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Merge branch 'release/v14.98'

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This commit is contained in:
Sander Roosendaal
2021-01-09 12:27:12 +01:00
parent 01c72e746f e3e0336f05
commit 068b97f7e0
9 changed files with 33722 additions and 82 deletions
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33541
coverage.xml Normal file
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75
rowers/dataprep.py
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@@ -1102,6 +1102,62 @@ def workout_goldmedalstandard(workout):
else:
return 0,0
def check_marker(workout):
r = workout.user
gmstandard,gmseconds = workout_goldmedalstandard(workout)
if gmseconds<60:
return None
dd = arrow.get(workout.date).datetime-datetime.timedelta(days=r.kfit)
ws = Workout.objects.filter(date__gte=dd,
date__lte=workout.date,
user=r,duplicate=False,
workouttype__in=mytypes.rowtypes,
).order_by("date")
ids = []
gms = []
for w in ws:
gmstandard,gmseconds = workout_goldmedalstandard(w)
if gmseconds>60:
ids.append(w.id)
gms.append(gmstandard)
df = pd.DataFrame({
'id':ids,
'gms':gms,
})
if df.empty:
workout.ranking = True
workout.save()
return workout
indexmax = df['gms'].idxmax()
theid = df.loc[indexmax,'id']
wmax = Workout.objects.get(id=theid)
gms_max = wmax.goldmedalstandard
# check if equal, bigger, or smaller than previous
if not wmax.rankingpiece:
rankingworkouts = ws.filter(rankingpiece=True)
if len(rankingworkouts) == 0:
wmax.rankingpiece = True
wmax.save()
return wmax
lastranking = rankingworkouts[len(rankingworkouts)-1]
if lastranking.goldmedalstandard+0.2 < wmax.goldmedalstandard:
wmax.rankingpiece = True
wmax.save()
return wmax
else:
return wmax
return None
def calculate_goldmedalstandard(rower,workout,recurrance=True):
cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
try:
@@ -1187,28 +1243,30 @@ def fetchcp_new(rower,workouts):
try:
df = pd.read_parquet(cpfile)
df['workout'] = str(workout)
df['url'] = workout.url()
data.append(df)
except:
# CP data file doesn't exist yet. has to be created
df, delta, cpvalues = setcp(workout)
df['workout'] = str(workout)
df['url'] = workout.url()
data.append(df)
if len(data) == 0:
return pd.Series(),pd.Series(),0,pd.Series()
return pd.Series(),pd.Series(),0,pd.Series(),pd.Series()
if len(data)>1:
df = pd.concat(data,axis=0)
try:
df = df[df['cp'] == df.groupby(['delta'])['cp'].transform('max')]
except KeyError:
pd.Series(),pd.Series(),0,pd.DataFrame()
pd.Series(),pd.Series(),0,pd.Series(),pd.Series()
df = df.sort_values(['delta']).reset_index()
return df['delta'],df['cp'],0,df['workout']
return df['delta'],df['cp'],0,df['workout'],df['url']
def setcp(workout,background=False,recurrance=True):
filename = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
@@ -1264,7 +1322,7 @@ def update_rolling_cp(r,types,mode='water'):
user = r
)
delta, cp, avgpower, workoutnames = fetchcp_new(r,workouts)
delta, cp, avgpower, workoutnames,urls = fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':delta,
@@ -1470,6 +1528,9 @@ def checkbreakthrough(w, r):
# submit email task to send email about breakthrough workout
if isbreakthrough:
if not w.duplicate:
w.rankingpiece = True
w.save()
if r.getemailnotifications and not r.emailbounced:
job = myqueue(queuehigh,handle_sendemail_breakthrough,
w.id,
@@ -1480,6 +1541,9 @@ def checkbreakthrough(w, r):
# submit email task to send email about breakthrough workout
if ishard:
if not w.duplicate:
w.rankingpiece = True
w.save()
if r.getemailnotifications and not r.emailbounced:
job = myqueue(queuehigh,handle_sendemail_hard,
w.id,
@@ -1724,6 +1788,8 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
# check for duplicate start times and duration
duplicate = checkduplicates(r,workoutdate,workoutstartdatetime,workoutenddatetime)
if duplicate:
rankingpiece = False
# test title length
if title is not None and len(title)>140:
@@ -1771,6 +1837,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
job = myqueue(queuehigh,handle_calctrimp,w.id,f2,r.ftp,r.sex,r.hrftp,r.max,r.rest)
isbreakthrough, ishard = checkbreakthrough(w, r)
marker = check_marker(w)
return (w.id, message)

4
rowers/forms.py
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@@ -721,14 +721,14 @@ class PerformanceManagerForm(forms.Form):
label='End Date')
metricchoices = (
('trimp','Use Heart Rate Data'),
('hrtss','Use Heart Rate Data'),
('rscore','Use Power and Heart Rate Data'),
)
metricchoice = forms.ChoiceField(
required=True,
choices=metricchoices,
initial='trimp',
initial='hrtss',
label='Data',
widget=forms.RadioSelect
)

138
rowers/interactiveplots.py
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@@ -106,7 +106,7 @@ from pandas.core.groupby.groupby import DataError
def newtestpower(x):
try:
if abs(x['testpower'] - x['testdup']) < 0.2:
if abs(x['testpower'] - x['testdup']) <= 0.2:
return np.nan
except (AttributeError,TypeError):
return np.nan
@@ -132,6 +132,14 @@ def newtestpowerdate(x):
return x['date']
def workoutname(id):
try:
w = Workout.objects.get(id=id)
except Workout.DoesNotExist:
return ''
return str(w)
def all_goldmedalstandards(workouts,startdate,enddate):
dates = []
testpowers = []
@@ -182,34 +190,21 @@ def build_goldmedalstandards(workouts,kfitness):
df.sort_values(['workoutdt'],inplace=True)
#for id, row in df.iterrows():
# d = row['workoutdt']
# dd = d-datetime.timedelta(days=90)
# mask = df['workoutdt']>dd
# mask2 = df['workoutdt']<=d
# df2 = df.where(mask & mask2)
# powertest = df2['goldmedalstandard'].max()
# idx = df2['goldmedalstandard'].argmax()
# durationtest = df2['goldmedalduration'].values[idx]
# dates.append(d)
# testpower.append(powertest)
# testduration.append(durationtest)
# fatigues.append(np.nan)
# fitnesses.append(np.nan)
for w in workouts:
ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=42),
ids = [ww.id for ww in workouts.filter(date__gte=w.date-datetime.timedelta(days=42),
date__lte=w.date)]
powerdf = df[df['workout'].isin(ids)]
indexmax = powerdf['goldmedalstandard'].idxmax()
theid = powerdf.loc[indexmax,'workout']
powertest = powerdf['goldmedalstandard'].max()
durationtest = powerdf.loc[indexmax,'goldmedalduration']
dates.append(arrow.get(w.date).datetime)
w2 = Workout.objects.get(id=theid)
dates.append(arrow.get(w2.date).datetime)
if powertest > 0:
testpower.append(powertest)
testduration.append(durationtest)
@@ -1756,34 +1751,26 @@ def getfatigues(
lambda_c = 2/(kfitness+1)
nrdays = (enddate-startdate).days
factor = 1.0
if metricchoice == 'rscore':
factor = 2.0
for i in range(nrdays+1):
date = startdate+datetime.timedelta(days=i)
ws = Workout.objects.filter(user=user.rower,date=date,duplicate=False)
weight = 0
for w in ws:
if getattr(w,metricchoice) > 0:
weight += factor*getattr(w,metricchoice)
weight += getattr(w,metricchoice)
if getattr(w,metricchoice) <= 0:
if metricchoice == 'rscore' and w.hrtss > 0:
weight+= factor*w.hrtss
elif metricchoice == 'rscore' and w.hrtss <= 0:
weight+= w.hrtss
else:
trimp,hrtss = dataprep.workout_trimp(w)
rscore,normp = dataprep.workout_rscore(w)
if w.rpe and w.rpe > 0:
dd = 3600*w.duration.hour+60*w.duration.minute+w.duration.second
dd = dd/3600
weight += factor*rpetotss[w.rpe]*dd
elif metricchoice == 'trimp' and w.trimp <= 0:
trimp,hrtss = dataprep.workout_trimp(w)
rscore,normp = dataprep.workout_rscore(w)
if w.rpe and w.rpe > 0:
dd = 3600*w.duration.hour+60*w.duration.minute+w.duration.second
dd = dd/3600
weight += 2*rpetotss[w.rpe]*dd
weight += rpetotss[w.rpe]*dd
impulses.append(weight)
@@ -1808,15 +1795,18 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
# marker workouts
workouts = Workout.objects.filter(user=user.rower,date__gte=startdate,
date__lte=enddate,
workouttype__in=mytypes.rowtypes,
duplicate=False)
duplicate=False).order_by('date')
# marker workouts
dates,testpower,testduration,fatigues,fitnesses,impulses, outids = build_goldmedalstandards(
workouts,42
)
markerworkouts = workouts.filter(rankingpiece=True)
outids = [w.id for w in markerworkouts]
dates = [arrow.get(w.date).datetime for w in markerworkouts]
testpower = [w.goldmedalstandard if w.rankingpiece else np.nan for w in markerworkouts]
testduration = [w.goldmedalseconds if w.rankingpiece else 0 for w in markerworkouts]
df = pd.DataFrame({
'id':outids,
@@ -1826,8 +1816,8 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
})
df.sort_values(['date'],inplace=True)
df['testdup'] = df['testpower'].shift(1)
df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
#df['testdup'] = df['testpower'].shift(1)
#df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
#df['date'] = df.apply(lambda x: newtestpowerdate(x), axis=1)
@@ -1842,6 +1832,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
# all workouts
alldates,alltestpower,allduration = all_goldmedalstandards(workouts,startdate,enddate)
allids = [w.id for w in workouts]
nrdays = (enddate-startdate).days
@@ -1850,6 +1841,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
score = []
markerduration = []
duration = []
workoutid = []
previous = 0
@@ -1863,6 +1855,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
markerduration.append(testduration[i])
score.append(np.nan)
duration.append(np.nan)
workoutid.append(id)
for i in range(len(alldates)):
td.append(arrow.get(alldates[i]).datetime)
@@ -1870,6 +1863,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
score.append(alltestpower[i])
markerduration.append(np.nan)
duration.append(allduration[i])
workoutid.append(allids[i])
for i in range(nrdays+1):
td.append(arrow.get(startdate+datetime.timedelta(days=i)).datetime)
@@ -1877,6 +1871,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
score.append(np.nan)
markerduration.append(np.nan)
duration.append(np.nan)
workoutid.append(0)
df = pd.DataFrame({
@@ -1885,6 +1880,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
'score':score,
'duration':duration,
'date':td,
'id':workoutid,
})
@@ -1893,6 +1889,9 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
df = df.groupby(['date']).max()
df['date'] = df.index.values
df['url'] = df['id'].apply(lambda x:settings.SITE_URL+'/rowers/workout/{id}/'.format(id=encoder.encode_hex(x)))
df['workout'] = df['id'].apply(lambda x:workoutname(x))
source = ColumnDataSource(
data = dict(
@@ -1902,6 +1901,8 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
duration = df['duration'].apply(lambda x:totaltime_sec_to_string(x,shorten=True)),
date = df['date'],
fdate = df['date'].map(lambda x: x.strftime('%d-%m-%Y')),
url = df['url'],
workout = df['workout']
)
)
@@ -1961,8 +1962,12 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
('Score','@score{int}'),
('Duration', '@duration'),
('Date','@fdate'),
('Workout','@workout')
])
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url='@url')
script, div = components(plot)
return script, div,outids
@@ -1994,42 +1999,20 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
outids = []
if showtests:
workouts = Workout.objects.filter(user=user.rower,date__gte=startdate,
date__lte=enddate,
workouttype__in=mytypes.rowtypes,
duplicate=False)
dates,testpower,testduration,fatigues,fitnesses,impulses, outids = build_goldmedalstandards(
workouts,kfitness
)
df = pd.DataFrame({
'id': outids,
'date':dates,
'testpower':testpower,
'testduration':testduration,
'fatigue':fatigues,
'fitness':fitnesses,
'impulse':impulses,
})
df.sort_values(['date'],inplace=True)
df['testdup'] = df['testpower'].shift(1)
df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
df['id'] = df.apply(lambda x: newtestpowerid(x),axis=1)
workouts = Workout.objects.filter(user=user.rower,date__gte=startdate,
date__lte=enddate,
workouttype__in=mytypes.rowtypes,
duplicate=False).order_by('date')
#try:
# df['testpower'].iloc[-1] = df['testdup'].iloc[-1]
#except IndexError:
# pass
dates = [d for d in df['date']]
testpower = df['testpower'].values.tolist()
fatigues = df['fatigue'].values.tolist()
fitnesses = df['fitness'].values.tolist()
testduration = df['testduration'].values.tolist()
impulses = df['impulse'].tolist()
outids = df['id'].unique()
markerworkouts = workouts.filter(rankingpiece=True)
outids = [w.id for w in markerworkouts]
dates = [arrow.get(w.date).datetime for w in workouts]
testpower = [w.goldmedalstandard if w.rankingpiece else np.nan for w in workouts]
impulses = [np.nan for w in workouts]
testduration = [w.goldmedalseconds if w.rankingpiece else 0 for w in workouts]
fitnesses = [np.nan for w in workouts]
fatigues = [np.nan for w in workouts]
fatigues,fitnesses,dates,testpower,testduration,impulses = getfatigues(fatigues,
fitnesses,
@@ -2041,7 +2024,6 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
kfatigue,kfitness)
df = pd.DataFrame({
'date':dates,
'testpower':testpower,
@@ -3914,6 +3896,8 @@ def interactive_otwcpchart(powerdf,promember=0,rowername="",r=None,cpfit='data',
deltas = fitt.apply(lambda x: timedeltaconv(x))
ftime = niceformat(deltas)
workouts = powerdf['workout']
urls = powerdf['url']
sourcecomplex = ColumnDataSource(
data = dict(
@@ -3922,6 +3906,7 @@ def interactive_otwcpchart(powerdf,promember=0,rowername="",r=None,cpfit='data',
duration = fitt/60.,
ftime = ftime,
workout = workouts,
url = urls,
)
)
@@ -3985,6 +3970,9 @@ def interactive_otwcpchart(powerdf,promember=0,rowername="",r=None,cpfit='data',
hover.mode = 'mouse'
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url='@url')
plot.line('duration','CP',source=sourcecomplex,legend_label="CP Model",
color='green')

8
rowers/models.py
View File

@@ -44,6 +44,7 @@ import datetime
#from rules.contrib.models import RulesModel
from rowers.rower_rules import *
from rowers.opaque import encoder
from rowers.rows import validate_file_extension
from collections import OrderedDict
@@ -2992,6 +2993,13 @@ class Workout(models.Model):
duplicate = models.BooleanField(default=False,verbose_name='Duplicate Workout')
impeller = models.BooleanField(default=False,verbose_name='Impeller')
def url(self):
str = '/rowers/workout/{id}/'.format(
id = encoder.encode_hex(self.id)
)
url = settings.SITE_URL+str
return url
def save(self, *args, **kwargs):
user = self.user
if self.notes is not None and len(self.notes)>1000:

5
rowers/templates/goldmedalscores.html
View File

@@ -80,6 +80,8 @@
<ul class="main-content">
<li class="grid_4">
<p>Hover over the workout to see details, click on the workout to open in a
separate page.</p>
<div id="id_chart">
{{ the_div|safe }}
</div>
@@ -129,6 +131,9 @@
</table>
</li>
{% endif %}
<li class="grid_4">
<a href="/rowers/createmarkerworkouts/user/{{ rower.user.id }}/">Automatically generate marker workouts</a>
</li>
</ul>

3
rowers/templates/performancemanager.html
View File

@@ -182,6 +182,9 @@
</table>
</li>
{% endif %}
<li class="grid_4">
<a href="/rowers/createmarkerworkouts/user/{{ rower.user.id }}/">Automatically generate marker workouts</a>
</li>
</ul>

4
rowers/urls.py
View File

@@ -355,6 +355,10 @@ urlpatterns = [
re_path(r'^fitness-fit/$',views.fitness_from_cp_view,name='fitness_from_cp_view'),
re_path(r'^fitness-fit/user/(?P<userid>\d+)/$',views.fitness_from_cp_view,name='fitness_from_cp_view'),
re_path(r'^fitness-fit/user/(?P<userid>\d+)/(?P<mode>\w+.*)/$',views.fitness_from_cp_view,name='fitness_from_cp_view'),
re_path(r'^createmarkerworkouts/user/(?P<userid>\d+)/$',views.create_marker_workouts_view,
name='create_marker_workouts_view'),
re_path(r'^createmarkerworkouts/$',views.create_marker_workouts_view,
name='create_marker_workouts_view'),
re_path(r'^goldmedalscores/$',views.goldmedalscores_view,name='goldmedalscores_view'),
re_path(r'^goldmedalscores/user/(?P<userid>\d+)/$',views.goldmedalscores_view,name='goldmedalscores_view'),
re_path(r'^goldmedalscores/user/(?P<userid>\d+)/(?P<mode>\w+.*)/$',views.goldmedalscores_view,name='goldmedalscores_view'),

26
rowers/views/analysisviews.py
View File

@@ -525,12 +525,13 @@ def cpdata(workouts, options):
ids = [w.id for w in workouts]
delta, cpvalue, avgpower,workoutnames = dataprep.fetchcp_new(r,workouts)
delta, cpvalue, avgpower,workoutnames,urls = dataprep.fetchcp_new(r,workouts)
powerdf = pd.DataFrame({
'Delta':delta,
'CP':cpvalue,
'workout':workoutnames,
'url':urls,
})
@@ -1543,6 +1544,29 @@ def fitnessmetric_view(request,userid=0,mode='rower',
'form':form,
})
@user_passes_test(ispromember, login_url="/rowers/paidplans",
message="This functionality requires a Pro plan or higher. If you are already a Pro user, please log in to access this functionality",
redirect_field_name=None)
def create_marker_workouts_view(request,userid=0,
startdate=timezone.now()-timezone.timedelta(days=365),
enddate=timezone.now()):
therower = getrequestrower(request,userid=userid)
theuser = therower.user
workouts = Workout.objects.filter(user=theuser.rower,date__gte=startdate,
date__lte=enddate,
workouttype__in=mytypes.rowtypes,
duplicate=False).order_by('date')
for workout in workouts:
w2 = dataprep.check_marker(workout)
url = reverse('goldmedalscores_view',
kwargs={
'userid':userid,
})
return HttpResponseRedirect(url)
@user_passes_test(ispromember, login_url="/rowers/paidplans",
message="This functionality requires a Pro plan or higher. If you are already a Pro user, please log in to access this functionality",
redirect_field_name=None)