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', params_map=None, 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. By default, the table is searched for column names that match model parameter names and the values specified by x_name and y_name. However, the user can specify a different mapping between model parameter names and column names using the params_map keyword.

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 or params_map will be set to the model default value. To attach units to model parameters, params_table must be input as a QTable. Rows that contain any non-finite model parameters will be skipped.

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 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 unless params_map is input.

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 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. If param_map is not input, then this value 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. If param_map is not input, then this value must also be a column name in params_table.

params_mapdict or None, optional

A dictionary mapping the model parameter names to the column names in the input params_table. The dictionary keys are the model parameter names and the values are the column names in the input params_table. This can be used to map column names to model parameter names that are different. For example, if the input column name is ‘flux_f200w’ and the model parameter name is ‘flux’, then use params_map={'flux': 'flux_f200w'}. This table may also be used if you want to map the model x and y parameters to different columns than x_name and y_name, but the x_name and y_name keys must be included in the dictionary.

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.

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 is added to each source over its model_shape region. In regions where the model_shape of sources 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)

fig, ax = plt.subplots()
ax.imshow(data, origin='lower')
fig.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')

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

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

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