noise_torch.py
import torch
import lenstronomy.Util.data_util as data_util
__all__ = ['NoiseModelTorch']
class NoiseModelTorch:
"""A combination of sky, readout, and Poisson flux noise to be added to the image
Note
----
This is a torch wrapper around the functionality provided by the `SingleBand` class in lenstronomy.
"""
def __init__(self, pixel_scale, exposure_time, magnitude_zero_point, read_noise=None, ccd_gain=None, sky_brightness=None, seeing=None, num_exposures=1, psf_type='GAUSSIAN', kernel_point_source=None, truncation=5, data_count_unit='ADU', background_noise=None):
"""
Parameters
----------
pixel_scale : float
pixel scale in arcsec/pixel
exposure_time : float
exposure time per image in seconds
magnitude_zero_point : float
magnitude at which 1 count per second per arcsecond square is registered
read_noise : float
std of noise generated by readout (in units of electrons)
ccd_gain : float
electrons/ADU (analog-to-digital unit). A gain of 8 means that the camera digitizes the CCD signal so that each ADU corresponds to 8 photoelectrons
sky_brightness : float
sky brightness (in magnitude per square arcsec)
seeing : float
fwhm of PSF
num_exposures : float
number of exposures that are combined
psf_type : str
type of PSF ('GAUSSIAN' and 'PIXEL' supported)
kernel_point_source : 2d numpy array
model of PSF centered with odd number of pixels per axis(optional when psf_type='PIXEL' is chosen)
truncation : float
Gaussian truncation (in units of sigma), only required for 'GAUSSIAN' model
data_count_unit : str
unit of the data (and other properties), 'e-': (electrons assumed to be IID), 'ADU': (analog-to-digital unit)
background_noise : float
sqrt(variance of background) as a total contribution from read noise, sky brightness, etc. in units of the data_count_units
If you set this parameter, it will override readout_noise, sky_brightness. Default: None
"""
self.pixel_scale = pixel_scale
self.exposure_time = exposure_time
self.magnitude_zero_point = magnitude_zero_point
self.ccd_gain = ccd_gain
self.sky_brightness = sky_brightness
self.seeing = seeing
self.num_exposures = num_exposures
self.psf_type = psf_type
self.kernel_point_source = kernel_point_source
self.truncation = truncation
self.data_count_unit = data_count_unit
self.background_noise = background_noise
#FIXME: seeing, psf_type, kernel_point_source, and truncation do not seem to be used at all.
if self.background_noise is None:
self.readout_noise = read_noise
if self.data_count_unit == 'ADU':
self.readout_noise /= self.ccd_gain
self.sky_brightness = data_util.magnitude2cps(self.sky_brightness, self.magnitude_zero_point)
if self.data_count_unit == 'ADU':
self.sky_brightness /= self.ccd_gain
self.exposure_time_tot = self.num_exposures * self.exposure_time
self.readout_noise_tot = self.num_exposures * self.readout_noise**2.0
self.sky_per_pixel = self.sky_brightness * pixel_scale**2.0
self.get_background_noise_sigma2 = getattr(self, 'get_background_noise_sigma2_composite') if self.background_noise is None else getattr(self, 'get_background_noise_sigma2_simple')
self.scaled_exposure_time = self.exposure_time_tot
if self.data_count_unit == 'ADU':
self.scaled_exposure_time *= self.ccd_gain
def get_sky_noise_sigma2(self):
"""Compute the variance in sky noise
Returns
-------
float
variance of the sky noise, in cps^2
"""
return self.sky_per_pixel**2.0 / self.exposure_time_tot
def get_readout_noise_sigma2(self):
"""Compute the variance in readout noise
Returns
-------
float
variance of the readout noise, in cps^2
"""
return self.readout_noise_tot / self.exposure_time_tot**2.0
def get_background_noise_sigma2_simple(self):
"""Get the variance in background noise from the specified estimate of the background noise, rather than computing it from the sky brightness and read noise
Returns
-------
float
variance of the background noise, in cps^2
"""
return self.background_noise**2.0
def get_background_noise_sigma2_composite(self):
"""Get the variance in background noise from the sky brightness and read noise
Returns
-------
float
variance of the background noise, in cps^2
"""
return self.get_sky_noise_sigma2() + self.get_readout_noise_sigma2()
def get_poisson_noise_sigma2(self, img):
"""Get the variance in Poisson flux noise from the image
Parameters
----------
img : 2D torch.Tensor
the image of flux values in cps on which to evaluate the noise
Returns
-------
float
variance of the Poisson flux noise, in cps^2
"""
return torch.max(img, torch.zeros_like(img))/self.scaled_exposure_time
def get_noise_sigma2(self, img):
"""Get the variance of total noise due to the combined effects of sky, readout, and Poisson flux noise
Parameters
----------
img : 2D torch.Tensor
the image of flux values in cps on which to evaluate the noise
Returns
-------
2D torch.Tensor
variance of total noise, in cps^2
"""
return self.get_background_noise_sigma2() + self.get_poisson_noise_sigma2(img)
def get_noise_map(self, img):
"""Get the total random noise map due to the combined effects of sky, readout, and Poisson flux noise
Parameters
----------
img : 2D torch.Tensor
the image of flux values in cps on which to evaluate the noise
Returns
-------
2D torch.Tensor
the noise map in cps
"""
return torch.randn_like(img)*self.get_noise_sigma2(img)**0.5