various minor bugfixes

bin/all_sky_search/pycbc_fit_sngls_split_binned

@@ -217,7 +217,7 @@ trigs = SingleDetTriggers(
     args.trigger_file,
     args.ifo,
     filter_rank=args.sngl_ranking,
-    filter_threshold=args.args.plot_lower_stat_limit,
+    filter_threshold=args.plot_lower_stat_limit,
 )
 # This is the direct pointer to the HDF file, used later on
 trigf = trigs.trigs_f

bin/minifollowups/pycbc_injection_minifollowup

@@ -103,7 +103,7 @@ if args.ifar_threshold is not None:
         ifars = f['found_after_vetoes']['ifar'][:]
     except KeyError:
         ifars = f['found_after_vetoes']['ifar_exc'][:]
-        logging.warn('Inclusive IFAR not found, using exclusive')
+        logging.warning('Inclusive IFAR not found, using exclusive')
     lgc_arr = ifars < args.ifar_threshold
     missed = numpy.append(missed,
                           f['found_after_vetoes']['injection_index'][lgc_arr])
@@ -178,6 +178,7 @@ def nearby_missedinj(endtime, snr):
 trigger_idx = {}
 trigger_snrs = {}
 trigger_times = {}
+# This finds the triggers near to _any_ missed injection
 for trig in single_triggers:
     ifo = trig.ifo
     with HFile(trig.lfn, 'r') as trig_f:
@@ -230,7 +231,7 @@ for num_event in range(num_events):
                       tags=args.tags + [str(num_event)])[0],)]
 
     for sngl in single_triggers:
-        # Find the triggers close to this injection at this IFO
+        # Find the triggers close to _this_ injection at this IFO
         ifo = sngl.ifo
         trig_tdiff = abs(inj_params[ifo + '_end_time'] - trigger_times[ifo])
         nearby = trig_tdiff < args.nearby_triggers_window
@@ -240,7 +241,7 @@ for num_event in range(num_events):
             continue
         # Find the loudest SNR in this window
         loudest = numpy.argmax(trigger_snrs[ifo][nearby])
-        # Convert to the indexin the trigger file
+        # Convert to the index in the trigger file
         nearby_trigger_idx = trigger_idx[ifo][nearby][loudest]
         # Make the info snippet
         sngl_info = mini.make_sngl_ifo(workflow, sngl, tmpltbank_file,
@@ -310,7 +311,7 @@ for num_event in range(num_events):
         single_fname = args.single_detector_triggers[curr_ifo]
         idx = HFile(single_fname).select(
             lambda t: abs(t - inj_params['tc']) < args.inj_window,
-            f'{ifo}/end_time',
+            f'{curr_ifo}/end_time',
             indices_only=True,
         )
 
@@ -327,7 +328,7 @@ for num_event in range(num_events):
         # Next, find the loudest within this set of triggers
         # Use SNR here or NewSNR, or other??
         loudest_idx = hd_sngl.snr.argmax()
-        hd_sngl.apply_mask(loudest_idx)
+        hd_sngl.apply_mask([loudest_idx])
 
         # What are the parameters of this trigger?
         curr_params = copy.deepcopy(inj_params)

bin/minifollowups/pycbc_page_snglinfo

@@ -76,12 +76,12 @@ sngl_file = hdf.SingleDetTriggers(
     args.instrument,
     bank_file=args.bank_file,
     veto_file=args.veto_file,
-    veto_segment_name=args.veto_segment_name,
+    segment_name=args.veto_segment_name,
 )
 
 if args.trigger_id is not None:
     # Apply a mask which is just the trigger of interest
-    sngl_file.apply_mask(np.array([args.trigger_id]))
+    sngl_file.apply_mask(numpy.array([args.trigger_id]))
 elif args.n_loudest is not None:
     # Cluster by a ranking statistic and retain only the loudest n clustered
     # triggers

pycbc/io/hdf.py

@@ -92,7 +92,7 @@ def select(self, fcn, *args, **kwds):
         size = None
         group = kwds.get('group', '')
         for ds in dsets:
-            refs[ds] = self[group][ds]
+            refs[ds] = self[group + '/' + ds]
             if (size is not None) and (refs[ds].size != size):
                 raise RuntimeError(f"Dataset {ds} is {self[ds].size} "
                                    "entries long, which does not match "
@@ -648,17 +648,18 @@ def and_masks(self, logic_mask):
 
         # Use intersection of the indices of True values in the masks
         if hasattr(logic_mask, 'dtype') and (logic_mask.dtype == 'bool'):
-            new_indices = self.mask.nonzero()[0][logic_mask]
+            new_indices = logic_mask.nonzero()[0]
         else:
             new_indices = np.array(logic_mask)
 
         if hasattr(self.mask, 'dtype') and (self.mask.dtype == 'bool'):
-            orig_indices = self.mask.nonzero()[0][logic_mask]
+            orig_indices = self.mask.nonzero()[0]
         else:
             orig_indices = np.array(self.mask)
 
         self.mask[:] = False
-        self.mask[np.intersect1d(new_indices, orig_indices)] = True
+        and_indices = np.intersect1d(new_indices, orig_indices)
+        self.mask[and_indices.astype(np.uint64)] = True
 
     def mask_to_n_loudest_clustered_events(self, rank_method,
                                            ranking_threshold=6,