added notification to rowing physics

rowers/datautils.py

@@ -0,0 +1,109 @@
+import pandas as pd
+import numpy as np
+from scipy.interpolate import griddata
+
+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':1000*(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 = []
+    avgpower = {}
+    #avgpower[0] = 0
+
+    for id,group in dfgrouped:
+        tt = group['time'].copy()
+        ww = group['power'].copy()
+
+        tmax = tt.max()
+        if tmax > 500000:
+            newlen = int(tmax/5000.)
+        else:
+            newlen = len(tt)
+        if newlen < len(tt):
+            newt = np.arange(newlen)*tmax/float(newlen)
+            ww = griddata(tt.values,
+                          ww.values,
+                          newt,method='linear',
+                          rescale=True)
+
+            tt = pd.Series(newt)
+            ww = pd.Series(ww)
+
+            
+        try:
+            avgpower[id] = int(ww.mean())
+        except ValueError:
+            avgpower[id] = '---'
+        if not np.isnan(ww.mean()):
+            length = len(ww)
+            dt = []
+            cpw = []
+            for i in xrange(length-2):
+                deltat,wmax = getmaxwattinterval(tt,ww,i)
+                if not np.isnan(deltat) and not np.isnan(wmax):
+                    dt.append(deltat)
+                    cpw.append(wmax)
+
+
+            
+            dt = pd.Series(dt)
+            cpw = pd.Series(cpw)
+            if len(dt):
+            
+                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 getmaxwattinterval(tt,ww,i):
+    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]
+        except KeyError:
+            pass
+    else:
+        wmax = 0
+        deltat = 0
+
+    return deltat,wmax