;********************************************************

 ; histogram normalized by rain and compute correleration

 ;********************************************************

 load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"

 load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"

 load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"

 procedure pminmax(data:numeric,name:string)

 begin

   print ("min/max " + name + " = " + min(data) + "/" + max(data))

   if(isatt(data,"units")) then

     print (name + " units = " + data@units)

   end if

 end

 ;

 ; Main code.

 ;

 begin

 ;nclass = 18

  nclass = 20

 ;************************************************

 ; read in data: observed

 ;************************************************

  diri1  = "/fis/cgd/cseg/people/jeff/clamp_data/lai/"

 ;fili1  = "land_class_T42.nc"

  fili1  = "land_class_T42_new.nc"

  fili2  = "LAI_2000-2005_mean_T42.nc"

  data_file_ob1 = addfile(diri1+fili1,"r")

  data_file_ob2 = addfile(diri1+fili2,"r")

  RAIN1 = tofloat(data_file_ob1->LAND_CLASS)               

  NPP1  = data_file_ob2->LAI      

 ;************************************************

 ; read in data: model       

 ;************************************************

  diri2  = "/fis/cgd/cseg/people/jeff/clamp_data/model/"

 ;fili3  = "i01.03cn_1545-1569_ANN_climo.nc"

  fili3  = "i01.04casa_1605-1629_ANN_climo.nc"

  data_file_model = addfile(diri2+fili3,"r")

  NPP2  = data_file_model->TLAI      

 ;************************************************

 ; print min/max and unit

 ;************************************************

   pminmax(RAIN1,"RAIN1")

   pminmax(NPP1,"NPP1")

   pminmax(NPP2,"NPP2")

   RAIN1_1D = ndtooned(RAIN1)

   NPP1_1D  = ndtooned(NPP1)

   NPP2_1D  = ndtooned(NPP2)

 ;

 ; Calculate some "nice" bins for binning the data in equally spaced

 ; ranges.

 ;

 ; nbins       = nclass + 1         ; Number of bins to use.

 ; nicevals    = nice_mnmxintvl(min(RAIN1_1D),max(RAIN1_1D),nbins,False)

 ; nvals       = floattoint((nicevals(1) - nicevals(0))/nicevals(2) + 1)

 ; range       = fspan(nicevals(0),nicevals(1),nvals)

   nclassn     = nclass + 1

   range       = fspan(0,nclassn-1,nclassn)

 ; print (nicevals)

 ; print (nvals)

   print (range)

 ; exit

 ;

 ; Use this range information to grab all the values in a

 ; particular range, and then take an average.

 ;

   nr      = dimsizes(range)

   nx      = nr-1

   xvalues     = new((/2,nx/),typeof(RAIN1_1D))

   xvalues(0,:) = range(0:nr-2) + (range(1:)-range(0:nr-2))/2.

   dx           = xvalues(0,1) - xvalues(0,0)       ; range width

   dx4          = dx/4                              ; 1/4 of the range

   xvalues(1,:) = xvalues(0,:) - dx/5.

   yvalues      = new((/2,nx/),typeof(RAIN1_1D))

   mn_yvalues   = new((/2,nx/),typeof(RAIN1_1D))

   mx_yvalues   = new((/2,nx/),typeof(RAIN1_1D))

   do nd=0,1

 ;

 ; See if we are doing model or observational data.

 ;

     if(nd.eq.0) then

       data     = RAIN1_1D

       npp_data = NPP1_1D

     else

       data     = RAIN1_1D

       npp_data = NPP2_1D

     end if

 ;

 ; Loop through each range and check for values.

 ;

     do i=0,nr-2

       if (i.ne.(nr-2)) then

          print("")

          print("In range ["+range(i)+","+range(i+1)+")")

         idx = ind((range(i).le.data).and.(data.lt.range(i+1)))

       else

          print("")

          print("In range ["+range(i)+",)")

         idx = ind(range(i).le.data)

       end if

 ;

 ; Calculate average, and get min and max.

 ;

       if(.not.any(ismissing(idx))) then

         yvalues(nd,i)    = avg(npp_data(idx))

         mn_yvalues(nd,i) = min(npp_data(idx))

         mx_yvalues(nd,i) = max(npp_data(idx))

         count = dimsizes(idx)

       else

         count            = 0

         yvalues(nd,i)    = yvalues@_FillValue

         mn_yvalues(nd,i) = yvalues@_FillValue

         mx_yvalues(nd,i) = yvalues@_FillValue

       end if

 ;

 ; Print out information.

 ;

        print(nd + ": " + count + " points, avg = " + yvalues(nd,i))

        print("Min/Max:  " + mn_yvalues(nd,i) + "/" + mx_yvalues(nd,i))

 ;

 ; Clean up for next time in loop.

 ;

       delete(idx)

     end do

     delete(data)

     delete(npp_data)

   end do

 ;

 ; Start the graphics.

 ;

  u = yvalues(0,:)

  v = yvalues(1,:)

  print (u)

  print (v)

  good = ind(.not.ismissing(u) .and. .not.ismissing(v))

  uu = u(good)

  vv = v(good)

  nz = dimsizes(uu)

  print (nz)

  ccr = esccr(uu,vv,0)

  print (ccr)

 ;new eq

  bias = sum(abs(vv-uu)/(vv+uu))

  M    = (1.- (bias/nz))*5.

  print (bias)

  print (M)

 end