The Cannon

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)

Installation

pip install https://github.com/andycasey/AnniesLasso/archive/master.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:

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 a vectorizer that defines our model form.
vectorizer = tc.vectorizer.PolynomialVectorizer(("TEFF", "LOGG", "FEH"), 2)

# Create the model that will run in parallel using all available cores.
model = tc.CannonModel(training_set, normalized_flux, normalized_ivar,
                       vectorizer=vectorizer, dispersion=dispersion, threads=-1)

# Train the model!
model.train()

You can follow this example further in the complete Getting Started tutorial.

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.