moved data field prep for stats to dataprep from views
This commit is contained in:
Sander Roosendaal
165
rowers/views.py
165
rowers/views.py
@@ -2738,97 +2738,17 @@ def cumstats(request,theuser=0,
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u = ''
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ids = [int(workout.id) for workout in allergworkouts]
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# Get field names and remove those that are not useful in stats
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fields = StrokeData._meta.get_fields()
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fielddict = {field.name:field.verbose_name for field in fields}
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fielddict.pop('workoutid')
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fielddict.pop('ergpace')
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fielddict.pop('hr_an')
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fielddict.pop('hr_tr')
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fielddict.pop('hr_at')
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fielddict.pop('hr_ut2')
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fielddict.pop('hr_ut1')
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fielddict.pop('time')
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fielddict.pop('distance')
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fielddict.pop('nowindpace')
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fielddict.pop('fnowindpace')
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fielddict.pop('fergpace')
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fielddict.pop('equivergpower')
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# fielddict.pop('workoutstate')
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fielddict.pop('fpace')
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fielddict.pop('pace')
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fielddict.pop('id')
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fielddict.pop('ftime')
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fielddict.pop('x_right')
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fielddict.pop('hr_max')
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fielddict.pop('hr_bottom')
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fielddict.pop('cumdist')
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fieldlist,fielddict = dataprep.getstatsfields()
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# prepare data frame
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datadf = dataprep.read_cols_df_sql(ids,fieldlist)
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# clean data remove zeros and negative values
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datadf=datadf.clip(lower=0)
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datadf.replace(to_replace=0,value=np.nan,inplace=True)
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# clean data for useful ranges per column
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mask = datadf['hr'] < 30
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datadf.loc[mask,'hr'] = np.nan
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mask = datadf['rhythm'] < 5
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datadf.loc[mask,'rhythm'] = np.nan
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mask = datadf['rhythm'] > 70
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datadf.loc[mask,'rhythm'] = np.nan
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mask = datadf['power'] < 20
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datadf.loc[mask,'power'] = np.nan
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mask = datadf['drivelength'] < 0.5
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datadf.loc[mask,'drivelength'] = np.nan
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mask = datadf['forceratio'] < 0.2
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datadf = dataprep.clean_df_stats(datadf,workstrokesonly=workstrokesonly)
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mask = datadf['forceratio'] > 1.0
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datadf.loc[mask,'forceratio'] = np.nan
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mask = datadf['spm'] < 10
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datadf.loc[mask,'spm'] = np.nan
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mask = datadf['spm'] > 60
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datadf.loc[mask,'spm'] = np.nan
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mask = datadf['drivespeed'] < 0.5
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datadf.loc[mask,'drivespeed'] = np.nan
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mask = datadf['drivespeed'] > 4
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datadf.loc[mask,'drivespeed'] = np.nan
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mask = datadf['driveenergy'] > 2000
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datadf.loc[mask,'driveenergy'] = np.nan
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mask = datadf['driveenergy'] < 100
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if datadf.empty:
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return HttpResponse("No data found")
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workoutstateswork = [1,4,5,8,9,6,7]
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workoutstatesrest = [3]
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workoutstatetransition = [0,2,10,11,12,13]
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if workstrokesonly=='True' or workstrokesonly==True:
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try:
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datadf = datadf[~datadf['workoutstate'].isin(workoutstatesrest)]
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except:
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# Create stats
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@@ -2908,51 +2828,8 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
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message = "You are not allowed to see the stats of this workout"
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url = reverse(workouts_view,args=[str(message)])
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return HttpResponseRedirect(url)
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datadf = dataprep.clean_df_stats(datadf,workstrokesonly=workstrokesonly)
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# clean data remove zeros and negative values
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datadf=datadf.clip(lower=0)
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datadf.replace(to_replace=0,value=np.nan,inplace=True)
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# clean data for useful ranges per column
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mask = datadf['hr'] < 30
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datadf.loc[mask,'hr'] = np.nan
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mask = datadf['rhythm'] < 5
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datadf.loc[mask,'rhythm'] = np.nan
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mask = datadf['rhythm'] > 70
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datadf.loc[mask,'rhythm'] = np.nan
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mask = datadf['power'] < 20
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datadf.loc[mask,'power'] = np.nan
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mask = datadf['drivelength'] < 0.5
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datadf.loc[mask,'drivelength'] = np.nan
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mask = datadf['forceratio'] < 0.2
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datadf.loc[mask,'forceratio'] = np.nan
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mask = datadf['forceratio'] > 1.0
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datadf.loc[mask,'forceratio'] = np.nan
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mask = datadf['spm'] < 10
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datadf.loc[mask,'spm'] = np.nan
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mask = datadf['spm'] > 60
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datadf.loc[mask,'spm'] = np.nan
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mask = datadf['drivespeed'] < 0.5
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datadf.loc[mask,'drivespeed'] = np.nan
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mask = datadf['drivespeed'] > 4
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datadf.loc[mask,'drivespeed'] = np.nan
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mask = datadf['driveenergy'] > 2000
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datadf.loc[mask,'driveenergy'] = np.nan
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mask = datadf['driveenergy'] < 100
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if datadf.empty:
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@@ -2961,44 +2838,12 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
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workoutstateswork = [1,4,5,8,9,6,7]
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workoutstatesrest = [3]
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workoutstatetransition = [0,2,10,11,12,13]
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if workstrokesonly=='True' or workstrokesonly==True:
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try:
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datadf = datadf[~datadf['workoutstate'].isin(workoutstatesrest)]
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except:
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pass
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# Create stats
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stats = {}
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# Get field names and remove those that are not useful in stats
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fields = StrokeData._meta.get_fields()
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fielddict = {field.name:field.verbose_name for field in fields}
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fielddict.pop('workoutid')
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fielddict.pop('ergpace')
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fielddict.pop('hr_an')
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fielddict.pop('hr_tr')
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fielddict.pop('hr_at')
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fielddict.pop('hr_ut2')
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fielddict.pop('hr_ut1')
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fielddict.pop('time')
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fielddict.pop('distance')
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fielddict.pop('nowindpace')
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fielddict.pop('fnowindpace')
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fielddict.pop('fergpace')
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fielddict.pop('equivergpower')
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fielddict.pop('workoutstate')
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fielddict.pop('fpace')
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fielddict.pop('pace')
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fielddict.pop('id')
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fielddict.pop('ftime')
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fielddict.pop('x_right')
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fielddict.pop('hr_max')
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fielddict.pop('hr_bottom')
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fieldlist,fielddict = dataprep.getstatsfields()
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for field,verbosename in fielddict.iteritems():
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