Live cleanup combine single fits

bin/live/pycbc_live_combine_single_fits

@@ -12,12 +12,15 @@
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
 # Public License for more details.
 
+"""Combine PyCBC Live single-detector trigger fitting parameters from several
+different files."""
+
 import h5py, numpy as np, argparse
 import logging
 import pycbc
 
-parser = argparse.ArgumentParser(usage="",
-    description="Combine fitting parameters from several different files")
+
+parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument("--verbose", action="store_true",
                     help="Print extra debugging information", default=False)
 parser.add_argument("--trfits-files", nargs="+", required=True,
@@ -30,7 +33,7 @@ parser.add_argument("--output", required=True,
 parser.add_argument("--ifos", required=True, nargs="+",
                     help="list of ifos fo collect info for")
 
-args=parser.parse_args()
+args = parser.parse_args()
 
 pycbc.init_logging(args.verbose)
 
@@ -42,8 +45,8 @@ if args.conservative_percentile < 50 or \
                  "otherwise it is either not a percentile, or not "
                  "conservative.")
 
-counts_all = {ifo:[] for ifo in args.ifos}
-alphas_all = {ifo:[] for ifo in args.ifos}
+counts_all = {ifo: [] for ifo in args.ifos}
+alphas_all = {ifo: [] for ifo in args.ifos}
 analysis_dates = []
 
 with h5py.File(args.trfits_files[0], 'r') as fit_f0:
@@ -58,7 +61,7 @@ with h5py.File(args.trfits_files[0], 'r') as fit_f0:
     fit_thresh = fit_f0.attrs['fit_threshold']
     fit_func = fit_f0.attrs['fit_function']
 
-live_times = {ifo : [] for ifo in args.ifos}
+live_times = {ifo: [] for ifo in args.ifos}
 
 trigger_file_starts = []
 trigger_file_ends = []
@@ -67,7 +70,6 @@ n_files = len(args.trfits_files)
 logging.info("Checking through %d files", n_files)
 
 for f in args.trfits_files:
-
     fits_f = h5py.File(f, 'r')
     # Check that the file uses the same setup as file 0, to make sure
     # all coefficients are comparable
@@ -84,10 +86,9 @@ for f in args.trfits_files:
     for ifo in args.ifos:
         if ifo not in fits_f:
             continue
-        else:
-            trig_times = fits_f[ifo]['triggers']['end_time'][:]
-            gps_last = max(gps_last, trig_times.max())
-            gps_first = min(gps_first, trig_times.min())
+        trig_times = fits_f[ifo]['triggers']['end_time'][:]
+        gps_last = max(gps_last, trig_times.max())
+        gps_first = min(gps_first, trig_times.min())
     trigger_file_starts.append(gps_first)
     trigger_file_ends.append(gps_last)
 
@@ -101,7 +102,7 @@ for f in args.trfits_files:
             counts_all[ifo].append(fits_f[ifo + '/counts'][:])
             alphas_all[ifo].append(fits_f[ifo + '/fit_coeff'][:])
             if any(np.isnan(fits_f[ifo + '/fit_coeff'][:])):
-                logging.info("nan in " + f + ", " + ifo)
+                logging.info("nan in %s, %s", f, ifo)
                 logging.info(fits_f[ifo + '/fit_coeff'][:])
     fits_f.close()
 
@@ -118,10 +119,10 @@ ad = trigger_file_ends[ad_order] - start_time_n
 counts_bin = {ifo: [c for c in zip(*counts_all[ifo])] for ifo in args.ifos}
 alphas_bin = {ifo: [a for a in zip(*alphas_all[ifo])] for ifo in args.ifos}
 
-alphas_out = {ifo : np.zeros(len(alphas_bin[ifo])) for ifo in args.ifos}
-counts_out = {ifo : np.inf * np.ones(len(counts_bin[ifo])) for ifo in args.ifos}
-cons_alphas_out = {ifo : np.zeros(len(alphas_bin[ifo])) for ifo in args.ifos}
-cons_counts_out = {ifo : np.inf * np.ones(len(alphas_bin[ifo])) for ifo in args.ifos}
+alphas_out = {ifo: np.zeros(len(alphas_bin[ifo])) for ifo in args.ifos}
+counts_out = {ifo: np.inf * np.ones(len(counts_bin[ifo])) for ifo in args.ifos}
+cons_alphas_out = {ifo: np.zeros(len(alphas_bin[ifo])) for ifo in args.ifos}
+cons_counts_out = {ifo: np.inf * np.ones(len(alphas_bin[ifo])) for ifo in args.ifos}
 
 logging.info("Writing results")
 fout = h5py.File(args.output, 'w')
@@ -132,23 +133,15 @@ fout['bins_edges'] = list(bl) + [bu[-1]]
 fout['fits_dates'] = ad + start_time_n
 
 for ifo in args.ifos:
+    logging.info(ifo)
     fout.create_group(ifo)
-    fout[ifo].attrs['live_time'] = sum(live_times[ifo])
-
-save_allmeanalpha = {}
-for ifo in args.ifos:
     fout_ifo = fout[ifo]
-    logging.info(ifo)
     l_times = np.array(live_times[ifo])
-    count_all = np.sum(counts_bin[ifo], axis=0) / l_times
-    invalphan = np.array(counts_bin[ifo]) / np.array(alphas_bin[ifo])
-    invalphan_all = np.mean(invalphan, axis=0)
-    alpha_all = np.mean(counts_bin[ifo], axis=0) / invalphan_all
-    meant = l_times.mean()
+    fout_ifo.attrs['live_time'] = l_times.sum()
 
-    fout_ifo[f'separate_fits/live_times'] = l_times[ad_order]
-    fout_ifo[f'separate_fits/start_time'] = trigger_file_starts[ad_order]
-    fout_ifo[f'separate_fits/end_time'] = trigger_file_ends[ad_order]
+    fout_ifo['separate_fits/live_times'] = l_times[ad_order]
+    fout_ifo['separate_fits/start_time'] = trigger_file_starts[ad_order]
+    fout_ifo['separate_fits/end_time'] = trigger_file_ends[ad_order]
 
     for counter, a_c_u_l in enumerate(zip(alphas_bin[ifo],
                                           counts_bin[ifo], bu, bl)):
@@ -181,15 +174,15 @@ for ifo in args.ifos:
     # Take some averages for plotting and summary values
     overall_invalphan = counts_out[ifo] / alphas_out[ifo]
     overall_meanalpha = counts_out[ifo].mean() / overall_invalphan.mean()
-    sum_counts_out = counts_out[ifo].sum() / sum(live_times[ifo])
-    save_allmeanalpha[ifo] = overall_meanalpha
 
     # For the fixed version, we just set this to 1
-    fout_ifo['fixed/counts'] = [1 for c in counts_out[ifo]]
-    fout_ifo['fixed/fit_coeff'] = [0 for a in alphas_out[ifo]]
+    fout_ifo['fixed/counts'] = [1] * len(counts_out[ifo])
+    fout_ifo['fixed/fit_coeff'] = [0] * len(alphas_out[ifo])
 
     # Add some useful info to the output file
-    fout_ifo.attrs['mean_alpha'] = save_allmeanalpha[ifo]
+    fout_ifo.attrs['mean_alpha'] = overall_meanalpha
     fout_ifo.attrs['total_counts'] = counts_out[ifo].sum()
 
 fout.close()
+
+logging.info('Done')