allow option to do a logical and on masks

bin/minifollowups/pycbc_plot_trigger_timeseries

@@ -67,7 +67,7 @@ for ifo in args.single_trigger_files.keys():
             group=ifo,
             indices_only=True
         )
-        data_mask = np.zeros(data[ifo]['snr'].size, dtype=bool)
+        data_mask = numpy.zeros(data[ifo]['snr'].size, dtype=bool)
         data_mask[idx] = True
 
     if not len(idx):

bin/minifollowups/pycbc_sngl_minifollowup

@@ -65,8 +65,9 @@ parser.add_argument('--inspiral-data-read-name',
 parser.add_argument('--inspiral-data-analyzed-name',
                     help="Name of inspiral segmentlist containing data "
                          "analyzed by each analysis job.")
-parser.add_argument('--min-snr', type=float, default=6.5,
-                    help="Minimum SNR to consider for loudest triggers")
+parser.add_argument('--min-sngl-ranking', type=float, default=6.5,
+                    help="Minimum sngl-ranking to consider for loudest "
+                         "triggers. Default=6.5.")
 parser.add_argument('--non-coinc-time-only', action='store_true',
                     help="If given remove (veto) single-detector triggers "
                          "that occur during a time when at least one other "
@@ -136,8 +137,8 @@ trigs = hdf.SingleDetTriggers(
     bank_file=args.bank_file,
     veto_file=args.foreground_censor_file,
     segment_name=args.foreground_segment_name,
-    filter_ranking='snr',
-    filter_threshold=args.min_snr
+    filter_rank=args.sngl_ranking,
+    filter_threshold=args.min_sngl_ranking
 )
 
 # Include gating vetoes

bin/plotting/pycbc_plot_singles_vs_params

@@ -107,7 +107,7 @@ if opts.z_var == 'density' or opts.min_z is None:
     filter_thresh = None
 else:
     # We can apply a/another filter on the z value
-    filter_rank = ranking.sngls_ranking_function_dict[opts.z_var]
+    filter_rank = opts.z_var
     filter_thresh = opts.min_z
 
 trigs = pycbc.io.SingleDetTriggers(

pycbc/io/hdf.py

@@ -537,7 +537,8 @@ def __init__(self, trig_file, detector, bank_file=None, veto_file=None,
                  chunksize=chunksize,
             )
             logging.info("%d triggers remain", idx.size)
-            self.apply_mask(idx)
+            # If self.mask already has values, need to take these into account:
+            self.and_masks(idx)
 
         if filter_func:
             # Apply a filter on the triggers which is _not_ a ranking statistic
@@ -617,7 +618,12 @@ def get_param_names(cls):
             if type(m[1]) == property]
 
     def apply_mask(self, logic_mask):
-        """Apply a mask over the top of the current mask"""
+        """Apply a mask over the top of the current mask
+
+        Parameters
+        ----------
+        logic_mask : boolean array or numpy array of indices
+        """
         if self.mask is None:
             self.mask = np.zeros(self.ntriggers, dtype=bool)
             self.mask[logic_mask] = True
@@ -628,6 +634,32 @@ def apply_mask(self, logic_mask):
         else:
             self.mask = list(np.array(self.mask)[logic_mask])
 
+    def and_masks(self, logic_mask):
+        """Apply a mask to be combined as a logical and with the current mask.
+
+        Parameters
+        ----------
+        logic_mask : boolean array or numpy array of indices
+        """
+        if self.mask is None:
+            # No mask exists, just update to use the given mask
+            self.apply_mask(logic_mask)
+            return
+
+        # 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]
+        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]
+        else:
+            orig_indices = np.array(self.mask)
+
+        self.mask[:] = False
+        self.mask[np.intersect1d(new_indices, orig_indices)] = True
+
     def mask_to_n_loudest_clustered_events(self, rank_method,
                                            ranking_threshold=6,
                                            n_loudest=10,