Binary Data Processing in MATLAB: Using memmapfile

If you have a binary file of data that is split periodically by a keyword into segments, you might wonder how to efficiently read and process the concatenated binary data as if using fread on a binary file. While you could write the entire string to a new file and then read it, there’s a more memory-efficient approach: using the memmapfile function in MATLAB.

With memmapfile, you can create a memory-mapped file object that allows you to treat a portion of memory as a binary file. Here’s how:

Convert your concatenated binary string into a uint8 array.
Create a memory-mapped file object using memmapfile with the Format specified as 'uint8'.
Write the binary data to the memory-mapped file object.
Read segments from the memory-mapped file as if they were binary files.

Efficient Binary Data Processing in MATLAB: Using memmapfile for Memory-Mapped Reading

Here’s an example code snippet that demonstrates this process:

// Sample concatenated binary string (replace this with your actual data)
concatenated_binary_string = '...'; // Your concatenated binary string here

// Convert binary string to uint8 array
binary_data = uint8(concatenated_binary_string);

// Define the size of each segment and the total number of segments
segment_size = 1000; // Example segment size
total_segments = numel(binary_data) / segment_size;

// Create a memory-mapped file object
mmf = memmapfile('temp.dat', 'Writable', true, 'Format', 'uint8');

// Write the binary data to the memory-mapped file
mmf.Data = binary_data;

// Read a segment from the memory-mapped file
segment_number = 1; // Example segment number
start_index = (segment_number - 1) * segment_size + 1;
end_index = start_index + segment_size - 1;
segment = mmf.Data(start_index:end_index);

// Use the segment as needed
disp(segment);

// Clean up: Close the memory-mapped file
clear mmf;

Conclusion – Efficient Binary Data Processing in MATLAB: Using memmapfile for Memory-Mapped Reading

By using the memmapfile function, you can efficiently work with concatenated binary data as if it were a binary file, without loading the entire dataset into memory. This approach can be particularly useful when dealing with large datasets and memory constraints.

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