added alternative updatecp

but it is slower ...

rowers/datautils.py

@@ -107,7 +107,104 @@ def getsinglecp(df):
 
     return delta,cpvalue,avgpower
 
+def getcp_new(dfgrouped,logarr):
+    delta = []
+    cpvalue = []
+    avgpower = {}
 
+    
+    for id, group in dfgrouped:
+        tt = group['time'].copy()
+
+        ww = group['power'].copy()
+
+        try:
+            avgpower[id] = int(ww.mean())
+        except ValueError:
+            avgpower[id] = '---'
+
+        tmax = tt.max()
+
+        if tmax > 500000:
+            newlen = int(tmax/2000.)
+            newt = np.arange(newlen)*tmax/float(newlen)
+            deltat = newt[1]-newt[0]
+        else:
+            newt = np.arange(0,tmax,10.)
+            deltat = 10.
+
+        ww = griddata(tt.values,
+                      ww.values,
+                      newt,method='linear',
+                      rescale=True)
+        
+        tt = pd.Series(newt)
+        ww = pd.Series(ww)
+
+        G = pd.Series(ww.cumsum())
+        G = pd.concat([pd.Series([0]),G])
+
+        h = np.mgrid[0:len(tt)+1:1,0:len(tt)+1:1]
+
+        distances = pd.DataFrame(h[1]-h[0])
+
+        ones = 1+np.zeros(len(G))
+
+        Ghor = np.outer(ones,G)
+        Gver = np.outer(G,ones)
+
+        Gdif = Ghor - Gver
+
+        Gdif = np.tril(Gdif.T).T
+
+        Gdif = pd.DataFrame(Gdif)
+
+        F = Gdif/distances
+
+        F.fillna(inplace=True,method='ffill',axis=1)
+        F.fillna(inplace=True,value=0)
+
+        restime = []
+        power = []
+        
+        for i in np.arange(0,len(tt)+1,1):
+            restime.append(deltat*i)
+            cp = np.diag(F,i).max()
+            power.append(cp)
+
+        power[0] = power[1]
+
+        restime = np.array(restime)
+        power = np.array(power)
+
+            
+        #power[0] = power[1]
+            
+        cpvalues = griddata(restime,power,
+                            logarr,method='linear', fill_value=0)
+
+
+    
+        for cpv in cpvalues:
+            cpvalue.append(cpv)
+        for d in logarr:
+            delta.append(d)
+                                     
+    df = pd.DataFrame({
+        'delta':delta,
+        'cpvalue':cpvalue
+        })
+
+    df.dropna(axis=0, how='any',inplace=True)
+    df = df.sort_values(['delta','cp'], ascending=[1, 0])
+    df = df.drop_duplicates(subset='Delta', keep='first')
+
+    delta = df['delta']
+    cpvalue = df['cpvalue']
+
+    return delta,cpvalue,avgpower
+            
+        
 def getcp(dfgrouped,logarr):
     delta = []
     cpvalue = []
@@ -165,7 +262,9 @@ def getcp(dfgrouped,logarr):
                     cpvalue.append(cpv)
                 for d in logarr:
                     delta.append(d)
-            
+
+                
+                    
     delta = pd.Series(delta,name='Delta')
     cpvalue = pd.Series(cpvalue,name='CP')