SourceProperties¶

class
photutils.segmentation.
SourceProperties
(data, segment_img, label, filtered_data=None, error=None, mask=None, background=None, wcs=None, localbkg_width=None, kron_params='mask', 2.5, 0.0, 'exact', 5)[source]¶ Bases:
object
Class to calculate photometry and morphological properties of a single labeled source.
 Parameters
 dataarray_like or
Quantity
The 2D array from which to calculate the source photometry and properties. If
filtered_data
is input, then it will be used instead ofdata
to calculate the source centroid and morphological properties. Source photometry is always measured fromdata
. For accurate source properties and photometry,data
should be backgroundsubtracted. Nonfinitedata
values (NaN and +/ inf) are automatically masked. segment_img
SegmentationImage
or array_like (int) A 2D segmentation image, either as a
SegmentationImage
object or anndarray
, with the same shape asdata
where sources are labeled by different positive integer values. A value of zero is reserved for the background. labelint
The label number of the source whose properties are calculated.
 filtered_dataarraylike or
Quantity
, optional The filtered version of the backgroundsubtracted
data
from which to calculate the source centroid and morphological properties. The kernel used to perform the filtering should be the same one used in defining the source segments (e.g., seedetect_sources()
). Ifdata
is aQuantity
array thenfiltered_data
must be aQuantity
array (and vise versa) with identical units. Nonfinitefiltered_data
values (NaN and +/ inf) are not automatically masked, unless they are at the same position of nonfinite values in the inputdata
array. Such pixels can be masked using themask
keyword. IfNone
, then the unfiltereddata
will be used instead. errorarray_like or
Quantity
, optional The total error array corresponding to the input
data
array.error
is assumed to include all sources of error, including the Poisson error of the sources (seecalc_total_error
) .error
must have the same shape as the inputdata
. Ifdata
is aQuantity
array thenerror
must be aQuantity
array (and vise versa) with identical units. Nonfiniteerror
values (NaN and +/ inf) are not automatically masked, unless they are at the same position of nonfinite values in the inputdata
array. Such pixels can be masked using themask
keyword. See the Notes section below for details on the error propagation. maskarray_like (bool), optional
A boolean mask with the same shape as
data
where aTrue
value indicates the corresponding element ofdata
is masked. Masked data are excluded from all calculations. Nonfinite values (NaN and +/ inf) in the inputdata
are automatically masked. backgroundfloat, array_like, or
Quantity
, optional The background level that was previously present in the input
data
.background
may either be a scalar value or a 2D image with the same shape as the inputdata
. Ifdata
is aQuantity
array thenbackground
must be aQuantity
array (and vise versa) with identical units. Inputting thebackground
merely allows for its properties to be measured within each source segment. The inputbackground
does not get subtracted from the inputdata
, which should already be backgroundsubtracted. Nonfinitebackground
values (NaN and +/ inf) are not automatically masked, unless they are at the same position of nonfinite values in the inputdata
array. Such pixels can be masked using themask
keyword. wcsWCS object or
None
, optional A world coordinate system (WCS) transformation that supports the astropy shared interface for WCS (e.g.,
astropy.wcs.WCS
,gwcs.wcs.WCS
). IfNone
, then all skybased properties will be set toNone
. localbkg_width
None
or positive int, optional The width of the rectangular annulus used to compute a local background around each source. If
None
then no local background subtraction is performed. The local background affects thesource_sum
,max_value
,min_value
, andkron_flux
properties. It does not affect the momentbased morphological properties of the source. kron_paramstuple of list, optional
A list of five parameters used to determine how the Kron radius and flux are calculated. The first item represents how data pixels are masked around the source. It must be one of:
 ‘none’: do not mask any pixels (equivalent to
MASK_TYPE=NONE in SourceExtractor).
 ‘mask’: mask pixels assigned to neighboring sources
(equivalent to MASK_TYPE=BLANK in SourceExtractor)
‘mask_all’: mask all pixels outside of the source segment.
 ‘correct’: replace pixels assigned to neighboring sources
by replacing them with pixels on the opposite side of the source (equivalent to MASK_TYPE=CORRECT in SourceExtractor).
The second item represents the scaling parameter of the Kron radius as a scalar float. The third item represents the minimum circular radius as a scalar float. If the Kron radius times sqrt(
semimajor_axis_sigma
*semiminor_axis_sigma
) is less than than this radius, then the Kron flux will be measured in a circle with this minimum radius. The forth and fifth items represent theaperture_photometry()
keywordsmethod
andsubpixels
, respectively, which are used to measure the flux in the Kron aperture.
 dataarray_like or
Notes
data
(and optionalfiltered_data
) should be backgroundsubtracted for accurate source photometry and properties.SExtractor’s centroid and morphological parameters are always calculated from a filtered “detection” image, i.e., the image used to define the segmentation image. The usual downside of the filtering is the sources will be made more circular than they actually are. If you wish to reproduce SExtractor centroid and morphology results, then input a filtered and backgroundsubtracted “detection” image into the
filtered_data
keyword. Iffiltered_data
isNone
, then the unfiltereddata
will be used for the source centroid and morphological parameters.Negative data values (
filtered_data
ordata
) within the source segment are set to zero when calculating morphological properties based on image moments. Negative values could occur, for example, if the segmentation image was defined from a different image (e.g., different bandpass) or if the background was oversubtracted. Note thatsource_sum
always includes the contribution of negativedata
values.The input
error
array is assumed to include all sources of error, including the Poisson error of the sources.source_sum_err
is simply the quadrature sum of the pixelwise total errors over the nonmasked pixels within the source segment:\[\Delta F = \sqrt{\sum_{i \in S} \sigma_{\mathrm{tot}, i}^2}\]where \(\Delta F\) is
source_sum_err
, \(S\) are the nonmasked pixels in the source segment, and \(\sigma_{\mathrm{tot}, i}\) is the inputerror
array.Custom errors for source segments can be calculated using the
error_cutout_ma
andbackground_cutout_ma
properties, which are 2DMaskedArray
cutout versions of the inputerror
andbackground
. The mask isTrue
for pixels outside of the source segment, masked pixels from themask
input, or any nonfinitedata
values (NaN and +/ inf).Attributes Summary
The total unmasked area of the source segment in units of pixels**2.
The value of the
background
at the position of the source centroid.A 2D
MaskedArray
cutout from the inputbackground
.The mean of
background
values within the source segment.The sum of
background
values within the source segment.The
BoundingBox
of the minimal rectangular region containing the source segment.The maximum
x
pixel location within the minimal bounding box containing the source segment.The minimum
x
pixel location within the minimal bounding box containing the source segment.The maximum
y
pixel location within the minimal bounding box containing the source segment.The minimum
y
pixel location within the minimal bounding box containing the source segment.The
(y, x)
coordinate of the centroid within the source segment.The
(0, 0)
element of thecovariance
matrix, representing \(\sigma_x^2\), in units of pixel**2.The
(0, 1)
and(1, 0)
elements of thecovariance
matrix, representing \(\sigma_x \sigma_y\), in units of pixel**2.The
(1, 1)
element of thecovariance
matrix, representing \(\sigma_y^2\), in units of pixel**2.The covariance matrix of the 2D Gaussian function that has the same secondorder moments as the source.
The two eigenvalues of the
covariance
matrix in decreasing order.The
(y, x)
coordinate, relative to thedata_cutout
, of the centroid within the source segment.SExtractor’s CXX ellipse parameter in units of pixel**(2).
SExtractor’s CXY ellipse parameter in units of pixel**(2).
SExtractor’s CYY ellipse parameter in units of pixel**(2).
A 2D
ndarray
cutout from the data using the minimal bounding box of the source segment.A 2D
MaskedArray
cutout from thedata
.The eccentricity of the 2D Gaussian function that has the same secondorder moments as the source.
1
minus the ratio of the lengths of the semimajor and semiminor axes (or1
minus theelongation
):The ratio of the lengths of the semimajor and semiminor axes:
The radius of a circle with the same
area
as the source segment.A 2D
MaskedArray
cutout from the inputerror
image.A 2D
MaskedArray
cutout from thefiltered_data
.The Gini coefficient of the source.
The source identification number corresponding to the object label in the segmentation image.
A tuple of two
ndarray
containing they
andx
pixel indices, respectively, of unmasked pixels within the source segment.The inertia tensor of the source for the rotation around its center of mass.
The Kron aperture.
The flux in the Kron aperture.
The flux error in the Kron aperture.
The unscaled firstmoment Kron radius.
The local background value estimated using a rectangular annulus aperture around the source.
The rectangular annulus aperture used to estimate the local background.
The maximum pixel value of the
data
within the source segment.The
(y, x)
coordinate, relative to thedata_cutout
, of the maximum pixel value of thedata
within the source segment.The
(y, x)
coordinate of the maximum pixel value of thedata
within the source segment.The
x
coordinate of the maximum pixel value of thedata
within the source segment.The
y
coordinate of the maximum pixel value of thedata
within the source segment.The minimum pixel value of the
data
within the source segment.The
(y, x)
coordinate, relative to thedata_cutout
, of the minimum pixel value of thedata
within the source segment.The
(y, x)
coordinate of the minimum pixel value of thedata
within the source segment.The
x
coordinate of the minimum pixel value of thedata
within the source segment.The
y
coordinate of the minimum pixel value of thedata
within the source segment.Spatial moments up to 3rd order of the source.
Central moments (translation invariant) of the source up to 3rd order.
The angle between the
x
axis and the major axis of the 2D Gaussian function that has the same secondorder moments as the source.The perimeter of the source segment, approximated as the total length of lines connecting the centers of the border pixels defined by a 4pixel connectivity.
The 1sigma standard deviation along the semimajor axis of the 2D Gaussian function that has the same secondorder central moments as the source.
The 1sigma standard deviation along the semiminor axis of the 2D Gaussian function that has the same secondorder central moments as the source.
The sky coordinates of the lowerleft vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The sky coordinates of the lowerright vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The sky coordinates of the upperleft vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The sky coordinates of the upperright vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The sky coordinates of the centroid within the source segment, returned as a
SkyCoord
object.The sky coordinates, in the International Celestial Reference System (ICRS) frame, of the centroid within the source segment, returned as a
SkyCoord
object.The sum of the unmasked
data
values within the source segment.The uncertainty of
source_sum
, propagated from the inputerror
array.The
x
coordinate of the centroid within the source segment.The
y
coordinate of the centroid within the source segment.Methods Summary
make_cutout
(data[, masked_array])Create a (masked) cutout array from the input
data
using the minimal bounding box of the source segment.to_table
([columns, exclude_columns])Create a
QTable
of properties.Attributes Documentation

area
¶ The total unmasked area of the source segment in units of pixels**2.
Note that the source area may be smaller than its segment area if a mask is input to
SourceProperties
orsource_properties
, or if thedata
within the segment contains invalid values (NaN and +/ inf).

background_at_centroid
¶ The value of the
background
at the position of the source centroid.The background value at fractional position values are determined using bilinear interpolation.

background_cutout_ma
¶ A 2D
MaskedArray
cutout from the inputbackground
.The mask is
True
for pixels outside of the source segment (labeled region of interest), masked pixels from themask
input, or any nonfinitedata
values (NaN and +/ inf).If
background
isNone
, thenbackground_cutout_ma
is alsoNone
.

background_mean
¶ The mean of
background
values within the source segment.Pixel values that are masked in the input
data
, including any nonfinite pixel values (NaN and +/ inf) that are automatically masked, are also masked in the background array.

background_sum
¶ The sum of
background
values within the source segment.Pixel values that are masked in the input
data
, including any nonfinite pixel values (NaN and +/ inf) that are automatically masked, are also masked in the background array.

bbox
¶ The
BoundingBox
of the minimal rectangular region containing the source segment.

bbox_xmax
¶ The maximum
x
pixel location within the minimal bounding box containing the source segment.Note that this value is inclusive, unlike numpy slice indices.

bbox_xmin
¶ The minimum
x
pixel location within the minimal bounding box containing the source segment.

bbox_ymax
¶ The maximum
y
pixel location within the minimal bounding box containing the source segment.Note that this value is inclusive, unlike numpy slice indices.

bbox_ymin
¶ The minimum
y
pixel location within the minimal bounding box containing the source segment.

centroid
¶ The
(y, x)
coordinate of the centroid within the source segment.

covar_sigx2
¶ The
(0, 0)
element of thecovariance
matrix, representing \(\sigma_x^2\), in units of pixel**2.Note that this is the same as SExtractor’s X2 parameter.

covar_sigxy
¶ The
(0, 1)
and(1, 0)
elements of thecovariance
matrix, representing \(\sigma_x \sigma_y\), in units of pixel**2.Note that this is the same as SExtractor’s XY parameter.

covar_sigy2
¶ The
(1, 1)
element of thecovariance
matrix, representing \(\sigma_y^2\), in units of pixel**2.Note that this is the same as SExtractor’s Y2 parameter.

covariance
¶ The covariance matrix of the 2D Gaussian function that has the same secondorder moments as the source.

covariance_eigvals
¶ The two eigenvalues of the
covariance
matrix in decreasing order.

cutout_centroid
¶ The
(y, x)
coordinate, relative to thedata_cutout
, of the centroid within the source segment.

cxx
¶ SExtractor’s CXX ellipse parameter in units of pixel**(2).
The ellipse is defined as
\[cxx (x  \bar{x})^2 + cxy (x  \bar{x}) (y  \bar{y}) + cyy (y  \bar{y})^2 = R^2\]where \(R\) is a parameter which scales the ellipse (in units of the axes lengths). SExtractor reports that the isophotal limit of a source is well represented by \(R \approx 3\).

cxy
¶ SExtractor’s CXY ellipse parameter in units of pixel**(2).
The ellipse is defined as
\[cxx (x  \bar{x})^2 + cxy (x  \bar{x}) (y  \bar{y}) + cyy (y  \bar{y})^2 = R^2\]where \(R\) is a parameter which scales the ellipse (in units of the axes lengths). SExtractor reports that the isophotal limit of a source is well represented by \(R \approx 3\).

cyy
¶ SExtractor’s CYY ellipse parameter in units of pixel**(2).
The ellipse is defined as
\[cxx (x  \bar{x})^2 + cxy (x  \bar{x}) (y  \bar{y}) + cyy (y  \bar{y})^2 = R^2\]where \(R\) is a parameter which scales the ellipse (in units of the axes lengths). SExtractor reports that the isophotal limit of a source is well represented by \(R \approx 3\).

data_cutout
¶ A 2D
ndarray
cutout from the data using the minimal bounding box of the source segment.

data_cutout_ma
¶ A 2D
MaskedArray
cutout from thedata
.The mask is
True
for pixels outside of the source segment (labeled region of interest), masked pixels from themask
input, or any nonfinitedata
values (NaN and +/ inf).

eccentricity
¶ The eccentricity of the 2D Gaussian function that has the same secondorder moments as the source.
The eccentricity is the fraction of the distance along the semimajor axis at which the focus lies.
\[e = \sqrt{1  \frac{b^2}{a^2}}\]where \(a\) and \(b\) are the lengths of the semimajor and semiminor axes, respectively.

ellipticity
¶ 1
minus the ratio of the lengths of the semimajor and semiminor axes (or1
minus theelongation
):\[\mathrm{ellipticity} = 1  \frac{b}{a}\]where \(a\) and \(b\) are the lengths of the semimajor and semiminor axes, respectively.
Note that this is the same as SExtractor’s ellipticity parameter.

elongation
¶ The ratio of the lengths of the semimajor and semiminor axes:
\[\mathrm{elongation} = \frac{a}{b}\]where \(a\) and \(b\) are the lengths of the semimajor and semiminor axes, respectively.
Note that this is the same as SExtractor’s elongation parameter.

error_cutout_ma
¶ A 2D
MaskedArray
cutout from the inputerror
image.The mask is
True
for pixels outside of the source segment (labeled region of interest), masked pixels from themask
input, or any nonfinitedata
values (NaN and +/ inf).

filtered_data_cutout_ma
¶ A 2D
MaskedArray
cutout from thefiltered_data
.If
filtered_data
was not input, then the cutout will be from the inputdata
.The mask is
True
for pixels outside of the source segment (labeled region of interest), masked pixels from themask
input, or any nonfinitedata
values (NaN and +/ inf).

gini
¶ The Gini coefficient of the source.
The Gini coefficient is calculated using the prescription from Lotz et al. 2004 as:
\[G = \frac{1}{\left  \bar{x} \right  n (n  1)} \sum^{n}_{i} (2i  n  1) \left  x_i \right \]where \(\bar{x}\) is the mean over all pixel values \(x_i\).
The Gini coefficient is a way of measuring the inequality in a given set of values. In the context of galaxy morphology, it measures how the light of a galaxy image is distributed among its pixels. A Gini coefficient value of 0 corresponds to a galaxy image with the light evenly distributed over all pixels while a Gini coefficient value of 1 represents a galaxy image with all its light concentrated in just one pixel.

id
¶ The source identification number corresponding to the object label in the segmentation image.

indices
¶ A tuple of two
ndarray
containing they
andx
pixel indices, respectively, of unmasked pixels within the source segment.Nonfinite
data
values (NaN and +/ inf) are excluded.If all
data
pixels are masked, a tuple of two empty arrays will be returned.

inertia_tensor
¶ The inertia tensor of the source for the rotation around its center of mass.

kron_aperture
¶ The Kron aperture.

kron_flux
¶ The flux in the Kron aperture.

kron_fluxerr
¶ The flux error in the Kron aperture.

kron_radius
¶ The unscaled firstmoment Kron radius.
The unscaled firstmoment Kron radius is given by:
\[k_r = \frac{\sum_{i \in A} \ r_i I_i}{\sum_{i \in A} I_i}\]where the sum is over all pixels in an elliptical aperture whose axes are defined by six times the
semimajor_axis_sigma
andsemiminor_axis_sigma
at the calculatedorientation
(all properties derived from the central image moments of the source). \(r_i\) is the elliptical “radius” to the pixel given by:\[r_i^2 = cxx(x_i  \bar{x})^2 + cxx \ cyy (x_i  \bar{x})(y_i  \bar{y}) + cyy(y_i  \bar{y})^2\]where \(\bar{x}\) and \(\bar{y}\) represent the source centroid.

local_background
¶ The local background value estimated using a rectangular annulus aperture around the source.

local_background_aperture
¶ The rectangular annulus aperture used to estimate the local background.

max_value
¶ The maximum pixel value of the
data
within the source segment.

maxval_cutout_pos
¶ The
(y, x)
coordinate, relative to thedata_cutout
, of the maximum pixel value of thedata
within the source segment.If there are multiple occurrences of the maximum value, only the first occurence is returned.

maxval_pos
¶ The
(y, x)
coordinate of the maximum pixel value of thedata
within the source segment.If there are multiple occurrences of the maximum value, only the first occurence is returned.

maxval_xpos
¶ The
x
coordinate of the maximum pixel value of thedata
within the source segment.If there are multiple occurrences of the maximum value, only the first occurence is returned.

maxval_ypos
¶ The
y
coordinate of the maximum pixel value of thedata
within the source segment.If there are multiple occurrences of the maximum value, only the first occurence is returned.

min_value
¶ The minimum pixel value of the
data
within the source segment.

minval_cutout_pos
¶ The
(y, x)
coordinate, relative to thedata_cutout
, of the minimum pixel value of thedata
within the source segment.If there are multiple occurrences of the minimum value, only the first occurence is returned.

minval_pos
¶ The
(y, x)
coordinate of the minimum pixel value of thedata
within the source segment.If there are multiple occurrences of the minimum value, only the first occurence is returned.

minval_xpos
¶ The
x
coordinate of the minimum pixel value of thedata
within the source segment.If there are multiple occurrences of the minimum value, only the first occurence is returned.

minval_ypos
¶ The
y
coordinate of the minimum pixel value of thedata
within the source segment.If there are multiple occurrences of the minimum value, only the first occurence is returned.

moments
¶ Spatial moments up to 3rd order of the source.

moments_central
¶ Central moments (translation invariant) of the source up to 3rd order.

orientation
¶ The angle between the
x
axis and the major axis of the 2D Gaussian function that has the same secondorder moments as the source. The angle increases in the counterclockwise direction.

perimeter
¶ The perimeter of the source segment, approximated as the total length of lines connecting the centers of the border pixels defined by a 4pixel connectivity.
If any masked pixels make holes within the source segment, then the perimeter around the inner hole (e.g., an annulus) will also contribute to the total perimeter.
References
 1
K. Benkrid, D. Crookes, and A. Benkrid. “Design and FPGA Implementation of a Perimeter Estimator”. Proceedings of the Irish Machine Vision and Image Processing Conference, pp. 5157 (2000). http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc

semimajor_axis_sigma
¶ The 1sigma standard deviation along the semimajor axis of the 2D Gaussian function that has the same secondorder central moments as the source.

semiminor_axis_sigma
¶ The 1sigma standard deviation along the semiminor axis of the 2D Gaussian function that has the same secondorder central moments as the source.

sky_bbox_ll
¶ The sky coordinates of the lowerleft vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The bounding box encloses all of the source segment pixels in their entirety, thus the vertices are at the pixel corners.

sky_bbox_lr
¶ The sky coordinates of the lowerright vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The bounding box encloses all of the source segment pixels in their entirety, thus the vertices are at the pixel corners.

sky_bbox_ul
¶ The sky coordinates of the upperleft vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The bounding box encloses all of the source segment pixels in their entirety, thus the vertices are at the pixel corners.

sky_bbox_ur
¶ The sky coordinates of the upperright vertex of the minimal bounding box of the source segment, returned as a
SkyCoord
object.The bounding box encloses all of the source segment pixels in their entirety, thus the vertices are at the pixel corners.

sky_centroid
¶ The sky coordinates of the centroid within the source segment, returned as a
SkyCoord
object.The output coordinate frame is the same as the input WCS.

sky_centroid_icrs
¶ The sky coordinates, in the International Celestial Reference System (ICRS) frame, of the centroid within the source segment, returned as a
SkyCoord
object.

source_sum
¶ The sum of the unmasked
data
values within the source segment.\[F = \sum_{i \in S} (I_i  B_i)\]where \(F\) is
source_sum
, \((I_i  B_i)\) is thedata
, and \(S\) are the unmasked pixels in the source segment.Nonfinite pixel values (NaN and +/ inf) are excluded (automatically masked).

source_sum_err
¶ The uncertainty of
source_sum
, propagated from the inputerror
array.source_sum_err
is the quadrature sum of the total errors over the nonmasked pixels within the source segment:\[\Delta F = \sqrt{\sum_{i \in S} \sigma_{\mathrm{tot}, i}^2}\]where \(\Delta F\) is
source_sum_err
, \(\sigma_{\mathrm{tot, i}}\) are the pixelwise total errors, and \(S\) are the nonmasked pixels in the source segment.Pixel values that are masked in the input
data
, including any nonfinite pixel values (NaN and +/ inf) that are automatically masked, are also masked in the error array.

xcentroid
¶ The
x
coordinate of the centroid within the source segment.

ycentroid
¶ The
y
coordinate of the centroid within the source segment.
Methods Documentation

make_cutout
(data, masked_array=False)[source]¶ Create a (masked) cutout array from the input
data
using the minimal bounding box of the source segment.If
masked_array
isFalse
(default), then the returned cutout array is simply andarray
. The returned cutout is a view (not a copy) of the inputdata
. No pixels are altered (e.g., set to zero) within the bounding box.If
masked_array` is `True`, then the returned cutout array is a `~numpy.ma.MaskedArray`. The mask is `True` for pixels outside of the source segment (labeled region of interest), masked pixels from the ``mask
input, or any nonfinitedata
values (NaN and +/ inf). The data part of the masked array is a view (not a copy) of the inputdata
. Parameters
 dataarraylike (2D)
The data array from which to create the masked cutout array.
data
must have the same shape as the segmentation image input intoSourceProperties
. masked_arraybool, optional
If
True
then aMaskedArray
will be returned, where the mask isTrue
for pixels outside of the source segment (labeled region of interest), masked pixels from themask
input, or any nonfinitedata
values (NaN and +/ inf). IfFalse
, then andarray
will be returned.
 Returns
 result2D
ndarray
orMaskedArray
The 2D cutout array.
 result2D

to_table
(columns=None, exclude_columns=None)[source]¶ Create a
QTable
of properties.If
columns
orexclude_columns
are not input, then theQTable
will include a default list of scalarvalued properties. Parameters
 columnsstr or list of str, optional
Names of columns, in order, to include in the output
QTable
. The allowed column names are any of the attributes ofSourceProperties
. exclude_columnsstr or list of str, optional
Names of columns to exclude from the default columns in the output
QTable
. The default columns are defined in thephotutils.segmentation.properties.DEFAULT_COLUMNS
variable.
 Returns
 table
QTable
A singlerow table of properties of the source.
 table