Merge branch 'bugfix/mikesbreakthrough' into develop

rowers/dataprep.py

@@ -425,13 +425,6 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
                 powerperc=powerperc,powerzones=r.powerzones)
     row = rdata(f2,rower=rr)
 
-    isbreakthrough = False
-    if workouttype == 'water':
-        delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
-        res,btvalues = utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3,r.cpratio)
-        if res:
-            isbreakthrough = True
-            res = datautils.updatecp(delta,cpvalues,r)
 
     dtavg = row.df['TimeStamp (sec)'].diff().mean()
 
@@ -590,6 +583,21 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
 
     w.save()
 
+    isbreakthrough = False
+    if workouttype == 'water':
+        df = getsmallrowdata_db(['power','workoutid','time'],ids=[w.id])
+        # delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
+        thesecs = totaltime
+        maxt = 1.05*thesecs
+        logarr = datautils.getlogarr(maxt)
+        dfgrouped = df.groupby(['workoutid'])
+        delta,cpvalues,avgpower = datautils.getcp(dfgrouped,logarr)
+        
+        res,btvalues = utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3,r.cpratio)
+        if res:
+            isbreakthrough = True
+            res = datautils.updatecp(delta,cpvalues,r)
+
     # 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')

rowers/dataprepnodjango.py

@@ -463,35 +463,14 @@ def testdata(time,distance,pace,spm):
 
 
 def getsmallrowdata_db(columns,ids=[]):
-    prepmultipledata(ids)
+
     data = read_cols_df_sql(ids,columns)
 
     return data
         
 
-def prepmultipledata(ids,verbose=False,debug=True):
-    query = sa.text('SELECT DISTINCT workoutid FROM strokedata')
-    if debug:
-        engine = create_engine(database_url_debug, echo=False)
-    else:
-        engine = create_engine(database_url, echo=False)
 
 
-    with engine.connect() as conn, conn.begin():
-        res = conn.execute(query)
-        res = list(itertools.chain.from_iterable(res.fetchall()))
-    conn.close()
-    engine.dispose()
-    
-    res = list(set(ids)-set(res))
-    for id in res:
-        rowdata,row = getrowdata(id=id)
-        if verbose:
-            print id
-        if rowdata:
-            data = dataprep(rowdata.df,id=id,bands=True,barchart=True,otwpower=True)    
-    return res
-
 def read_cols_df_sql(ids,columns,debug=True):
     columns = list(columns)+['distance','spm']
     columns = [x for x in columns if x != 'None']

rowers/datautils.py

@@ -118,6 +118,8 @@ def getcp(dfgrouped,logarr):
         ww = group['power'].copy()
 
         tmax = tt.max()
+
+        
         if tmax > 500000:
             newlen = int(tmax/2000.)
         else:
@@ -143,6 +145,7 @@ def getcp(dfgrouped,logarr):
             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)
@@ -164,6 +167,18 @@ def getcp(dfgrouped,logarr):
             
     delta = pd.Series(delta,name='Delta')
     cpvalue = pd.Series(cpvalue,name='CP')
+
+    
+    cpdf = pd.DataFrame({
+        'delta':delta,
+        'cpvalue':cpvalue
+        })
+
+    cpdf.dropna(axis=0, how='any',inplace=True)
+
+    delta = cpdf['delta']
+    cpvalue = cpdf['cpvalue']
+
     return delta,cpvalue,avgpower
 
 def getmaxwattinterval(tt,ww,i):

rowers/tasks.py

@@ -26,6 +26,7 @@ from utils import serialize_list,deserialize_list
 
 from rowers.dataprepnodjango import update_strokedata
 from rowers.dataprepnodjango import new_workout_from_file
+from rowers.dataprepnodjango import getsmallrowdata_db
 
 from django.core.mail import send_mail, BadHeaderError,EmailMessage
 
@@ -245,7 +246,20 @@ 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)
+    totaltime = rowdata.df['TimeStamp (sec)'].max()-rowdata.df['TimeStamp (sec)'].min()
+    try:
+        totaltime = totaltime+rowdata.df.ix[0,' ElapsedTime (sec)']
+    except KeyError:
+        pass
+    df = getsmallrowdata_db(['power','workoutid','time'],ids=[workoutid])
+    thesecs = totaltime
+    maxt = 1.05*thesecs
+    logarr = datautils.getlogarr(maxt)
+    dfgrouped = df.groupby(['workoutid'])
+    delta,cpvalues,avgpower = datautils.getcp(dfgrouped,logarr)
+    
+    
+    #delta,cpvalues,avgpower = datautils.getsinglecp(rowdata.df)
     res,btvalues =  utils.isbreakthrough(delta,cpvalues,ps[0],ps[1],ps[2],ps[3],ratio)
     if res:
         handle_sendemail_breakthrough(workoutid,email,

rowers/templates/analysis.html

@@ -100,6 +100,7 @@
         {% if user.rower.rowerplan == 'pro' or user.rower.rowerplan == 'coach' %}
         <a class="button blue small" href="/rowers/team-compare-select/team/0/">Multi Compare</a>
         {% else %}
+        <a class="button blue small" href="/rowers/promembership">Multi Compare</a>		
         {% endif %}
       </p>
       <p>

rowers/utils.py

@@ -77,11 +77,14 @@ def geo_distance(lat1,lon1,lat2,lon2):
 
 
 def isbreakthrough(delta,cpvalues,p0,p1,p2,p3,ratio):
-  pwr = p0/(1+delta/p2)
+  pwr = abs(p0)/(1+(delta/abs(p2)))
-  pwr += p1/(1+delta/p3)
+  pwr += abs(p1)/(1+(delta/abs(p3)))
 
   pwr *= ratio
 
+  delta = delta.values
+  cpvalues = cpvalues.values
+
   res = np.sum(cpvalues>pwr)
 
   btdf = pd.DataFrame(
@@ -92,6 +95,7 @@ def isbreakthrough(delta,cpvalues,p0,p1,p2,p3,ratio):
     }
   )
 
+
   btdf.sort_values('delta',axis=0,inplace=True)
 
   

rowers/views.py

@@ -2926,7 +2926,7 @@ def otwrankings_view(request,theuser=0,
 
 
     if len(thesecs) != 0:
-        maxt = 2*pd.Series(thesecs).max()
+        maxt = 1.05*pd.Series(thesecs).max()
     else:
         maxt = 1000.
 
@@ -2936,13 +2936,13 @@ def otwrankings_view(request,theuser=0,
     dfgrouped = df.groupby(['workoutid'])
     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)