pro raman_lidar_file_write, time, year, month, day, height, bscat_copol, bscat_crosspol, bscat_raman, calibration, copol_nd_filter_correction, crosspol_nd_filter_correction 



;  estimate cloud base
common colors,r_orig,g_orig,b_orig,r_curr,g_curr,b_curr

dumb=where(time[1:*] eq 0.0, count)
if count gt 0 then begin
  num_times=min(where(time[1:*] eq 0.0))-1
endif else begin
  num_times=n_elements(time)
endelse

cloud_base_index=lonarr(num_times)

loadct, 11 ;  This is the stern color scheme


r_curr[250:255]=255 & r_orig=r_curr
g_curr[250:255]=255 & g_orig=g_curr
b_curr[250:255]=255 & b_orig=b_curr

for j=0,num_times-1 do begin
  
  if max(bscat_copol[j,*]) gt 0. then begin
  
max_co_diff=0. & max_co_diff_index=0
max_cr_diff=0. & max_cr_diff_index=0

max_index=where(bscat_copol[j,*] eq max(bscat_copol[j,*]))

if max_index lt n_elements(height)-10 then begin
  mean_bscat=mean(bscat_copol[j,max_index+10:n_elements(height)-1])
   bscat_max_ratio=bscat_copol[j,max_index]/mean_bscat
endif

base_flag=0 & k=10
;for k=11,n_elements(height)-12 do begin
while base_flag eq 0 and k lt n_elements(height)-10 do begin
  
  lower_mean_copol=mean(bscat_copol[j,k-5:k])/(2.*(mean(height[k-5:k])^2))
  upper_mean_copol=mean(bscat_copol[j,k:k+5])/(2.*(mean(height[k-5:k])^2))
  
  lower_mean_crpol=mean(bscat_crosspol[j,k-5:k])/(2.*(mean(height[k-5:k])^2))
  upper_mean_crpol=mean(bscat_crosspol[j,k:k+5])/(2.*(mean(height[k-5:k])^2))
  
  if upper_mean_copol/lower_mean_copol gt 5. and upper_mean_crpol/lower_mean_crpol gt 3. and $
    upper_mean_copol gt 0. and lower_mean_copol gt 0. and upper_mean_crpol gt 0. and lower_mean_crpol gt 0. and min(bscat_copol[j,k-5:k+5]) gt 0. then begin 

  prev_co_rat=upper_mean_copol/lower_mean_copol & prev_cr_rat=upper_mean_crpol/lower_mean_crpol
  
  k=k+1
  lower_mean_copol=mean(bscat_copol[j,k-5:k])/(2.*(mean(height[k-5:k])^2))
  upper_mean_copol=mean(bscat_copol[j,k:k+5])/(2.*(mean(height[k-5:k])^2))

  lower_mean_crpol=mean(bscat_crosspol[j,k-5:k])/(2.*(mean(height[k-5:k])^2))
  upper_mean_crpol=mean(bscat_crosspol[j,k:k+5])/(2.*(mean(height[k-5:k])^2))
while upper_mean_copol/lower_mean_copol gt prev_co_rat or upper_mean_crpol/lower_mean_crpol gt prev_cr_rat and k lt n_elements(height)-10 and $
    upper_mean_copol gt 0. and lower_mean_copol gt 0. and upper_mean_crpol gt 0. and lower_mean_crpol gt 0. do begin

prev_co_rat=upper_mean_copol/lower_mean_copol & prev_cr_rat=upper_mean_crpol/lower_mean_crpol

k=k+1

lower_mean_copol=mean(bscat_copol[j,k-5:k])/(2.*(mean(height[k-5:k])^2))
upper_mean_copol=mean(bscat_copol[j,k:k+5])/(2.*(mean(height[k-5:k])^2))

lower_mean_crpol=mean(bscat_crosspol[j,k-5:k])/(2.*(mean(height[k-5:k])^2))
upper_mean_crpol=mean(bscat_crosspol[j,k:k+5])/(2.*(mean(height[k-5:k])^2))

endwhile
if upper_mean_copol gt 0. and lower_mean_copol gt 0. and upper_mean_crpol gt 0. and lower_mean_crpol gt 0. then begin
  cloud_base_index[j]=k
 base_flag=1 
 
 ;if k gt 150 then stop

endif

endif


k=k+1 
endwhile

if bscat_max_ratio gt 10 and abs(cloud_base_index[j]-max_index) gt 5 then cloud_base_index[j]=max_index


;gamma_ct, 1.0
;set_plot, 'Z'
;device, set_resolution=[3000,2000]
;!p.charsize=3.
;!p.font=-1
;!p.background=!d.n_colors-2
;!p.thick=3.
;  
;bscatco=bscat_copol[j,*]
;bscatcr=bscat_crosspol[j,*]
;  
;  plot, bscatco[where(height lt 3.)], height[where(height lt 3.)], psym=-2, color=0
;  oplot, bscatcr[where(height lt 3.)], height[where(height lt 3.)], psym=-3, color=200
;  oplot, [-1.e6, 1.e6], [height[cloud_base_index[j]],height[cloud_base_index[j]]], psym=-3, color=0
;  oplot, [-1.e6, 1.e6], [height[cloud_base_index[j]],height[cloud_base_index[j]]], psym=-3, linestyle=2, color=0
;  write_gif, '/Users/u0029340/Documents/data/Capricorn/Lidar/test_profile.gif', tvrd()
;    
  
 endif  ; bscat_copol[j,*] 
  
  
endfor

set_plot, 'Z'
device, set_resolution=[3000,2000]
!p.charsize=3.
!p.font=-1
!p.background=!d.n_colors-2
!p.thick=3.

;bscatco=bscat_copol[j,*]
;bscatcr=bscat_crosspol[j,*]

plot, time, height[cloud_base_index], psym=-2, color=0
;oplot, bscatcr[where(height lt 3.)], height[where(height lt 3.)], psym=-3, color=200
;oplot, [-1.e6, 1.e6], [height[cloud_base_index[j]],height[cloud_base_index[j]]], psym=-3, color=0
;oplot, [-1.e6, 1.e6], [height[cloud_base_index[j]],height[cloud_base_index[j]]], psym=-3, linestyle=2, color=0
write_gif, '/Users/u0029340/Documents/data/Capricorn/Lidar/test_profile.gif', tvrd()



output_path='/Users/u0029340/Documents/data/Capricorn/Lidar/'

; create a base_time and time_offset
; 

 t=float(fix(time))
frac_hr=time-t
 min_t=frac_hr*60.
 sec_t=(min_t-float(fix(min_t)))*60.

julian_day=julday(float(month),float(day),float(year),float(fix(frac_hr[0:num_times-1])),float(fix(min_t[0:num_times-1])),float(fix(sec_t[0:num_times-1])))

base_time=julian_day[0]
time_offset=julian_day-julian_day[0]

output_fname=output_path+'Capricorn_Lidar_'+year+month+day+'.cdf

cdfid=ncdf_create(output_fname,/clobber)


time_did=ncdf_dimdef(cdfid,'time_offset',n_elements(time_offset))

heights_did=ncdf_dimdef(cdfid,'heights',n_elements(height))


basetime_id=ncdf_vardef(cdfid, 'base_time', /long)
time_offset_id=ncdf_vardef(cdfid, 'time_offset', /double)
year_id=ncdf_vardef(cdfid, 'year', /long)
month_id=ncdf_vardef(cdfid, 'month', /long)
day_id=ncdf_vardef(cdfid, 'day', /long)
hrfrac_id=ncdf_vardef(cdfid, 'fractional_hour',[time_did], /float)
height_id=ncdf_vardef(cdfid, 'heights', [heights_did], /float)
bscat_copol_id=ncdf_vardef(cdfid, 'bscat_copol', [time_did, heights_did], /float)
bscat_crosspol_id=ncdf_vardef(cdfid, 'bscat_crosspol', [time_did, heights_did], /float)
bscat_raman_id=ncdf_vardef(cdfid, 'bscat_raman', [time_did, heights_did], /float)
calibration_id=ncdf_vardef(cdfid, 'calibration', /float)
copol_nd_filter_correction_id=ncdf_vardef(cdfid, 'copol_nd_filter_correction', /float)
crosspol_nd_filter_correction_id=ncdf_vardef(cdfid, 'crosspol_nd_filter_correction', /float)
cloud_base_height_index_id=ncdf_vardef(cdfid, 'cloud_base_height_index',[time_did], /long)

ncdf_control, cdfid, /endef

ncdf_varput, cdfid, basetime_id, long(base_time)
ncdf_varput, cdfid, time_offset_id, time_offset
ncdf_varput, cdfid, year_id, year
ncdf_varput, cdfid, month_id, month
ncdf_varput, cdfid, day_id, day
ncdf_varput, cdfid, hrfrac_id, time[0:num_times-1]
ncdf_varput, cdfid, height_id, height
ncdf_varput, cdfid, bscat_copol_id, bscat_copol[0:num_times-1,*]
ncdf_varput, cdfid, bscat_crosspol_id, bscat_crosspol[0:num_times-1,*]
ncdf_varput, cdfid, bscat_raman_id, bscat_raman[0:num_times-1,*]
ncdf_varput, cdfid, calibration_id, calibration
ncdf_varput, cdfid, copol_nd_filter_correction_id, copol_nd_filter_correction
ncdf_varput, cdfid, crosspol_nd_filter_correction_id, crosspol_nd_filter_correction
ncdf_varput, cdfid, cloud_base_height_index_id, cloud_base_index[0:num_times-1]

ncdf_close, cdfid

return
end