Metadata-Version: 2.1
Name: crillab-metrics
Version: 1.2.4
Summary: rEproducible sofTware peRformance analysIs in perfeCt Simplicity
Home-page: https://github.com/crillab/metrics
Author: Thibault Falque, Romain Wallon, Hugues Wattez
Author-email: metrics@cril.fr
License: LGPLv3+
Description: # mETRICS - rEproducible sofTware peRformance analysIs in perfeCt Simplicity
        
        [![License](https://img.shields.io/pypi/l/crillab-metrics)](LICENSE.md)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/crillab-metrics)
        ![PyPI - Status](https://img.shields.io/pypi/status/crillab-metrics)
        ![Travis (.org)](https://img.shields.io/travis/crillab/metrics)
        ![Sonar Quality Gate](https://img.shields.io/sonar/quality_gate/crillab_metrics?server=https%3A%2F%2Fsonarcloud.io)
        ![Sonar Coverage](https://img.shields.io/sonar/coverage/crillab_metrics?server=https%3A%2F%2Fsonarcloud.io)
        
        ## Authors
        
        - Thibault Falque - Exakis Nelite
        - [Romain Wallon - CRIL, Univ Artois & CNRS](https://www.cril.univ-artois.fr/~wallon) 
        - [Hugues Wattez - Laboratoire d'Informatique de l'X (LIX), École Polytechnique](https://hwattez.github.io/markdown-cv/)
        
        ## About *Metrics*
        
        *Metrics* is an open-source Python library developed at
        [CRIL](http://www.cril.fr), designed to facilitate the conduction of
        experiments and their analysis.
        
        The main objective of *Metrics* is to provide a complete toolchain from
        the execution of software programs to the analysis of their performance.
        In particular, the development of *Metrics* started with the observation
        that, in the SAT community, the process of experimenting solver remains
        mostly the same: everybody collects almost the same statistics about the
        solver execution.
        However, there are probably as many scripts as researchers in the domain
        for retrieving experimental data and drawing figures.
        There is thus clearly a need for a tool that unifies and makes easier the
        analysis of solver experiments.
        
        The ambition of Metrics is thus to simplify the retrieval of experimental data
        from many different kinds of inputs (including the solver's output), and
        provide a nice interface for drawing commonly used plots, computing statistics
        about the execution of the solver, and effortlessly organizing them.
        In the end, the main purpose of Metrics is to favor the sharing and
        reproducibility of experimental results and their analysis.
        
        ## Installation
        
        To execute *Metrics* on your computer, you first need to install
        [Python](https://www.python.org/downloads/) (at least version **3.8**).
        
        You may install *Metrics* using `pip`, as the `metrics` library is
        [available on PyPI](https://pypi.org/project/crillab-metrics/).
        
        ```bash
        pip install crillab-metrics
        ```
        
        Note that, depending on your Python installation, you may need to use `pip3`
        to install it, or to execute `pip` as a module, as follows.
        
        ```bash
        python3 -m pip install crillab-metrics
        ```
        
        To improve the reproducibility of the experiments, we highly recommend to use
        a [*virtual environment*](https://docs.python.org/3/tutorial/venv.html) for
        each analysis you create with *Metrics*, and thus to install the `metrics`
        library in this virtual environment rather than with a system-wide
        installation.
        
        ## Using *Metrics*
        
        You may find more information on how to use *Metrics* in the
        [documentation](https://metrics.readthedocs.io) we provide for the package.
        
        ## Citing *Metrics*
        
        If you are using *Metrics* in your papers, we kindly ask you to either refer to
        this repository or to one of the following papers:
        
        + [*Metrics : Mission Expérimentations*.](https://hal.archives-ouvertes.fr/hal-03295285/document)
          Thibault Falque, Romain Wallon and Hugues Wattez.
          16es Journées Francophones de Programmation par Contraintes (JFPC'21), 2021.
        + *Metrics: Towards a Unified Library for Experimenting Solvers*.
          Thibault Falque, Romain Wallon and Hugues Wattez.
          11th International Workshop on Pragmatics of SAT (POS'20), 2020.
        
Keywords: reproducible software performance analysis
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
