Modernize Scientific Computing Stack

This skill provides guidance for migrating legacy Python 2 scientific computing code to modern Python 3 with contemporary libraries and best practices.

When to Use This Skill

Apply this skill when:

• Migrating Python 2 scientific scripts to Python 3
• Updating legacy data processing code using outdated patterns
• Modernizing scripts that use deprecated file handling, string encoding, or numerical libraries
• Converting scripts from csv module to pandas for data analysis
• Replacing os.path with pathlib for path manipulation

Approach

Phase 1: Complete Code Discovery

Before making any changes, ensure complete understanding of the existing codebase:

1. Read all source files completely - If a file read is truncated, request the full content before proceeding. Never assume file contents based on partial reads.

2. Identify all dependencies - Check for:

   • Import statements (standard library and third-party)
   • Configuration files (JSON, YAML, INI)
   • Data files (CSV, Excel, pickle)
   • Environment requirements

3. Map the data flow - Understand:

   • Input file formats and encodings
   • Data transformations applied
   • Output format requirements
   • Any intermediate files or caches

Phase 2: Identify Migration Requirements

Common Python 2 to Python 3 migration patterns in scientific code:

Legacy Pattern	Modern Replacement
print "text"	print("text")
unicode() / str()	str() with explicit encoding
open(file)	open(file, encoding='utf-8')
os.path.join()	pathlib.Path()
csv module	pandas.read_csv()
for key in dict.keys()	for key in dict
dict.has_key(x)	x in dict
Manual file iteration	Context managers (with statements)
xrange()	range()
Integer division /	Explicit // or float division

Phase 3: Implementation Strategy

1. Create the modernized script with these priorities:

   • UTF-8 encoding for all file operations
   • pathlib.Path for all file path manipulations
   • pandas for CSV/data processing
   • Type hints where beneficial
   • Context managers for resource handling

2. Handle configuration files - Check for file existence before reading:

   config_path = Path("config.json")
   if config_path.exists():
       config = json.loads(config_path.read_text(encoding='utf-8'))
   

3. Create requirements.txt - Include all dependencies with version constraints

Phase 4: Verification Protocol

Critical: Always verify file operations

After writing any file, read it back to confirm:

• The complete content was written (not truncated)
• The syntax is valid
• All imports are present

Testing sequence:

1. Syntax validation - Run Python syntax check:

   python -m py_compile script.py
   

2. Import verification - Test all imports resolve:

   python -c "from script import *"
   

3. Functional test - Run the script and compare output to expected results

4. Output validation - Verify output format matches requirements exactly

Common Pitfalls to Avoid

1. Truncated file content - Never proceed with partial file reads. If a response shows ... [truncated] or incomplete content, request the full file before continuing.

2. Unverified writes - After using a write operation, always read the file back to confirm the complete content was written correctly.

3. Encoding issues - Always specify encoding='utf-8' explicitly in file operations. Legacy scripts often have implicit ASCII assumptions.

4. Path string concatenation - Replace all os.path.join() and string concatenation for paths with pathlib.Path operations.

5. Missing edge case handling:

   • Empty data files or datasets
   • Missing required files
   • Invalid data types in CSV columns
   • Stations/entities with no matching data

6. Environment setup repetition - When setting up environments (venv, PATH), verify the setup persists rather than repeating in each command.

Verification Checklist

Before marking the task complete, confirm:

• All source files were read completely (no truncation)
• Written files were verified by reading back
• All Python 2 patterns have been converted
• File encodings are explicitly specified
• pathlib is used for all path operations
• pandas is used for data processing (where appropriate)
• requirements.txt includes all dependencies
• Script runs without errors
• Output matches expected format exactly
• Edge cases are handled (empty data, missing files)

Output Validation

When the task specifies an expected output format, verify the output matches exactly:

1. Run the modernized script
2. Capture the output
3. Compare against expected format character-by-character if needed
4. Pay attention to:
   • Decimal precision in numerical output
   • Whitespace and formatting
   • Order of output items
   • Units and labels