Metadata-Version: 2.1
Name: sora-astro
Version: 0.2
Summary: Stellar Occultation Library
Home-page: https://github.com/riogroup/SORA
Author: SORA Team
Author-email: rio.occteam@gmail.com
License: MIT
Project-URL: Bug Reports, https://github.com/riogroup/SORA/issues
Project-URL: Documentation, https://sora.readthedocs.io/
Description: SORA
        ====
        
        PACKAGE DESCRIPTION
        -------------------
        
        SORA is the acronym for *Stellar Occultation Reduction and Analysis*.
        It is a library developed in Python3 with the tools to analyze stellar
        occultation data. It is based on Astropy functions and Classes.
        **Full documentation at https://sora.readthedocs.io/**
        
        A stellar occultation occurs when a solar system object passes in front
        of a star for an observer on Earth, and its shadow causes a temporary
        drop in the observed flux of the star. This technique allows the
        determination of sizes and shapes with kilometre precision and to obtain
        characteristics of the object, such as its albedo, the presence of an
        atmosphere, rings, jets, or other structures around the body or even
        the detection of topographic features (Sicardy et al. 2011, 2016
        Braga-Ribas et al. 2013, 2014, 2019, Dias-Oliveira et al., 2015, 2017,
        Benedetti-Rossi et al., 2016, 2019, Ortiz et al., 2015, 2017, 2020,
        Leiva et al., 2017, Bérard et al., 2017, Morgado et al., 2019, Gomes-Júnior et al., 2020,
        Souami et al., 2020, Santos-Sanz et al., 2021).
        
        SORA is a Python-based, object-oriented library for optimal analysis of
        stellar occultation data. The user can use this package to build pipelines
        to analyse their stellar occultation’s data. It includes processes starting
        on the prediction of such events to the resulting size, shape and position of
        the Solar System object. The main modules available at version 0.2
        are: **star**, **body**, **observer**, **lightcurve** and
        **occultation**. It is important to note that new modules and other
        improvements and implementations can be available in future versions.
        
        AUTHORS
        -------
        
        Altair R. Gomes-Júnior (1, 2),
        Bruno E. Morgado (3, 2, 4),
        Gustavo Benedetti-Rossi (1, 3, 2),
        Rodrigo C. Boufleur (4, 2),
        Flavia L. Rommel (4, 2),
        Martin B. Huarca (2, 4)
        
        (1) UNESP - São Paulo State University, Grupo de Dinâmica Orbital e Planetologia, CEP 12516-410, Guaratinguetá, SP 12516-410, Brazil</br>
        (2) Laboratório Interinstitucional de e-Astronomia - LIneA and INCT do e-Universo, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil</br>
        (3) LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Univ. Paris Diderot, Sorbonne Paris Cité, 5 place Jules Janssen, 92195 Meudon, France</br>
        (4) Observatório Nacional/MCTIC, R. General José Cristino 77, Rio de Janeiro, RJ 20.921-400, Brazil</br>
        
        CITATION
        --------
        
        If you use SORA in a scientific publication, we would appreciate that you add at your acknowledgement the following statement:
        
            This research made use of SORA, a python package for stellar occultations reduction and analysis, developed with the support of ERC Lucky Star and LIneA/Brazil.
        
        SYSTEM REQUIREMENTS AND INSTALLATION
        ------------------------------------
        
        SORA was developed in Python 3.7 and requires the following packages:
        
        -  Astropy (4.0): For astronomical related functions, mainly coordinates and time.
        
        -  Astroquery (0.4.1): To query astronomical database as JPL and Vizier.
        
        -  Matplotlib (3.1.1): For easy and beautiful plots.
        
        -  NumPy (1.18.1): Otimized mathematical functions.
        
        -  SciPy (1.4.1): Otimized functions for mathematics, science, and engineering.
        
        -  SpiceyPy (3.0.2): SPICE/NAIF functions in python.
        
        -  PyERFA (2.0): Python wrapper for the ERFA library based on the SOFA library.
        
        -  Cartopy (0.17): Geospatial data processing to produce maps.
        
        The user can install SORA and most of its requirements using **pip**, only
        Cartopy should be installed from conda afterwards.
        
        ```shell
        pip install sora-astro
        conda install -c conda-forge cartopy
        ```
        
        If you are a GitHub user, you can also use:
        
        ```shell
        git clone https://github.com/riogroup/SORA.git
        cd SORA
        pip install .
        conda install -c conda-forge cartopy
        ```
        
        For a better experience with SORA, we recommend the use of [Jupyter]. The creation of a dedicated Conda environment for SORA is suggested to avoid requirement issues.
        
        Acknowledgements
        ----------------
        
        The SORA package is hosted on a GitHub repository. It was developed with support
        of the LuckyStar, that agglomerates the efforts of the Paris, Granada, and Rio
        teams. The LuckyStar is funded by the ERC (European Research Council)
        under the European Community’s H2020 (2014-2020/ERC Grant Agreement No. 669416). Also,
        this project is supported by LIneA (Laboratório Interinstitucional de e-Astronomia),
        INCT do e-Universo (CNPQ grants 465376/2014-2), by FAPESP (proc. 2018/11239-8), by CNPQ
        (proc. 300472/2020-0, 150612/2020-6), and by CAPES-PRINT/UNESP (88887.571156/2020-00)
        in Brazil.
        
        The Paris, Granada, and Rio teams are professionals astronomers affiliated mainly in the following
        institutions:
        
        * LESIA - Observatoire de Paris, France;
        * Institut Polytechnique des Sciences Avancées, France;
        * IMCCE - Observatoire de Paris, France;
        * Instituto de Astrofísica de Andalucía, Spain;
        * Laboratório Interinstitucional de e-Astronomia, Brazil;
        * INCT do e-Universo, Brazil;
        * Observatório Nacional/MCTI, Brazil;
        * Federal University of Technology - Paraná, Brazil;
        * UNESP - São Paulo State University, Brazil;
        * Universidade Federal do Rio de Janeiro - Observatório do Valongo, Brazil;
        
Keywords: science,astronomy,occultation
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research 
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Astronomy
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
