automatic import of python-madminer

3 files changed, 557 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..2352054 100644
--- a/.gitignore
+++ b/.gitignore
@@ -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
diff --git a/sources b/sources
new file mode 100644
index 0000000..b295658
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d6ade79a2a37f93b851f464227752e2d  madminer-0.9.6.tar.gz