PixelAperture#

class photutils.aperture.PixelAperture[source]#

Bases: Aperture

Abstract base class for apertures defined in pixel coordinates.

Attributes Summary

bbox

The minimal bounding box for the aperture.

isscalar

Whether the instance is scalar (i.e., a single position).

shape

The shape of the instance.

Methods Summary

area()

The exact geometric area of the aperture shape.

area_overlap(data, *[, mask, method, subpixels])

Return the area of overlap between the data and the aperture.

copy()

Make an deep copy of this object.

do_photometry(data[, error, mask, method, ...])

Perform aperture photometry on the input data.

plot([ax, origin])

Plot the aperture on a matplotlib Axes instance.

positions()

The aperture positions, as an array of (x, y) coordinates or a SkyCoord.

to_mask([method, subpixels])

Return a mask for the aperture.

to_sky(wcs)

Convert the aperture to a SkyAperture object defined in celestial coordinates.

Attributes Documentation

bbox#

The minimal bounding box for the aperture.

If the aperture is scalar then a single BoundingBox is returned, otherwise a list of BoundingBox is returned.

isscalar#

Whether the instance is scalar (i.e., a single position).

shape#

The shape of the instance.

Methods Documentation

abstract area()[source]#

The exact geometric area of the aperture shape.

Use the area_overlap method to return the area of overlap between the data and the aperture, taking into account the aperture mask method, masked data pixels (mask keyword), and partial/no overlap of the aperture with the data.

Returns:
areafloat

The aperture area.

See also

area_overlap
area_overlap(data, *, mask=None, method='exact', subpixels=5)[source]#

Return the area of overlap between the data and the aperture.

This method takes into account the aperture mask method, masked data pixels (mask keyword), and partial/no overlap of the aperture with the data. In other words, it returns the area that used to compute the aperture sum (assuming identical inputs).

Use the area method to calculate the exact analytical area of the aperture shape.

Parameters:
dataarray_like or Quantity

A 2D array.

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 the area overlap.

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'.

Returns:
areasfloat or array_like

The area (in pixels**2) of overlap between the data and the aperture.

See also

area
copy()#

Make an deep copy of this object.

Returns:
resultAperture

A deep copy of the Aperture object.

do_photometry(data, error=None, mask=None, method='exact', subpixels=5)[source]#

Perform aperture photometry on the input data.

Parameters:
dataarray_like or Quantity instance

The 2D array on which to perform photometry. data should be background subtracted.

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.

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'.

Returns:
aperture_sumsndarray or Quantity

The sums within each aperture.

aperture_sum_errsndarray or Quantity

The errors on the sums within each aperture.

Notes

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 recommend to set method='subpixel' with a larger subpixels size.

plot(ax=None, origin=(0, 0), **kwargs)[source]#

Plot the aperture on a matplotlib Axes instance.

Parameters:
axmatplotlib.axes.Axes or None, optional

The matplotlib axes on which to plot. If None, then the current Axes instance is used.

originarray_like, optional

The (x, y) position of the origin of the displayed image.

**kwargsdict, optional

Any keyword arguments accepted by matplotlib.patches.Patch.

Returns:
patchlist of Patch

A list of matplotlib patches for the plotted aperture. The patches can be used, for example, when adding a plot legend.

abstract positions()#

The aperture positions, as an array of (x, y) coordinates or a SkyCoord.

abstract to_mask(method='exact', subpixels=5)[source]#

Return a mask for the aperture.

Parameters:
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'.

Returns:
maskApertureMask or list of ApertureMask

A mask for the aperture. If the aperture is scalar then a single ApertureMask is returned, otherwise a list of ApertureMask is returned.

abstract to_sky(wcs)[source]#

Convert the aperture to a SkyAperture object defined in celestial coordinates.

Parameters:
wcsWCS object

A world coordinate system (WCS) transformation that supports the astropy shared interface for WCS (e.g., astropy.wcs.WCS, gwcs.wcs.WCS).

Returns:
apertureSkyAperture object

A SkyAperture object.