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

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.

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.