Actually purge

bin/all_sky_search/pycbc_add_statmap

@@ -266,19 +266,12 @@ def filter_dataset(h5file, name, idx):
 # multiple files
 for key in f['foreground'].keys():
     if key not in all_ifos:
-        if f['foreground/%s' % key].size == 0:
-            # This is an indicator that the column was specifically
-            # not calculated
-            logging.info('foreground/%s dataset is being ignored', key)
-            continue
         filter_dataset(f, 'foreground/%s' % key, cidx)
     else:  # key is an ifo
         for k in f['foreground/%s' % key].keys():
             filter_dataset(f, 'foreground/{}/{}'.format(key, k), cidx)
 
-fg_ifar_exc_init = f['foreground/ifar_exc'][:]
-
-n_triggers = fg_ifar_exc_init.size
+n_triggers = f['foreground/stat'].size
 
 logging.info('Calculating event times to determine which types of coinc '
              'are available')

bin/all_sky_search/pycbc_coinc_statmap_inj

@@ -82,36 +82,20 @@ f.attrs['background_time'] = background_time
 f.attrs['foreground_time'] = coinc_time
 
 if len(zdata) > 0:
+
     _, fnlouder_exc = significance.get_n_louder(
         back_stat,
         zdata.stat,
         dec_fac,
         **significance_dict[ifo_key])
+
     ifar_exc = background_time / (fnlouder_exc + 1)
     fap_exc = 1 - numpy.exp(- coinc_time / ifar_exc)
     f['foreground/ifar_exc'] = conv.sec_to_year(ifar_exc)
     f['foreground/fap_exc'] = fap_exc
 
-    logging.info('calculating injection backgrounds')
-    ifotimes = numpy.array([zdata.data['%s/time' % ifo] for ifo in args.ifos])
-    ftimes = ifotimes.mean(axis=0)
-    start, end = ftimes - args.veto_window, ftimes + args.veto_window
-
-    fnlouder = numpy.zeros(len(ftimes), dtype=numpy.float32)
-    ifar = numpy.zeros(len(ftimes), dtype=numpy.float32)
-    fap = numpy.zeros(len(ftimes), dtype=numpy.float32)
-
-    # most of the triggers are here so presort to speed up later sorting
-    bsort = back_stat.argsort()
-    dec_fac = dec_fac[bsort]
-    back_stat = back_stat[bsort]
-
 else:
     f['foreground/ifar_exc'] = numpy.array([])
     f['foreground/fap_exc'] = numpy.array([])
 
-# Indicate that inclusive IFAR is not calculated
-f['foreground/ifar'] = numpy.array([])
-f['foreground/fap'] = numpy.array([])
-
 logging.info("Done")

bin/all_sky_search/pycbc_sngls_statmap_inj

@@ -108,16 +108,6 @@ fore_stat = all_trigs.stat[fore_locs]
 
 significance_dict = significance.digest_significance_options([ifo], args)
 
-# Cumulative array of inclusive background triggers and the number of
-# inclusive background triggers louder than each foreground trigger
-back_cnum, fnlouder = significance.get_n_louder(
-    back_stat,
-    fore_stat,
-    bkg_dec_facs,
-    **significance_dict[ifo])
-
-bg_ifar = fg_time / (back_cnum + 1)
-
 # Cumulative array of exclusive background triggers and the number
 # of exclusive background triggers louder than each foreground trigger
 back_cnum_exc, fnlouder_exc = significance.get_n_louder(
@@ -129,15 +119,6 @@ back_cnum_exc, fnlouder_exc = significance.get_n_louder(
 fg_ifar_exc = fg_time_exc / (fnlouder_exc + 1)
 bg_ifar_exc = fg_time_exc / (back_cnum_exc + 1)
 
-# Not sure if these are actually used later as we
-# don't do inclusive
-f['background/ifar'] = conv.sec_to_year(bg_ifar)
-f.attrs['background_time'] = fg_time
-f.attrs['foreground_time'] = fg_time
-# Indicate not doing inclusive IFAR/FAP
-f['foreground/ifar'] = numpy.array([])
-f['foreground/fap'] = numpy.array([])
-
 f['background_exc/ifar'] = conv.sec_to_year(bg_ifar_exc)
 f.attrs['background_time_exc'] = fg_time_exc
 f.attrs['foreground_time_exc'] = fg_time_exc