astra.contrib.thecannon.tasks.test

Module Contents

Classes

TestTheCannon	A mixin class all tasks related to The Cannon.
EstimateStellarLabelsGivenApStarFileBase	A task to train The Cannon, given some file that contains high-quality labels,

class astra.contrib.thecannon.tasks.test.TestTheCannon

A mixin class all tasks related to The Cannon.

Parameters:

• label_names – A list of label names.
• order – (optional) The polynomial order to use for this model (default: 2).

N_initialisations

use_derivatives

task_namespace = TheCannon

label_names

order

requires(self)

output(self)

read_observation(self)

Read the input observation, and if continuum is a requirement, normalize it.

prepare_observation(self, dispersion, spectrum=None, continuum=None)

read_model(self)

run(self)

class astra.contrib.thecannon.tasks.test.EstimateStellarLabelsGivenApStarFileBase

A task to train The Cannon, given some file that contains high-quality labels, and pseudo-continuum-normalised fluxes and inverse variances.

Parameters:

• training_set_path –

  The path to a pickle file that contains a dictionary with the following keys:

  • wavelength: an array of shape (P, ) where P is the number of pixels
  • flux: an array of flux values with shape (N, P) where N is the number of observed spectra and P is the number of pixels
  • ivar: an array of inverse variance values with shape (N, P) where N is the number of observed spectra and P is the number of pixels
  • labels: an array of shape (L, N) where L is the number of labels and N is the number observed spectra
  • label_names: a tuple of length L that describes the names of the labels
• regularization – (optional) The L1 regularization strength to use during training (default: 0.0).
• threads – (optional) The number of threads to use during training (default: 1).
• plot – (optional) Produce quality assurance figures after training (default: True).

training_set_path

regularization

threads

plot

task_namespace = TheCannon

label_names

order

N_initialisations

use_derivatives

requires(self)

Requirements of this task.

run(self)

Execute this task.

output(self)

The output of this task.

read_observation(self)

Read the input observation, and if continuum is a requirement, normalize it.

prepare_observation(self, dispersion, spectrum=None, continuum=None)

read_model(self)