photutils.datasets.make_gaussian_sources_image(shape, source_table, oversample=1)[source]

Make an image containing 2D Gaussian sources.

shape2-tuple of int

The shape of the output 2D image.


Table of parameters for the Gaussian sources. Each row of the table corresponds to a Gaussian source whose parameters are defined by the column names. With the exception of 'flux', column names that do not match model parameters will be ignored (flux will be converted to amplitude). If both 'flux' and 'amplitude' are present, then 'flux' will be ignored. Model parameters not defined in the table will be set to the default value.

oversamplefloat, optional

The sampling factor used to discretize the models on a pixel grid. If the value is 1.0 (the default), then the models will be discretized by taking the value at the center of the pixel bin. Note that this method will not preserve the total flux of very small sources. Otherwise, the models will be discretized by taking the average over an oversampled grid. The pixels will be oversampled by the oversample factor.

image2D ndarray

Image containing 2D Gaussian sources.


# make a table of Gaussian sources
from astropy.table import QTable
table = QTable()
table['amplitude'] = [50, 70, 150, 210]
table['x_mean'] = [160, 25, 150, 90]
table['y_mean'] = [70, 40, 25, 60]
table['x_stddev'] = [15.2, 5.1, 3., 8.1]
table['y_stddev'] = [2.6, 2.5, 3., 4.7]
table['theta'] = np.radians(np.array([145., 20., 0., 60.]))

# make an image of the sources without noise, with Gaussian
# noise, and with Poisson noise
from photutils.datasets import make_gaussian_sources_image
from photutils.datasets import make_noise_image
shape = (100, 200)
image1 = make_gaussian_sources_image(shape, table)
image2 = image1 + make_noise_image(shape, distribution='gaussian',
                                   mean=5., stddev=5.)
image3 = image1 + make_noise_image(shape, distribution='poisson',

# plot the images
import matplotlib.pyplot as plt
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 12))
ax1.imshow(image1, origin='lower', interpolation='nearest')
ax1.set_title('Original image')
ax2.imshow(image2, origin='lower', interpolation='nearest')
ax2.set_title('Original image with added Gaussian noise'
              ' ($\mu = 5, \sigma = 5$)')
ax3.imshow(image3, origin='lower', interpolation='nearest')
ax3.set_title('Original image with added Poisson noise ($\mu = 5$)')

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