iminuit 2.8.2

Jupyter-friendly Python frontend for MINUIT2 in C++

iminuit is a Jupyter-friendly Python interface for the Minuit2 C++ library maintained by CERN’s ROOT team.

Minuit was designed to minimise statistical cost functions, for likelihood and least-squares fits of parametric models to data. It provides the best-fit parameters and error estimates from likelihood profile analysis.

• Supported CPython versions: 3.6+

• Supported PyPy versions: 3.6

• Supported platforms: Linux, OSX and Windows.

The iminuit package comes with additional features:

• Included cost functions for binned and unbinned maximum-likelihood and (robust) least-squares fits

• Support for SciPy minimisers

• Numba support (optional)

Checkout the comprehensive list of tutorials that demonstrate these features.

In a nutshell

from iminuit import Minuit

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

fcn.errordef = Minuit.LEAST_SQUARES

m = Minuit(cost_function, x=0, y=0, z=0)

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

Versions

The current 2.x series has introduced breaking interfaces changes with respect to the 1.x series.

All interface changes are documented in the changelog with recommendations how to upgrade. To keep existing scripts running, pin your major iminuit version to <2, i.e. pip install 'iminuit<2' installs the 1.x series.