import numpy as np
from scipy.stats import iqr
import torch
from ..utils.decorators import ignore_numpy_warnings, default_internal
from ..param import Param_Unlock, Param_SoftLimits
from .sky_model_object import Sky_Model
from ._shared_methods import select_target
__all__ = ["Flat_Sky"]
[docs]
class Flat_Sky(Sky_Model):
"""Model for the sky background in which all values across the image
are the same.
Parameters:
sky: brightness for the sky, represented as the log of the brightness over pixel scale squared, this is proportional to a surface brightness
"""
model_type = f"flat {Sky_Model.model_type}"
parameter_specs = {
"F": {"units": "log10(flux/arcsec^2)"},
}
_parameter_order = Sky_Model._parameter_order + ("F",)
useable = True
[docs]
@torch.no_grad()
@ignore_numpy_warnings
@select_target
@default_internal
def initialize(self, target=None, parameters=None, **kwargs):
super().initialize(target=target, parameters=parameters)
with Param_Unlock(parameters["F"]), Param_SoftLimits(parameters["F"]):
if parameters["F"].value is None:
parameters["F"].value = torch.log10(torch.median(target[self.window].data) / target.pixel_area)
if parameters["F"].uncertainty is None:
parameters["F"].uncertainty = (
(
iqr(
target[self.window].data.detach().cpu().numpy(),
rng=(31.731 / 2, 100 - 31.731 / 2),
)
/ (2.0 * target.pixel_area.item())
)
/ np.sqrt(np.prod(self.window.shape.detach().cpu().numpy()))
) / (10 ** parameters["F"].value.item() * np.log(10))
[docs]
def evaluate_model(self, X=None, Y=None, image=None, parameters=None, **kwargs):
ref = image.data if X is None else X
return torch.ones_like(ref) * (image.pixel_area * 10 ** parameters["F"].value)