solved losing Empower data

rowers/dataprep.py

@@ -730,11 +730,11 @@ def new_workout_from_df(r,df,
 
     csvfilename ='media/Fusion_'+timestr+'.csv'
 
+
     df.rename(columns = columndict,inplace=True)
     starttimeunix = mktime(startdatetime.utctimetuple())
     df[' ElapsedTime (sec)'] = df['TimeStamp (sec)']
     df['TimeStamp (sec)'] = df['TimeStamp (sec)']+starttimeunix
-    print df.info()
 
     row = rrdata(df=df)
     row.write_csv(csvfilename,gzip=True)
@@ -888,8 +888,9 @@ def getrowdata_db(id=0,doclean=False):
     if doclean:
         data = clean_df_stats(data,ignorehr=True)
 
-    data['averageforce'] = data['averageforce']
+    # these two lines seem redundant ??
-    data['peakforce'] = data['peakforce']
+    #data['averageforce'] = data['averageforce']
+    #data['peakforce'] = data['peakforce']
 
     return data,row
 
@@ -1106,10 +1107,13 @@ def datafusion(id1,id2,columns,offset):
     offsetmillisecs += offset.days*(3600*24*1000)
     df2['time'] = df2['time']+offsetmillisecs
 
+    
     keep1 = {c:c for c in set(df1.columns)}
+
     for c in columns:
         keep1.pop(c)
     
+        
     for c in df1.columns:
         if not c in keep1:
             df1 = df1.drop(c,1,errors='ignore')
@@ -1246,40 +1250,62 @@ def dataprep(rowdatadf,id=0,bands=True,barchart=True,otwpower=True,
     if empower:
         try:
             wash = rowdatadf.ix[:,'wash']
-            catch = rowdatadf.ix[:,'catch']
-            finish = rowdatadf.ix[:,'finish']
-            peakforceangle = rowdatadf.ix[:,'peakforceangle']
-            driveenergy = rowdatadf.ix[:,'driveenergy']
-            arclength = (inboard-0.05)*(np.radians(finish)-np.radians(catch))
-            if arclength.mean()>0:
-                drivelength = arclength
-            else:
-                drivelength = driveenergy/(averageforce*4.44822)
-
-            slip = rowdatadf.ix[:,'slip']
-            totalangle = finish-catch
-            effectiveangle = finish-wash-catch-slip
-            if windowsize > 3 and windowsize<len(slip):
-                wash = savgol_filter(wash,windowsize,3)
-                slip = savgol_filter(slip,windowsize,3)
-                catch = savgol_filter(catch,windowsize,3)
-                finish = savgol_filter(finish,windowsize,3)
-                peakforceangle = savgol_filter(peakforceangle,windowsize,3)
-                driveenergy = savgol_filter(driveenergy,windowsize,3)
-                drivelength = savgol_filter(drivelength,windowsize,3)
-                totalangle = savgol_filter(totalangle,windowsize,3)
-                effectiveangle = savgol_filter(effectiveangle,windowsize,3)
-            data['wash'] = wash
-            data['catch'] = catch
-            data['slip'] = slip
-            data['finish'] = finish
-            data['peakforceangle'] = peakforceangle
-            data['driveenergy'] = driveenergy
-            data['drivelength'] = drivelength
-            data['totalangle'] = totalangle
-            data['effectiveangle'] = effectiveangle
         except KeyError:
-            pass
+            wash = 0*power
+
+        try:
+            catch = rowdatadf.ix[:,'catch']
+        except KeyError:
+            catch = 0*power
+
+        try:
+            finish = rowdatadf.ix[:,'finish']
+        except KeyError:
+            finish = 0*power
+
+        try:
+            peakforceangle = rowdatadf.ix[:,'peakforceangle']
+        except KeyError:
+            peakforceangle = 0*power
+
+        try:
+            driveenergy = rowdatadf.ix[:,'driveenergy']
+        except KeyError:
+            driveenergy = 0*power
+
+        arclength = (inboard-0.05)*(np.radians(finish)-np.radians(catch))
+        if arclength.mean()>0:
+            drivelength = arclength
+        else:
+            drivelength = driveenergy/(averageforce*4.44822)
+
+        try:
+            slip = rowdatadf.ix[:,'slip']
+        except KeyError:
+            slip = 0*power
+        
+        totalangle = finish-catch
+        effectiveangle = finish-wash-catch-slip
+        if windowsize > 3 and windowsize<len(slip):
+            wash = savgol_filter(wash,windowsize,3)
+            slip = savgol_filter(slip,windowsize,3)
+            catch = savgol_filter(catch,windowsize,3)
+            finish = savgol_filter(finish,windowsize,3)
+            peakforceangle = savgol_filter(peakforceangle,windowsize,3)
+            driveenergy = savgol_filter(driveenergy,windowsize,3)
+            drivelength = savgol_filter(drivelength,windowsize,3)
+            totalangle = savgol_filter(totalangle,windowsize,3)
+            effectiveangle = savgol_filter(effectiveangle,windowsize,3)
+
+        data['wash'] = wash
+        data['catch'] = catch
+        data['slip'] = slip
+        data['finish'] = finish
+        data['peakforceangle'] = peakforceangle
+        data['driveenergy'] = driveenergy
+        data['drivelength'] = drivelength
+        data['totalangle'] = totalangle
+        data['effectiveangle'] = effectiveangle
         
     if otwpower:
 	try:

rowers/forms.py

@@ -343,6 +343,7 @@ class FusionMetricChoiceForm(ModelForm):
 
     def __init__(self, *args, **kwargs):
         super(FusionMetricChoiceForm, self).__init__(*args, **kwargs)
+        formaxlabels2 = formaxlabels.copy()
         # need to add code to remove "empty" fields
 
         if self.instance.id is not None:
@@ -354,10 +355,10 @@ class FusionMetricChoiceForm(ModelForm):
             for label in labeldict:
                 if df.ix[:,label].std() == 0:
                     try:
-                        formaxlabels.pop(label)
+                        formaxlabels2.pop(label)
                     except KeyError:
                         pass
                     
-                    metricchoices = list(sorted(formaxlabels.items(), key = lambda x:x[1]))
+                    metricchoices = list(sorted(formaxlabels2.items(), key = lambda x:x[1]))
                     self.fields['columns'].choices = metricchoices
 

rowers/views.py

@@ -925,8 +925,6 @@ def add_workout_from_underarmourdata(user,importid,data):
 
     times_distance = res[0]
 
-    print distance[0:5]
-    print times_distance[0:5]
 
     try:
 	l = timeseries['position']
@@ -6941,7 +6939,9 @@ def workout_fusion_view(request,id1=0,id2=1):
             posneg = cd['posneg']
             if posneg == 'neg':
                 timeoffset = -timeoffset
+
             df = dataprep.datafusion(id1,id2,columns,timeoffset)
+
             idnew,message = dataprep.new_workout_from_df(r,df,
                                                          title='Fused data',
                                                          parent=w1)