iminuit

Jupyter-friendly Python frontend for MINUIT2 in C++

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Maintainers

Christoph.Deil hdembins Piti.Ongmongkolkul scikit-hep

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Project description

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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 optimize statistical cost functions, for maximum-likelihood and least-squares fits. It provides the best-fit parameters and error estimates from likelihood profile analysis.

The iminuit package brings additional features:

Builtin cost functions for statistical fits to N-dimensional data
- Unbinned and binned maximum-likelihood + extended versions
- Template fits with error propagation
- Least-squares (optionally robust to outliers)
- Gaussian penalty terms for parameters
- Cost functions can be combined by adding them: total_cost = cost_1 + cost_2
- Visualization of the fit in Jupyter notebooks
Support for SciPy minimizers as alternatives to Minuit’s MIGRAD algorithm (optional)
Support for Numba accelerated functions (optional)

Minimal dependencies

iminuit is promised to remain a lean package which only depends on numpy, but additional features are enabled if the following optional packages are installed.

numba: Cost functions are partially JIT-compiled if numba is installed.
matplotlib: Visualization of fitted model for builtin cost functions
ipywidgets: Interactive fitting, see example below (also requires matplotlib)
scipy: Compute Minos intervals for arbitrary confidence levels
unicodeitplus: Render names of model parameters in simple LaTeX as Unicode

Documentation

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.

Lecture by Glen Cowan

In the exercises to his lecture for the KMISchool 2022, Glen Cowan shows how to solve statistical problems in Python with iminuit. You can find the lectures and exercises on the Github page, which covers both frequentist and Bayesian methods.

Glen Cowan is a known for his papers and international lectures on statistics in particle physics, as a member of the Particle Data Group, and as author of the popular book Statistical Data Analysis.

In a nutshell

iminuit can be used with a user-provided cost functions in form of a 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 to perform an unbinned maximum likelihood fit.

import numpy as np
from iminuit import Minuit
from iminuit.cost import UnbinnedNLL
from scipy.stats import norm

x = norm.rvs(size=1000, random_state=1)

def pdf(x, mu, sigma):
    return norm.pdf(x, mu, sigma)

# Negative unbinned log-likelihood, you can write your own
cost = UnbinnedNLL(x, pdf)

m = Minuit(cost, mu=0, sigma=1)
m.limits["sigma"] = (0, np.inf)
m.migrad()  # find minimum
m.hesse()   # compute uncertainties

Output of the demo in a Jupyter notebook

Interactive fitting

iminuit optionally supports an interactive fitting mode in Jupyter notebooks.

Animated demo of an interactive fit in a Jupyter notebook

High performance when combined with numba

When iminuit is used with cost functions that are JIT-compiled with numba (JIT-compiled pdfs are provided by numba_stats ), the speed is comparable to RooFit with the fastest backend. numba with auto-parallelization is considerably faster than the parallel computation in RooFit.

More information about this benchmark is given in the Benchmark section of the documentation.

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.
boost-histogram from Scikit-HEP provides fast generalized histograms that you can use with the builtin cost functions.
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.

Project details

These details have been verified by PyPI

Maintainers

Christoph.Deil hdembins Piti.Ongmongkolkul scikit-hep

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

2.30.1

Oct 8, 2024

2.30.0

Sep 24, 2024

2.29.1

Aug 25, 2024

2.29.0

Aug 25, 2024

2.28.0

Aug 1, 2024

This version

2.27.0

Jul 30, 2024

2.26.0

Jun 3, 2024

2.25.2

Feb 9, 2024

2.25.1

Feb 8, 2024

2.25.0

Jan 31, 2024

2.24.0

Aug 15, 2023

2.23.0

Jul 28, 2023

2.22.0

Jun 22, 2023

2.21.3

Apr 3, 2023

2.21.2

Mar 19, 2023

2.21.1

Mar 18, 2023

2.21.0

Mar 3, 2023

2.20.0

Feb 13, 2023

2.19.0

Feb 10, 2023

2.18.0

Dec 14, 2022

2.17.0

Sep 27, 2022

2.17.0b1 pre-release

Sep 27, 2022

2.16.0

Aug 16, 2022

2.15.2

Aug 2, 2022

2.15.1

Jul 28, 2022

2.15.0

Jul 27, 2022

2.14.0

Jul 25, 2022

2.13.0

Jul 17, 2022

2.12.3b2 pre-release

Jul 16, 2022

2.12.3b1 pre-release

Jul 16, 2022

2.12.2

Jul 15, 2022

2.12.1

Jul 1, 2022

2.12.0

Jun 21, 2022

2.11.2

Mar 28, 2022

2.11.1

Mar 27, 2022

2.11.0

Mar 27, 2022

2.10.0

Mar 4, 2022

2.9.0

Jan 9, 2022

2.8.4

Oct 11, 2021

2.8.3

Sep 3, 2021

2.8.2

Aug 15, 2021

2.8.1

Aug 4, 2021

2.8.1rc1 pre-release

Aug 3, 2021

2.8.0

Jul 25, 2021

2.7.0

Jul 4, 2021

2.6.1

May 13, 2021

2.6.0

May 2, 2021

2.5.0

Apr 30, 2021

2.4.0

Feb 10, 2021

2.3.0

Jan 24, 2021

2.2.1

Dec 22, 2020

2.2.0

Dec 20, 2020

2.2.0rc2 pre-release

Dec 20, 2020

2.1.0

Dec 18, 2020

2.0.0

Dec 7, 2020

1.5.4

Nov 21, 2020

1.5.3

Nov 19, 2020

1.5.2 yanked

Sep 24, 2020

Reason this release was yanked:

1.5.x series has performance regression

1.5.1 yanked

Sep 20, 2020

Reason this release was yanked:

1.5.x series has performance regression

1.5.0 yanked

Sep 17, 2020

Reason this release was yanked:

1.5.x series has performance regression

1.4.9

Jul 19, 2020

1.4.8

Jul 16, 2020

1.4.7

Jul 15, 2020

1.4.6

Jul 11, 2020

1.4.5

Jun 25, 2020

1.4.4

Jun 25, 2020

1.4.3

Jun 24, 2020

1.4.2

Jun 14, 2020

1.4.1

Jun 13, 2020

1.4.0

Jun 12, 2020

1.3.10

Mar 31, 2020

1.3.9

Mar 31, 2020

1.3.8

Oct 17, 2019

1.3.7

Jun 12, 2019

1.3.6

May 22, 2019

1.3.3

Sep 12, 2018

1.3.2

Aug 11, 2018

1.3.1

Jul 10, 2018

1.3

Jul 9, 2018

1.2

Nov 10, 2015

1.1.1

Jan 28, 2014

1.1.0

Dec 6, 2013

1.0.9

Nov 20, 2013

1.0.8

Sep 14, 2013

1.0.7

Jun 4, 2013

1.0.6

Jan 27, 2013

1.0.5

Jan 16, 2013

1.0.4

Jan 6, 2013

1.0.3

Jan 2, 2013

1.0.2

Dec 27, 2012

1.0.1

Dec 21, 2012

1.0.0

Dec 18, 2012

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iminuit 2.27.0

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SHA256	`b7d2db901f2737608bdb61109a4f00efa6979e74b4b135fb1c50f21455679286`
MD5	`807cefe7b957ee625bb23a71acfffd8e`
BLAKE2b-256	`df00420e633c84f6610d15f89ca039d61baaac03c5b6c1f4311dda52a6059738`

Algorithm	Hash digest
SHA256	`d9381c93ad3480b7c2a4b722a6017ada7bf563125d8b88124402aefa5e29ee63`
MD5	`6bd265970cd09bc1f10e6a0b71678b62`
BLAKE2b-256	`a4f4cf0f44a3593ab6c4a6aac083b19ca096ae4233437f9d83e1a9b0c561ee29`

Algorithm	Hash digest
SHA256	`a4a78bc0b50409fd64d6ec445e22d526c8f1ae65c6f526cc6b318ca88dbfa830`
MD5	`55a1b7727ab97c877e42870e1fd37295`
BLAKE2b-256	`d510347f360dcd68da85d4225184c541040c34b0b1e372c5a21982c211bf011d`

Algorithm	Hash digest
SHA256	`4f115d314ed7a5cf35ec08c034fcfc6e89609007a555677f7cae00bf62760ee2`
MD5	`d120827888914134323e8f9b5fce65cb`
BLAKE2b-256	`d55d66b80bef4551714f99c06dd06f345a2850437ceec7e27aba5c6540e15432`

Algorithm	Hash digest
SHA256	`d6470a25645011a462a8f9a493105e38c465c30e8a375488be41c8dd702de547`
MD5	`02c77a2f612d528ec6b7ade8cc986aac`
BLAKE2b-256	`f8a9b52303164056121526eb74d4d5f3f8ad75c3f715a79fccd4519b6bbfb838`

Algorithm	Hash digest
SHA256	`4566065f844e60df56df2545ba84485b2437b71cf3e87e05589b61d5d25070f9`
MD5	`6f140b5f54f0bd2cbd5110df5a84bf3a`
BLAKE2b-256	`8b8f4fa2236cf1f4e8d796bb6c81fe066b24594f3067503aa5e517f495760f80`

Algorithm	Hash digest
SHA256	`c7c6a8cee66bdcee30931dc82505f27141476f0ed7bf4882bf73f3e4287dbb48`
MD5	`03d2a8e907e7316f8054913a3c6f5ded`
BLAKE2b-256	`61685d510f468db404c3c9421a08d6ef2964d422215acae0ddc4a566efb3ce11`

Algorithm	Hash digest
SHA256	`cdd9499203705bcd048d4c575d1c7d64b9502f085baeda2de8d9fdbc4ce5d6b1`
MD5	`96dfe0a14ac0f11315afa912e6295a3e`
BLAKE2b-256	`65a41467c2c4563218892cad26e88bcb574725193b17994aae149ee2db2fea42`

Algorithm	Hash digest
SHA256	`62341b7753b19af63754fd52f21ca6a8aac281cec128942843629a06a5f76dee`
MD5	`032b1426e2dccb0b1874ccfb78948930`
BLAKE2b-256	`948d1016cd85934702e38f9af77189b43a0a20c813ed913864d55f171ffeb28d`

Algorithm	Hash digest
SHA256	`be13f810c344b79f107400e433f4262c92813020ac7418796b930ad5e0780ec7`
MD5	`39c1ea5a18327a8ded61c5db184afea7`
BLAKE2b-256	`ef28f155bcc164b989b926f98232436693e80725fdf6b4433d24fa3be6ec1b53`