iminuit

Jupyter-friendly Python frontend for MINUIT2 in C++

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

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 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.

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.

from iminuit import Minuit

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

cost_function.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

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.

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

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

This version

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 2.12.0

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BLAKE2b-256	`ed967dc1113eb26345ec7f4b997e5796c21cbe5826856a42a33d79d72daaa113`

Algorithm	Hash digest
SHA256	`1648917736fb6abcf17ac61faf321f4535eb0ced42761c1f9564d56d3088b39f`
MD5	`ebc00e36a7929ffcc5f610bd53c6ebcd`
BLAKE2b-256	`395185a3378d27c0c32d0e70511009525a9a7d0e511f86b2d847054f0c15625c`

Algorithm	Hash digest
SHA256	`a0582528e652b5838353671d10b2e103fc2f1c47277dffc87d83fb8236225e92`
MD5	`d778bf5b8dfa19609d3a2173d74191e4`
BLAKE2b-256	`b074e21af2510b2226b74f8f73b30c01731ab3dc941e6c5e4603300770ea0391`

Algorithm	Hash digest
SHA256	`ca3ca18e67a9ceb7721b64adf85e6dbf83cde856ed673d9adcbaccbc8d0bbf83`
MD5	`f041d13833f8eabf6372ad88b2c66ab6`
BLAKE2b-256	`2b5212f795c67e6f0c6ebf25e1909e5e56bf5e2bf8e02f46ff0743a0d18b62be`

Algorithm	Hash digest
SHA256	`9fb5b6aced0e1533bdc3f6e121ed58ced8b4f3e718e0df69bad9a4b556b15de0`
MD5	`6f2b14b4eba0d0cba8b096defe318bba`
BLAKE2b-256	`107c006ed31b73b79cd13e6957f40ae68b465f96721d084a731883c7a67de6c4`

Algorithm	Hash digest
SHA256	`20aa35e62466aa5b2b98a653a98a2f05ae9673705a8bee8bf2fa68951bdf9c82`
MD5	`cfbda702302461d2b9feb6762022c707`
BLAKE2b-256	`9b783170812c0653c203cd861dc1cde7f57cf1a533b4ea5ecb31a86df68cae00`

Algorithm	Hash digest
SHA256	`78273b92416c3e947321655c2bb2899aab323c59909f4ad5b61a751f6da390d3`
MD5	`11bc05fb0c42ac0bfe018f7b679c9739`
BLAKE2b-256	`437b20352d3f147f76e512e40fdd93ccb04443cd6e4fb526a37a76bd46b78ef0`