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does fitscore

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This commit is contained in:
Sander Roosendaal
2020-11-26 09:52:14 +01:00
parent 4550a4fe53
commit a7d6292a11
4 changed files with 133 additions and 70 deletions
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11
rowers/dataprep.py
View File

@@ -1078,9 +1078,16 @@ def fitscore(rower,workout):
wcpowers = fitfunc(p1wc,times)
scores = 100.*powers/wcpowers
indexmax = scores.idxmax()
try:
indexmax = scores.idxmax()
delta = df.loc[indexmax,'delta']
maxvalue = scores.max()
except ValueError:
indexmax = 0
delta = 0
maxvalue = 0
return scores.max(),df.loc[indexmax,'delta']
return maxvalue,delta
def fetchcp_new(rower,workouts):

3
rowers/forms.py
View File

@@ -732,6 +732,9 @@ class FitnessFitForm(forms.Form):
fitnesstest = forms.IntegerField(required=True,initial=20,
label='Test Duration (minutes)')
usefitscore = forms.BooleanField(required=False,initial=False,
label='Use best performance against world class')
kfitness = forms.IntegerField(initial=42,required=True,
label='Fitness Time Constant (days)')

186
rowers/interactiveplots.py
View File

@@ -102,6 +102,103 @@ import rowers.datautils as datautils
from pandas.core.groupby.groupby import DataError
def get_fitscore(workouts,kfitness):
dates = []
testpower = []
fatigues = []
fitnesses = []
data = []
fitscores = []
ids = []
for w in workouts:
fitscore,fitnesstestsecs = dataprep.fitscore(w.user,w)
ids.append(w.id)
fitscores.append(fitscore)
df = pd.DataFrame({'workout':ids,'fitscore':fitscores})
for w in workouts:
ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=kfitness),
date__lte=w.date)]
powerdf = df[df['workout'].isin(ids)]
powertest = powerdf['fitscore'].max()
dates.append(datetime.datetime.combine(w.date,datetime.datetime.min.time()))
testpower.append(powertest)
fatigues.append(np.nan)
fitnesses.append(np.nan)
return dates, testpower, fatigues, fitnesses
def get_testpower(workouts,fitnesstestsecs,kfitness):
dates = []
testpower = []
fatigues = []
fitnesses = []
data = []
for w in workouts:
cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=w.id)
try:
df = pd.read_parquet(cpfile)
df['workout'] = w.id
df['workoutdate'] = w.date.strftime('%d-%m-%Y')
data.append(df)
except:
strokesdf = dataprep.getsmallrowdata_db(['power','workoutid','time'],ids=[w.id])
res = myqueue(queuelow,
handle_setcp,
strokesdf,
cpfile,w.id)
if len(data)>1:
df = pd.concat(data,axis=0)
fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
errfunc = lambda pars,x,y: fitfunc(pars,x)-y
for w in workouts:
# Create CP data point for date range
ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=kfitness),
date__lte=w.date)]
try:
powerdf = df[df['workout'].isin(ids)]
powerdf = powerdf[powerdf['cp'] == powerdf.groupby(['delta'])['cp'].transform('max')]
powerdf = powerdf.sort_values(['delta']).reset_index()
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)
except KeyError:
powerdf = pd.DataFrame()
# p1,fitt,fitpower,ratio = datautils.cpfit(powerdf)
if len(powerdf['delta'])>= 4:
thesecs = powerdf['delta'].values
theavpower = powerdf['cp'].values
if thesecs.min() < fitnesstestsecs and thesecs.max() > fitnesstestsecs:
ww = griddata(thesecs,theavpower,np.array([fitnesstestsecs]),method='linear',rescale=True)
powertest = ww[0]
else:
powertest = np.nan
dates.append(datetime.datetime.combine(w.date,datetime.datetime.min.time()))
testpower.append(powertest)
fatigues.append(np.nan)
fitnesses.append(np.nan)
return dates,testpower,fatigues,fitnesses
def errorbar(fig, x, y, source=ColumnDataSource(),
xerr=False, yerr=False, color='black',
point_kwargs={}, error_kwargs={}):
@@ -1538,81 +1635,27 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
enddate=None,kfitness=42,kfatigue=7,fitnesstest=20,
metricchoice='rscore',
k1=1,k2=1,p0=100,
modelchoice='tsb'):
modelchoice='tsb',
usefitscore=False):
TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
dates = []
testpower = []
fatigues = []
fitnesses = []
workouts = workouts.order_by('date')
data = []
fitnesstestsecs = fitnesstest*60
df = pd.DataFrame()
if not usefitscore:
dates,testpower,fatigues,fitnesses = get_testpower(
workouts,fitnesstestsecs,kfitness
)
else:
dates,testpower,fatigues,fitnesses = get_fitscore(
workouts,kfitness
)
# create CP data
for w in workouts:
cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=w.id)
try:
df = pd.read_parquet(cpfile)
df['workout'] = w.id
df['workoutdate'] = w.date.strftime('%d-%m-%Y')
data.append(df)
except:
strokesdf = dataprep.getsmallrowdata_db(['power','workoutid','time'],ids=[w.id])
res = myqueue(queuelow,
handle_setcp,
strokesdf,
cpfile,w.id)
if len(data)>1:
df = pd.concat(data,axis=0)
fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
errfunc = lambda pars,x,y: fitfunc(pars,x)-y
for w in workouts:
# Create CP data point for date range
ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=kfitness),
date__lte=w.date)]
try:
powerdf = df[df['workout'].isin(ids)]
powerdf = powerdf[powerdf['cp'] == powerdf.groupby(['delta'])['cp'].transform('max')]
powerdf = powerdf.sort_values(['delta']).reset_index()
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)
except KeyError:
powerdf = pd.DataFrame()
# p1,fitt,fitpower,ratio = datautils.cpfit(powerdf)
if len(powerdf['delta'])>= 4:
thesecs = powerdf['delta'].values
theavpower = powerdf['cp'].values
if thesecs.min() < fitnesstestsecs and thesecs.max() > fitnesstestsecs:
ww = griddata(thesecs,theavpower,np.array([fitnesstestsecs]),method='linear',rescale=True)
powertest = ww[0]
else:
powertest = np.nan
dates.append(datetime.datetime.combine(w.date,datetime.datetime.min.time()))
testpower.append(powertest)
fatigues.append(np.nan)
fitnesses.append(np.nan)
df = pd.DataFrame({
'date':dates,
@@ -1654,7 +1697,7 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
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()))
@@ -1743,11 +1786,18 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
formlabel = 'TSB'
rightaxlabel = 'Coggan CTL/ATL/TSB'
if usefitscore:
legend_label = 'Test Score'
yaxlabel = 'Test Score'
else:
legend_label = '{fitnesstest} min power'
yaxlabel = 'Test Power (Watt)'
plot.circle('date','testpower',source=source,fill_color='green',size=10,
legend_label='{fitnesstest} min power'.format(fitnesstest=fitnesstest))
legend_label=legend_label.format(fitnesstest=fitnesstest))
plot.xaxis.axis_label = 'Date'
plot.yaxis.axis_label = 'Test Power (Watt)'
plot.yaxis.axis_label = yaxlabel
y2rangemin = df.loc[:,['fitness','fatigue','form']].min().min()

3
rowers/views/analysisviews.py
View File

@@ -1557,6 +1557,7 @@ def fitness_from_cp_view(request,userid=0,mode='rower',
fitnesstest = 20
metricchoice = 'rscore'
modelchoice = 'tsb'
usefitscore = False
# temp fit parameters
k1 = 1
@@ -1578,6 +1579,7 @@ def fitness_from_cp_view(request,userid=0,mode='rower',
k2 = form.cleaned_data['k2']
p0 = form.cleaned_data['p0']
modelchoice = form.cleaned_data['modelchoice']
usefitscore = form.cleaned_data['usefitscore']
else:
form = FitnessFitForm()
@@ -1602,6 +1604,7 @@ def fitness_from_cp_view(request,userid=0,mode='rower',
metricchoice=metricchoice,
k1=k1,k2=k2,p0=p0,
modelchoice=modelchoice,
usefitscore=usefitscore,
)
breadcrumbs = [