Added adaptation of p values on breakthrough workouts

rowers/dataprep.py

@@ -430,6 +430,7 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
         delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
         if utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3):
             isbreakthrough = True
+            res = datautils.updatecp(delta,cpvalues,r)
 
     dtavg = row.df['TimeStamp (sec)'].diff().mean()
 

rowers/datautils.py

@@ -3,6 +3,35 @@ import numpy as np
 from scipy.interpolate import griddata
 from scipy import optimize
 
+def updatecp(delta,cpvalues,r):
+    cp2 = r.p0/(1+delta/r.p2)
+    cp2 += r.p1/(1+delta/r.p3)
+
+    delta = delta.append(delta)
+    cp = cpvalues.append(cp2)
+
+    powerdf = pd.DataFrame({
+        'Delta':delta,
+        'CP':cp,
+        })
+
+    powerdf.dropna(axis=0,inplace=True)
+    powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
+    powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
+    
+
+    res = cpfit(powerdf)
+    p1 = res[0]
+
+    r.p0 = p1[0]
+    r.p1 = p1[1]
+    r.p2 = p1[2]
+    r.p3 = p1[3]
+
+    r.save()
+
+    return 1
+
 def cpfit(powerdf):
     # Fit the data to thee parameter CP model
     fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))