From 90591e91a8bf1415366827b63e81e2f7bb4576ad Mon Sep 17 00:00:00 2001
From: Georg Raiser <grg2rsr@gmail.com>
Date: Wed, 11 Dec 2024 16:05:09 +0000
Subject: [PATCH] small fix: skipping files for qc that raise an exception

---
 src/iblphotometry/qc.py | 144 +++++++++++++++++++++-------------------
 1 file changed, 77 insertions(+), 67 deletions(-)

diff --git a/src/iblphotometry/qc.py b/src/iblphotometry/qc.py
index 5276026..7d9c7bd 100644
--- a/src/iblphotometry/qc.py
+++ b/src/iblphotometry/qc.py
@@ -113,74 +113,84 @@ def run_qc(
     qc_results = []
     for eid in tqdm(eids):
         print(eid)
-        # get photometry data
-        raw_dfs = data_loader.load_photometry_data(eid=eid)
-        signal_bands = list(raw_dfs.keys())
-        brain_regions = raw_dfs[signal_bands[0]]
-
-        # get behavioral data
-        # TODO this should be provided
-        sl = SessionLoader(eid=eid, one=data_loader.one)
-        # for caroline
-        # trials = sl.load_trials(
-        #     collection='alf/task_00'
-        # )  # this is necessary fo caroline
-        trials = sl.load_trials()  # should be good for all others
-
-        # the old way
-        # trials = data_loader.one.load_dataset(eid, '*trials.table.pqt')
-
-        for band in signal_bands:
-            raw_tf = raw_dfs[band]
-            for region in brain_regions:
-                qc_result = qc_series(
-                    raw_tf[region], qc_metrics['raw'], sliding_kwargs=None, eid=eid
-                )
-                qc_results.append(
-                    dict(eid=eid, pipeline='raw', band=band, region=region, **qc_result)
-                )
-
-        # run the pipelines and qc on the processed data
-        # here it needs to be specified if one band is a reference of the other
-        for pipeline_name, pipeline in pipelines_reg.items():
-            if 'reference' in sigref_mapping:  # this is for isosbestic pipelines
-                proc_tf = run_pipeline(
-                    pipeline,
-                    raw_dfs[sigref_mapping['signal']],
-                    raw_dfs[sigref_mapping['reference']],
-                )
-            else:
-                # FIXME this fails for true-multiband
-                # this hack works for single-band
-                # possible fix could be that signal could be a list
-                proc_tf = run_pipeline(pipeline, raw_dfs[sigref_mapping['signal']])
-
-            for region in brain_regions:
-                # sliding qc of the processed data
-                qc_proc = qc_series(
-                    proc_tf[region],
-                    qc_metrics=qc_metrics['processed'],
-                    sliding_kwargs=qc_metrics['sliding_kwargs'],
-                    eid=eid,
-                    brain_region=region,
-                )
-
-                # qc with metrics that use behavior
-                qc_resp = qc_series(
-                    proc_tf[region],
-                    qc_metrics['response'],
-                    trials=trials,
-                    eid=eid,
-                    brain_region=region,
-                )
-                qc_result = qc_proc | qc_resp
-                qc_results.append(
-                    dict(
+        try:
+            # get photometry data
+            raw_dfs = data_loader.load_photometry_data(eid=eid)
+            signal_bands = list(raw_dfs.keys())
+            brain_regions = raw_dfs[signal_bands[0]]
+
+            # get behavioral data
+            # TODO this should be provided
+            sl = SessionLoader(eid=eid, one=data_loader.one)
+            # for caroline
+            # trials = sl.load_trials(
+            #     collection='alf/task_00'
+            # )  # this is necessary fo caroline
+            trials = sl.load_trials()  # should be good for all others
+
+            # the old way
+            # trials = data_loader.one.load_dataset(eid, '*trials.table.pqt')
+
+            for band in signal_bands:
+                raw_tf = raw_dfs[band]
+                for region in brain_regions:
+                    qc_result = qc_series(
+                        raw_tf[region], qc_metrics['raw'], sliding_kwargs=None, eid=eid
+                    )
+                    qc_results.append(
+                        dict(
+                            eid=eid,
+                            pipeline='raw',
+                            band=band,
+                            region=region,
+                            **qc_result,
+                        )
+                    )
+
+            # run the pipelines and qc on the processed data
+            # here it needs to be specified if one band is a reference of the other
+            for pipeline_name, pipeline in pipelines_reg.items():
+                if 'reference' in sigref_mapping:  # this is for isosbestic pipelines
+                    proc_tf = run_pipeline(
+                        pipeline,
+                        raw_dfs[sigref_mapping['signal']],
+                        raw_dfs[sigref_mapping['reference']],
+                    )
+                else:
+                    # FIXME this fails for true-multiband
+                    # this hack works for single-band
+                    # possible fix could be that signal could be a list
+                    proc_tf = run_pipeline(pipeline, raw_dfs[sigref_mapping['signal']])
+
+                for region in brain_regions:
+                    # sliding qc of the processed data
+                    qc_proc = qc_series(
+                        proc_tf[region],
+                        qc_metrics=qc_metrics['processed'],
+                        sliding_kwargs=qc_metrics['sliding_kwargs'],
                         eid=eid,
-                        pipeline=pipeline_name,
-                        region=region,
-                        **qc_result,
+                        brain_region=region,
                     )
-                )
+
+                    # qc with metrics that use behavior
+                    qc_resp = qc_series(
+                        proc_tf[region],
+                        qc_metrics['response'],
+                        trials=trials,
+                        eid=eid,
+                        brain_region=region,
+                    )
+                    qc_result = qc_proc | qc_resp
+                    qc_results.append(
+                        dict(
+                            eid=eid,
+                            pipeline=pipeline_name,
+                            region=region,
+                            **qc_result,
+                        )
+                    )
+        except Exception as e:
+            logger.warning(f'{eid}: failure: {type(e).__name__}:{e}')
+
         gc.collect()
     return qc_results