added notification to rowing physics

rowers/dataprep.py

@@ -36,7 +36,7 @@ import pandas as pd
 import numpy as np
 import itertools
 import math
-from tasks import handle_sendemail_unrecognized
+from tasks import handle_sendemail_unrecognized,handle_sendemail_breakthrough
 
 from django.conf import settings
 from sqlalchemy import create_engine
@@ -44,6 +44,7 @@ import sqlalchemy as sa
 import sys
 
 import utils
+import datautils
 from utils import lbstoN
 from scipy.interpolate import griddata
 
@@ -140,107 +141,6 @@ def filter_df(datadf,fieldname,value,largerthan=True):
 
     return datadf
 
-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':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()
-        newlen = int(tmax/5000.)
-        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
 
 def df_resample(datadf):
     # time stamps must be in seconds
@@ -527,7 +427,7 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
 
     isbreakthrough = False
     if workouttype == 'water':
-        delta,cpvalues,avgpower = getsinglecp(row.df)
+        delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
         if utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3):
             isbreakthrough = True
 
@@ -689,16 +589,21 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
     # submit email task to send email about breakthrough workout
     if isbreakthrough:
         a_messages.info(r.user,'It looks like you have a new breakthrough workout')
-        if settings.DEBUG:
+        if settings.DEBUG and r.getemailnotifications:
-            res = handle_sendemail_breakthrough(w.id,r.user.email,
+            res = handle_sendemail_breakthrough.delay(w.id,r.user.email,
                                                 r.user.first_name,
                                                 r.user.last_name)
+        elif r.getemailnotifications:
+            try:
+                res = queuehigh.enqueue(
+                    handle_sendemail_breakthrough(w.id,
+                                                  r.user.email,
+                                                  r.user.first_name,
+                                                  r.user.last_name))
+            except AttributeError:
+                pass
         else:
-            res = queuehigh.enqueue(
+            pass
-                handle_sendemail_breakthrough(w.id,
-                                              r.user.email,
-                                              r.user.first_name,
-                                              r.user.last_name))
 
     if privacy == 'visible':
         ts = Team.objects.filter(rower=r)

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

rowers/models.py

@@ -232,6 +232,9 @@ class Rower(models.Model):
         ('hidden','Hidden'),
         )
 
+    getemailnotifications = models.BooleanField(default=True,
+                                                verbose_name='Receive email notifications')
+
     rowerplan = models.CharField(default='basic',max_length=30,
 				 choices=plans)
 
@@ -746,7 +749,7 @@ class RowerPowerZonesForm(ModelForm):
 class AccountRowerForm(ModelForm):
     class Meta:
         model = Rower
-        fields = ['weightcategory']
+        fields = ['weightcategory','getemailnotifications']
 
 class UserForm(ModelForm):
     class Meta:

rowers/tasks.py

@@ -69,6 +69,8 @@ def handle_sendemail_breakthrough(workoutid,useremail,userfirstname,userlastname
     message += str(workoutid)
     message += "/updatecp\n\n"
 
+    message += "To opt out of these email notifications, deselect the checkbox on your Profile page under Account Information.\n\n"
+
     message += "Best Regards, the Rowsandall Team"
 
     email = EmailMessage(subject, message,
@@ -79,7 +81,6 @@ def handle_sendemail_breakthrough(workoutid,useremail,userfirstname,userlastname
     res = email.send()
 
     # remove tcx file
-    os.remove(unrecognizedfile)
     return 1
 
 
@@ -177,7 +178,7 @@ def handle_sendemailcsv(first_name,last_name,email,csvfile):
 # Calculate wind and stream corrections for OTW rowing
 @app.task
 def handle_otwsetpower(f1,boattype,weightvalue,
-                       first_name,last_name,email,workoutid,
+                       first_name,last_name,email,workoutid,ps=[1,1,1,1],
                        debug=False):
     try:
         rowdata = rdata(f1)
@@ -219,6 +220,12 @@ def handle_otwsetpower(f1,boattype,weightvalue,
     rowdata.write_csv(f1,gzip=True)
     update_strokedata(workoutid,rowdata.df,debug=debug)
 
+    delta,cpvalues,avgpower = datautils.getsinglecp(rowdata.df)
+    if utils.isbreakthrough(delta,cpvalues,ps[0],ps[1],ps[2],ps[3]):
+        handle_sendemail_breakthrough(workoutid,email,
+                                      first_name,
+                                      last_name)
+
     # send email
     fullemail = first_name + " " + last_name + " " + "<" + email + ">"
     subject = "Your Rowsandall OTW calculations are ready"

rowers/tests.py

@@ -68,6 +68,9 @@ class C2Objects(DjangoTestCase):
 	u = User.objects.create_user('john',
 				     'sander@ds.ds',
 				     'koeinsloot')
+        u.first_name = 'John'
+        u.last_name = 'Sander'
+        u.save()
 	r = Rower.objects.create(user=u)
 
 	res = add_workout_from_strokedata(u,1,data,strokedata,source='c2')
@@ -88,6 +91,9 @@ class C2Objects(DjangoTestCase):
 	u = User.objects.create_user('john',
 				     'sander@ds.ds',
 				     'koeinsloot')
+        u.first_name = 'John'
+        u.last_name = 'Sander'
+        u.save()
 	r = Rower.objects.create(user=u)
 
 	res = add_workout_from_strokedata(u,1,data,strokedata,source='c2')
@@ -162,6 +168,9 @@ class StravaObjects(DjangoTestCase):
 	u = User.objects.create_user('john',
 				     'sander@ds.ds',
 				     'koeinsloot')
+        u.first_name = 'John'
+        u.last_name = 'Sander'
+        u.save()
 	r = Rower.objects.create(user=u)
 
 	res = add_workout_from_strokedata(u,1,workoutsummary,strokedata,
@@ -235,6 +244,9 @@ class StravaObjects(DjangoTestCase):
 	u = User.objects.create_user('john',
 				     'sander@ds.ds',
 				     'koeinsloot')
+        u.first_name = 'John'
+        u.last_name = 'Sander'
+        u.save()
 	r = Rower.objects.create(user=u)
 
 	res = add_workout_from_strokedata(u,1,workoutsummary,strokedata,

rowers/urls.py

@@ -183,6 +183,7 @@ urlpatterns = [
     url(r'^workout/compare/(?P<id>\d+)/(?P<startdatestring>\d+-\d+-\d+)/(?P<enddatestring>\w+.*)$',views.workout_comparison_list),
     url(r'^workout/(?P<id>\d+)/edit$',views.workout_edit_view),
     url(r'^workout/(?P<id>\d+)/setprivate$',views.workout_setprivate_view),
+    url(r'^workout/(?P<id>\d+)/updatecp$',views.workout_update_cp_view),
     url(r'^workout/(?P<id>\d+)/makepublic$',views.workout_makepublic_view),
     url(r'^workout/(?P<id>\d+)/geeky$',views.workout_geeky_view),
     url(r'^workout/(?P<id>\d+)/advanced$',views.workout_advanced_view),

rowers/utils.py

@@ -77,6 +77,7 @@ def geo_distance(lat1,lon1,lat2,lon2):
 
 
 def isbreakthrough(delta,cpvalues,p0,p1,p2,p3):
+
   pwr = p0/(1+delta/p2)
   pwr += p1/(1+delta/p3)
 

rowers/views.py

@@ -274,6 +274,8 @@ from utils import (
     str2bool
     )
 
+import datautils
+
 from rowers.models import checkworkoutuser
 
 # Check if a user is a Coach member
@@ -2778,6 +2780,27 @@ def rankings_view(request,theuser=0,
                    'teams':get_my_teams(request.user),
 		   })
 
+@user_passes_test(ispromember,login_url="/",redirect_field_name=None)
+def workout_update_cp_view(request,id=0):
+    try:
+        row = Workout.objects.get(id=id)
+    except Workout.DoesNotExist:
+	raise Http404("Workout doesn't exist")
+
+    if (checkworkoutuser(request.user,row)==False):
+	message = "You are not allowed to edit this workout"
+        messages.error(request,message)
+	url = reverse(workouts_view)
+
+	return HttpResponseRedirect(url)
+
+    row.rankingpiece = True
+    row.save()
+
+    url = reverse(otwrankings_view)
+
+    return HttpResponseRedirect(url)
+
 # Show ranking distances including predicted paces
 @user_passes_test(ispromember,login_url="/",redirect_field_name=None)
 def otwrankings_view(request,theuser=0,
@@ -2919,7 +2942,7 @@ def otwrankings_view(request,theuser=0,
     
     
     dfgrouped = df.groupby(['workoutid'])
-    delta,cpvalue,avgpower = dataprep.getcp(dfgrouped,logarr)
+    delta,cpvalue,avgpower = datautils.getcp(dfgrouped,logarr)
     
 
     powerdf = pd.DataFrame({
@@ -7779,6 +7802,7 @@ def rower_edit_view(request,message=""):
             last_name = ucd['last_name']
             email = ucd['email']
             weightcategory = cd['weightcategory']
+            getemailnotifications = cd['getemailnotifications']
             u = request.user
             if len(first_name):
                 u.first_name = first_name
@@ -7788,6 +7812,7 @@ def rower_edit_view(request,message=""):
             u.save()
             r = getrower(u)
             r.weightcategory = weightcategory
+            r.getemailnotifications = getemailnotifications
             r.save()
             form = RowerForm(instance=r)
 	    powerform = RowerPowerForm(instance=r)