working on low level plannedsession
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@@ -36,7 +36,7 @@ from rowingdata import (
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summarydata, get_file_type,
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)
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from rowers.metrics import axes
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from rowers.metrics import axes,calc_trimp
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from async_messages import messages as a_messages
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import os
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import zipfile
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@@ -2212,3 +2212,40 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
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conn.close()
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engine.dispose()
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return data
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def workout_trimp(workout):
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r = workout.user
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df,row = getrowdata_db(id=workout.id)
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df = clean_df_stats(df)
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if df.empty:
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df,row = getrowdata_db(id=workout.id)
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df = clean_df_stats(df,workstrokesonly=False)
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trimp = calc_trimp(df,r.sex,r.max,r.rest)
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trimp = int(trimp)
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return trimp
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def workout_rscore(w):
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r = workout.user
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df,row = getrowdata_db(id=workout.id)
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df = clean_df_stats(df)
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if df.empty:
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df,row = getrowdata_db(id=workout.id)
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df = clean_df_stats(df,workstrokesonly=False)
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duration = df['time'].max()-df['time'].min()
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duration /= 1.0e3
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pwr4 = df['power']**(4.0)
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normp = (pwr4.mean())**(0.25)
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if not np.isnan(normp):
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ftp = float(r.ftp)
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if w.workouttype in ('water','coastal'):
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ftp = ftp*(100.-r.otwslack)/100.
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intensityfactor = df['power'].mean()/float(ftp)
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intensityfactor = normp/float(ftp)
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tss = 100.*((duration*normp*intensityfactor)/(3600.*ftp))
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else:
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tss = 0
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return tss
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@@ -307,6 +307,7 @@ This value should be fairly constant across all stroke rates.""",
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},
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)
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def calc_trimp(df,sex,hrmax,hrmin):
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if sex == 'male':
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f = 1.92
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@@ -321,6 +322,7 @@ def calc_trimp(df,sex,hrmax,hrmin):
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return trimp
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def getagegrouprecord(age,sex='male',weightcategory='hwt',
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distance=2000,duration=None,indf=pd.DataFrame()):
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@@ -761,7 +761,9 @@ class PlannedSession(models.Model):
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sessionmodechoices = (
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('distance','Distance'),
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('time','Time')
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('time','Time'),
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('rScore','rScore'),
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('TRIMP','TRIMP'),
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)
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criteriumchoices = (
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@@ -779,7 +781,8 @@ class PlannedSession(models.Model):
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sessionunitchoices = (
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('min','minutes'),
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('km','km'),
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('m','meters')
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('m','meters'),
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('None',None),
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)
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manager = models.ForeignKey(User)
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@@ -20,20 +20,76 @@ from rowers.models import (
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TrainingPlan,
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)
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import metrics
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import numpy as np
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import dataprep
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# Low Level functions - to be called by higher level methods
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# dummies for now
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def submit_workout(w,ps):
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def add_workouts_plannedsession(ws,ps):
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for w in ws:
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w.plannedsession = ps
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w.save()
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return 1
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def remove_workout_plannedsession(w,ps):
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return 1
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if w.plannedsession == ps:
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w.plannedsession = None
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w.save()
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return 1
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return 0
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def clone_planned_session(ps):
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ps.save()
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ps.pk = None # creates new instance
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ps.save()
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def timefield_to_seconds_duration(t):
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duration = t.hour*3600.
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duration += t.minute * 60.
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duration += t.second
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duration += t.microsecond/1.e6
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return duration
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def is_session_complete(ps):
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ws = Workout.objects.filter(plannedsession=ps)
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score = 0
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for w in ws:
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if ps.sessionmode == 'distance':
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score += w.distance
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elif ps.sessionmode == 'time':
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durationseconds = timefield_to_seconds_duration(w.duration)
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score += durationseconds
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elif ps.sessionmode == 'TRIMP':
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trimp = dataprep.workout_trimp(w)
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score += trimp
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elif ps.sessionmode == 'rScore':
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rscore = dataprep.workout_rscore(w)
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score += rscore
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ratio = score/float(ps.value)
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if ratio>0.8 and ratio<1.2:
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return True
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return False
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def rank_results(ps):
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return 1
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def add_team(t,ps):
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ps.team.add(t)
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ps.save()
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return 1
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def add_rower(r,ps):
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ps.rower.add(r)
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ps.save()
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return 1
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@@ -7496,7 +7496,6 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
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if w.workouttype in ('water','coastal'):
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ftp = ftp*(100.-r.otwslack)/100.
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intensityfactor = datadf['power'].mean()/float(ftp)
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intensityfactor = normp/float(ftp)
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tss = 100.*((duration*normp*intensityfactor)/(3600.*ftp))
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