- sep.sum_circle(data, x, y, r, err=None, var=None, mask=None, maskthresh=0.0, segmap=None, seg_id=None, bkgann=None, gain=None, subpix=5)¶
Sum data in circular aperture(s).
2-d array to be summed.
- x, y, rarray_like
Center coordinates and radius (radii) of aperture(s).
xcorresponds to the second (“fast”) axis of the input array and
ycorresponds 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.
- err, varfloat or
Error or variance (specify at most one).
Mask array. If supplied, a given pixel is masked if its value is greater than
- maskthreshfloat, optional
Threshold for a pixel to be masked. Default is
Segmentation image with dimensions of
np.int32. This is an optional input and corresponds to the segmentation map output by
- seg_idarray_like, optional
Array of segmentation ids used to mask additional pixels in the image. Dimensions correspond to the dimensions of
y. The behavior differs depending on whether
seg_idis negative or positive. If
seg_idis positive, all pixels belonging to other objects are masked. (Pixel
j, iis masked if
seg[j, i] != seg_id and seg[j, i] != 0). If
seg_idis negative, all pixels other than those belonging to the object of interest are masked. (Pixel
j, iis masked if
seg[j, i] != -seg_id). NB: must be included if ``segmap` is provided.
- bkganntuple, 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
- gainfloat, 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.
- subpixint, optional
Subpixel sampling factor. If 0, exact overlap is calculated. Default is 5.