# correctionlib
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## 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
def f(*args: 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
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[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
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[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/
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[rtd-link]: https://cms-nanoAOD.github.io/correctionlib/
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