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:
-            res = handle_sendemail_breakthrough(w.id,r.user.email,
+        if settings.DEBUG and r.getemailnotifications:
+            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(
-                handle_sendemail_breakthrough(w.id,
-                                              r.user.email,
-                                              r.user.first_name,
-                                              r.user.last_name))
+            pass
 
     if privacy == 'visible':
         ts = Team.objects.filter(rower=r)