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    "info": {
        "author": "Filipe Pereira",
        "author_email": "filipe.pereira@astro.up.pt",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "# gptransits\n\n**Fit planetary transits and stellar signals at the same time with the help of gaussian processes**\n\n## Installation\nAll python dependencies necessary for gptransits are available in pip (one of the package dependencies requires numpy to be installed beforehand). These can be seen in [setup.py](https://github.com/Fill4/gptransits/tree/master/setup.py).\n\n*gptransits* can be installed from pip:\n~~~ bash\npip install gptransits\n~~~\nOr from the source:\n~~~ bash\ngit clone https://github.com/Fill4/gptransits.git\ncd gptransits\npython setup.py install\n~~~\n\n## Getting started\nScript template (or ipython/jupyter notebook):\n~~~ python\nimport gptransits\n\nmodel = gptransits.Model(\"lightcurve_file.txt\", \"config_file.py\")\nmodel.run()\nmodel.analysis(plot=True, fout=\"results_file.txt\")\n~~~\n\nThe model is initialized by providing a light curve file and a configuration file.\n~~~ python\nmodel = gptransits.Model(\"lightcurve_file.txt\", \"config_file.py\")\n~~~\n\nThe configuration file follows the structure in config_template.py. The available settings options can be seen in [settings.py](https://github.com/Fill4/gptransits/tree/master/gptransits/settings.py).  \nThe light curve file follows the format: \n~~~ \n#            time (days)              flux (frac)          flux_err (frac)\n0.000000000000000000e+00 9.995755553299999763e-01 5.522560000000000197e-05\n2.043337615740740618e-02 9.992535114099999616e-01 5.521756000000000196e-05\n4.086675347222221838e-02 9.992965459599999489e-01 5.521029000000000160e-05\n~~~\n\nRunning the MCMC algorithm to sample the posterior:\n~~~ python\nmodel.run()\n~~~\n\n\nAnalysis calls a collection of functions that evaluate the MCMC convergence:\n~~~ python\nmodel.analysis(plot=True, fout=\"results_file.txt\")\n~~~\nThe plot flag defines if the analysis also creates some plots of interest, in a created figures folder.  \nThe fout flag, if defined, defines the file where the calculated results for the model parameters are saved.\n\nSome examples ready to be run are available in the [examples](https://github.com/Fill4/gptransits/tree/master/examples) folder.  \nEach folder has a light curve and a configuration file. Light curve files have the .lc extension.  \nThe output folder saves the chain and posterior probabilities from emcee in case the save flag is set in the configuration.  \nThe figures folder holds the plots from the analysis function.\n\n\n",
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        "license": "MIT",
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        "name": "gptransits",
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