Extend source classification to all kinds of GraceDB uploads

bin/pycbc_live

@@ -28,6 +28,7 @@ from pycbc.events.ranking import newsnr
 from pycbc.events.coinc import LiveCoincTimeslideBackgroundEstimator as Coincer
 from pycbc.events.single import LiveSingle
 from pycbc.io.live import SingleCoincForGraceDB
+from pycbc.io.hdf import recursively_save_dict_contents_to_group
 import pycbc.waveform.bank
 from pycbc.vetoes.sgchisq import SingleDetSGChisq
 from pycbc.waveform.waveform import props
@@ -331,6 +332,10 @@ class LiveEventManager(object):
                         hdfp['channel_names/{}'.format(ifo)] = \
                             args.channel_name[ifo]
 
+                    recursively_save_dict_contents_to_group(hdfp,
+                                                            'mc_area_args/',
+                                                            self.mc_area_args)
+
                 cmd += '--params-file {} '.format(curr_fname)
                 cmd += '--approximant {} '.format(apr)
                 cmd += '--gracedb-server {} '.format(args.gracedb_server)
@@ -371,7 +376,8 @@ class LiveEventManager(object):
             event = SingleCoincForGraceDB([ifo], single, bank=bank,
                                           psds=psds, followup_data=fud,
                                           low_frequency_cutoff=f_low,
-                                          channel_names=args.channel_name)
+                                          channel_names=args.channel_name,
+                                          mc_area_args=self.mc_area_args)
 
             end_time = int(single['foreground/%s/end_time' % ifo])
             fname = 'single-%s-%s.xml.gz' % (ifo, end_time)

bin/pycbc_optimize_snr

@@ -20,6 +20,7 @@ from pycbc.conversions import (
 )
 from scipy.optimize import differential_evolution
 from pycbc.io import live
+from pycbc.io.hdf import load_hdf5_to_dict
 from pycbc.detector import Detector
 from pycbc.psd import interpolate
 
@@ -316,10 +317,13 @@ channel_names = {}
 for ifo in fp['channel_names'].keys():
     channel_names[ifo] = fp['channel_names'][ifo][()]
 
+mc_area_args = load_hdf5_to_dict(fp, 'mc_area_args/')
+
 kwargs = {'psds': {ifo: followup_data[ifo]['psd'] for ifo in ifos},
           'low_frequency_cutoff': flow,
           'followup_data': followup_data,
-          'channel_names': channel_names}
+          'channel_names': channel_names,
+          'mc_area_args': mc_area_args}
 
 doc = live.SingleCoincForGraceDB(ifos, coinc_results,
                                  upload_snr_series=True, **kwargs)

pycbc/io/hdf.py

@@ -960,13 +960,38 @@ def recursively_save_dict_contents_to_group(h5file, path, dic):
         python dictionary to be converted to hdf5 format
     """
     for key, item in dic.items():
-        if isinstance(item, (np.ndarray, np.int64, np.float64, str, bytes, tuple, list)):
+        if isinstance(item, (np.ndarray, np.int64, np.float64, str, int, float,
+                             bytes, tuple, list)):
             h5file[path + str(key)] = item
         elif isinstance(item, dict):
             recursively_save_dict_contents_to_group(h5file, path + key + '/', item)
         else:
             raise ValueError('Cannot save %s type' % type(item))
 
+def load_hdf5_to_dict(h5file, path):
+    """
+    Parameters
+    ----------
+    h5file:
+        h5py file to be loaded as a dictionary
+    path:
+        path within h5py file to load: '/' for the whole h5py file
+
+    Returns
+    -------
+    dic:
+        dictionary with hdf5 file group content
+    """
+    dic = {}
+    for key, item in h5file[path].items():
+        if isinstance(item, h5py.Dataset):
+            dic[key] = item[()]
+        elif isinstance(item, h5py.Group):
+            dic[key] = load_hdf5_to_dict(h5file, path + key + '/')
+        else:
+            raise ValueError('Cannot load %s type' % type(item))
+    return dic
+
 def combine_and_copy(f, files, group):
     """ Combine the same column from multiple files and save to a third"""
     # ensure that the files input is stable for iteration order

pycbc/mchirp_area.py

@@ -320,7 +320,7 @@ def calc_probabilities(mchirp, snr, eff_distance, src_args):
     # equal to the maximum mass, the probability for BBH is 100%
     mc_max = mass_limits['max_m1'] / (2 ** 0.2)
     if trig_mc_det['central'] > mc_max * (1 + z['central']):
-        if mass_gap is not False:
+        if mass_gap:
             probabilities = {"BNS": 0.0, "GNS": 0.0, "NSBH": 0.0, "GG": 0.0,
                              "BHG": 0.0, "BBH": 1.0}
         else: