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Merge branch 'feature/fitnesschart' into develop

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This commit is contained in:
Sander Roosendaal
2021-01-03 19:05:32 +01:00
parent 1f50bdd061 545cfa0dec
commit f5ad179c3c
7 changed files with 225 additions and 48 deletions
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3
rowers/dataprep.py
View File

@@ -1092,6 +1092,9 @@ def workout_goldmedalstandard(workout):
return workout.goldmedalstandard,workout.goldmedalseconds
if workout.workouttype in rowtypes:
goldmedalstandard,goldmedalseconds = calculate_goldmedalstandard(workout.user,workout)
if workout.workouttype in otwtypes:
factor = 100./(100.-workout.user.otwslack)
goldmedalstandard = goldmedalstandard*factor
workout.goldmedalstandard = goldmedalstandard
workout.goldmedalseconds = goldmedalseconds
workout.save()

1
rowers/forms.py
View File

@@ -739,6 +739,7 @@ class PerformanceManagerForm(forms.Form):
doform = forms.BooleanField(required=False,initial=False,
label='Freshness')
class FitnessFitForm(forms.Form):
startdate = forms.DateField(
initial=timezone.now()-datetime.timedelta(days=365),

188
rowers/interactiveplots.py
View File

@@ -25,6 +25,8 @@ import itertools
from bokeh.plotting import figure, ColumnDataSource, Figure,curdoc
from bokeh.models import CustomJS,Slider, TextInput,BoxAnnotation, Band
import arrow
from rowers.utils import myqueue, totaltime_sec_to_string
import django_rq
queue = django_rq.get_queue('default')
@@ -102,46 +104,104 @@ import rowers.datautils as datautils
from pandas.core.groupby.groupby import DataError
def newtestpower(x):
try:
if abs(x['testpower'] - x['testdup']) < 1:
return np.nan
except (AttributeError,TypeError):
return np.nan
return x['testpower']
def newtestpowerid(x):
try:
if np.isnan(x['testpower']):
return np.nan
except (AttributeError,TypeError):
return np.nan
return x['id']
def build_goldmedalstandards(workouts,kfitness):
dates = []
testpower = []
testduration = []
fatigues = []
fitnesses = []
impulses = []
data = []
goldmedalstandards = []
goldmedaldurations = []
workoutdt = []
ids = []
outids = []
for w in workouts:
goldmedalstandard,goldmedalseconds = dataprep.workout_goldmedalstandard(w)
ids.append(w.id)
goldmedalstandards.append(goldmedalstandard)
goldmedaldurations.append(goldmedalseconds)
goldmedalstandard,goldmedalseconds = dataprep.workout_goldmedalstandard(w)
if goldmedalseconds > 60:
goldmedalstandards.append(goldmedalstandard)
goldmedaldurations.append(goldmedalseconds)
else:
goldmedalstandards.append(0)
goldmedaldurations.append(0)
workoutdt.append(arrow.get(w.startdatetime).datetime)
df = pd.DataFrame({
'workout':ids,
'workoutdt': workoutdt,
'goldmedalstandard':goldmedalstandards,
'goldmedalduration':goldmedaldurations,
})
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=kfitness),
ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=90),
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(datetime.datetime.combine(w.date,datetime.datetime.min.time()))
testpower.append(powertest)
testduration.append(durationtest)
dates.append(arrow.get(w.date).datetime)
if powertest > 0:
testpower.append(powertest)
testduration.append(durationtest)
outids.append(theid)
else:
testpower.append(np.nan)
testduration.append(np.nan)
outids.append(np.nan)
fatigues.append(np.nan)
fitnesses.append(np.nan)
impulses.append(np.nan)
return dates, testpower, testduration, fatigues, fitnesses
return dates, testpower, testduration, fatigues, fitnesses,impulses,outids
def get_testpower(workouts,fitnesstestsecs,kfitness):
@@ -156,7 +216,7 @@ def get_testpower(workouts,fitnesstestsecs,kfitness):
try:
df = pd.read_parquet(cpfile)
df['workout'] = w.id
df['workoutdate'] = w.date.strftime('%d-%m-%Y')
df['workoutdate'] = arrow.get(w.date.strftime('%d-%m-%Y')).datetime
data.append(df)
except:
strokesdf = dataprep.getsmallrowdata_db(['power','workoutid','time'],ids=[w.id])
@@ -203,7 +263,7 @@ def get_testpower(workouts,fitnesstestsecs,kfitness):
dates.append(datetime.datetime.combine(w.date,datetime.datetime.min.time()))
dates.append(arrow.get(w.date).datetime)
testpower.append(powertest)
testduration.append(fitnesstestsecs)
fatigues.append(np.nan)
@@ -1660,6 +1720,7 @@ def interactive_forcecurve(theworkouts,workstrokesonly=True,plottype='scatter'):
def getfatigues(
fatigues,fitnesses,dates,testpower,testduration,
impulses,
startdate,enddate,user,metricchoice,kfatigue,kfitness):
fatigue = 0
@@ -1704,42 +1765,88 @@ def getfatigues(
impulses.append(weight)
fatigue = (1-lambda_a)*fatigue+weight*lambda_a
fitness = (1-lambda_c)*fitness+weight*lambda_c
fatigues.append(fatigue)
fitnesses.append(fitness)
dates.append(datetime.datetime.combine(date,datetime.datetime.min.time()))
dates.append(arrow.get(date).datetime)
testpower.append(np.nan)
testduration.append(np.nan)
return fatigues,fitnesses,dates,testpower,testduration,impulses
def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
metricchoice='trimp',doform=False,dofatigue=False):
metricchoice='trimp',doform=False,dofatigue=False,
showtests=False):
TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
TOOLS2 = 'box_zoom,hover'
# to avoid data mess later on
startdate = arrow.get(startdate).datetime.replace(hour=0,minute=0,second=0,microsecond=0)
enddate = enddate+datetime.timedelta(days=1)
enddate = arrow.get(enddate).datetime.replace(hour=0,minute=0,second=0,microsecond=0)
fatigues = []
fitnesses = []
dates = []
testpower = []
testduration = []
modelchoice = 'coggan'
p0 = 0
k1 = 1
k2 = 1
dates = []
testpower = []
fatigues = []
fitnesses = []
testduration = []
impulses = []
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)
#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()
fatigues,fitnesses,dates,testpower,testduration,impulses = getfatigues(fatigues,
fitnesses,
dates,
testpower,testduration,
impulses,
startdate,enddate,
user,metricchoice,
kfatigue,kfitness)
@@ -1749,11 +1856,13 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
df = pd.DataFrame({
'date':dates,
'testpower':testpower,
'testduration': testduration,
'fatigue':fatigues,
'fitness':fitnesses,
'impulse':impulses,
})
endfitness = fitnesses[-1]
endfatigue = fatigues[-1]
endform = endfitness-endfatigue
@@ -1771,9 +1880,14 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
df = df.groupby(['date']).max()
df['date'] = df.index.values
#for row in df.iterrows():
# print(row)
source = ColumnDataSource(
data = dict(
testpower = df['testpower'],
testduration = df['testduration'].apply(lambda x:totaltime_sec_to_string(x,shorten=True)),
date = df['date'],
fdate = df['date'].map(lambda x: x.strftime('%d-%m-%Y')),
fitness = df['fitness'],
@@ -1836,8 +1950,9 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
yaxlabel = 'Fitness'
#plot.circle('date','testpower',source=source,fill_color='green',size=10,
# legend_label=legend_label.format(fitnesstest=fitnesstest))
#if showtests:
# plot.circle('date','testpower',source=source,fill_color='green',size=10,
# legend_label='Your best workouts')
plot.xaxis.axis_label = None
plot.yaxis.axis_label = yaxlabel
@@ -1845,12 +1960,18 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
y2rangemin = df.loc[:,['form']].min().min()
y2rangemax = df.loc[:,['form']].max().max()
#if dofatigue and showtests:
# y1rangemin = df.loc[:,['testpower','fitness','fatigue']].min().min()
# y1rangemax = df.loc[:,['testpower','fitness','fatigue']].max().max()*1.02
#elif showtests:
# y1rangemin = df.loc[:,['testpower','fitness']].min().min()
# y1rangemax = df.loc[:,['testpower','fitness']].max().max()*1.02
if dofatigue:
y1rangemin = df.loc[:,['fitness','fatigue']].min().min()
y1rangemax = df.loc[:,['fitness','fatigue']].max().max()
y1rangemax = df.loc[:,['fitness','fatigue']].max().max()*1.02
else:
y1rangemin = df.loc[:,['fitness']].min().min()
y1rangemax = df.loc[:,['fitness']].max().max()
y1rangemax = df.loc[:,['fitness']].max().max()*1.02
if doform:
plot.extra_y_ranges["yax2"] = Range1d(start=y2rangemin,end=y2rangemax)
@@ -1892,6 +2013,7 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
linked_crosshair = CrosshairTool(dimensions='height')
hover.tooltips = OrderedDict([
#(legend_label,'@testpower'),
('Date','@fdate'),
@@ -1901,7 +2023,17 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
('Impulse','@impulse{int}')
])
if showtests:
hover.tooltips = OrderedDict([
#(legend_label,'@testpower'),
('Date','@fdate'),
(fitlabel,'@fitness{int}'),
(fatiguelabel,'@fatigue{int}'),
(formlabel,'@form{int}'),
('Impulse','@impulse{int}'),
('Gold Medal Score','@testpower{int}'),
('Test', '@testduration'),
])
plot2 = Figure(tools=TOOLS2,x_axis_type='datetime',
plot_width=900,plot_height=150,
@@ -1914,6 +2046,7 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
plot2.y_range = Range1d(0,df['impulse'].max())
plot2.vbar(x = df['date'], top = df['impulse'],color='gray')
plot2.vbar(x = df['date'], top = 0*df['testpower']+df['impulse'], color='red')
plot2.sizing_mode = 'scale_both'
plot2.yaxis.axis_label = 'Impulse'
@@ -1935,10 +2068,10 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
nrworkouts = workouts.count(),
nrdata = len(df),
e = e,
)
),0,0,0,[]
)
return [script,div,endfitness,endfatigue,endform]
return [script,div,endfitness,endfatigue,endform,outids]
def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
@@ -1962,7 +2095,7 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
workouts,fitnesstestsecs,kfitness
)
else:
dates,testpower, testduration,fatigues,fitnesses = build_goldmedalstandards(
dates,testpower, testduration,fatigues,fitnesses,impulses = build_goldmedalstandards(
workouts,kfitness
)
# create CP data
@@ -1978,8 +2111,7 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
df['testdup'] = df['testpower'].shift(1)
df['testpower'] = df.apply(lambda x: np.nan if abs(x['testpower'] - x['testdup']) < 1 \
else x['testpower'],axis=1)
df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
try:
df['testpower'].iloc[-1] = df['testdup'].iloc[-1]

39
rowers/templates/performancemanager.html
View File

@@ -112,6 +112,14 @@
on the left. The model balances out after a few weeks of regular training, so don't
make this chart shorter than a few months.
</p>
<p>
The bottom chart shows the training impulse of each individual workout. A gray bar
denotes a regular workout. The red bars denote workouts that stand out in terms
of your power/time performance for that period. This is only available for workouts
where Power (Watts) is measured. How well you performed is expressed as a
Gold Medal Score, where 100 means you are as good as the world class
athletes of your gender, weight and age category.
</p>
<p>
For this chart to reflect your fitness and freshness, it is important to have all workouts on
Rowsandall.com. You can automatically import workouts from other fitness platforms. Change
@@ -143,6 +151,37 @@
</p>
</div>
</li>
{% if bestworkouts %}
<h2>Marker Workouts</h2>
<li class="grid_4">
<table width="100%" class="listtable">
<thead>
<tr>
<th>Date</th>
<th>Workout</th>
<th>Gold Medal Score</th>
<th>Duration</th>
</tr>
</thead>
<tbody>
{% for w in bestworkouts %}
<tr>
<td>{{ w.date }}</td>
<td>
<a href="/rowers/workout/{{ w.id|encode }}/">{{ w.name }}
</td>
<td>
{{ w.goldmedalstandard|floatformat:"0" }} %
</td>
<td>
{{ w.goldmedalseconds|secondstotimestring }}
</td>
</tr>
{% endfor %}
</tbody>
</table>
</li>
{% endif %}
</ul>

3
rowers/templatetags/rowerfilters.py
View File

@@ -215,7 +215,6 @@ def alertenddate(list,i):
def is_coach(rower,rowers):
for r in rowers:
if rower not in rower_get_managers(r):
print(r,rower)
return False
return True
@@ -256,7 +255,6 @@ def hrmajorticks(maxval,minval):
for t in ticks:
newticks.append(100+t*20)
print(newticks)
return newticks
def strfdeltah(tdelta):
@@ -272,6 +270,7 @@ def strfdeltah(tdelta):
return res
@register.filter
def secondstotimestring(tdelta):
hours, rest = divmod(tdelta,3600)
minutes,seconds = divmod(rest,60)

15
rowers/views/analysisviews.py
View File

@@ -1585,15 +1585,21 @@ def performancemanager_view(request,userid=0,mode='rower',
'dofatigue':dofatigue,
})
script, thediv, endfitness, endfatigue, endform = performance_chart(
script, thediv, endfitness, endfatigue, endform, ids = performance_chart(
theuser,startdate=startdate,enddate=enddate,
kfitness = kfitness,
kfatigue = kfatigue,
metricchoice = metricchoice,
doform = doform,
dofatigue = dofatigue,
showtests = True,
)
ids = pd.Series(ids).dropna().values
bestworkouts = Workout.objects.filter(id__in=ids).order_by('date')
breadcrumbs = [
{
'url':'/rowers/analysis',
@@ -1629,6 +1635,7 @@ def performancemanager_view(request,userid=0,mode='rower',
'endfitness':int(endfitness),
'endfatigue':int(endfatigue),
'endform':int(endform),
'bestworkouts':bestworkouts,
})
@@ -1678,12 +1685,8 @@ def fitness_from_cp_view(request,userid=0,mode='rower',
workouts = Workout.objects.filter(user=therower,date__gte=startdate,
date__lte=enddate,
workouttype__in=mytypes.otwtypes,
workouttype__in=mytypes.rowtypes,
duplicate=False)
if mode == 'rower':
workouts = Workout.objects.filter(user=therower,date__gte=startdate,
date__lte=enddate,workouttype__in=mytypes.otetypes,
duplicate=False)

24
rowers/views/workoutviews.py
View File

@@ -3502,19 +3502,19 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
goldmedalstandard,goldmedalseconds = dataprep.workout_goldmedalstandard(w)
#if not np.isnan(goldmedalstandard) and goldmedalstandard > 0:
# otherstats['goldmedalstandard'] = {
# 'verbose_name': 'Gold Medal Standard',
# 'value': int(goldmedalstandard),
# 'unit': '%',
# }
if not np.isnan(goldmedalstandard) and goldmedalstandard > 0:
otherstats['goldmedalstandard'] = {
'verbose_name': 'Gold Medal Standard',
'value': int(goldmedalstandard),
'unit': '%',
}
#if not np.isnan(goldmedalseconds) and goldmedalseconds > 0:
# otherstats['goldmedalseconds'] = {
# 'verbose_name': 'Gold Medal Standard Duration',
# 'value': utils.totaltime_sec_to_string(goldmedalseconds,shorten=True),
# 'unit': '',
# }
if not np.isnan(goldmedalseconds) and goldmedalseconds > 0:
otherstats['goldmedalseconds'] = {
'verbose_name': 'Gold Medal Standard Duration',
'value': utils.totaltime_sec_to_string(goldmedalseconds,shorten=True),
'unit': '',
}
if not np.isnan(tss) and tss != 0: