diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 89266aba..fb7ef68c 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -117,6 +117,30 @@ from scipy.signal import savgol_filter
 
 import datetime
 
+def polarization_index(df,rower):
+    df['dt'] = df['time'].diff()/6.e4
+    # remove rest (spm<15)
+    df.dropna(axis=0,inplace=True)
+    df['dt'] = df['dt'].clip(upper=4,lower=0)
+
+    
+    masklow = (df['power']>0) & (df['power']<int(rower.pw_at))
+    maskmid = (df['power']>=rower.pw_at) & (df['power']<int(rower.pw_an))
+    maskhigh = (df['power']>rower.pw_an)
+
+    
+    time_low_pw = df.loc[masklow,'dt'].sum()
+    time_mid_pw = df.loc[maskmid,'dt'].sum()
+    time_high_pw = df.loc[maskhigh,'dt'].sum()
+
+    frac_low = time_low_pw/(time_low_pw+time_mid_pw+time_high_pw)
+    frac_mid = time_mid_pw/(time_low_pw+time_mid_pw+time_high_pw)
+    frac_high = time_high_pw/(time_low_pw+time_mid_pw+time_high_pw)
+
+    index = math.log10(frac_high*100.*frac_low/frac_mid)
+    
+    return index
+
 
 def get_latlon(id):
     try:
diff --git a/rowers/metrics.py b/rowers/metrics.py
index c556e3c8..c06debdd 100644
--- a/rowers/metrics.py
+++ b/rowers/metrics.py
@@ -11,6 +11,8 @@ import pandas as pd
 from scipy import optimize
 from django.utils import timezone
 
+from math import log10
+
 nometrics = [
     'originalvelo',
     'cumdist',
@@ -359,8 +361,6 @@ This value should be fairly constant across all stroke rates.""",
     )
 
 
-def polarization_index(df,rower):
-    return 0
 
 def calc_trimp(df,sex,hrmax,hrmin,hrftp):
     if sex == 'male':
diff --git a/rowers/views/workoutviews.py b/rowers/views/workoutviews.py
index 92c7e7ee..4a53bce4 100644
--- a/rowers/views/workoutviews.py
+++ b/rowers/views/workoutviews.py
@@ -1304,6 +1304,11 @@ def workouts_view(request,message='',successmessage='',
     workoutsnohr = workouts.exclude(averagehr__isnull=False)
     for w in workoutsnohr:
         res = dataprep.workout_trimp(w)
+
+    # ids = [w.id for w in workouts]
+    # df = dataprep.getsmallrowdata_db(['time','power'],ids=ids)
+    # polarization = dataprep.polarization_index(df,r)
+    
         
     if query:
         query_list = query.split()