import torch
import numpy as np
from .galaxy_model_object import Galaxy_Model
from .psf_model_object import PSF_Model
from ._shared_methods import parametric_initialize, select_target
from ..utils.decorators import ignore_numpy_warnings, default_internal
from ..utils.parametric_profiles import moffat_np
from ..utils.conversions.functions import moffat_I0_to_flux, general_uncertainty_prop
__all__ = ["Moffat_Galaxy", "Moffat_PSF"]
def _x0_func(model_params, R, F):
return 2.0, R[4], F[0]
def _wrap_moffat(R, n, rd, i0):
return moffat_np(R, n, rd, 10 ** (i0))
[docs]
class Moffat_Galaxy(Galaxy_Model):
"""basic galaxy model with a Moffat profile for the radial light
profile. The functional form of the Moffat profile is defined as:
I(R) = I0 / (1 + (R/Rd)^2)^n
where I(R) is the brightness profile as a function of semi-major
axis, R is the semi-major axis length, I0 is the central flux
density, Rd is the scale length for the profile, and n is the
concentration index which controls the shape of the profile.
Parameters:
n: Concentration index which controls the shape of the brightness profile
I0: brightness at the center of the profile, represented as the log of the brightness divided by pixel scale squared.
Rd: scale length radius
"""
model_type = f"moffat {Galaxy_Model.model_type}"
parameter_specs = {
"n": {"units": "none", "limits": (0.1, 10), "uncertainty": 0.05},
"Rd": {"units": "arcsec", "limits": (0, None)},
"I0": {"units": "log10(flux/arcsec^2)"},
}
_parameter_order = Galaxy_Model._parameter_order + ("n", "Rd", "I0")
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)
parametric_initialize(
self, parameters, target, _wrap_moffat, ("n", "Rd", "I0"), _x0_func
)
[docs]
@default_internal
def total_flux(self, parameters=None):
return moffat_I0_to_flux(
10 ** parameters["I0"].value,
parameters["n"].value,
parameters["Rd"].value,
parameters["q"].value,
)
[docs]
@default_internal
def total_flux_uncertainty(self, parameters=None):
return general_uncertainty_prop(
(10 ** parameters["I0"].value,
parameters["n"].value,
parameters["Rd"].value,
parameters["q"].value
),
((10 ** parameters["I0"].value) * parameters["I0"].uncertainty * torch.log(10*torch.ones_like(parameters["I0"].value)),
parameters["n"].uncertainty,
parameters["Rd"].uncertainty,
parameters["q"].uncertainty
),
moffat_I0_to_flux
)
from ._shared_methods import moffat_radial_model as radial_model
[docs]
class Moffat_PSF(PSF_Model):
"""basic point source model with a Moffat profile for the radial light
profile. The functional form of the Moffat profile is defined as:
I(R) = I0 / (1 + (R/Rd)^2)^n
where I(R) is the brightness profile as a function of semi-major
axis, R is the semi-major axis length, I0 is the central flux
density, Rd is the scale length for the profile, and n is the
concentration index which controls the shape of the profile.
Parameters:
n: Concentration index which controls the shape of the brightness profile
I0: brightness at the center of the profile, represented as the log of the brightness divided by pixel scale squared.
Rd: scale length radius
"""
model_type = f"moffat {PSF_Model.model_type}"
parameter_specs = {
"n": {"units": "none", "limits": (0.1, 10), "uncertainty": 0.05},
"Rd": {"units": "arcsec", "limits": (0, None)},
"I0": {"units": "log10(flux/arcsec^2)", "value": 0., "locked": True},
}
_parameter_order = PSF_Model._parameter_order + ("n", "Rd", "I0")
useable = True
model_integrated = False
[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)
parametric_initialize(
self, parameters, target, _wrap_moffat, ("n", "Rd", "I0"), _x0_func
)
from ._shared_methods import moffat_radial_model as radial_model
[docs]
@default_internal
def total_flux(self, parameters=None):
return moffat_I0_to_flux(
10 ** parameters["I0"].value,
parameters["n"].value,
parameters["Rd"].value,
torch.ones_like(parameters["n"].value),
)
[docs]
@default_internal
def total_flux_uncertainty(self, parameters=None):
return general_uncertainty_prop(
(10 ** parameters["I0"].value,
parameters["n"].value,
parameters["Rd"].value,
torch.ones_like(parameters["n"].value)
),
((10 ** parameters["I0"].value) * parameters["I0"].uncertainty * torch.log(10*torch.ones_like(parameters["I0"].value)),
parameters["n"].uncertainty,
parameters["Rd"].uncertainty,
torch.zeros_like(parameters["n"].value)
),
moffat_I0_to_flux
)
from ._shared_methods import radial_evaluate_model as evaluate_model