Read Binary Data In Matlab: Fread Function Guide

MATLAB supports various methods for reading binary data using functions like fread, which interprets data according to a specified precision. Reading binary data involves dealing with file access and data type considerations, especially when handling data written in a different programming language like C, which organizes data in memory differently than MATLAB. Understanding these nuances ensures accurate data representation and manipulation within the MATLAB environment.

Ever felt like you’re staring at a jumbled mess of 1s and 0s? That, my friends, is binary data, and it’s everywhere! Think of your favorite cat video, the chart detailing the latest trends, or even your favorite songs. All of them can be read as binary data.

What is Binary Data Anyway?

In essence, binary data is information represented using only two symbols: 0 and 1. It’s the language computers speak, and it’s used to store all kinds of information, from images and audio to scientific measurements and sensor readings. You’ll find it lurking in image files (.jpg, .png), audio files (.wav, .mp3), scientific data files, and all sorts of other places where information needs to be stored efficiently. Think of it as the digital DNA of nearly everything we interact with on our devices.

Why Should You Care About Reading Binary Data?

Imagine having a treasure chest but not knowing how to open it. That’s what it’s like having binary data without the tools to read and interpret it. Being able to decode this data unlocks a world of possibilities. You can analyze images, process audio signals, and extract valuable insights from scientific datasets. You can even build your own custom tools and applications to work with specific file formats. In short, understanding binary data gives you the power to manipulate and understand the raw information that fuels our digital world. It allows you to do cool stuff like analyze data, reverse engineer applications, or just satisfy your curiosity about how things work under the hood.

MATLAB to the Rescue!

Now, you might be thinking, “Binary data sounds complicated. Do I need to become a computer scientist to understand it?” Thankfully, no! MATLAB is here to make your life easier.

MATLAB, with its powerful built-in functions and intuitive environment, is an excellent tool for handling binary data. It provides functions specifically designed for reading, writing, and manipulating binary files, allowing you to extract the information you need without getting bogged down in the nitty-gritty details of low-level programming.

Throughout this post, we’ll be your guide, and we’ll explore the following essential MATLAB functions:

• fopen: Opens binary files.
• fread: Reads data from binary files.
• fclose: Closes binary files.
• fseek: Moves the file pointer to specific locations.
• ftell: Tells you the current position of the file pointer.
• feof: Checks for the end of the file.

With these tools in your arsenal, you’ll be able to confidently tackle any binary data challenge that comes your way. Let’s dive in and decode the secrets hidden within those 1s and 0s!

fopen: Opening the Gateway to Binary Files

Okay, imagine you’re about to embark on a treasure hunt! The treasure? Raw, unfiltered data! And the map leading to it? A binary file! But you can’t just grab the treasure; you need to open the map first. That’s where fopen comes in – it’s the key to unlocking those binary files in MATLAB. Think of it as the handshake you need to perform before diving into the data.

• Syntax and File Paths: fopen‘s syntax is pretty straightforward: fileID = fopen('your_file.bin', 'mode'). See that 'your_file.bin'? That’s the file path. It tells MATLAB exactly where to find your precious data stash. Make sure you get it right, or you’ll be wandering around aimlessly! If the file isn’t in MATLAB’s current folder, you’ll need to provide the full path (e.g., 'C:\Data\Important\your_file.bin').

• File Modes: Choose Wisely! The 'mode' part is super important. It tells MATLAB what you plan to do with the file. Want to read the data? Use 'rb' (read binary). Need to write new data? 'wb' (write binary) is your friend. There are other modes too, like 'ab' (append binary) for adding data to the end of an existing file. Choosing the wrong mode can lead to data loss or errors, so double-check!

• File Identifiers: Your File’s Name Tag: fopen returns a File Identifier (File ID), which is simply a number. MATLAB uses this number to keep track of all the open files. It’s like giving each file a unique name tag so it doesn’t get mixed up with the others. If fopen fails, it’ll return -1.

• Examples:

  % Opening for reading
  fileID = fopen('my_data.bin', 'rb');
  
  % Opening for writing (careful, this will overwrite the file if it exists!)
  fileID = fopen('new_data.bin', 'wb');
  

• Best Practice: Always, always check if fopen returns a valid File ID before proceeding. If fileID is -1, something went wrong (file not found, wrong permissions, etc.). Handle this gracefully with an if statement to avoid crashes later on.

fread: Extracting Data from Binary Files

Alright, you’ve unlocked the file with fopen. Now comes the fun part: extracting the treasure – the actual data! fread is your shovel and pickaxe in this scenario. It’s how you dig out the information you need from the binary file.

• Understanding the Parameters: fread has a few key ingredients: data = fread(fileID, size, precision, skip). Let’s break it down:

  • fileID: The File ID you got from fopen.
  • size: How much data you want to read. This can be a number (e.g., 10 for 10 elements), 'inf' to read until the end of the file, or [rows, columns] to read into a matrix.
  • precision: Crucial! This tells MATLAB what type of data you’re reading (e.g., integers, floating-point numbers). More on this below.
  • skip: The number of bytes to skip after reading each value. Useful for files with interleaved data.

• Data Types: This is where things get interesting. Binary files don’t inherently know what kind of data they contain; it’s all just bytes. You have to tell fread how to interpret those bytes. Here are some common options:

  • 'int8', 'uint8': 8-bit signed/unsigned integers
  • 'int16', 'uint16': 16-bit signed/unsigned integers
  • 'int32', 'uint32': 32-bit signed/unsigned integers
  • 'int64', 'uint64': 64-bit signed/unsigned integers
  • 'single': Single-precision floating-point numbers (32 bits)
  • 'double': Double-precision floating-point numbers (64 bits)
  • 'char': Characters (usually 8 bits)
  • 'logical': Logical values (typically 1 bit, but often stored as 8 bits)

• Examples:

  % Read 10 integers from the file
  data = fread(fileID, 10, 'int32');
  
  % Read the entire file as doubles
  data = fread(fileID, 'inf', 'double');
  
  % Read a 5x5 matrix of unsigned 16-bit integers
  data = fread(fileID, [5, 5], 'uint16');
  

• Best Practice: Always specify the 'precision' argument. If you don’t, MATLAB will assume 'uint8', which might be wrong and lead to completely garbled data.

fclose: Closing Files and Freeing Resources

You’ve plundered the binary file, extracted your data, and now… it’s time to clean up! fclose is the broom and dustpan of binary file I/O. It closes the file, freeing up system resources and preventing potential problems.

• Why Close Files?: Leaving files open is like leaving the water running. It hogs resources and can lead to data corruption if your program crashes unexpectedly. Closing files is just good housekeeping.

• Syntax: The syntax is simple: fclose(fileID). You just pass in the File ID that you got from fopen.

• Closing All Files: If you have multiple files open, you can close them all at once with fclose('all'). This is useful for cleaning up at the end of your script.

• Best Practice: Use a try-catch block to ensure that files are closed even if errors occur during processing. This is crucial for robust code.

  fileID = fopen('my_data.bin', 'rb');
  try
      % Do some processing
      data = fread(fileID, 'inf', 'double');
      % ... more operations ...
  catch ME
      % Handle the error
      disp(['Error: ' ME.message]);
  end
  fclose(fileID); % Always close the file!
  

fseek and ftell: Navigating Through Binary Data

Sometimes, you don’t need to read the entire binary file. You might only be interested in specific sections. That’s where fseek and ftell come in. They’re your GPS and odometer for navigating the binary landscape.

• fseek: Jumping Around fseek lets you move the file pointer (the “cursor” that indicates where you’ll read from next) to a specific location within the file. The syntax is: fseek(fileID, offset, origin).

  • fileID: Your trusty File ID.
  • offset: The number of bytes to move. Positive values move forward, negative values move backward.
  • origin: Where the offset is relative to. There are three options:
    • 'bof' (beginning of file): Move relative to the start of the file.
    • 'cof' (current position): Move relative to the current position.
    • 'eof' (end of file): Move relative to the end of the file.

• ftell: Where Am I? ftell tells you the current position of the file pointer, in bytes, relative to the beginning of the file. The syntax is: position = ftell(fileID).

• Examples:

  % Move to the 100th byte from the beginning of the file
  fseek(fileID, 100, 'bof');
  
  % Move 20 bytes forward from the current position
  fseek(fileID, 20, 'cof');
  
  % Move 50 bytes backward from the end of the file
  fseek(fileID, -50, 'eof');
  
  % Get the current file position
  currentPosition = ftell(fileID);
  

• Best Practice: Use ftell to verify the position after using fseek. This helps ensure that you’ve moved to the correct location, especially when dealing with complex file structures.

feof: Detecting the End of the Line (or File)

Ever been reading a book and wondered, “Am I almost done?” feof answers that question for binary files. It tells you whether you’ve reached the end-of-file (EOF) marker.

• How it Works: feof(fileID) returns a logical value: true if the end of the file has been reached, false otherwise.

• Using feof in a Loop: feof is particularly useful in loops when you’re reading variable-length data or don’t know the exact file size beforehand.

  while ~feof(fileID)
      data = fread(fileID, 1, 'int32');
      % Process the data
      disp(data);
  end
  

• Why is it Important?: Using feof prevents read errors that can occur when you try to read beyond the end of the file. It ensures data integrity and helps you avoid unexpected behavior.

• Best Practice: Use feof in conjunction with fread to handle variable-length data. Read until feof returns true, then stop.

Understanding Binary File Structures: Decoding the Blueprint

Alright, so you’ve got the basic tools down – fopen, fread, fclose, the whole shebang. But now comes the fun part: actually understanding what you’re digging out of those binary files! Think of it like this: you’ve got a treasure chest (the binary file), and now you need the map (the file structure) to find the gold (your data). This section is all about becoming Indiana Jones of binary data!

Headers: Reading and Interpreting Metadata

First up, Headers. Imagine the header as the table of contents or the title page of a book. It’s usually at the very beginning of the file and contains all sorts of juicy information about the data that follows. We’re talking metadata like:

• What kind of data is in the file?
• How is the data organized?
• What version of the file format is it?

Basically, it’s the file’s way of saying, “Hey, before you go rummaging around in here, here’s what you need to know!”.

Now, how do we actually read this? With our trusty fread, of course! You’ll need to know the size and data types of the header fields. For example:

fileID = fopen('my_binary_file.dat', 'rb');

% Assuming the first 4 bytes represent an integer indicating the version number
version_number = fread(fileID, 1, 'int32');

% Assuming the next 8 bytes represent a double indicating a creation timestamp
timestamp = fread(fileID, 1, 'double');

% ... read other header information ...

Common header formats? Well, there’s no one-size-fits-all, but many formats use a combination of integer values, character strings, and flags. Learning to recognize these common elements is key to deciphering different file types.

Data Blocks: Extracting the Numerical Information

Okay, you’ve read the title page. Now, it’s time to get to the actual story – the Data Blocks! This is where the real data lives, whether it’s sensor readings, image pixels, audio samples, or whatever else you’re working with.

Reading data blocks is all about using fread strategically. You’ll need to know:

• What type of data are you expecting (int16, double, char, etc.)?
• How many elements do you want to read at once (a single value, an array, a matrix)?

Here’s a taste:

% Assuming the data block contains a 10x10 matrix of doubles
data_matrix = fread(fileID, [10, 10], 'double');

Easy peasy, right? The key is to understand the structure of the data block – is it a simple array, a matrix, or something more complex?

Endianness (Byte Ordering): Tackling Big-Endian vs. Little-Endian

Endianness is where things get a little weird, but don’t worry, we’ll break it down. Imagine you have the number 1234. In memory, that number is stored as a sequence of bytes. The question is, which byte comes first – the most significant byte (1) or the least significant byte (4)?

• Big-Endian: The most significant byte comes first.
• Little-Endian: The least significant byte comes first.

Why does this matter? Because if you read a file created on a big-endian system on a little-endian system (or vice versa), the numbers will be wrong!

Luckily, MATLAB has a solution: swapbytes. This function flips the byte order of your data, correcting the problem.

% Assuming we read an integer from a big-endian file
value = fread(fileID, 1, 'int32');

% Swap the bytes to correct the endianness
value = swapbytes(value);

How do you know if you need to use swapbytes? Look for telltale signs like numbers that are completely out of range or data that just doesn’t make sense. You might also find clues in the file header (if the format is well-defined).

Record Structures: Decoding Organized Data

Finally, let’s talk about Record Structures. Often, binary files aren’t just a jumble of numbers; they’re organized into records. Think of it like a spreadsheet, where each row is a record containing related pieces of information.

These records can be:

• Fixed-Length: Each record has the same size.
• Variable-Length: Records can have different sizes, often with a length indicator at the beginning.

To read record structures, you’ll often use a loop to iterate through the records, reading each field one by one. Here’s a simplified example:

while ~feof(fileID)
    % Assuming each record contains an ID (int32) and a value (double)
    record_id = fread(fileID, 1, 'int32');
    record_value = fread(fileID, 1, 'double');

    % Do something with the data
    disp(['Record ID: ' num2str(record_id) ', Value: ' num2str(record_value)]);
end

The trick here is to carefully analyze the file format to understand how the records are structured. Look for delimiters or length indicators that help you separate the records.

With these insights, you’re well on your way to decoding the mysteries of binary file formats.

Advanced Techniques: Data Type Conversion and Manipulation

Ever feel like you’re staring at a jumbled mess of raw bytes and wishing you had a secret decoder ring? Well, in MATLAB, typecast is pretty darn close to that ring! It’s your go-to function when you need to reinterpret those bytes without actually changing the underlying data itself. Think of it like this: you’re looking at a LEGO structure, and typecast lets you see it as a spaceship instead of a house, all without taking the bricks apart.

typecast: Reinterpreting Raw Bytes

So, how does this magic work? Let’s break it down:

• What it does: typecast takes an array of data (usually bytes) and recasts it as a different data type. The key thing to remember is that the total number of bytes remains the same. It’s like fitting the same amount of playdough into a different mold.

• Bytes to Numbers (and Back Again!): Imagine you’ve read a sequence of bytes from a binary file, and you know it represents a floating-point number. Instead of writing complex code to manually piece together the bits, you can simply use typecast to transform those bytes directly into a single-precision or double-precision number.

  byteData = uint8([64 153 153 153]); % Example: Represents single(pi)
  floatValue = typecast(byteData, 'single'); % Convert to single-precision float
  disp(floatValue); % Output: 3.1416
  

  In this example, we’re taking a uint8 array representing the bytes of pi and turning it into its floating-point equivalent.

• Reinterpreting Data: Want to see those same bytes as an integer instead? No problem! typecast lets you peek at the data from a completely different perspective. This can be incredibly handy for understanding how data is structured and encoded.

  byteData = uint8([64 153 153 153]); % Example: Represents single(pi)
  intValue = typecast(byteData, 'int32'); % Convert to a 32-bit integer
  disp(intValue); % Output: 1099919041 (the integer representation of those bytes)
  

  Here, the same byte array is now viewed as an integer, revealing the underlying bit pattern in a different light. Pretty neat, right?

In essence, typecast provides a powerful way to manipulate binary data at a low level, giving you the flexibility to interpret bytes in various ways depending on your needs. Just remember to keep an eye on the size and structure of your data to avoid any unexpected results. It’s a bit like being a data whisperer, understanding the hidden language of bytes!

Error Handling: Safeguarding Your Data – Because Nobody Likes a Data Disaster!

Let’s face it, dealing with binary data can sometimes feel like navigating a minefield. One wrong step, and BOOM, your program crashes, your data gets corrupted, or worse, your cat starts judging you. Okay, maybe not the cat part (unless your cat is exceptionally tech-savvy). But seriously, error handling is crucial when you’re wrangling binary files. Think of it as wearing a hard hat and safety goggles – it’s not the most glamorous part of the job, but it can save you from a serious headache.

So, why all the fuss about error handling? Well, binary files are, by their nature, unforgiving. If your code assumes a certain file structure or data type and the reality doesn’t match, things can go south quickly. A robust error handling strategy acts as your early warning system, flagging potential problems before they turn into full-blown disasters.

Checking Return Values: Your First Line of Defense

MATLAB functions like `fopen` and `fread` are like diligent workers – they’ll try their best to do what you ask. But sometimes, things don’t go as planned. That’s why they often provide return values that tell you whether they succeeded or encountered a problem.

• `fopen`: Did the Door Open?

  `fopen` is like the doorkeeper to your binary file. It tries to open the file, and if it succeeds, it hands you a shiny File ID. If it fails (maybe the file doesn’t exist, you don’t have permission, or something else went wrong), it returns a negative value (usually -1). Always check this!

  fileID = fopen('my_binary_file.dat', 'rb');
  if fileID < 0
      error('Could not open file!');
  end
  

• `fread`: Did You Get the Goods?

  `fread` attempts to read data from the file. The return value tells you how many elements it successfully read. If this number is less than what you expected, something might be amiss (maybe you reached the end of the file unexpectedly, or there was an error).

  [data, count] = fread(fileID, 10, 'int32');
  if count < 10
      warning('Could not read all expected elements.');
  end
  

`ferror`: Digging Deeper into the Problem

Sometimes, just knowing that an error occurred isn’t enough. You need to know why. That’s where `ferror` comes in. It’s like a detective that investigates the scene of the crime and gives you a more detailed report.

`ferror(fileID)` returns an error message associated with the file. It helps you pinpoint the cause of the problem. You can use this to provide more informative error messages to the user or to take specific actions based on the type of error.

`try-catch` Blocks: Your Safety Net

`try-catch` blocks are a cornerstone of robust error handling. They allow you to gracefully handle exceptions (errors that occur during the execution of your code). The idea is simple: you put the code that might cause an error inside the `try` block, and if an error occurs, the code inside the `catch` block is executed.

A common and important use is to ensure that files are always closed, even if an error occurs. Failing to close files can lead to data corruption or resource leaks.

fileID = -1; % Initialize fileID
try
    fileID = fopen('my_binary_file.dat', 'rb');
    if fileID < 0
        error('Could not open file!');
    end

    data = fread(fileID, 10, 'int32');

catch ME % ME is a MATLAB exception object

    disp(['An error occurred: ' ME.message]); % Display error message

    % Optionally perform other error handling tasks like logging

    rethrow(ME) % Re-throw the exception to stop execution

end

• Best Practice: Comprehensive Error Handling is a Must

  By implementing comprehensive error handling, you significantly improve the resilience and reliability of your MATLAB code when dealing with binary data.

The Takeaway

Don’t be lazy with error handling! A few extra lines of code can save you hours of debugging and prevent data disasters. Check return values, use `ferror` to investigate problems, and wrap your code in `try-catch` blocks to handle exceptions gracefully. Your future self (and your cat) will thank you.

Practical Examples: Let’s Get Our Hands Dirty!

Alright, enough theory! Let’s roll up our sleeves and dive into some real-world scenarios. These examples will show you how to put those MATLAB functions to work and conquer binary data like a pro.

Example 1: Peeking Inside a Simple Binary File – Easy Peasy!

Imagine you’ve got a binary file just chock-full of integers. Nothing fancy, just a sequence of numbers waiting to be unleashed. Here’s how you can grab that data and give it some MATLAB love:

1. First, we’ll whip up a simple binary file. Let’s say it contains the numbers 1 through 5, stored as 16-bit integers. We would use this handy code:

%create data
data = int16([1 2 3 4 5]);

%open a file
fileID = fopen('simpleBinary.bin', 'wb');

% Write the data to the file
fwrite(fileID, data, 'int16');

% Close the file
fclose(fileID);

2. Next, it’s time to open the file in MATLAB. This is like knocking on the door and getting permission to come inside. Remember that fopen function? Time to use it!

fileID = fopen('simpleBinary.bin', 'rb'); % 'rb' for read binary

3. Now for the magic use fread to suck all the data from the file. The second argument dictates how many values from the binary to read. The third argument tell what to read the binary data as:

data = fread(fileID, 5, 'int16');

4. Finally, remember to close the file using fclose. It’s like saying goodbye and shutting the door behind you.

fclose(fileID);

That’s it! The data variable now holds the integers from your binary file. Easy peasy, lemon squeezy!

Example 2: Deciphering Header Information – Like a Binary Detective!

Some binary files have a header, a little section at the beginning that contains metadata (data about the data). This could include information about the file format, the data type, or the number of elements in the file. Think of it as the file’s business card.

1. So first we create a binary file. This binary file contains the dimensions of the data set. For instance, the number of rows. Then it contains that amount of data. To read this we need to know how many rows to load.

% Parameters for the data
numRows = 10;
numCols = 5;

% Create some sample data (a matrix of doubles)
data = rand(numRows, numCols);

% Open a file for writing in binary mode
fileID = fopen('headeredBinary.bin', 'wb');

% Write the number of rows and columns as header information
fwrite(fileID, numRows, 'int32'); % Write number of rows
fwrite(fileID, numCols, 'int32'); % Write number of columns

% Write the actual data to the file
fwrite(fileID, data, 'double'); % Write the data as doubles

% Close the file
fclose(fileID);

2. Now, the adventure begins! Open the file in MATLAB, ready to decode:

fileID = fopen('headeredBinary.bin', 'rb');

3. Use fread to grab the header information. We know we wrote row and columns to the binary file, so let’s read that data using fread.

numRows = fread(fileID, 1, 'int32'); % Read number of rows
numCols = fread(fileID, 1, 'int32'); % Read number of columns

4. Now that you know that number of columns and rows, you can read the data.

% Read the data
data = fread(fileID, [numRows, numCols], 'double');

5. And the final step close the binary file.

fclose(fileID);

With that info, you can now interpret the rest of the file!

Example 3: Conquering Endianness – Byte Order Blues Be Gone!

Ah, Endianness, the bane of many a programmer’s existence! This refers to the order in which bytes are stored in memory. Some systems use big-endian (most significant byte first), while others use little-endian (least significant byte first). If you’re reading a binary file created on a different system, you might need to swap the bytes to get the correct values.

1. To demonstrate this, imagine we have file that was created on a big-endian system. Let’s create a little binary file, to read the other binary file!

% Sample data (a single 32-bit integer)
data = int32(12345);

% Open a file for writing in binary mode
fileID = fopen('littleEndian.bin', 'wb');

% Write the data to the file
fwrite(fileID, data, 'int32');

% Close the file
fclose(fileID);

2. Open the little endian file using fopen.

fileID = fopen('littleEndian.bin', 'rb');

3. Read the data.

data = fread(fileID, 1, 'int32');

4. Detect Endianness using computer to see if the systems are different.

[~,maxSize,endian] = computer;

5. Swap the data!

% Check if swapping is needed (if the file's endianness is different from the system's endianness)
if strcmp(endian, 'L') % If the system is little-endian
    data = swapbytes(data); % Swap bytes to convert to big-endian
end

6. Close the file

fclose(fileID)

Detecting and correcting Endianness issues can be a bit tricky, but with swapbytes in your toolkit, you’ll be ready to handle cross-platform data like a seasoned traveler.

Organizing Read Data: Structuring Your Results

Alright, you’ve wrestled the raw binary data out of its file prison using fopen, fread, and the gang. Now what? You can’t just leave it lying around like a pile of unsorted LEGO bricks! The real magic happens when you take that jumble of bits and bytes and mold it into something meaningful within MATLAB. Think of it as turning raw ingredients into a gourmet meal! We’re going to be taking this data and turning it into a more appropriate data structure.

Arrays: The Workhorse of Sequential Data

When dealing with a stream of data – sensor readings, time-series data, or image pixels – arrays are your best friend. They’re like the trusty pickup truck of the data world: reliable, versatile, and can haul a lot of stuff. After using fread to pull the sequence of data in, directly assign it to an array. MATLAB makes this ridiculously easy. Just ensure you’ve specified the correct precision in fread to get the data types right.

For example, if you’re reading a series of temperature readings, each represented as a double-precision floating-point number, you would read this data into an array:

fileID = fopen('temperature_data.bin', 'rb');
temperatureReadings = fread(fileID, Inf, 'double'); % Read all doubles
fclose(fileID);

Now, temperatureReadings is a tidy array, ready for analysis, plotting, or whatever your scientific heart desires. Easy peasy.

Structs: Giving Your Data a Name and a Home

But what if your binary file contains structured data? Imagine a file holding information about a collection of books, where each book record has fields like title, author, publication year, and ISBN. Throwing all that into a single array would be like throwing all your clothes into one giant pile. Enter structs!

Structs allow you to create custom data structures with named fields. It’s like giving each piece of data its own little labeled drawer in a well-organized cabinet.

Here’s how you might approach this. First, read each book’s data from the file. Then, populate a struct with the extracted values. Repeat for each book and store all those structs into a single array!

fileID = fopen('books.bin', 'rb');

bookData = struct('title', {}, 'author', {}, 'year', {}, 'isbn', {}); % Pre-allocate
i = 1;

while ~feof(fileID)
    bookData(i).title = fread(fileID, 50, '*char')';  % Read 50 characters for the title
    bookData(i).author = fread(fileID, 50, '*char')'; % Read 50 characters for the author
    bookData(i).year = fread(fileID, 1, 'int32');    % Read the publication year
    bookData(i).isbn = fread(fileID, 20, '*char')';   % Read 20 characters for the ISBN
    i = i + 1;
end

fclose(fileID);

Warning: Reading character arrays like this can be dangerous, especially if you’re not 100% sure about file formatting.

Now you have a struct array, bookData, where you can easily access information about each book:

disp(bookData(1).title); % Displays the title of the first book
disp(bookData(1).author) % Displays the Author of the first book

Picking the Right Tool for the Job

Choosing between arrays and structs (or even combining them!) depends entirely on the structure of your binary data. If it’s a simple sequence, arrays are your go-to. If it’s a collection of related but distinct pieces of information, structs are the way to go. And, of course, you can always use both, creating arrays of structs or even structs containing arrays. The possibilities are endless!

The key is to think about the data you’re reading and how you’ll be using it later. A little planning upfront can save you a ton of headaches down the road. Now go forth and organize your data! Make it beautiful!

So, there you have it! Reading binary files in MATLAB might seem daunting at first, but with these tools and a little practice, you’ll be fluent in “binary-ese” in no time. Happy coding, and may your data always be properly interpreted!