diff --git a/rowers/dataprep.py b/rowers/dataprep.py index 1d18c90a..844693e7 100644 --- a/rowers/dataprep.py +++ b/rowers/dataprep.py @@ -332,6 +332,7 @@ def workout_summary_to_df( types = [] names = [] + ids = [] startdatetimes = [] timezones = [] distances = [] @@ -346,11 +347,15 @@ def workout_summary_to_df( goldstandards = [] goldstandarddurations = [] rscores = [] + hrtss = [] trimps = [] + rankingpieces = [] + boattypes = [] for w in ws: types.append(w.workouttype) names.append(w.name) + ids.append(encoder.encode_hex(w.id)) startdatetimes.append(w.startdatetime) timezones.append(w.timezone) distances.append(w.distance) @@ -358,6 +363,7 @@ def workout_summary_to_df( weightcategories.append(w.weightcategory) adaptivetypes.append(w.adaptiveclass) weightvalues.append(w.weightvalue) + boattypes.append(w.boattype) notes.append(w.notes) tcx_link = SITE_URL+'/rowers/workout/{id}/emailtcx'.format( id=encoder.encode_hex(w.id) @@ -374,28 +380,34 @@ def workout_summary_to_df( trimps.append(workout_trimp(w)[0]) rscore = workout_rscore(w) rscores.append(int(rscore[0])) + hrtss.append(int(w.hrtss)) goldstandard,goldstandardduration = workout_goldmedalstandard(w) goldstandards.append(int(goldstandard)) goldstandarddurations.append(int(goldstandardduration)) + rankingpieces.append(w.rankingpiece) df = pd.DataFrame({ + 'ID': ids, + 'date':startdatetimes, 'name':names, 'link':workout_links, - 'date':startdatetimes, 'timezone':timezones, 'type':types, + 'boat type':boattypes, 'distance (m)':distances, 'duration ':durations, + 'ranking piece':rankingpieces, 'weight category':weightcategories, 'adaptive classification':adaptivetypes, 'weight (kg)':weightvalues, - 'notes':notes, 'Stroke Data TCX':tcx_links, 'Stroke Data CSV':csv_links, 'TRIMP Training Load':trimps, 'TSS Training Load':rscores, + 'hrTSS Training Load':hrtss, 'GS':goldstandards, 'GS_secs':goldstandarddurations, + 'notes':notes, }) return df @@ -1046,7 +1058,8 @@ def get_workoutsummaries(userid,startdate): u = User.objects.get(id=userid) r = u.rower df = workout_summary_to_df(r,startdate=startdate) - df = df.sort_values('date') + df.drop(['Stroke Data TCX','Stroke Data CSV'],axis=1,inplace=True) + df = df.sort_values('date',ascending=False) return df