trivial changes white space

rowers/dataprep.py

@@ -49,7 +49,7 @@ from rowingdata import (
     SpeedCoach2Parser, FITParser, fitsummarydata,
     RitmoTimeParser,KinoMapParser,
     make_cumvalues,cumcpdata,ExcelTemplate,
-    summarydata, get_file_type, 
+    summarydata, get_file_type,
 )
 
 from rowingdata.csvparsers import HumonParser
@@ -128,12 +128,12 @@ def polarization_index(df,rower):
     df.dropna(axis=0,inplace=True)
     df['dt'] = df['dt'].clip(upper=4,lower=0)
 
-    
+
     masklow = (df['power']>0) & (df['power']<int(rower.pw_at))
     maskmid = (df['power']>=rower.pw_at) & (df['power']<int(rower.pw_an))
     maskhigh = (df['power']>rower.pw_an)
 
-    
+
     time_low_pw = df.loc[masklow,'dt'].sum()
     time_mid_pw = df.loc[maskmid,'dt'].sum()
     time_high_pw = df.loc[maskhigh,'dt'].sum()
@@ -143,7 +143,7 @@ def polarization_index(df,rower):
     frac_high = time_high_pw/(time_low_pw+time_mid_pw+time_high_pw)
 
     index = math.log10(frac_high*100.*frac_low/frac_mid)
-    
+
     return index
 
 
@@ -157,7 +157,7 @@ def get_latlon(id):
     rowdata = rdata(w.csvfilename)
 
     if rowdata.df.empty:
-        return [pd.Series([]), pd.Series([])]        
+        return [pd.Series([]), pd.Series([])]
 
     try:
         try:
@@ -216,7 +216,7 @@ def workout_summary_to_df(
         trimps.append(workout_trimp(w)[0])
         rscore = workout_rscore(w)
         rscores.append(int(rscore[0]))
-        
+
     df = pd.DataFrame({
         'name':names,
         'date':startdatetimes,
@@ -285,7 +285,7 @@ def join_workouts(r,ids,title='Joined Workout',
         else:
             makeprivate = False
 
-        startdatetime = parent.startdatetime 
+        startdatetime = parent.startdatetime
     else:
         oarlength = 2.89
         inboard = 0.88
@@ -311,7 +311,7 @@ def join_workouts(r,ids,title='Joined Workout',
         workouttype = parent.workouttype
         notes = parent.notes
         summary = parent.summary
-    
+
     files = [w.csvfilename for w in ws]
 
     row = rdata(files[0])
@@ -353,7 +353,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
                    ignoreadvanced=False):
     # clean data remove zeros and negative values
 
-    
+
     # bring metrics which have negative values to positive domain
     if len(datadf)==0:
         return datadf
@@ -377,7 +377,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
         datadf['spm'] = datadf['spm'] + 1.0
     except (KeyError,TypeError) as e:
         pass
-        
+
     try:
         datadf = datadf.clip(lower=0)
     except TypeError:
@@ -421,13 +421,13 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
         datadf.mask(mask,inplace=True)
     except (KeyError,TypeError):
         pass
-        
+
     try:
         mask = datadf['efficiency'] > 200.
         datadf.mask(mask,inplace=True)
     except (KeyError,TypeError):
         pass
-        
+
     try:
         mask = datadf['spm'] < 10
         datadf.mask(mask,inplace=True)
@@ -648,7 +648,7 @@ def fitnessmetric_to_sql(m,table='powertimefitnessmetric',debug=False):
     placeholders  = ", ".join(["?"] * len(m))
 
     query = "INSERT into %s ( %s ) Values (%s)" % (table, columns, placeholders)
-    
+
     values = tuple(m[key] for key in m.keys())
     with engine.connect() as conn, conn.begin():
         result = conn.execute(query,values)
@@ -684,8 +684,8 @@ def deletecpdata_sql(rower_id,table='cpdata'):
     conn.close()
     engine.dispose()
 
-    
+
-    
+
 def updatecpdata_sql(rower_id,delta,cp,table='cpdata',distance=[]):
     deletecpdata_sql(rower_id)
     df = pd.DataFrame(
@@ -836,7 +836,7 @@ def create_row_df(r,distance,duration,startdatetime,workouttype='rower',
             spm = 20.
     else:
         spm = avgspm
-            
+
     step = totalseconds/float(nr_strokes)
 
     elapsed = np.arange(nr_strokes)*totalseconds/(float(nr_strokes-1))
@@ -867,7 +867,7 @@ def create_row_df(r,distance,duration,startdatetime,workouttype='rower',
         hr = avghr
     else:
         hr = 0
-        
+
     df = pd.DataFrame({
         'TimeStamp (sec)': unixtime,
 	' Horizontal (meters)': d,
@@ -902,7 +902,7 @@ def create_row_df(r,distance,duration,startdatetime,workouttype='rower',
     return (id, message)
 
 from rowers.utils import totaltime_sec_to_string
-    
+
 # Processes painsled CSV file to database
 def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
                           boattype='1x',
@@ -934,7 +934,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
 
     if row.df.empty:
         return (0, 'Error: CSV data file was empty')
-    
+
     dtavg = row.df['TimeStamp (sec)'].diff().mean()
 
     if dtavg < 1:
@@ -1025,7 +1025,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
     if dosummary:
         summary = row.allstats()
 
-    
+
 
     timezone_str = 'UTC'
     try:
@@ -1094,7 +1094,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
     )
 
     ws2 = []
-    
+
     for ww in ws:
         t = ww.duration
         delta = datetime.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second)
@@ -1146,7 +1146,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
 
     rscore,normp = workout_rscore(w)
     trimp,hrtss = workout_trimp(w)
-    
+
     isbreakthrough = False
     ishard = False
     if workouttype == 'water':
@@ -1184,7 +1184,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
                           r.user.first_name,
                           r.user.last_name,
                           btvalues=btvalues.to_json())
-            
+
     # submit email task to send email about breakthrough workout
     if ishard:
         if r.getemailnotifications and not r.emailbounced:
@@ -1194,7 +1194,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
                           r.user.first_name,
                           r.user.last_name,
                           btvalues=btvalues.to_json())
- 
+
 
     return (w.id, message)
 
@@ -1266,7 +1266,7 @@ def handle_nonpainsled(f2, fileformat, summary=''):
 
     if not hasrecognized:
         return (0,'',0,0,'')
-    
+
     f_to_be_deleted = f2
     # should delete file
     f2 = f2[:-4] + 'o.csv'
@@ -1322,7 +1322,7 @@ def new_workout_from_file(r, f2,
         f3 = f3[6:]
         a = MessageAttachment(message=msg,document=f3)
         a.save()
-        
+
         return -1, message, f2
 
     # Some people try to upload Concept2 logbook summaries
@@ -1340,7 +1340,7 @@ def new_workout_from_file(r, f2,
         os.remove(f2)
         message = "KML files are not supported"
         return (0, message, f2)
-    
+
     # Some people upload corrupted zip files
     if fileformat == 'notgzip':
         os.remove(f2)
@@ -1371,7 +1371,7 @@ def new_workout_from_file(r, f2,
                       handle_sendemail_unrecognized,
                       f4,
                       r.user.email)
-        
+
         return (0, message, f2)
     if fileformat == 'att':
         # email attachment which can safely be ignored
@@ -1393,7 +1393,7 @@ def new_workout_from_file(r, f2,
 
     if workoutsource is None:
         workoutsource = fileformat
-        
+
     id, message = save_workout_database(
         f2, r,
         notes=notes,
@@ -1657,7 +1657,7 @@ def getrowdata_db(id=0, doclean=False, convertnewtons=True,
     else:
         row = Workout.objects.get(id=id)
 
-    
+
     if not data.empty and data['efficiency'].mean() == 0 and data['power'].mean() != 0 and checkefficiency == True:
         data = add_efficiency(id=id)
 
@@ -1692,10 +1692,10 @@ def getsmallrowdata_db(columns, ids=[], doclean=True,workstrokesonly=True,comput
 
         df = pd.concat(data,axis=0)
         # df = dd.concat(data,axis=0)
-    
+
     else:
         try:
-            df = pd.read_parquet(csvfilenames[0],columns=columns)            
+            df = pd.read_parquet(csvfilenames[0],columns=columns)
         except OSError:
             rowdata,row = getrowdata(id=ids[0])
             if rowdata and len(rowdata.df):
@@ -1707,7 +1707,7 @@ def getsmallrowdata_db(columns, ids=[], doclean=True,workstrokesonly=True,comput
 
                 # df = df.loc[:,~df.columns.duplicated()]
 
-    
+
 
     if compute:
         data = df.copy()
@@ -1717,7 +1717,7 @@ def getsmallrowdata_db(columns, ids=[], doclean=True,workstrokesonly=True,comput
         data.dropna(axis=1,how='all',inplace=True)
         data.dropna(axis=0,how='any',inplace=True)
         return data
-    
+
     return df
 
 def getsmallrowdata_db_dask(columns, ids=[], doclean=True,workstrokesonly=True,compute=True):
@@ -1744,10 +1744,10 @@ def getsmallrowdata_db_dask(columns, ids=[], doclean=True,workstrokesonly=True,c
 
         df = dd.concat(data,axis=0)
         # df = dd.concat(data,axis=0)
-    
+
     else:
         try:
-            df = dd.read_parquet(csvfilenames[0],columns=columns)            
+            df = dd.read_parquet(csvfilenames[0],columns=columns)
         except OSError:
             rowdata,row = getrowdata(id=ids[0])
             if rowdata and len(rowdata.df):
@@ -1759,7 +1759,7 @@ def getsmallrowdata_db_dask(columns, ids=[], doclean=True,workstrokesonly=True,c
 
                 # df = df.loc[:,~df.columns.duplicated()]
 
-    
+
 
     if compute:
         data = df.compute()
@@ -1769,7 +1769,7 @@ def getsmallrowdata_db_dask(columns, ids=[], doclean=True,workstrokesonly=True,c
         data.dropna(axis=1,how='all',inplace=True)
         data.dropna(axis=0,how='any',inplace=True)
         return data
-    
+
     return df
 
 def getsmallrowdata_db_old(columns, ids=[], doclean=True, workstrokesonly=True):
@@ -1787,7 +1787,7 @@ def getsmallrowdata_db_old(columns, ids=[], doclean=True, workstrokesonly=True):
             f = row.df['TimeStamp (sec)'].diff().mean()
         except (AttributeError,KeyError) as e:
             f = 0
-            
+
         if f != 0 and not np.isnan(f):
             windowsize = 2 * (int(10. / (f))) + 1
         else:
@@ -1808,7 +1808,7 @@ def getsmallrowdata_db_old(columns, ids=[], doclean=True, workstrokesonly=True):
             except (KeyError, AttributeError):
                 data[c] = 0
 
-                
+
     # convert newtons
 
     if doclean:
@@ -1907,7 +1907,7 @@ def read_cols_df_sql(ids, columns, convertnewtons=True):
                     data.append(df)
 
         df = pd.concat(data,axis=0)
-                
+
 
     df = df.fillna(value=0)
 
@@ -2215,9 +2215,9 @@ def add_efficiency(id=0):
     rowdata = remove_invalid_columns(rowdata)
     rowdata = rowdata.replace([-np.inf, np.inf], np.nan)
     rowdata = rowdata.fillna(method='ffill')
-    
+
     delete_strokedata(id)
-    
+
     if id != 0:
         rowdata['workoutid'] = id
         filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
@@ -2250,7 +2250,7 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
         velo = rowdatadf.loc[:,' AverageBoatSpeed (m/s)']
     except KeyError:
         velo = 500./p
-        
+
     hr = rowdatadf.loc[:, ' HRCur (bpm)']
     spm = rowdatadf.loc[:, ' Cadence (stokes/min)']
     cumdist = rowdatadf.loc[:, 'cum_dist']
@@ -2316,7 +2316,7 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
 
     powerhr = 60.*power/hr
     powerhr = powerhr.fillna(value=0)
-            
+
     if driveenergy.mean() == 0 and driveenergy.std() == 0:
         driveenergy = 0*driveenergy+100
 
@@ -2521,7 +2521,7 @@ def workout_trimp(w):
 
     if w.trimp > 0:
         return w.trimp,w.hrtss
-    
+
     r = w.user
     ftp = float(r.ftp)
     if w.workouttype in otwtypes:
@@ -2557,13 +2557,13 @@ def workout_trimp(w):
         r.hrftp,
         r.max,
         r.rest)
-        
+
     return 0,0
 
 def workout_rscore(w):
     if w.rscore > 0:
         return w.rscore,w.normp
-    
+
     r = w.user
     ftp = float(r.ftp)
     if w.workouttype in otwtypes:
@@ -2574,7 +2574,7 @@ def workout_rscore(w):
         r.hrftp = int(hrftp)
         r.save()
 
-        
+
 
     job = myqueue(
         queuehigh,
@@ -2586,7 +2586,7 @@ def workout_rscore(w):
         r.hrftp,
         r.max,
         r.rest)
-        
+
     return 0,0
 
 def workout_normv(w,pp=4.0):
@@ -2603,7 +2603,7 @@ def workout_normv(w,pp=4.0):
         r.hrftp = int(hrftp)
         r.save()
 
-        
+
 
     job = myqueue(
         queuehigh,
@@ -2615,7 +2615,5 @@ def workout_normv(w,pp=4.0):
         r.hrftp,
         r.max,
         r.rest)
-        
-    return 0,0
 
-    
+    return 0,0