pysersic.rendering

Attributes

`base_profile_types`
`base_profile_params`

Exceptions

`RMSWarning`	Base class for warning categories.
`KernelError`	Common base class for all non-exit exceptions.
`PSFNormalizationWarning`	Base class for warning categories.
`MaskWarning`	Base class for warning categories.
`ShapeMatchError`	Common base class for all non-exit exceptions.

Classes

`BaseRenderer`
`PixelRenderer`	Render class based on rendering in pixel space and then convolving with the PSF
`FourierRenderer`	Class to render sources based on rendering them in Fourier space. Sersic profiles are modeled as a series of Gaussian following Shajib (2019) (https://arxiv.org/abs/1906.08263) and the implementation in lenstronomy (https://github.com/lenstronomy/lenstronomy/blob/main/lenstronomy/LensModel/Profiles/gauss_decomposition.py)
`HybridRenderer`	Class to render sources based on the hybrid rendering scheme introduced in Lang (2020). This avoids some of the artifacts introduced by rendering sources purely in Fourier space. Sersic profiles are modeled as a series of Gaussian following Shajib (2019) (https://arxiv.org/abs/1906.08263) and the implementation in lenstronomy (https://github.com/lenstronomy/lenstronomy/blob/main/lenstronomy/LensModel/Profiles/gauss_decomposition.py).

Functions

`sersic1D`(→ Union[float, jax.numpy.array])	Evaluate a 1D sersic profile
`render_gaussian_fourier`(→ jax.numpy.array)	Render Gaussian components in the Fourier domain
`render_pointsource_fourier`(→ jax.numpy.array)	Render a point source in the Fourier domain
`render_gaussian_pixel`(→ jax.numpy.array)	Render Gaussian components in pixel space
`render_sersic_2d`(→ jax.numpy.array)	Evalulate a 2D Sersic distribution at given locations
`calculate_etas_betas`(→ Tuple[jax.numpy.array, ...)	Calculate the weights and nodes for the Gaussian decomposition described in Shajib (2019) (https://arxiv.org/abs/1906.08263)
`sersic_gauss_decomp`(→ Tuple[jax.numpy.array, ...)	Calculate a gaussian decomposition of a given sersic profile, following Shajib (2019) (https://arxiv.org/abs/1906.08263)

Module Contents

exception pysersic.rendering.RMSWarning

Bases: Warning

Base class for warning categories.

exception pysersic.rendering.KernelError

Bases: Exception

Common base class for all non-exit exceptions.

exception pysersic.rendering.PSFNormalizationWarning

Bases: Warning

Base class for warning categories.

exception pysersic.rendering.MaskWarning

Bases: Warning

Base class for warning categories.

exception pysersic.rendering.ShapeMatchError

Bases: Exception

Common base class for all non-exit exceptions.

pysersic.rendering.base_profile_types = ['sersic', 'doublesersic', 'sersic_exp', 'sersic_pointsource', 'pointsource', 'exp', 'dev']

pysersic.rendering.base_profile_params

class pysersic.rendering.BaseRenderer(im_shape: Iterable, pixel_PSF: jax.numpy.array)

Bases: equinox.Module

im_shape: tuple

psf_shape: tuple

fft_shape: tuple

profile_func_dict: dict

pixel_PSF: jax.numpy.array

PSF_fft: jax.numpy.array

X: jax.numpy.array

Y: jax.numpy.array

FX: jax.numpy.array

FY: jax.numpy.array

x_mid: float

y_mid: float

fft_zeros: jax.numpy.array

img_zeros: jax.numpy.array

conv_img(image)

conv_fft(F_im)

combine_scene(F_im, int_im, obs_im)

abstract render_sersic(params: dict)

render_doublesersic(params: dict)

render_sersic_exp(params: dict)

render_sersic_pointsource(params: dict)

abstract render_pointsource(params: dict)

render_exp(params: dict) → jax.numpy.array

Thin wrapper for an exponential profile based on render_sersic

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux
r_eff (float) – Effective radius
ellip (float) – Ellipticity
theta (float) – Position angle in radians

Returns:

Rendered Exponential model

Return type:

jax.numpy.array

render_dev(params: dict) → jax.numpy.array

Thin wrapper for a De Vaucouleurs profile based on render_sersic

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux
r_eff (float) – Effective radius
ellip (float) – Ellipticity
theta (float) – Position angle in radians

Returns:

Rendered Exponential model

Return type:

jax.numpy.array

render_for_model(param_dict, types, suffix)

render_source(params: dict, profile_type: str, suffix: str | None = '') → jax.numpy.array

Render an observed source of a given type and parameters

Parameters:

params (jax.numpy.array) – Parameters specifying the source
profile_type (str) – Type of profile to use

Returns:

Rendered, observed model

Return type:

jax.numpy.array

class pysersic.rendering.PixelRenderer(im_shape: Iterable, pixel_PSF: jax.numpy.array, os_pixel_size: int | None = 6, num_os: int | None = 12)

Bases: BaseRenderer

Render class based on rendering in pixel space and then convolving with the PSF

os_pixel_size: int

num_os: int

w_os: jax.numpy.array

x_os_lo: int

x_os_hi: int

y_os_lo: int

y_os_hi: int

X_os: jax.numpy.array

Y_os: jax.numpy.array

render_int_sersic(xc, yc, flux, r_eff, n, ellip, theta)

render_sersic(params: dict) → jax.numpy.array

Render a sersic profile

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux
r_eff (float) – Effective radius
n (float) – Sersic index
ellip (float) – Ellipticity
theta (float) – Position angle in radians

Returns:

Rendered Sersic model

Return type:

jax.numpy.array

render_pointsource(params: dict) → jax.numpy.array

Render a Point source by interpolating given PSF into image. Currently jax only supports linear intepolation.

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux

Returns:

rendered pointsource model

Return type:

jax.numpy.array

class pysersic.rendering.FourierRenderer(im_shape: Iterable, pixel_PSF: jax.numpy.array, frac_start: float | None = 0.01, frac_end: float | None = 15.0, n_sigma: int | None = 15, precision: int | None = 10, use_interp_amps: bool | None = True)

Bases: BaseRenderer

Class to render sources based on rendering them in Fourier space. Sersic profiles are modeled as a series of Gaussian following Shajib (2019) (https://arxiv.org/abs/1906.08263) and the implementation in lenstronomy (https://github.com/lenstronomy/lenstronomy/blob/main/lenstronomy/LensModel/Profiles/gauss_decomposition.py)

frac_start: float

frac_end: float

n_sigma: int

precision: int

use_interp_amps: bool

etas: jax.numpy.array

betas: jax.numpy.array

n_ax: jax.numpy.array

amps_n_ax: jax.numpy.array

get_amps_sigmas(flux, r_eff, n)

render_sersic_mog_fourier(xc, yc, flux, r_eff, n, ellip, theta)

render_sersic(params: dict) → jax.numpy.array

Render a Sersic profile

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux
r_eff (float) – Effective radius
n (float) – Sersic index
ellip (float) – Ellipticity
theta (float) – Position angle in radians

Returns:

Rendered Sersic model

Return type:

jax.numpy.array

render_pointsource(params: dict) → jax.numpy.array

Render a Point source

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux

Returns:

rendered pointsource model

Return type:

jax.numpy.array

Bases: FourierRenderer

Class to render sources based on the hybrid rendering scheme introduced in Lang (2020). This avoids some of the artifacts introduced by rendering sources purely in Fourier space. Sersic profiles are modeled as a series of Gaussian following Shajib (2019) (https://arxiv.org/abs/1906.08263) and the implementation in lenstronomy (https://github.com/lenstronomy/lenstronomy/blob/main/lenstronomy/LensModel/Profiles/gauss_decomposition.py).

Our scheme is implemented slightly differently than Lang (2020), specifically in how it chooses which gaussian components to render in Fourier vs. Real space. Lang (2020) employs a cutoff based on distance to the edge of the image. However given some of jax’s limitation with dynamic shapes (see more here -> https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#dynamic-shapes), we have not implemented that specific criterion. Instead we use a simpler version where the user must decide how many components to render in real space, starting from the largest ones. While this is not ideal in all circumstances it still overcomes many of the issues of rendering purely in fourier space discussed in Lang (2020).

num_pixel_render: int

w_real: jax.numpy.array

w_fourier: jax.numpy.array

sig_psf_approx: float

render_sersic_hybrid(xc, yc, flux, r_eff, n, ellip, theta)

render_sersic(params: dict) → jax.numpy.array

Render a Sersic profile

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux
r_eff (float) – Effective radius
n (float) – Sersic index
ellip (float) – Ellipticity
theta (float) – Position angle in radians

Returns:

Rendered Sersic model

Return type:

jax.numpy.array

render_pointsource(params: dict) → jax.numpy.array

Render a Point source

Parameters:

xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux

Returns:

rendered pointsource model

Return type:

jax.numpy.array

pysersic.rendering.sersic1D(r: float | jax.numpy.array, flux: float, re: float, n: float) → float | jax.numpy.array

Evaluate a 1D sersic profile

Parameters:

r (float) – radii to evaluate profile at
flux (float) – Total flux
re (float) – Effective radius
n (float) – Sersic index

Returns:

Sersic profile evaluated at r

Return type:

jax.numpy.array

pysersic.rendering.render_gaussian_fourier(FX: jax.numpy.array, FY: jax.numpy.array, amps: jax.numpy.array, sigmas: jax.numpy.array, xc: float, yc: float, theta: float, q: float) → jax.numpy.array

Render Gaussian components in the Fourier domain

Parameters:

FX (jax.numpy.array) – X frequency positions to evaluate
FY (jax.numpy.array) – Y frequency positions to evaluate
amps (jax.numpy.array) – Amplitudes of each component
sigmas (jax.numpy.array) – widths of each component
xc (float) – Central x position
yc (float) – Central y position
theta (float) – position angle
q (float) – Axis ratio

Returns:

Sum of components evaluated at FX and FY

Return type:

jax.numpy.array

pysersic.rendering.render_pointsource_fourier(FX: jax.numpy.array, FY: jax.numpy.array, xc: float, yc: float, flux: float) → jax.numpy.array

Render a point source in the Fourier domain

Parameters:

FX (jax.numpy.array) – X frequency positions to evaluate
FY (jax.numpy.array) – Y frequency positions to evaluate
xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux of source

Returns:

Point source evaluated at FX FY

Return type:

jax.numpy.array

pysersic.rendering.render_gaussian_pixel(X: jax.numpy.array, Y: jax.numpy.array, amps: jax.numpy.array, sigmas: jax.numpy.array, xc: float, yc: float, theta: float, q: float | jax.numpy.array) → jax.numpy.array

Render Gaussian components in pixel space

Parameters:

FX (jax.numpy.array) – X positions to evaluate
FY (jax.numpy.array) – Y positions to evaluate
amps (jax.numpy.array) – Amplitudes of each component
sigmas (jax.numpy.array) – widths of each component
xc (float) – Central x position
yc (float) – Central y position
theta (float) – position angle
q (Union[float,jax.numpy.array]) – Axis ratio

Returns:

Sum of components evaluated at X and Y

Return type:

jax.numpy.array

pysersic.rendering.render_sersic_2d(X: jax.numpy.array, Y: jax.numpy.array, xc: float, yc: float, flux: float, r_eff: float, n: float, ellip: float, theta: float) → jax.numpy.array

Evalulate a 2D Sersic distribution at given locations

Parameters:

X (jax.numpy.array) – x locations to evaluate at
Y (jax.numpy.array) – y locations to evaluate at
xc (float) – Central x position
yc (float) – Central y position
flux (float) – Total flux
r_eff (float) – Effective radius
n (float) – Sersic index
ellip (float) – Ellipticity
theta (float) – Position angle in radians [now measured from north]

Returns:

Sersic model evaluated at given locations

Return type:

jax.numpy.array

pysersic.rendering.calculate_etas_betas(precision: int) → Tuple[jax.numpy.array, jax.numpy.array]

Calculate the weights and nodes for the Gaussian decomposition described in Shajib (2019) (https://arxiv.org/abs/1906.08263)

Parameters:: precision (int) – Precision, higher number implies more precise decomposition but more nodes. Effective upper limit is 12 for 32 bit numbers, 27 for 64 bit numbers.
Returns:: etas and betas array to be use in gaussian decomposition
Return type:: Tuple[jax.numpy.array, jax.numpy.array]

pysersic.rendering.sersic_gauss_decomp(flux: float, re: float, n: float, etas: jax.numpy.array, betas: jax.numpy.array, sigma_start: float, sigma_end: float, n_comp: int) → Tuple[jax.numpy.array, jax.numpy.array]

Calculate a gaussian decomposition of a given sersic profile, following Shajib (2019) (https://arxiv.org/abs/1906.08263)

Parameters:

flux (float) – Total flux
re (float) – half light radius
n (float) – Sersic index
etas (jax.numpy.array) – Weights for decomposition, can be calcualted using pysersic.rendering_utils.calculate_etas_betas
betas (jax.numpy.array) – Nodes for decomposition, can be calcualted using pysersic.rendering_utils.calculate_etas_betas
sigma_start (float) – width for the smallest Gaussian component
sigma_end (float) – width for the largest Gaussian component
n_comp (int) – Number of Gaussian components

Returns:

Amplitudes and sigmas of Gaussian decomposition

Return type:

Tuple[jax.numpy.array, jax.numpy.array]