RadialProfile

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

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 CurveOfGrowth class, where the input radii are the radii of the circular apertures used to compute the cumulative flux.

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

CurveOfGrowth

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 photutils.centroids import centroid_2dg
>>> 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)
>>> bkg_sig = 2.1
>>> noise = make_noise_image(data.shape, mean=0., stddev=bkg_sig, seed=0)
>>> data += noise
>>> error = np.zeros_like(data) + bkg_sig

Create the radial profile.

>>> xycen = centroid_2dg(data)
>>> edge_radii = np.arange(25)
>>> rp = RadialProfile(data, xycen, edge_radii, error=error)

>>> 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]

>>> print(rp.profile)
[ 4.30187860e+01  4.02502046e+01  3.57758011e+01  3.16071235e+01
  2.61511082e+01  2.10539746e+01  1.63701300e+01  1.16674718e+01
  8.12828014e+00  5.78962699e+00  3.59342666e+00  2.35353336e+00
  1.20355937e+00  7.67093923e-01  4.24650784e-01  8.67989701e-02
  5.11484374e-02 -9.82041768e-02  2.37482124e-02 -3.66602855e-02
  6.84802299e-02  1.72239596e-01 -3.86056497e-02  7.30423743e-02]

>>> print(rp.profile_error)
[1.18479813 0.68404352 0.52985783 0.4478116  0.39493271 0.35723008
 0.32860388 0.30591356 0.28735575 0.27181133 0.25854415 0.24704749
 0.23695963 0.22801451 0.22001149 0.21279603 0.20624688 0.20026744
 0.19477961 0.18971954 0.18503438 0.18068002 0.17661928 0.17282057]

Plot the radial profile, including the raw data profile.

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

Normalize the profile and plot the normalized radial profile.

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

Plot three of the annulus apertures on the data.

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

Fit 1D Gaussian and Moffat models to the normalized radial profile.

>>> rp.normalize()
>>> rp.gaussian_fit
<Gaussian1D(amplitude=0.98231088, mean=0., stddev=4.67512776)>
>>> rp.moffat_fit
<Moffat1D(amplitude=..., x_0=0., gamma=..., alpha=...)>
>>> print(rp.gaussian_fwhm)
11.009084813327846
>>> print(rp.moffat_fwhm)
10.868426507928344

Plot the fitted 1D Gaussian and Moffat models on the radial profile.

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

Attributes Summary

apertures	A list of the circular annulus apertures used to measure the radial profile, as CircularAnnulus objects.
area	The unmasked area in each circular annulus (or aperture) as a function of radius as a 1D ndarray.
data_profile	The raw data profile as a 1D ndarray.
data_radius	The radii of the raw data profile 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.
moffat_fit	The fitted 1D Moffat to the radial profile as a Moffat1D model.
moffat_fwhm	The full-width at half-maximum (FWHM) in pixels of the 1D Moffat fitted to the radial profile.
moffat_profile	The fitted 1D Moffat 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, as CircularAnnulus objects.

If the minimum of radii is zero, then the innermost element will be a CircularAperture with radius equal to radii[1].

area

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

data_profile

The raw data profile as a 1D ndarray.

data_radius

The radii of the raw data profile as a 1D ndarray.

gaussian_fit

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

The cached fit is automatically invalidated when the profile normalization is changed, so the fit is always consistent with the current profile.

gaussian_fwhm

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

The cached value is automatically invalidated when the profile normalization is changed.

Returns None if the fit failed (e.g., the profile is entirely non-finite or masked).

gaussian_profile

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

The cached profile is automatically invalidated when the profile normalization is changed.

Returns None if the fit failed (e.g., the profile is entirely non-finite or masked).

moffat_fit

The fitted 1D Moffat to the radial profile as a Moffat1D model.

The cached fit is automatically invalidated when the profile normalization is changed, so the fit is always consistent with the current profile.

moffat_fwhm

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

The cached value is automatically invalidated when the profile normalization is changed.

Returns None if the fit failed (e.g., the profile is entirely non-finite or masked).

moffat_profile

The fitted 1D Moffat profile to the radial profile as a 1D ndarray.

The cached profile is automatically invalidated when the profile normalization is changed.

Returns None if the fit failed (e.g., the profile is entirely non-finite or masked).

profile

The radial profile as a 1D ndarray.

profile_error

The radial profile errors as a 1D ndarray.

If no error array was provided, an empty array with shape (0,) is returned.

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:

polymatplotlib.collections.PolyCollection or None

A PolyCollection containing the plotted polygons, or None if no errors were input.

unnormalize()

Unnormalize the profile back to the original state before any calls to normalize.