Frame filtering by candidate list

This implements the frame filtering by the candidate list with formats that we discussed: h5, csv. In both formats we expect three datasets: trainId, pulseId, classId, which can be given under any name (parameter). The third options is EXDF format, which also can be read as h5 but it is read here with extra-data and due to that can be split on sequence files

@schmidtp @kluyvert @turkot

src/exdf/data_reduction/builtins.py

@@ -3,7 +3,7 @@ from logging import getLogger
 
 import numpy as np
 from exdf.data_reduction import ReductionMethod
-from extra_data import SourceNameError, by_id
+from extra_data import SourceNameError, by_id, RunDirectory
 
 
 def parse_slice(value):
@@ -399,3 +399,169 @@ class LitFrames(ReductionMethod):
             )
         if nrec > max_lines:
             self.log.info(f"... {nrec - max_lines + 1} more lines skipped")
+
+
+class FrameList(ReductionMethod):
+    log = getLogger('exdf.data_reduction.builtins.FrameList')
+
+    event_type = np.dtype([
+        ('tid', np.uint64), ('pid', np.uint64), ('cls', np.uint16)])
+
+    @staticmethod
+    def arguments(ap):
+        group = ap.add_argument_group(
+            'Candidate frame selection',
+            'Allows to filter frames by candidate list')
+
+        group.add_argument(
+            '--framelist-det-sources',
+            action='store', type=str,
+            help='Detector sources to filter',
+        )
+        group.add_argument(
+            '--framelist-file',
+            action='store', type=str,
+            help='Candidate list file, if none use data collection',
+        )
+        group.add_argument(
+            '--framelist-filetype',
+            action='store', type=str, choices=['exdf', 'h5', 'csv'], default='exdf',
+            help='Candidate list file, if none use data collection',
+        )
+        group.add_argument(
+            '--framelist-entry',
+            action='store', type=str, default='',
+            help='Source name in EXDF format or dataset prefix in other formats',
+        )
+        group.add_argument(
+            '--framelist-class-key',
+            action='store', type=str,
+            help='Key of frame class dataset',
+        )
+        group.add_argument(
+            '--framelist-train-id-key',
+            action='store', type=str,
+            help='Key of frame class dataset',
+        )
+        group.add_argument(
+            '--framelist-pulse-id-key',
+            action='store', type=str,
+            help='Key of frame class dataset',
+        )
+        group.add_argument(
+            '--framelist-keep-missed',
+            action='store_true',
+            help='Key of frame class dataset',
+        )
+
+    def from_exdf(self, data, args):
+        clist = (
+            data if args.framelist_file is None
+            else RunDirectory(args.framelist_file)
+        )
+        source = args.framelist_entry
+        class_key = args.framelist_class_key
+        if class_key is None:
+            raise ValueError(
+                "The --framelist-class-key option is not specified.")
+        train_id_key = args.framelist_train_id_key
+        if train_id_key is None:
+            train_id_key = class_key.rpartition('.')[0] + '.trainId'
+        pulse_id_key = args.framelist_pulse_id_key
+        if pulse_id_key is None:
+            pulse_id_key = class_key.rpartition('.')[0] + '.pulseId'
+
+        group = clist[source]
+        cls = group[class_key].ndarray()
+        tid = group[train_id_key].ndarray()
+        pid = group[pulse_id_key].ndarray()
+        return np.rec.fromarrays([tid, pid, cls], dtype=self.event_type)
+
+    def from_h5(self, args):
+        import h5py
+        with h5py.File(args.framelist_file) as f:
+            entry = f[args.framelist_entry]
+            tid = entry[args.framelist_train_id_key][:]
+            pid = entry[args.framelist_pulse_id_key][:]
+            cls = entry[args.framelist_class_key][:]
+        return np.rec.fromarrays([tid, pid, cls], dtype=self.event_type)
+
+    def from_csv(self, args):
+        import pandas as pd
+        ds = pd.read_csv(args.framelist_file)
+        return np.rec.fromarrays(
+            [
+                ds[args.framelist_entry + args.framelist_train_id_key],
+                ds[args.framelist_entry + args.framelist_pulse_id_key],
+                ds[args.framelist_entry + args.framelist_class_key]
+            ],
+            dtype=self.event_type
+        )
+
+    def __init__(self, data, args):
+        try:
+            if args.framelist_filetype == 'exdf':
+                evt = self.from_exdf(data, args)
+            elif args.framelist_filetype == 'h5':
+                evt = self.from_h5(args)
+            elif args.framelist_filetype == 'csv':
+                evt = self.from_csv(args)
+            else:
+                raise ValueError("Unknown filetype.")
+        except FileNotFoundError:
+            self.log.error(f"File '{args.framelist_file}' not found.")
+            return
+        except (SourceNameError, KeyError):
+            self.log.error("Required keys are not found in data.")
+            return
+        except Exception as e:
+            self.log.error(f"Exception when reading the candidate list: {e}.")
+            return
+
+        # group by trains
+        trains, first, count = np.unique(evt.tid, return_index=True, return_counts=True)
+
+        # loop over sources
+        det = data.select(args.framelist_det_sources, "image.*")
+        for source_req in det.instrument_sources:
+            # resolve legacy source
+            src = data.legacy_sources.get(source_req, source_req)
+            self.log.info("select frames in " + src)
+
+            num_drop = 0
+            num_keep = 0
+            num_missed_frames = 0
+            num_missed_trains = 0
+
+            key = data[src, "image.pulseId"].drop_empty_trains()
+            for train_id, pulse_ids in key.trains():
+                num_frames = len(pulse_ids)
+                if args.framelist_keep_missed:
+                    mask = np.ones(num_frames, bool)
+                else:
+                    mask = np.zeros(num_frames, bool)
+                i = np.argmax(trains == train_id)
+                if trains[i] == train_id:
+                    f0 = first[i]
+                    fN = f0 + count[i]
+                    classified = np.isin(pulse_ids, evt.pid[f0:fN])
+                    available = np.isin(evt.pid[f0:fN], pulse_ids[classified])
+                    mask[classified] = evt.cls[f0:fN][available] != 0
+                    num_missed_frames += abs(num_frames - np.sum(classified))
+                else:
+                    num_missed_trains += 1
+
+                reduced_num_frames = np.sum(mask)
+                num_keep += reduced_num_frames
+                num_drop += num_frames - reduced_num_frames
+
+                if src.endswith(':xtdf'):
+                    self.select_xtdf(src, by_id[[train_id]], mask)
+                else:
+                    self.select_entries(src, 'image', by_id[[train_id]], mask)
+
+            ratio = num_keep / (num_keep + num_drop)
+            self.log.info(f" - {num_keep} frames keep, {num_drop} frames drop, "
+                          f"ratio: {ratio:.2%}")
+            self.log.info(f" - decision is missed for {num_missed_trains} trains, "
+                          f"{num_missed_frames} frames")