added trend flex chart

rowers/views/analysisviews.py

@@ -215,6 +215,200 @@ def analysis_new(request,userid=0,function='boxplot'):
                    'teams':get_my_teams(request.user),
                    })
 
+def trendflexdata(workouts, options,userid=0):
+
+    includereststrokes = options['includereststrokes']
+    palette = options['palette']
+    groupby = options['groupby']
+    binsize = options['binsize']
+    xparam  = options['xparam']
+    yparam = options['yparam']
+    spmmin = options['spmmin']
+    spmmax = options['spmmax']
+    workmin = options['workmin']
+    workmax = options['workmax']
+    ploterrorbars = options['ploterrorbars']
+    ids = options['ids']
+    workstrokesonly = not includereststrokes
+    
+    labeldict = {
+        int(w.id): w.__str__() for w in workouts
+    }
+    
+    fieldlist,fielddict = dataprep.getstatsfields()
+    fieldlist = [xparam,yparam,groupby,
+                 'workoutid','spm','driveenergy',
+                 'workoutstate']
+    
+    # prepare data frame
+    datadf,extracols = dataprep.read_cols_df_sql(ids,fieldlist)
+
+    if xparam == groupby:
+        datadf['groupby'] = datadf[xparam]
+        groupy = 'groupby'
+
+    datadf = dataprep.clean_df_stats(datadf,workstrokesonly=workstrokesonly)
+
+
+    datadf = dataprep.filter_df(datadf,'spm',spmmin,
+                                largerthan=True)
+    datadf = dataprep.filter_df(datadf,'spm',spmmax,
+                                largerthan=False)
+
+    datadf = dataprep.filter_df(datadf,'driveenergy',workmin,
+                                largerthan=True)
+    datadf = dataprep.filter_df(datadf,'driveneergy',workmax,
+                                largerthan=False)
+
+
+    datadf.dropna(axis=0,how='any',inplace=True)
+
+
+    datemapping = {
+        w.id:w.date for w in workouts
+    }
+
+    datadf['date'] = datadf['workoutid']
+    datadf['date'].replace(datemapping,inplace=True)
+
+    today = datetime.date.today()
+    datadf['days ago'] = map(lambda x : x.days, datadf.date - today)
+
+    if groupby != 'date':
+        try:
+            bins = np.arange(datadf[groupby].min()-binsize,
+                             datadf[groupby].max()+binsize,
+                             binsize)
+            groups = datadf.groupby(pd.cut(datadf[groupby],bins,labels=False))
+        except ValueError:
+            messages.error(
+                request,
+                "Unable to compete. Probably not enough data selected"
+            )
+            url = reverse(user_multiflex_select)
+            return HttpResponseRedirect(url)
+    else:
+        bins = np.arange(datadf['days ago'].min()-binsize,
+                         datadf['days ago'].max()+binsize,
+                         binsize,
+        )
+        groups = datadf.groupby(pd.cut(datadf['days ago'], bins,
+                                           labels=False))
+            
+
+    xvalues = groups.mean()[xparam]    
+    yvalues = groups.mean()[yparam]
+    xerror = groups.std()[xparam]
+    yerror = groups.std()[yparam]
+    groupsize = groups.count()[xparam]
+
+    mask = groupsize <= min([0.01*groupsize.sum(),0.2*groupsize.mean()])
+    xvalues.loc[mask] = np.nan
+
+    yvalues.loc[mask] = np.nan
+    xerror.loc[mask] = np.nan
+    yerror.loc[mask] = np.nan
+    groupsize.loc[mask] = np.nan
+
+    xvalues.dropna(inplace=True)
+    yvalues.dropna(inplace=True)
+    xerror.dropna(inplace=True)
+    yerror.dropna(inplace=True)
+    groupsize.dropna(inplace=True)
+
+    if len(groupsize) == 0:
+        messages.error(request,'No data in selection')
+        url = reverse(user_multiflex_select)
+        return HttpResponseRedirect(url)
+    else:
+        groupsize = 30.*np.sqrt(groupsize/float(groupsize.max()))
+    
+    df = pd.DataFrame({
+        xparam:xvalues,
+        yparam:yvalues,
+        'x':xvalues,
+        'y':yvalues,
+        'xerror':xerror,
+        'yerror':yerror,
+        'groupsize':groupsize,
+    })
+
+
+    if yparam == 'pace':
+        df['y'] = dataprep.paceformatsecs(df['y']/1.0e3)
+    
+    aantal = len(df)
+    
+    if groupby != 'date':
+        try:
+            df['groupval'] = groups.mean()[groupby]
+            df['groupval'].loc[mask] = np.nan
+                    
+            groupcols = df['groupval']
+        except ValueError:
+            df['groupval'] =  groups.mean()[groupby].fillna(value=0)
+            df['groupval'].loc[mask] = np.nan
+            groupcols = df['groupval']
+        except KeyError:
+            messages.error(request,'Data selection error')
+            url = reverse(user_multiflex_select)
+            return HttpResponseRedirect(url)            
+    else:
+        try:
+            dates = groups.min()[groupby]
+            dates.loc[mask] = np.nan
+            dates.dropna(inplace=True)
+            df['groupval'] = [x.strftime("%Y-%m-%d") for x in dates]
+            df['groupval'].loc[mask] = np.nan
+            groupcols = 100.*np.arange(aantal)/float(aantal)
+        except AttributeError:
+            df['groupval'] = groups.mean()['days ago'].fillna(value=0)
+            groupcols = 100.*np.arange(aantal)/float(aantal)
+                    
+            
+    groupcols = (groupcols-groupcols.min())/(groupcols.max()-groupcols.min())
+
+    if aantal == 1:
+        groupcols = np.array([1.])
+        
+
+    colors = range_to_color_hex(groupcols,palette=palette)
+    
+    df['color'] = colors
+
+    clegendx = np.arange(0,1.2,.2)
+    legcolors = range_to_color_hex(clegendx,palette=palette)
+    if groupby != 'date':
+        clegendy = df['groupval'].min()+clegendx*(df['groupval'].max()-df['groupval'].min())
+    else:
+        clegendy = df.index.min()+clegendx*(df.index.max()-df.index.min())
+
+
+    
+    colorlegend =  zip(range(6),clegendy,legcolors)
+
+
+    if userid == 0:
+        extratitle = ''
+    else:
+        u = User.objects.get(id=userid)
+        extratitle = ' '+u.first_name+' '+u.last_name
+                
+
+            
+    script,div = interactive_multiflex(df,xparam,yparam,
+                                       groupby,
+                                       extratitle=extratitle,
+                                       ploterrorbars=ploterrorbars,
+                                       binsize=binsize,
+                                       colorlegend=colorlegend,
+                                       spmmin=spmmin,spmmax=spmmax,
+                                       workmin=workmin,workmax=workmax)
+                
+    scripta= script.split('\n')[2:-1]
+    script = ''.join(scripta)
+
+    return(script,div)
     
 def boxplotdata(workouts,options):
 
@@ -320,6 +514,8 @@ def analysis_view_data(request,userid=0):
 
     if function == 'boxplot':
         script, div = boxplotdata(workouts,options)
+    elif function == 'trendflex':
+        script, div = trendflexdata(workouts, options,userid=userid)
     else:
         script = ''
         div = 'Unknown analysis functions'