allow psd var sample rate to change as needed

bin/pycbc_inspiral

@@ -295,7 +295,7 @@ def template_triggers(t_num):
                                       out_vals['time_index'],
                                       opt.gps_start_time, opt.sample_rate)
         #print(idx, out_vals['time_index'])
-        
+
         out_vals_all.append(copy.deepcopy(out_vals))
         #print(out_vals_all)
     return out_vals_all, tparam
@@ -358,9 +358,10 @@ with ctx:
     if opt.psdvar_segment is not None:
         logging.info("Calculating PSD variation")
         psd_var = pycbc.psd.calc_filt_psd_variation(gwstrain, opt.psdvar_segment,
-                opt.psdvar_short_segment, opt.psdvar_long_segment, 
+                opt.psdvar_short_segment, opt.psdvar_long_segment,
                 opt.psdvar_psd_duration, opt.psdvar_psd_stride,
-                opt.psd_estimation, opt.psdvar_low_freq, opt.psdvar_high_freq)
+                opt.psd_estimation, opt.psdvar_low_freq, opt.psdvar_high_freq,
+                opt.sample_rate)
 
     if opt.enable_q_transform:
         logging.info("Performing q-transform on analysis segments")
@@ -453,16 +454,16 @@ with ctx:
 
     tsetup = time.time() - tstart
     tcheckpoint = time.time()
-    
+
     tanalyze = list(range(tnum_start, len(bank)))
     n = opt.finalize_events_template_rate
     n = 1 if n is None else n
-    tchunks = [tanalyze[i:i + n] for i in range(0, len(tanalyze), n)] 
-    
+    tchunks = [tanalyze[i:i + n] for i in range(0, len(tanalyze), n)]
+
     mmap = map
     if opt.multiprocessing_nprocesses:
         mmap = Pool(opt.multiprocessing_nprocesses).map
-    
+
     for tchunk in tchunks:
         data = list(mmap(template_triggers, tchunk))
 
@@ -472,11 +473,11 @@ with ctx:
                 event_mgr.new_template(tmplt=tparam)
 
                 for edata in out_vals_all:
-                    event_mgr.add_template_events(names, [edata[n] for n in names])              
-                                    
+                    event_mgr.add_template_events(names, [edata[n] for n in names])
+
                 event_mgr.cluster_template_events("time_index", "snr", cluster_window)
                 event_mgr.finalize_template_events()
-        
+
         if opt.finalize_events_template_rate is not None:
             event_mgr.consolidate_events(opt, gwstrain=gwstrain)
 
@@ -488,7 +489,7 @@ with ctx:
         if opt.checkpoint_exit_maxtime and \
             (time.time() - tstart > opt.checkpoint_exit_maxtime):
             event_mgr.save_state(max(tchunk), opt.output + '.checkpoint')
-            sys.exit(opt.checkpoint_exit_code)           
+            sys.exit(opt.checkpoint_exit_code)
 
 event_mgr.consolidate_events(opt, gwstrain=gwstrain)
 event_mgr.finalize_events()

pycbc/psd/variation.py

@@ -61,7 +61,7 @@ def mean_square(data, delta_t, srate, short_stride, stride):
 
 def calc_filt_psd_variation(strain, segment, short_segment, psd_long_segment,
                             psd_duration, psd_stride, psd_avg_method, low_freq,
-                            high_freq):
+                            high_freq, sample_rate):
     """ Calculates time series of PSD variability
 
     This function first splits the segment up into 512 second chunks. It
@@ -113,10 +113,10 @@ def calc_filt_psd_variation(strain, segment, short_segment, psd_long_segment,
     # Convert start and end times immediately to floats
     start_time = float(strain.start_time)
     end_time = float(strain.end_time)
+    srate = int(sample_rate)
 
     # Resample the data
-    strain = resample_to_delta_t(strain, 1.0 / 2048)
-    srate = int(strain.sample_rate)
+    strain = resample_to_delta_t(strain, 1. / srate)
 
     # Fix the step for the PSD estimation and the time to remove at the
     # edge of the time series.