diff --git a/bin/pycbc_inspiral b/bin/pycbc_inspiral
index dc71bd367d0..4d5e6e9a585 100644
--- a/bin/pycbc_inspiral
+++ b/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
@@ -358,10 +358,11 @@ 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)
 
+
     if opt.enable_q_transform:
         logging.info("Performing q-transform on analysis segments")
         q_trans = qtransform.inspiral_qtransform_generator(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()
diff --git a/pycbc/psd/variation.py b/pycbc/psd/variation.py
index 50a006faf99..6e4b8de5483 100644
--- a/pycbc/psd/variation.py
+++ b/pycbc/psd/variation.py
@@ -7,7 +7,6 @@
 
 import pycbc.psd
 from pycbc.types import TimeSeries
-from pycbc.filter import resample_to_delta_t
 
 
 def mean_square(data, delta_t, srate, short_stride, stride):
@@ -113,9 +112,6 @@ 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)
-
-    # Resample the data
-    strain = resample_to_delta_t(strain, 1.0 / 2048)
     srate = int(strain.sample_rate)
 
     # Fix the step for the PSD estimation and the time to remove at the