sep.sum_ellipse¶
-
sep.
sum_ellipse
(data, x, y, a, b, theta, r, err=None, var=None, mask=None, maskthresh=0.0, bkgann=None, gain=None, subpix=5)¶ Sum data in elliptical aperture(s).
Parameters: data :
ndarray
2-d array to be summed.
x, y : array_like
Center coordinates and radius (radii) of aperture(s).
x
corresponds to the second (“fast”) axis of the input array andy
corresponds to the first (“slow”) axis.x, y = (0.0, 0.0)
corresponds to the center of the first element of the array. These inputs obey numpy broadcasting rules.a, b, theta : array_like
Ellipse parameters. These inputs, along with
x
,y
, andr
, obey numpy broadcasting rules.a
is the semi-major axis,b
is the semi-minor axis andtheta
is angle in radians between the positive x axis and the major axis. It must be in the range[-pi/2, pi/2]
. It is also required thata >= b >= 0.0
.r : array_like, optional
Scaling factor for the semi-minor and semi-major axes. The actual ellipse used will have semi-major axis
a * r
and semi-minor axisb * r
. Setting this parameter to a value other than 1.0 is exactly equivalent to scaling botha
andb
by the same value. Default is 1.0.err, var : float or
ndarray
Error or variance (specify at most one).
mask :
ndarray
, optionalMask array. If supplied, a given pixel is masked if its value is greater than
maskthresh
.maskthresh : float, optional
Threshold for a pixel to be masked. Default is
0.0
.bkgann : tuple, optional
Length 2 tuple giving the inner and outer radius of a “background annulus”. If supplied, the background is estimated by averaging unmasked pixels in this annulus. If supplied, the inner and outer radii obey numpy broadcasting rules, along with
x
,y
, and ellipse parameters.gain : float, optional
Conversion factor between data array units and poisson counts, used in calculating poisson noise in aperture sum. If
None
(default), do not add poisson noise.subpix : int, optional
Subpixel sampling factor. Default is 5.
Returns: sum :
ndarray
The sum of the data array within the aperture.
sumerr :
ndarray
Error on the sum.
flags :
ndarray
Integer giving flags. (0 if no flags set.)