# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module provides tools for generating radial profiles.
"""
import warnings
import numpy as np
from astropy.modeling.fitting import TRFLSQFitter
from astropy.modeling.models import Gaussian1D
from astropy.stats import gaussian_sigma_to_fwhm
from astropy.utils import lazyproperty
from photutils.profiles.core import ProfileBase
__all__ = ['RadialProfile']
[docs]
class RadialProfile(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
----------
data : 2D `~numpy.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.
xycen : tuple of 2 floats
The ``(x, y)`` pixel coordinate of the source center.
radii : 1D float `~numpy.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.
error : 2D `~numpy.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 `~photutils.utils.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.
mask : 2D bool `~numpy.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.
subpixels : int, 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 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) # doctest: +FLOAT_CMP
[ 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) # doctest: +FLOAT_CMP
[ 4.15632243e+01 3.93402079e+01 3.59845746e+01 3.15540506e+01
2.62300757e+01 2.07297033e+01 1.65106801e+01 1.19376723e+01
7.75743772e+00 5.56759777e+00 3.44112671e+00 1.91350281e+00
1.17092981e+00 4.22261078e-01 9.70256904e-01 4.16355795e-01
1.52328707e-02 -6.69985111e-02 4.15522650e-01 2.48494731e-01
4.03348112e-01 1.43482678e-01 -2.62777461e-01 7.30653622e-02
7.84616804e-04]
>>> print(rp.profile_error) # doctest: +FLOAT_CMP
[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.
.. plot::
import numpy as np
from astropy.modeling.models import Gaussian2D
from photutils.centroids import centroid_quadratic
from photutils.datasets import make_noise_image
from photutils.profiles import RadialProfile
# create an artificial single source
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
# find the source centroid
xycen = centroid_quadratic(data, xpeak=48, ypeak=52)
# create the radial profile
edge_radii = np.arange(26)
rp = RadialProfile(data, xycen, edge_radii, error=error, mask=None)
# plot the radial profile
rp.plot()
rp.plot_error()
Normalize the profile and plot the normalized radial profile.
.. plot::
import numpy as np
from astropy.modeling.models import Gaussian2D
from photutils.centroids import centroid_quadratic
from photutils.datasets import make_noise_image
from photutils.profiles import RadialProfile
# create an artificial single source
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
# find the source centroid
xycen = centroid_quadratic(data, xpeak=48, ypeak=52)
# create the radial profile
edge_radii = np.arange(26)
rp = RadialProfile(data, xycen, edge_radii, error=error, mask=None)
# plot the radial profile
rp.normalize()
rp.plot()
rp.plot_error()
Plot three of the annulus apertures on the data.
.. plot::
import matplotlib.pyplot as plt
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
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
# find the source centroid
xycen = centroid_quadratic(data, xpeak=48, ypeak=52)
# create the radial profile
edge_radii = np.arange(26)
rp = RadialProfile(data, xycen, edge_radii, error=error, mask=None)
norm = simple_norm(data, 'sqrt')
plt.figure(figsize=(5, 5))
plt.imshow(data, norm=norm, origin='lower')
rp.apertures[5].plot(color='C0', lw=2)
rp.apertures[10].plot(color='C1', lw=2)
rp.apertures[15].plot(color='C3', lw=2)
Fit a 1D Gaussian to the radial profile and return the Gaussian
model.
>>> rp.gaussian_fit # doctest: +FLOAT_CMP
<Gaussian1D(amplitude=41.54880743, mean=0., stddev=4.71059406)>
>>> print(rp.gaussian_fwhm) # doctest: +FLOAT_CMP
11.09260130738712
Plot the fitted 1D Gaussian on the radial profile.
.. plot::
import matplotlib.pyplot as plt
import numpy as np
from astropy.modeling.models import Gaussian2D
from photutils.centroids import centroid_quadratic
from photutils.datasets import make_noise_image
from photutils.profiles import RadialProfile
# create an artificial single source
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
# find the source centroid
xycen = centroid_quadratic(data, xpeak=48, ypeak=52)
# create the radial profile
edge_radii = np.arange(26)
rp = RadialProfile(data, xycen, edge_radii, error=error, mask=None)
# plot the radial profile
rp.normalize()
rp.plot(label='Radial Profile')
rp.plot_error()
plt.plot(rp.radius, rp.gaussian_profile, label='Gaussian Fit')
plt.legend()
"""
@lazyproperty
def radius(self):
"""
The profile radius (bin centers) in pixels as a 1D
`~numpy.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:])``.
"""
# define the radial bin centers from the radial bin edges
return (self.radii[:-1] + self.radii[1:]) / 2
@lazyproperty
def apertures(self):
"""
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.
"""
from photutils.aperture import CircularAnnulus, CircularAperture
apertures = []
for i in range(len(self.radii) - 1):
try:
aperture = CircularAnnulus(self.xycen, self.radii[i],
self.radii[i + 1])
except ValueError: # zero radius
aperture = CircularAperture(self.xycen,
self.radii[i + 1])
apertures.append(aperture)
return apertures
@lazyproperty
def _flux(self):
"""
The flux in a circular annulus.
"""
return np.diff(self._photometry[0])
@lazyproperty
def _fluxerr(self):
"""
The flux error in a circular annulus.
"""
return np.sqrt(np.diff(self._photometry[1] ** 2))
@lazyproperty
def area(self):
"""
The unmasked area in each circular annulus (or aperture) as a
function of radius as a 1D `~numpy.ndarray`.
"""
return np.diff(self._photometry[2])
@lazyproperty
def profile(self):
"""
The radial profile as a 1D `~numpy.ndarray`.
"""
# ignore divide-by-zero RuntimeWarning
with warnings.catch_warnings():
warnings.simplefilter('ignore', RuntimeWarning)
return self._flux / self.area
@lazyproperty
def profile_error(self):
"""
The radial profile errors as a 1D `~numpy.ndarray`.
"""
if self.error is None:
return self._fluxerr
# ignore divide-by-zero RuntimeWarning
with warnings.catch_warnings():
warnings.simplefilter('ignore', RuntimeWarning)
return self._fluxerr / self.area
@lazyproperty
def _profile_nanmask(self):
return np.isfinite(self.profile)
@lazyproperty
def gaussian_fit(self):
"""
The fitted 1D Gaussian to the radial profile as a
`~astropy.modeling.functional_models.Gaussian1D` model.
"""
profile = self.profile[self._profile_nanmask]
radius = self.radius[self._profile_nanmask]
amplitude = np.max(profile)
std = np.sqrt(abs(np.sum(profile * radius**2) / np.sum(profile)))
g_init = Gaussian1D(amplitude=amplitude, mean=0.0, stddev=std)
g_init.mean.fixed = True
fitter = TRFLSQFitter()
return fitter(g_init, radius, profile)
@lazyproperty
def gaussian_profile(self):
"""
The fitted 1D Gaussian profile to the radial profile as a 1D
`~numpy.ndarray`.
"""
return self.gaussian_fit(self.radius)
@lazyproperty
def gaussian_fwhm(self):
"""
The full-width at half-maximum (FWHM) in pixels of the 1D
Gaussian fitted to the radial profile.
"""
return self.gaussian_fit.stddev.value * gaussian_sigma_to_fwhm
