Jupyter-friendly Python frontend for MINUIT2 in C++

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  • License: GNU Library or Lesser General Public License (LGPL), MIT License
  • Author: Piti Ongmongkolkul and the iminuit team
  • Maintainer: Hans Dembinski
  • Requires: Python >=3.5
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Project description

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.

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}

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