`mag_noise`#

Simulators for magnitude-dependent noise on the magnitudes

Taken from: https://github.com/jiwoncpark/node-to-joy

Module Contents#

Classes#

`MagNoise`	LSST-like noise on ugrizy magnitudes in numpy
`MagNoiseTorch`	LSST-like noise on ugrizy magnitudes in torch

class mag_noise.MagNoise(mag_idx=[0, 1, 2, 3, 4, 5], which_bands=list('ugrizy'), override_kwargs=None, depth=5, airmass=1.15304)[source]#

LSST-like noise on ugrizy magnitudes in numpy

bands = ['u', 'g', 'r', 'i', 'z', 'y'][source]#

m_skys = [22.5, 22.19191, 21.10172, 19.93964, 18.3, 17.7][source]#

seeings = [1.029668, 0.951018, 0.8996875, 0.868422, 1, 1][source]#

gammas = [0.038, 0.039, 0.039, 0.039, 0.039, 0.039][source]#

k_ms = [0.491, 0.213, 0.126, 0.096, 0.069, 0.17][source]#

C_ms = [23.09, 24.42, 24.44, 24.32, 24.16, 23.73][source]#

delta_C_m_infs = [0.62, 0.18, 0.1, 0.07, 0.05, 0.04][source]#

num_visits_10_year = [56, 80, 184, 184, 160, 160][source]#

_format_input_params()[source]#: Convert param lists into arrays for vectorized computation

_slice_input_params()[source]#: Slice and reorder input params so only the relevant bands in self.which_bands remain, in that order

calculate_delta_C_ms()[source]#: Returns delta_C_m correction for num_visits > 1 (i.e. exposure times > 30s), for ugrizy following Eq 7 in Science Drivers.

calculate_5sigma_depths()[source]#: Returns m_5 found using Eq 6 in Science Drivers, using eff seeing, sky brightness, exposure time, extinction coeff, airmass, for ugrizy. Includes dependence on number of visits.

calculate_rand_err(mags)[source]#: “Returns sigma_rand_squared

get_sigmas(mags)[source]#: “Returns sigma (photometric error in mag). Calculated using figures and formulae from Science Drivers Params: - AB mag (float)

__call__(x)[source]#

class mag_noise.MagNoiseTorch(mag_idx=[2, 3, 4, 5, 6, 7], which_bands=list('ugrizy'), override_kwargs=None, depth=5, airmass=1.15304)[source]#

LSST-like noise on ugrizy magnitudes in torch

bands = ['u', 'g', 'r', 'i', 'z', 'y'][source]#

m_skys = [22.5, 22.19191, 21.10172, 19.93964, 18.3, 17.7][source]#

seeings = [1.029668, 0.951018, 0.8996875, 0.868422, 1, 1][source]#

gammas = [0.038, 0.039, 0.039, 0.039, 0.039, 0.039][source]#

k_ms = [0.491, 0.213, 0.126, 0.096, 0.069, 0.17][source]#

C_ms = [23.09, 24.42, 24.44, 24.32, 24.16, 23.73][source]#

delta_C_m_infs = [0.62, 0.18, 0.1, 0.07, 0.05, 0.04][source]#

num_visits_10_year = [56, 80, 184, 184, 160, 160][source]#

_format_input_params()[source]#: Convert param lists into arrays for vectorized computation

_slice_input_params()[source]#: Slice and reorder input params so only the relevant bands in self.which_bands remain, in that order

calculate_delta_C_ms()[source]#: Returns delta_C_m correction for num_visits > 1 (i.e. exposure times > 30s), for ugrizy following Eq 7 in Science Drivers.

calculate_5sigma_depths()[source]#: Returns m_5 found using Eq 6 in Science Drivers, using eff seeing, sky brightness, exposure time, extinction coeff, airmass, for ugrizy. Includes dependence on number of visits.

calculate_rand_err(mags)[source]#: “Returns sigma_rand_squared

get_sigmas(mags)[source]#: “Returns sigma (photometric error in mag). Calculated using figures and formulae from Science Drivers Params: - AB mag (float)

__call__(x)[source]#

mag_noise#

Module Contents#

Classes#

`mag_noise`#