astrophot.models.func package

Submodules

astrophot.models.func.base module

astrophot.models.func.base.all_subclasses(cls)

astrophot.models.func.base.downsample(img: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], scale: int = 1)

astrophot.models.func.base.downsample_mean(img: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], scale: int = 1)

astrophot.models.func.convolution module

astrophot.models.func.convolution.convolve(image: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], psf: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.convolution.curvature_kernel(dtype, device)

astrophot.models.func.exponential module

astrophot.models.func.exponential.exponential(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Re: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Ie: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Exponential 1d profile function, specifically designed for pytorch operations.

Args: - R: Radius tensor at which to evaluate the exponential function - Re: Effective radius in the same units as R - Ie: Effective surface density

astrophot.models.func.ferrer module

astrophot.models.func.ferrer.ferrer(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], rout: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], beta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], I0: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Modified Ferrer profile.

Args: - R: Radius tensor at which to evaluate the modified Ferrer function - rout: Outer radius of the profile - alpha: Power-law index - beta: Exponent for the modified Ferrer function - I0: Central intensity

astrophot.models.func.gaussian module

astrophot.models.func.gaussian.gaussian(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], sigma: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], flux: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Gaussian 2d profile function.

Args: - R: Radii array at which to evaluate the gaussian function - sigma: Standard deviation of the gaussian in the same units as R - flux: Total flux of the Gaussian

astrophot.models.func.gaussian_ellipsoid module

astrophot.models.func.gaussian_ellipsoid.euler_rotation_matrix(alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], beta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], gamma: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Compute the rotation matrix from Euler angles.

See the Z_alpha X_beta Z_gamma convention for the order of rotations here: https://en.wikipedia.org/wiki/Euler_angles

astrophot.models.func.integration module

astrophot.models.func.integration.bright_integrate(z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], brightness_ij: callable, bright_frac: float, scale: float = 1.0, quad_order: int = 3, gridding: int = 5, max_depth: int = 2)

astrophot.models.func.integration.pixel_center_integrator(Z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.integration.pixel_quad_integrator(Z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], w: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'] = None, order: int = 3) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Integrate the pixel values using quadrature weights.

Args: - Z: The tensor containing pixel values. - w: The quadrature weights. - order: The order of the quadrature.

astrophot.models.func.integration.pixel_simpsons_integrator(Z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.integration.recursive_quad_integrate(i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], brightness_ij: callable, curve_frac: float, scale: float = 1.0, quad_order: int = 3, gridding: int = 5, _current_depth: int = 0, max_depth: int = 1) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.integration.single_quad_integrate(i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], brightness_ij, scale: float, quad_order: int = 3) → Tuple[Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']]

astrophot.models.func.integration.upsample(i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], order: int, scale: float) → Tuple[Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']]

astrophot.models.func.king module

astrophot.models.func.king.king(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rc: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rt: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], I0: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Empirical King profile.

Args: - R: Radial distance from the center of the profile. - Rc: Core radius of the profile. - Rt: Truncation radius of the profile. - alpha: Power-law index of the profile. - I0: Central intensity of the profile.

astrophot.models.func.moffat module

astrophot.models.func.moffat.moffat(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], n: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rd: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], I0: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Moffat 1d profile function

Args: - R: Radii tensor at which to evaluate the moffat function - n: concentration index - Rd: scale length in the same units as R - I0: central surface density

astrophot.models.func.nuker module

astrophot.models.func.nuker.nuker(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rb: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Ib: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], beta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], gamma: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Nuker 1d profile function

Args: - R: Radii tensor at which to evaluate the nuker function - Ib: brightness at the scale length, represented as the log of the brightness divided by pixel scale squared. - Rb: scale length radius - alpha: sharpness of transition between power law slopes - beta: outer power law slope - gamma: inner power law slope

astrophot.models.func.sersic module

astrophot.models.func.sersic.sersic(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], n: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Re: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Ie: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Seric 1d profile function, specifically designed for pytorch operations

Args: - R: Radii tensor at which to evaluate the sersic function - n: sersic index restricted to n > 0.36 - Re: Effective radius in the same units as R - Ie: Effective surface density

astrophot.models.func.sersic.sersic_n_to_b(n: float) → float

Compute the b(n) for a sersic model. This factor ensures that the $R_e$ and $I_e$ parameters do in fact correspond to the half light values and not some other scale radius/intensity.

astrophot.models.func.spline module

astrophot.models.func.spline.cubic_spline_torch(x: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], y: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], xs: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Compute the 1D cubic spline interpolation for the given data points using PyTorch.

Args: - x (Tensor): A 1D tensor representing the x-coordinates of the known data points. - y (Tensor): A 1D tensor representing the y-coordinates of the known data points. - xs (Tensor): A 1D tensor representing the x-coordinates of the positions where

the cubic spline function should be evaluated.

astrophot.models.func.spline.spline(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], profR: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], profI: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], extend: str = 'zeros') → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Spline 1d profile function, cubic spline between points up to second last point beyond which is linear

Args: - R: Radii tensor at which to evaluate the spline function - profR: radius values for the surface density profile in the same units as R - profI: surface density values for the surface density profile - extend: How to extend the spline beyond the last point. Options are ‘zeros’ or ‘const’.

astrophot.models.func.transform module

astrophot.models.func.transform.rotate(theta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], x: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], y: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Tuple[Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']]

Applies a rotation matrix to the X,Y coordinates

astrophot.models.func.zernike module

astrophot.models.func.zernike.coefficients(n: int, m: int) → list[tuple[int, float]]

astrophot.models.func.zernike.zernike_basis(order: int, N: int) → ndarray

astrophot.models.func.zernike.zernike_n_m_list(n: int) → list[tuple[int, int]]

astrophot.models.func.zernike.zernike_n_m_modes(rho: ndarray, phi: ndarray, n: int, m: int) → ndarray

Module contents

astrophot.models.func.all_subclasses(cls)

astrophot.models.func.bright_integrate(z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], brightness_ij: callable, bright_frac: float, scale: float = 1.0, quad_order: int = 3, gridding: int = 5, max_depth: int = 2)

astrophot.models.func.convolve(image: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], psf: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.curvature_kernel(dtype, device)

astrophot.models.func.downsample(img: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], scale: int = 1)

astrophot.models.func.downsample_mean(img: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], scale: int = 1)

astrophot.models.func.euler_rotation_matrix(alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], beta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], gamma: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Compute the rotation matrix from Euler angles.

See the Z_alpha X_beta Z_gamma convention for the order of rotations here: https://en.wikipedia.org/wiki/Euler_angles

astrophot.models.func.exponential(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Re: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Ie: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Exponential 1d profile function, specifically designed for pytorch operations.

Args: - R: Radius tensor at which to evaluate the exponential function - Re: Effective radius in the same units as R - Ie: Effective surface density

astrophot.models.func.ferrer(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], rout: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], beta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], I0: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Modified Ferrer profile.

Args: - R: Radius tensor at which to evaluate the modified Ferrer function - rout: Outer radius of the profile - alpha: Power-law index - beta: Exponent for the modified Ferrer function - I0: Central intensity

astrophot.models.func.gaussian(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], sigma: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], flux: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Gaussian 2d profile function.

Args: - R: Radii array at which to evaluate the gaussian function - sigma: Standard deviation of the gaussian in the same units as R - flux: Total flux of the Gaussian

astrophot.models.func.king(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rc: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rt: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], I0: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Empirical King profile.

Args: - R: Radial distance from the center of the profile. - Rc: Core radius of the profile. - Rt: Truncation radius of the profile. - alpha: Power-law index of the profile. - I0: Central intensity of the profile.

astrophot.models.func.moffat(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], n: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rd: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], I0: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Moffat 1d profile function

Args: - R: Radii tensor at which to evaluate the moffat function - n: concentration index - Rd: scale length in the same units as R - I0: central surface density

astrophot.models.func.nuker(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Rb: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Ib: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], alpha: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], beta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], gamma: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Nuker 1d profile function

Args: - R: Radii tensor at which to evaluate the nuker function - Ib: brightness at the scale length, represented as the log of the brightness divided by pixel scale squared. - Rb: scale length radius - alpha: sharpness of transition between power law slopes - beta: outer power law slope - gamma: inner power law slope

astrophot.models.func.pixel_center_integrator(Z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.pixel_quad_integrator(Z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], w: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'] = None, order: int = 3) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Integrate the pixel values using quadrature weights.

Args: - Z: The tensor containing pixel values. - w: The quadrature weights. - order: The order of the quadrature.

astrophot.models.func.pixel_simpsons_integrator(Z: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.quad_table(order, dtype, device)

Generate a meshgrid for quadrature points using Legendre-Gauss quadrature.

Parameters

nint

The number of quadrature points in each dimension.

dtypetorch.dtype

The desired data type of the tensor.

devicetorch.device

The device on which to create the tensor.

Returns

Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

The generated meshgrid as a tuple of Tensors.

astrophot.models.func.recursive_quad_integrate(i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], brightness_ij: callable, curve_frac: float, scale: float = 1.0, quad_order: int = 3, gridding: int = 5, _current_depth: int = 0, max_depth: int = 1) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

astrophot.models.func.rotate(theta: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], x: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], y: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Tuple[Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']]

Applies a rotation matrix to the X,Y coordinates

astrophot.models.func.sersic(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], n: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Re: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Ie: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']) → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Seric 1d profile function, specifically designed for pytorch operations

Args: - R: Radii tensor at which to evaluate the sersic function - n: sersic index restricted to n > 0.36 - Re: Effective radius in the same units as R - Ie: Effective surface density

astrophot.models.func.sersic_n_to_b(n: float) → float

Compute the b(n) for a sersic model. This factor ensures that the $R_e$ and $I_e$ parameters do in fact correspond to the half light values and not some other scale radius/intensity.

astrophot.models.func.single_quad_integrate(i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], brightness_ij, scale: float, quad_order: int = 3) → Tuple[Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']]

astrophot.models.func.spline(R: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], profR: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], profI: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], extend: str = 'zeros') → Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']

Spline 1d profile function, cubic spline between points up to second last point beyond which is linear

Args: - R: Radii tensor at which to evaluate the spline function - profR: radius values for the surface density profile in the same units as R - profI: surface density values for the surface density profile - extend: How to extend the spline beyond the last point. Options are ‘zeros’ or ‘const’.

astrophot.models.func.upsample(i: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], j: Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], order: int, scale: float) → Tuple[Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.'], Annotated[Tensor, 'One of: torch.Tensor or jax.numpy.ndarray depending on the chosen backend.']]

astrophot.models.func.zernike_basis(order: int, N: int) → ndarray

astrophot.models.func.zernike_n_m_list(n: int) → list[tuple[int, int]]

astrophot.models.func.zernike_n_m_modes(rho: ndarray, phi: ndarray, n: int, m: int) → ndarray