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some warnings

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This commit is contained in:
Sander Roosendaal
2021-04-27 15:02:30 +02:00
parent b4ed7a0a6b
commit 6f55a975c1
11 changed files with 63 additions and 51 deletions
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36
rowers/dataprep.py
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@@ -265,7 +265,7 @@ def get_latlon(id):
rowdata = rdata(w.csvfilename)
if rowdata.df.empty: # pragma: no cover
return [pd.Series([]), pd.Series([])]
return [pd.Series([],dtype='float'), pd.Series([],dtype='float')]
try:
try:
@@ -276,9 +276,9 @@ def get_latlon(id):
longitude = 0 * rowdata.df.loc[:, 'TimeStamp (sec)']
return [latitude, longitude]
except AttributeError: # pragma: no cover
return [pd.Series([]), pd.Series([])]
return [pd.Series([],dtype='float'), pd.Series([],dtype='float')]
return [pd.Series([]), pd.Series([])] # pragma: no cover
return [pd.Series([],dtype='float'), pd.Series([],dtype='float')] # pragma: no cover
def get_latlon_time(id):
try:
@@ -290,7 +290,7 @@ def get_latlon_time(id):
rowdata = rdata(w.csvfilename)
if rowdata.df.empty: # pragma: no cover
return [pd.Series([]), pd.Series([])]
return [pd.Series([],dtype='float'), pd.Series([],dtype='float')]
try:
try:
@@ -1152,8 +1152,8 @@ def calculate_goldmedalstandard(rower,workout,recurrance=True):
job = myqueue(queuelow,handle_getagegrouprecords,
jsondf,distances,durations,age,rower.sex,rower.weightcategory)
wcpower = pd.Series(wcpower)
wcdurations = pd.Series(wcdurations)
wcpower = pd.Series(wcpower,dtype='float')
wcdurations = pd.Series(wcdurations,dtype='float')
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
@@ -1203,14 +1203,14 @@ def fetchcp_new(rower,workouts):
if len(data) == 0:
return pd.Series(),pd.Series(),0,pd.Series(),pd.Series()
return pd.Series(dtype='float'),pd.Series(dtype='float'),0,pd.Series(dtype='float'),pd.Series(dtype='float')
if len(data)>1:
df = pd.concat(data,axis=0)
try:
df = df[df['cp'] == df.groupby(['delta'])['cp'].transform('max')]
except KeyError: # pragma: no cover
return pd.Series(),pd.Series(),0,pd.Series(),pd.Series()
return pd.Series(dtype='float'),pd.Series(dtype='float'),0,pd.Series(dtype='float'),pd.Series(dtype='float')
df = df.sort_values(['delta']).reset_index()
@@ -1224,13 +1224,13 @@ def setcp(workout,background=False,recurrance=True):
try:
if strokesdf['power'].std()==0:
return pd.DataFrame(),pd.Series(),pd.Series()
return pd.DataFrame(),pd.Series(dtype='float'),pd.Series(dtype='float')
except KeyError:
return pd.DataFrame(),pd.Series(),pd.Series()
return pd.DataFrame(),pd.Series(dtype='float'),pd.Series(dtype='float')
if background: # pragma: no cover
job = myqueue(queuelow,handle_setcp,strokesdf,filename,workout.id)
return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(),pd.Series()
return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(dtype='float'),pd.Series(dtype='float')
if not strokesdf.empty:
totaltime = strokesdf['time'].max()
@@ -1261,7 +1261,7 @@ def setcp(workout,background=False,recurrance=True):
workout.save()
return df,delta,cpvalues
return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(),pd.Series()
return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(dtype='float'),pd.Series(dtype='float')
def update_rolling_cp(r,types,mode='water'):
firstdate = datetime.date.today()-datetime.timedelta(days=r.cprange)
@@ -1315,20 +1315,20 @@ def fetchcp(rower,theworkouts,table='cpdata'): # pragma: no cover
avgpower2 = {}
for id in theids:
avgpower2[id] = 0
return pd.Series([]),pd.Series([]),avgpower2
return pd.Series([],dtype='float'),pd.Series([],dtype='float'),avgpower2
try:
dfgrouped = df.groupby(['workoutid'])
except KeyError:
avgpower2 = {}
return pd.Series([]),pd.Series([]),avgpower2
return pd.Series([],dtype='float'),pd.Series([],dtype='float'),avgpower2
try:
avgpower2 = dict(dfgrouped.mean()['power'].astype(int))
except KeyError:
avgpower2 = {}
for id in theids:
avgpower2[id] = 0
return pd.Series([]),pd.Series([]),avgpower2
return pd.Series([],dtype='float'),pd.Series([],dtype='float'),avgpower2
cpdf = getcpdata_sql(rower.id,table=table)
@@ -1341,10 +1341,10 @@ def fetchcp(rower,theworkouts,table='cpdata'): # pragma: no cover
theids,
table=table)
return pd.Series([]),pd.Series([]),avgpower2
return pd.Series([],dtype='float'),pd.Series([],dtype='float'),avgpower2
return pd.Series([]),pd.Series([]),avgpower2
return pd.Series([],dtype='float'),pd.Series([],dtype='float'),avgpower2
# create a new workout from manually entered data
@@ -1631,7 +1631,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
else: # pragma: no cover
velo2 = velo
velo3 = pd.Series(velo2)
velo3 = pd.Series(velo2,dtype='float')
velo3 = velo3.replace([-np.inf, np.inf], np.nan)
velo3 = velo3.fillna(method='ffill')