Can REGEXP be utilized in searching M x N matrix values?

The following text file contains numerous lines of data, including a 9 x 9 state covariance matrix:

State Time: 13267.000

State Date: 20120101

State Covariance:

          -0.11000000000000e-01 0.12000000000000e01 0.13000000000000e00 0.14000000000000e-01 0.15000000000000e-01 0.16000000000000e-01 0.17000000000000e-01 0.18000000000000e-01 0.19000000000000e-01
          0.21000000000000e-01 0.22000000000000e-01 0.23000000000000e-01 0.24000000000000e-01 0.25000000000000e-01 0.26000000000000e-01 0.27000000000000e-01 0.28000000000000e-01 0.29000000000000e-01
          0.31000000000000e-01 0.32000000000000e-01 0.33000000000000e-01 0.34000000000000e-01 0.35000000000000e-01 0.36000000000000e-01 0.37000000000000e-01 0.38000000000000e-01 0.39000000000000e-01
          0.41000000000000e-01 0.42000000000000e-01 0.43000000000000e-01 0.44000000000000e-01 0.45000000000000e-01 0.46000000000000e-01 0.47000000000000e-01 0.48000000000000e-01 0.49000000000000e-01
          0.51000000000000e-01 0.52000000000000e-01 0.53000000000000e-01 0.54000000000000e-01 0.55000000000000e-01 0.56000000000000e-01 0.57000000000000e-01 0.58000000000000e-01 0.59000000000000e-01
          0.61000000000000e-01 0.62000000000000e-01 0.63000000000000e-01 0.64000000000000e-01 0.65000000000000e-01 0.66000000000000e-01 0.67000000000000e-01 0.68000000000000e-01 0.69000000000000e-01
          0.71000000000000e-01 0.72000000000000e-01 0.73000000000000e-01 0.74000000000000e-01 0.75000000000000e-01 0.76000000000000e-01 0.77000000000000e-01 0.78000000000000e-01 0.79000000000000e-01
          0.81000000000000e-01 0.82000000000000e-01 0.83000000000000e-01 0.84000000000000e-01 0.85000000000000e-01 0.86000000000000e-01 0.87000000000000e-01 0.88000000000000e-01 0.89000000000000e-01
          0.91000000000000e-01 0.92000000000000e-01 0.93000000000000e-01 0.94000000000000e-01 0.95000000000000e-01 0.96000000000000e-01 0.97000000000000e-01 0.98000000000000e-01 0.99000000000000e-01

State Confidence: -1

I’m looking for a method to extract the 9x9 matrix values and save them for processing. I’m using the following code to extract the matrix:

% Prompt user for file to open
[fn,pn]=uigetfile('*.*','Select SNER Text File');
filename = fullfile(pn,fn);

% Read in entire file
buffer = fileread(filename);

% Parse out the Matrix (9x9) and store as SCD
pattern = '*?State Covariance:\s+([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+).([-?\d\.]+\w[-?\d\.]+)';
tokens4 = regexp(buffer, pattern, 'tokens');
SCD = reshape(str2double([tokens4{:}]), 9, []).';

This code, even with the long search pattern, does a great job in finding all nine matrix values in row 1 and saving them. But how would I go about extracting the remaining rows? Is there a more practical approach than using the REGEXP for this task?

Answer 1

[per isakson]

If the entire file fits in memory I recommend that you

• read the entire file as character
• extracts the "numerical chunks" (an appropriate job for regexp)
• convert to double

Try

    >> M = cssm();
    >> whos M
      Name      Size             Bytes  Class     Attributes

      M         9x9x3             1944  double

where cssm.m is

    function  M = cssm()
        xpr = '(?<=State Covariance:)[ 0-9\.\+\-e\r\n]++(?=State Confidence:)';
        str = fileread( 'cssm.txt' );
        cac = regexp( str, xpr, 'match' );
        M = nan( 9, 9, length( cac ) ); 
        for jj = 1 : length( cac )
           M( :, :, jj ) = str2num( cac{jj} ); 
        end
    end

and cssm.txt contains copy&paste three times of the data of your question

.

See Lookahead Assertions in Regular Expressions

.

"contains numerous lines of data" made me think your file contains many matrices. However, the code above shall work with one.

Answer 2

[dpb]

Surely it could be made to work, but is way overkill for the job...

>> c=textread('brad.txt','%f',81,'headerlines',6);
>> c=reshape(c,9,[]).'
c =
 -0.0110    1.2000    0.1300    0.0140    0.0150    0.0160    0.0170    0.0180    0.0190
  0.0210    0.0220    0.0230    0.0240    0.0250    0.0260    0.0270    0.0280    0.0290
  ...
  0.0810    0.0820    0.0830    0.0840    0.0850    0.0860    0.0870    0.0880    0.0890
  0.0910    0.0920    0.0930    0.0940    0.0950    0.0960    0.0970    0.0980    0.0990
>>

The above does presume you know the size a priori...if not need a little more work to not run off into the weeds at the end...