Morphological Properties (photutils.morphology)#
Introduction#
The data_properties() function can
be used to calculate the basic morphological properties (e.g.,
elliptical shape properties) of a single source in a cutout
image. data_properties() returns a
SourceCatalog object. Please see
SourceCatalog for the list of the many
properties that are calculated.
The photutils.morphology subpackage also includes the
gini() function, which calculates the Gini
coefficient of a source in an image.
If you have a segmentation image, the
SourceCatalog class can be used
to calculate the properties for all (or a specified subset) of the
segmented sources. Please see Source Photometry and Properties
from Image Segmentation for more details.
Getting Started#
Let’s extract a single object from a synthetic dataset and find calculate its morphological properties. For this example, we will subtract the background using simple sigma-clipped statistics.
First, we create the source image and subtract its background:
>>> from astropy.stats import sigma_clipped_stats
>>> from photutils.datasets import make_4gaussians_image
>>> data = make_4gaussians_image()[40:80, 75:105]
>>> mean, median, std = sigma_clipped_stats(data, sigma=3.0)
>>> data -= median # subtract background
Then, calculate its properties:
>>> from photutils.morphology import data_properties
>>> mask = data < 50
>>> cat = data_properties(data, mask=mask)
>>> columns = ['label', 'xcentroid', 'ycentroid', 'semimajor_sigma',
... 'semiminor_sigma', 'orientation']
>>> tbl = cat.to_table(columns=columns)
>>> tbl['xcentroid'].info.format = '.10f' # optional format
>>> tbl['ycentroid'].info.format = '.10f'
>>> tbl['semiminor_sigma'].info.format = '.10f'
>>> tbl['orientation'].info.format = '.10f'
>>> print(tbl)
label xcentroid ycentroid ... semiminor_sigma orientation
... pix deg
----- ------------- ------------- ... --------------- -------------
1 15.0203353055 20.0876025118 ... 3.2260911267 59.6896286141
Now let’s use the measured morphological properties to define an approximate isophotal ellipse for the source:
>>> import astropy.units as u
>>> from photutils.aperture import EllipticalAperture
>>> xypos = (cat.xcentroid, cat.ycentroid)
>>> r = 2.5 # approximate isophotal extent
>>> a = cat.semimajor_sigma.value * r
>>> b = cat.semiminor_sigma.value * r
>>> theta = cat.orientation.to(u.rad).value
>>> apertures = EllipticalAperture(xypos, a, b, theta=theta)
>>> plt.imshow(data, origin='lower', cmap='viridis',
... interpolation='nearest')
>>> apertures.plot(color='C3')
(Source code, png, hires.png, pdf, svg)
Gini Coefficient#
The Gini coefficient is a measure of the inequality in the distribution of flux values in an image. The Gini coefficient ranges from 0 to 1, where 0 indicates that the flux is equally distributed among all pixels and 1 indicates that the flux is concentrated in a single pixel.
The gini() function calculates the Gini
coefficient of a single source using the values in a cutout image.
An optional boolean mask can be used to exclude pixels from the
calculation.
Let’s calculate the Gini coefficient of the source in the above example:
>>> from photutils.morphology import gini
>>> g = gini(data, mask=mask)
>>> print(g)
0.21943786993407582