make_psf_model_image

photutils.psf.make_psf_model_image(shape, psf_model, n_sources, *, model_shape=None, min_separation=1, border_size=None, seed=0, progress_bar=False, **kwargs)

Make an example image containing PSF model images.

Source parameters are randomly generated using an optional seed.

Parameters:

shape2-tuple of int

The shape of the output image.

psf_model2D astropy.modeling.Model

The PSF model. The model must have parameters named x_0, y_0, and flux, corresponding to the center (x, y) position and flux, or it must have ‘x_name’, ‘y_name’, and ‘flux_name’ attributes that map to the x, y, and flux parameters (i.e., a model output from make_psf_model). The model must be two-dimensional such that it accepts 2 inputs (e.g., x and y) and provides 1 output.

n_sourcesint

The number of sources to generate. If min_separation is too large, the number of requested sources may not fit within the given shape and therefore the number of sources generated may be less than n_sources.

model_shapeNone or 2-tuple of int, optional

The shape around the center (x, y) position that will used to evaluate the psf_model. If None, then the shape will be determined from the psf_model bounding box (an error will be raised if the model does not have a bounding box).

min_separationfloat, optional

The minimum separation between the centers of two sources. Note that if the minimum separation is too large, the number of sources generated may be less than n_sources.

border_sizeNone, tuple of 2 int, or int, optional

The (ny, nx) size of the exclusion border around the image edges where no sources will be generated that have centers within the border region. If a single integer is provided, it will be used for both dimensions. If None, then a border size equal to half the (y, x) size of the evaluated PSF model (taking any oversampling into account) will be used.

seedint, optional

A seed to initialize the numpy.random.BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS.

progress_barbool, optional

Whether to display a progress bar when creating the sources. 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.

**kwargs

Keyword arguments are accepted for additional model parameters. The values should be 2-tuples of the lower and upper bounds for the parameter range. The parameter values will be uniformly distributed between the lower and upper bounds, inclusively. If the parameter is not in the input psf_model parameter names, it will be ignored.

Returns:

data2D ndarray

The simulated image.

tableTable

A table containing the (x, y, flux) parameters of the generated sources. The column names will correspond to the names of the input psf_model (x, y, flux) parameter names. The table will also contain an 'id' column with unique source IDs.

Examples

>>> from photutils.psf import CircularGaussianPRF, make_psf_model_image
>>> shape = (150, 200)
>>> psf_model = CircularGaussianPRF(fwhm=3.5)
>>> n_sources = 10
>>> data, params = make_psf_model_image(shape, psf_model, n_sources,
...                                     flux=(100, 250),
...                                     min_separation=10,
...                                     seed=0)
>>> params['x_0'].info.format = '.4f'  # optional format
>>> params['y_0'].info.format = '.4f'
>>> params['flux'].info.format = '.4f'
>>> print(params)  
 id   x_0      y_0      flux
--- -------- -------- --------
  1 125.2010  72.3184 147.9522
  2  57.6408  39.1380 128.1262
  3  15.5391 115.4520 200.8790
  4  11.0411 131.7530 129.2661
  5 157.6417  43.6615 186.6532
  6 175.9470  80.2172 190.3359
  7 142.2274 132.7563 244.3635
  8 108.0270  13.4284 110.8398
  9 180.0533 106.0888 174.9959
 10 158.1171  90.3260 211.6146

import matplotlib.pyplot as plt
from photutils.psf import CircularGaussianPRF, make_psf_model_image
shape = (150, 200)
psf_model = CircularGaussianPRF(fwhm=3.5)
n_sources = 10
data, params = make_psf_model_image(shape, psf_model, n_sources,
                                    flux=(100, 250),
                                    min_separation=10,
                                    seed=0)
plt.imshow(data, origin='lower')

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

import matplotlib.pyplot as plt
from photutils.psf import CircularGaussianPRF, make_psf_model_image
shape = (150, 200)
psf_model = CircularGaussianPRF(fwhm=3.5)
n_sources = 10
data, params = make_psf_model_image(shape, psf_model, n_sources,
                                    flux=(100, 250),
                                    min_separation=10,
                                    seed=0, sigma=(1, 2))
plt.imshow(data, origin='lower')

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