MATLAB (Matrix Laboratory) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
MATLAB Anti-Patterns Overview
MATLAB, despite being a powerful platform for numerical computing and data analysis, has several common anti-patterns that can lead to performance issues, maintainability problems, and bugs. Here are the most important anti-patterns to avoid when writing MATLAB code.
Growing Arrays in Loops
Avoid growing arrays incrementally in loops. MATLAB needs to reallocate memory and copy the entire array each time it grows, which is very inefficient. Instead, pre-allocate arrays to their final size before filling them, or use vectorized operations to avoid loops entirely.
Using Loops Instead of Vectorization
Avoid using loops for element-wise operations on arrays. MATLAB is optimized for vectorized operations, which are much faster and more concise. Use MATLAB’s built-in vectorized functions and operators whenever possible.
Using eval and str2func
Avoid using eval
and str2func
with dynamically constructed strings. These functions can execute arbitrary code, making your program vulnerable to code injection if the strings come from external sources. They also make code harder to debug and maintain. Use arrays, cell arrays, or containers.Map to store multiple values or function handles instead.
Using Global Variables
Avoid using global variables. They create hidden dependencies between functions, make code harder to understand and test, and can lead to unexpected behavior. Instead, pass variables explicitly as function parameters and return values.
Not Using Functions for Code Organization
Avoid writing long scripts without breaking them into functions. This makes code harder to understand, test, and reuse. Instead, organize your code into functions with clear responsibilities, and use a main script or function to coordinate their execution.
Using clear all/close all/clc
Avoid using clear all
, close all
, and clc
in scripts and functions, especially those that might be called by other scripts or functions. These commands affect the entire MATLAB environment, not just your script’s scope, which can lead to unexpected behavior and make debugging more difficult. Instead, be specific about what variables to clear and what figures to close.
Not Handling Errors Properly
Always handle errors properly in your code. Use try-catch blocks to catch and handle exceptions, check return values from functions that might fail, and provide meaningful error messages. This makes your code more robust and easier to debug.
Using i and j as Variable Names
Avoid using i
and j
as variable names, especially for loop indices. In MATLAB, i
and j
are predefined as the imaginary unit (√-1), and overwriting them can lead to unexpected behavior in complex arithmetic. Use ii
, jj
, or other names for loop indices, and consider using 1i
instead of i
for the imaginary unit.
Using Magic Numbers
Avoid using magic numbers (hardcoded numeric literals) in your code. Instead, define constants with meaningful names at the beginning of your script or function. This makes your code more readable, maintainable, and less prone to errors. For mathematical constants like π, use MATLAB’s built-in constants (e.g., pi
).
Ignoring Code Vectorization Opportunities
Don’t miss opportunities for vectorization. MATLAB provides many ways to avoid loops, such as logical indexing, vectorized functions, and array operations. Look for patterns in your code where you’re performing the same operation on many elements, and try to vectorize them.
Not Using Logical Indexing
Use logical indexing to filter arrays instead of loops. Logical indexing is a powerful MATLAB feature that allows you to select elements based on conditions. It’s much faster and more concise than using loops.
Inefficient File I/O
Avoid inefficient file I/O operations, such as reading a file line by line in a loop. Instead, use functions like textscan
, readtable
, readmatrix
, or readlines
(in newer versions) to read the entire file at once. These functions are optimized for performance and make your code more concise.
Not Using Built-in Functions
Don’t reinvent the wheel by implementing functionality that already exists in MATLAB. MATLAB provides a vast library of built-in functions that are optimized for performance. Familiarize yourself with common functions for statistics, linear algebra, signal processing, and other domains relevant to your work.
Using Nested Loops for Matrix Operations
Avoid using nested loops for matrix operations like multiplication, transposition, or inversion. MATLAB’s built-in matrix operations are highly optimized and much faster. Use operators like *
, '
, and functions like inv
instead of implementing these operations manually.
Not Using Appropriate Data Structures
Use appropriate data structures for your data. For heterogeneous data with named fields, use structures or tables instead of parallel arrays. For large, homogeneous numerical data, use arrays. For sparse matrices, use sparse arrays. Choosing the right data structure can make your code more readable and efficient.
Not Using Cell Arrays Appropriately
Use cell arrays appropriately. Cell arrays are useful for storing heterogeneous data or data of varying sizes, like strings of different lengths. However, they’re less efficient than regular arrays for homogeneous numeric data. Use regular arrays for numeric data and cell arrays for mixed data types or varying-size data.
Not Using Function Handles
Use function handles to pass functions as arguments to other functions. This is more flexible and efficient than passing function names as strings and using conditional statements. Function handles also enable you to use anonymous functions for simple operations.
Not Using Profiler for Performance Optimization
Don’t guess where performance bottlenecks are in your code. Use the MATLAB Profiler to identify which parts of your code are taking the most time. The Profiler provides detailed information about execution time for each line and function call, helping you focus your optimization efforts where they’ll have the most impact.
Not Using Appropriate Comments and Documentation
Use appropriate comments and documentation in your code. Comments should explain why the code does something, not what it does (which should be clear from the code itself). Use MATLAB’s help comment format for functions to provide documentation that can be accessed with the help
command.
Not Using Meaningful Variable Names
Use meaningful variable and function names that clearly indicate their purpose. This makes your code more readable and easier to maintain. Avoid single-letter variable names except for very common conventions (like i
and j
for loop indices, but even then, be careful as mentioned earlier).