make_model_image#

photutils.datasets.make_model_image(shape, model, params_table, *, model_shape=None, bbox_factor=None, x_name='x_0', y_name='y_0', discretize_method='center', discretize_oversample=10, progress_bar=False)[source]#

Make a 2D image containing sources generated from a user-specified astropy 2D model.

The model parameters for each source are taken from the input params_table table.

Parameters:

shape2-tuple of int

The shape of the output image.

model2D astropy.modeling.Model

The 2D model to be used to render the sources. The model must be two-dimensional where it accepts 2 inputs (i.e., (x, y)) and has 1 output. The model must have parameters for the x and y positions of the sources. Typically, these parameters are named ‘x_0’ and ‘y_0’, but the parameter names can be specified using the x_name and y_name keywords.

params_tableTable

A table containing the model parameters for each source. Each row of the table corresponds to a source whose model parameters are defined by the column names, which must match the model parameter names. The table must contain columns for the x and y positions of the sources. The column names for the x and y positions can be specified using the x_name and y_name keywords. Model parameters not defined in the table will be set to the model default value. To attach units to model parameters, params_table must be input as a QTable.

If the table contains a column named ‘model_shape’, then the values in that column will be used to override the model_shape keyword and model bounding_box for each source. This can be used to render each source with a different shape.

If the table contains a column named ‘local_bkg’, then the per-pixel local background values in that column will be used to added to each model source over the region defined by its model_shape. The ‘local_bkg’ column must have the same flux units as the output image (e.g., if the input model has ‘amplitude’ or ‘flux’ parameters with units). Including ‘local_bkg’ should be used with care, especially in crowded fields where the model_shape of sources overlap (see Notes below).

Except for model_shape and local_bkg column names, column names that do not match model parameters will be ignored.

model_shape2-tuple of int, int, or None, optional

The shape around the (x, y) center of each source that will used to evaluate the model. If model_shape is a scalar integer, then a square shape of size model_shape will be used. If None, then the bounding box of the model will be used (which can optionally be scaled using the bbox_factor keyword if the model supports it). This keyword must be specified if the model does not have a bounding_box attribute. If specified, this keyword overrides the model bounding_box attribute. To use a different shape for each source, include a column named 'model_shape' in the params_table. For that case, this keyword is ignored.

bbox_factorNone or float, optional

The multiplicative factor to pass to the model bounding_box method to determine the model shape. If the model bounding_box method does not accept a factor keyword, then this keyword is ignored. If None, the default model bounding box will be used. This keyword is ignored if model_shape is specified or if the params_table contains a 'model_shape' column. Note that some Photutils PSF models have a bbox_factor keyword that is be used to define the model bounding box. In that case, this keyword is ignored.

x_namestr, optional

The name of the model parameter that corresponds to the x position of the sources. This parameter must also be a column name in params_table.

y_namestr, optional

The name of the model parameter that corresponds to the y position of the sources. This parameter must also be a column name in params_table.

discretize_method{‘center’, ‘interp’, ‘oversample’, ‘integrate’}, optional

One of the following methods for discretizing the model on the pixel grid:

'center' (default)
Discretize model by taking the value at the center of the pixel bins. This method should be used for ePSF/PRF single or gridded models.

'interp'
Discretize model by bilinearly interpolating between the values at the corners of the pixel bins.

'oversample'
Discretize model by taking the average of model values in the pixel bins on an oversampled grid. Use the discretize_oversample keyword to set the integer oversampling factor.

'integrate'
Discretize model by integrating the model over the pixel bins using scipy.integrate.quad. This mode conserves the model integral on a subpixel scale, but it is extremely slow.

discretize_oversampleint, optional

The integer oversampling factor used when descretize_method='oversample'. This keyword is ignored otherwise.

progress_barbool, optional

Whether to display a progress bar while adding the sources to the image. The progress bar requires that the tqdm optional dependency be installed. Note that the progress bar does not currently work in the Jupyter console due to limitations in tqdm.

Returns:

array2D ndarray: The rendered image containing the model sources.

Notes

The local background value around each source is optionally included using the local_bkg column in the input params_table. This local background added to each source over its model_shape region. In regions where the model_shape of source overlap, the local background will be added multiple times. This is not an issue if the sources are well-separated, but for crowded fields, this option should be used with care.

Examples

import matplotlib.pyplot as plt
from astropy.modeling.models import Moffat2D
from photutils.datasets import (make_model_image,
                                make_random_models_table)

model = Moffat2D()
n_sources = 25
shape = (100, 100)
param_ranges = {'amplitude': [100, 200],
                'x_0': [0, shape[1]],
                'y_0': [0, shape[0]],
                'gamma': [1, 2],
                'alpha': [1, 2]}
params = make_random_models_table(n_sources, param_ranges, seed=0)

model_shape = (15, 15)
data = make_model_image(shape, model, params, model_shape=model_shape)

plt.imshow(data, origin='lower')
plt.tight_layout()

(Source code, png, hires.png, pdf, svg)

../_images/photutils-datasets-make_model_image-1.png

import matplotlib.pyplot as plt
import numpy as np
from astropy.modeling.models import Gaussian2D
from photutils.datasets import make_model_image, make_model_params

model = Gaussian2D()
shape = (500, 500)
n_sources = 100
params = make_model_params(shape, n_sources, x_name='x_mean',
                           y_name='y_mean', min_separation=25,
                           amplitude=(100, 500), x_stddev=(1, 3),
                           y_stddev=(1, 3), theta=(0, np.pi))
model_shape = (25, 25)
data = make_model_image(shape, model, params, model_shape=model_shape,
                        x_name='x_mean', y_name='y_mean')

plt.imshow(data, origin='lower')
plt.tight_layout()

(Source code, png, hires.png, pdf, svg)

../_images/photutils-datasets-make_model_image-2.png