got to point where chart is generated (L2936)

rowers/urls.py

@@ -135,6 +135,12 @@ urlpatterns = [
     url(r'^ote-bests/(?P<deltadays>\d+)$',views.rankings_view),
     url(r'^ote-bests/$',views.rankings_view),
     url(r'^(?P<theuser>\d+)/ote-bests/$',views.rankings_view),
+    url(r'^(?P<theuser>\d+)/otw-bests/(?P<startdatestring>\w+.*)/(?P<enddatestring>\w+.*)$',views.otwrankings_view),
+    url(r'^(?P<theuser>\d+)/otw-bests/(?P<deltadays>\d+)$',views.otwrankings_view),
+    url(r'^otw-bests/(?P<startdatestring>\w+.*)/(?P<enddatestring>\w+.*)$',views.otwrankings_view),
+    url(r'^otw-bests/(?P<deltadays>\d+)$',views.otwrankings_view),
+    url(r'^otw-bests/$',views.otwrankings_view),
+    url(r'^(?P<theuser>\d+)/otw-bests/$',views.otwrankings_view),
     url(r'^(?P<theuser>\d+)/flexall/(?P<xparam>\w+.*)/(?P<yparam1>\w+.*)/(?P<yparam2>\w+.*)/(?P<startdatestring>\w+.*)/(?P<enddatestring>\w+.*)$',views.cum_flex),
     url(r'^flexall/(?P<xparam>\w+.*)/(?P<yparam1>\w+.*)/(?P<yparam2>\w+.*)/(?P<startdatestring>\w+.*)/(?P<enddatestring>\w+.*)$',views.cum_flex),
     url(r'^flexall/(?P<xparam>\w+.*)/(?P<yparam1>\w+.*)/(?P<yparam2>\w+.*)$',views.cum_flex),

rowers/views.py

@@ -2856,13 +2856,6 @@ def otwrankings_view(request,theuser=0,
     thedistances = []
     theworkouts = []
     thesecs = []
-
-
-
-    rankingdistances.sort()
-    rankingdurations.sort()
-
-
     
     theworkouts = Workout.objects.filter(user=r,rankingpiece=True,
 				          workouttype='water',
@@ -2878,10 +2871,10 @@ def otwrankings_view(request,theuser=0,
     thesecs = []
     
     for w in theworkouts:
-        timesecs = 3600*workouts[0].duration.hour
+        timesecs = 3600*w.duration.hour
-	timesecs += 60*workouts[0].duration.minute
+	timesecs += 60*w.duration.minute
-	timesecs += workouts[0].duration.second
+	timesecs += w.duration.second
-	timesecs += 1.e-5*workouts[0].duration.microsecond
+	timesecs += 1.e-5*w.duration.microsecond
 	
 	thesecs.append(timesecs)
 
@@ -2890,7 +2883,10 @@ def otwrankings_view(request,theuser=0,
 
     maxlog10 = np.log10(maxt)
     logarr = np.arange(100)*maxlog10/100.
-    logarr = 10.**(logarr)
+    logarr = [int(10.**(la)) for la in logarr]
+    logarr = pd.Series(logarr)
+    logarr.drop_duplicates(keep='first',inplace=True)
+    logarr = logarr.values
 
     delta = []
     cpvalue = []
@@ -2899,25 +2895,34 @@ def otwrankings_view(request,theuser=0,
     for id,group in dfgrouped:
         tt = group['time']
         ww = group['power']
-        length = len(ww)
+        if not np.isnan(ww.mean()):
-        dt = []
+            length = len(ww)
-        cpw = []
+            dt = []
-        for i in range(length):
+            cpw = []
-            w_roll = ww.rolling(i)
+            for i in range(length-2):
-            # now goes with # data points - should be fixed seconds
+                w_roll = ww.rolling(i+2,min_periods=2).mean()
-            t_0 = tt.ix[w_roll.idxmax(axis=1)]
+                # now goes with # data points - should be fixed seconds
-            t_1 = tt.ix[w_roll.idxmax(axis=1)-i]
+                indexmax = w_roll.idxmax(axis=1)
-            deltat = t_1-t_0
+                try:
-            wmax = w_roll.ix[w_roll.idxmax(axis=1)]
+                    t_0 = tt.ix[indexmax]
-            dt.append(deltat)
+                    t_1 = tt.ix[indexmax-i-2]
-            cpw.append(wmax)
+                    deltat = 1.0e-3*(t_0-t_1)
+                    wmax = w_roll.ix[indexmax]
+                    dt.append(deltat)
+                    cpw.append(wmax)
+                except KeyError:
+                    pass
 
-        cpvalues = griddata(dt,cpw,logarr,method='linear',fill_value=0)
+            dt = pd.Series(dt)
+            cpw = pd.Series(cpw)
+            cpvalues = griddata(dt.values,
+                                cpw.values,
+                                logarr,method='linear',fill_value=0)
 
-        for cpv in cpvalues:
+            for cpv in cpvalues:
-            cpvalue.append(cpv)
+                cpvalue.append(cpv)
-        for d in logarr:
+            for d in logarr:
-            delta.append(d)
+                delta.append(d)
             
     dt = pd.Series(delta,name='Delta')
     cpvalue = pd.Series(cpvalue,name='CP')
@@ -2928,9 +2933,9 @@ def otwrankings_view(request,theuser=0,
         'CP':cpvalue,
         })
 
-    powerdf.sort_value(['Delta','CP'],ascending=[1,0]
+    powerdf.sort_values(['Delta','CP'],ascending=[1,0])
     powerdf.drop_duplicates(subset='Delta',keep='first')
-        
+
 
     # create interactive plot
     if len(thedistances) !=0 :
@@ -2966,61 +2971,11 @@ def otwrankings_view(request,theuser=0,
     else:
 	form = PredictedPieceForm()
 
-    rankingdistances.sort()
-    rankingdurations.sort()
-
 
     predictions = []
     cpredictions = []
 
 
-    for rankingdistance in rankingdistances:
-	# Paul's model
-	p = paulslope*np.log10(rankingdistance)+paulintercept
-	velo = 500./p
-	t = rankingdistance/velo
-	pwr = 2.8*(velo**3)
-	a = {'distance':rankingdistance,
-	     'duration':timedeltaconv(t),
-	     'pace':timedeltaconv(p),
-	     'power':int(pwr)}
-	predictions.append(a)
-
-	# CP model -
-	pwr2 = p1[0]/(1+t/p1[2])
-	pwr2 += p1[1]/(1+t/p1[3])
-
-	if pwr2 <= 0:
-	    pwr2 = 50.
-	
-	velo2 = (pwr2/2.8)**(1./3.)
-
-	if np.isnan(velo2) or velo2 <= 0:
-	    velo2 = 1.0
-	    
-	t2 = rankingdistance/velo2
-	
-	pwr3 = p1[0]/(1+t2/p1[2])
-	pwr3 += p1[1]/(1+t2/p1[3])
-
-	if pwr3 <= 0:
-	    pwr3 = 50.
-	
-	velo3 = (pwr3/2.8)**(1./3.)
-	if np.isnan(velo3) or velo3 <= 0:
-	    velo3 = 1.0
-	    
-	t3 = rankingdistance/velo3
-	p3 = 500./velo3
-
-	a = {'distance':rankingdistance,
-	     'duration':timedeltaconv(t3),
-	     'pace':timedeltaconv(p3),
-	     'power':int(pwr3)}
-	cpredictions.append(a)
-
-
-
 
     for rankingduration in rankingdurations:
 	t = 3600.*rankingduration.hour