synthesizer.parametric.morphology

A submodule for defining parametric morphologies for use in making images.

This module provides a base class for defining parametric morphologies, and specific classes for the Sersic profile and point sources. The base class provides a common interface for defining morphologies, and the specific classes provide the functionality for the Sersic profile and point sources.

Example usage:

# Import the module
from synthesizer import morphology

# Define a Sersic profile
sersic = morphology.Sersic(r_eff=10.0, sersic_index=4, ellipticity=0.5)

# Define a point source
point_source = morphology.PointSource(offset=[0.0, 0.0])

Classes

class synthesizer.parametric.morphology.Gaussian2D(x_mean, y_mean, stddev_x, stddev_y, rho=0)[source]

A class holding a 2-dimensional Gaussian distribution.

This is a morphology where a 2-dimensional Gaussian density grid is populated based on provided x and y values.

x_mean

(float): The mean of the Gaussian along the x-axis.

y_mean

(float): The mean of the Gaussian along the y-axis.

stddev_x

(float): The standard deviation along the x-axis.

stddev_y

(float): The standard deviation along the y-axis.

rho

(float): The population correlation coefficient between x and y.

compute_density_grid(x, y)[source]

Compute density grid.

Parameters:

  • x (unyt_array of float) – x values on a 2D grid.

  • y (unyt_array of float) – y values on a 2D grid.

Returns:

A 2D array representing the Gaussian density values at each

(x, y) point.

float:

The normalisation factor for the Gaussian density grid.

Return type:

np.ndarray

Raises:

ValueError – If either x or y is None.

class synthesizer.parametric.morphology.Gaussian2DAnnuli(x_mean, y_mean, stddev_x, stddev_y, radii, rho=0)[source]

A subclass of Gaussian2D that supports masking of concentric annuli.

x_mean

(float): The mean of the Gaussian along the x-axis.

y_mean

(float): The mean of the Gaussian along the y-axis.

stddev_x

(float): The standard deviation along the x-axis.

stddev_y

(float): The standard deviation along the y-axis.

rho

(float): The population correlation coefficient between x and y.

radii

The radii defining the annuli.

Type:

list of float

annulus_index

Index of the annulus to be used.

Type:

int

compute_density_grid(x, y, annulus)[source]

Compute the Gaussian density grid with optional annulus masking.

Parameters:

  • x (array-like) – x values on a 2D grid.

  • y (array-like) – y values on a 2D grid.

  • annulus (int) – Index of the annulus to be used.

Returns:

The masked Gaussian density grid. float: The normalisation factor for the density grid.

Return type:

np.ndarray

class synthesizer.parametric.morphology.MorphologyBase[source]

A base class holding common methods for parametric morphologies.

r_eff_kpc

The effective radius in kpc.

Type:

float

r_eff_mas

The effective radius in milliarcseconds.

Type:

float

sersic_index

The Sersic index.

Type:

float

ellipticity

The ellipticity.

Type:

float

theta

The rotation angle.

Type:

float

cosmo

The cosmology object.

Type:

astropy.cosmology

redshift

The redshift.

Type:

float

grid

The Sersic2D model in kpc.

Type:

astropy.modeling.models.Sersic2D

model_mas

The Sersic2D model in

Type:

astropy.modeling.models.Sersic2D

abstract compute_density_grid(*args)[source]

Compute the density grid from coordinate grids.

This is a place holder method to be overwritten by child classes.

get_density_grid(resolution, npix, **kwargs)[source]

Get the density grid based on resolution and npix.

Parameters:

  • resolution (unyt_quantity) – The resolution of the grid.

  • npix (tuple, int) – The number of pixels in each dimension.

  • **kwargs – Additional keyword arguments to pass to the compute_density_grid method.

plot_density_grid(resolution, npix)[source]

Make a quick density plot.

Parameters:

  • resolution (float) – The resolution (in the same units provded to the child class).

  • npix (int) – The number of pixels.

Examples using synthesizer.parametric.morphology.MorphologyBase

Image addition example

Image addition example

Create image example

Create image example

Generate parametric morphology profiles

Generate parametric morphology profiles
class synthesizer.parametric.morphology.PointSource(offset=unyt_array([0., 0.], 'kpc'), cosmo=None, redshift=None)[source]

A class holding a PointSource profile.

This is a morphology where a single cell of the density grid is populated.

cosmo

The cosmology object.

Type:

astropy.cosmology

redshift

The redshift.

Type:

float

offset_kpc

The offset of the point source relative to the centre of the image in kpc.

Type:

float

compute_density_grid(xx, yy)[source]

Compute the density grid.

This acts as a wrapper to astropy functionality (defined above) which only work in units of kpc or milliarcseconds (mas)

Parameters:

  • xx – array-like (unyt_array of float): x values on a 2D grid.

  • yy – array-like (unyt_array of float): y values on a 2D grid.

Returns:

The density grid produced float:

The normalisation factor for the density grid.

Return type:

np.ndarray

class synthesizer.parametric.morphology.Sersic2D(r_eff, amplitude=1, sersic_index=1, x_0=unyt_quantity(0, 'kpc'), y_0=unyt_quantity(0, 'kpc'), theta=0, ellipticity=0, cosmo=None, redshift=None)[source]

A class holding a 2D Sersic profile.

r_eff_kpc

The effective radius in kpc.

Type:

float

r_eff_mas

The effective radius in milliarcseconds.

Type:

float

sersic_index

The Sersic index.

Type:

float

ellipticity

The ellipticity.

Type:

float

theta

The rotation angle.

Type:

float

cosmo

The cosmology object.

Type:

astropy.cosmology

redshift

The redshift.

Type:

float

grid

The 2D Sersic model in kpc.

model_mas

The 2D Sersic model in milliarcseconds.

compute_density_grid(x, y)[source]

Compute the density grid.

Parameters:

  • x – array-like (float): x values on a 2D grid.

  • y – array-like (float): y values on a 2D grid.

Returns:

The density grid produced from either

the kpc or mas Sersic profile.

float:

The normalisation factor for the density grid.

Return type:

np.ndarray

class synthesizer.parametric.morphology.Sersic2DAnnuli(r_eff, radii, amplitude=1, sersic_index=1, x_0=0, y_0=0, theta=0, ellipticity=0, cosmo=None, redshift=None)[source]

A subclass of Sersic2D that supports masking of concentric annuli.

r_eff_kpc

The effective radius in kpc.

Type:

float

r_eff_mas

The effective radius in milliarcseconds.

Type:

float

sersic_index

The Sersic index.

Type:

float

ellipticity

The ellipticity.

Type:

float

theta

The rotation angle.

Type:

float

cosmo

The cosmology object.

Type:

astropy.cosmology

redshift

The redshift.

Type:

float

grid

The 2D Sersic model in kpc.

model_mas

The 2D Sersic model in milliarcseconds.

radii

The radii defining the annuli.

Type:

list of float

annulus_index

Index of the annulus to be used.

Type:

int

compute_density_grid(x, y, annulus)[source]

Compute the density grid with optional annulus masking.

Parameters:

  • x (array-like) – x values on a 2D grid.

  • y (array-like) – y values on a 2D grid.

  • annulus (int) – Index of the annulus to be used.

Returns:

The computed density grid, optionally masked by annuli. float: The normalisation factor for the density grid.

Return type:

np.ndarray