diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 23375f3c..b24de134 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -6,6 +6,7 @@ import rowingdata.tcxtools as tcxtools
 from rowers.utils import totaltime_sec_to_string
 from rowers.datautils import p0
 from scipy import optimize
+from scipy.signal import find_peaks
 from rowers.utils import calculate_age
 import datetime
 import gzip
@@ -1207,8 +1208,15 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
     except KeyError:
         pass
 
-    # smoothen power
-    row.df[' Power (watts)'].replace(to_replace=0,method='ffill', inplace=True)
+
+    # remove negative power peaks
+    x = row.df[' Power (watts)'].values
+    x = x * - 1    
+    neg_peaks, _ = find_peaks(x, height=0) # hieght is the threshold value
+
+    row.df[' Power (watts)'][neg_peaks] = row.df[' Power (watts)'][neg_peaks-1]
+    
+    #row.df[' Power (watts)'].replace(to_replace=0,method='ffill', inplace=True)
 
     if dosmooth:
         # auto smoothing
diff --git a/rowers/tests/test_units.py b/rowers/tests/test_units.py
index 198deef6..b540eeb3 100644
--- a/rowers/tests/test_units.py
+++ b/rowers/tests/test_units.py
@@ -160,7 +160,7 @@ class ForceUnits(TestCase):
         df = dataprep.read_data(['averageforce'],ids=[13])
         df = dataprep.remove_nulls_pl(df)
         average_N = int(df['averageforce'].mean())
-        self.assertEqual(average_N,119)
+        self.assertEqual(average_N,120)
 
 @override_settings(TESTING=True)
 class TestForceUnit(TestCase):
diff --git a/rowers/tests/testdata/testdata.tcx.gz b/rowers/tests/testdata/testdata.tcx.gz
index d906cfbf..346692ec 100644
Binary files a/rowers/tests/testdata/testdata.tcx.gz and b/rowers/tests/testdata/testdata.tcx.gz differ
diff --git a/rowers/views/analysisviews.py b/rowers/views/analysisviews.py
index a2c254f9..782c63bd 100644
--- a/rowers/views/analysisviews.py
+++ b/rowers/views/analysisviews.py
@@ -433,14 +433,16 @@ def trendflexdata(workouts, options, userid=0):
     yerror = []
     groupsize = []
     groupval = []
-    for key, item in groups:
-        xvalues.append(groups.get_group(key)[xparam].mean())
-        yvalues.append(groups.get_group(key)[yparam].mean())
-        xerror.append(groups.get_group(key)[xparam].std())
-        yerror.append(groups.get_group(key)[yparam].std())
-        groupsize.append(len(groups.get_group(key)[xparam]))
-        groupval.append(groups.get_group(key)[groupby].mean())
-
+    try:
+        for key, item in groups:
+            xvalues.append(groups.get_group(key)[xparam].mean())
+            yvalues.append(groups.get_group(key)[yparam].mean())
+            xerror.append(groups.get_group(key)[xparam].std())
+            yerror.append(groups.get_group(key)[yparam].std())
+            groupsize.append(len(groups.get_group(key)[xparam]))
+            groupval.append(groups.get_group(key)[groupby].mean())
+    except TypeError:
+        return('','Error: Unable to compute data')
 
     xvalues = pd.Series(xvalues)
     yvalues = pd.Series(yvalues)