Scientific Programming

Best practices for research software and reproducible computation.

Project Structure

project/
├── README.md              # Project overview, how to reproduce
├── LICENSE                 # MIT, Apache 2.0, or GPL
├── requirements.txt       # or environment.yml (conda)
├── setup.py / pyproject.toml
├── data/
│   ├── raw/               # Never modify raw data
│   ├── processed/         # Cleaned/transformed data
│   └── external/          # Third-party data
├── src/ or scripts/
│   ├── data_processing.py
│   ├── analysis.py
│   ├── models.py
│   └── visualization.py
├── notebooks/             # Exploratory analysis
│   ├── 01_eda.ipynb
│   ├── 02_modeling.ipynb
│   └── 03_figures.ipynb
├── results/
│   ├── figures/
│   └── tables/
├── tests/
└── docs/

Reproducibility Checklist

1. Environment: Pin all dependencies with versions

   pip freeze > requirements.txt
   # or conda
   conda env export > environment.yml
   

2. Random seeds: Set and document all random seeds

   import numpy as np
   import random
   SEED = 42
   np.random.seed(SEED)
   random.seed(SEED)
   # torch.manual_seed(SEED)
   # tf.random.set_seed(SEED)
   

3. Data versioning: Use DVC or git-lfs for large data

   dvc init
   dvc add data/raw/dataset.csv
   git add data/raw/dataset.csv.dvc
   

4. Configuration: Separate config from code

   # config.yaml
   # experiment:
   #   learning_rate: 0.001
   #   batch_size: 32
   #   epochs: 100
   import yaml
   with open('config.yaml') as f:
       config = yaml.safe_load(f)
   

5. Logging: Record all experiments

   import logging
   logging.basicConfig(level=logging.INFO, 
                       format='%(asctime)s %(levelname)s: %(message)s',
                       filename='experiment.log')
   

Parallel Computing

# Multiprocessing (CPU-bound)
from multiprocessing import Pool
import numpy as np