/usr/lib/python2.7/dist-packages/photutils/segmentation.py is in python-photutils 0.2.1-2.
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
from distutils.version import LooseVersion
import numpy as np
from astropy.table import Table
from astropy.utils import lazyproperty
import astropy.units as u
from astropy.wcs.utils import pixel_to_skycoord
from .utils.convolution import _convolve_data
from .utils.prepare_data import _prepare_data
__all__ = ['SegmentationImage', 'SourceProperties', 'source_properties',
'properties_table']
# outline_segments requires scikit-image >= 0.11
__doctest_skip__ = {'SegmentationImage.outline_segments'}
__doctest_requires__ = {('SegmentationImage', 'SegmentationImage.*',
'SourceProperties', 'SourceProperties.*',
'source_properties', 'properties_table'): ['scipy'],
('SegmentationImage', 'SegmentationImage.*',
'SourceProperties', 'SourceProperties.*',
'source_properties', 'properties_table'):
['skimage']}
class SegmentationImage(object):
"""
Class for a segmentation image.
Parameters
----------
data : array_like (int)
A 2D segmentation image where sources are labeled by different
positive integer values. A value of zero is reserved for the
background.
"""
def __init__(self, data):
if np.min(data) < 0:
raise ValueError('The segmentation image cannot contain '
'negative integers.')
self._data = np.asanyarray(data, dtype=np.int)
self._update_slices()
def _update_slices(self):
"""
Update the segmentation slices after changes to self._data made
by the class methods.
"""
from scipy.ndimage import find_objects
self.slices = find_objects(self._data)
@property
def data(self):
"""
The 2D segmentation image.
"""
return self._data
@property
def array(self):
"""
The 2D segmentation image.
"""
return self._data
def __array__(self):
"""
Array representation of the segmentation image (e.g., for
matplotlib).
"""
return self._data
@property
def data_masked(self):
"""
A `~numpy.ma.MaskedArray` version of the segmentation image
where the background (label = 0) has been masked.
"""
return np.ma.masked_where(self.data == 0, self.data)
@staticmethod
def _labels(data):
"""
Return a sorted array of the non-zero labels in the segmentation
image.
Parameters
----------
data : array_like (int)
A 2D segmentation image where sources are labeled by
different positive integer values. A value of zero is
reserved for the background.
Returns
-------
result : `~numpy.ndarray`
An array of non-zero label numbers.
Notes
-----
This is a separate static method so it can be used on masked
versions of the segmentation image (cf.
``~photutils.SegmentationImage.remove_masked_labels``.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm._labels(segm.data)
array([1, 3, 4, 5, 7])
"""
return np.unique(data[data != 0])
@property
def shape(self):
"""
The shape of the 2D segmentation image.
"""
return self._data.shape
@property
def labels(self):
"""The sorted non-zero labels in the segmentation image."""
return self._labels(self.data)
@property
def nlabels(self):
"""The number of non-zero labels in the segmentation image."""
return len(self.labels)
@property
def max(self):
"""The maximum non-zero label in the segmentation image."""
return np.max(self.data)
@property
def is_sequential(self):
"""
Determine whether or not the non-zero labels in the segmenation
image are sequential (with no missing values).
"""
if (self.labels[-1] - self.labels[0] + 1) == self.nlabels:
return True
else:
return False
def check_label(self, label):
"""
Check for a valid label label number within the segmentation
image.
Parameters
----------
label : int
The label number to check.
Raises
------
ValueError
If the input ``label`` is invalid.
"""
if label == 0:
raise ValueError('label "0" is reserved for the background')
if label < 0:
raise ValueError('label must be a positive integer, got '
'"{0}"'.format(label))
if label not in self.data:
raise ValueError('label "{0}" is not in the segmentation '
'image'.format(label))
def outline_segments(self, mask_background=False):
"""
Outline the labeled segments.
The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified. This corresponds to
the ``mode='inner'`` in `skimage.segmentation.find_boundaries`.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned image. This is useful for overplotting the
segment outlines on an image. The default is `False`.
Returns
-------
boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An image with the same shape of the segmenation image
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation image. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]])
"""
import skimage
if LooseVersion(skimage.__version__) < LooseVersion('0.11'):
raise ImportError('The outline_segments() function requires '
'scikit-image >= 0.11')
from skimage.segmentation import find_boundaries
outlines = self.data * find_boundaries(self.data, mode='inner')
if mask_background:
outlines = np.ma.masked_where(outlines == 0, outlines)
return outlines
def relabel(self, labels, new_label):
"""
Relabel one or more label numbers.
The input ``labels`` will all be relabeled to ``new_label``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to relabel.
new_label : int
The relabeled label number.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel(labels=[1, 7], new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[2, 0, 0, 0, 0, 5],
[2, 2, 0, 5, 5, 5],
[2, 2, 0, 0, 5, 5]])
"""
labels = np.atleast_1d(labels)
for label in labels:
self._data[np.where(self.data == label)] = new_label
self._update_slices()
def relabel_sequential(self, start_label=1):
"""
Relabel the label numbers sequentially, such that there are no
missing label numbers (up to the maximum label number).
Parameters
----------
start_label : int, optional
The starting label number, which should be a positive
integer. The default is 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel_sequential()
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[5, 0, 0, 0, 0, 4],
[5, 5, 0, 4, 4, 4],
[5, 5, 0, 0, 4, 4]])
"""
if start_label <= 0:
raise ValueError('start_label must be > 0.')
if self.is_sequential and (self.labels[0] == start_label):
return
forward_map = np.zeros(self.max + 1, dtype=np.int)
forward_map[self.labels] = np.arange(self.nlabels) + start_label
self._data = forward_map[self.data]
self._update_slices()
def keep_labels(self, labels, relabel=False):
"""
Keep only the specified label numbers.
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to keep. Labels of zero and those not
in the segmentation image will be ignored.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=3)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3])
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 5, 5, 5],
[0, 0, 0, 0, 5, 5]])
"""
labels = np.atleast_1d(labels)
labels_tmp = list(set(self.labels) - set(labels))
self.remove_labels(labels_tmp, relabel=relabel)
def remove_labels(self, labels, relabel=False):
"""
Remove one or more label numbers.
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to remove. Labels of zero and those not
in the segmentation image will be ignored.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=5)
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3])
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
"""
self.relabel(labels, new_label=0)
if relabel:
self.relabel_sequential()
def remove_border_labels(self, border_width, partial_overlap=True,
relabel=False):
"""
Remove labeled segments near the image border.
Labels within the defined border region will be removed.
Parameters
----------
border_width : int
The width of the border region in pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into the border region will be removed.
Segments that are completely within the border region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1,
... partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if border_width >= min(self.shape) / 2:
raise ValueError('border_width must be smaller than half the '
'image size in either dimension')
border = np.zeros(self.shape, dtype=np.bool)
border[:border_width, :] = True
border[-border_width:, :] = True
border[:, :border_width] = True
border[:, -border_width:] = True
self.remove_masked_labels(border, partial_overlap=partial_overlap,
relabel=relabel)
def remove_masked_labels(self, mask, partial_overlap=True,
relabel=False):
"""
Remove labeled segments located within a masked region.
Parameters
----------
mask : array_like (bool)
A boolean mask, with the same shape as the segmentation
image (``.data``), where `True` values indicate masked
pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into a masked region will also be removed.
Segments that are completely within a masked region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> mask = np.zeros_like(segm.data, dtype=np.bool)
>>> mask[0, :] = True # mask the first row
>>> segm.remove_masked_labels(mask)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_masked_labels(mask, partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if mask.shape != self.shape:
raise ValueError('mask must have the same shape as the '
'segmentation image')
remove_labels = self._labels(self.data[mask])
if not partial_overlap:
interior_labels = self._labels(self.data[~mask])
remove_labels = list(set(remove_labels) - set(interior_labels))
self.remove_labels(remove_labels, relabel=relabel)
class SourceProperties(object):
"""
Class to calculate photometry and morphological properties of a
single labeled source.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The 2D array from which to calculate the source photometry and
properties. If ``filtered_data`` is input, then it will be used
instead of ``data`` to calculate the source centroid and
morphological properties. Source photometry is always measured
from ``data``. ``data`` should be background-subtracted.
segment_img : `SegmentationImage` or array_like (int)
A 2D segmentation image, either as a `SegmentationImage` object
or an `~numpy.ndarray`, with the same shape as ``data`` where
sources are labeled by different positive integer values. A
value of zero is reserved for the background.
label : int
The label number of the source whose properties to calculate.
filtered_data : array-like or `~astropy.units.Quantity`, optional
The filtered version of the background-subtracted ``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., see
:func:`~photutils.detect_sources`). If `None`, then the
unfiltered ``data`` will be used instead. Note that
`SExtractor`_'s centroid and morphological parameters are
calculated from the filtered "detection" image.
error : array_like or `~astropy.units.Quantity`, optional
The pixel-wise Gaussian 1-sigma errors of the input ``data``.
If ``effective_gain`` is input, then ``error`` should include
all sources of "background" error but *exclude* the Poisson
error of the sources. If ``effective_gain`` is `None`, then
``error`` is assumed to include *all* sources of error,
including the Poisson error of the sources. ``error`` must have
the same shape as ``data``. See the Notes section below for
details on the error propagation.
effective_gain : float, array-like, or `~astropy.units.Quantity`, optional
Ratio of counts (e.g., electrons or photons) to the units of
``data``. This ratio is used to calculate the Poisson error of
the sources when it is not included in ``error``. If
``effective_gain`` is `None`, then ``error`` is assumed to
include *all* sources of error. See the Notes section below for
details on the error propagation.
If you are calculating the properties of many sources from the
same data, it is highly recommended that you input a *total*
error array instead of using ``effective_gain``. Otherwise a
total error array will need to be repeatedly recalculated.
mask : array_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.
background : float, array_like, or `~astropy.units.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 input ``data``. Inputting the
``background`` merely allows for its properties to be measured
within each source segment. The input ``background`` does *not*
get subtracted from the input ``data``, which should already be
background-subtracted.
wcs : `~astropy.wcs.WCS`
The WCS transformation to use. If `None`, then
`~photutils.SourceProperties.icrs_centroid`,
`~photutils.SourceProperties.ra_icrs_centroid`, and
`~photutils.SourceProperties.dec_icrs_centroid` will be `None`.
Notes
-----
`SExtractor`_'s centroid and morphological parameters are always
calculated from the filtered "detection" 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`_ results, then
use the ``filtered_data`` input. If ``filtered_data`` is `None`,
then the unfiltered ``data`` will be used for the source centroid
and morphological parameters.
Negative (background-subtracted) data values within the source
segment are set to zero when measuring morphological properties
based on image moments. This 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
that `~photutils.SourceProperties.source_sum` includes the
contribution of negative (background-subtracted) data values.
`~photutils.SourceProperties.source_sum_err` will ignore such pixels
when calculating the source Poission error (i.e. when if
``effective_gain`` is input; see below).
If ``effective_gain`` is input, then ``error`` should include all
sources of "background" error but *exclude* the Poisson error of the
sources. The total error image, :math:`\sigma_{\mathrm{tot}}` is
then:
.. math:: \\sigma_{\\mathrm{tot}} = \\sqrt{\\sigma_{\\mathrm{b}}^2 +
\\frac{(I - B)}{g}}
where :math:`\sigma_b`, :math:`(I - B)`, and :math:`g` are the
background ``error`` image, the background-subtracted ``data``
image, and ``effective_gain``, respectively.
Pixels where :math:`(I_i - B_i)` is negative do not contribute
additional Poisson noise to the total error, i.e.
:math:`\sigma_{\mathrm{tot}, i} = \sigma_{\mathrm{b}, i}`. Note
that this is different from `SExtractor`_, which sums the total
variance in the segment, including pixels where :math:`(I_i - B_i)`
is negative. In such cases, `SExtractor`_ underestimates the total
errors.
If ``effective_gain`` is `None`, then ``error`` is assumed to
include *all* sources of error, including the Poisson error of the
sources, i.e. :math:`\sigma_{\mathrm{tot}} = \sigma_{\mathrm{b}} =
\mathrm{error}`.
For example, if your input ``data`` are in units of ADU, then
``effective_gain`` should represent electrons/ADU. If your input
``data`` are in units of electrons/s then ``effective_gain`` should
be the exposure time or an exposure time map (e.g., for mosaics with
non-uniform exposure times).
``effective_gain`` can be a 2D gain image with the same shape as the
``data``. This is useful with mosaic images that have variable
depths (i.e., exposure times) across the field. For example, one
should use an exposure-time map as the ``effective_gain`` for a
variable depth mosaic image in count-rate units.
`~photutils.SourceProperties.source_sum_err` is simply the
quadrature sum of the pixel-wise total errors over the non-masked
pixels within the source segment:
.. math:: \\Delta F = \\sqrt{\\sum_{i \\in S}
\\sigma_{\\mathrm{tot}, i}^2}
where :math:`\Delta F` is
`~photutils.SourceProperties.source_sum_err` and :math:`S` are the
non-masked pixels in the source segment.
Custom errors for source segments can be calculated using the
`~photutils.SourceProperties.error_cutout_ma` and
`~photutils.SourceProperties.background_cutout_ma` properties, which
are 2D `~numpy.ma.MaskedArray` cutout versions of the input
``error`` and ``background``. The mask is `True` for both pixels
outside of the source segment and masked pixels.
.. _SExtractor: http://www.astromatic.net/software/sextractor
"""
def __init__(self, data, segment_img, label, filtered_data=None,
error=None, effective_gain=None, mask=None, background=None,
wcs=None):
if not isinstance(segment_img, SegmentationImage):
segment_img = SegmentationImage(segment_img)
if segment_img.shape != data.shape:
raise ValueError('The data and segmentation image must have '
'the same shape')
if mask is not None:
if mask.shape != data.shape:
raise ValueError('The data and mask must have the same shape')
segment_img.check_label(label)
self.label = label
self._slice = segment_img.slices[label - 1]
self._segment_img = segment_img
self._mask = mask
self._wcs = wcs
data, error, background = _prepare_data(
data, error=error, effective_gain=effective_gain,
background=background)
# data and filtered_data should be background-subtracted
self._data = data
if filtered_data is None:
self._filtered_data = data
else:
self._filtered_data = filtered_data
self._error = error # *total* error
self._background = background # 2D array
def __getitem__(self, key):
return getattr(self, key, None)
def make_cutout(self, data, masked_array=False):
"""
Create a (masked) cutout array from the input ``data`` using the
minimal bounding box of the source segment.
Parameters
----------
data : array-like (2D)
The data array from which to create the masked cutout array.
``data`` must have the same shape as the segmentation image
input into `SourceProperties`.
masked_array : bool, optional
If `True` then a `~numpy.ma.MaskedArray` will be created
where the mask is `True` for both pixels outside of the
source segment and any masked pixels. If `False`, then a
`~numpy.ndarray` will be generated.
Returns
-------
result : `~numpy.ndarray` or `~numpy.ma.MaskedArray` (2D)
The 2D cutout array or masked array.
"""
if data is None:
return None
data = np.asarray(data)
if data.shape != self._data.shape:
raise ValueError('data must have the same shape as the '
'segmentation image input to SourceProperties')
if masked_array:
return np.ma.masked_array(data[self._slice],
mask=self._cutout_total_mask)
else:
return data[self._slice]
def to_table(self, columns=None, exclude_columns=None):
"""
Create a `~astropy.table.Table` of properties.
If ``columns`` or ``exclude_columns`` are not input, then the
`~astropy.table.Table` will include all scalar-valued
properties. Multi-dimensional properties, e.g.
`~photutils.SourceProperties.data_cutout`, can be included in
the ``columns`` input.
Parameters
----------
columns : str or list of str, optional
Names of columns, in order, to include in the output
`~astropy.table.Table`. The allowed column names are any of
the attributes of `SourceProperties`.
exclude_columns : str or list of str, optional
Names of columns to exclude from the default properties list
in the output `~astropy.table.Table`. The default
properties are those with scalar values.
Returns
-------
table : `~astropy.table.Table`
A single-row table of properties of the source.
"""
return properties_table(self, columns=columns,
exclude_columns=exclude_columns)
@lazyproperty
def _cutout_segment_bool(self):
"""
_cutout_segment_bool is `True` only for pixels in the source
segment of interest. Pixels from other sources within the
rectangular cutout are not included.
"""
return self._segment_img.data[self._slice] == self.label
@lazyproperty
def _cutout_total_mask(self):
"""
_cutout_total_mask is `True` for regions outside of the source
segment or where the input mask is `True`.
"""
mask = ~self._cutout_segment_bool
if self._mask is not None:
mask |= self._mask[self._slice]
return mask
@lazyproperty
def data_cutout(self):
"""
A 2D cutout from the (background-subtracted) data of the source
segment.
"""
return self.make_cutout(self._data, masked_array=False)
@lazyproperty
def data_cutout_ma(self):
"""
A 2D `~numpy.ma.MaskedArray` cutout from the
(background-subtracted) data, where the mask is `True` for both
pixels outside of the source segment and masked pixels.
"""
return self.make_cutout(self._data, masked_array=True)
@lazyproperty
def _data_cutout_maskzeroed_double(self):
"""
A 2D cutout from the (background-subtracted) (filtered) data,
where pixels outside of the source segment and masked pixels are
set to zero. Negative data values are also set to zero because
negative pixels (especially at large radii) can result in image
moments that result in negative variances. The cutout image is
double precision, which is required for scikit-image's
Cython-based moment functions.
"""
cutout = self.make_cutout(self._filtered_data, masked_array=False)
cutout = np.where(cutout > 0, cutout, 0.) # negative pixels -> 0
return (cutout * ~self._cutout_total_mask).astype(np.float64)
@lazyproperty
def error_cutout_ma(self):
"""
A 2D `~numpy.ma.MaskedArray` cutout from the input ``error``
image, where the mask is `True` for both pixels outside of the
source segment and masked pixels. If ``error`` is `None`, then
``error_cutout_ma`` is also `None`.
"""
return self.make_cutout(self._error, masked_array=True)
@lazyproperty
def background_cutout_ma(self):
"""
A 2D `~numpy.ma.MaskedArray` cutout from the input
``background``, where the mask is `True` for both pixels outside
of the source segment and masked pixels. If ``background`` is
`None`, then ``background_cutout_ma`` is also `None`.
"""
return self.make_cutout(self._background, masked_array=True)
@lazyproperty
def coords(self):
"""
A tuple of `~numpy.ndarray`\s containing the ``y`` and ``x``
pixel coordinates of the source segment. Masked pixels are not
included.
"""
yy, xx = np.nonzero(self.data_cutout_ma)
coords = (yy + self._slice[0].start, xx + self._slice[1].start)
return coords
@lazyproperty
def values(self):
"""
A `~numpy.ndarray` of the (background-subtracted) pixel values
within the source segment. Masked pixels are not included.
"""
return self.data_cutout[~self._cutout_total_mask]
@lazyproperty
def moments(self):
"""Spatial moments up to 3rd order of the source."""
from skimage.measure import moments
return moments(self._data_cutout_maskzeroed_double, 3)
@lazyproperty
def moments_central(self):
"""
Central moments (translation invariant) of the source up to 3rd
order.
"""
from skimage.measure import moments_central
ycentroid, xcentroid = self.cutout_centroid.value
return moments_central(self._data_cutout_maskzeroed_double,
ycentroid, xcentroid, 3)
@lazyproperty
def id(self):
"""
The source identification number corresponding to the object
label in the segmentation image.
"""
return self.label
@lazyproperty
def cutout_centroid(self):
"""
The ``(y, x)`` coordinate, relative to the `data_cutout`, of
the centroid within the source segment.
"""
m = self.moments
if m[0, 0] != 0:
ycentroid = m[0, 1] / m[0, 0]
xcentroid = m[1, 0] / m[0, 0]
return (ycentroid, xcentroid) * u.pix
else:
return (np.nan, np.nan) * u.pix
@lazyproperty
def centroid(self):
"""
The ``(y, x)`` coordinate of the centroid within the source
segment.
"""
ycen, xcen = self.cutout_centroid.value
return (ycen + self._slice[0].start,
xcen + self._slice[1].start) * u.pix
@lazyproperty
def xcentroid(self):
"""
The ``x`` coordinate of the centroid within the source segment.
"""
return self.centroid[1]
@lazyproperty
def ycentroid(self):
"""
The ``y`` coordinate of the centroid within the source segment.
"""
return self.centroid[0]
@lazyproperty
def icrs_centroid(self):
"""
The International Celestial Reference System (ICRS) coordinates
of the centroid within the source segment, returned as a
`~astropy.coordinates.SkyCoord` object.
"""
if self._wcs is not None:
return pixel_to_skycoord(self.xcentroid.value,
self.ycentroid.value,
self._wcs, origin=1).icrs
else:
return None
@lazyproperty
def ra_icrs_centroid(self):
"""
The ICRS Right Ascension coordinate (in degrees) of the centroid
within the source segment.
"""
if self._wcs is not None:
return self.icrs_centroid.ra.degree * u.deg
else:
return None
@lazyproperty
def dec_icrs_centroid(self):
"""
The ICRS Declination coordinate (in degrees) of the centroid
within the source segment.
"""
if self._wcs is not None:
return self.icrs_centroid.dec.degree * u.deg
else:
return None
@lazyproperty
def bbox(self):
"""
The bounding box ``(ymin, xmin, ymax, xmax)`` of the minimal
rectangular region containing the source segment.
"""
# (stop - 1) to return the max pixel location, not the slice index
return (self._slice[0].start, self._slice[1].start,
self._slice[0].stop - 1, self._slice[1].stop - 1) * u.pix
@lazyproperty
def xmin(self):
"""
The minimum ``x`` pixel location of the minimal bounding box
(`~photutils.SourceProperties.bbox`) of the source segment.
"""
return self.bbox[1]
@lazyproperty
def xmax(self):
"""
The maximum ``x`` pixel location of the minimal bounding box
(`~photutils.SourceProperties.bbox`) of the source segment.
"""
return self.bbox[3]
@lazyproperty
def ymin(self):
"""
The minimum ``y`` pixel location of the minimal bounding box
(`~photutils.SourceProperties.bbox`) of the source segment.
"""
return self.bbox[0]
@lazyproperty
def ymax(self):
"""
The maximum ``y`` pixel location of the minimal bounding box
(`~photutils.SourceProperties.bbox`) of the source segment.
"""
return self.bbox[2]
@lazyproperty
def min_value(self):
"""
The minimum pixel value of the (background-subtracted) data
within the source segment.
"""
return np.min(self.values)
@lazyproperty
def max_value(self):
"""
The maximum pixel value of the (background-subtracted) data
within the source segment.
"""
return np.max(self.values)
@lazyproperty
def minval_cutout_pos(self):
"""
The ``(y, x)`` coordinate, relative to the `data_cutout`, of the
minimum pixel value of the (background-subtracted) data.
"""
return np.argwhere(self.data_cutout_ma == self.min_value)[0] * u.pix
@lazyproperty
def maxval_cutout_pos(self):
"""
The ``(y, x)`` coordinate, relative to the `data_cutout`, of the
maximum pixel value of the (background-subtracted) data.
"""
return np.argwhere(self.data_cutout_ma == self.max_value)[0] * u.pix
@lazyproperty
def minval_pos(self):
"""
The ``(y, x)`` coordinate of the minimum pixel value of the
(background-subtracted) data.
"""
yp, xp = np.array(self.minval_cutout_pos)
return (yp + self._slice[0].start, xp + self._slice[1].start) * u.pix
@lazyproperty
def maxval_pos(self):
"""
The ``(y, x)`` coordinate of the maximum pixel value of the
(background-subtracted) data.
"""
yp, xp = np.array(self.maxval_cutout_pos)
return (yp + self._slice[0].start, xp + self._slice[1].start) * u.pix
@lazyproperty
def minval_xpos(self):
"""
The ``x`` coordinate of the minimum pixel value of the
(background-subtracted) data.
"""
return self.minval_pos[1]
@lazyproperty
def minval_ypos(self):
"""
The ``y`` coordinate of the minimum pixel value of the
(background-subtracted) data.
"""
return self.minval_pos[0]
@lazyproperty
def maxval_xpos(self):
"""
The ``x`` coordinate of the maximum pixel value of the
(background-subtracted) data.
"""
return self.maxval_pos[1]
@lazyproperty
def maxval_ypos(self):
"""
The ``y`` coordinate of the maximum pixel value of the
(background-subtracted) data.
"""
return self.maxval_pos[0]
@lazyproperty
def area(self):
"""The area of the source segment in units of pixels**2."""
return len(self.values) * u.pix**2
@lazyproperty
def equivalent_radius(self):
"""
The radius of a circle with the same `area` as the source
segment.
"""
return np.sqrt(self.area / np.pi)
@lazyproperty
def perimeter(self):
"""
The perimeter of the source segment, approximated lines through
the centers of the border pixels using a 4-connectivity.
"""
from skimage.measure import perimeter
return perimeter(self._cutout_segment_bool, 4) * u.pix
@lazyproperty
def inertia_tensor(self):
"""
The inertia tensor of the source for the rotation around its
center of mass.
"""
mu = self.moments_central
a = mu[2, 0]
b = -mu[1, 1]
c = mu[0, 2]
return np.array([[a, b], [b, c]]) * u.pix**2
@lazyproperty
def covariance(self):
"""
The covariance matrix of the 2D Gaussian function that has the
same second-order moments as the source.
"""
mu = self.moments_central
if mu[0, 0] != 0:
m = mu / mu[0, 0]
covariance = self._check_covariance(
np.array([[m[2, 0], m[1, 1]], [m[1, 1], m[0, 2]]]))
return covariance * u.pix**2
else:
return np.empty((2, 2)) * np.nan * u.pix**2
@staticmethod
def _check_covariance(covariance):
"""
Check and modify the covariance matrix in the case of
"infinitely" thin detections. This follows SExtractor's
prescription of incrementally increasing the diagonal elements
by 1/12.
"""
p = 1. / 12 # arbitrary SExtractor value
val = (covariance[0, 0] * covariance[1, 1]) - covariance[0, 1]**2
if val >= p**2:
return covariance
else:
covar = np.copy(covariance)
while val < p**2:
covar[0, 0] += p
covar[1, 1] += p
val = (covar[0, 0] * covar[1, 1]) - covar[0, 1]**2
return covar
@lazyproperty
def covariance_eigvals(self):
"""
The two eigenvalues of the `covariance` matrix in decreasing
order.
"""
if not np.isnan(np.sum(self.covariance)):
eigvals = np.linalg.eigvals(self.covariance)
if np.any(eigvals < 0): # negative variance
return (np.nan, np.nan) * u.pix**2
return (np.max(eigvals), np.min(eigvals)) * u.pix**2
else:
return (np.nan, np.nan) * u.pix**2
@lazyproperty
def semimajor_axis_sigma(self):
"""
The 1-sigma standard deviation along the semimajor axis of the
2D Gaussian function that has the same second-order central
moments as the source.
"""
# this matches SExtractor's A parameter
return np.sqrt(self.covariance_eigvals[0])
@lazyproperty
def semiminor_axis_sigma(self):
"""
The 1-sigma standard deviation along the semiminor axis of the
2D Gaussian function that has the same second-order central
moments as the source.
"""
# this matches SExtractor's B parameter
return np.sqrt(self.covariance_eigvals[1])
@lazyproperty
def eccentricity(self):
"""
The eccentricity of the 2D Gaussian function that has the same
second-order moments as the source.
The eccentricity is the fraction of the distance along the
semimajor axis at which the focus lies.
.. math:: e = \\sqrt{1 - \\frac{b^2}{a^2}}
where :math:`a` and :math:`b` are the lengths of the semimajor
and semiminor axes, respectively.
"""
l1, l2 = self.covariance_eigvals
if l1 == 0:
return 0.
return np.sqrt(1. - (l2 / l1))
@lazyproperty
def orientation(self):
"""
The angle in radians between the ``x`` axis and the major axis
of the 2D Gaussian function that has the same second-order
moments as the source. The angle increases in the
counter-clockwise direction.
"""
a, b, b, c = self.covariance.flat
if a < 0 or c < 0: # negative variance
return np.nan * u.rad
return 0.5 * np.arctan2(2. * b, (a - c))
@lazyproperty
def elongation(self):
"""
The ratio of the lengths of the semimajor and semiminor axes:
.. math:: \mathrm{elongation} = \\frac{a}{b}
where :math:`a` and :math:`b` are the lengths of the semimajor
and semiminor axes, respectively.
Note that this is the same as `SExtractor`_'s elongation
parameter.
"""
return self.semimajor_axis_sigma / self.semiminor_axis_sigma
@lazyproperty
def ellipticity(self):
"""
``1`` minus the ratio of the lengths of the semimajor and
semiminor axes (or ``1`` minus the `elongation`):
.. math:: \mathrm{ellipticity} = 1 - \\frac{b}{a}
where :math:`a` and :math:`b` are the lengths of the semimajor
and semiminor axes, respectively.
Note that this is the same as `SExtractor`_'s ellipticity
parameter.
"""
return 1.0 - (self.semiminor_axis_sigma / self.semimajor_axis_sigma)
@lazyproperty
def covar_sigx2(self):
"""
The ``(0, 0)`` element of the `covariance` matrix, representing
:math:`\sigma_x^2`, in units of pixel**2.
Note that this is the same as `SExtractor`_'s X2 parameter.
"""
return self.covariance[0, 0]
@lazyproperty
def covar_sigy2(self):
"""
The ``(1, 1)`` element of the `covariance` matrix, representing
:math:`\sigma_y^2`, in units of pixel**2.
Note that this is the same as `SExtractor`_'s Y2 parameter.
"""
return self.covariance[1, 1]
@lazyproperty
def covar_sigxy(self):
"""
The ``(0, 1)`` and ``(1, 0)`` elements of the `covariance`
matrix, representing :math:`\sigma_x \sigma_y`, in units of
pixel**2.
Note that this is the same as `SExtractor`_'s XY parameter.
"""
return self.covariance[0, 1]
@lazyproperty
def cxx(self):
"""
`SExtractor`_'s CXX ellipse parameter in units of pixel**(-2).
The ellipse is defined as
.. math::
cxx (x - \\bar{x})^2 + cxy (x - \\bar{x}) (y - \\bar{y}) +
cyy (y - \\bar{y})^2 = R^2
where :math:`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 :math:`R
\\approx 3`.
"""
return ((np.cos(self.orientation) / self.semimajor_axis_sigma)**2 +
(np.sin(self.orientation) / self.semiminor_axis_sigma)**2)
@lazyproperty
def cyy(self):
"""
`SExtractor`_'s CYY ellipse parameter in units of pixel**(-2).
The ellipse is defined as
.. math::
cxx (x - \\bar{x})^2 + cxy (x - \\bar{x}) (y - \\bar{y}) +
cyy (y - \\bar{y})^2 = R^2
where :math:`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 :math:`R
\\approx 3`.
"""
return ((np.sin(self.orientation) / self.semimajor_axis_sigma)**2 +
(np.cos(self.orientation) / self.semiminor_axis_sigma)**2)
@lazyproperty
def cxy(self):
"""
`SExtractor`_'s CXY ellipse parameter in units of pixel**(-2).
The ellipse is defined as
.. math::
cxx (x - \\bar{x})^2 + cxy (x - \\bar{x}) (y - \\bar{y}) +
cyy (y - \\bar{y})^2 = R^2
where :math:`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 :math:`R
\\approx 3`.
"""
return (2. * np.cos(self.orientation) * np.sin(self.orientation) *
((1. / self.semimajor_axis_sigma**2) -
(1. / self.semiminor_axis_sigma**2)))
@lazyproperty
def source_sum(self):
"""
The sum of the non-masked (background-subtracted) data values
within the source segment.
.. math:: F = \\sum_{i \\in S} (I_i - B_i)
where :math:`F` is ``source_sum``, :math:`(I_i - B_i)` is the
background-subtracted input ``data``, and :math:`S` are the
non-masked pixels in the source segment.
"""
return np.sum(np.ma.masked_array(self._data[self._slice],
mask=self._cutout_total_mask))
@lazyproperty
def source_sum_err(self):
"""
The uncertainty of `~photutils.SourceProperties.source_sum`,
propagated from the input ``error`` array.
``source_sum_err`` is the quadrature sum of the total errors
over the non-masked pixels within the source segment:
.. math:: \\Delta F = \\sqrt{\\sum_{i \\in S}
\\sigma_{\\mathrm{tot}, i}^2}
where :math:`\Delta F` is ``source_sum_err``,
:math:`\sigma_{\mathrm{tot, i}}` are the pixel-wise total
errors, and :math:`S` are the non-masked pixels in the source
segment.
"""
if self._error is not None:
# power doesn't work here, see astropy #2968
# return np.sqrt(np.sum(self.error_cutout_ma**2))
return np.sqrt(np.sum(
np.ma.masked_array(self.error_cutout_ma.data**2,
mask=self.error_cutout_ma.mask)))
else:
return None
@lazyproperty
def background_sum(self):
"""The sum of ``background`` values within the source segment."""
if self._background is not None:
return np.sum(self.background_cutout_ma)
else:
return None
@lazyproperty
def background_mean(self):
"""The mean of ``background`` values within the source segment."""
if self._background is not None:
return np.mean(self.background_cutout_ma)
else:
return None
@lazyproperty
def background_at_centroid(self):
"""
The value of the ``background`` at the position of the source
centroid. Fractional position values are determined using
bilinear interpolation.
"""
from scipy.ndimage import map_coordinates
if self._background is None:
return None
else:
return map_coordinates(
self._background, [[self.ycentroid.value],
[self.xcentroid.value]])[0]
def source_properties(data, segment_img, error=None, effective_gain=None,
mask=None, background=None, filter_kernel=None,
wcs=None, labels=None):
"""
Calculate photometry and morphological properties of sources defined
by a labeled segmentation image.
Parameters
----------
data : array_like or `~astropy.units.Quantity`
The 2D array from which to calculate the source photometry and
properties. ``data`` should be background-subtracted.
segment_img : `SegmentationImage` or array_like (int)
A 2D segmentation image, either as a `SegmentationImage` object
or an `~numpy.ndarray`, with the same shape as ``data`` where
sources are labeled by different positive integer values. A
value of zero is reserved for the background.
error : array_like or `~astropy.units.Quantity`, optional
The pixel-wise Gaussian 1-sigma errors of the input ``data``.
If ``effective_gain`` is input, then ``error`` should include
all sources of "background" error but *exclude* the Poisson
error of the sources. If ``effective_gain`` is `None`, then
``error`` is assumed to include *all* sources of error,
including the Poisson error of the sources. ``error`` must have
the same shape as ``data``. See the Notes section below for
details on the error propagation.
effective_gain : float, array-like, or `~astropy.units.Quantity`, optional
Ratio of counts (e.g., electrons or photons) to the units of
``data``. This ratio is used to calculate the Poisson error of
the sources when it is not included in ``error``. If
``effective_gain`` is `None`, then ``error`` is assumed to
include *all* sources of error. See the Notes section below for
details on the error propagation.
If you are calculating the properties of many sources from the
same data, it is highly recommended that you input a *total*
error array instead of using ``effective_gain``. Otherwise a
total error array will need to be repeatedly recalculated.
mask : array_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.
background : float, array_like, or `~astropy.units.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 input ``data``. Inputting the
``background`` merely allows for its properties to be measured
within each source segment. The input ``background`` does *not*
get subtracted from the input ``data``, which should already be
background-subtracted.
filter_kernel : array-like (2D) or `~astropy.convolution.Kernel2D`, optional
The 2D array of the kernel used to filter the data prior to
calculating the source centroid and morphological parameters.
The kernel should be the same one used in defining the source
segments (e.g., see :func:`~photutils.detect_sources`). If
`None`, then the unfiltered ``data`` will be used instead. Note
that `SExtractor`_'s centroid and morphological parameters are
calculated from the filtered "detection" image.
wcs : `~astropy.wcs.WCS`
The WCS transformation to use. If `None`, then
`~photutils.SourceProperties.icrs_centroid`,
`~photutils.SourceProperties.ra_icrs_centroid`, and
`~photutils.SourceProperties.dec_icrs_centroid` will be `None`.
labels : int or list of ints
Subset of segmentation labels for which to calculate the
properties. If `None`, then the properties will be calculated
for all labeled sources (the default).
Returns
-------
output : list of `SourceProperties` objects
A list of `SourceProperties` objects, one for each source. The
properties can be accessed as attributes or keys.
Notes
-----
`SExtractor`_'s centroid and morphological parameters are always
calculated from the filtered "detection" 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`_ results, then
use the ``filtered_data`` input. If ``filtered_data`` is `None`,
then the unfiltered ``data`` will be used for the source centroid
and morphological parameters.
Negative (background-subtracted) data values within the source
segment are set to zero when measuring morphological properties
based on image moments. This 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
that `~photutils.SourceProperties.source_sum` includes the
contribution of negative (background-subtracted) data values.
`~photutils.SourceProperties.source_sum_err` will ignore such pixels
when calculating the source Poission error (i.e. when if
``effective_gain`` is input; see below).
If ``effective_gain`` is input, then ``error`` should include all
sources of "background" error but *exclude* the Poisson error of the
sources. The total error image, :math:`\sigma_{\mathrm{tot}}` is
then:
.. math:: \\sigma_{\\mathrm{tot}} = \\sqrt{\\sigma_{\\mathrm{b}}^2 +
\\frac{(I - B)}{g}}
where :math:`\sigma_b`, :math:`(I - B)`, and :math:`g` are the
background ``error`` image, the background-subtracted ``data``
image, and ``effective_gain``, respectively.
Pixels where :math:`(I_i - B_i)` is negative do not contribute
additional Poisson noise to the total error, i.e.
:math:`\sigma_{\mathrm{tot}, i} = \sigma_{\mathrm{b}, i}`. Note
that this is different from `SExtractor`_, which sums the total
variance in the segment, including pixels where :math:`(I_i - B_i)`
is negative. In such cases, `SExtractor`_ underestimates the total
errors.
If ``effective_gain`` is `None`, then ``error`` is assumed to
include *all* sources of error, including the Poisson error of the
sources, i.e. :math:`\sigma_{\mathrm{tot}} = \sigma_{\mathrm{b}} =
\mathrm{error}`.
For example, if your input ``data`` are in units of ADU, then
``effective_gain`` should represent electrons/ADU. If your input
``data`` are in units of electrons/s then ``effective_gain`` should
be the exposure time or an exposure time map (e.g., for mosaics with
non-uniform exposure times).
``effective_gain`` can be a 2D gain image with the same shape as the
``data``. This is useful with mosaic images that have variable
depths (i.e., exposure times) across the field. For example, one
should use an exposure-time map as the ``effective_gain`` for a
variable depth mosaic image in count-rate units.
`~photutils.SourceProperties.source_sum_err` is simply the
quadrature sum of the pixel-wise total errors over the non-masked
pixels within the source segment:
.. math:: \\Delta F = \\sqrt{\\sum_{i \\in S}
\\sigma_{\\mathrm{tot}, i}^2}
where :math:`\Delta F` is
`~photutils.SourceProperties.source_sum_err` and :math:`S` are the
non-masked pixels in the source segment.
.. _SExtractor: http://www.astromatic.net/software/sextractor
See Also
--------
SegmentationImage, SourceProperties, properties_table,
:func:`photutils.detection.detect_sources`
Examples
--------
>>> import numpy as np
>>> from photutils import SegmentationImage, source_properties
>>> image = np.arange(16.).reshape(4, 4)
>>> print(image)
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]
[ 12. 13. 14. 15.]]
>>> segm = SegmentationImage([[1, 1, 0, 0],
... [1, 0, 0, 2],
... [0, 0, 2, 2],
... [0, 2, 2, 0]])
>>> props = source_properties(image, segm)
Print some properties of the first object (labeled with ``1`` in the
segmentation image):
>>> props[0].id # id corresponds to segment label number
1
>>> props[0].centroid # doctest: +FLOAT_CMP
<Quantity [ 0.8, 0.2] pix>
>>> props[0].source_sum # doctest: +FLOAT_CMP
5.0
>>> props[0].area # doctest: +FLOAT_CMP
<Quantity 3.0 pix2>
>>> props[0].max_value # doctest: +FLOAT_CMP
4.0
Print some properties of the second object (labeled with ``2`` in
the segmentation image):
>>> props[1].id # id corresponds to segment label number
2
>>> props[1].centroid # doctest: +FLOAT_CMP
<Quantity [ 2.36363636, 2.09090909] pix>
>>> props[1].perimeter # doctest: +FLOAT_CMP
<Quantity 5.414213562373095 pix>
>>> props[1].orientation # doctest: +FLOAT_CMP
<Quantity -0.7417593069227176 rad>
"""
if not isinstance(segment_img, SegmentationImage):
segment_img = SegmentationImage(segment_img)
if segment_img.shape != data.shape:
raise ValueError('The data and segmentation image must have '
'the same shape')
if labels is None:
labels = segment_img.labels
labels = np.atleast_1d(labels)
# prepare the input data once, instead of repeating for each source
data, error_total, background = _prepare_data(
data, error=error, effective_gain=effective_gain,
background=background)
# filter the data once, instead of repeating for each source
if filter_kernel is not None:
filtered_data = _convolve_data(data, filter_kernel, mode='constant',
fill_value=0.0,
check_normalization=True)
else:
filtered_data = None
sources_props = []
for label in labels:
if label not in segment_img.labels:
continue # skip invalid labels (without warnings)
sources_props.append(SourceProperties(
data, segment_img, label, filtered_data=filtered_data,
error=error_total, effective_gain=None, mask=mask,
background=background, wcs=wcs))
return sources_props
def properties_table(source_props, columns=None, exclude_columns=None):
"""
Construct a `~astropy.table.Table` of properties from a list of
`SourceProperties` objects.
If ``columns`` or ``exclude_columns`` are not input, then the
`~astropy.table.Table` will include all scalar-valued properties.
Multi-dimensional properties, e.g.
`~photutils.SourceProperties.data_cutout`, can be included in the
``columns`` input.
Parameters
----------
source_props : `SourceProperties` or list of `SourceProperties`
A `SourceProperties` object or list of `SourceProperties`
objects, one for each source.
columns : str or list of str, optional
Names of columns, in order, to include in the output
`~astropy.table.Table`. The allowed column names are any of the
attributes of `SourceProperties`.
exclude_columns : str or list of str, optional
Names of columns to exclude from the default properties list in
the output `~astropy.table.Table`. The default properties are
those with scalar values.
Returns
-------
table : `~astropy.table.Table`
A table of properties of the segmented sources, one row per
source.
See Also
--------
SegmentationImage, SourceProperties, source_properties,
:func:`photutils.detection.detect_sources`
Examples
--------
>>> import numpy as np
>>> from photutils import source_properties, properties_table
>>> image = np.arange(16.).reshape(4, 4)
>>> print(image)
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]
[ 12. 13. 14. 15.]]
>>> segm = SegmentationImage([[1, 1, 0, 0],
... [1, 0, 0, 2],
... [0, 0, 2, 2],
... [0, 2, 2, 0]])
>>> props = source_properties(image, segm)
>>> columns = ['id', 'xcentroid', 'ycentroid', 'source_sum']
>>> tbl = properties_table(props, columns=columns)
>>> print(tbl)
id xcentroid ycentroid source_sum
pix pix
--- ------------- ------------- ----------
1 0.2 0.8 5.0
2 2.09090909091 2.36363636364 55.0
"""
if isinstance(source_props, list) and len(source_props) == 0:
raise ValueError('source_props is an empty list')
source_props = np.atleast_1d(source_props)
# all scalar-valued properties
columns_all = ['id', 'xcentroid', 'ycentroid', 'ra_icrs_centroid',
'dec_icrs_centroid', 'source_sum',
'source_sum_err', 'background_sum', 'background_mean',
'background_at_centroid', 'xmin', 'xmax', 'ymin', 'ymax',
'min_value', 'max_value', 'minval_xpos', 'minval_ypos',
'maxval_xpos', 'maxval_ypos', 'area', 'equivalent_radius',
'perimeter', 'semimajor_axis_sigma',
'semiminor_axis_sigma', 'eccentricity', 'orientation',
'ellipticity', 'elongation', 'covar_sigx2',
'covar_sigxy', 'covar_sigy2', 'cxx', 'cxy', 'cyy']
table_columns = None
if exclude_columns is not None:
table_columns = [s for s in columns_all if s not in exclude_columns]
if columns is not None:
table_columns = np.atleast_1d(columns)
if table_columns is None:
table_columns = columns_all
# it's *much* faster to calculate world coordinates using the
# complete list of (x, y) instead of from the individual (x, y).
# The assumption here is that the wcs is the same for each
# element of source_props.
if ('ra_icrs_centroid' in table_columns or
'dec_icrs_centroid' in table_columns or
'icrs_centroid' in table_columns):
xcentroid = [props.xcentroid.value for props in source_props]
ycentroid = [props.ycentroid.value for props in source_props]
if source_props[0]._wcs is not None:
icrs_centroid = pixel_to_skycoord(
xcentroid, ycentroid, source_props[0]._wcs, origin=1).icrs
icrs_ra = icrs_centroid.ra.degree * u.deg
icrs_dec = icrs_centroid.dec.degree * u.deg
else:
nprops = len(source_props)
icrs_ra = [None] * nprops
icrs_dec = [None] * nprops
icrs_centroid = [None] * nprops
props_table = Table()
for column in table_columns:
if column == 'ra_icrs_centroid':
props_table[column] = icrs_ra
elif column == 'dec_icrs_centroid':
props_table[column] = icrs_dec
elif column == 'icrs_centroid':
props_table[column] = icrs_centroid
else:
values = [getattr(props, column) for props in source_props]
if isinstance(values[0], u.Quantity):
# turn list of Quantities into a Quantity array
values = u.Quantity(values)
props_table[column] = values
return props_table
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