Merge branch 'release/v3.12'

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

rowers/models.py

@@ -173,6 +173,12 @@ class Rower(models.Model):
 
     # Power Zone Data
     ftp = models.IntegerField(default=226,verbose_name="Functional Threshold Power")
+
+    p0 = models.FloatField(default=1.0,verbose_name="CP p1")
+    p1 = models.FloatField(default=1.0,verbose_name="CP p2")
+    p2 = models.FloatField(default=1.0,verbose_name="CP p3")
+    p3 = models.FloatField(default=1.0,verbose_name="CP p4")
+    
     otwslack = models.IntegerField(default=0,verbose_name="OTW Power slack")
 
     pw_ut2 = models.IntegerField(default=124,verbose_name="UT2 Power")

rowers/views.py

@@ -2905,7 +2905,7 @@ def otwrankings_view(request,theuser=0,
 
 
     maxlog10 = np.log10(maxt)
-    logarr = np.arange(100)*maxlog10/100.
+    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)
@@ -2914,56 +2914,9 @@ def otwrankings_view(request,theuser=0,
 
     
     
-    delta = []
-    cpvalue = []
-    avgpower = {}
-    
     dfgrouped = df.groupby(['workoutid'])
-    for id,group in dfgrouped:
-        tt = group['time'].copy()
-        ww = group['power'].copy()
-
-        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')
+    delta,cpvalue,avgpower = dataprep.getcp(dfgrouped,logarr)
+    
 
     powerdf = pd.DataFrame({
         'Delta':delta,
@@ -2983,6 +2936,11 @@ def otwrankings_view(request,theuser=0,
 	script = res[0]
 	div = res[1]
 	p1 = res[2]
+        r.p0 = p1[0]
+        r.p1 = p1[1]
+        r.p2 = p1[2]
+        r.p3 = p1[3]
+        r.save()
         paulslope = 1
         paulintercept = 1
 	message = res[3]