import torch
import numpy as np
from ...param import forward
from ...backend_obj import ArrayLike
from ...utils.decorators import ignore_numpy_warnings
from .._shared_methods import _sample_image
from ...utils.interpolate import default_prof
from .. import func
[docs]
class SplineMixin:
"""Spline radial model for brightness.
The ``radial_model`` function for this model is defined as a spline
interpolation from the parameter ``I_R``. The ``I_R`` parameter is a tensor
that contains the radial profile of the brightness in units of
flux/arcsec^2. The radius of each node is determined from ``I_R.prof``.
:param I_R: Tensor of radial brightness values in units of flux/arcsec^2.
"""
_model_type = "spline"
_parameter_specs = {
"I_R": {
"units": "flux/arcsec^2",
"valid": (0, None),
"shape": (None,),
"dynamic": True,
"description": "Tensor of radial brightness values in units of flux/arcsec^2.",
}
}
[docs]
@torch.no_grad()
@ignore_numpy_warnings
def initialize(self):
super().initialize()
if self.I_R.initialized:
return
target_area = self.target[self.window]
# Create the I_R profile radii if needed
if self.I_R.prof is None:
prof = default_prof(self.window.shape, target_area.pixelscale, 2, 0.2)
prof = np.append(prof, prof[-1] * 10)
self.I_R.prof = prof
else:
prof = self.I_R.prof
R, I, S = _sample_image(
target_area,
self.transform_coordinates,
self.radius_metric,
rad_bins=[0] + list((prof[:-1] + prof[1:]) / 2) + [prof[-1] * 100],
)
self.I_R.value = 10**I
[docs]
@forward
def radial_model(self, R: ArrayLike, I_R: ArrayLike) -> ArrayLike:
ret = func.spline(R, self.I_R.prof, I_R)
return ret
[docs]
class iSplineMixin:
"""Batched spline radial model for brightness.
The ``radial_model`` function for this model is defined as a spline
interpolation from the parameter ``I_R``. The ``I_R`` parameter is a tensor that
contains the radial profile of the brightness in units of flux/arcsec^2. The
radius of each node is determined from ``I_R.prof``.
Both ``I_R`` and ``I_R.prof`` are batched by their first dimension, allowing for
multiple spline profiles to be defined at once. Each individual spline model
is then ``I_R[i]`` and ``I_R.prof[i]`` where ``i`` indexes the profiles.
:param I_R: Tensor of radial brightness values in units of flux/arcsec^2.
"""
_model_type = "spline"
_parameter_specs = {
"I_R": {
"units": "flux/arcsec^2",
"valid": (0, None),
"shape": (None, None),
"dynamic": True,
"description": "Tensor of radial brightness values in units of flux/arcsec^2.",
}
}
[docs]
@torch.no_grad()
@ignore_numpy_warnings
def initialize(self):
super().initialize()
if self.I_R.initialized:
return
target_area = self.target[self.window]
# Create the I_R profile radii if needed
if self.I_R.prof is None:
prof = default_prof(self.window.shape, target_area.pixelscale, 2, 0.2)
prof = np.append(prof, prof[-1] * 10)
prof = np.stack([prof] * self.segments)
self.I_R.prof = prof
else:
prof = self.I_R.prof
value = np.zeros(prof.shape)
cycle = np.pi if self.symmetric else 2 * np.pi
w = cycle / self.segments
v = w * np.arange(self.segments)
for s in range(self.segments):
angle_range = (v[s] - w / 2, v[s] + w / 2)
R, I, S = _sample_image(
target_area,
self.transform_coordinates,
self.radius_metric,
angle=self.angular_metric,
rad_bins=[0] + list((prof[s][:-1] + prof[s][1:]) / 2) + [prof[s][-1] * 100],
angle_range=angle_range,
cycle=cycle,
)
value[s] = I
self.I_R.value = 10**value
[docs]
@forward
def iradial_model(self, i: int, R: ArrayLike, I_R: ArrayLike) -> ArrayLike:
return func.spline(R, self.I_R.prof[i], I_R[i])
[docs]
class SplinePSFMixin:
"""Spline radial model for brightness.
The ``radial_model`` function for this model is defined as a spline
interpolation from the parameter ``I_R``. The ``I_R`` parameter is a tensor
that contains the radial profile of the brightness in units of
flux/pix^2. The radius of each node is determined from ``I_R.prof``.
:param I_R: Tensor of radial brightness values in units of flux/pix^2.
"""
_model_type = "spline"
_parameter_specs = {
"I_R": {
"units": "flux/pix^2",
"valid": (0, None),
"shape": (None,),
"dynamic": True,
"description": "Tensor of radial brightness values in units of flux/pix^2.",
}
}
[docs]
@torch.no_grad()
@ignore_numpy_warnings
def initialize(self):
super().initialize()
if self.I_R.initialized:
return
target_area = self.target[self.window]
# Create the I_R profile radii if needed
if self.I_R.prof is None:
prof = default_prof(self.window.shape, target_area.pixelscale, 2, 0.2)
prof = np.append(prof, prof[-1] * 10)
self.I_R.prof = prof
else:
prof = self.I_R.prof
R, I, S = _sample_image(
target_area,
self.transform_coordinates,
self.radius_metric,
rad_bins=[0] + list((prof[:-1] + prof[1:]) / 2) + [prof[-1] * 100],
)
self.I_R.value = 10**I
[docs]
@forward
def radial_model(self, R: ArrayLike, I_R: ArrayLike) -> ArrayLike:
ret = func.spline(R, self.I_R.prof, I_R)
return ret
