semvec/pypi-packages huggingface dataset:
dibisoplot 0.9
A Python library for plotting bibliometric research data, developed at the DiBISO.
Requires Python: >=3.10
Keywords: DiBISO, Data analysis, Scientific research, Plotly

# DiBISO plot

A Python library for plotting bibliometric research data, developed at the [DiBISO](https://www.bibliotheques.universite-paris-saclay.fr/en/department-libraries-information-and-open-science-dibiso-and-its-missions).

For more details about this project, you can read our [technical report](https://universite-paris-saclay.hal.science/hal-05336463).

Install with:

```bash
pip install dibisoplot
```

The library contains the following submodules:

  - `biso`: plot data for the BiSO (Open-Science Report)
  - `pubpart`: plot data about publications and partnerships


Homepage: https://pypi.org/project/dibisoplot/

Documentation: https://dibiso-upsaclay.github.io/dibisoplot/

Repository URL: https://github.com/dibiso-upsaclay/dibisoplot

Repository DOI: https://doi.org/10.5281/zenodo.17251536

Technical report: https://universite-paris-saclay.hal.science/hal-05336463


## About the BiSO

The BiSO (Bilan Science Ouverte - Open Science Report) is an annual report produced for each research laboratory under 
the responsibility of the Open Science teams (DiBISO) at Université Paris-Saclay. 
Prepared in collaboration with the laboratories, it is based on open data, mainly coming from the [HAL](https://hal.science/) repository but 
also from [OpenAlex](https://openalex.org/), [scanR](https://scanr.enseignementsup-recherche.gouv.fr/) and the [BSO](https://barometredelascienceouverte.esr.gouv.fr/). 
The BiSO presents indicators such as publication types, open access rates, and collaborations. 
Primarily intended for the laboratories under our scope, the report supports the development of open science practices. 
The code and template are shared under open-source licenses, allowing other institutions to reuse and adapt the 
methodology.

## About the Publications and Partnerships

This submodule uses data from [OpenAlex](https://openalex.org/) to make plots about publication topics, institutions, and citations, as 
well as common publication topics and potential collaboration topics with other institutions.

Romain THOMAS 2025  
Department of Libraries, Information and Open Science (DiBISO)  
Université Paris-Saclay
madevent7 0.3.1
Fast and flexible phase space mappings
Requires Python: >=3.11

## madevent7

### Installation

#### Package

Packages on PyPI are available for Linux and MacOS X (with Apple silicon),
for Python 3.11 to 3.14.

```sh
pip install madevent7
```

#### Build

First install `scikit_build_core` with

```sh
pip install scikit_build_core
```

The pre-installed version of `cmake` under MacOS is outdated, so you might need to install a
newer version, for example with

```sh
brew install cmake
```

Then check out the `madevent7` repository and build and install it with

```sh
git clone git@github.com:madgraph-ml/madevent7.git
cd madevent7
pip install .
```

For a development version allowing for incremental build, use the following command instead:

```sh
pip install --no-build-isolation -Cbuild-dir=build -Ccmake.build-type=RelWithDebInfo .
```

This will create a directory `build` where you can run make directly to make development
easier. To update the python module itself, make sure to also run the `pip install` command
above again. This will not happen automatically, even if you make the installation editable!
Build type `RelWithDebInfo` generates optimized code but includes debug symbols, so you
can use `lldb` or `gdb` to debug the code.

### Tests

To run the tests, you need to have the `pytest`, `numpy` and `torch` packages installed.
One test optionally requires the `lhapdf` package (can be installed via conda or built from
source) and the `NNPDF40_nlo_as_01180` PDF set.

To run the tests, go to the root directory of the repository and run
```sh
pytest tests
```