From dd48d66c3aa73dc337ce42ee76cab43df2a92931 Mon Sep 17 00:00:00 2001
From: Sander Roosendaal <roosendaalsander@gmail.com>
Date: Tue, 16 Jan 2018 17:04:30 +0100
Subject: [PATCH] first attempt

---
 rowers/datautils.py |  3 +-
 rowers/models.py    |  4 ++-
 rowers/urls.py      |  2 ++
 rowers/views.py     | 80 +++++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 87 insertions(+), 2 deletions(-)

diff --git a/rowers/datautils.py b/rowers/datautils.py
index 5aba0141..1358e0d6 100644
--- a/rowers/datautils.py
+++ b/rowers/datautils.py
@@ -300,7 +300,8 @@ def getmaxwattinterval(tt,ww,i):
                 wmax = 0
                 deltat = 0
         except KeyError:
-            pass
+            wmax = 0
+            deltat = 0
     else:
         wmax = 0
         deltat = 0
diff --git a/rowers/models.py b/rowers/models.py
index db2fb086..f96ff624 100644
--- a/rowers/models.py
+++ b/rowers/models.py
@@ -248,13 +248,15 @@ class PowerTimeFitnessMetric(models.Model):
         ('water','On the water')
         )
     
-    data = models.DateField(default=timezone.now)
+    date = models.DateField(default=timezone.now)
     user = models.ForeignKey(User)
     PowerFourMin = models.FloatField(default=0)
     PowerTwoK = models.FloatField(default=0)
     PowerOneHour = models.FloatField(default=0)
     workoutmode = models.CharField(default='rower',choices=modechoices,
                                    max_length=40)
+    class Meta:
+        db_table = 'powertimefitnessmetric'
         
 class C2WorldClassAgePerformance(models.Model):
     weightcategories = (
diff --git a/rowers/urls.py b/rowers/urls.py
index 7d345028..b43cc09d 100644
--- a/rowers/urls.py
+++ b/rowers/urls.py
@@ -125,6 +125,8 @@ urlpatterns = [
     url(r'^agegroupcp/(?P<age>\d+)/(?P<normalize>\d+)$',views.agegroupcpview),
     url(r'^ajax_agegroup/(?P<age>\d+)/(?P<weightcategory>\w+.*)/(?P<sex>\w+.*)/(?P<userid>\d+)$',
         views.ajax_agegrouprecords),
+    url(r'^updatefitness/(?P<mode>\w+.*)/(?P<days>\d+)$',views.fitness_metric_view),
+    url(r'^updatefitness$',views.fitness_metric_view),
     url(r'^agegrouprecords/(?P<sex>\w+.*)/(?P<weightcategory>\w+.*)/(?P<distance>\d+)m$',
         views.agegrouprecordview),
     url(r'^agegrouprecords/(?P<sex>\w+.*)/(?P<weightcategory>\w+.*)/(?P<duration>\d+)min$',
diff --git a/rowers/views.py b/rowers/views.py
index 09571a41..61aa9644 100644
--- a/rowers/views.py
+++ b/rowers/views.py
@@ -238,6 +238,7 @@ from io import BytesIO
 from scipy.special import lambertw
 
 from dataprep import timedeltaconv
+from dataprep import getsmallrowdata_db
 
 from scipy.interpolate import griddata
 
@@ -3206,6 +3207,85 @@ def addmanual_view(request):
                   {'form':form,
                    })
 
+@login_required()
+def fitness_metric_view(request,mode='rower',days=42):
+    r = getrower(request.user)
+    startdate = timezone.now()-datetime.timedelta(days=days)
+    if mode == 'rower':
+        workouts = Workout.objects.filter(
+            user=r,
+            workouttype__in=['rower','dynamic','slides'],
+            startdatetime__gte=startdate)
+    else:
+        workouts = Workout.objects.filter(
+            user=r,
+            workouttype__in=['water','coastal'],
+            startdatetime__gte=startdate)
+
+    theids = [int(w.id) for w in workouts]
+    columns = ['power','workoutid','time']
+    df = getsmallrowdata_db(columns,ids=theids)
+    df.dropna(inplace=True,axis=0)
+
+    if df.empty:
+        # change this
+        return 'aap'
+
+    # df is not empty. We continue
+    dfgrouped = df.groupby(['workoutid'])
+    maxt = 1.05*df['time'].max()/1000.
+
+    logarr = datautils.getlogarr(maxt)
+        
+    delta,cpvalue,avgpower = datautils.getcp(dfgrouped,logarr)
+
+    powerdf = pd.DataFrame({
+        'Delta':delta,
+        'CP':cpvalue,
+        })
+
+    powerdf = powerdf[powerdf['CP']>0]
+    powerdf.dropna(axis=0,inplace=True)
+    powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
+    powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
+
+    p1,fitt,fitpower,ratio = datautils.cpfit(powerdf)
+    # This is code duplication from datautils -- correct asap
+    fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
+
+    powerfourmin = fitfunc(p1,240.)
+    powerhour = fitfunc(p1,3600.)
+
+    # 2k power
+    velofourmin = (powerfourmin/2.8)**(1./3.)
+    dfourmin = 240.*velofourmin
+    dratio = 2000./dfourmin
+    pacefourmin = 500./velofourmin
+
+    # assume 5 sec per doubling drop
+    pace2k = pacefourmin + 5.*np.log10(dratio)/np.log10(2.)
+    velo2k = 500./pace2k
+    t2k = 2000./velo2k
+    pwr2k = fitfunc(p1,t2k)
+    velo2 = (pwr2k/2.8)**(1./3.)
+    if np.isnan(velo2) or velo2 <= 0:
+        velo2 = 1.0
+
+    t2 = 2000./velo2
+
+    pwr2k = fitfunc(p1,t2)
+
+    velo3 = (pwr2k/2.8)**(1./3.)
+
+    t3 = 2000./velo3
+
+    power2k = fitfunc(p1,t3)
+
+    print powerfourmin,power2k,powerhour
+
+    return HttpResponse("success")
+
+
 # Show ranking distances including predicted paces
 @login_required()
 def rankings_view(request,theuser=0,