k_clst =   n_cnts -1 ; k_clst should always be less than the numnber of points to be clustered.
 print("n_cnts = " + n_cnts)
 printVarSummary(n_cnts)
 if (n_cnts.gt.1)then
 lat_y := new(n_cnts,float)
 lon_x := new(n_cnts,float)
 n_cnts = 0
 print("dims_var = " + dims_var)
 do n = 0,dims_var-1
   check = ismissing(var_zoom_1d(n))
   if (check.eq.False)
   if (var_zoom_1d(n).ge.2)then
     lat_y(n_cnts) = lat_zoom_1d(n)
     lon_x(n_cnts) = lon_zoom_1d(n)
     n_cnts = n_cnts + 1
   end if
   end if
 end do
 print("n_cnts = " + n_cnts)
 x  := ((/lon_x,lat_y/)) 
 printVarSummary(x)
  print("i_file = " + i_file)
   dimx = dimsizes(x)
   n    = dimx(0)         ; # variables    (2)
   m    = dimx(1)         ; # observations (7)
;  k =  5              ; # clusters

   opt            = True
   opt@iter       = 40     ; change the default max # iterations 

   clcntr := kmeans_as136(x, k_clst, opt)  
   print("x = " + x)
   print("clcntr = " + clcntr)      
   k_found = False
   k_end = k_clst
   do k_c = 1,k_clst,1 
    k_end = k_clst-k_c
    if (k_found.eq.False)then
      clcntr := kmeans_as136(x, k_end, opt)  
      points := clcntr@npts
;     qsort(points)
      min_p =min(points)
      print("min_p = " + min_p) 
      if (min_p.gt.0)then
       k_found = True 
       kk_end =k_end 
      end if
    end if
   end do
   print("points after = " + points)
   print(k_found)
   print(kk_end)
   k_clst_n = kk_end
   k_found = False
   min_points = min(clcntr@npts) 
   print("min_points = "+ min_points)
   do k_c = 1,k_clst_n,1 
    k_end = k_clst_n-k_c
    print("k_end = " + k_end)
    if (k_end.ne.0)then
    if (k_found.eq.False)then
      clcntr := kmeans_as136(x, k_end, opt)  
      points := clcntr@npts
      print("points 2nd = " + points)
      unique_values := get_unique_values(points)
      print("num_unique = " + unique_values)
      num_one = num(points.eq.min_points)
      print("num_one = " + num_one)
;     print(xyz)
      if (num_one .eq. 1.and.k_found.eq.False)then
       k_found = True 
       kk_end = k_end
      end if
    end if
    end if
   end do
   print("kk_end = " + kk_end)
   if (kk_end.eq.0)then
    kk_end = 1
   end if
    
   k_clst_n = kk_end 
   printVarSummary(clcntr)
   clcntr := kmeans_as136(x, k_clst_n, opt)  
   print("clcntr@npts= " + clcntr@npts)      
   print("clcntr@ss2 = " + clcntr@ss2)      
   print("clcntr@id " + clcntr@id)      
   print("clcntr before = " + clcntr)      
   print("k_clst_n = " + k_clst_n) 
   print("finished find")
; my adaptation to the clustering program
   dat := x 
   printVarSummary(x)
   members := x
   print(x)
   print("k_clst_n = " + k_clst_n)
   m = dimsizes(lon_x); integer M, the number of points.
   n = 2 ; integer N, the number of spatial dimensions (aka, variables).
   k = k_clst_n
   iter = 40
   ier  = 1 ; not used
   ic1 := clcntr@id
   print("ic1 = " + ic1)
   nc := clcntr@npts
   print("nc = " + nc)
   wss := clcntr@ss2
   print("wss = " + wss)
;  members = new((/n,m/),float)
   iseed = 1 ; results in the center clusters being set to the first k sets of observations
    CLUSTER::kmns136_mine (dat, m, n, clcntr, k,  ic1, nc ,iter, iseed, wss, members,ier)
;  ic1 =  ;the cluster to which each point is assigned.
;  nc = ; the number of points in each cluster
;  wss = ;  the within-cluster sum of squares
   print("ic1 = " + ic1)
   print("nc = " + nc)
   print("wss = " + wss)
   print("clcntr after" + clcntr)
   print(members)