RadialProfile#

class photutils.profiles.RadialProfile(data, xycen, radii, *, error=None, mask=None, method='exact', subpixels=5)[source]#

Bases: ProfileBase

Class to create a radial profile using concentric circular annulus apertures.

The radial profile represents the azimuthally-averaged flux in circular annuli apertures as a function of radius.

For this class, the input radii represent the edges of the radial bins. This differs from the RadialProfile class, where the inputs represent the centers of the radial bins.

The output radius attribute represents the bin centers.

Parameters:

data2D ndarray

The 2D data array. The data should be background-subtracted. Non-finite values (e.g., NaN or inf) in the data or error array are automatically masked.

xycentuple of 2 floats

The (x, y) pixel coordinate of the source center.

radii1D float ndarray

An array of radii defining the edges of the radial bins. radii must be strictly increasing with a minimum value greater than or equal to zero, and contain at least 2 values. The radial spacing does not need to be constant. The output radius attribute will be defined at the bin centers.

error2D ndarray, optional

The 1-sigma errors of the input data. error is assumed to include all sources of error, including the Poisson error of the sources (see calc_total_error) . error must have the same shape as the input data. Non-finite values (e.g., NaN or inf) in the data or error array are automatically masked.

mask2D bool ndarray, optional

A boolean mask with the same shape as data where a True value indicates the corresponding element of data is masked. Masked data are excluded from all calculations.

method{‘exact’, ‘center’, ‘subpixel’}, optional

The method used to determine the overlap of the aperture on the pixel grid:

'exact' (default): The exact fractional overlap of the aperture and each pixel is calculated. The aperture weights will contain values between 0 and 1.

'center': A pixel is considered to be entirely in or out of the aperture depending on whether its center is in or out of the aperture. The aperture weights will contain values only of 0 (out) and 1 (in).

'subpixel': A pixel is divided into subpixels (see the subpixels keyword), each of which are considered to be entirely in or out of the aperture depending on whether its center is in or out of the aperture. If subpixels=1, this method is equivalent to 'center'. The aperture weights will contain values between 0 and 1.

subpixelsint, optional

For the 'subpixel' method, resample pixels by this factor in each dimension. That is, each pixel is divided into subpixels**2 subpixels. This keyword is ignored unless method='subpixel'.

See also

RadialProfile

Notes

If the minimum of radii is zero, then a circular aperture with radius equal to radii[1] will be used for the innermost aperture.

Examples

>>> import numpy as np
>>> from astropy.modeling.models import Gaussian2D
>>> from astropy.visualization import simple_norm
>>> from photutils.centroids import centroid_quadratic
>>> from photutils.datasets import make_noise_image
>>> from photutils.profiles import RadialProfile

Create an artificial single source. Note that this image does not have any background.

>>> gmodel = Gaussian2D(42.1, 47.8, 52.4, 4.7, 4.7, 0)
>>> yy, xx = np.mgrid[0:100, 0:100]
>>> data = gmodel(xx, yy)
>>> error = make_noise_image(data.shape, mean=0., stddev=2.4, seed=123)
>>> data += error

Create the radial profile.

>>> xycen = centroid_quadratic(data, xpeak=48, ypeak=52)
>>> edge_radii = np.arange(26)
>>> rp = RadialProfile(data, xycen, edge_radii, error=error, mask=None)

>>> print(rp.radius)  
[ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5  9.5 10.5 11.5 12.5 13.5
 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5]

>>> print(rp.profile)  
[ 4.15632243e+01  3.93402079e+01  3.59845746e+01  3.15540506e+01
62300757e+01  2.07297033e+01  1.65106801e+01  1.19376723e+01
75743772e+00  5.56759777e+00  3.44112671e+00  1.91350281e+00
17092981e+00  4.22261078e-01  9.70256904e-01  4.16355795e-01
52328707e-02 -6.69985111e-02  4.15522650e-01  2.48494731e-01
03348112e-01  1.43482678e-01 -2.62777461e-01  7.30653622e-02
84616804e-04]

>>> print(rp.profile_error)  
[1.69588246 0.81797694 0.61132694 0.44670831 0.49499835 0.38025361
 0.40844702 0.32906672 0.36466713 0.33059274 0.29661894 0.27314739
 0.25551933 0.27675376 0.25553986 0.23421017 0.22966813 0.21747036
 0.23654884 0.22760386 0.23941711 0.20661313 0.18999134 0.17469024
 0.19527558]

Plot the radial profile.

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

../_images/photutils-profiles-RadialProfile-1.png

Normalize the profile and plot the normalized radial profile.

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

../_images/photutils-profiles-RadialProfile-2.png

Plot two of the annulus apertures on the data.

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

../_images/photutils-profiles-RadialProfile-3.png

Fit a 1D Gaussian to the radial profile and return the Gaussian model.

>>> rp.gaussian_fit  
<Gaussian1D(amplitude=41.54880743, mean=0., stddev=4.71059406)>

>>> print(rp.gaussian_fwhm)  
11.09260130738712

Plot the fitted 1D Gaussian on the radial profile.

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

../_images/photutils-profiles-RadialProfile-4.png

Attributes Summary

`apertures`	A list of the circular annulus apertures used to measure the radial profile.
`area`	The unmasked area in each circular annulus (or aperture) as a function of radius as a 1D `ndarray`.
`gaussian_fit`	The fitted 1D Gaussian to the radial profile as a `Gaussian1D` model.
`gaussian_fwhm`	The full-width at half-maximum (FWHM) in pixels of the 1D Gaussian fitted to the radial profile.
`gaussian_profile`	The fitted 1D Gaussian profile to the radial profile as a 1D `ndarray`.
`profile`	The radial profile as a 1D `ndarray`.
`profile_error`	The radial profile errors as a 1D `ndarray`.
`radius`	The profile radius (bin centers) in pixels as a 1D `ndarray`.

Methods Summary

`normalize`([method])	Normalize the profile.
`plot`([ax])	Plot the profile.
`plot_error`([ax])	Plot the profile errors.
`unnormalize`()	Unnormalize the profile back to the original state before any calls to `normalize`.

Attributes Documentation

apertures#

A list of the circular annulus apertures used to measure the radial profile.

If the min_radius is less than or equal to half the radius_step, then a circular aperture with radius equal to min_radius + 0.5 * radius_step will be used for the innermost aperture.

area#: The unmasked area in each circular annulus (or aperture) as a function of radius as a 1D ndarray.

gaussian_fit#: The fitted 1D Gaussian to the radial profile as a Gaussian1D model.

gaussian_fwhm#: The full-width at half-maximum (FWHM) in pixels of the 1D Gaussian fitted to the radial profile.

gaussian_profile#: The fitted 1D Gaussian profile to the radial profile as a 1D ndarray.

profile#: The radial profile as a 1D ndarray.

profile_error#: The radial profile errors as a 1D ndarray.

radius#

The profile radius (bin centers) in pixels as a 1D ndarray.

The returned radius values are defined as the arithmetic means of the input radial-bins edges (radii).

For logarithmically-spaced input radii, one could instead use a radius array defined using the geometric mean of the bin edges, i.e. np.sqrt(radii[:-1] * radii[1:]).

Methods Documentation

normalize(method='max')#

Normalize the profile.

Parameters:

method{‘max’, ‘sum’}, optional

The method used to normalize the profile:

‘max’ (default): The profile is normalized such that its maximum value is 1.

‘sum’: The profile is normalized such that its sum (integral) is 1.

plot(ax=None, **kwargs)#

Plot the profile.

Parameters:

axmatplotlib.axes.Axes or None, optional: The matplotlib axes on which to plot. If None, then the current Axes instance is used.
**kwargsdict, optional: Any keyword arguments accepted by matplotlib.pyplot.plot.

Returns:

lineslist of Line2D: A list of lines representing the plotted data.

plot_error(ax=None, **kwargs)#

Plot the profile errors.

Parameters:

axmatplotlib.axes.Axes or None, optional: The matplotlib axes on which to plot. If None, then the current Axes instance is used.
**kwargsdict, optional: Any keyword arguments accepted by matplotlib.pyplot.fill_between.

Returns:

linesmatplotlib.collections.PolyCollection: A PolyCollection containing the plotted polygons.

unnormalize()#: Unnormalize the profile back to the original state before any calls to normalize.