Jupyter-friendly Python frontend for MINUIT2 in C++
Project description
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 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
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.
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