From 3d4f88d5d8c873f1052e0d31170a888d6e0a4e42 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Wed, 31 Jul 2024 16:15:39 +0200 Subject: [PATCH] fix --- doc/index.rst | 150 -------------------------------------------------- 1 file changed, 150 deletions(-) delete mode 100644 doc/index.rst diff --git a/doc/index.rst b/doc/index.rst deleted file mode 100644 index d9159b85..00000000 --- a/doc/index.rst +++ /dev/null @@ -1,150 +0,0 @@ -.. |iminuit| image:: _static/iminuit_logo.svg - :alt: iminuit - -|iminuit| -========= - -**These docs are for iminuit version:** |release| - -.. 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 `_. - -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. - -.. 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:: _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:: _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:: _static/roofit_vs_iminuit+numba.svg - -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. - -.. _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.ch/master/namespaceRooFit.html -.. include:: bibliography.txt - -.. toctree:: - :hidden: - - about - install - reference - tutorials - studies - faq - changelog - benchmark - contribute - citation