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:
$$I(R) = \\frac{{\\rm flux}}{\\sqrt{2\\pi}\\sigma} \\exp(-R^2 / (2 \\sigma^2))$$
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.
**Parameters:**
- `sigma`: Standard deviation of the Gaussian profile in arcseconds.
- `flux`: Total flux of the Gaussian profile.
"""
_model_type = "gaussian"
_parameter_specs = {
"sigma": {"units": "arcsec", "valid": (0, None), "shape": (), "dynamic": True},
"flux": {"units": "flux", "valid": (0, None), "shape": (), "dynamic": True},
}
[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:
$$I(R) = \\frac{{\\rm flux}}{\\sqrt{2\\pi}\\sigma} \\exp(-R^2 / (2 \\sigma^2))$$
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.
**Parameters:**
- `sigma`: Standard deviation of the Gaussian profile in arcseconds.
- `flux`: Total flux of the Gaussian profile.
"""
_model_type = "gaussian"
_parameter_specs = {
"sigma": {"units": "arcsec", "valid": (0, None), "shape": (None,), "dynamic": True},
"flux": {"units": "flux", "valid": (0, None), "shape": (None,), "dynamic": True},
}
[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:
$$I(R) = \\frac{{\\rm flux}}{\\sqrt{2\\pi}\\sigma} \\exp(-R^2 / (2 \\sigma^2))$$
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.
**Parameters:**
- `sigma`: Standard deviation of the Gaussian profile in pixels.
- `flux`: Total flux of the Gaussian profile.
"""
_model_type = "gaussian"
_parameter_specs = {
"sigma": {"units": "pix", "valid": (0, None), "shape": (), "dynamic": True},
"flux": {"units": "flux", "valid": (0, None), "shape": (), "dynamic": False, "value": 1.0},
}
[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)
