Metadata-Version: 2.1
Name: amical
Version: 1.3.1.post1
Summary: UNKNOWN
Home-page: https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL
Author: Anthony Soulain
Author-email: anthony.soulain@sydney.edu.au.com
License: MIT
Description: <a href="https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL">
        <img src="https://raw.githubusercontent.com/SydneyAstrophotonicInstrumentationLab/AMICAL/master/doc/Figures/amical_logo.png" width="300"></a>
        
        (**A**perture **M**asking **I**nterferometry **C**alibration and **A**nalysis
        **L**ibrary)
        
        [![PyPI](https://img.shields.io/pypi/v/amical)](https://pypi.org/project/amical/)
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        isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
        
        ## Installation
        
        ```shell
        $ python -m pip install amical
        ```
        
        ## What can AMICAL do for you ?
        
        AMICAL is developed to provide an easy-to-use solution to process
        **A**perture **M**asking **I**nterferometry (AMI) data from major existing
        facilities:
        [NIRISS](https://jwst-docs.stsci.edu/near-infrared-imager-and-slitless-spectrograph)
        on the JWST (first scientific interferometer operating in space),
        [SPHERE](https://www.eso.org/sci/facilities/paranal/instruments/sphere.html) and
        [VISIR](https://www.eso.org/sci/facilities/paranal/instruments/visir.html) from
        the European Very Large Telescope (VLT) and
        [VAMPIRES](https://www.naoj.org/Projects/SCEXAO/scexaoWEB/030openuse.web/040vampires.web/indexm.html)
        from SUBARU telescope (and more to come).
        
        We focused our efforts to propose a user-friendly interface, though different
        sub-classes allowing to (1) **Clean** the reduced datacube from the standard
        instrument pipelines, (2) **Extract** the interferometrical quantities
        (visibilities and closure phases) using a Fourier sampling approach and (3)
        **Calibrate** those quantities to remove the instrumental biases.
        
        In addition (4), we include two external packages called
        [CANDID](https://github.com/amerand/CANDID) and
        [Pymask](https://github.com/AnthonyCheetham/pymask) to **analyse** the final
        outputs obtained from a binary-like sources (star-star or star-planet). We
        interfaced these stand-alone packages with AMICAL to quickly estimate our
        scientific results (e.g., separation, position angle, contrast ratio, contrast
        limits, etc.) using different approaches (chi2 grid, MCMC, see
        [example_analysis.py](https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL/blob/master/doc/example_analysis.py) for details).
        
        ## Getting started
        
        Looking for a quickstart into AMICAL? You can go through our **[tutorial](https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL/blob/master/doc/tutorial.md)** explaining
        how to use its different features.
        
        You can also have a look to the example scripts
        made for [NIRISS](https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL/blob/master/doc/example_NIRISS.py) and [SPHERE](https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL/blob/master/doc/example_NIRISS.py) or get details about the CANDID/Pymask uses with [example_analysis.py](https://github.com/SydneyAstrophotonicInstrumentationLab/AMICAL/blob/master/doc/example_analysis.py).
        
        ## Use policy and reference publication
        
        If you use AMICAL in a publication, we encourage you to properly cite the
        reference paper published during the 2020 SPIE conference: [The James Webb Space
        Telescope aperture masking
        interferometer](https://ui.adsabs.harvard.edu/abs/2020SPIE11446E..11S/abstract).
        The library explanation is part of a broader description of the interferometric
        mode of NIRISS, so feel free to have a look at the exciting possibilities of
        AMI!
        
        ## Acknowledgements
        
        This work is mainly a modern Python translation of the very well known (and old)
        IDL pipeline used to process and analyze Sparse Aperture Masking data. This
        pipeline, called "Sydney code", was developed by a lot of people over many
        years. Credit goes to the major developers, including Peter Tuthill, Mike
        Ireland and John Monnier. Many forks exist across the web and the last IDL
        version can be found [here](https://github.com/AnthonyCheetham/idl_masking).
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: AIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Astronomy
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
