iminuit is a Jupyter-friendly Python frontend to the MINUIT2 C++ library.

It can be used as a general robust function minimisation method, but is most commonly used for likelihood fits of models to data, and to get model parameter error estimates from likelihood profile analysis.

• Supported CPython versions: 3.5+
• Supported PyPy versions: 3.5, 3.6
• Supported platforms: Linux, OSX and Windows.

• PyPI: https://pypi.org/project/iminuit
• Documentation: http://iminuit.readthedocs.org
• Source: https://github.com/scikit-hep/iminuit
• Gitter: https://gitter.im/Scikit-HEP/community
• License: MINUIT2 is LGPL and iminuit is MIT
• Citation: https://doi.org/10.5281/zenodo.3949207

In a nutshell

from iminuit import Minuit

def f(x, y, z):
    return (x - 2) ** 2 + (y - 3) ** 2 + (z - 4) ** 2

m = Minuit(f)

m.migrad()  # run optimiser
print(m.values)  # {'x': 2,'y': 3,'z': 4}

m.hesse()   # run covariance estimator
print(m.errors)  # {'x': 1,'y': 1,'z': 1}