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setcp function

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This commit is contained in:
Sander Roosendaal
2020-10-16 18:52:05 +02:00
parent 33156ce13c
commit ee4ee05f33
1 changed files with 88 additions and 101 deletions
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189
rowers/dataprep.py
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@@ -1026,30 +1026,7 @@ def fetchcp_new(rower,workouts):
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)
data.append(setcp(workout)[0])
if len(data) == 0:
@@ -1064,6 +1041,35 @@ def fetchcp_new(rower,workouts):
return df['delta'],df['cp'],0
def setcp(workout):
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')
return df,delta,cpvalues
return pd.DataFrame(),pd.Series(),pd.Series()
def fetchcp(rower,theworkouts,table='cpdata'):
# get all power data from database (plus workoutid)
theids = [int(w.id) for w in theworkouts]
@@ -1482,91 +1488,72 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
isbreakthrough = False
ishard = False
if workouttype in rowtypes:
df = getsmallrowdata_db(['power', 'workoutid', 'time'], ids=[w.id])
try:
powermean = df['power'].mean()
except KeyError:
powermean = 0
cpdf,delta,cpvalues = setcp(w)
if not cpdf.empty:
if workouttype in otwtypes:
res, btvalues, res2 = utils.isbreakthrough(
delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio)
cprange = r.cprange
firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
if powermean != 0:
thesecs = totaltime
maxt = 1.05 * thesecs
if maxt > 0:
logarr = datautils.getlogarr(maxt)
dfgrouped = df.groupby(['workoutid'])
delta, cpvalues, avgpower = datautils.getcp(dfgrouped, logarr)
filename = 'media/cpdata_{id}.parquet.gz'.format(id=w.id)
cpdf = pd.DataFrame({
'delta':delta,
'cp':cpvalues,
'id':w.id,
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=otwtypes,
)
dd,cpcp,avgpower = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
cpdf.to_parquet(filename,engine='fastparquet',compression='GZIP')
if workouttype in otwtypes:
res, btvalues, res2 = utils.isbreakthrough(
delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio)
cprange = r.cprange
firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=otwtypes,
)
dd,cpcp,avgpower = 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)
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()
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:
res, btvalues, res2 = utils.isbreakthrough(
delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio)
cprange = r.cprange
firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=otetypes,
)
dd,cpcp,avgpower = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':dd,
'CP':cpcp,
})
elif workouttype in otetypes:
res, btvalues, res2 = utils.isbreakthrough(
delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio)
cprange = r.cprange
firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
workouts = Workout.objects.filter(
date__gte=firstdate,
workouttype__in=otetypes,
)
dd,cpcp,avgpower = 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()
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:
res = 0
res2 = 0