Writing Reusable Utility Scripts

Why Write Reusable Utility Scripts

When you start automating tasks in MATLAB, it is tempting to write one long script that does everything in one go. This can work for very small problems, but it quickly becomes hard to maintain, hard to debug, and hard to adapt when requirements change. Reusable utility scripts give you a way to separate common tasks from one-off work, so you can call the same code from many different scripts or projects.

A reusable utility script is a script file that focuses on one clear job, and is written so that you can use it in more than one project with little or no change. Typical jobs include setting paths, loading a standard dataset, applying the same visualization style, or running the same analysis steps on different input files.

In this chapter the focus is on how to design and organize such scripts so that they are easy to reuse. Detailed comparisons with functions as reusable units belong to other chapters, so here you will mostly see how to make script-style utilities behave more predictably and be more convenient in everyday work.

Characteristics of a Reusable Script

A reusable script is usually small, focused, and predictable. It should do one type of task and do it consistently. For example, a script might always prepare the environment for a project, or always perform the same formatting of plots, or always clean a newly imported table in a standard way.

Predictability is important. If you run the same script multiple times, it should either give the same result, or it should change things in a clearly understood way. Scripts that make hidden changes in many variables, or rely on variables that must already exist in the workspace, are hard to reuse, because they are fragile and context dependent.

Good utility scripts are also easy to read. They include a short header comment that explains what they do, and they use clear section titles to divide steps. This readability makes it easier to decide whether a script is suitable for reuse in a new project.

Designing Scripts with Clear Inputs and Outputs

Scripts do not have formal input and output arguments like functions, but you can still design them as if they had. In practice, this means you decide in advance which workspace variables your script will read, and which variables it will produce or modify, and you document this at the top of the file.

Suppose you have a script that standardizes numeric data stored in a variable x. You can agree that before running the script, x must exist in the workspace, and that after running it, x_std will exist and contain the standardized values. At the top of the script, you then describe these expectations in comments. For example, you might write that the script requires x to be a column vector, and that it will create x_std only, without touching any other variables.

You should avoid reading or modifying variables that are not part of this clear contract. If you silently depend on many existing variables, any change in the workspace can break the script. If you silently overwrite many variables, you can easily lose important data.

Sometimes you can mimic input parameters with configuration variables that the user edits at the top of the script. For instance, you can define inputFolder, outputFolder, and filePattern in a configuration section, then keep the rest of the script unchanged. By treating these few variables as the input to the script, you make the behavior easy to control without editing the main logic.

Documentation and Headers for Utility Scripts

A reusable script should always begin with a descriptive comment block. This header is not only for other people, it is also for your future self. The header should briefly say what the script does, what it expects, what it creates or changes, and any important options a user may want to adjust.

A simple structure for a header is to start with a one line summary, then a short paragraph with details, then a list of required inputs and produced outputs described only in text. You can also include usage hints, for example how and when to run the script within a project.

Even a small header can be very helpful. The header reminds you that this file is meant to be a utility, not a one-off experiment, so you are more likely to keep it clean. It also makes the script searchable, since you can look for keywords like "export", "preprocess", or "batch" inside your utility folder when you forget which script does what.

MATLAB will not show script headers in the same way as function help text, but clear comments still work as in-editor documentation. They also show up when you use search features in the editor or in your operating system.

Parameterizing Behavior with Configuration Sections

To make a script reusable across different inputs, avoid hard coding values that are likely to change. Instead, move such values into a clearly marked configuration section at the top of the file. This section can define folder paths, file patterns, thresholds, flags that turn features on or off, and similar parameters.

For example, rather than scatter specific folder names inside the script, define rootFolder, inputSubfolder, and outputSubfolder at the top, and build full paths from them later. Rather than embed a numeric threshold directly in a calculation, define outlierThreshold or sampleRate at the top and refer to these variables in the calculations.

This approach has several advantages. When you want to reuse the script for a new dataset, you usually have to change only a small number of lines in the configuration section. If you accidentally change logic deeper in the script while adjusting parameters, you may introduce errors, so it is safer to treat the configuration section as the only place that should be edited for customization.

You can also separate configuration for development and configuration for production. For example, include a variable like isTestRun and use it inside the script to decide how many files to process. When you move the script to real data, you only change isTestRun in the configuration section, and the rest of the code stays identical.

Avoiding Unwanted Side Effects on the Workspace

Because scripts run in the base workspace, any variables they create stay there after the script ends. This is convenient when you are exploring interactively, but a reusable utility script should try to limit the number of variables it leaves behind. If you fill the workspace with many temporary intermediate results, they may conflict with variables from other parts of your project.

To improve reusability, first decide which variables are part of the intended output, and which ones are only intermediate steps. For intermediate variables, choose names that clearly mark them as internal, or clean them up at the end of the script with a clear statement. For example, you may create a temporary list of filenames, use it to do batch processing, then remove it so that only the final result remains.

It is also wise to avoid using very generic names as outputs. If your script always creates a variable called data or result, it becomes easy to accidentally overwrite an existing variable with the same name. Consider adding a short prefix or suffix that relates to the script purpose, such as imgFiles, statsTable, or summaryResult, so that clashes are less likely across different utilities.

Another aspect of side effects is random number generation and changes to global state like the current folder. If a script uses random numbers, you can set the random seed at the top explicitly, or document that the script will affect the random number generator. If a script changes the current folder, consider returning to the original folder at the end, so that other scripts that rely on the current folder are not affected.

Naming Conventions and File Organization

Reusable scripts become much more helpful if you can find them easily and recognize their purpose from the file name. A clear naming convention gives structure to your growing collection of utilities. You can pick a simple and consistent pattern, such as using verbs at the beginning of names that describe what the script does.

For example, you might name scripts setup_project, batch_process_signals, export_plots_pdf, or cleanup_imported_data. Starting with an action verb suggests that you are meant to run the script directly. Including a short noun that describes the main data or operation helps distinguish similar utilities.

It is also a good idea to group reusable scripts in a dedicated folder, separate from one-off experiments. Many people create a utils or scripts subfolder inside each project, or even a shared utility folder across projects. By putting general purpose scripts in such a place, and adding that folder to the MATLAB path, you can reuse the same code in many different workspaces.

Within a utilities folder, you can reflect broad categories in subfolders, for instance separate folders for importing data, cleaning data, plotting, and exporting. This organization makes it easier to browse and avoid accidental duplication of similar scripts.

You should also try to keep one main idea per script. If you notice that one script starts to do several unrelated tasks, consider splitting it into smaller scripts or into functions. This separation makes the naming clearer and improves long term maintainability.

Creating Project Setup and Initialization Scripts

One very common type of reusable utility script is the project setup or initialization script. This is a script that you run when you start working on a project, and it prepares your MATLAB environment for that project. Typical tasks include setting the current folder, adding project subfolders to the path, defining common configuration variables, and possibly loading small reference data that you use often.

A good setup script can significantly reduce friction. Instead of manually navigating to the project folder and adding paths every time you reopen MATLAB, you simply run the script and are ready to work. The same script can be reused on a different computer or by a collaborator, as long as the folder structure is similar.

To keep a setup script reusable, avoid including personal paths that only exist on your machine. Instead, base file locations on the project root folder and relative paths. You can use functions that find the folder of the script itself, then build other paths from there. Then, as long as the whole project folder is moved together, the script continues to work.

It can also be useful to have a separate teardown script that removes project-specific paths or clears particular variables when you are done. This is less common, but in some environments it helps keep your MATLAB session clean when you switch between different projects during the same day.

Batch Processing Scripts as Utilities

Another common pattern is to write a batch processing script that applies the same operation to many files or datasets. For example, you may have a script that loops over all .csv files in a folder, reads each file, performs a standard cleaning step, and saves the results to an output folder. If you design such a script carefully, it can become a general utility for processing any collection of files with the same structure.

To increase reusability, use configuration variables at the top to control the root folder, the input file pattern, the type of processing, and the output location. Keep the core processing logic in a small section of the script or in a separate function that the script calls. In this way, you can often adapt the batch script to new projects by changing only the configuration and perhaps a small analysis step.

It is helpful to include some basic reporting inside batch scripts, such as displaying which file is currently being processed, or counting the number of files successfully handled. These messages provide feedback when you reuse the script on larger datasets, and they make it easier to tell whether the automation is working as intended.

If you notice that the same batch structure appears in several places, with only small variations in the processing step, you can move shared parts into a single reusable script and treat the variable parts as configuration or separate functions. This is a gradual way to move from ad hoc scripts towards a more modular and maintainable style.

Logging and Minimal User Feedback

Utility scripts often run longer or touch many files and folders. To make them easier to reuse and to trust, include simple logging or progress messages. This does not have to be complex. Printing a message when the script starts, showing intermediate milestones, and reporting success or failure at the end is usually enough.

In addition to printed messages, you can store a simple log in a variable or write it to a text file. For example, you might keep a cell array of filenames that were processed successfully and another for files that failed. When you reuse the script on new data, this logging makes it more transparent what happened, especially if you run it in the background and return later.

When you design feedback messages, be concise and informative. Instead of generic output, mention specific actions, such as which folder is being scanned or which file caused an error. Clear messages are a form of documentation in action, and they are very valuable when a script is reused months later on a different dataset.

When to Turn a Script into a Function

Although this chapter focuses on scripts, reusable logic often grows to a point where moving parts into functions is beneficial. In practice, you might keep a thin script wrapper that sets up configuration and calls several functions that do the actual work. The script remains the convenient entry point you run from the command window, while the functions provide proper inputs and outputs.

A good sign that some code should be moved from a utility script into a function is that it would be useful to call it with different parameters in different contexts. Another sign is that the same block of code appears in several scripts, possibly with small modifications. Instead of copying and editing that block, you can define a function once and call it from multiple places.

You do not have to convert entire scripts at once. Many useful utilities remain scripts for a long time, especially those that mostly coordinate other operations rather than carry detailed algorithms. The important point is to stay aware of the difference between ad hoc code and shared utility code, and to refactor as needed to keep your automation clean and reusable.

Maintaining and Evolving Utility Scripts

As your projects evolve, your collection of utility scripts will also evolve. You will find that some scripts are used often and others rarely. For the scripts that matter, treat them with the same care as any important resource. When you improve a script, try to keep it backward compatible so that older projects that depend on it still work. If you must introduce large changes, consider saving a copy with a versioned name and documenting the difference.

Keep comments and headers up to date when you change behavior. Outdated documentation makes reuse harder and can be more confusing than no documentation at all. If you add a new configuration variable, describe it in the header. If the expected inputs or outputs change, adjust the description accordingly.

Periodically review your utilities folder. You may discover scripts that duplicate similar logic, which you can merge into a single more flexible script. You may also find that some scripts have become obsolete or are tightly tied to a single old project. In such cases, you can either archive them or move them out of the shared utilities area, so that the remaining scripts are more clearly reusable.

Finally, consider using simple version control for your utility scripts, even if you do not use it for every experiment. This provides a history of changes and makes it easier to retrieve earlier versions of a script when you realize that a newer change breaks an older project.