pro fhist_average,x,h,ri,number=w,mean=xm,sigma=xs,weight=weight  $
   ,range=mm,nbins=nbins,geth=geth

if keyword_set(geth) or not keyword_set(h) then begin
  if n_elements(mm) ne 2 then mm = minmax_range(x)   
  if not keyword_set(nbins) then nbins = 20
  binsize = (mm[1]-mm[0])/nbins
  h = histogram( x,min=mm[0],max=mm[1],binsize=binsize,reverse=ri)
  ; note: histogram  does not work properly with the nan keyword set
  ; the size of h will be nbins+1
  print,h[nbins]
  h = h[0:nbins-1]
;  help,names='*'
endif 


;nbins = n_elements(h)

;xm = replicate(!values.f_nan,nbins)

w = make_array(value=0.,dimen=dimen(h))
wx = w
wx2 = w

if not keyword_set(weight) then weight= replicate(1.,n_elements(x))
weight_x = weight * x
weight_x2= weight_x * x

whn0 = where(h ne 0,count)

for j=0,count-1 do begin
  i = whn0[j]
  ind = ri[ ri[i]: ri[i+1]-1 ]
  w[i] = total(weight[ind])
  wx[i] = total(weight_x[ind])
  wx2[i] = total(weight_x2[ind])
endfor
 
xm = wx / w
xs = sqrt( wx2/w - xm^2 ) 

end