adding resample function

rowers/dataprep.py

@@ -547,6 +547,49 @@ def df_resample(datadf):
     newdf = datadf.groupby(['timestamps']).mean()
     return newdf
 
+def resample(id,r,parent,overwrite='copy'):
+    data, row = getrowdata_db(id=id)
+    messages = []
+
+    # resample
+    startdatetime = row.startdatetime
+    data['datetime'] = data['time'].apply(lambda x:startdatetime+datetime.timedelta(seconds=x/1000.))
+
+    data = data.resample('S',on='datetime').mean()
+    data.interpolate(method='linear',inplace=True)
+    data.reset_index(drop=True,inplace=True)
+
+    #data.drop('datetime',inplace=True)
+    data['pace'] = data['pace'] / 1000.
+    data['time'] = data['time'] / 1000.
+
+    if overwrite=='overwrite':
+        # remove CP data
+        try:
+            cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=parent.id)
+            os.remove(cpfile)
+        except FileNotFoundError:
+            pass
+        # save
+        data.rename(columns=columndict,inplace=True)
+
+        starttimeunix = arrow.get(startdatetime).timestamp()
+        data[' ElapsedTime (sec)'] = data['TimeStamp (sec)']
+
+        data['TimeStamp (sec)'] = data['TimeStamp (sec)'] + starttimeunix
+
+        row = rrdata(df=data)
+
+        row.write_csv(parent.csvfilename,gzip=True)
+
+        res = dataprep(row.df, id=parent.id, bands=True, barchart=True, otwpower=True, empower=True,inboard=parent.inboard)
+    else:
+        id, message = new_workout_from_df(r, data, title=parent.name + '(Resampled)',
+                                          parent=parent,forceunit='N')
+        messages.append(message)
+
+    return data, id, messages
+
 
 def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
                    ignoreadvanced=False):
@@ -1239,6 +1282,7 @@ def setcp(workout,background=False,recurrance=True):
 
     strokesdf = getsmallrowdata_db(['power','workoutid','time'],ids = [workout.id])
 
+
     try:
         if strokesdf['power'].std()==0:
             return pd.DataFrame(),pd.Series(dtype='float'),pd.Series(dtype='float')