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        "author": "Andrew R. Casey, David W. Hogg, Melissa Ness",
        "author_email": "andrew.casey@monash.edu",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Astronomy",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "# The Cannon\n\n[![Build Status](https://img.shields.io/travis/andycasey/AnniesLasso/refactor.svg)](https://travis-ci.org/andycasey/AnniesLasso)\n[![Coverage Status](https://img.shields.io/coveralls/andycasey/AnniesLasso/refactor.svg)](https://coveralls.io/github/andycasey/AnniesLasso?branch=refactor)\n[![Scrutinizer](https://img.shields.io/scrutinizer/g/andycasey/AnniesLasso.svg?b=refactor)](https://scrutinizer-ci.com/g/andycasey/AnniesLasso/?branch=refactor)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/andycasey/AnniesLasso/blob/refactor/LICENSE)\n\n\n[See the documentation.](#)\n\n## Authors\n- **Andy Casey** (Cambridge) (Monash)\n- **David W. Hogg** (NYU) (MPIA) (SCDA)\n- **Melissa K. Ness** (MPIA)\n- **Hans-Walter Rix** (MPIA)\n- **Anna Y. Q. Ho** (Caltech)\n- **Gerry Gilmore** (Cambridge)\n\n\n## License\n**Copyright 2017 the authors**.\nThe code in this repository is released under the open-source **MIT License**.\nSee the file `LICENSE` for more details.\n\n\n## Installation\n\nTo install:\n\n``\npip install https://github.com/andycasey/AnniesLasso/archive/refactor.zip\n``\n\n\n## Getting Started\n\nLet us assume that you have rest-frame continuum-normalized spectra for a set of\nstars for which the stellar parameters and chemical abundances (which we will\ncollectively call *labels*) are known with high fidelity.  The labels for those\nstars (and the locations of the spectrum fluxes and inverse variances) are\nassumed to be stored in a table.  In this example all stars are assumed to be \nsampled on the same wavelength (dispersion) scale.\n\n\nHere we will create and train a 3-label (effective temperature, surface gravity,\nmetallicity) quadratic (e.g., `Teff^2`) model:\n\n\n````python\nimport numpy as np\nfrom astropy.table import Table\n\nimport AnniesLasso as tc\n\n# Load the table containing the training set labels, and the spectra.\ntraining_set = Table.read(\"training_set_labels.fits\")\n\n# Here we will assume that the flux and inverse variance arrays are stored in\n# different ASCII files. The end goal is just to produce flux and inverse\n# variance arrays of shape (N_stars, N_pixels).\nnormalized_flux = np.array([np.loadtxt(star[\"flux_filename\"]) for star in training_set])\nnormalized_ivar = np.array([np.loadtxt(star[\"ivar_filename\"]) for star in training_set])\n\n# Providing the dispersion to the model is optional, but handy later on.\ndispersion = np.loadtxt(\"common_wavelengths.txt\")\n\n# Create the model that will run in parallel using all available cores.\nmodel = tc.CannonModel(training_set, normalized_flux, normalized_ivar,\n    dispersion=dispersion, threads=-1)\n\n# Specify the complexity of the model:\nmodel.vectorizer = tc.vectorizer.NormalizedPolynomialVectorizer(labelled_set,\n    tc.vectorizer.polynomial.terminator((\"TEFF\", \"LOGG\", \"FEH\"), 2))\n\n# Train the model!\nmodel.train()\n````\n\nYou can follow this example further in the complete [Getting Started](#) tutorial.\n",
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