Reference/API¶

APOGEE Data¶

Functions for reading in APOGEE spectra and training labels

TheCannon.apogee.continuum_normalize_training(ds)[source]¶

Continuum normalize the training set, using an iterative Cannon approach

Parameters:	ds: dataset object
Returns:	updated dataset object

TheCannon.apogee.get_pixmask(fluxes, flux_errs)[source]¶

Create and return a bad pixel mask for an APOGEE spectrum

Bad pixels are defined as follows: fluxes or errors are not finite, or reported errors are <= 0, or fluxes are 0

Parameters:	fluxes: ndarray Flux data values flux_errs: ndarray Flux uncertainty data values
Returns:	mask: ndarray Bad pixel mask, value of True corresponds to bad pixels

TheCannon.apogee.get_starmask(ids, labels, aspcapflag, paramflag)[source]¶

Identifies which APOGEE objects have unreliable physical parameters, as laid out in Holzman et al 2015 and on the APOGEE DR12 website

Parameters:	data: np array all APOGEE DR12 IDs and labels
Returns:	bad: np array mask where 1 corresponds to a star with unreliable parameters

TheCannon.apogee.load_labels(filename)[source]¶

Extracts reference labels from a file

Parameters:	filename: str Name of the file containing the table of reference labels ids: array The IDs of stars to retrieve labels for
Returns:	labels: ndarray Reference label values for all reference objects

TheCannon.apogee.load_spectra(data_dir)[source]¶

Reads wavelength, flux, and flux uncertainty data from apogee fits files

Parameters:	data_dir: str Name of the directory containing all of the data files
Returns:	wl: ndarray Rest-frame wavelength vector fluxes: ndarray Flux data values ivars: ndarray Inverse variance values corresponding to flux values

Dataset¶

class TheCannon.dataset.Dataset(wl, tr_ID, tr_flux, tr_ivar, tr_label, test_ID, test_flux, test_ivar)[source]¶

A class to represent Cannon input: a dataset of spectra and labels

bin_flux(flux, ivar)[source]¶

bin two neighboring flux values

continuum_normalize(cont)[source]¶

Continuum normalize spectra, in chunks if spectrum has regions

Parameters:	cont: ndarray Flux values corresponding to the continuum
Returns:	norm_tr_flux: ndarray Normalized flux values for the training objects norm_tr_ivar: ndarray Rescaled inverse variance values for the training objects norm_test_flux: ndarray Normalized flux values for the test objects norm_test_ivar: numpy ndarray Rescaled inverse variance values for the test objects

continuum_normalize_gaussian_smoothing(L)[source]¶

Continuum normalize using a Gaussian-weighted smoothed spectrum

Parameters:	dataset: Dataset the dataset to continuum normalize L: float the width of the Gaussian used for weighting

continuum_normalize_training_q(q, delta_lambda, n_proc=1, verbose=True)[source]¶

Continuum normalize the training set using a running quantile

Parameters:	q: float The quantile cut delta_lambda: float The width of the pixel range used to calculate the median Modified by: [08 Jun 2016] Bo Zhang (NAOC): add multiprocessing option Note: Multiprocessing calls for more memory on computer.

diagnostics_1to1(figname='1to1_label')[source]¶

Plots survey labels vs. training labels, color-coded by survey SNR

diagnostics_SNR()[source]¶

Plots SNR distributions of ref and test object spectra

diagnostics_best_fit_spectra(model)[source]¶

Plot results of best-fit spectra for ten random test objects

diagnostics_ref_labels()[source]¶

Plots all training labels against each other

diagnostics_survey_labels()[source]¶

Plot all survey labels against each other

diagnostics_test_step_flagstars()[source]¶

Write files listing stars whose inferred labels lie outside 2 standard deviations from the reference label space

fit_continuum(deg, ffunc, n_proc=1)[source]¶

Fit a continuum to the continuum pixels

Parameters:	deg: int Degree of the fitting function ffunc: str Type of fitting function, ‘sinusoid’ or ‘chebyshev’
Returns:	tr_cont: ndarray Flux values corresponding to the fitted continuum of training objects test_cont: ndarray Flux values corresponding to the fitted continuum of test objects

get_plotting_labels()[source]¶

Return the label names used make plots

Returns:	label_names: ndarray The label names

make_contmask(fluxes, ivars, frac)[source]¶

Identify continuum pixels using training spectra

Does this for each region of the spectrum if dataset.ranges is not None

Parameters:	fluxes: ndarray Flux data values ivars: ndarray Inverse variances corresponding to flux data values frac: float The fraction of pixels that should be identified as continuum
Returns:	contmask: ndarray Mask with True indicating that the pixel is continuum

set_continuum(contmask)[source]¶

Set the contmask attribute

Parameters:	contmask: ndarray Mask with True indicating that the pixel is continuum

set_label_names(names)[source]¶

Set the label names for plotting

Parameters:	names: ndarray or list The names of the labels used for plotting, ex. in LaTeX syntax

set_test_label_vals(vals)[source]¶

Set test label values

Parameters:	vals: ndarray Test label values

smooth_dataset()[source]¶

Bins down all of the spectra and updates the dataset

smooth_spectra(wl, fluxes, ivars)[source]¶

Bins down a block of spectra

smooth_spectrum(wl, flux, ivar)[source]¶

Bins down one spectrum

Parameters:	wl: numpy ndarray wavelengths flux: numpy ndarray flux values ivar: numpy ndarray inverse variances associated with fluxes
Returns:	wl: numpy ndarray updated binned pixel wavelengths flux: numpy ndarray updated binned flux values ivar: numpy ndarray updated binned inverse variances

Model¶

class TheCannon.model.CannonModel(order, useErrors)[source]¶

diagnostics()[source]¶

Produce a set of diagnostics plots about the model.

diagnostics_contpix(data, nchunks=10, fig='baseline_spec_with_cont_pix')[source]¶

Call plot_contpix once for each nth of the spectrum

diagnostics_plot_chisq(ds, figname='modelfit_chisqs.png')[source]¶

Produce a set of diagnostic plots for the model

Parameters:	(optional) chisq_dist_plot_name: str Filename of output saved plot

fit(ds)¶

Run training step: solve for best-fit spectral model

infer_labels(ds, starting_guess=None)[source]¶

Uses the model to solve for labels of the test set, updates Dataset Then use those inferred labels to set the model.test_spectra attribute

Parameters:	ds: Dataset Dataset that needs label inference
Returns:	errs_all: ndarray Covariance matrix of the fit

infer_spectra(ds)[source]¶

After inferring labels for the test spectra, infer the model spectra and update the dataset model_spectra attribute.

Parameters:	ds: Dataset object

model()[source]¶

Return the model definition or raise an error if not trained

plot_contpix(x, y, contpix_x, contpix_y, figname)[source]¶

Plot baseline spec with continuum pix overlaid

predict(model, dataset, starting_guess=None)¶

Uses the model to solve for labels of the test set.

Parameters:	model: tuple Coeffs_all, covs, scatters, chis, chisqs, pivots dataset: Dataset Dataset that needs label inference
Returns:	errs_all: Covariance matrix of the fit

train(ds)[source]¶

Run training step: solve for best-fit spectral model