AI Summarypycse is a Python library that assists with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments, and neural network-based modeling. It's useful for researchers, engineers, and data scientists working on numerical optimization, experimental design, and uncertainty analysis.
Install
# Install skill into your project (87 files) mkdir -p .cursor/skills/pycse && curl --retry 3 --retry-delay 2 --retry-all-errors -o .cursor/skills/pycse/SKILL.md "https://raw.githubusercontent.com/jkitchin/pycse/master/src/pycse/SKILL.md" && curl --retry 3 --retry-delay 2 --retry-all-errors -o .cursor/skills/pycse/PYCSE.py "https://raw.githubusercontent.com/jkitchin/pycse/master/src/pycse/PYCSE.py"
Run in your IDE terminal (bash). On Windows, use Git Bash, WSL, or your IDE's built-in terminal. If curl fails with an SSL error, your network may block raw.githubusercontent.com — try using a VPN or download the files directly from the source repo.
Description
Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.
pycse - Python Computations in Science and Engineering
pycse is a comprehensive library for scientific computing, data analysis, and uncertainty quantification in Python.
1. Nonlinear Regression and Curve Fitting
• nlinfit: Nonlinear least squares fitting with uncertainty quantification • regress: Linear regression with statistics • Supports parameter uncertainty estimation and confidence intervals
2. Design of Experiments (DOE)
• Latin Hypercube Sampling (LHC): Space-filling designs for efficient parameter exploration • Surface Response Modeling: Fit polynomial response surfaces to experimental data • Useful for optimizing experimental conditions with minimal trials
3. Uncertainty Quantification with DPOSE
• DPOSE (Direct Propagation of Shallow Ensembles): Neural network ensemble for UQ • Provides per-sample uncertainty estimates (heteroscedastic) • Handles gaps, extrapolation, and nonlinear relationships • Trained using CRPS or NLL loss for calibrated uncertainties
Quality Score
Acceptable
70/100
Trust & Transparency
Open Source — GPL-2.0
Source code publicly auditable
Verified Open Source
Hosted on GitHub — publicly auditable
Actively Maintained
Last commit 8d ago
274 stars — Growing Community
74 forks
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