Python Validation Naming

Ensuring Your Files Follow a Consistent Naming Scheme

Are you wrestling with a bunch of files that need to conform to a specific naming convention? If you’ve found yourself with TV episode files (or any other type of files) and are unsure how to ensure they fit a required pattern, you’re in the right place. Let’s break down how to validate your files while making your code more organized and expandable for future needs.

The Challenge: File Naming Validation

For a seamless experience when handling files, especially for media libraries or datasets, it’s critical to adhere to specific naming conventions. Here are some typical requirements you might have:

  • File Format: Your file names might need to look like Show Name - [01x23] - Episode Name.avi or variations thereof.
  • Missing Metadata: If a file is just titled Show Name - [01x23].avi, it should be flagged because it lacks a designated episode name.
  • Directory Structure: The file paths should reflect the correct organization, like Show Name/season 2/the_file.avi.
  • Folder Content: Each show directory should include a folder.jpg file to keep things tidy.

A Proposed Solution: Organizing Validation Checks

Rather than letting your validation code spiral into complexity, consider structuring it using a dictionary-based approach. This method allows for scalability and readability as you add more rules; here’s how you can set it up.

Step 1: Define Your Checker Dictionary

Using a dictionary makes it easy to define the rules and requirements for your validation. Here’s a general structure you can adopt:

check_dict = {
  'delim': /\-/,
  'pattern_rules': {
    'parts': ['Show Name', 'Episode Name', 'Episode Number'],
    'patterns': [/valid name/, /valid episode name/, /valid number/]
  },
  'required_files': ['folder.jpg'],
  'ignored_files': ['.*', 'hidden.txt'],
  'start_dir': '/path/to/dir/to/test/'
}

Step 2: Breaking Down the Validation Process

  1. File Retrieval: Begin by obtaining a comprehensive list of files within your specified start directory.
  2. Pattern Checking:
    • Split each filename using the designated delimiter.
    • Validate each section of the filename against defined patterns.
  3. Required File Validation:
    • Ensure that each directory contains a folder.jpg (or any other necessary files).
  4. Error Handling: Flag any files that don’t conform to the expected patterns or are missing required files with clear error codes.

Organized Error Codes

Defining specific error codes can help simplify troubleshooting:

  • Error Code 1: Missing episode name
  • Error Code 2: Malformed filename
  • Error Code 3: Required files not present

A Flexible Framework

The beauty of this approach lies in its potential for expansion. Adding new rules or adjusting existing ones can easily be done by modifying the dictionary. For instance, should you decide to introduce additional file formats or requirements, just extend the pattern_rules or required_files.

Conclusion: A Clean Solution for File Validation

By adopting a structured and dictionary-driven approach to validate your file naming conventions, you can maintain both clean code and effective checks. This strategy provides an intuitive way to scale and adapt your validation solution as your needs evolve—be it TV episodes or any files needing organization.

Does this method align with what you were envisioning for your file validation project? If you have any further questions or need assistance tweaking your code, feel free to ask!