moved CP segment findin/sorting to dataprep

rowers/dataprep.py

@@ -44,6 +44,7 @@ import sqlalchemy as sa
 import sys
 
 from utils import lbstoN
+from scipy.interpolate import griddata
 
 import django_rq
 queue = django_rq.get_queue('default')
@@ -138,6 +139,71 @@ def filter_df(datadf,fieldname,value,largerthan=True):
 
     return datadf
 
+def getcp(dfgrouped,logarr):
+    delta = []
+    cpvalue = []
+    avgpower = {}
+    #avgpower[0] = 0
+
+    for id,group in dfgrouped:
+        tt = group['time'].copy()
+        ww = group['power'].copy()
+
+        tmax = tt.max()
+        newlen = int(tmax/2000.)
+        print newlen,len(ww)
+        newt = np.arange(newlen)*tmax/float(newlen)
+        neww = griddata(tt.values,
+                        ww.values,
+                        newt,method='linear',
+                        rescale=True)
+
+        #tt = pd.Series(newt)
+        #ww = pd.Series(neww)
+                               
+        try:
+            avgpower[id] = int(ww.mean())
+        except ValueError:
+            avgpower[id] = '---'
+        if not np.isnan(ww.mean()):
+            length = len(ww)
+            dt = []
+            cpw = []
+            for i in range(length-2):
+                w_roll = ww.rolling(i+2).mean().dropna()
+                if len(w_roll):
+                    # now goes with # data points - should be fixed seconds
+                    indexmax = w_roll.idxmax(axis=1)
+                    try:
+                        t_0 = tt.ix[indexmax]
+                        t_1 = tt.ix[indexmax-i]
+                        deltat = 1.0e-3*(t_0-t_1)
+                        wmax = w_roll.ix[indexmax]
+                        if not np.isnan(deltat) and not np.isnan(wmax):
+                            dt.append(deltat)
+                            cpw.append(wmax)
+                    except KeyError:
+                        pass
+
+            
+            dt = pd.Series(dt)
+            cpw = pd.Series(cpw)
+
+            
+            cpvalues = griddata(dt.values,
+                                cpw.values,
+                                logarr,method='linear',
+                                rescale=True)
+
+            for cpv in cpvalues:
+                cpvalue.append(cpv)
+            for d in logarr:
+                delta.append(d)
+            
+    delta = pd.Series(delta,name='Delta')
+    cpvalue = pd.Series(cpvalue,name='CP')
+    return delta,cpvalue,avgpower
+
 def clean_df_stats(datadf,workstrokesonly=True,ignorehr=True,
                    ignoreadvanced=False):
     # clean data remove zeros and negative values