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filtered out non meaningul values

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Sander Roosendaal
2017-02-07 10:49:42 +01:00
parent 955d5aa070
commit 24565e2ef2
1 changed files with 91 additions and 1 deletions
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92
rowers/views.py
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@@ -2737,7 +2737,52 @@ def cumstats(request,theuser=0,
fieldlist = [field for field,value in fielddict.iteritems()]
# prepare data frame
datadf = dataprep.read_cols_df_sql(ids,fieldlist)
datadf = dataprep.read_cols_df_sql(ids,fieldlist)
# clean data remove zeros and negative values
datadf=datadf.clip(lower=0)
datadf.replace(to_replace=0,value=np.nan,inplace=True)
# clean data for useful ranges per column
mask = datadf['hr'] < 30
datadf.loc[mask,'hr'] = np.nan
mask = datadf['rhythm'] < 5
datadf.loc[mask,'rhythm'] = np.nan
mask = datadf['rhythm'] > 70
datadf.loc[mask,'rhythm'] = np.nan
mask = datadf['power'] < 20
datadf.loc[mask,'power'] = np.nan
mask = datadf['drivelength'] < 0.5
datadf.loc[mask,'drivelength'] = np.nan
mask = datadf['forceratio'] < 0.2
datadf.loc[mask,'forceratio'] = np.nan
mask = datadf['forceratio'] > 1.0
datadf.loc[mask,'forceratio'] = np.nan
mask = datadf['spm'] < 10
datadf.loc[mask,'spm'] = np.nan
mask = datadf['spm'] > 60
datadf.loc[mask,'spm'] = np.nan
mask = datadf['drivespeed'] < 0.5
datadf.loc[mask,'drivespeed'] = np.nan
mask = datadf['drivespeed'] > 4
datadf.loc[mask,'drivespeed'] = np.nan
mask = datadf['driveenergy'] > 2000
datadf.loc[mask,'driveenergy'] = np.nan
mask = datadf['driveenergy'] < 100
datadf.loc[mask,'driveenergy'] = np.nan
if datadf.empty:
return HttpResponse("No data found")
@@ -2812,6 +2857,51 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
message = "You are not allowed to see the stats of this workout"
url = reverse(workouts_view,args=[str(message)])
return HttpResponseRedirect(url)
# clean data remove zeros and negative values
datadf=datadf.clip(lower=0)
datadf.replace(to_replace=0,value=np.nan,inplace=True)
# clean data for useful ranges per column
mask = datadf['hr'] < 30
datadf.loc[mask,'hr'] = np.nan
mask = datadf['rhythm'] < 5
datadf.loc[mask,'rhythm'] = np.nan
mask = datadf['rhythm'] > 70
datadf.loc[mask,'rhythm'] = np.nan
mask = datadf['power'] < 20
datadf.loc[mask,'power'] = np.nan
mask = datadf['drivelength'] < 0.5
datadf.loc[mask,'drivelength'] = np.nan
mask = datadf['forceratio'] < 0.2
datadf.loc[mask,'forceratio'] = np.nan
mask = datadf['forceratio'] > 1.0
datadf.loc[mask,'forceratio'] = np.nan
mask = datadf['spm'] < 10
datadf.loc[mask,'spm'] = np.nan
mask = datadf['spm'] > 60
datadf.loc[mask,'spm'] = np.nan
mask = datadf['drivespeed'] < 0.5
datadf.loc[mask,'drivespeed'] = np.nan
mask = datadf['drivespeed'] > 4
datadf.loc[mask,'drivespeed'] = np.nan
mask = datadf['driveenergy'] > 2000
datadf.loc[mask,'driveenergy'] = np.nan
mask = datadf['driveenergy'] < 100
datadf.loc[mask,'driveenergy'] = np.nan
if datadf.empty: