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@@ -0,0 +1 @@ +/madminer-0.9.6.tar.gz diff --git a/python-madminer.spec b/python-madminer.spec new file mode 100644 index 0000000..7f27142 --- /dev/null +++ b/python-madminer.spec @@ -0,0 +1,555 @@ +%global _empty_manifest_terminate_build 0 +Name: python-madminer +Version: 0.9.6 +Release: 1 +Summary: Mining gold from MadGraph to improve limit setting in particle physics. +License: MIT +URL: https://github.com/madminer-tool/madminer +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2d/88/a1d87a6642f13ef182eb49e40beb4d69c2f860a9b658ad30bde40b516d86/madminer-0.9.6.tar.gz +BuildArch: noarch + +Requires: python3-h5py +Requires: python3-matplotlib +Requires: python3-numpy +Requires: python3-particle +Requires: python3-scipy +Requires: python3-torch +Requires: python3-uproot3 +Requires: python3-vector +Requires: python3-sympy +Requires: python3-myst-parser +Requires: python3-numpydoc +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-bqplot +Requires: python3-pandas +Requires: python3-black[jupyter] +Requires: python3-isort +Requires: python3-pytest + +%description +# MadMiner: ML based inference for particle physics + +**By Johann Brehmer, Felix Kling, Irina Espejo, Sinclert Pérez, and Kyle Cranmer** + +[![PyPI version][pypi-version-badge]][pypi-version-link] +[![CI/CD Status][ci-status-badge]][ci-status-link] +[![Docs Status][docs-status-badge]][docs-status-link] +[![Gitter chat][chat-gitter-badge]][chat-gitter-link] +[![Code style][code-style-badge]][code-style-link] +[![MIT license][mit-license-badge]][mit-license-link] +[![DOI reference][ref-zenodo-badge]][ref-zenodo-link] +[![ArXiv reference][ref-arxiv-badge]][ref-arxiv-link] + + +## Introduction + +![Schematics of the simulation and inference workflow][image-rascal-diagram] + +Particle physics processes are usually modeled with complex Monte-Carlo simulations of the hard process, parton shower, +and detector interactions. These simulators typically do not admit a tractable likelihood function: given a (potentially +high-dimensional) set of observables, it is usually not possible to calculate the probability of these observables +for some model parameters. Particle physicists usually tackle this problem of "likelihood-free inference" by +hand-picking a few "good" observables or summary statistics and filling histograms of them. But this conventional +approach discards the information in all other observables and often does not scale well to high-dimensional problems. + +In the three publications ["Constraining Effective Field Theories with Machine Learning"][ref-arxiv-madminer-1], +["A Guide to Constraining Effective Field Theories with Machine Learning"][ref-arxiv-madminer-2], and +["Mining gold from implicit models to improve likelihood-free inference"][ref-arxiv-madminer-3], +a new approach has been developed. In a nutshell, additional information is extracted from the simulations that is +closely related to the matrix elements that determine the hard process. This "augmented data" can be used to train +neural networks to efficiently approximate arbitrary likelihood ratios. We playfully call this process "mining gold" +from the simulator, since this information may be hard to get, but turns out to be very valuable for inference. + +But the gold does not have to be hard to mine: MadMiner automates these modern multivariate inference strategies. It +wraps around the simulators MadGraph and Pythia, with different options for the detector simulation. It streamlines all +steps in the analysis chain from the simulation to the extraction of the augmented data, their processing, the training +and evaluation of the neural networks, and the statistical analysis are implemented. + + +## Resources + +### Paper +Our main publication [MadMiner: Machine-learning-based inference for particle physics][ref-arxiv-link] +provides an overview over this package. We recommend reading it first before jumping into the code. + +### Installation instructions +Please have a look at our [installation instructions][docs-installation-guide]. + +### Tutorials +In the [examples][examples-folder-path] folder in this repository, we provide two tutorials. The first is called +[_Toy simulator_][examples-simulator-path], and it is based on a toy problem rather than a full particle-physics simulation. +It demonstrates inference with MadMiner without spending much time on the more technical steps of running the simulation. +The second, called [_Particle physics_][examples-physics-path], shows all steps of a particle-physics analysis with MadMiner. + +These examples are the basis of [the online tutorial][jupyter-tutorial-link] built on Jupyter Books. It also walks +through how to run MadMiner using Docker so that you do not have to install Fortran, MadGraph, Pythia, Delphes, etc. +You can even run it with no install using Binder. + +### Documentation +The madminer API is documented on [Read the Docs][docs-index]. + +### Support +If you have any questions, please chat to us in our [Gitter community][chat-gitter-link]. + + +## Citations + +If you use MadMiner, please cite our main publication, +``` +@article{Brehmer:2019xox, + author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Cranmer, Kyle", + title = "{MadMiner: Machine learning-based inference for particle physics}", + journal = "Comput. Softw. Big Sci.", + volume = "4", + year = "2020", + number = "1", + pages = "3", + doi = "10.1007/s41781-020-0035-2", + eprint = "1907.10621", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + SLACcitation = "%%CITATION = ARXIV:1907.10621;%%" +} +``` + +The code itself can be cited as +``` +@misc{MadMiner_code, + author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Perez, Sinclert and Cranmer, Kyle", + title = "{MadMiner}", + doi = "10.5281/zenodo.1489147", + url = {https://github.com/madminer-tool/madminer} +} +``` + +The main references for the implemented inference techniques are the following: + +- CARL: [1506.02169][ref-arxiv-carl]. +- MAF: [1705.07057][ref-arxiv-maf]. +- CASCAL, RASCAL, ROLR, SALLY, SALLINO, SCANDAL: + - [1805.00013][ref-arxiv-madminer-1]. + - [1805.00020][ref-arxiv-madminer-2]. + - [1805.12244][ref-arxiv-madminer-3]. +- ALICE, ALICES: [1808.00973][ref-arxiv-alice]. + + +## Acknowledgements + +We are immensely grateful to all [contributors][repo-madminer-contrib] and bug reporters! In particular, we would like +to thank Zubair Bhatti, Philipp Englert, Lukas Heinrich, Alexander Held, Samuel Homiller and Duccio Pappadopulo. + +The SCANDAL inference method is based on [Masked Autoregressive Flows][ref-arxiv-scandal], where our implementation is +a PyTorch port of the original code by George Papamakarios, available at [this repository][repo-maf-main-page]. + +![IRIS-HEP logo][image-iris-logo] + +We are grateful for the support of [IRIS-HEP][web-iris-hep] and [DIANA-HEP][web-diana-hep]. + + +[chat-gitter-badge]: https://badges.gitter.im/madminer/community.svg +[chat-gitter-link]: https://gitter.im/madminer/community +[ci-status-badge]: https://github.com/madminer-tool/madminer/actions/workflows/ci.yml/badge.svg?branch=main +[ci-status-link]: https://github.com/madminer-tool/madminer/actions/workflows/ci.yml?query=branch%3Amain +[code-style-badge]: https://img.shields.io/badge/code%20style-black-000000.svg +[code-style-link]: https://github.com/psf/black +[docs-status-badge]: https://readthedocs.org/projects/madminer/badge/?version=latest +[docs-status-link]: https://madminer.readthedocs.io/en/latest/?badge=latest +[mit-license-badge]: https://img.shields.io/badge/License-MIT-blue.svg +[mit-license-link]: https://github.com/madminer-tool/madminer/blob/main/LICENSE.md +[pypi-version-badge]: https://badge.fury.io/py/madminer.svg +[pypi-version-link]: https://badge.fury.io/py/madminer +[ref-arxiv-badge]: http://img.shields.io/badge/arXiv-1907.10621-B31B1B.svg +[ref-arxiv-link]: https://arxiv.org/abs/1907.10621 +[ref-zenodo-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.1489147.svg +[ref-zenodo-link]: https://doi.org/10.5281/zenodo.1489147 + +[docs-index]: https://madminer.readthedocs.io/en/latest/ +[docs-installation-guide ]: https://madminer.readthedocs.io/en/latest/installation.html +[examples-folder-path]: https://github.com/madminer-tool/madminer/tree/main/examples +[examples-physics-path]: https://github.com/madminer-tool/madminer/tree/main/examples/tutorial_particle_physics +[examples-simulator-path]: https://github.com/madminer-tool/madminer/tree/main/examples/tutorial_toy_simulator +[image-iris-logo]: https://iris-hep.org/assets/logos/Iris-hep-4-no-long-name.png +[image-rascal-diagram]: https://raw.githubusercontent.com/madminer-tool/madminer/main/docs/img/rascal-explainer.png +[jupyter-tutorial-link]: https://madminer-tool.github.io/madminer-tutorial +[ref-arxiv-alice]: https://arxiv.org/abs/1808.00973 +[ref-arxiv-carl]: https://arxiv.org/abs/1506.02169 +[ref-arxiv-maf]: https://arxiv.org/abs/1705.07057 +[ref-arxiv-madminer-1]: https://arxiv.org/abs/1805.00013 +[ref-arxiv-madminer-2]: https://arxiv.org/abs/1805.00020 +[ref-arxiv-madminer-3]: https://arxiv.org/abs/1805.12244 +[ref-arxiv-scandal]: https://arxiv.org/abs/1705.07057 +[repo-madminer-contrib]: https://github.com/madminer-tool/madminer/graphs/contributors +[repo-maf-main-page]: https://github.com/gpapamak/maf +[web-diana-hep]: https://diana-hep.org +[web-iris-hep]: https://iris-hep.org + + +%package -n python3-madminer +Summary: Mining gold from MadGraph to improve limit setting in particle physics. +Provides: python-madminer +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-madminer +# MadMiner: ML based inference for particle physics + +**By Johann Brehmer, Felix Kling, Irina Espejo, Sinclert Pérez, and Kyle Cranmer** + +[![PyPI version][pypi-version-badge]][pypi-version-link] +[![CI/CD Status][ci-status-badge]][ci-status-link] +[![Docs Status][docs-status-badge]][docs-status-link] +[![Gitter chat][chat-gitter-badge]][chat-gitter-link] +[![Code style][code-style-badge]][code-style-link] +[![MIT license][mit-license-badge]][mit-license-link] +[![DOI reference][ref-zenodo-badge]][ref-zenodo-link] +[![ArXiv reference][ref-arxiv-badge]][ref-arxiv-link] + + +## Introduction + +![Schematics of the simulation and inference workflow][image-rascal-diagram] + +Particle physics processes are usually modeled with complex Monte-Carlo simulations of the hard process, parton shower, +and detector interactions. These simulators typically do not admit a tractable likelihood function: given a (potentially +high-dimensional) set of observables, it is usually not possible to calculate the probability of these observables +for some model parameters. Particle physicists usually tackle this problem of "likelihood-free inference" by +hand-picking a few "good" observables or summary statistics and filling histograms of them. But this conventional +approach discards the information in all other observables and often does not scale well to high-dimensional problems. + +In the three publications ["Constraining Effective Field Theories with Machine Learning"][ref-arxiv-madminer-1], +["A Guide to Constraining Effective Field Theories with Machine Learning"][ref-arxiv-madminer-2], and +["Mining gold from implicit models to improve likelihood-free inference"][ref-arxiv-madminer-3], +a new approach has been developed. In a nutshell, additional information is extracted from the simulations that is +closely related to the matrix elements that determine the hard process. This "augmented data" can be used to train +neural networks to efficiently approximate arbitrary likelihood ratios. We playfully call this process "mining gold" +from the simulator, since this information may be hard to get, but turns out to be very valuable for inference. + +But the gold does not have to be hard to mine: MadMiner automates these modern multivariate inference strategies. It +wraps around the simulators MadGraph and Pythia, with different options for the detector simulation. It streamlines all +steps in the analysis chain from the simulation to the extraction of the augmented data, their processing, the training +and evaluation of the neural networks, and the statistical analysis are implemented. + + +## Resources + +### Paper +Our main publication [MadMiner: Machine-learning-based inference for particle physics][ref-arxiv-link] +provides an overview over this package. We recommend reading it first before jumping into the code. + +### Installation instructions +Please have a look at our [installation instructions][docs-installation-guide]. + +### Tutorials +In the [examples][examples-folder-path] folder in this repository, we provide two tutorials. The first is called +[_Toy simulator_][examples-simulator-path], and it is based on a toy problem rather than a full particle-physics simulation. +It demonstrates inference with MadMiner without spending much time on the more technical steps of running the simulation. +The second, called [_Particle physics_][examples-physics-path], shows all steps of a particle-physics analysis with MadMiner. + +These examples are the basis of [the online tutorial][jupyter-tutorial-link] built on Jupyter Books. It also walks +through how to run MadMiner using Docker so that you do not have to install Fortran, MadGraph, Pythia, Delphes, etc. +You can even run it with no install using Binder. + +### Documentation +The madminer API is documented on [Read the Docs][docs-index]. + +### Support +If you have any questions, please chat to us in our [Gitter community][chat-gitter-link]. + + +## Citations + +If you use MadMiner, please cite our main publication, +``` +@article{Brehmer:2019xox, + author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Cranmer, Kyle", + title = "{MadMiner: Machine learning-based inference for particle physics}", + journal = "Comput. Softw. Big Sci.", + volume = "4", + year = "2020", + number = "1", + pages = "3", + doi = "10.1007/s41781-020-0035-2", + eprint = "1907.10621", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + SLACcitation = "%%CITATION = ARXIV:1907.10621;%%" +} +``` + +The code itself can be cited as +``` +@misc{MadMiner_code, + author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Perez, Sinclert and Cranmer, Kyle", + title = "{MadMiner}", + doi = "10.5281/zenodo.1489147", + url = {https://github.com/madminer-tool/madminer} +} +``` + +The main references for the implemented inference techniques are the following: + +- CARL: [1506.02169][ref-arxiv-carl]. +- MAF: [1705.07057][ref-arxiv-maf]. +- CASCAL, RASCAL, ROLR, SALLY, SALLINO, SCANDAL: + - [1805.00013][ref-arxiv-madminer-1]. + - [1805.00020][ref-arxiv-madminer-2]. + - [1805.12244][ref-arxiv-madminer-3]. +- ALICE, ALICES: [1808.00973][ref-arxiv-alice]. + + +## Acknowledgements + +We are immensely grateful to all [contributors][repo-madminer-contrib] and bug reporters! In particular, we would like +to thank Zubair Bhatti, Philipp Englert, Lukas Heinrich, Alexander Held, Samuel Homiller and Duccio Pappadopulo. + +The SCANDAL inference method is based on [Masked Autoregressive Flows][ref-arxiv-scandal], where our implementation is +a PyTorch port of the original code by George Papamakarios, available at [this repository][repo-maf-main-page]. + +![IRIS-HEP logo][image-iris-logo] + +We are grateful for the support of [IRIS-HEP][web-iris-hep] and [DIANA-HEP][web-diana-hep]. + + +[chat-gitter-badge]: https://badges.gitter.im/madminer/community.svg +[chat-gitter-link]: https://gitter.im/madminer/community +[ci-status-badge]: https://github.com/madminer-tool/madminer/actions/workflows/ci.yml/badge.svg?branch=main +[ci-status-link]: https://github.com/madminer-tool/madminer/actions/workflows/ci.yml?query=branch%3Amain +[code-style-badge]: https://img.shields.io/badge/code%20style-black-000000.svg +[code-style-link]: https://github.com/psf/black +[docs-status-badge]: https://readthedocs.org/projects/madminer/badge/?version=latest +[docs-status-link]: https://madminer.readthedocs.io/en/latest/?badge=latest +[mit-license-badge]: https://img.shields.io/badge/License-MIT-blue.svg +[mit-license-link]: https://github.com/madminer-tool/madminer/blob/main/LICENSE.md +[pypi-version-badge]: https://badge.fury.io/py/madminer.svg +[pypi-version-link]: https://badge.fury.io/py/madminer +[ref-arxiv-badge]: http://img.shields.io/badge/arXiv-1907.10621-B31B1B.svg +[ref-arxiv-link]: https://arxiv.org/abs/1907.10621 +[ref-zenodo-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.1489147.svg +[ref-zenodo-link]: https://doi.org/10.5281/zenodo.1489147 + +[docs-index]: https://madminer.readthedocs.io/en/latest/ +[docs-installation-guide ]: https://madminer.readthedocs.io/en/latest/installation.html +[examples-folder-path]: https://github.com/madminer-tool/madminer/tree/main/examples +[examples-physics-path]: https://github.com/madminer-tool/madminer/tree/main/examples/tutorial_particle_physics +[examples-simulator-path]: https://github.com/madminer-tool/madminer/tree/main/examples/tutorial_toy_simulator +[image-iris-logo]: https://iris-hep.org/assets/logos/Iris-hep-4-no-long-name.png +[image-rascal-diagram]: https://raw.githubusercontent.com/madminer-tool/madminer/main/docs/img/rascal-explainer.png +[jupyter-tutorial-link]: https://madminer-tool.github.io/madminer-tutorial +[ref-arxiv-alice]: https://arxiv.org/abs/1808.00973 +[ref-arxiv-carl]: https://arxiv.org/abs/1506.02169 +[ref-arxiv-maf]: https://arxiv.org/abs/1705.07057 +[ref-arxiv-madminer-1]: https://arxiv.org/abs/1805.00013 +[ref-arxiv-madminer-2]: https://arxiv.org/abs/1805.00020 +[ref-arxiv-madminer-3]: https://arxiv.org/abs/1805.12244 +[ref-arxiv-scandal]: https://arxiv.org/abs/1705.07057 +[repo-madminer-contrib]: https://github.com/madminer-tool/madminer/graphs/contributors +[repo-maf-main-page]: https://github.com/gpapamak/maf +[web-diana-hep]: https://diana-hep.org +[web-iris-hep]: https://iris-hep.org + + +%package help +Summary: Development documents and examples for madminer +Provides: python3-madminer-doc +%description help +# MadMiner: ML based inference for particle physics + +**By Johann Brehmer, Felix Kling, Irina Espejo, Sinclert Pérez, and Kyle Cranmer** + +[![PyPI version][pypi-version-badge]][pypi-version-link] +[![CI/CD Status][ci-status-badge]][ci-status-link] +[![Docs Status][docs-status-badge]][docs-status-link] +[![Gitter chat][chat-gitter-badge]][chat-gitter-link] +[![Code style][code-style-badge]][code-style-link] +[![MIT license][mit-license-badge]][mit-license-link] +[![DOI reference][ref-zenodo-badge]][ref-zenodo-link] +[![ArXiv reference][ref-arxiv-badge]][ref-arxiv-link] + + +## Introduction + +![Schematics of the simulation and inference workflow][image-rascal-diagram] + +Particle physics processes are usually modeled with complex Monte-Carlo simulations of the hard process, parton shower, +and detector interactions. These simulators typically do not admit a tractable likelihood function: given a (potentially +high-dimensional) set of observables, it is usually not possible to calculate the probability of these observables +for some model parameters. Particle physicists usually tackle this problem of "likelihood-free inference" by +hand-picking a few "good" observables or summary statistics and filling histograms of them. But this conventional +approach discards the information in all other observables and often does not scale well to high-dimensional problems. + +In the three publications ["Constraining Effective Field Theories with Machine Learning"][ref-arxiv-madminer-1], +["A Guide to Constraining Effective Field Theories with Machine Learning"][ref-arxiv-madminer-2], and +["Mining gold from implicit models to improve likelihood-free inference"][ref-arxiv-madminer-3], +a new approach has been developed. In a nutshell, additional information is extracted from the simulations that is +closely related to the matrix elements that determine the hard process. This "augmented data" can be used to train +neural networks to efficiently approximate arbitrary likelihood ratios. We playfully call this process "mining gold" +from the simulator, since this information may be hard to get, but turns out to be very valuable for inference. + +But the gold does not have to be hard to mine: MadMiner automates these modern multivariate inference strategies. It +wraps around the simulators MadGraph and Pythia, with different options for the detector simulation. It streamlines all +steps in the analysis chain from the simulation to the extraction of the augmented data, their processing, the training +and evaluation of the neural networks, and the statistical analysis are implemented. + + +## Resources + +### Paper +Our main publication [MadMiner: Machine-learning-based inference for particle physics][ref-arxiv-link] +provides an overview over this package. We recommend reading it first before jumping into the code. + +### Installation instructions +Please have a look at our [installation instructions][docs-installation-guide]. + +### Tutorials +In the [examples][examples-folder-path] folder in this repository, we provide two tutorials. The first is called +[_Toy simulator_][examples-simulator-path], and it is based on a toy problem rather than a full particle-physics simulation. +It demonstrates inference with MadMiner without spending much time on the more technical steps of running the simulation. +The second, called [_Particle physics_][examples-physics-path], shows all steps of a particle-physics analysis with MadMiner. + +These examples are the basis of [the online tutorial][jupyter-tutorial-link] built on Jupyter Books. It also walks +through how to run MadMiner using Docker so that you do not have to install Fortran, MadGraph, Pythia, Delphes, etc. +You can even run it with no install using Binder. + +### Documentation +The madminer API is documented on [Read the Docs][docs-index]. + +### Support +If you have any questions, please chat to us in our [Gitter community][chat-gitter-link]. + + +## Citations + +If you use MadMiner, please cite our main publication, +``` +@article{Brehmer:2019xox, + author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Cranmer, Kyle", + title = "{MadMiner: Machine learning-based inference for particle physics}", + journal = "Comput. Softw. Big Sci.", + volume = "4", + year = "2020", + number = "1", + pages = "3", + doi = "10.1007/s41781-020-0035-2", + eprint = "1907.10621", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + SLACcitation = "%%CITATION = ARXIV:1907.10621;%%" +} +``` + +The code itself can be cited as +``` +@misc{MadMiner_code, + author = "Brehmer, Johann and Kling, Felix and Espejo, Irina and Perez, Sinclert and Cranmer, Kyle", + title = "{MadMiner}", + doi = "10.5281/zenodo.1489147", + url = {https://github.com/madminer-tool/madminer} +} +``` + +The main references for the implemented inference techniques are the following: + +- CARL: [1506.02169][ref-arxiv-carl]. +- MAF: [1705.07057][ref-arxiv-maf]. +- CASCAL, RASCAL, ROLR, SALLY, SALLINO, SCANDAL: + - [1805.00013][ref-arxiv-madminer-1]. + - [1805.00020][ref-arxiv-madminer-2]. + - [1805.12244][ref-arxiv-madminer-3]. +- ALICE, ALICES: [1808.00973][ref-arxiv-alice]. + + +## Acknowledgements + +We are immensely grateful to all [contributors][repo-madminer-contrib] and bug reporters! In particular, we would like +to thank Zubair Bhatti, Philipp Englert, Lukas Heinrich, Alexander Held, Samuel Homiller and Duccio Pappadopulo. + +The SCANDAL inference method is based on [Masked Autoregressive Flows][ref-arxiv-scandal], where our implementation is +a PyTorch port of the original code by George Papamakarios, available at [this repository][repo-maf-main-page]. + +![IRIS-HEP logo][image-iris-logo] + +We are grateful for the support of [IRIS-HEP][web-iris-hep] and [DIANA-HEP][web-diana-hep]. + + +[chat-gitter-badge]: https://badges.gitter.im/madminer/community.svg +[chat-gitter-link]: https://gitter.im/madminer/community +[ci-status-badge]: https://github.com/madminer-tool/madminer/actions/workflows/ci.yml/badge.svg?branch=main +[ci-status-link]: https://github.com/madminer-tool/madminer/actions/workflows/ci.yml?query=branch%3Amain +[code-style-badge]: https://img.shields.io/badge/code%20style-black-000000.svg +[code-style-link]: https://github.com/psf/black +[docs-status-badge]: https://readthedocs.org/projects/madminer/badge/?version=latest +[docs-status-link]: https://madminer.readthedocs.io/en/latest/?badge=latest +[mit-license-badge]: https://img.shields.io/badge/License-MIT-blue.svg +[mit-license-link]: https://github.com/madminer-tool/madminer/blob/main/LICENSE.md +[pypi-version-badge]: https://badge.fury.io/py/madminer.svg +[pypi-version-link]: https://badge.fury.io/py/madminer +[ref-arxiv-badge]: http://img.shields.io/badge/arXiv-1907.10621-B31B1B.svg +[ref-arxiv-link]: https://arxiv.org/abs/1907.10621 +[ref-zenodo-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.1489147.svg +[ref-zenodo-link]: https://doi.org/10.5281/zenodo.1489147 + +[docs-index]: https://madminer.readthedocs.io/en/latest/ +[docs-installation-guide ]: https://madminer.readthedocs.io/en/latest/installation.html +[examples-folder-path]: https://github.com/madminer-tool/madminer/tree/main/examples +[examples-physics-path]: https://github.com/madminer-tool/madminer/tree/main/examples/tutorial_particle_physics +[examples-simulator-path]: https://github.com/madminer-tool/madminer/tree/main/examples/tutorial_toy_simulator +[image-iris-logo]: https://iris-hep.org/assets/logos/Iris-hep-4-no-long-name.png +[image-rascal-diagram]: https://raw.githubusercontent.com/madminer-tool/madminer/main/docs/img/rascal-explainer.png +[jupyter-tutorial-link]: https://madminer-tool.github.io/madminer-tutorial +[ref-arxiv-alice]: https://arxiv.org/abs/1808.00973 +[ref-arxiv-carl]: https://arxiv.org/abs/1506.02169 +[ref-arxiv-maf]: https://arxiv.org/abs/1705.07057 +[ref-arxiv-madminer-1]: https://arxiv.org/abs/1805.00013 +[ref-arxiv-madminer-2]: https://arxiv.org/abs/1805.00020 +[ref-arxiv-madminer-3]: https://arxiv.org/abs/1805.12244 +[ref-arxiv-scandal]: https://arxiv.org/abs/1705.07057 +[repo-madminer-contrib]: https://github.com/madminer-tool/madminer/graphs/contributors +[repo-maf-main-page]: https://github.com/gpapamak/maf +[web-diana-hep]: https://diana-hep.org +[web-iris-hep]: https://iris-hep.org + + +%prep +%autosetup -n madminer-0.9.6 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-madminer -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.6-1 +- Package Spec generated @@ -0,0 +1 @@ +d6ade79a2a37f93b851f464227752e2d madminer-0.9.6.tar.gz |