Metadata-Version: 1.1
Name: VESPA
Version: 0.5
Summary: Calculate astrophysical false positive probabilities for transiting exoplanet signals
Home-page: https://github.com/timothydmorton/VESPA
Author: Timothy D. Morton
Author-email: tim.morton@gmail.com
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: VESPA
        ======
        .. image:: https://zenodo.org/badge/6253/timothydmorton/VESPA.svg   
            :target: http://dx.doi.org/10.5281/zenodo.16467
        
        Validation of Exoplanet Signals using a Probabilistic Algorithm--- calculating false positive probabilities for transit signals
        
        For usage and more info, `check out the documentation <http://vespa.rtfd.org>`_.
        
        [NOTE: While `isochrones <http://github.com/timothydmorton/isochrones>`_ has been updated to version 1.0, vespa only works with isochrones version 0.9.1.  This is an awkward situation that I plan to resolve before too long, pending partially on NASA funding for me to continue to support these packages.]
        
        Installation
        ------------
        
        To install, you can get the most recently released version from PyPI::
        
            pip install vespa [--user]
        
        Or you can clone the repository::
        
            git clone https://github.com/timothydmorton/vespa.git
            cd vespa
            python setup.py install [--user]
        
        The ``--user`` argument may be necessary if you don't have root privileges.
        
        Depends on typical scientific packages (e.g. `numpy`, `scipy`, `pandas`),
        as well as `isochrones <http://github.com/timothydmorton/isochrones>`_, and (in several corners of the code), another package of mine called `simpledist <http://github.com/timothydmorton/simpledist>`_.  All dependencies *should* get resolved upon install, though this has only been tested under the anaconda Python distribution, which has all the scientific stuff already well-organized.
        
        For best results, it is also recommended to have ``MultiNest`` and ``pymultinest`` installed.  Without this, ``emcee`` will be used for stellar modeling, but the ``MulitNest`` results are a bit more trustworthy given the often multi-modal nature of stellar model fitting.
        
        Basic Usage
        -----------
        
        The simplest way to run an FPP calculation straight out of the box is
        as follows.
        
        1. Make a text file containing the transit photometry in three columns: ``t_from_midtransit`` [days], ``flux`` [relative, where out-of-transit is normalized to unity], and ``flux_err``.  The file should not have a header row (no titles); and can be either whitespace or comma-delimited (will be ingested by ``np.loadtxt``).  
        
        2. Make a ``star.ini`` file that contains the observed properties of the target star (photometric and/or spectroscopic, whatever is available):: 
        
        	    #provide spectroscopic properties if available
                    #Teff = 3503, 80  #value, uncertainty
                    #feh = 0.09, 0.09
                    #logg = 4.89, 0.1
        
        	    #observed magnitudes of target star
        	    # If uncertainty provided, will be used to fit StarModel
                    J = 9.763, 0.03
                    H = 9.135, 0.03
                    K = 8.899, 0.02
                    Kepler = 12.473
        
        3. Make a ``fpp.ini`` file containing the following information::
        
                    name = k2oi #anything
                    ra = 11:30:14.510 #can be decimal form too
                    dec = +07:35:18.21
        
                    period = 32.988 #days
                    rprs = 0.0534   #Rp/Rstar best estimate
                    photfile = lc_k2oi.csv #contains transit photometry
        
        	    [constraints]
        	    maxrad = 12  # aperture radius [arcsec] 
        	    secthresh = 1e-4 # Maximum allowed depth of potential secondary eclipse 
        
        4. Run the following from the command line (from within the same folder that has ``star.ini`` and ``fpp.ini``)::
        
        	$  calcfpp 
        	 
        Or, if you put the files in a folder called ``mycandidate``, then you can run ``calcfpp mycandidate``::
        	 
        This will run the calculation for you, creating result files, diagnostic plots, etc.  
        It should take 20-30 minutes.  If you want to do a shorter
        version to test, you can try ``calcfpp -n 1000`` (the default is 20000).  The first
        time you run it though, about half the time is doing the stellar modeling, so it will still
        take a few minutes.
        
        
        Attribution
        -----------
        
        If you use this code, please cite both the paper and the code.
        
        Paper citation::
        
            @ARTICLE{2012ApJ...761....6M,
            author = {{Morton}, T.~D.},
            title = "{An Efficient Automated Validation Procedure for Exoplanet Transit Candidates}",
            journal = {\apj},
            archivePrefix = "arXiv",
            eprint = {1206.1568},
            primaryClass = "astro-ph.EP",
            keywords = {planetary systems, stars: statistics },
            year = 2012,
            month = dec,
            volume = 761,
            eid = {6},
            pages = {6},
            doi = {10.1088/0004-637X/761/1/6},
            adsurl = {http://adsabs.harvard.edu/abs/2012ApJ...761....6M},
            adsnote = {Provided by the SAO/NASA Astrophysics Data System}
            }
        
        code::
        
        	@MISC{2015ascl.soft03011M,
        	   author = {{Morton}, T.~D.},
        	    title = "{VESPA: False positive probabilities calculator}",
        	howpublished = {Astrophysics Source Code Library},
        	     year = 2015,
        	    month = mar,
        	archivePrefix = "ascl",
        	   eprint = {1503.011},
        	   adsurl = {http://adsabs.harvard.edu/abs/2015ascl.soft03011M},
        	  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
        	}
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Astronomy
