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|
%global _empty_manifest_terminate_build 0
Name: python-correctionlib
Version: 2.2.2
Release: 1
Summary: A generic correction library
License: BSD 3-Clause License
URL: https://github.com/cms-nanoAOD/correctionlib
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d7/a4/221534a863f2390d9c4011b68c819bc67537c17d6b2e82641437904886ff/correctionlib-2.2.2.tar.gz
Requires: python3-numpy
Requires: python3-pydantic
Requires: python3-rich
Requires: python3-typing
Requires: python3-dataclasses
Requires: python3-typing-extensions
Requires: python3-pandas
Requires: python3-uproot
Requires: python3-requests
Requires: python3-scipy
Requires: python3-pytest
Requires: python3-pre-commit
Requires: python3-mypy
Requires: python3-uhi
Requires: python3-recommonmark
Requires: python3-Sphinx
Requires: python3-sphinx-copybutton
Requires: python3-sphinx-rtd-theme
Requires: python3-sphinx-jsonschema
Requires: python3-myst-parser
Requires: python3-nbsphinx
Requires: python3-pytest
Requires: python3-scipy
%description
# correctionlib
[![Actions Status][actions-badge]][actions-link]
[![Documentation Status][rtd-badge]][rtd-link]
[![Code style: black][black-badge]][black-link]
[![conda version][conda-badge]][conda-link]
[![PyPI version][pypi-version]][pypi-link]
[![PyPI platforms][pypi-platforms]][pypi-link]
[![GitHub Discussion][github-discussions-badge]][github-discussions-link]
## Introduction
The purpose of this library is to provide a well-structured JSON data format for a
wide variety of ad-hoc correction factors encountered in a typical HEP analysis and
a companion evaluation tool suitable for use in C++ and python programs.
Here we restrict our definition of correction factors to a class of functions with
scalar inputs that produce a scalar output.
In python, the function signature is:
```python
from typing import Union
def f(*args: Union[str,int,float]) -> float:
return ...
```
In C++, the evaluator implements this currently as:
```cpp
double Correction::evaluate(const std::vector<std::variant<int, double, std::string>>& values) const;
```
The supported function classes include:
* multi-dimensional binned lookups;
* binned lookups pointing to multi-argument formulas with a restricted
math function set (`exp`, `sqrt`, etc.);
* categorical (string or integer enumeration) maps;
* input transforms (updating one input value in place); and
* compositions of the above.
Each function type is represented by a "node" in a call graph and holds all
of its parameters in a JSON structure, described by the JSON schema.
Possible future extension nodes might include weigted sums (which, when composed with
the others, could represent a BDT) and perhaps simple MLPs.
The tool should provide:
* standardized, versioned [JSON schemas](https://json-schema.org/);
* forward-porting tools (to migrate data written in older schema versions); and
* a well-optimized C++ evaluator and python bindings (with numpy vectorization support).
This tool will definitely not provide:
* support for `TLorentzVector` or other object-type inputs (such tools should be written
as a higher-level tool depending on this library as a low-level tool)
Formula support currently includes a mostly-complete subset of the ROOT library `TFormula` class,
and is implemented in a threadsafe standalone manner. The parsing grammar is formally defined
and parsed through the use of a header-only [PEG parser library](https://github.com/yhirose/cpp-peglib).
The supported features mirror CMSSW's [reco::formulaEvaluator](https://github.com/cms-sw/cmssw/pull/11516)
and fully passes the test suite for that utility with the purposeful exception of the `TMath::` namespace.
The python bindings may be able to call into [numexpr](https://numexpr.readthedocs.io/en/latest/user_guide.html),
though, due to the tree-like structure of the corrections, it may prove difficult to exploit vectorization
at levels other than the entrypoint.
Detailed instructions for installing and using this package are provided in the [documentation][rtd-link].
## Creating new corrections
A demo/tutorial of the features is available in the [documentation][rtd-link] and also available interactively
on [binder](https://mybinder.org/v2/gh/cms-nanoAOD/correctionlib/HEAD?labpath=binder%2Fcorrectionlib_tutorial.ipynb)
The `correctionlib.schemav2` module provides a helpful framework for defining correction objects
and `correctionlib.convert` includes select conversion routines for common types. Nodes can be type-checked as they are
constructed using the [parse_obj](https://pydantic-docs.helpmanual.io/usage/models/#helper-functions)
class method or by directly constructing them using keyword arguments.
## Developing
See CONTRIBUTING.md
[actions-badge]: https://github.com/cms-nanoAOD/correctionlib/workflows/CI/badge.svg
[actions-link]: https://github.com/cms-nanoAOD/correctionlib/actions
[black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg
[black-link]: https://github.com/psf/black
[conda-badge]: https://img.shields.io/conda/vn/conda-forge/correctionlib.svg
[conda-link]: https://github.com/conda-forge/correctionlib-feedstock
[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github
[github-discussions-link]: https://github.com/cms-nanoAOD/correctionlib/discussions
[gitter-badge]: https://badges.gitter.im/https://github.com/cms-nanoAOD/correctionlib/community.svg
[gitter-link]: https://gitter.im/https://github.com/cms-nanoAOD/correctionlib/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
[pypi-link]: https://pypi.org/project/correctionlib/
[pypi-platforms]: https://img.shields.io/pypi/pyversions/correctionlib
[pypi-version]: https://badge.fury.io/py/correctionlib.svg
[rtd-badge]: https://github.com/cms-nanoAOD/correctionlib/actions/workflows/docs.yml/badge.svg
[rtd-link]: https://cms-nanoAOD.github.io/correctionlib/
%package -n python3-correctionlib
Summary: A generic correction library
Provides: python-correctionlib
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-correctionlib
# correctionlib
[![Actions Status][actions-badge]][actions-link]
[![Documentation Status][rtd-badge]][rtd-link]
[![Code style: black][black-badge]][black-link]
[![conda version][conda-badge]][conda-link]
[![PyPI version][pypi-version]][pypi-link]
[![PyPI platforms][pypi-platforms]][pypi-link]
[![GitHub Discussion][github-discussions-badge]][github-discussions-link]
## Introduction
The purpose of this library is to provide a well-structured JSON data format for a
wide variety of ad-hoc correction factors encountered in a typical HEP analysis and
a companion evaluation tool suitable for use in C++ and python programs.
Here we restrict our definition of correction factors to a class of functions with
scalar inputs that produce a scalar output.
In python, the function signature is:
```python
from typing import Union
def f(*args: Union[str,int,float]) -> float:
return ...
```
In C++, the evaluator implements this currently as:
```cpp
double Correction::evaluate(const std::vector<std::variant<int, double, std::string>>& values) const;
```
The supported function classes include:
* multi-dimensional binned lookups;
* binned lookups pointing to multi-argument formulas with a restricted
math function set (`exp`, `sqrt`, etc.);
* categorical (string or integer enumeration) maps;
* input transforms (updating one input value in place); and
* compositions of the above.
Each function type is represented by a "node" in a call graph and holds all
of its parameters in a JSON structure, described by the JSON schema.
Possible future extension nodes might include weigted sums (which, when composed with
the others, could represent a BDT) and perhaps simple MLPs.
The tool should provide:
* standardized, versioned [JSON schemas](https://json-schema.org/);
* forward-porting tools (to migrate data written in older schema versions); and
* a well-optimized C++ evaluator and python bindings (with numpy vectorization support).
This tool will definitely not provide:
* support for `TLorentzVector` or other object-type inputs (such tools should be written
as a higher-level tool depending on this library as a low-level tool)
Formula support currently includes a mostly-complete subset of the ROOT library `TFormula` class,
and is implemented in a threadsafe standalone manner. The parsing grammar is formally defined
and parsed through the use of a header-only [PEG parser library](https://github.com/yhirose/cpp-peglib).
The supported features mirror CMSSW's [reco::formulaEvaluator](https://github.com/cms-sw/cmssw/pull/11516)
and fully passes the test suite for that utility with the purposeful exception of the `TMath::` namespace.
The python bindings may be able to call into [numexpr](https://numexpr.readthedocs.io/en/latest/user_guide.html),
though, due to the tree-like structure of the corrections, it may prove difficult to exploit vectorization
at levels other than the entrypoint.
Detailed instructions for installing and using this package are provided in the [documentation][rtd-link].
## Creating new corrections
A demo/tutorial of the features is available in the [documentation][rtd-link] and also available interactively
on [binder](https://mybinder.org/v2/gh/cms-nanoAOD/correctionlib/HEAD?labpath=binder%2Fcorrectionlib_tutorial.ipynb)
The `correctionlib.schemav2` module provides a helpful framework for defining correction objects
and `correctionlib.convert` includes select conversion routines for common types. Nodes can be type-checked as they are
constructed using the [parse_obj](https://pydantic-docs.helpmanual.io/usage/models/#helper-functions)
class method or by directly constructing them using keyword arguments.
## Developing
See CONTRIBUTING.md
[actions-badge]: https://github.com/cms-nanoAOD/correctionlib/workflows/CI/badge.svg
[actions-link]: https://github.com/cms-nanoAOD/correctionlib/actions
[black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg
[black-link]: https://github.com/psf/black
[conda-badge]: https://img.shields.io/conda/vn/conda-forge/correctionlib.svg
[conda-link]: https://github.com/conda-forge/correctionlib-feedstock
[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github
[github-discussions-link]: https://github.com/cms-nanoAOD/correctionlib/discussions
[gitter-badge]: https://badges.gitter.im/https://github.com/cms-nanoAOD/correctionlib/community.svg
[gitter-link]: https://gitter.im/https://github.com/cms-nanoAOD/correctionlib/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
[pypi-link]: https://pypi.org/project/correctionlib/
[pypi-platforms]: https://img.shields.io/pypi/pyversions/correctionlib
[pypi-version]: https://badge.fury.io/py/correctionlib.svg
[rtd-badge]: https://github.com/cms-nanoAOD/correctionlib/actions/workflows/docs.yml/badge.svg
[rtd-link]: https://cms-nanoAOD.github.io/correctionlib/
%package help
Summary: Development documents and examples for correctionlib
Provides: python3-correctionlib-doc
%description help
# correctionlib
[![Actions Status][actions-badge]][actions-link]
[![Documentation Status][rtd-badge]][rtd-link]
[![Code style: black][black-badge]][black-link]
[![conda version][conda-badge]][conda-link]
[![PyPI version][pypi-version]][pypi-link]
[![PyPI platforms][pypi-platforms]][pypi-link]
[![GitHub Discussion][github-discussions-badge]][github-discussions-link]
## Introduction
The purpose of this library is to provide a well-structured JSON data format for a
wide variety of ad-hoc correction factors encountered in a typical HEP analysis and
a companion evaluation tool suitable for use in C++ and python programs.
Here we restrict our definition of correction factors to a class of functions with
scalar inputs that produce a scalar output.
In python, the function signature is:
```python
from typing import Union
def f(*args: Union[str,int,float]) -> float:
return ...
```
In C++, the evaluator implements this currently as:
```cpp
double Correction::evaluate(const std::vector<std::variant<int, double, std::string>>& values) const;
```
The supported function classes include:
* multi-dimensional binned lookups;
* binned lookups pointing to multi-argument formulas with a restricted
math function set (`exp`, `sqrt`, etc.);
* categorical (string or integer enumeration) maps;
* input transforms (updating one input value in place); and
* compositions of the above.
Each function type is represented by a "node" in a call graph and holds all
of its parameters in a JSON structure, described by the JSON schema.
Possible future extension nodes might include weigted sums (which, when composed with
the others, could represent a BDT) and perhaps simple MLPs.
The tool should provide:
* standardized, versioned [JSON schemas](https://json-schema.org/);
* forward-porting tools (to migrate data written in older schema versions); and
* a well-optimized C++ evaluator and python bindings (with numpy vectorization support).
This tool will definitely not provide:
* support for `TLorentzVector` or other object-type inputs (such tools should be written
as a higher-level tool depending on this library as a low-level tool)
Formula support currently includes a mostly-complete subset of the ROOT library `TFormula` class,
and is implemented in a threadsafe standalone manner. The parsing grammar is formally defined
and parsed through the use of a header-only [PEG parser library](https://github.com/yhirose/cpp-peglib).
The supported features mirror CMSSW's [reco::formulaEvaluator](https://github.com/cms-sw/cmssw/pull/11516)
and fully passes the test suite for that utility with the purposeful exception of the `TMath::` namespace.
The python bindings may be able to call into [numexpr](https://numexpr.readthedocs.io/en/latest/user_guide.html),
though, due to the tree-like structure of the corrections, it may prove difficult to exploit vectorization
at levels other than the entrypoint.
Detailed instructions for installing and using this package are provided in the [documentation][rtd-link].
## Creating new corrections
A demo/tutorial of the features is available in the [documentation][rtd-link] and also available interactively
on [binder](https://mybinder.org/v2/gh/cms-nanoAOD/correctionlib/HEAD?labpath=binder%2Fcorrectionlib_tutorial.ipynb)
The `correctionlib.schemav2` module provides a helpful framework for defining correction objects
and `correctionlib.convert` includes select conversion routines for common types. Nodes can be type-checked as they are
constructed using the [parse_obj](https://pydantic-docs.helpmanual.io/usage/models/#helper-functions)
class method or by directly constructing them using keyword arguments.
## Developing
See CONTRIBUTING.md
[actions-badge]: https://github.com/cms-nanoAOD/correctionlib/workflows/CI/badge.svg
[actions-link]: https://github.com/cms-nanoAOD/correctionlib/actions
[black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg
[black-link]: https://github.com/psf/black
[conda-badge]: https://img.shields.io/conda/vn/conda-forge/correctionlib.svg
[conda-link]: https://github.com/conda-forge/correctionlib-feedstock
[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github
[github-discussions-link]: https://github.com/cms-nanoAOD/correctionlib/discussions
[gitter-badge]: https://badges.gitter.im/https://github.com/cms-nanoAOD/correctionlib/community.svg
[gitter-link]: https://gitter.im/https://github.com/cms-nanoAOD/correctionlib/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
[pypi-link]: https://pypi.org/project/correctionlib/
[pypi-platforms]: https://img.shields.io/pypi/pyversions/correctionlib
[pypi-version]: https://badge.fury.io/py/correctionlib.svg
[rtd-badge]: https://github.com/cms-nanoAOD/correctionlib/actions/workflows/docs.yml/badge.svg
[rtd-link]: https://cms-nanoAOD.github.io/correctionlib/
%prep
%autosetup -n correctionlib-2.2.2
%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-correctionlib -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.2-1
- Package Spec generated
|
