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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:

• Builtin cost functions for statistical fits

  • Binned and unbinned maximum-likelihood
  • Non-linear regression with (optionally robust) weighted least-squares
  • Gaussian penalty terms
  • Cost functions can be combined by adding them: total_cost = cost_1 + cost_2

• Support for SciPy minimisers as alternatives to Minuit's Migrad algorithm (optional)

• Support for Numba accelerated functions (optional)

Checkout our large and comprehensive list of tutorials_ that take you all the way from beginner to power user. For help and how-to questions, please use the discussions_ on GitHub or gitter_.

In a nutshell

iminuit is intended to be used with a user-provided negative log-likelihood function or least-squares function. Standard functions are included in iminuit.cost, so you don't have to write them yourself. The following example shows how iminuit is used with a dummy least-squares function.

.. code-block:: python

from iminuit import Minuit

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

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

m.migrad()  # run optimiser
m.hesse()   # run covariance estimator

print(m.values)  # x: 2, y: 3, z: 4
print(m.errors)  # x: 1, y: 1, z: 1

Interactive fitting

iminuit optionally supports an interactive fitting mode in Jupyter notebooks.

.. image:: doc/_static/interactive_demo.gif :alt: Animated demo of an interactive fit in a Jupyter notebook

Partner projects

• numba_stats_ provides faster implementations of probability density functions than scipy, and a few specific ones used in particle physics that are not in scipy.
• jacobi_ provides a robust, fast, and accurate calculation of the Jacobi matrix of any transformation function and building a function for generic error propagation.

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.

.. _changelog: https://iminuit.readthedocs.io/en/stable/changelog.html .. _tutorials: https://iminuit.readthedocs.io/en/stable/tutorials.html .. _discussions: https://github.com/scikit-hep/iminuit/discussions .. _gitter: https://gitter.im/Scikit-HEP/iminuit .. _jacobi: https://github.com/hdembinski/jacobi .. _numba_stats: https://github.com/HDembinski/numba-stats