Code Refactor For Reproducibility

Source: https://github.com/aipoch/medical-research-skills

Research Code Reproducibility Refactoring Tool

When to Use

• Use this skill when the task needs Use when refactoring research code for publication, adding documentation to existing analysis scripts, creating reproducible computational workflows, or preparing code for sharing with collaborators. Transforms research code into publication-ready, reproducible workflows. Adds documentation, implements error handling, creates environment specifications, and ensures computational reproducibility for scientific publications.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Use when refactoring research code for publication, adding documentation to existing analysis scripts, creating reproducible computational workflows, or preparing code for sharing with collaborators. Transforms research code into publication-ready, reproducible workflows. Adds documentation, implements error handling, creates environment specifications, and ensures computational reproducibility for scientific publications.
• Packaged executable path(s): scripts/main.py.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• numpy: unspecified. Declared in requirements.txt.
• pandas: unspecified. Declared in requirements.txt.
• pytest: unspecified. Declared in requirements.txt.
• scipy: unspecified. Declared in requirements.txt.
• src: unspecified. Declared in requirements.txt.

Example Usage

cd "20260318/scientific-skills/Data Analytics/code-refactor-for-reproducibility"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Workflow Overview

Follow this sequence when refactoring a research codebase:

1. Analyze — identify reproducibility issues in existing code
2. Refactor — apply documentation, parameterization, and error handling
3. Specify environment — pin dependencies and create environment files
4. Validate — run tests and verify behaviour is unchanged

Step 1: Analyze Code for Reproducibility Issues

Read each source file and check for the following problems. Document findings before making any changes.

Checklist: missing docstrings · hardcoded absolute paths · missing random seeds · bare except: clauses · unpinned imports · unexplained magic numbers

Example — detecting issues manually:

import ast, pathlib

def find_hardcoded_paths(source: str) -> list[str]:
    """Return string literals that look like absolute paths."""
    tree = ast.parse(source)
    return [
        node.s for node in ast.walk(tree)
        if isinstance(node, ast.Constant)
        and isinstance(node.s, str)
        and node.s.startswith("/")
    ]

source = pathlib.Path("analysis.py").read_text()
print(find_hardcoded_paths(source))

Step 2: Refactor for Best Practices

Apply improvements in place. Always back up originals first.

2a. Add docstrings

# Before
def load_data(path):
    import pandas as pd
    return pd.read_csv(path)

# After
def load_data(path: str) -> "pd.DataFrame":
    """Load a CSV dataset from disk.

    Parameters
    ----------
    path : str
        Path to the CSV file (relative to project root).

    Returns
    -------
    pd.DataFrame
        Raw dataset with original column names preserved.
    """
    import pandas as pd
    return pd.read_csv(path)

2b. Parameterize hardcoded values

from pathlib import Path
import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--data", type=Path, default=Path("data/raw.csv"))
    parser.add_argument("--output", type=Path, default=Path("results/"))
    return parser.parse_args()

args = parse_args()
df = pd.read_csv(args.data)
args.output.mkdir(parents=True, exist_ok=True)

2c. Set random seeds

import random
import numpy as np

SEED = 42  # document this constant at module level

random.seed(SEED)
np.random.seed(SEED)

# scikit-learn
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(random_state=SEED)

# PyTorch
import torch
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True

2d. Add error handling and logging

import logging
from pathlib import Path

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)

def load_data(path: Path) -> "pd.DataFrame":
    """Load dataset with validation."""
    import pandas as pd
    if not path.exists():
        raise FileNotFoundError(f"Data file not found: {path}")
    logger.info("Loading data from %s", path)
    df = pd.read_csv(path)
    if df.empty:
        raise ValueError(f"Loaded dataframe is empty: {path}")
    logger.info("Loaded %d rows, %d columns", *df.shape)
    return df

Step 3: Generate Environment Specifications

See references/environment-setup.md for full Dockerfile and Conda environment templates.