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

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.

• Code: https://github.com/scikit-hep/iminuit
• Documentation: http://iminuit.readthedocs.org/
• Gitter: https://gitter.im/Scikit-HEP/community
• Mailing list: https://groups.google.com/forum/#!forum/scikit-hep-forum
• PyPI: https://pypi.org/project/iminuit/
• License: MINUIT2 is LGPL and iminuit is MIT
• Citation: https://github.com/scikit-hep/iminuit/blob/master/CITATION

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}