sort of gets CP data

rowers/dataprep.py

@@ -1016,6 +1016,68 @@ def fetchcperg(rower,theworkouts):
 
     return cpdf
 
+def fetchcp_new(rower,workouts):
+
+    data = []
+    for workout in workouts:
+        cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
+        try:
+            df = pd.read_parquet(cpfile)
+            data.append(df)
+        except OSError:
+            # CP data file doesn't exist yet. has to be created
+            strokesdf = getsmallrowdata_db(['power','workoutid','time'],ids = [workout.id])
+            if not strokesdf.empty:
+                totaltime = strokesdf['time'].max()
+                try:
+                    powermean = strokesdf['power'].mean()
+                except KeyError:
+                    powermean = 0
+
+
+                if powermean != 0:
+                    thesecs = totaltime
+                    maxt = 1.05 * thesecs
+                    if maxt > 0:
+                        logarr = datautils.getlogarr(maxt)
+                        dfgrouped = strokesdf.groupby(['workoutid'])
+                        delta, cpvalues, avgpower = datautils.getcp(dfgrouped, logarr)
+                        filename = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
+                        df = pd.DataFrame({
+                            'delta':delta,
+                            'cp':cpvalues,
+                            'id':workout.id,
+                        })
+                        df.to_parquet(filename,engine='fastparquet',compression='GZIP')
+                        data.append(df)
+
+
+    if len(data)>1:
+        df = pd.concat(data,axis=0)
+
+    df = df.groupby(['delta']).max()
+
+
+    df = df.sort_values(['delta']).reset_index()
+    dindex = df['id'].shift(1)-df['id']
+    dpowerplus = df['cp'].shift(1)-df['cp']
+    dpowermin = df['cp'].shift(-1)-df['cp']
+    badrows = []
+    badid = 0
+    for index,row in df.iterrows():
+        if dindex[index] != 0 and dpowermin[index] > 0:
+            badrows.append(index)
+            badid = row['id']
+        elif row['id'] == badid:
+            badrows.append(index)
+        else:
+            badid = 0
+
+
+    df = df.drop(index = badrows)
+
+
+    return df['delta'],df['cp'],0
 
 def fetchcp(rower,theworkouts,table='cpdata'):
     # get all power data from database (plus workoutid)
@@ -1449,7 +1511,12 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
                 dfgrouped = df.groupby(['workoutid'])
                 delta, cpvalues, avgpower = datautils.getcp(dfgrouped, logarr)
                 filename = 'media/cpdata_{id}.parquet.gz'.format(id=w.id)
-                df.to_parquet(filename,engine='fastparquet',compression='GZIP')
+                cpdf = pd.DataFrame({
+                    'delta':delta,
+                    'cp':cpvalues,
+                    'id':w.id,
+                })
+                cpdf.to_parquet(filename,engine='fastparquet',compression='GZIP')
 
                 if workouttype in otwtypes:
                     res, btvalues, res2 = utils.isbreakthrough(