MicroLIA.noise_models

Created on Thu July 28 20:30:11 2018

@author: danielgodinez

Module Contents

Functions

create_noise(median, rms[, degree])

Creates a noise model by fitting a one-dimensional smoothing

add_noise(mag, fn[, zp, exptime])

Adds noise to magnitudes given a noise function.

add_gaussian_noise(mag[, zp, exptime])

Adds noise to lightcurve given the magnitudes.
MicroLIA.noise_models.create_noise(median, rms, degree=3)[source]

Creates a noise model by fitting a one-dimensional smoothing spline of degree k.

Parameters:

  • median (array) – Baseline magnitudes.

  • rms (array) – Corresponding RMS per baseline.

  • k (int) – Degree of the smoothing spline. Default is a cubic spline of degree 3.

Returns:

fn

Return type:

The kth degree spline fit.

MicroLIA.noise_models.add_noise(mag, fn, zp=24, exptime=60)[source]

Adds noise to magnitudes given a noise function.

Parameters:

  • mag (array) – Magnitude to add noise to.

  • fn (function) – Spline fit, must be defined using the create_noise function.

  • zp (Zeropoint) – Zeropoint of the instrument, default is 24.

  • exptime (Exposure time) – The exposure time of the observations.

Returns:

  • mag (array) – The noise-added magnitudes.

  • magerr (array) – The corresponding magnitude errors.

MicroLIA.noise_models.add_gaussian_noise(mag, zp=24, exptime=60)[source]

Adds noise to lightcurve given the magnitudes.

Parameters:

  • mag (array) – Mag array to add noise to.

  • zp (zeropoint) – Zeropoint of the instrument, default is 24.

  • convert (boolean, optional) –

Returns:

  • noisy_mag (array) – The noise-added magnitude.

  • magerr (array) – The corresponding magnitude errors.