iminuit

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.. |iminuit| image:: doc/_static/iminuit_logo.svg :alt: iminuit :target: http://iminuit.readthedocs.io/en/latest

|iminuit|

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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:

• Cost functions for binned and unbinned maximum-likelihood and (robust) least-squares fits
• Support for SciPy minimisers
• Numba support (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.

In a nutshell

.. code-block:: python

``````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)

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.

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