| Name | iminuit JSON |
| Version |
2.30.1
JSON |
| download |
| home_page | None |
| Summary | Jupyter-friendly Python frontend for MINUIT2 in C++ |
| upload_time | 2024-10-08 07:39:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Minuit is from SEAL Minuit It's LGPL v2 http://seal.web.cern.ch/seal/main/license.html. For iminuit, I'm releasing it as MIT license: Copyright (c) 2012 Piti Ongmongkolkul Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Note: MIT license is GPL compatible, so it is an acceptable license for a wrapper, as can be seen here: http://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html#GPLWrapper http://www.gnu.org/licenses/old-licenses/gpl-2.0-faq.html#OrigBSD (L)GPL can be combined or included in code that does not impose more restrictive conditions. |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
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.. |iminuit| image:: doc/_static/iminuit_logo.svg
:alt: iminuit
|iminuit|
=========
.. version-marker-do-not-remove
.. image:: https://scikit-hep.org/assets/images/Scikit--HEP-Project-blue.svg
:target: https://scikit-hep.org
.. image:: https://img.shields.io/pypi/v/iminuit.svg
:target: https://pypi.org/project/iminuit
.. image:: https://img.shields.io/conda/vn/conda-forge/iminuit.svg
:target: https://github.com/conda-forge/iminuit-feedstock
.. image:: https://coveralls.io/repos/github/scikit-hep/iminuit/badge.svg?branch=develop
:target: https://coveralls.io/github/scikit-hep/iminuit?branch=develop
.. image:: https://github.com/scikit-hep/iminuit/actions/workflows/docs.yml/badge.svg?branch=main
:target: https://scikit-hep.org/iminuit
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3949207.svg
:target: https://doi.org/10.5281/zenodo.3949207
.. image:: https://img.shields.io/badge/ascl-2108.024-blue.svg?colorB=262255
:target: https://ascl.net/2108.024
:alt: ascl:2108.024
.. image:: https://img.shields.io/gitter/room/Scikit-HEP/iminuit
:target: https://gitter.im/Scikit-HEP/iminuit
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-hep/iminuit/develop?filepath=doc%2Ftutorial
``iminuit`` is a Jupyter-friendly Python interface for the ``Minuit2`` C++ library maintained by CERN's `ROOT team <https://root.cern.ch>`_.
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 <https://doi.org/10.1140/epjc/s10052-022-11019-z>`_
- 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 <https://github.com/KMISchool2022>`_, 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 <https://scholar.google.com/citations?hl=en&user=ljQwt8QAAAAJ&view_op=list_works>`_ 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 <https://www.pp.rhul.ac.uk/~cowan/sda/>`_.
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.
.. code:: python
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
.. image:: doc/_static/demo_output.png
:alt: Output of the demo in a Jupyter notebook
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
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`_.
.. image:: doc/_static/roofit_vs_iminuit+numba.svg
More information about this benchmark is given `in the Benchmark section of the documentation <https://scikit-hep.org/iminuit/benchmark.html>`_.
Citation
--------
If you use iminuit in a scientific work, please cite us. A generic BibTeX entry is::
@article{iminuit,
author={Hans Dembinski and Piti Ongmongkolkul et al.},
title={scikit-hep/iminuit},
DOI={10.5281/zenodo.3949207},
publisher={Zenodo},
year={2020},
month={Dec},
url={https://doi.org/10.5281/zenodo.3949207}
}
The DOI and URL in this entry point always to the latest release of iminuit. You can also cite the actual release that you used, please follow the `Zenodo link <https://doi.org/10.5281/zenodo.3949207>`_, which offers entries for common bibliography formats for all iminuit releases.
The recommended scientific reference for the MINUIT algorithms is::
@article{James:1975dr,
author = "James, F. and Roos, M.",
title = "{Minuit: A System for Function Minimization and Analysis of the Parameter Errors and Correlations}",
reportNumber = "CERN-DD-75-20",
doi = "10.1016/0010-4655(75)90039-9",
journal = "Comput. Phys. Commun.",
volume = "10",
pages = "343--367",
year = "1975"
}
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 2.x series has introduced breaking interfaces changes with respect to the 1.x series. There are no plans to introduce further breaking changes.
All interface changes from 1.x to 2.x are documented in the `changelog`_ with recommendations how to upgrade. To keep old scripts running, pin your major iminuit version to <2: the command ``pip install 'iminuit<2'`` installs the 1.x series.
.. _changelog: https://scikit-hep.org/iminuit/changelog.html
.. _tutorials: https://scikit-hep.org/iminuit/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
.. _boost-histogram: https://github.com/scikit-hep/boost-histogram
.. _numba: https://numba.pydata.org
.. _RooFit: https://root.cern/manual/roofit/
Raw data
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"description": ".. |iminuit| image:: doc/_static/iminuit_logo.svg\n :alt: iminuit\n\n|iminuit|\n=========\n\n.. version-marker-do-not-remove\n\n.. image:: https://scikit-hep.org/assets/images/Scikit--HEP-Project-blue.svg\n :target: https://scikit-hep.org\n.. image:: https://img.shields.io/pypi/v/iminuit.svg\n :target: https://pypi.org/project/iminuit\n.. image:: https://img.shields.io/conda/vn/conda-forge/iminuit.svg\n :target: https://github.com/conda-forge/iminuit-feedstock\n.. image:: https://coveralls.io/repos/github/scikit-hep/iminuit/badge.svg?branch=develop\n :target: https://coveralls.io/github/scikit-hep/iminuit?branch=develop\n.. image:: https://github.com/scikit-hep/iminuit/actions/workflows/docs.yml/badge.svg?branch=main\n :target: https://scikit-hep.org/iminuit\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3949207.svg\n :target: https://doi.org/10.5281/zenodo.3949207\n.. image:: https://img.shields.io/badge/ascl-2108.024-blue.svg?colorB=262255\n :target: https://ascl.net/2108.024\n :alt: ascl:2108.024\n.. image:: https://img.shields.io/gitter/room/Scikit-HEP/iminuit\n :target: https://gitter.im/Scikit-HEP/iminuit\n.. image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/scikit-hep/iminuit/develop?filepath=doc%2Ftutorial\n\n``iminuit`` is a Jupyter-friendly Python interface for the ``Minuit2`` C++ library maintained by CERN's `ROOT team <https://root.cern.ch>`_.\n\nMinuit 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.\n\nThe iminuit package brings additional features:\n\n- Builtin cost functions for statistical fits to N-dimensional data\n\n - Unbinned and binned maximum-likelihood + extended versions\n - `Template fits with error propagation <https://doi.org/10.1140/epjc/s10052-022-11019-z>`_\n - Least-squares (optionally robust to outliers)\n - Gaussian penalty terms for parameters\n - Cost functions can be combined by adding them: ``total_cost = cost_1 + cost_2``\n - Visualization of the fit in Jupyter notebooks\n- Support for SciPy minimizers as alternatives to Minuit's MIGRAD algorithm (optional)\n- Support for Numba accelerated functions (optional)\n\nMinimal dependencies\n--------------------\n\n``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.\n\n- ``numba``: Cost functions are partially JIT-compiled if ``numba`` is installed.\n- ``matplotlib``: Visualization of fitted model for builtin cost functions\n- ``ipywidgets``: Interactive fitting, see example below (also requires ``matplotlib``)\n- ``scipy``: Compute Minos intervals for arbitrary confidence levels\n- ``unicodeitplus``: Render names of model parameters in simple LaTeX as Unicode\n\nDocumentation\n-------------\n\nCheckout 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`_.\n\n**Lecture by Glen Cowan**\n\n`In the exercises to his lecture for the KMISchool 2022 <https://github.com/KMISchool2022>`_, 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.\n\n`Glen Cowan <https://scholar.google.com/citations?hl=en&user=ljQwt8QAAAAJ&view_op=list_works>`_ 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 <https://www.pp.rhul.ac.uk/~cowan/sda/>`_.\n\nIn a nutshell\n-------------\n\n``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.\n\n.. code:: python\n\n import numpy as np\n from iminuit import Minuit\n from iminuit.cost import UnbinnedNLL\n from scipy.stats import norm\n\n x = norm.rvs(size=1000, random_state=1)\n\n def pdf(x, mu, sigma):\n return norm.pdf(x, mu, sigma)\n\n # Negative unbinned log-likelihood, you can write your own\n cost = UnbinnedNLL(x, pdf)\n\n m = Minuit(cost, mu=0, sigma=1)\n m.limits[\"sigma\"] = (0, np.inf)\n m.migrad() # find minimum\n m.hesse() # compute uncertainties\n\n.. image:: doc/_static/demo_output.png\n :alt: Output of the demo in a Jupyter notebook\n\nInteractive fitting\n-------------------\n\n``iminuit`` optionally supports an interactive fitting mode in Jupyter notebooks.\n\n.. image:: doc/_static/interactive_demo.gif\n :alt: Animated demo of an interactive fit in a Jupyter notebook\n\nHigh performance when combined with numba\n-----------------------------------------\n\nWhen ``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`_.\n\n.. image:: doc/_static/roofit_vs_iminuit+numba.svg\n\nMore information about this benchmark is given `in the Benchmark section of the documentation <https://scikit-hep.org/iminuit/benchmark.html>`_.\n\nCitation\n--------\n\nIf you use iminuit in a scientific work, please cite us. A generic BibTeX entry is::\n\n @article{iminuit,\n author={Hans Dembinski and Piti Ongmongkolkul et al.},\n title={scikit-hep/iminuit},\n DOI={10.5281/zenodo.3949207},\n publisher={Zenodo},\n year={2020},\n month={Dec},\n url={https://doi.org/10.5281/zenodo.3949207}\n }\n\nThe DOI and URL in this entry point always to the latest release of iminuit. You can also cite the actual release that you used, please follow the `Zenodo link <https://doi.org/10.5281/zenodo.3949207>`_, which offers entries for common bibliography formats for all iminuit releases.\n\nThe recommended scientific reference for the MINUIT algorithms is::\n\n @article{James:1975dr,\n author = \"James, F. and Roos, M.\",\n title = \"{Minuit: A System for Function Minimization and Analysis of the Parameter Errors and Correlations}\",\n reportNumber = \"CERN-DD-75-20\",\n doi = \"10.1016/0010-4655(75)90039-9\",\n journal = \"Comput. Phys. Commun.\",\n volume = \"10\",\n pages = \"343--367\",\n year = \"1975\"\n }\n\nPartner projects\n----------------\n\n* `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.\n* `boost-histogram`_ from Scikit-HEP provides fast generalized histograms that you can use with the builtin cost functions.\n* `jacobi`_ provides a robust, fast, and accurate calculation of the Jacobi matrix of any transformation function and building a function for generic error propagation.\n\nVersions\n--------\n\nThe 2.x series has introduced breaking interfaces changes with respect to the 1.x series. There are no plans to introduce further breaking changes.\n\nAll interface changes from 1.x to 2.x are documented in the `changelog`_ with recommendations how to upgrade. To keep old scripts running, pin your major iminuit version to <2: the command ``pip install 'iminuit<2'`` installs the 1.x series.\n\n.. _changelog: https://scikit-hep.org/iminuit/changelog.html\n.. _tutorials: https://scikit-hep.org/iminuit/tutorials.html\n.. _discussions: https://github.com/scikit-hep/iminuit/discussions\n.. _gitter: https://gitter.im/Scikit-HEP/iminuit\n.. _jacobi: https://github.com/hdembinski/jacobi\n.. _numba_stats: https://github.com/HDembinski/numba-stats\n.. _boost-histogram: https://github.com/scikit-hep/boost-histogram\n.. _numba: https://numba.pydata.org\n.. _RooFit: https://root.cern/manual/roofit/\n",
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