Metadata-Version: 1.1
Name: the-cannon
Version: 0.2.6
Summary: A data-driven approach to stellar spectroscopy
Home-page: http://thecannon.io
Author: Andrew R. Casey, David W. Hogg, Melissa Ness
Author-email: andrew.casey@monash.edu
License: MIT
Description: # The Cannon
        
        [![Build Status](https://img.shields.io/travis/andycasey/AnniesLasso/refactor.svg)](https://travis-ci.org/andycasey/AnniesLasso)
        [![Coverage Status](https://img.shields.io/coveralls/andycasey/AnniesLasso/refactor.svg)](https://coveralls.io/github/andycasey/AnniesLasso?branch=refactor)
        [![Scrutinizer](https://img.shields.io/scrutinizer/g/andycasey/AnniesLasso.svg?b=refactor)](https://scrutinizer-ci.com/g/andycasey/AnniesLasso/?branch=refactor)
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/andycasey/AnniesLasso/blob/refactor/LICENSE)
        
        
        [See the documentation.](#)
        
        ## Authors
        - **Andy Casey** (Cambridge) (Monash)
        - **David W. Hogg** (NYU) (MPIA) (SCDA)
        - **Melissa K. Ness** (MPIA)
        - **Hans-Walter Rix** (MPIA)
        - **Anna Y. Q. Ho** (Caltech)
        - **Gerry Gilmore** (Cambridge)
        
        
        ## License
        **Copyright 2017 the authors**.
        The code in this repository is released under the open-source **MIT License**.
        See the file `LICENSE` for more details.
        
        
        ## Installation
        
        To install:
        
        ``
        pip install https://github.com/andycasey/AnniesLasso/archive/refactor.zip
        ``
        
        
        ## Getting Started
        
        Let us assume that you have rest-frame continuum-normalized spectra for a set of
        stars for which the stellar parameters and chemical abundances (which we will
        collectively call *labels*) are known with high fidelity.  The labels for those
        stars (and the locations of the spectrum fluxes and inverse variances) are
        assumed to be stored in a table.  In this example all stars are assumed to be 
        sampled on the same wavelength (dispersion) scale.
        
        
        Here we will create and train a 3-label (effective temperature, surface gravity,
        metallicity) quadratic (e.g., `Teff^2`) model:
        
        
        ````python
        import numpy as np
        from astropy.table import Table
        
        import AnniesLasso as tc
        
        # Load the table containing the training set labels, and the spectra.
        training_set = Table.read("training_set_labels.fits")
        
        # Here we will assume that the flux and inverse variance arrays are stored in
        # different ASCII files. The end goal is just to produce flux and inverse
        # variance arrays of shape (N_stars, N_pixels).
        normalized_flux = np.array([np.loadtxt(star["flux_filename"]) for star in training_set])
        normalized_ivar = np.array([np.loadtxt(star["ivar_filename"]) for star in training_set])
        
        # Providing the dispersion to the model is optional, but handy later on.
        dispersion = np.loadtxt("common_wavelengths.txt")
        
        # Create the model that will run in parallel using all available cores.
        model = tc.CannonModel(training_set, normalized_flux, normalized_ivar,
            dispersion=dispersion, threads=-1)
        
        # Specify the complexity of the model:
        model.vectorizer = tc.vectorizer.NormalizedPolynomialVectorizer(labelled_set,
            tc.vectorizer.polynomial.terminator(("TEFF", "LOGG", "FEH"), 2))
        
        # Train the model!
        model.train()
        ````
        
        You can follow this example further in the complete [Getting Started](#) tutorial.
        
Keywords: The Cannon
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
