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Merge branch 'release/v14.66'

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This commit is contained in:
Sander Roosendaal
2020-11-26 10:21:57 +01:00
parent aea8d5e4e0 a7d6292a11
commit 56662c6083
6 changed files with 223 additions and 85 deletions
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80
rowers/dataprep.py
View File

@@ -6,7 +6,9 @@ from __future__ import unicode_literals
# All the data preparation, data cleaning and data mangling should
# be defined here
from __future__ import unicode_literals, absolute_import
from rowers.models import Workout, Team
from rowers.models import (
Workout, Team, CalcAgePerformance,C2WorldClassAgePerformance,
)
import pytz
import collections
@@ -23,7 +25,10 @@ from rowingdata import (
get_file_type, get_empower_rigging,get_empower_firmware
)
from rowers.tasks import handle_sendemail_unrecognized,handle_setcp
from rowers.tasks import (
handle_sendemail_unrecognized,handle_setcp,
handle_getagegrouprecords
)
from rowers.tasks import handle_zip_file
from pandas import DataFrame, Series
@@ -643,7 +648,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
pass
try:
mask = datadf['spm'] > 60
mask = datadf['spm'] > 120
datadf.mask(mask,inplace=True)
except (KeyError,TypeError):
pass
@@ -1016,6 +1021,74 @@ def fetchcperg(rower,theworkouts):
return cpdf
from rowers.datautils import p0
from rowers.utils import calculate_age
from scipy import optimize
def fitscore(rower,workout):
cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
try:
df = pd.read_parquet(cpfile)
except:
df, delta, cpvalues = setcp(workout)
age = calculate_age(rower.birthdate,today=workout.date)
agerecords = CalcAgePerformance.objects.filter(
age=age,
sex=rower.sex,
weightcategory = rower.weightcategory
)
wcdurations = []
wcpower = []
for record in agerecords:
wcdurations.append(record.duration)
wcpower.append(record.power)
if len(agerecords)==0:
durations = [1,4,10,20,30,60]
distances = []
df2 = pd.DataFrame(
list(
C2WorldClassAgePerformance.objects.filter(
sex=rower.sex,
weightcategory=rower.weightcategory
).values()
)
)
jsondf = df2.to_json()
job = myqueue(queue,handle_getagegrouprecords,
jsondf,distances,durations,age,rower.sex,rower.weightcategory)
wcpower = pd.Series(wcpower)
wcdurations = pd.Series(wcdurations)
fitfunc = lambda pars,x: pars[0]/(1+(x/pars[2])) + pars[1]/(1+(x/pars[3]))
errfunc = lambda pars,x,y: fitfunc(pars,x)-y
if len(wcdurations)>4:
p1wc, success = optimize.leastsq(errfunc, p0[:],args=(wcdurations,wcpower))
else:
factor = fitfunc(p0,wcdurations.mean()/wcpower.mean())
p1wc = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
success = 0
times = df['delta']
powers = df['cp']
wcpowers = fitfunc(p1wc,times)
scores = 100.*powers/wcpowers
try:
indexmax = scores.idxmax()
delta = df.loc[indexmax,'delta']
maxvalue = scores.max()
except ValueError:
indexmax = 0
delta = 0
maxvalue = 0
return maxvalue,delta
def fetchcp_new(rower,workouts):
data = []
@@ -3009,6 +3082,7 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
return data
def workout_trimp(w):
r = w.user

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)')

200
rowers/interactiveplots.py
View File

@@ -6,7 +6,7 @@ from __future__ import unicode_literals
import colorsys
from rowers.models import (
Workout, User, Rower, WorkoutForm,RowerForm,
GraphImage,GeoPolygon,GeoCourse,GeoPoint
GraphImage,GeoPolygon,GeoCourse,GeoPoint,
)
from rowers.tasks import handle_setcp
from rowingdata import rower as rrower
@@ -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,
@@ -1651,12 +1694,9 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
weight = 0
for w in ws:
weight += getattr(w,metricchoice)
if modelchoice == 'tsb':
fatigue = (1-lambda_a)*fatigue+weight*lambda_a
fitness = (1-lambda_c)*fitness+weight*lambda_c
else:
fatigue = fatigue*math.exp(-1./kfatigue) + weight
fitness = fitness*math.exp(-1./kfitness) + weight
fatigue = (1-lambda_a)*fatigue+weight*lambda_a
fitness = (1-lambda_c)*fitness+weight*lambda_c
fatigues.append(fatigue)
fitnesses.append(fitness)
@@ -1739,22 +1779,31 @@ def fitnessfit_chart(workouts,user,workoutmode='water',startdate=None,
fitlabel = 'PTE (fitness)'
fatiguelabel = 'NTE (fatigue)'
formlabel = 'Performance'
rightaxlabel = 'Banister PTE/NTE/Performance'
else:
fitlabel = 'CTL'
fatiguelabel = 'ATL'
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 = 'Power (W)'
plot.yaxis.axis_label = yaxlabel
y2rangemin = df.loc[:,['fitness','fatigue','form']].min().min()
y2rangemax = df.loc[:,['fitness','fatigue','form']].max().max()
plot.extra_y_ranges["yax2"] = Range1d(start=y2rangemin,end=y2rangemax)
plot.add_layout(LinearAxis(y_range_name="yax2",axis_label="Score"),"right")
plot.add_layout(LinearAxis(y_range_name="yax2",axis_label=rightaxlabel),"right")
plot.line('date','fitness',source=source,color='blue',
legend_label=fitlabel,y_range_name="yax2")
@@ -4757,7 +4806,6 @@ def interactive_flexchart_stacked(id,r,xparam='time',
workstrokesonly=False)
if r.usersmooth > 1:
for column in columns:
try:

17
rowers/models.py
View File

@@ -265,15 +265,15 @@ def update_records(url=c2url,verbose=True):
for nr,row in df.iterrows():
if 'm' in row['Record']:
df.ix[nr,'Distance'] = row['Record'][:-1]
df.ix[nr,'Duration'] = 60*row['Event']
df.loc[nr,'Distance'] = row['Record'][:-1]
df.loc[nr,'Duration'] = 60*row['Event']
else:
df.ix[nr,'Distance'] = row['Event']
df.loc[nr,'Distance'] = row['Event']
try:
tobj = datetime.datetime.strptime(row['Record'],'%M:%S.%f')
except ValueError:
tobj = datetime.datetime.strptime(row['Record'],'%H:%M:%S.%f')
df.ix[nr,'Duration'] = 3600.*tobj.hour+60.*tobj.minute+tobj.second+tobj.microsecond/1.e6
df.loc[nr,'Duration'] = 3600.*tobj.hour+60.*tobj.minute+tobj.second+tobj.microsecond/1.e6
for nr,row in df.iterrows():
try:
@@ -334,6 +334,15 @@ class CalcAgePerformance(models.Model):
class Meta:
db_table = 'calcagegrouprecords'
def __str_(self):
stri = 'Calculated World Class Performance for {s}, {a}, {d} secs, {p} Watts'.format(
s = self.sex,
a = self.age,
d = self.duration,
p = self.power
)
return stri
class PowerTimeFitnessMetric(models.Model):
modechoices = (
('rower','Rower'),

5
rowers/utils.py
View File

@@ -323,8 +323,9 @@ def myqueue(queue,function,*args,**kwargs):
from datetime import date
def calculate_age(born):
today = date.today()
def calculate_age(born,today=None):
if not today:
today = date.today()
if born:
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
else:

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 = [