function get_latest(info)
% get_latest(info)
%
% Last modified:  Time-stamp: <2005-01-20 10:32:38 haines>
%
  
% Abstract: Function that loads temporary latest raw data from NOS, parses obs,
%    subset to last 48 hours, and saves netCDF
%
% Usage: get_latest(info)
  
% Processing steps
% (1) read data from stripped text file from earlier screen-scrape
%  (Data is nicely formatted text file so no editing necessary)
% (2) set NaN to values used for no-data (e.g. -99.9, -999.9)
% (3) Since this data has predicted WL data there is data into the future
%     we must only get everything older than current date and time.
% (4) calculate u, v, seallevel pressure (if needed), and time stuff and
% temperature conversions
% (5) change all NaN's to missing_value for netcdf
% (6) create new file of global attributes, dimensions, and variables
% (7) save (ncclose) file

% Author: Sara Haines, Sep 18, 2003 

% Raw data looks like this (without column header)
% Date/Time                   Pre.   Obs.  Res.  Wsp. Wdr. Wgt. Bar.   Air  Water
% (Local Time)               (ft.)  (ft.) (ft.) (kts.)(true)(kts.)(mb.)(F)  (F)
% 11/30/2003 09:18:00  EST   2.01   0.35  -1.66   17  241   19 1014.7 -99.9  47.2
% 11/30/2003 09:48:00  EST   2.47   0.93  -1.54 -999 -999 -999 -999.9 -99.9 -99.9

  fid = fopen(info.rawdirname);
  tline = fgetl(fid);
  fclose(fid);

  file_ok = isempty(regexp(tline, '[S|s]orry'));

  % (1) read data from stripped text file 
  if file_ok
    format_str = '%d/%d/%d %d:%d:%d %s %f %f %f %f %f %f %f %f %f';
    [mm, dd, yyyy, HH, MM, SS, TZ_str, ...
     pre_wl, obs_wl, res_wl, wi_spd, wi_dir, wi_gst, baro, atemp, wtemp] = ...
	textread(info.rawdirname, format_str);

    % param names to use and associated missing value from posted data
    params = {'pre_wl', 'obs_wl', 'res_wl', ...
	      'wi_spd', 'wi_dir', 'wi_gst', ...
	      'baro', 'atemp', 'wtemp'};
    missing_value = [-99.99, -99.99, -99.99, ...
		     -999, -999, -999, ...
		     -999.9, -99.9, -99.9];

    % (2) replace missing values with NaN
    for i=1:length(params)
      param_str = char(params(i));
      eval(['which_miss = find(' param_str ' == ' num2str(missing_value(i)) ');'])
      eval([param_str '(which_miss) = ones(size(which_miss))*NaN;'])
    end

    % Adjust timezone of data to UTC
    TZ_type = {'AST', 'EST', 'CST', 'MST', 'PST', 'YST', 'AKST', 'HST', ...
	       'ADT', 'EDT', 'CDT', 'MDT', 'PDT', 'AKDT'};
    hours_from_UTC = [4, 5, 6, 7, 8, 9, 9, 10, ...
		      3, 4, 5, 6, 7, 8];
    TZ = zeros(size(TZ_str)); % assume UTC until shifted

    for i=1:length(TZ_type)
      type_str = char(TZ_type(i));
      eval(['which_zone = find(strcmp(TZ_str,''' type_str '''));'])
      eval(['TZ(which_zone) = ones(size(which_zone))*' int2str(hours_from_UTC(i)) ';'])
    end
        
    % calculate datenum adjusted to UTC
    dn = datenum(yyyy,mm,dd,HH+TZ,MM,SS);

    % (3) get only last 2 days (based on UTC)
    two_days_ago = info.current_time_dn-2; % datenum is in units of days
    which_latest = find(two_days_ago<=dn & dn<info.current_time_dn);
    dn = dn(which_latest);

    for i=1:length(params)
      param_str = char(params(i));
      eval([param_str ' = ' param_str '(which_latest);'])
    end

  else,
    disp(['No data file for ' info.platform_name]);
    return;
  end; % if file_okay
  
  % (4) convert kts to m/s
  wi_spd = wi_spd*0.5141; % from knots to mps (m/sec)
  wi_gst = wi_gst*0.5141; 
  
  % (4a) calc wind components (northward and eastward)
  r=pi/180;
  wi_dir_toward = wi_dir -180;
  wi_u = wi_spd .*(sin(wi_dir_toward*r));
  wi_v = wi_spd .*(cos(wi_dir_toward*r));
      
  params = [ params, {'wi_u', 'wi_v'}];
  
  % (4b) convert deg F to deg F for air temp and water temp 
  % celsTemp = 5/9 (fahrenTemp - 32) 
  atemp = (5/9).*(atemp-32);
  wtemp = (5/9).*(wtemp-32);
  
  % (4c) convert water level feet to meters (3.2808 feet = 1 meter)
  pre_wl = pre_wl/3.2808;
  obs_wl = obs_wl/3.2808;
  res_wl = res_wl/3.2808;
  
  % (4c) time variables for wind product exercise
  %     (i) seconds since  1995-1-1 00:00:00 UTC
  seacoos_time_1995 = (dn-datenum(1995, 1, 1, 0, 0, 0))*24*60*60;
  seacoos_time_1995 = round(seacoos_time_1995);
  
  % (4c) time variables for wind product exercise
  %     (i) seconds since  1970-1-1 00:00:00 UTC
  seacoos_time_1970 = (dn-datenum(1970, 1, 1, 0, 0, 0))*24*60*60;
  seacoos_time_1970 = round(seacoos_time_1970);
  
  %     (iii) date_time_string
  date_time_string = datestr(dn,'yyyy-mm-dd HH:MM:SS' );
     
  ncfile_v20 = [info.out_nc_v20_dir ...
		'/nos-' info.platform_name '-' info.package_name ...
		'-latest.nc'];
  
  % determine if have wind or sst data to write to file
  which_wind = find(~isnan(wi_spd) & ~isnan(wi_dir));
  which_sst = find(~isnan(wtemp));
  which_wl = find(~isnan(obs_wl));
  
  if length(which_wind)>0
    have_wind = 1;
  else
    have_wind = 0;
  end
  
  if length(which_wl)>0
    have_wl = 1;
  else
    have_wl = 0;
  end

  if length(which_sst)>0
    have_sst = 1;
  else
    have_sst = 0;
  end
  
  % have_data = have_wind | have_sst | have_wl;
  have_data = have_wind | have_sst;

  % (5) change all Nan to missing value (-9999)
  for i=1:length(params)
    param_str = char(params(i));
    eval(['which_nan = find(isnan(' param_str '));'])
    eval([param_str '(which_nan) = ones(size(which_nan))*-9999;'])
  end
  
  % delete old netcdf file of latest data
  if exist(ncfile_v20, 'file')
    delete(ncfile_v20);
  end
  
  % one or more obs in desired time period?
  have_dn = exist('dn');
  if have_dn
    have_latest = length(dn)>0;
  else
    have_latest = 0;
  end

  % (5) create new file of global attributes, dimensions, and variables
  % if have data then create new netcdf file and populate
  if have_data & have_latest
    if have_wind | have_sst | have_wl
      fprintf(2, ['... ... writing %s\n'], ncfile_v20);
      nc = netcdf(ncfile_v20, 'clobber');    
      netcdf_create_v20;
      nc = close(nc);
    end
  end
  

%