Release v2.1.6 (#4701)

* SNR optimizer option types (#4650) * SNR optimizer option types Fix issue in non-default settings for some options that Max complained about * Use standard verbosity argument --------- Co-authored-by: maxtrevor <[email protected]> * Need to use atol for isclose here (#4655) * Update how live fitting deals with invalid values (#4653) * Update how live fitting deals with invalid values * TDC comments * Tito's fixes * Add check for nan trigger fit values in the singles IFAR calculation * Properly fix ASD plot minimum value (#4671) * Properly fix ASD plot minimum value Rather than a hard coded minimum use the minimum value we are going to plot. Also put the calculation of the asd we are plotting on a single line rather than splitting over 2 * Fix bug and grid * ignore samples above minimum freq rather than above 0 * curr_psd does exist ! * specify an older branch of BBHx in tox.ini (#4672) (#4674) * specify an older branch of BBHx in tox.ini * Update tox.ini Co-authored-by: Shichao Wu <[email protected]> --------- Co-authored-by: Shichao Wu <[email protected]> * Modify logging in pycbc_optimize_snr to avoid dependencies * Add live singles significance fitting supervision script to github repo (#4687) * tidy up code before adding to github repo * log starting fitting script * Fix IndexError in PyCBC Live's followup code (#4676) * Draft fix for followup indexing error * Use a nonzero min required lookback, better variable names * Alex's comment on td_samples vs delta_f * Add docstring for `followup_event_significance` * Docstring fixes * Work on comments * Try to fix Sphinx error * Simplify definition of strain buffer length * Explicit kwarg name Co-authored-by: maxtrevor <[email protected]> * Simplify `from_cli()` args * Fix error * Update example --------- Co-authored-by: maxtrevor <[email protected]> * Supervision script typo (#4696) * increment version * Fix import broken by change in lscsoft-glue (#4662) * Drop lscsoft-glue dependency * Glue still needed, do not drop it! * Fix bug from scipy version changing (#4683) --------- Co-authored-by: Thomas Dent <[email protected]> Co-authored-by: Ian Harry <[email protected]> Co-authored-by: Gareth S Cabourn Davies <[email protected]> Co-authored-by: Shichao Wu <[email protected]> Co-authored-by: Tito Dal Canton <[email protected]>
gwastro · Apr 19, 2024 · 8b33176 · 8b33176
1 parent 4dc8cec
commit 8b33176
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Showing 20 changed files with 838 additions and 140 deletions.
diff --git a/bin/live/pycbc_live_combine_single_fits → ...cbc_live_combine_single_significance_fits b/bin/live/pycbc_live_combine_single_fits → ...cbc_live_combine_single_significance_fits
@@ -154,31 +154,43 @@ for ifo in args.ifos:
     logging.info(ifo)
     fout_ifo = fout.create_group(ifo)
     l_times = np.array(live_times[ifo])
-    fout_ifo.attrs['live_time'] = l_times.sum()
+    total_time = l_times.sum()
+    fout_ifo.attrs['live_time'] = total_time
 
     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)):
-        a, c, u, l = a_c_u_l
+    for counter, (a, c) in enumerate(zip(alphas_bin[ifo],
+                                         counts_bin[ifo])):
         # Sort alpha and counts by date
         a = np.array(a)[ad_order]
         c = np.array(c)[ad_order]
-        invalphan = c / a
-        mean_alpha = c.mean() / invalphan.mean()
-        cons_alpha = np.percentile(a, 100 - args.conservative_percentile)
+
+        # ignore anything with the 'invalid' salient values
+        valid = c > 0
+
+        fout_ifo[f'separate_fits/bin_{counter:d}/fit_coeff'] = a
+        fout_ifo[f'separate_fits/bin_{counter:d}/counts'] = c
+
+        if not any(valid):
+            cons_alphas_out[ifo][counter] = np.nan
+            alphas_out[ifo][counter] = np.nan
+            cons_counts_out[ifo][counter] = np.nan
+            counts_out[ifo][counter] = np.nan
+            continue
+
+        invalphan = c[valid] / a[valid]
+        mean_alpha = c[valid].mean() / invalphan.mean()
+        cons_alpha = np.percentile(a[valid], 100 - args.conservative_percentile)
         cons_alphas_out[ifo][counter] = cons_alpha
         alphas_out[ifo][counter] = mean_alpha
+
         # To get the count values, we need to convert to rates and back again
-        r = c / l_times[ad_order]
+        r = c[valid]/ l_times[ad_order][valid]
         cons_rate = np.percentile(r, args.conservative_percentile)
-        cons_counts_out[ifo][counter] = cons_rate * l_times[ad_order].sum()
-        counts_out[ifo][counter] = np.mean(r) * l_times[ad_order].sum()
-
-        fout_ifo[f'separate_fits/bin_{counter:d}/fit_coeff'] = a
-        fout_ifo[f'separate_fits/bin_{counter:d}/counts'] = c
+        cons_counts_out[ifo][counter] = cons_rate * total_time
+        counts_out[ifo][counter] = np.mean(r) * total_time
 
     # Output the mean average values
     fout_ifo['mean/fit_coeff'] = alphas_out[ifo]
@@ -188,17 +200,18 @@ for ifo in args.ifos:
     fout_ifo['conservative/fit_coeff'] = cons_alphas_out[ifo]
     fout_ifo['conservative/counts'] = cons_counts_out[ifo]
 
-    # 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()
-
     # For the fixed version, we just set this to 1
     fout_ifo['fixed/counts'] = [1] * len(counts_out[ifo])
     fout_ifo['fixed/fit_coeff'] = [0] * len(alphas_out[ifo])
 
+    # Take some averages for plotting and summary values
+    overall_invalphan = counts_out[ifo] / alphas_out[ifo]
+    overall_meanalpha = np.nanmean(counts_out[ifo]) \
+        / np.nanmean(overall_invalphan)
+
     # Add some useful info to the output file
     fout_ifo.attrs['mean_alpha'] = overall_meanalpha
-    fout_ifo.attrs['total_counts'] = counts_out[ifo].sum()
+    fout_ifo.attrs['total_counts'] = np.nansum(counts_out[ifo])
 
 fout.close()
 

diff --git a/...live/pycbc_live_plot_combined_single_fits → ...ve_plot_combined_single_significance_fits b/...live/pycbc_live_plot_combined_single_fits → ...ve_plot_combined_single_significance_fits
@@ -140,7 +140,8 @@ for ifo in ifos:
             continue
 
         l_times = separate_times[ifo]
-        rate = counts / l_times
+        with np.errstate(divide='ignore'):
+            rate = counts / l_times
 
         ma = mean_alpha[ifo][i]
         ca = cons_alpha[ifo][i]
@@ -151,7 +152,8 @@ for ifo in ifos:
         bin_colour = plt.get_cmap(args.colormap)(bin_prop)
         bin_label = f"duration {bl:.2f}-{bu:.2f}"
         alpha_lines += ax_alpha.plot(separate_starts[ifo], alphas, c=bin_colour,
-                                     label=bin_label)
+                                     label=bin_label, marker='.',
+                                     markersize=10)
         alpha_lines.append(ax_alpha.axhline(ma,
                                             label="total fit = %.2f" % ma,
                                             c=bin_colour, linestyle='--',))
@@ -161,14 +163,22 @@ for ifo in ifos:
                                             label=alpha_lab))
 
         count_lines += ax_count.plot(separate_starts[ifo], rate, c=bin_colour,
-                                     label=bin_label)
+                                     label=bin_label, marker='.',
+                                     markersize=10)
+
+        if mr < 1e-3:
+            mlab = f"mean = {mr:.3e}"
+            clab = f"{conservative_percentile:d}th %ile = {cr:.3e}"
+        else:
+            mlab = f"mean = {mr:.3f}"
+            clab = f"{conservative_percentile:d}th %ile = {cr:.3f}"
+
         count_lines.append(ax_count.axhline(mr,
                                             c=bin_colour, linestyle='--',
-                                            label=f"mean = {mr:.3f}"))
-        count_lab = f"{conservative_percentile:d}th %ile = {cr:.3f}"
+                                            label=mlab))
         count_lines.append(ax_count.axhline(cr,
                                             c=bin_colour, linestyle=':',
-                                            label=count_lab))
+                                            label=clab))
 
     alpha_labels = [l.get_label() for l in alpha_lines]
     ax_alpha.legend(alpha_lines, alpha_labels, loc='lower center',

diff --git a/bin/live/pycbc_live_plot_single_trigger_fits → .../pycbc_live_plot_single_significance_fits b/bin/live/pycbc_live_plot_single_trigger_fits → .../pycbc_live_plot_single_significance_fits
diff --git a/bin/live/pycbc_live_single_trigger_fits → bin/live/pycbc_live_single_significance_fits b/bin/live/pycbc_live_single_trigger_fits → bin/live/pycbc_live_single_significance_fits