function raw_data = LTspice2Matlab( filename, varargin )
%
%
% LTSPICE2MATLAB -- Reads an LTspice IV .RAW waveform file containing data from a Transient Analysis (.tran) or
%           AC Analysis (.ac) simulation, and converts voltages and currents vs. time into Matlab variables.  
%           This function can read compressed binary, uncompressed binary, and ASCII file formats.  It does not 
%           currently support files saved in the Fast Access Format.  In the case of compressed binary, 
%           the data is automatically uncompressed using fast quadratic point insertion.
%
%           LTspice IV is an excellent Spice III simulator & schematic capture tool freely avaliable for
%           download at www.linear.com/designtools/software.  It is optimized for simulation of switching regulators, but
%           can simulate many other types of circuits as well and comes with a wide variety of component models.  Note
%           that the LTspice uses a lossy compression format (enabled by default) with user adjustable error bounds.
%
%           Use LTSPICE2MATLAB to import LTspice waveforms into Matlab for additional analysis or to 
%           compare with measured data.
% 
%           This function has been tested with LTspice IV version 4.01p, and Matlab versions 6.1 and 7.5.  Regression
%           testing has been used to expose the function to a wide range of LTspice settings.
%           Author:  Paul Wagner  4/25/2009
%
% 
%    Calling Convention:
%        RAW_DATA = LTSPICE2MATLAB( FILENAME );                  %Returns all variables found in FILENAME
%                             (or)
%        RAW_DATA = LTSPICE2MATLAB( FILENAME, SELECTED_VARS );   %Returns only those variables covered by SELECTED_VARS
%               Set SELECTED_VARS to [] to quickly determine the number and names of variables present in FILENAME without
%               actually loading the variables.
%                             (or)
%        RAW_DATA = LTSPICE2MATLAB( FILENAME, SELECTED_VARS, N );   
%               Returns variables listed in SELECTED_VARS, with all waveforms downsampled by N.  Set N > 1 to load 
%               very large data files using less memory, at the price of degraded waveform accuracy and possible aliasing.
%
%    Inputs:  FILENAME is a string containing the name and path of the LTspiceIV .raw file to be converted.
%
%             SELECTED_VARS (optional) is a vector of indexes indicating which variables to extract from the .raw file.  
%                  For example, if a .raw file has 14 variables and SELECTED_VARS is [1 8 9], then the output 
%                  RAW_DATA.VARIABLE_MAT will be a 3 x NUM_DATA_PNTS matrix containing waveforms for
%                  variables 1, 8, and 9 only.  Note that SELECTED_VARS does not cover the time (or frequency) variable 
%                  (index 0), which is returned separately in RAW_DATA.TIME_VECT (or RAW_DATA.FREQ_VECT).  Extracting only 
%                  of subset of variables is a way to use less memory when loading very large simulation files.
%
%                  If this parameter is not specified, then all variables are returned by default.  Setting  
%                  SELECTED_VARS to 'all' will also cause all variables to be returned.
%
%             *  To quickly determine the number and names of variables present in a .raw file, call LTspice2Matlab with 
%                  SELECTED_VARS set to [].  In this case, all fields in RAW_DATA will be populated, except 
%                  .TIME_VECT (or .FREQ_VECT) and .VARIABLE_MAT, which will both be empty ([]).  Since only the header 
%                  is read, the function call should execute very quickly, even for large files.
%
%             N (optional) must be a positive integer >= 1.  If N is specified, then SELECTED_VARS must also be specified.  
%                  If N is unspecified, it defaults to 1, which does not change the sampling rate.  If this value is 2 
%                  or larger, the returned voltage, current, and time data will be downsampled by keeping every N-th sample 
%                  in the original data, starting with the first.  
%                  Caution:  No lowpass filtering is applied prior to downsampling, so aliasing may occur.  Also, 
%                  in many cases LTspice saves data with a non-constant sampling rate, in which case downsampling can
%                  result in substantial waveform distortion.  This option should only be used if the waveform of 
%                  interest is initially oversampled.
%
%    Outputs:  RAW_DATA is a Matlab structure containing the following fields ...
%                  title:                String containing the title appearing in the .RAW file header.
%                  date:                 String containing the date appearing in the .RAW file header.
%                  plotname:             String indicating simulation type ('Transient Analysis', 'AC Analysis')
%                  conversion_notes:     Description of modifications (if any) done to the data during conversion.  
%                  num_variables:        Number of variables (does not include the "time" or "frequency" variable)
%                  variable_type_list:   A cell of strings indicating the variable type (i.e. voltage, current etc.)
%                  variable_name_list:   A cell of strings indicating the name of each variable.
%                  selected_vars:        A vector of indicies referencing VARIABLE_TYPE_LIST cells, corresponding 
%                                        to each row in VARIABLE_MAT.
%                  num_data_pnts:        Number of data points for each variable.
%                  variable_mat:         Double precision matrix with NUM_VARIABLES rows and NUM_DATA_PNTS columns.
%                                        This matrix contains node voltages (in Volts) and device currents (in Amps) 
%                                        for each variable and each time point listed in TIME_VECT (or FREQ_VECT).
%                                        For AC Analysis simulations, VARIABLE_MAT will have complex values showing
%                                        the real and imaginary components of the voltage or current at the
%                                        corresponding frequency.  To convert this to log magnitude and normalized
%                                        phase representation used in LTspice plots, use the following formulas:
%                                            Log_Magnitude_dB    =  20*log10(abs(variable_mat))
%                                            Norm_Phase_degrees  =  angle(variable_mat)*180/pi
%                  time_vect:            [Field returned for Transient Analysis only] Double precision row vector of 
%                                        time values (in seconds) at each simulation point
%                    (or)
%                  freq_vect:            [Field returned for AC Analysis only] Double precision row vector of 
%                                        frequency values (in Hz) at each simulation point
%
%    ** Currently this function is able to import results from Transient Analysis (.tran) and AC Analysis (.ac)
%       simulations only. 
%
%
%    Examples
%    --------
%    These examples assume you've run a .TRAN simulation in LTspice for a hypothetical file called 
%    BASIC_CIRCUIT.ASC, and that an output file called BASIC_CIRCUIT.RAW has been created.  It also assumes your   
%    current Matlab directory is pointing to the directory where the .RAW file is located (or that you prepended
%    the full path to the input parameter FILENAME).
%
%    To import BASIC_CIRCUIT.RAW into Matlab and create a labeled plot of a single variable vs. time:
%
%       raw_data = LTspice2Matlab('BASIC_CIRCUIT.RAW');
%       variable_to_plot = 1;   %This example plots the first variable in the data structure.
%       plot(raw_data.time_vect, raw_data.variable_mat(variable_to_plot,:), 'k');
%       title(sprintf('Waveform %s', raw_data.variable_name_list{variable_to_plot}));
%       ylabel(raw_data.variable_type_list{variable_to_plot} );
%       xlabel('Time (sec)' );
% 
%   To superimpose all variables in BASIC_CIRCUIT.RAW on a single graph with a legend:
% 
%       raw_data = LTspice2Matlab('BASIC_CIRCUIT.RAW');
%       plot(raw_data.time_vect, raw_data.variable_mat);
%       title(sprintf( 'File:  %s', raw_data.title));
%       legend(raw_data.variable_name_list);
%       ylabel('Voltage (V) or Current (A)');
%       xlabel('Time (sec)');
% 
%   To quickly determine the number and names of variables in BASIC_CIRCUIT.RAW without loading the entire file:
% 
%       raw_data = LTspice2Matlab('BASIC_CIRCUIT.RAW', []);
%       disp(sprintf( '\n\nThis file contains %.0f variables:\n', raw_data.num_variables));
%       disp(sprintf('NAME         TYPE\n-------------------------'));
%       disp([char(raw_data.variable_name_list), char(zeros(raw_data.num_variables,5)), char(raw_data.variable_type_list)]);
%
%
%
      
        

    raw_data = [];  %Initialize the output structure.

    if nargin==0,
        error( 'LTspice2Matlab takes 1, 2, or 3 input parameters.  Type "help LTspice2Matlab" for details' );
    elseif nargin==1,
        selected_vars = 'all';
        downsamp_N = 1;
    elseif nargin==2,
        selected_vars = varargin{1};
        if ischar(selected_vars),  selected_vars = lower(selected_vars);  end
        downsamp_N = 1;
    elseif nargin==3,
        selected_vars = varargin{1};
        if ischar(selected_vars),  selected_vars = lower(selected_vars);  end
        downsamp_N = varargin{2};
    else
        error( 'LTspice2Matlab takes only 1, 2, or 3 input parameters.  Type "help LTspice2Matlab" for details' );
    end
    
    
    if length(downsamp_N)~=1 | ~isnumeric(downsamp_N) | isnan(downsamp_N) | mod(downsamp_N,1)~=0.0 | downsamp_N<=0,
        error( 'Optional parameter DOWNSAMP_N must be a positive integer >= 1' );
    end
    
    
    filename = fliplr(deblank(fliplr(deblank(filename))));   %Remove leading and trailing spaces from filename.
	fid = fopen(filename, 'rb');
    if length(fid)==1 & isnumeric(fid) & fid==-1,
        %try to append ".raw" to the file name ...
        fid = fopen(sprintf( '%s.raw', filename ), 'rb');
        if length(fid)==1 & isnumeric(fid) & fid==-1,
            error( sprintf( 'Could not open file "%s"', filename ) );
        end
    end
       
    
	[filename, the_permision, machineformat] = fopen(fid);

    %Load header tags & information
    variable_name_list = {};  variable_type_list = {};   %These include voltages and currents only.  Does not include the time vector.
    variable_flag = 0;
    file_format = '';
    while 1,   
        the_line = fgetl(fid);
        if length(the_line)==1 & isnumeric(the_line) & double(the_line)==-1,  
            try fclose( fid );  catch  end
            error( sprintf( 'Format error in LTspice file "%s" ... End of file unexpectedly encountered', filename ));
        end
        
        the_line = char(the_line);
        
        if length(strfind( the_line, 'Binary:' ))~=0,  file_format = 'binary';  break;  end
        if length(strfind( the_line, 'Values:' ))~=0,  file_format = 'ascii';   break;  end
        
        if variable_flag==0,  %Non-variable header section
            if length(the_line)==0,  colon_index = [];
            else,  colon_index = find( the_line == ':' );  end
            if length(colon_index)==0,  
                try fclose( fid );  catch end
                error( sprintf( 'Format error in LTspice file "%s"', filename ));  
            end
            var_name = the_line(1:(colon_index(1)-1));
            var_value = fliplr(deblank(fliplr(deblank(the_line((colon_index(1)+1):end)))));

            vn_keep_index = find( var_name~=' ' & var_name~='.' & var_name~=char(9) & var_name~=char(10) & var_name~=char(13) );
            var_name = lower(var_name(vn_keep_index));
            if length(var_name)==0 | (var_name(1)>='0' & var_name(1)<='9'),  
                try fclose( fid );  catch end
                error( sprintf('Format error in LTspice file "%s" ... Bad tag name found', filename ));  
            end

            if strcmpi( var_name, 'variables' ) | strcmpi( var_name, 'variable' ),  variable_flag = 1;  continue;  end
            value_try = str2num(var_value);
            try
                if length(value_try)==0,  raw_data = setfield( raw_data, var_name, var_value );
                else raw_data = setfield( raw_data, var_name, value_try );  end
            catch
                try fclose( fid );  catch end
                error( sprintf('Format error in LTspice file "%s" ... Bad tag name found', filename ));  
            end
            
        else  %Variable header section
            leading_ch_index = find( (the_line(1:end-1)==' ' | the_line(1:end-1)==char(9)) & (the_line(2:end)~=' ' & the_line(2:end)~=char(9)) );
            if length(leading_ch_index)~=3,  
                try fclose( fid );  catch end
                error( sprintf('Format error in LTspice file "%s" ... Wrong number of columns in the variable define section', filename ));  
            end
            
            part1 = fliplr(deblank(fliplr(deblank(the_line( (leading_ch_index(1)+1) : leading_ch_index(2) )))));
            part2 = fliplr(deblank(fliplr(deblank(the_line( (leading_ch_index(2)+1) : leading_ch_index(3) )))));
            part3 = fliplr(deblank(fliplr(deblank(the_line( (leading_ch_index(3)+1) : end )))));
            
            if str2num(part1)~=length(variable_name_list),  
                try fclose( fid );  catch end
                error( sprintf('Format error in LTspice file "%s" ... Inconsistency found in the variable define section', filename ));  
            end
            variable_name_list{end+1} = part2;
            variable_type_list{end+1} = part3;
        end
    end
    
    %Check raw_data structure for required fields
    expected_tags      = {'title', 'date', 'plotname', 'flags', 'novariables',   'nopoints'    };
    expected_tags_full = {'Title', 'Date', 'Plotname', 'Flags', 'No. Variables', 'No. Points'  };
    for q=1:length(expected_tags),
        if ~isfield( raw_data, lower(expected_tags{q}) ),  
            try fclose( fid );  catch end
            error( sprintf('Format error in LTspice file "%s" ... tag "%s" not found', filename, expected_tags_full{q} ));  
        end
    end
    
    raw_data.conversion_notes = '';
    raw_data.num_data_pnts = raw_data.nopoints;  raw_data = rmfield( raw_data, 'nopoints' );
    raw_data.num_variables = raw_data.novariables-1;  raw_data = rmfield( raw_data, 'novariables' );
    %"raw_data.num_variables" does not include the time vector (index 0 in the .raw file)

    if isfield( raw_data, 'command' ),  raw_data = rmfield( raw_data, 'command' );  end
    if isfield( raw_data, 'backannotation' ),  raw_data = rmfield( raw_data, 'backannotation' );  end
    if isfield( raw_data, 'offset' ),  
        general_offset = raw_data.offset;  %(sec)
        raw_data = rmfield( raw_data, 'offset' );  
    else
        general_offset = 0.0;
    end
    
    
    raw_data.variable_name_list = {variable_name_list{2:end}};        %cut off the time variable.
    raw_data.variable_type_list = {variable_type_list{2:end}};
    
    simulation_type = '';
    if     length(strfind( lower(raw_data.plotname), 'transient analysis' ))~=0,           simulation_type = '.tran';   %SUPPORTED
    elseif length(strfind( lower(raw_data.plotname), 'ac analysis' ))~=0,                  simulation_type = '.ac';     %SUPPORTED
    elseif length(strfind( lower(raw_data.plotname), 'dc transfer characteristic' ))~=0,   simulation_type = '.dc';     %This is a DC sweep (Not supported)
    elseif length(strfind( lower(raw_data.plotname), 'operating point' ))~=0,              simulation_type = '.op';     %This is a DC operating point (Not supported)
    end
    
    if length(simulation_type)==0 | ~(strcmpi(simulation_type, '.tran') | strcmpi(simulation_type, '.ac')),
        try fclose( fid );  catch end
        error( 'Currently LTspice2Matlab is only able to import results from Transient Analysis (.tran) and AC Analysis (.ac) simulations.' );
    end

    if length(strfind( lower(raw_data.flags), 'fastaccess' ))~=0,
        try fclose( fid );  catch end
        error( 'LTspice2Matlab cannot convert files saved in the "Fast Access" format.' );
    end
    if strcmpi(simulation_type, '.tran') & length(strfind( lower(raw_data.flags), 'real' ))==0,
        try fclose( fid );  catch end
        error( 'Expected to find "real" flag for a Transient Analysis (.tran) simulation.  Unsure how to convert the data' );
    end
    if strcmpi(simulation_type, '.tran') & length(strfind( lower(raw_data.flags), 'forward' ))==0,
        try fclose( fid );  catch end
        error( 'Expected to find "forward" flag for a Transient Analysis (.tran) simulation.  Unsure how to convert the data' );
    end
    
    if strcmpi(simulation_type, '.ac') & length(strfind( lower(raw_data.flags), 'complex' ))==0,
        try fclose( fid );  catch end
        error( 'Expected to find "complex" flag for an AC Analysis (.ac) simulation.  Unsure how to convert the data' );
    end
    if strcmpi(simulation_type, '.ac') & length(strfind( lower(raw_data.flags), 'forward' ))==0,
        try fclose( fid );  catch end
        error( 'Expected to find "forward" flag for an AC Analysis (.ac) simulation.  Unsure how to convert the data' );
    end
    if isfield( raw_data, 'flags' ),  raw_data = rmfield( raw_data, 'flags' );  end

    
    if ischar(selected_vars),
        if strcmpi(selected_vars, 'all') | strcmpi(selected_vars, 'everything') | strcmpi(selected_vars, 'complete') | strcmpi(selected_vars, 'all variables') | ...
                strcmpi(selected_vars, 'all vars') | strcmpi(selected_vars, 'every thing') | strcmpi(selected_vars, 'every'),  
            selected_vars = 1:raw_data.num_variables;     %Return all variables
        else
            try fclose( fid );  catch end
            error( 'Bad value for optional input parameter SELECTED_VARS' );
        end
    end
    
    
    if size(selected_vars,1)==0 | size(selected_vars,2)==0,  
        raw_data.selected_vars = [];
        raw_data.variable_mat = [];
        raw_data.time_vect = [];
        try fclose( fid );  catch end
        return;
    end
    if size(selected_vars,1)>1 & size(selected_vars,2)>1,  
        try fclose( fid );  catch end
        error( 'SELECTED_VARS must be a row or column vector, not a matrix' );  
    end
    if length(find(selected_vars==0))~=0,  
        try fclose( fid );  catch end
        error( 'The time vector (index 0) is returned separately.  \n   Values in input parameter SELECTED_VARS must be positive integers >= 1 and <= NUM_VARIABLES' );  
    end
    non_integer_index = find(isnan(selected_vars) | ~isnumeric(selected_vars) | mod( selected_vars, 1 )~=0.0);
    if length(non_integer_index)~=0,  
        try fclose( fid );  catch end
        error( 'Values in input parameter SELECTED_VARS must be positive integers >= 1 and <= NUM_VARIABLES' );  
    end
    missing_index = find( ~ismember( selected_vars, 1:raw_data.num_variables ) );
    if length(missing_index)~=0,  
        try fclose( fid );  catch end
        error( 'Error in input parameter SELECTED_VARS ... Out of range value(s) found' );  
    end
    
    selected_vars = unique(selected_vars);   %remove duplicates and sort in ascending order.
    raw_data.selected_vars = selected_vars;

    
    NumPnts = raw_data.num_data_pnts;
    NumPnts_DS = floor(NumPnts/downsamp_N);
    raw_data.num_data_pnts = NumPnts_DS;   %Updated # of points
    NumVars = raw_data.num_variables+1;
    
    
    %READ IN THE ACTUAL WAVEFORM DATA
    if strcmpi(file_format, 'binary'),
        binary_start = ftell(fid);   %start of binary data section.
        
        if strcmpi( simulation_type, '.tran' ),
            % For Transient Analysis simulations, the time data is stored in double precision floating point binary format, 
            % and everything else is stored in single precision format. 

            %Extract the binary data in the fewest possible number of contiguous blocks
            if length(selected_vars)>1,
                g_border = find( [2, diff(selected_vars), 2]~=1 );
                block_list = {};
                for k=1:length(g_border)-1,  block_list{k} = g_border(k):(g_border(k+1)-1);  end
            else
                block_list = {1:length(selected_vars)};
            end

            raw_data.variable_mat = zeros(length(selected_vars), NumPnts_DS);  %Initialize.
            for k=1:length(block_list),
                target_var_index = selected_vars(block_list{k});
                fseek(fid, binary_start + (target_var_index(1)+1)*4, 'bof'); 
                TVIL = length(target_var_index);
                bytes_skip = (NumVars+1-TVIL)*4  +  (downsamp_N-1)*(NumVars+1)*4;
                precision_str = sprintf('%.0f*float',TVIL);
                raw_data.variable_mat(block_list{k},:) = reshape( fread(fid, NumPnts_DS*TVIL, precision_str, bytes_skip, machineformat), TVIL, NumPnts_DS );
            end    

            fseek(fid, binary_start, 'bof');    %rewind to start, then extract the time vector.
            raw_data.time_vect = fread( fid, NumPnts_DS, 'double', (NumVars-1)*4 + (downsamp_N-1)*(NumVars+1)*4, machineformat ).';
            if downsamp_N==1,  raw_data.conversion_notes = 'Converted from Binary format';
            else raw_data.conversion_notes = sprintf( 'Converted from Binary format.  Downsampled from %.0f to %.0f points', NumPnts, NumPnts_DS );  end
            
        elseif strcmpi( simulation_type, '.ac' ),
            % For AC Analysis simulations, the frequency data is stored in double precision floating point binary format (8 bytes),
            % and the variables are stored as complex double precision arrays (8 bytes real followed by 8 bytes imag)
            
            %Extract the binary data in the fewest possible number of contiguous blocks
            if length(selected_vars)>1,
                g_border = find( [2, diff(selected_vars), 2]~=1 );
                block_list = {};
                for k=1:length(g_border)-1,  block_list{k} = g_border(k):(g_border(k+1)-1);  end
            else
                block_list = {1:length(selected_vars)};
            end

            raw_data.variable_mat = zeros(length(selected_vars), NumPnts_DS);  %Initialize.
            if prod(size(raw_data.variable_mat))~=0,  raw_data.variable_mat(1,1) = 0.0 + j*0.0;  end  %Allocate memory for complex double.
            for k=1:length(block_list),
                target_var_index = selected_vars(block_list{k});
                fseek(fid, binary_start + target_var_index(1)*16, 'bof'); 
                TVIL = length(target_var_index);
                bytes_skip = (NumVars-TVIL)*16  +  (downsamp_N-1)*NumVars*16;
                precision_str = sprintf('%.0f*double',TVIL*2);
                temp_buff = reshape(fread(fid, NumPnts_DS*TVIL*2, precision_str, bytes_skip, machineformat), TVIL*2, NumPnts_DS );
                raw_data.variable_mat(block_list{k},:) = temp_buff(1:2:end-1,:) + j*temp_buff(2:2:end,:);
                clear temp_buff;
            end    

            fseek(fid, binary_start, 'bof');    %rewind to start, then extract the time vector.
            raw_data.freq_vect = fread( fid, NumPnts_DS, 'double', (NumVars-1)*16 + 8 + (downsamp_N-1)*NumVars*16, machineformat ).';
            
        else
            try fclose( fid );  catch end
            error( sprintf('Simulation type (%s) not currently supported', simulation_type ));
        end
        
        
    elseif strcmpi(file_format, 'ascii' ),
        
        if strcmpi( simulation_type, '.tran' ),
            %Format:  point number, time value, var1, var2, var3 ... varN
            raw_data.variable_mat = fscanf( fid, '%g', [raw_data.num_variables+2, raw_data.num_data_pnts] );   %matrix is filled in column order.
            if (size(raw_data.variable_mat,1)~=raw_data.num_variables+2) | (size(raw_data.variable_mat,2)~=raw_data.num_data_pnts),
                error( sprintf('Format error in ASCII Transient Analysis LTspice file "%s" ... Incorrect number of data values read', filename ));
            end
            raw_data.time_vect = raw_data.variable_mat(2,1:downsamp_N:end);
            raw_data.variable_mat = raw_data.variable_mat(2+selected_vars,1:downsamp_N:end);
            
        elseif strcmpi( simulation_type, '.ac' ),
            %Format:  point number, freq value, 0, var1 real, var1 imag, var2 real, var2 imag, var3 real, var3 imag ... varN real, varN imag
            all_data = fread( fid, inf, 'uchar' );
            all_data( find( all_data == ',' ) ) = sprintf( '\t' );  %Replace commas with tab characters
            raw_data.variable_mat = sscanf( char(all_data), '%g', [3+2*raw_data.num_variables, raw_data.num_data_pnts] );
            clear all_data;
            %raw_data.variable_mat = fscanf( fid, '%g', [3+2*raw_data.num_variables, raw_data.num_data_pnts] );   %matrix is filled in column order.
            
            if (size(raw_data.variable_mat,1)~=(3+2*raw_data.num_variables)) | (size(raw_data.variable_mat,2)~=raw_data.num_data_pnts),
                error( sprintf('Format error in ASCII AC Analysis LTspice file "%s" ... Incorrect number of data values read', filename ));
            end
            raw_data.freq_vect = raw_data.variable_mat(2,1:downsamp_N:end);
            raw_data.variable_mat = raw_data.variable_mat(3+selected_vars*2-1,1:downsamp_N:end) + j*raw_data.variable_mat(3+selected_vars*2,1:downsamp_N:end);
            
        else
            try fclose( fid );  catch end
            error( sprintf('Simulation type (%s) not currently supported', simulation_type ));
        end
        
        if downsamp_N==1,  raw_data.conversion_notes = 'Converted from ASCII format';
        else raw_data.conversion_notes = sprintf( 'Converted from ASCII format.  Downsampled from %.0f to %.0f points', NumPnts, NumPnts_DS );  end
        
    else
        try fclose( fid );  catch end
        error( sprintf('Format error in LTspice file "%s" ... Data type ID tag not found', filename ));
    end

    try fclose( fid );  catch end
    
    
    
    %Deal with potential compression in Transient Analysis simulations
    if strcmpi( simulation_type, '.tran' ) & (min(diff(raw_data.time_vect)) < 0.0),   %Check to see if the time vector is monotonically increasing.
        
        if downsamp_N~=1,  %If we have already downsampled then we can't uncompress.
            raw_data.time_vect = abs(raw_data.time_vect);            
            
        else
            %The binary file contains 2nd order compression ... use 2nd-order interpolation to add data points in the vicinity of negative time points

            t_vect = raw_data.time_vect;   %We will add in the offset later.
            neg_pnt_index = find( t_vect < 0.0 & [0,ones(1,length(t_vect)-1)] );
            t_vect = abs(t_vect);

            x1 = t_vect(neg_pnt_index-1);  x2 = t_vect(neg_pnt_index);  x3 = t_vect(neg_pnt_index+1);
            x_new = [(2*x1 + x2)/3;  (x1 + 2*x2)/3;  (2*x2 + x3)/3;  (x2 + 2*x3)/3];   %New sample points

            t_vect_big = NaN*zeros(6,length(t_vect));
            t_vect_big(1,:) = t_vect;
            t_vect_big(4,neg_pnt_index) = t_vect(neg_pnt_index);
            t_vect_big(1,neg_pnt_index) = NaN;
            t_vect_big([2 3 5 6],neg_pnt_index) = x_new;

            full_index = find(~isnan(t_vect_big));
            time_vect_new = t_vect_big(full_index).';   %This is the new time vector with the inserted points.
            t_vect_big([1,4],:) = NaN;
            nan_vect = isnan(t_vect_big(full_index));
            new_index = find( ~nan_vect );  %Index into time_vect_new indicating the new points only.
            old_index = find( nan_vect );
            clear t_vect t_vect_big full_index nan_vect;

            x1sqr = repmat( x1.^2, [4,1] );  x2sqr = repmat( x2.^2, [4,1] );  x3sqr = repmat( x3.^2, [4,1] );
            x1 = repmat( x1, [4,1] );  x2 = repmat( x2, [4,1] );  x3 = repmat( x3, [4,1] );
            denom = (x1sqr-x2sqr).*(x2-x3) - (x2sqr-x3sqr).*(x1-x2);
            r1 = (x_new.^2 - x1sqr)./denom;
            r2 = (x_new - x1)./denom;
            p1 = (x2-x3).*r1 - (x2sqr-x3sqr).*r2;
            p3 = (x1-x2).*r1 - (x1sqr-x2sqr).*r2;
            p2 = -p1 - p3;
            p1 = p1 + 1;
            clear x_new x1sqr x2sqr x3sqr x1 x2 x3 denom r1 r2;

            raw_data.variable_mat(:,end+1:length(time_vect_new)) = 0.0;   %Init the memory
            for k=1:size(raw_data.variable_mat,1),
                y_vect = raw_data.variable_mat(k,1:length(raw_data.time_vect));
                raw_data.variable_mat(k,old_index) = y_vect;
                y_new = repmat(y_vect(neg_pnt_index-1),[4,1]).*p1  +  repmat(y_vect(neg_pnt_index),[4,1]).*p2  +  repmat(y_vect(neg_pnt_index+1),[4,1]).*p3;
                raw_data.variable_mat(k,new_index) = y_new(:).';
            end
            raw_data.time_vect = time_vect_new;
            clear time_vect_new y_vect y_new new_index old_index neg_pnt_index p1 p2 p3;

            raw_data.conversion_notes = sprintf( 'Converted from Binary format with 2nd Order compression.  Upsampled waveforms from %.0f to %.0f points', ...
                raw_data.num_data_pnts, length(raw_data.time_vect) );
            raw_data.num_data_pnts = length(raw_data.time_vect);
        end
        
    end
    
    if isfield( raw_data, 'time_vect' ),      raw_data.time_vect = raw_data.time_vect + general_offset;
    elseif isfield( raw_data, 'freq_vect' ),  raw_data.freq_vect = raw_data.freq_vect + general_offset;
    end