@ -115,7 +115,7 @@ def _patch_dataset(self, path, dset_id=None, revision=None, dry=False, ftp=False
         assert is_uuid_string(dset_id)
         # If the revision is not None then we need to add the revision into the path. Note the moving of the file
         # is handled by one registration client
-        if revision is not None and f'#{revision}' not in str(path):
+        if revision and f'#{revision}' not in str(path):
             path = path.parent.joinpath(f'#{revision}#', path.name)
         assert path.exists()
         dset = self.one.alyx.rest('datasets', 'read', id=dset_id)

ibllib/pipes/dynamic_pipeline.py

@@ -41,8 +41,7 @@
 import ibllib.pipes.video_tasks as vtasks
 import ibllib.pipes.ephys_tasks as etasks
 import ibllib.pipes.audio_tasks as atasks
-import ibllib.pipes.photometry_tasks as ptasks
-# from ibllib.pipes.photometry_tasks import FibrePhotometryPreprocess, FibrePhotometryRegisterRaw
+import ibllib.pipes.neurophotometrics as ptasks
 
 _logger = logging.getLogger(__name__)
 
@@ -582,14 +581,12 @@ def make_pipeline(session_path, **pkwargs):
         tasks['MesoscopeCompress'] = type('MesoscopeCompress', (mscope_tasks.MesoscopeCompress,), {})(
             **kwargs, **mscope_kwargs, parents=[tasks['MesoscopePreprocess']])
 
-    if 'photometry' in devices:
-        # {'collection': 'raw_photometry_data', 'sync_label': 'frame_trigger', 'regions': ['Region1G', 'Region3G']}
-        photometry_kwargs = devices['photometry']
-        tasks['FibrePhotometryRegisterRaw'] = type('FibrePhotometryRegisterRaw', (
-            ptasks.FibrePhotometryRegisterRaw,), {})(**kwargs, **photometry_kwargs)
-        tasks['FibrePhotometryPreprocess'] = type('FibrePhotometryPreprocess', (
-            ptasks.FibrePhotometryPreprocess,), {})(**kwargs, **photometry_kwargs, **sync_kwargs,
-                                                    parents=[tasks['FibrePhotometryRegisterRaw']] + sync_tasks)
+    if 'neurophotometrics' in devices:
+        # {'collection': 'raw_photometry_data', 'datetime': '2024-09-18T16:43:55.207000',
+        #   'fibers': {'G0': {'location': 'NBM'}, 'G1': {'location': 'SI'}}, 'sync_channel': 1}
+        photometry_kwargs = devices['neurophotometrics']
+        tasks['FibrePhotometrySync'] = type('FibrePhotometrySync', (
+            ptasks.FibrePhotometrySync,), {})(**kwargs, **photometry_kwargs)
 
     p = mtasks.Pipeline(session_path=session_path, **pkwargs)
     p.tasks = tasks

ibllib/pipes/mesoscope_tasks.py

@@ -790,7 +790,7 @@ def _run(self, rename_files=True, use_badframes=True, **kwargs):
 
         # If applicable, save as bad_frames.npy in first raw_imaging_folder for suite2p
         # badframes.mat contains QC values that do affect ROI detection (e.g. no PMT, lens artefacts)
-        badframes = np.array([], dtype='i8')
+        badframes = np.array([], dtype='uint32')
         total_frames = 0
         # Ensure all indices are relative to total cumulative frames
         for m, collection in zip(all_meta, raw_image_collections):