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A data-driven approach to stellar spectroscopy

Project description

# The Cannon

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[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

## 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 ="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!

You can follow this example further in the complete [Getting Started](#) tutorial.

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