import torch
from ...param import forward
from ...backend_obj import ArrayLike
from ...utils.decorators import ignore_numpy_warnings
from .._shared_methods import parametric_initialize, parametric_segment_initialize
from ...utils.parametric_profiles import gaussian_np
from .. import func
def _x0_func(model_params, R, F):
return R[4], 10 ** F[0]
[docs]
class GaussianMixin:
"""Gaussian radial light profile.
The Gaussian profile is a simple and widely used model for extended objects.
The functional form of the Gaussian profile is defined as:
.. math::
I(R) = \\frac{{\\rm flux}}{\\sqrt{2\\pi}\\sigma} \\exp(-R^2 / (2 \\sigma^2))
where ``I_0`` is the intensity at the center of the profile and ``sigma`` is the
standard deviation which controls the width of the profile.
:param sigma: Standard deviation of the Gaussian profile in arcseconds.
:param flux: Total flux of the Gaussian profile.
"""
_model_type = "gaussian"
_parameter_specs = {
"sigma": {
"units": "arcsec",
"valid": (0, None),
"shape": (),
"dynamic": True,
"description": "Standard deviation of the Gaussian profile in arcseconds.",
},
"flux": {
"units": "flux",
"valid": (0, None),
"shape": (),
"dynamic": True,
"description": "Total flux of the Gaussian profile.",
},
}
[docs]
@torch.no_grad()
@ignore_numpy_warnings
def initialize(self):
super().initialize()
parametric_initialize(
self,
self.target[self.window],
gaussian_np,
("sigma", "flux"),
_x0_func,
)
[docs]
@forward
def radial_model(self, R: ArrayLike, sigma: ArrayLike, flux: ArrayLike) -> ArrayLike:
return func.gaussian(R, sigma, flux)
[docs]
class iGaussianMixin:
"""Gaussian radial light profile.
The Gaussian profile is a simple and widely used model for extended objects.
The functional form of the Gaussian profile is defined as:
.. math::
I(R) = \\frac{{\\rm flux}}{\\sqrt{2\\pi}\\sigma} \\exp(-R^2 / (2 \\sigma^2))
where ``sigma`` is the standard deviation which controls the width of the
profile and ``flux`` gives the total flux of the profile (assuming no
perturbations).
``sigma`` and ``flux`` are batched by their first dimension, allowing for
multiple Gaussian profiles to be defined at once.
:param sigma: Standard deviation of the Gaussian profile in arcseconds.
:param flux: Total flux of the Gaussian profile.
"""
_model_type = "gaussian"
_parameter_specs = {
"sigma": {
"units": "arcsec",
"valid": (0, None),
"shape": (None,),
"dynamic": True,
"description": "Standard deviation of the Gaussian profile in arcseconds.",
},
"flux": {
"units": "flux",
"valid": (0, None),
"shape": (None,),
"dynamic": True,
"description": "Total flux of the Gaussian profile.",
},
}
[docs]
@torch.no_grad()
@ignore_numpy_warnings
def initialize(self):
super().initialize()
parametric_segment_initialize(
model=self,
target=self.target[self.window],
prof_func=gaussian_np,
params=("sigma", "flux"),
x0_func=_x0_func,
segments=self.segments,
)
[docs]
@forward
def iradial_model(self, i: int, R: ArrayLike, sigma: ArrayLike, flux: ArrayLike) -> ArrayLike:
return func.gaussian(R, sigma[i], flux[i])
[docs]
class GaussianPSFMixin:
"""Gaussian radial light profile.
The Gaussian profile is a simple and widely used model for extended objects.
The functional form of the Gaussian profile is defined as:
.. math::
I(R) = \\frac{{\\rm flux}}{\\sqrt{2\\pi}\\sigma} \\exp(-R^2 / (2 \\sigma^2))
where ``I_0`` is the intensity at the center of the profile and ``sigma`` is the
standard deviation which controls the width of the profile.
:param sigma: Standard deviation of the Gaussian profile in pixels.
:param flux: Total flux of the Gaussian profile.
"""
_model_type = "gaussian"
_parameter_specs = {
"sigma": {
"units": "pix",
"valid": (0, None),
"shape": (),
"dynamic": True,
"description": "Standard deviation of the Gaussian profile in pixels.",
},
"flux": {
"units": "flux",
"valid": (0, None),
"shape": (),
"dynamic": False,
"value": 1.0,
"description": "Total flux of the Gaussian profile.",
},
}
[docs]
@torch.no_grad()
@ignore_numpy_warnings
def initialize(self):
super().initialize()
parametric_initialize(
self,
self.target[self.window],
gaussian_np,
("sigma", "flux"),
_x0_func,
)
[docs]
@forward
def radial_model(self, R: ArrayLike, sigma: ArrayLike, flux: ArrayLike) -> ArrayLike:
return func.gaussian(R, sigma, flux)
