def functions_to_perform_onDF(df,stock,F2scores,shift,QUANTILES,date1,date2,out_dir='',xVar="???",yVar="RelRet"):
print "\n Performing functions on a DF subset with number of observations = ", len(df)
print stock.name
print yVar, "is being explored"
print date1.strftime('%d-%m-%y'), date2.strftime('%d-%m-%y')
STAT_functions.ols_F2vsYvar(df,stock,F2scores,yVar,QUANTILES)
"""prep stuff for plotting"""
s_dates = date1.strftime('%d-%m-%y')+"_"+date2.strftime('%d-%m-%y')
fig_fn = out_dir+ stock.name+"_"+xVar+"_vs_"+yVar + "_" +s_dates #+ "_shift("+str(abs(shift))+")"
suptitle = xVar+" vs "+yVar #+" (with Shift_Days="+str(abs(shift))+")" #the super-title above all subplots; can be omitted
#Plotting.ScatterSubplots_F2vsYvar(df,stock,F2scores,QUANTILES,fig_fn+".jpg",date1,date2,suptitle,yVar,ymin=-0.04,ymax=0.04)
fig_fn = out_dir+ stock_name+"_"+yVar+"_behaviour_"+s_dates+".jpg"
#Plotting.stacked_TimeSeries(df,stock_name,[yVar],yVar+" behaviour of "+stock_name,fig_fn,date1,date2,mean_per=8,ymin=-0.04,ymax=0.04)
F2scores = [F2scores[0]]
for fscore in F2scores:#F2scores if all are needed
if fscore.find('8')> -1:
roll_mean = 8
else:
roll_mean = 15
depVar = "ooRelRet(nextDay)" #yVar
fig_fn = out_dir+ stock.name+"_behaviour_"+fscore+"_"+depVar+"_"+yVar + "_" +s_dates+".jpg"
suptitle = stock.name+": behaviour of "+fscore
Plotting.overlays_TimeSeries(df,stock_name,fscore,yVar1=yVar,yVar2=depVar,suptitle=suptitle,
fig_fn=fig_fn,stock = stock,date1=date1,date2=date2,Quantiles=QUANTILES,
mean_per=roll_mean,ymin=-0.04,ymax=0.04,)
fig_fn = out_dir+ stock_name+"_"+yVar+"_Hist_"+s_dates+".jpg"
