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refactoring around CP charts

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This commit is contained in:
Sander Roosendaal
2020-10-27 08:12:40 +01:00
parent b498251864
commit d64114f6dc
2 changed files with 47 additions and 169 deletions
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160
rowers/dataprep.py
View File

@@ -1080,6 +1080,47 @@ def setcp(workout,background=False):
return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(),pd.Series() return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(),pd.Series()
def update_rolling_cp(r,types,mode='water'):
firstdate = datetime.date.today()-datetime.timedelta(days=r.cprange)
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=types,
user = r
)
delta, cp, avgpower, workoutnames = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':delta,
'CP':cp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
if mode == 'water':
p1 = res2[0]
r.p0 = p1[0]
r.p1 = p1[1]
r.p2 = p1[2]
r.p3 = p1[3]
r.cpratio = res2[3]
r.save()
else:
p1 = res2[0]
r.ep0 = p1[0]
r.ep1 = p1[1]
r.ep2 = p1[2]
r.ep3 = p1[3]
r.ecpratio = res2[3]
r.save()
return True
return False
def fetchcp(rower,theworkouts,table='cpdata'): def fetchcp(rower,theworkouts,table='cpdata'):
# get all power data from database (plus workoutid) # get all power data from database (plus workoutid)
@@ -1504,69 +1545,12 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
if workouttype in otwtypes: if workouttype in otwtypes:
res, btvalues, res2 = utils.isbreakthrough( res, btvalues, res2 = utils.isbreakthrough(
delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio) delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio)
cprange = r.cprange success = update_rolling_cp(r,otwtypes,'water')
firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=otwtypes,
user = w.user,
)
dd,cpcp,avgpower,workoutnames = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
p1 = res2[0]
r.p0 = p1[0]
r.p1 = p1[1]
r.p2 = p1[2]
r.p3 = p1[3]
r.cpratio = res2[3]
r.save()
elif workouttype in otetypes: elif workouttype in otetypes:
res, btvalues, res2 = utils.isbreakthrough( res, btvalues, res2 = utils.isbreakthrough(
delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio) delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio)
cprange = r.cprange success = update_rolling_cp(r,otetypes,'erg')
firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=otetypes,
user = w.user,
)
dd,cpcp,avgpower,workoutnames = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
res = datautils.cpfit(powerdf)
p1 = res2[0]
r.ep0 = p1[0]
r.ep1 = p1[1]
r.ep2 = p1[2]
r.ep3 = p1[3]
r.ecpratio = res2[3]
r.save()
else: else:
res = 0 res = 0
res2 = 0 res2 = 0
@@ -2506,62 +2490,8 @@ def read_cols_df_sql_old(ids, columns, convertnewtons=True):
return df,extracols return df,extracols
def initiate_cp(r): def initiate_cp(r):
firstdate = datetime.date.today()-datetime.timedelta(days=r.cprange) success = update_rolling_cp(r,otwtypes,'water')
workouts = Workout.objects.filter( success = update_rolling_cp(r,otetypes,'erg')
date__gte=firstdate,
workouttype__in = otwtypes,
user = r,
)
dd,cpcp,avgpower,workoutnames = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
p1 = res2[0]
r.p0 = p1[0]
r.p1 = p1[1]
r.p2 = p1[2]
r.p3 = p1[3]
r.cpratio = res2[3]
r.save()
workouts = Workout.objects.filter(
date__gte = firstdate,
workouttype__in = otetypes,
user = r,
)
dd,cpcp,avgpower,workoutnames = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
res = datautils.cpfit(powerdf)
p1 = res2[0]
r.ep0 = p1[0]
r.ep1 = p1[1]
r.ep2 = p1[2]
r.ep3 = p1[3]
r.ecpratio = res2[3]
r.save()
# Read stroke data from the DB for a Workout ID. Returns a pandas dataframe # Read stroke data from the DB for a Workout ID. Returns a pandas dataframe
def read_df_sql(id): def read_df_sql(id):

56
rowers/views/userviews.py
View File

@@ -566,60 +566,8 @@ def rower_prefs_view(request,userid=0,message=""):
r.cprange = cprange r.cprange = cprange
r.save() r.save()
messages.info(request,'Updated CP range value') messages.info(request,'Updated CP range value')
firstdate = datetime.date.today()-datetime.timedelta(days=cprange) success = dataprep.update_rolling_cp(r,mytypes.otwtypes,'water')
workouts = Workout.objects.filter( success = dataprep.update_rolling_cp(r,mytypes.otetypes,'erg')
date__gte=datetime.date.today()-datetime.timedelta(days=cprange),
workouttype__in=mytypes.otwtypes,
user = r,
)
dd,cpcp,avgpower,workoutnames = dataprep.fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
p1 = res2[0]
r.p0 = p1[0]
r.p1 = p1[1]
r.p2 = p1[2]
r.p3 = p1[3]
r.cpratio = res2[3]
r.save()
workouts = Workout.objects.filter(
date__gte=datetime.date.today()-datetime.timedelta(days=cprange),
workouttype__in=mytypes.otetypes,
user = r,
)
dd,cpcp,avgpower,workoutnames = dataprep.fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
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)
res2 = datautils.cpfit(powerdf)
if len(powerdf) != 0:
p1 = res2[0]
r.ep0 = p1[0]
r.ep1 = p1[1]
r.ep2 = p1[2]
r.ep3 = p1[3]
r.ecpratio = res2[3]
r.save()
return render(request, 'rower_preferences.html', return render(request, 'rower_preferences.html',
{ {