cannon_basic_usage.py

import numpy as np
from astropy.table import Table

import thecannon as tc

# The Ness et al. (2015) data is accessible from [URL]
labelled_set = Table.read("Ness_2015_labelled.csv")
normalized_flux = np.memmap("Ness_2015_normalized_flux.memmap",
    mode="r", dtype=float)
normalized_ivar = np.memmap("Ness_2015_normalized_ivar.memmap",
    mode="r", dtype=float)

# Reshape the flux and inverse variance arrays.
normalized_flux = normalized_flux.reshape((len(labelled_set), -1))
normalized_ivar = normalized_ivar.reshape((len(labelled_set), -1))

# Create a second-order polynomial model that contains a set of 
# labelled fluxes and inverse variances.
model = tc.CannonModel(labelled_set, normalized_flux, normalized_ivar)
model.vectorizer = tc.vectorizer.NormalizedPolynomialVectorizer(labelled_set, 
    tc.vectorizer.polynomial.terminator(["TEFF", "LOGG", "PARAM_M_H"], 2))

# Train the model!
model.train()

# Plot some results.
fig = tc.diagnostics.label_residuals(model)