code-science

name

code-science

description

Scientific programming best practices including reproducible research, computational notebooks, version control for research, data management, HPC/parallel computing, and research software engineering. Use when user needs help with research code organization, reproducibility, scientific Python/R workflows, or computational infrastructure. Triggers on "reproducible research", "research code", "scientific computing", "HPC", "parallel computing", "Jupyter", "notebook", "data management plan", "research software", "code review for science".

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

def process_chunk(data):
    return heavy_computation(data)

with Pool(processes=8) as pool:
    results = pool.map(process_chunk, data_chunks)

# Concurrent futures (simpler API)
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

with ProcessPoolExecutor(max_workers=8) as executor:
    results = list(executor.map(process_func, items))

# For I/O-bound tasks (API calls, file reading)
with ThreadPoolExecutor(max_workers=20) as executor:
    results = list(executor.map(fetch_data, urls))

Performance Optimization

# Profiling
import cProfile
cProfile.run('my_function()', sort='cumulative')

# Line profiling
# pip install line_profiler
# @profile decorator, then: kernprof -l -v script.py

# NumPy vectorization (avoid loops)
# Bad:
result = [x**2 + 2*x + 1 for x in data]
# Good:
result = data**2 + 2*data + 1

# Memory profiling
# pip install memory_profiler
# @profile decorator, then: python -m memory_profiler script.py

Data Management

FAIR Principles

• Findable: Persistent identifiers (DOI), rich metadata
• Accessible: Open protocols, authentication when needed
• Interoperable: Standard formats (CSV, JSON, HDF5, NetCDF)
• Reusable: Clear license, provenance, community standards

File Formats for Science

Format	Best For	Size	Speed
CSV	Small tabular, universal	Large	Slow
Parquet	Large tabular, columnar	Small	Fast
HDF5	Multidimensional arrays	Small	Fast
NetCDF	Climate/geospatial	Small	Fast
FITS	Astronomy	Medium	Fast
Feather	DataFrame interchange	Small	Very fast

# Parquet (recommended for large datasets)
df.to_parquet('data.parquet', compression='snappy')
df = pd.read_parquet('data.parquet')

# HDF5 (for arrays)
import h5py
with h5py.File('data.h5', 'w') as f:
    f.create_dataset('experiment1', data=array)

Testing Scientific Code

import numpy as np
import pytest

def test_conservation_law():
    """Physical quantities should be conserved"""
    initial_energy = compute_energy(initial_state)
    final_energy = compute_energy(simulate(initial_state))
    np.testing.assert_allclose(initial_energy, final_energy, rtol=1e-6)

def test_known_solution():
    """Compare against analytical solution"""
    numerical = solve_numerically(params)
    analytical = analytical_solution(params)
    np.testing.assert_allclose(numerical, analytical, atol=1e-4)

def test_symmetry():
    """Result should be symmetric under transformation"""
    result1 = compute(data)
    result2 = compute(transform(data))
    np.testing.assert_array_equal(result1, result2)

Tips

• Raw data is sacred — never modify it, only create processed copies
• Use version control (git) from day one
• Write README before writing code
• Automate the full pipeline (Makefile or Snakemake)
• Document assumptions and decisions in code comments
• Use type hints for clarity in scientific code
• Publish code alongside papers (GitHub + Zenodo for DOI)