aperture_photometry#

photutils.aperture.aperture_photometry(data, apertures, error=None, mask=None, method='exact', subpixels=5, wcs=None)[source]#

Perform aperture photometry on the input data by summing the flux within the given aperture(s).

Note that this function returns the sum of the (weighted) input data values within the aperture. It does not convert data in surface brightness units to flux or counts. Conversion from surface-brightness units should be performed before using this function.

Parameters:
dataarray_like, Quantity, NDData

The 2D array on which to perform photometry. data should be background-subtracted. If data is a Quantity array, then error (if input) must also be a Quantity array with the same units. See the Notes section below for more information about NDData input.

aperturesAperture, supported regions.Region, list of Aperture or regions.Region

The aperture(s) to use for the photometry. If apertures is a list of Aperture or regions.Region, then they all must have the same position(s). If apertures contains a SkyAperture or SkyRegion object, then a WCS must be input using the wcs keyword. Region objects are converted to aperture objects.

errorarray_like or Quantity, optional

The pixel-wise Gaussian 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. If a Quantity array, then data must also be a Quantity array with the same units.

maskarray_like (bool), 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. Not all options are available for all aperture types. Note that the more precise methods are generally slower. The following methods are available:

  • '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'.

wcsWCS object, optional

A world coordinate system (WCS) transformation that supports the astropy shared interface for WCS (e.g., astropy.wcs.WCS, gwcs.wcs.WCS). If provided, the output table will include a 'sky_center' column with the sky coordinates of the input aperture center(s). This keyword is required if the input apertures contains a SkyAperture or SkyRegion.

Returns:
tableQTable

A table of the photometry with the following columns:

  • 'id': The source ID.

  • 'x_center', 'y_center': The x and y pixel coordinates of the input aperture center(s).

  • 'sky_center': The sky coordinates of the input aperture center(s). Returned if a wcs is input.

  • 'aperture_sum': The sum of the values within the aperture(s).

  • 'aperture_sum_err': The corresponding uncertainty in the 'aperture_sum' values. Returned only if the input error is not None.

The table metadata includes the Astropy and Photutils version numbers and the aperture_photometry calling arguments.

Notes

Region objects are converted to Aperture objects using the region_to_aperture() function.

RectangularAperture and RectangularAnnulus photometry with the “exact” method uses a subpixel approximation by subdividing each data pixel by a factor of 1024 (subpixels = 32). For rectangular aperture widths and heights in the range from 2 to 100 pixels, this subpixel approximation gives results typically within 0.001 percent or better of the exact value. The differences can be larger for smaller apertures (e.g., aperture sizes of one pixel or smaller). For such small sizes, it is recommended to set method='subpixel' with a larger subpixels size.

If the input data is a NDData instance, then the error, mask, and wcs keyword inputs are ignored. Instead, these values should be defined as attributes in the NDData object. In the case of error, it must be defined in the uncertainty attribute with a StdDevUncertainty instance.

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