clamp: fire/24x.table+tseries.ncl@0c6405ab2ff4

     1 ;********************************************************

     2 ; using observed biome class

     3 ; landfrac applied to area only.

4 ;

     5 ; required command line input parameters:

     6 ;  ncl 'model_name="10cn" model_grid="T42" dirm="/.../ film="..."' 01.npp.ncl

7 ;

     8 ; histogram normalized by rain and compute correleration

     9 ;**************************************************************

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

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

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

    13 ;**************************************************************

    14 procedure set_line(lines:string,nline:integer,newlines:string)

    15 begin

    16 ; add line to ascci/html file

    18   nnewlines = dimsizes(newlines)

    19   if(nline+nnewlines-1.ge.dimsizes(lines))

    20     print("set_line: bad index, not setting anything.")

    21     return

    22   end if

    23   lines(nline:nline+nnewlines-1) = newlines

    24 ;  print ("lines = " + lines(nline:nline+nnewlines-1))

    25   nline = nline + nnewlines

    26   return

    27 end

    28 ;**************************************************************

    29 ; Main code.

    30 begin

    32   plot_type     = "ps"

    33   plot_type_new = "png"

    35 ;---------------------------------------------------

    36 ; model name and grid

    38   model_grid = "T42"

    40   model_name  = "cn"

    41   model_name1 = "i01.06cn"

    42   model_name2 = "i01.10cn"

    44 ;------------------------------------------------

    45 ; read biome data: observed

    47   biome_name_ob = "MODIS LandCover"

    49   diro = "/fis/cgd/cseg/people/jeff/clamp_data/lai/ob/"

    50   filo = "land_class_"+model_grid+".nc"

    52   fo = addfile(diro+filo,"r")

    54   classob = tofloat(fo->LAND_CLASS)

    56   delete (fo)

    58 ; observed data has 20 land-type classes

    60   nclass_ob = 20

    62 ;---------------------------------------------------

    63 ; get biome data: model

    65   biome_name_mod = "Model PFT Class"

    67   dirm = "/fis/cgd/cseg/people/jeff/clamp_data/model/"

    68   film = "class_pft_"+model_grid+".nc"

    69   fm   = addfile(dirm+film,"r")

    71   classmod = fm->CLASS_PFT

    73   delete (fm)

    75 ; model data has 17 land-type classes

    77   nclass_mod = 17

    79 ;--------------------------------------------------

    80 ; get model data: landmask, landfrac and area

    82   dirm = "/fis/cgd/cseg/people/jeff/surface_data/"

    83   film = "lnd_T42.nc"

    84   fm   = addfile (dirm+film,"r")

    86   landmask = fm->landmask

    87   landfrac = fm->landfrac

    88   area     = fm->area

    90   delete (fm)

    92 ; change area from km**2 to m**2

    93   area = area * 1.e6

    95 ;---------------------------------------------------

    96 ; take into account landfrac

    98  area = area * landfrac

   100 ;----------------------------------------------------

   101 ; read data: time series, model

   103  dirm = "/fis/cgd/cseg/people/jeff/clamp_data/model/"

   104  film = model_name2 + "_Fire_C_1979-2004_monthly.nc"

   105  fm   = addfile (dirm+film,"r")

   107  data_mod = fm->COL_FIRE_CLOSS(18:25,:,:,:)

   109  delete (fm)

   111 ; Units for these variables are:

   112 ; g C/m^2/s

   114 ; change unit to g C/m^2/month

   116   nsec_per_month = 60*60*24*30

   118   data_mod = data_mod * nsec_per_month

   120   data_mod@unit = "gC/m2/month"

   121 ;----------------------------------------------------

   122 ; read data: time series, observed

   124  dirm = "/fis/cgd/cseg/people/jeff/fire_data/ob/GFEDv2_C/"

   125  film = "Fire_C_1997-2006_monthly_"+ model_grid+".nc"

   126  fm   = addfile (dirm+film,"r")

   128  data_ob = fm->FIRE_C(0:7,:,:,:)

   130  delete (fm)

   132  ob_name = "GFEDv2"

   134 ; Units for these variables are:

   135 ; g C/m^2/month

   137 ;-------------------------------------------------------------

   138 ; html table1 data

   140 ; column (not including header column)

   142   col_head  = (/"Observed Fire_Flux (PgC/yr)" \

   143                ,"Model Fire_Flux (PgC/yr)" \

   144                ,"Correlation Coefficient" \

   145                ,"Ratio model/observed" \

   146                ,"M_score" \

   147                ,"Timeseries plot" \

   148                /)

   150   ncol = dimsizes(col_head)

   152 ; row (not including header row)

   154 ;----------------------------------------------------

   155 ; using observed biome class:

   156   row_head  = (/"Evergreen Needleleaf Forests" \

   157                ,"Evergreen Broadleaf Forests" \

   158                ,"Deciduous Needleleaf Forest" \

   159                ,"Deciduous Broadleaf Forests" \

   160                ,"Mixed Forests" \

   161                ,"Closed Bushlands" \

   162                ,"Open Bushlands" \

   163                ,"Woody Savannas (S. Hem.)" \

   164                ,"Savannas (S. Hem.)" \

   165                ,"Grasslands" \

   166                ,"Permanent Wetlands" \

   167                ,"Croplands" \

   168                ,"Cropland/Natural Vegetation Mosaic" \

   169                ,"Barren or Sparsely Vegetated" \

   170                ,"Woody Savannas (N. Hem.)" \

   171                ,"Savannas (N. Hem.)" \

   172                ,"All Biome" \

   173                /)

   176 ; using model biome class:

   177 ; row_head  = (/"Not Vegetated" \

   178 ;              ,"Needleleaf Evergreen Temperate Tree" \

   179 ;              ,"Needleleaf Evergreen Boreal Tree" \

   180 ;;             ,"Needleleaf Deciduous Boreal Tree" \

   181 ;              ,"Broadleaf Evergreen Tropical Tree" \

   182 ;              ,"Broadleaf Evergreen Temperate Tree" \

   183 ;              ,"Broadleaf Deciduous Tropical Tree" \

   184 ;              ,"Broadleaf Deciduous Temperate Tree" \

   185 ;;             ,"Broadleaf Deciduous Boreal Tree" \

   186 ;;             ,"Broadleaf Evergreen Shrub" \

   187 ;              ,"Broadleaf Deciduous Temperate Shrub" \

   188 ;              ,"Broadleaf Deciduous Boreal Shrub" \

   189 ;              ,"C3 Arctic Grass" \

   190 ;              ,"C3 Non-Arctic Grass" \

   191 ;              ,"C4 Grass" \

   192 ;              ,"Corn" \

   193 ;;             ,"Wheat" \

   194 ;              ,"All Biome" \

   195 ;              /)

   196   nrow = dimsizes(row_head)

   198 ; arrays to be passed to table.

   199   text = new ((/nrow, ncol/),string )

   201 ;*****************************************************************

   202 ; (A) get time-mean

   203 ;*****************************************************************

   205   x          = dim_avg_Wrap(data_mod(lat|:,lon|:,month|:,year|:))

   206   data_mod_m = dim_avg_Wrap(       x(lat|:,lon|:,month|:))

   207   delete (x)

   209   x          = dim_avg_Wrap( data_ob(lat|:,lon|:,month|:,year|:))

   210   data_ob_m  = dim_avg_Wrap(       x(lat|:,lon|:,month|:))

   211   delete (x)

   213 ;----------------------------------------------------

   214 ; compute correlation coef: space

   216   landmask_1d = ndtooned(landmask)

   217   data_mod_1d = ndtooned(data_mod_m)

   218   data_ob_1d  = ndtooned(data_ob_m )

   219   area_1d     = ndtooned(area)

   220   landfrac_1d = ndtooned(landfrac)

   222   good = ind(landmask_1d .gt. 0.)

   224   global_mod = sum(data_mod_1d(good)*area_1d(good)) * 1.e-15 * 12.

   225   global_ob  = sum(data_ob_1d(good) *area_1d(good)) * 1.e-15 * 12.

   227   print (global_mod)

   228   print (global_ob)

   230   global_area= sum(area_1d)

   231   global_land= sum(area_1d(good))

   232   print (global_area)

   233   print (global_land)

   235   cc_space = esccr(data_mod_1d(good)*landfrac_1d(good),data_ob_1d(good)*landfrac_1d(good),0)

   237   delete (landmask_1d)

   238   delete (landfrac_1d)

   239 ; delete (area_1d)

   240   delete (data_mod_1d)

   241   delete (data_ob_1d)

   242   delete (good)

   244 ;----------------------------------------------------

   245 ; compute M_global

   247   score_max = 1.

   249   Mscore1 = cc_space * cc_space * score_max

   251   M_global = sprintf("%.2f", Mscore1)

   253 ;----------------------------------------------------

   254 ; global res

   256   resg                      = True             ; Use plot options

   257   resg@cnFillOn             = True             ; Turn on color fill

   258   resg@gsnSpreadColors      = True             ; use full colormap

   259   resg@cnLinesOn            = False            ; Turn off contourn lines

   260   resg@mpFillOn             = False            ; Turn off map fill

   261   resg@cnLevelSelectionMode = "ManualLevels"   ; Manual contour invtervals

   263 ;----------------------------------------------------

   264 ; global contour: model vs ob

   266   plot_name = "global_model_vs_ob"

   268   wks = gsn_open_wks (plot_type,plot_name)

   269   gsn_define_colormap(wks,"gui_default")

   271   plot=new(3,graphic)                        ; create graphic array

   273   resg@gsnFrame             = False          ; Do not draw plot

   274   resg@gsnDraw              = False          ; Do not advance frame

   276 ;----------------------

   277 ; plot correlation coef

   279   gRes               = True

   280   gRes@txFontHeightF = 0.02

   281   gRes@txAngleF      = 90

   283   correlation_text = "(correlation coef = "+sprintf("%.2f", cc_space)+")"

   285   gsn_text_ndc(wks,correlation_text,0.20,0.50,gRes)

   287 ;-----------------------

   288 ; plot ob

   290   data_ob_m = where(landmask .gt. 0., data_ob_m, data_ob_m@_FillValue)

   292   title     = ob_name

   293   resg@tiMainString  = title

   295   resg@cnMinLevelValF       = 1.

   296   resg@cnMaxLevelValF       = 10.

   297   resg@cnLevelSpacingF      = 1.

   299   plot(0) = gsn_csm_contour_map_ce(wks,data_ob_m,resg)

   301 ;-----------------------

   302 ; plot model

   304   data_mod_m = where(landmask .gt. 0., data_mod_m, data_mod_m@_FillValue)

   306   title     = "Model "+ model_name

   307   resg@tiMainString  = title

   309   resg@cnMinLevelValF       = 1.

   310   resg@cnMaxLevelValF       = 10.

   311   resg@cnLevelSpacingF      = 1.

   313   plot(1) = gsn_csm_contour_map_ce(wks,data_mod_m,resg)

   315 ;-----------------------

   316 ; plot model-ob

   318   resg@cnMinLevelValF  = -8.

   319   resg@cnMaxLevelValF  =  2.

   320   resg@cnLevelSpacingF =  1.

   322   zz = data_ob_m

   323   zz = data_mod_m - data_ob_m

   324   title = "Model_"+model_name+" - Observed"

   325   resg@tiMainString    = title

   327   plot(2) = gsn_csm_contour_map_ce(wks,zz,resg)

   329 ; plot panel

   331   pres                            = True        ; panel plot mods desired

   332   pres@gsnMaximize                = True        ; fill the page

   334   gsn_panel(wks,plot,(/3,1/),pres)              ; create panel plot

   336   system("convert "+plot_name+"."+plot_type+" "+plot_name+"."+plot_type_new+";"+ \

   337         "rm "+plot_name+"."+plot_type)

   339   clear (wks)

   340   delete (plot)

   342   delete (data_ob_m)

   343   delete (data_mod_m)

   344   delete (zz)

   346   resg@gsnFrame             = True          ; Do advance frame

   347   resg@gsnDraw              = True          ; Do draw plot

   349 ;*******************************************************************

   350 ; (B) Time series : per biome

   351 ;*******************************************************************

   353  data_n = 2

   355  dsizes = dimsizes(data_mod)

   356  nyear  = dsizes(0)

   357  nmonth = dsizes(1)

   358  ntime  = nyear * nmonth

   360  year_start = 1997

   361  year_end   = 2004

   363 ;-------------------------------------------

   364 ; Calculate "nice" bins for binning the data

   366 ; using ob biome class

   367   nclass = nclass_ob

   368 ; using model biome class

   369 ; nclass = nclass_mod

   371   range  = fspan(0,nclass,nclass+1)

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

   374 ; particular range, and then take an average.

   376   nx = dimsizes(range) - 1

   378 ;-------------------------------------------

   379 ; put data into bins

   381 ; using observed biome class

   382   base  = ndtooned(classob)

   383 ; using model biome class

   384 ; base  = ndtooned(classmod)

   386 ; output

   388   area_bin = new((/nx/),float)

   389   yvalues  = new((/ntime,data_n,nx/),float)

   391 ; Loop through each range, using base.

   393   do i=0,nx-1

   395      if (i.ne.(nx-1)) then

   396         idx = ind((base.ge.range(i)).and.(base.lt.range(i+1)))

   397      else

   398         idx = ind(base.ge.range(i))

   399      end if

   400 ;---------------------

   401 ;    for area

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

   404         area_bin(i) = sum(area_1d(idx))

   405      else

   406         area_bin(i) = area_bin@_FillValue

   407      end if

   409 ;#############################################################

   410 ;using observed biome class:

   411 ;     set the following 4 classes to _FillValue:

   412 ;     Water Bodies(0), Urban and Build-Up(13),

   413 ;     Permenant Snow and Ice(15), Unclassified(17)

   415       if (i.eq.0 .or. i.eq.13 .or. i.eq.15 .or. i.eq.17) then

   416          area_bin(i) = yvalues@_FillValue

   417       end if

   418 ;#############################################################

   421 ;#############################################################

   422 ; using model biome class:

   423 ;     set the following 4 classes to _FillValue:

   424 ;     (3)Needleleaf Deciduous Boreal Tree,

   425 ;     (8)Broadleaf Deciduous Boreal Tree,

   426 ;     (9)Broadleaf Evergreen Shrub,

   427 ;     (16)Wheat

   429 ;    if (i.eq.3 .or. i.eq.8 .or. i.eq.9 .or. i.eq.16) then

   430 ;       area_bin(i) = area_bin@_FillValue

   431 ;    end if

   432 ;#############################################################

   434 ;---------------------

   435 ; for data_mod and data_ob

   437   do n = 0,data_n-1

   439      t = -1

   440      do m = 0,nyear-1

   441      do k = 0,nmonth-1

   443         t = t + 1

   445         if (n.eq.0) then

   446            data = ndtooned(data_ob(m,k,:,:))

   447         end if

   449         if (n.eq.1) then

   450            data = ndtooned(data_mod(m,k,:,:))

   451         end if

   453 ;       Calculate average

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

   456            yvalues(t,n,i) = sum(data(idx)*area_1d(idx))

   457         else

   458            yvalues(t,n,i) = yvalues@_FillValue

   459         end if

   461 ;#############################################################

   462 ;using observed biome class:

   463 ;     set the following 4 classes to _FillValue:

   464 ;     Water Bodies(0), Urban and Build-Up(13),

   465 ;     Permenant Snow and Ice(15), Unclassified(17)

   467       if (i.eq.0 .or. i.eq.13 .or. i.eq.15 .or. i.eq.17) then

   468          yvalues(t,n,i) = yvalues@_FillValue

   469       end if

   470 ;#############################################################

   473 ;#############################################################

   474 ; using model biome class:

   475 ;     set the following 4 classes to _FillValue:

   476 ;     (3)Needleleaf Deciduous Boreal Tree,

   477 ;     (8)Broadleaf Deciduous Boreal Tree,

   478 ;     (9)Broadleaf Evergreen Shrub,

   479 ;     (16)Wheat

   481 ;       if (i.eq.3 .or. i.eq.8 .or. i.eq.9 .or. i.eq.16) then

   482 ;          yvalues(t,n,i) = yvalues@_FillValue

   483 ;       end if

   484 ;#############################################################

   486      end do

   487      end do

   489      delete(data)

   490   end do

   492     delete(idx)

   493   end do

   495   delete (base)

   496   delete (data_mod)

   497   delete (data_ob)

   499   global_bin = sum(area_bin)

   500   print (global_bin)

   502 ;----------------------------------------------------------------

   503 ; get area_good

   505   good = ind(.not.ismissing(area_bin))

   507   area_g = area_bin(good)

   509   n_biome = dimsizes(good)

   511   global_good = sum(area_g)

   512   print (global_good)

   514 ;----------------------------------------------------------------

   515 ; data for tseries plot

   517   yvalues_g = new((/ntime,data_n,n_biome/),float)

   519   yvalues_g@units = "TgC/month"

   521 ; change unit to Tg C/month

   522 ; change unit from g to Tg (Tera gram)

   523   factor_unit = 1.e-12

   525   yvalues_g = yvalues(:,:,good) * factor_unit

   527   delete (good)

   529 ;-------------------------------------------------------------------

   530 ; general settings for line plot

   532   res                   = True

   533   res@xyDashPatterns    = (/0,0/)          ; make lines solid

   534   res@xyLineThicknesses = (/2.0,2.0/)      ; make lines thicker

   535   res@xyLineColors      = (/"blue","red"/) ; line color

   537   res@trXMinF   = year_start

   538   res@trXMaxF   = year_end + 1

   540   res@vpHeightF = 0.4                 ; change aspect ratio of plot

   541 ; res@vpWidthF  = 0.8

   542   res@vpWidthF  = 0.75

   544   res@tiMainFontHeightF = 0.025       ; size of title

   546   res@tmXBFormat  = "f"               ; not to add trailing zeros

   548 ; res@gsnMaximize = True

   550 ;----------------------------------------------

   551 ; Add a boxed legend using the simple method

   553   res@pmLegendDisplayMode    = "Always"

   554 ; res@pmLegendWidthF         = 0.1

   555   res@pmLegendWidthF         = 0.08

   556   res@pmLegendHeightF        = 0.06

   557   res@pmLegendOrthogonalPosF = -1.17

   558 ; res@pmLegendOrthogonalPosF = -1.00  ;(downward)

   559 ; res@pmLegendOrthogonalPosF = -0.30  ;(downward)

   561 ; res@pmLegendParallelPosF   =  0.18

   562   res@pmLegendParallelPosF   =  0.23  ;(rightward)

   563   res@pmLegendParallelPosF   =  0.73  ;(rightward)

   564   res@pmLegendParallelPosF   =  0.83  ;(rightward)

   566 ; res@lgPerimOn             = False

   567   res@lgLabelFontHeightF     = 0.015

   568   res@xyExplicitLegendLabels = (/"observed",model_name/)

   570 ;*******************************************************************

   571 ; (A) time series plot: monthly ( 2 lines per plot)

   572 ;*******************************************************************

   574 ; x-axis in time series plot

   576   timeI = new((/ntime/),integer)

   577   timeF = new((/ntime/),float)

   578   timeI = ispan(1,ntime,1)

   579   timeF = year_start + (timeI-1)/12.

   580   timeF@long_name = "year"

   582   plot_data = new((/2,ntime/),float)

   583   plot_data@long_name = "TgC/month"

   585 ;----------------------------------------------

   586 ; time series plot : per biome

   588   do m = 0, n_biome-1

   590      plot_name = "monthly_biome_"+ m

   592      wks = gsn_open_wks (plot_type,plot_name)

   594      title = "Fire : "+ row_head(m)

   595      res@tiMainString = title

   597      plot_data(0,:) = yvalues_g(:,0,m)

   598      plot_data(1,:) = yvalues_g(:,1,m)

   600      plot = gsn_csm_xy(wks,timeF,plot_data,res)

   602      system("convert "+plot_name+"."+plot_type+" "+plot_name+"."+plot_type_new+";"+ \

   603             "rm "+plot_name+"."+plot_type)

   605      clear (wks)

   606      delete (plot)

   608   end do

   610 ;------------------------------------------

   611 ; data for table : per biome

   613 ; unit change from TgC/month to PgC/month

   614   unit_factor = 1.e-3

   616   score_max = 1.

   618   tmp_ob    = new((/ntime/),float)

   619   tmp_mod   = new((/ntime/),float)

   621   total_ob  = new((/n_biome/),float)

   622   total_mod = new((/n_biome/),float)

   623   Mscore2   = new((/n_biome/),float)

   625   do m = 0, n_biome-1

   627      tmp_ob  = yvalues_g(:,0,m)

   628      tmp_mod = yvalues_g(:,1,m)

   630      total_ob(m)  = avg(month_to_annual(tmp_ob, 0)) * unit_factor

   631      total_mod(m) = avg(month_to_annual(tmp_mod,0)) * unit_factor

   633      cc_time = esccr(tmp_mod,tmp_ob,0)

   635      ratio = total_mod(m)/total_ob(m)

   637      good = ind(tmp_ob .ne. 0. .and. tmp_mod .ne. 0.)

   639      bias = sum( abs( tmp_mod(good)-tmp_ob(good) )/( abs(tmp_mod(good))+abs(tmp_ob(good)) ) )

   640      Mscore2(m) = (1.- (bias/dimsizes(good)))*score_max

   642      delete (good)

   644      text(m,0) = sprintf("%.2f",total_ob(m))

   645      text(m,1) = sprintf("%.2f",total_mod(m))

   646      text(m,2) = sprintf("%.2f",cc_time)

   647      text(m,3) = sprintf("%.2f",ratio)

   648      text(m,4) = sprintf("%.2f",Mscore2(m))

   649      text(m,5) = "<a href=./monthly_biome_"+m+".png>model_vs_ob</a>"

   650   end do

   652   delete (tmp_ob)

   653   delete (tmp_mod)

   655 ;--------------------------------------------

   656 ; time series plot: all biome

   658      plot_name = "monthly_global"

   660      wks = gsn_open_wks (plot_type,plot_name)

   662      title = "Fire : "+ row_head(n_biome)

   663      res@tiMainString = title

   665      do k = 0,ntime-1

   666         plot_data(0,k) = sum(yvalues_g(k,0,:))

   667         plot_data(1,k) = sum(yvalues_g(k,1,:))

   668      end do

   670      plot = gsn_csm_xy(wks,timeF,plot_data,res)

   672      system("convert "+plot_name+"."+plot_type+" "+plot_name+"."+plot_type_new+";"+ \

   673             "rm "+plot_name+"."+plot_type)

   675      clear (wks)

   676      delete (plot)

   678 ;------------------------------------------

   679 ; data for table : global

   681   score_max = 1.

   683   tmp_ob  = ndtooned(yvalues_g(:,0,:))

   684   tmp_mod = ndtooned(yvalues_g(:,1,:))

   686   cc_time = esccr(tmp_mod,tmp_ob,0)

   688   ratio = sum(total_mod)/sum(total_ob)

   690   good = ind(tmp_ob .ne. 0. .and. tmp_mod .ne. 0.)

   692   bias = sum( abs( tmp_mod(good)-tmp_ob(good) )/( abs(tmp_mod(good))+abs(tmp_ob(good)) ) )

   693   Mscore3 = (1.- (bias/dimsizes(good)))*score_max

   695   print (Mscore3)

   697   delete (good)

   699   text(nrow-1,0) = sprintf("%.2f",sum(total_ob))

   700   text(nrow-1,1) = sprintf("%.2f",sum(total_mod))

   701   text(nrow-1,2) = sprintf("%.2f",cc_time)

   702   text(nrow-1,3) = sprintf("%.2f",ratio)

   703 ; text(nrow-1,4) = sprintf("%.2f",avg(Mscore2))

   704   text(nrow-1,4) = sprintf("%.2f",    Mscore3)

   705   text(nrow-1,5) = "<a href=./monthly_global.png>model_vs_ob</a>"

   707 ;**************************************************

   708 ; create html table

   709 ;**************************************************

   711   header_text = "<H1>Fire Emission (1997-2004):  Model "+model_name+"</H1>"

   713   header = (/"<HTML>" \

   714             ,"<HEAD>" \

   715             ,"<TITLE>CLAMP metrics</TITLE>" \

   716             ,"</HEAD>" \

   717             ,header_text \

   718             /)

   719   footer = "</HTML>"

   721   table_header = (/ \

   722         "<table border=1 cellspacing=0 cellpadding=3 width=60%>" \

   723        ,"<tr>" \

   724        ,"   <th bgcolor=DDDDDD >Biome Type</th>" \

   725        ,"   <th bgcolor=DDDDDD >"+col_head(0)+"</th>" \

   726        ,"   <th bgcolor=DDDDDD >"+col_head(1)+"</th>" \

   727        ,"   <th bgcolor=DDDDDD >"+col_head(2)+"</th>" \

   728        ,"   <th bgcolor=DDDDDD >"+col_head(3)+"</th>" \

   729        ,"   <th bgcolor=DDDDDD >"+col_head(4)+"</th>" \

   730        ,"   <th bgcolor=DDDDDD >"+col_head(5)+"</th>" \

   731        ,"</tr>" \

   732        /)

   733   table_footer = "</table>"

   734   row_header = "<tr>"

   735   row_footer = "</tr>"

   737   lines = new(50000,string)

   738   nline = 0

   740   set_line(lines,nline,header)

   741   set_line(lines,nline,table_header)

   742 ;-----------------------------------------------

   743 ;row of table

   745   do n = 0,nrow-1

   746      set_line(lines,nline,row_header)

   748      txt0  = row_head(n)

   749      txt1  = text(n,0)

   750      txt2  = text(n,1)

   751      txt3  = text(n,2)

   752      txt4  = text(n,3)

   753      txt5  = text(n,4)

   754      txt6  = text(n,5)

   756      set_line(lines,nline,"<th>"+txt0+"</th>")

   757      set_line(lines,nline,"<th>"+txt1+"</th>")

   758      set_line(lines,nline,"<th>"+txt2+"</th>")

   759      set_line(lines,nline,"<th>"+txt3+"</th>")

   760      set_line(lines,nline,"<th>"+txt4+"</th>")

   761      set_line(lines,nline,"<th>"+txt5+"</th>")

   762      set_line(lines,nline,"<th>"+txt6+"</th>")

   764      set_line(lines,nline,row_footer)

   765   end do

   766 ;-----------------------------------------------

   767   set_line(lines,nline,table_footer)

   768   set_line(lines,nline,footer)

   770 ; Now write to an HTML file.

   772   output_html = "table_fire.html"

   774   idx = ind(.not.ismissing(lines))

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

   776     asciiwrite(output_html,lines(idx))

   777   else

   778    print ("error?")

   779   end if

   781   delete (idx)

   783 end

author	Forrest Hoffman <forrest@climatemodeling.org>
	Mon, 26 Jan 2009 22:08:20 -0500
changeset 0	0c6405ab2ff4
permissions	-rw-r--r--