first test on breakthrough workouts (only on upload)

rowers/dataprep.py

@@ -43,6 +43,7 @@ from sqlalchemy import create_engine
 import sqlalchemy as sa
 import sys
 
+import utils
 from utils import lbstoN
 from scipy.interpolate import griddata
 
@@ -139,6 +140,34 @@ def filter_df(datadf,fieldname,value,largerthan=True):
 
     return datadf
 
+def getsinglecp(df):
+    thesecs = df['TimeStamp (sec)'].max()-df['TimeStamp (sec)'].min()
+    if thesecs != 0:
+        maxt = 2*thesecs
+    else:
+        maxt = 1000.
+
+    maxlog10 = np.log10(maxt)
+    logarr = np.arange(50)*maxlog10/50.
+    logarr = [int(10.**(la)) for la in logarr]
+    logarr = pd.Series(logarr)
+    logarr.drop_duplicates(keep='first',inplace=True)
+
+    logarr = logarr.values
+
+
+    dfnew = pd.DataFrame({
+        'time':df['TimeStamp (sec)']-df.ix[0,'TimeStamp (sec)'],
+        'power':df[' Power (watts)']
+    })
+    
+    dfnew['workoutid'] = 0
+    
+    dfgrouped = dfnew.groupby(['workoutid'])
+    delta,cpvalue,avgpower = getcp(dfgrouped,logarr)
+
+    return delta,cpvalue,avgpower
+
 def getcp(dfgrouped,logarr):
     delta = []
     cpvalue = []
@@ -488,6 +517,12 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
                 powerperc=powerperc,powerzones=r.powerzones)
     row = rdata(f2,rower=rr)
 
+    isbreakthrough = False
+    if workouttype == 'water':
+        delta,cpvalues,avgpower = getsinglecp(row.df)
+        if utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3):
+            isbreakthrough = True
+
     dtavg = row.df['TimeStamp (sec)'].diff().mean()
 
     if dtavg < 1:
@@ -643,6 +678,10 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
 
     w.save()
 
+    # submit email task to send email about breakthrough workout
+    if isbreakthrough:
+        pass
+
     if privacy == 'visible':
         ts = Team.objects.filter(rower=r)
         for t in ts:

rowers/utils.py

@@ -74,3 +74,12 @@ def geo_distance(lat1,lon1,lat2,lon2):
     bearing = math.degrees(tc1)
 
     return [distance,bearing]
+
+
+def isbreakthrough(delta,cpvalues,p0,p1,p2,p3):
+  pwr = p0/(1+delta/p2)
+  pwr += p1/(1+delta/p3)
+
+  res = np.sum(cpvalues>pwr)
+
+  return res>1