fixing ub

rowers/datautils.py

@@ -26,7 +26,7 @@ def updatecp(delta,cpvalues,r):
     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)
-    
+
 
     res = cpfit(powerdf)
     p1 = res[0]
@@ -45,7 +45,7 @@ def cpfit(powerdf):
     # Fit the data to thee parameter CP model
     fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
     errfunc = lambda pars,x,y: fitfunc(pars,x)-y
-    
+
 
     p1 = p0
 
@@ -58,7 +58,7 @@ def cpfit(powerdf):
         except:
             factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
             p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
-            
+
     else:
         factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
         p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
@@ -82,7 +82,7 @@ def cpfit(powerdf):
         dd = fitpoints-theavpower
         ddmin = dd.min()
         frac = abs(ddmin)/fitpoints.mean()
-    
+
     ratio = fitpoints.mean()/fitpoints0.mean()
 
     return p1,fitt,fitpower,ratio
@@ -90,8 +90,15 @@ def cpfit(powerdf):
 def getlogarr(maxt):
     maxlog10 = np.log10(maxt-5)
     logarr = np.arange(50)*maxlog10/50.
-    logarr = [5+int(10.**(la)) for la in logarr]
-    logarr = pd.Series(logarr)
+    res = []
+    for la in logarr:
+        try:
+            v = 5+int(10.**(la))
+        except ValueError:
+            v = 0
+        res.append(v)
+
+    logarr = pd.Series(res)
     logarr.drop_duplicates(keep='first',inplace=True)
 
     logarr = logarr.values
@@ -111,9 +118,9 @@ def getsinglecp(df):
         'time':1000*(df['TimeStamp (sec)']-df.loc[:,'TimeStamp (sec)'].iloc[0]),
         'power':df[' Power (watts)']
     })
-    
+
     dfnew['workoutid'] = 0
-    
+
     dfgrouped = dfnew.groupby(['workoutid'])
     delta,cpvalue,avgpower = getcp(dfgrouped,logarr)
 
@@ -124,7 +131,7 @@ def getcp_new(dfgrouped,logarr):
     cpvalue = []
     avgpower = {}
 
-    
+
     for id, group in dfgrouped:
         tt = group['time'].copy()
 
@@ -149,7 +156,7 @@ def getcp_new(dfgrouped,logarr):
                       ww.values,
                       newt,method='linear',
                       rescale=True)
-        
+
         tt = pd.Series(newt)
         ww = pd.Series(ww)
 
@@ -178,7 +185,7 @@ def getcp_new(dfgrouped,logarr):
 
         restime = []
         power = []
-        
+
         for i in np.arange(0,len(tt)+1,1):
             restime.append(deltat*i)
             cp = np.diag(F,i).max()
@@ -189,19 +196,19 @@ def getcp_new(dfgrouped,logarr):
         restime = np.array(restime)
         power = np.array(power)
 
-            
+
         #power[0] = power[1]
-            
+
         cpvalues = griddata(restime,power,
                             logarr,method='linear', fill_value=0)
 
 
-    
+
         for cpv in cpvalues:
             cpvalue.append(cpv)
         for d in logarr:
             delta.append(d)
-                                     
+
     df = pd.DataFrame({
         'delta':delta,
         'cpvalue':cpvalue
@@ -215,8 +222,8 @@ def getcp_new(dfgrouped,logarr):
     cpvalue = df['cpvalue']
 
     return delta,cpvalue,avgpower
-            
-        
+
+
 def getcp(dfgrouped,logarr):
     delta = []
     cpvalue = []
@@ -228,7 +235,7 @@ def getcp(dfgrouped,logarr):
         ww = group['power'].copy()
 
         # Remove data where PM is repeating final power value
-        # of an interval during the rest 
+        # of an interval during the rest
         rolling_std = ww.rolling(window=4).std()
         deltas = tt.diff()
 
@@ -240,7 +247,7 @@ def getcp(dfgrouped,logarr):
 
         tmax = tt.max()
 
-        
+
         if tmax > 500000:
             newlen = int(tmax/2000.)
         else:
@@ -255,7 +262,7 @@ def getcp(dfgrouped,logarr):
             tt = pd.Series(newt)
             ww = pd.Series(ww)
 
-            
+
         try:
             avgpower[id] = int(ww.mean())
         except ValueError:
@@ -272,7 +279,7 @@ def getcp(dfgrouped,logarr):
                     cpw.append(wmax)
 
 
-            
+
             dt = pd.Series(dt)
             cpw = pd.Series(cpw)
             if len(dt)>2:
@@ -286,12 +293,12 @@ def getcp(dfgrouped,logarr):
                 for d in logarr:
                     delta.append(d)
 
-                
-                    
+
+
     delta = pd.Series(delta,name='Delta')
     cpvalue = pd.Series(cpvalue,name='CP')
 
-    
+
     cpdf = pd.DataFrame({
         'delta':delta,
         'cpvalue':cpvalue
@@ -332,4 +339,3 @@ def getmaxwattinterval(tt,ww,i):
         deltat = 0
 
     return deltat,wmax
-

rowers/tasks.py

@@ -30,6 +30,7 @@ import datetime
 import pytz
 import iso8601
 
+from json.decoder import JSONDecodeError
 
 from matplotlib.backends.backend_agg import FigureCanvas
 #from matplotlib.backends.backend_cairo import FigureCanvasCairo as FigureCanvas
@@ -2028,7 +2029,10 @@ def handle_makeplot(f1, f2, t, hrdata, plotnr, imagename,
     elif (plotnr == 2):
         fig1 = row.get_metersplot_erg(t,pacerange=oterange,**kwargs)
     elif (plotnr == 3):
-        t += ' - Heart Rate Distribution'
+        try:
+            t += ' - Heart Rate Distribution'
+        except TypeError:
+            t = 'Heart Rate Distribution'
         fig1 = row.get_piechart(t,**kwargs)
     elif (plotnr == 4):
         if haspower: