# sep.sum_ellipse¶

sep.sum_ellipse(data, x, y, a, b, theta, r, err=None, var=None, mask=None, maskthresh=0.0, seg_id=None, segmap=None, 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 and y 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, and r, obey numpy broadcasting rules. a is the semi-major axis, b is the semi-minor axis and theta 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 that a >= 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 axis b * r. Setting this parameter to a value other than 1.0 is exactly equivalent to scaling both a and b by the same value. Default is 1.0. err, var : float or ndarray Error or variance (specify at most one). mask : ndarray, optional Mask 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. segmap : ndarray, optional Segmentation image with dimensions of data and dtype np.int32. This is an optional input and corresponds to the segmentation map output by extract. seg_id : array_like, optional Array of segmentation ids used to mask additional pixels in the image. Dimensions correspond to the dimensions of x and y. The behavior differs depending on whether seg_id is negative or positive. If seg_id is positive, all pixels belonging to other objects are masked. (Pixel j, i is masked if seg[j, i] != seg_id and seg[j, i] != 0). If seg_id is negative, all pixels other than those belonging to the object of interest are masked. (Pixel j, i is masked if seg[j, i] != -seg_id). NB: must be included if segmap is provided. 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. 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.)