pycse - Python Computations in Science and Engineering

Overview

pycse extends numpy/scipy with convenience functions that automatically return confidence intervals for regression, making statistical analysis faster and less error-prone. Instead of manually extracting covariance matrices and calculating confidence intervals, pycse returns them directly.

Core value: Turn 100+ lines of scipy boilerplate into 10 lines of clear, reusable code.

When to Use

Use pycse when:

• Fitting models to experimental data and need parameter confidence intervals
• Performing regression analysis (linear, nonlinear, polynomial)
• Comparing models with statistical criteria (BIC, R²)
• Generating predictions with error bounds
• Caching expensive computational results
• Reading data from Google Sheets into pandas
• Solving ODEs (wraps scipy with convenient interface)