Junna preproc

sotodlib/obs_ops/splits.py

@@ -150,11 +150,19 @@ def get_split_flags(aman, proc_aman=None, split_cfg=None):
 
     if 't2p' in proc_aman:
         # T2P Leakage split
-        if not 'high_leakage' in split_cfg:
+        if 'high_leakage' not in split_cfg:
             split_cfg['high_leakage'] = 1e-3
-        fm.wrap_dets('high_leakage', np.sqrt(proc_aman.t2p.lamQ**2 + proc_aman.t2p.lamU**2) > split_cfg['high_leakage'])
-        fm.wrap_dets('low_leakage', np.sqrt(proc_aman.t2p.lamQ**2 + proc_aman.t2p.lamU**2) <= split_cfg['high_leakage'])
-        split_aman.wrap('leakage_avg', np.nanmean(np.sqrt(proc_aman.t2p.lamQ**2 + proc_aman.t2p.lamU**2)),
+        if 'AQ' in proc_aman.t2p:
+            leak_fldQ = 'lamQ'
+            leak_fldU = 'lamU'
+        elif 'coeffsQ' in proc_aman.t2p:
+            leak_fldQ = 'coeffsQ'
+            leak_fldU = 'coeffsU'
+        else:
+            raise ValueError('no leakage coefficients found in axis manager') 
+        fm.wrap_dets('high_leakage', np.sqrt(proc_aman.t2p[leak_fldQ]**2 + proc_aman.t2p[leak_fldU]**2) > split_cfg['high_leakage'])
+        fm.wrap_dets('low_leakage', np.sqrt(proc_aman.t2p[leak_fldQ]**2 + proc_aman.t2p[leak_fldU]**2) <= split_cfg['high_leakage'])
+        split_aman.wrap('leakage_avg', np.nanmean(np.sqrt(proc_aman.t2p[leak_fldQ]**2 + proc_aman.t2p[leak_fldU]**2)),
                     [(0, 'dets')])
     if 'hwpss_stats' in proc_aman:
         # High 2f amplitude split

sotodlib/preprocess/preprocess_util.py

@@ -562,11 +562,19 @@ def multilayer_load_and_preprocess_sim(obs_id, configs_init, configs_proc,
             pipe_proc = Pipeline(configs_proc["process_pipe"], logger=logger)
 
             logger.info("Restricting detectors on all proc pipeline processes")
-            keep_all = np.ones(meta_proc.dets.count, dtype=bool)
-            for process in pipe_proc[:]:
-                keep = process.select(meta_proc, in_place=False)
-                if isinstance(keep, np.ndarray):
-                    keep_all &= keep
+
+            if (
+                'valid_data' in meta_proc.preprocess and
+                isinstance(meta_proc.preprocess.valid_data, core.AxisManager)
+               ):
+                keep_all = has_any_cuts(meta_proc.preprocess.valid_data.valid_data)
+            else:
+                keep_all = np.ones(meta_proc.dets.count, dtype=bool)
+                for process in pipe_proc[:]:
+                    keep = process.select(meta_proc, in_place=False)
+                    if isinstance(keep, np.ndarray):
+                        keep_all &= keep
+
             meta_proc.restrict("dets", meta_proc.dets.vals[keep_all])
             meta_init.restrict('dets', meta_proc.dets.vals)
             aman = context_init.get_obs(meta_proc, no_signal=True)