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(

rowers/models.py

@@ -2985,6 +2985,13 @@ def auto_delete_file_on_delete(sender, instance, **kwargs):
     except FileNotFoundError:
         pass
 
+    # remove parquet file
+    try:
+        dirname = 'media/cpdata_{id}.parquet.gz'.format(id=instance.id)
+        shutil.rmtree(dirname)
+    except FileNotFoundError:
+        pass
+
 @receiver(models.signals.post_delete,sender=Workout)
 def update_duplicates_on_delete(sender, instance, **kwargs):
     if instance.id:

rowers/templates/user_analysis_select.html

@@ -104,7 +104,11 @@
       yaxis1.hide();
       yaxis2.hide();
       plottype.hide();
-			reststrokes.show();
+			reststrokes.hide();
+			workmin.hide();
+			workmax.hide();
+			spmmin.hide();
+			spmmax.hide();
 
       if (functionfield.val() == 'boxplot') {
 	  plotfield.show();

rowers/views/analysisviews.py

@@ -501,7 +501,58 @@ def histodata(workouts, options):
     return(script,div)
 
 def cpdata(workouts, options):
-    return ('','Not Yet Implemented')
+    userid = options['userid']
+
+
+    u = User.objects.get(id=userid)
+    r = u.rower
+
+
+    ids = [w.id for w in workouts]
+    delta, cpvalue, avgpower = dataprep.fetchcp_new(r,workouts)
+    powerdf = pd.DataFrame({
+        'Delta':delta,
+        'CP':cpvalue,
+    })
+
+
+    if powerdf.empty:
+        return('','<p>No valid data found</p>')
+
+    powerdf = powerdf[powerdf['CP']>0]
+    powerdf.dropna(axis=0,inplace=True)
+    powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
+    powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
+
+    rowername = r.user.first_name+" "+r.user.last_name
+
+    if len(powerdf) !=0 :
+        res = interactive_otwcpchart(powerdf,promember=True,rowername=rowername)
+        script = res[0]
+        div = res[1]
+        p1 = res[2]
+        ratio = res[3]
+        r.p0 = p1[0]
+        r.p1 = p1[1]
+        r.p2 = p1[2]
+        r.p3 = p1[3]
+        r.cpratio = ratio
+        r.save()
+        paulslope = 1
+        paulintercept = 1
+        message = res[4]
+    else:
+        script = ''
+        div = '<p>No ranking pieces found.</p>'
+        paulslope = 1
+        paulintercept = 1
+        p1 = [1,1,1,1]
+        message = ""
+
+    scripta = script.split('\n')[2:-1]
+    script = ''.join(scripta)
+
+    return (script,div)
 
 def statsdata(workouts, options):
     includereststrokes = options['includereststrokes']