# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module defines the base aperture classes.
"""
import abc
import inspect
import warnings
from copy import deepcopy
import astropy.units as u
import numpy as np
from astropy.coordinates import SkyCoord
from astropy.utils import lazyproperty
from photutils.aperture.bounding_box import BoundingBox
from photutils.utils._wcs_helpers import _pixel_scale_angle_at_skycoord
__all__ = ['Aperture', 'PixelAperture', 'SkyAperture']
[docs]
class Aperture(metaclass=abc.ABCMeta):
"""
Abstract base class for all apertures.
"""
_params = ()
def __len__(self):
if self.isscalar:
raise TypeError(f'A scalar {self.__class__.__name__!r} object '
'has no len()')
return self.shape[0]
def __getitem__(self, index):
if self.isscalar:
raise TypeError(f'A scalar {self.__class__.__name__!r} object '
'cannot be indexed')
kwargs = {}
for param in self._params:
if param == 'positions':
# slice the positions array
kwargs[param] = getattr(self, param)[index]
else:
kwargs[param] = getattr(self, param)
return self.__class__(**kwargs)
def __iter__(self):
for i in range(len(self)):
yield self.__getitem__(i)
def _positions_str(self, prefix=None):
if isinstance(self, PixelAperture):
return np.array2string(self.positions, separator=', ',
prefix=prefix)
if isinstance(self, SkyAperture):
return repr(self.positions)
raise TypeError('Aperture must be a subclass of PixelAperture '
'or SkyAperture')
def __repr__(self):
prefix = f'{self.__class__.__name__}'
cls_info = []
for param in self._params:
if param == 'positions':
cls_info.append(self._positions_str(prefix))
else:
cls_info.append(f'{param}={getattr(self, param)}')
cls_info = ', '.join(cls_info)
return f'<{prefix}({cls_info})>'
def __str__(self):
cls_info = [('Aperture', self.__class__.__name__)]
for param in self._params:
if param == 'positions':
prefix = 'positions'
cls_info.append((prefix, self._positions_str(prefix + ': ')))
else:
cls_info.append((param, getattr(self, param)))
fmt = [f'{key}: {val}' for key, val in cls_info]
return '\n'.join(fmt)
def __eq__(self, other):
"""
Equality operator for `Aperture`.
All Aperture properties are compared for strict equality except
for Quantity parameters, which allow for different units if they
are directly convertible.
"""
if not isinstance(other, self.__class__):
return False
self_params = list(self._params)
other_params = list(other._params)
# check that both have identical parameters
if self_params != other_params:
return False
# now check the parameter values
# Note that Quantity comparisons allow for different units if they
# are directly convertible (e.g., 1.0 * u.deg == 60.0 * u.arcmin)
try:
for param in self_params:
# np.any is used for SkyCoord array comparisons
if np.any(getattr(self, param) != getattr(other, param)):
return False
except TypeError:
# TypeError is raised from SkyCoord comparison when they do
# not have equivalent frames. Here return False instead of
# the TypeError.
return False
return True
def __ne__(self, other):
"""
Inequality operator for `Aperture`.
"""
return not self == other
@property
def _lazyproperties(self):
"""
A list of all class lazyproperties (even in superclasses).
"""
def islazyproperty(obj):
return isinstance(obj, lazyproperty)
return [i[0] for i in inspect.getmembers(self.__class__,
predicate=islazyproperty)]
[docs]
def copy(self):
"""
Make an deep copy of this object.
Returns
-------
result : `Aperture`
A deep copy of the Aperture object.
"""
params_copy = {}
for param in list(self._params):
params_copy[param] = deepcopy(getattr(self, param))
return self.__class__(**params_copy)
[docs]
@abc.abstractmethod
def positions(self):
"""
The aperture positions, as an array of (x, y) coordinates or a
`~astropy.coordinates.SkyCoord`.
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
@lazyproperty
def shape(self):
"""
The shape of the instance.
"""
if isinstance(self.positions, SkyCoord):
return self.positions.shape
return self.positions.shape[:-1]
@lazyproperty
def isscalar(self):
"""
Whether the instance is scalar (i.e., a single position).
"""
return self.shape == ()
[docs]
class PixelAperture(Aperture):
"""
Abstract base class for apertures defined in pixel coordinates.
"""
@lazyproperty
def _default_patch_properties(self):
"""
A dictionary of default matplotlib.patches.Patch properties.
"""
mpl_params = {}
# matplotlib.patches.Patch default is ``fill=True``
mpl_params['fill'] = False
return mpl_params
@staticmethod
def _translate_mask_mode(mode, subpixels, rectangle=False):
if mode not in ('center', 'subpixel', 'exact'):
raise ValueError(f'Invalid mask mode: {mode}')
if rectangle and mode == 'exact':
mode = 'subpixel'
subpixels = 32
if ((mode == 'subpixel')
and (not isinstance(subpixels, int) or subpixels <= 0)):
raise ValueError('subpixels must be a strictly positive integer')
if mode == 'center':
use_exact = 0
subpixels = 1
elif mode == 'subpixel':
use_exact = 0
elif mode == 'exact':
use_exact = 1
subpixels = 1
return use_exact, subpixels
@abc.abstractmethod
def _xy_extents(self):
"""
The (x, y) extents of the aperture measured from the center
position.
In other words, the (x, y) extents are half of the aperture
minimal bounding box size in each dimension.
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
@lazyproperty
def _positions(self):
"""
The aperture positions, always as a 2D ndarray.
"""
return np.atleast_2d(self.positions)
@lazyproperty
def _bbox(self):
"""
The minimal bounding box for the aperture, always as a list of
`~photutils.aperture.BoundingBox` instances.
"""
x_delta, y_delta = self._xy_extents
xmin = self._positions[:, 0] - x_delta
xmax = self._positions[:, 0] + x_delta
ymin = self._positions[:, 1] - y_delta
ymax = self._positions[:, 1] + y_delta
return [BoundingBox.from_float(x0, x1, y0, y1)
for x0, x1, y0, y1 in zip(xmin, xmax, ymin, ymax, strict=True)]
@lazyproperty
def bbox(self):
"""
The minimal bounding box for the aperture.
If the aperture is scalar then a single
`~photutils.aperture.BoundingBox` is returned, otherwise a list
of `~photutils.aperture.BoundingBox` is returned.
"""
if self.isscalar:
return self._bbox[0]
return self._bbox
@lazyproperty
def _centered_edges(self):
"""
A list of ``(xmin, xmax, ymin, ymax)`` tuples, one for each
position, of the pixel edges after recentering the aperture at
the origin.
These pixel edges are used by the low-level `photutils.geometry`
functions.
"""
edges = []
for position, bbox in zip(self._positions, self._bbox, strict=True):
xmin = bbox.ixmin - 0.5 - position[0]
xmax = bbox.ixmax - 0.5 - position[0]
ymin = bbox.iymin - 0.5 - position[1]
ymax = bbox.iymax - 0.5 - position[1]
edges.append((xmin, xmax, ymin, ymax))
return edges
[docs]
@abc.abstractmethod
def area(self):
"""
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
-------
area : float
The aperture area.
See Also
--------
area_overlap
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
[docs]
def area_overlap(self, data, *, mask=None, method='exact', subpixels=5):
"""
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
----------
data : array_like or `~astropy.units.Quantity`
A 2D array.
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 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.
subpixels : int, 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
-------
areas : float or array_like
The area (in pixels**2) of overlap between the data and the
aperture.
See Also
--------
area
"""
apermasks = self.to_mask(method=method, subpixels=subpixels)
if self.isscalar:
apermasks = (apermasks,)
if mask is not None:
mask = np.asarray(mask)
if mask.shape != data.shape:
raise ValueError('mask and data must have the same shape')
areas = []
for apermask in apermasks:
slc_large, slc_small = apermask.get_overlap_slices(data.shape)
# if the aperture does not overlap the data return np.nan
if slc_large is None:
area = np.nan
else:
aper_weights = apermask.data[slc_small]
if mask is not None:
aper_weights[mask[slc_large]] = 0.0
area = np.sum(aper_weights)
areas.append(area)
areas = np.array(areas)
if self.isscalar:
return areas[0]
return areas
[docs]
@abc.abstractmethod
def to_mask(self, method='exact', subpixels=5):
"""
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.
subpixels : int, 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
-------
mask : `~photutils.aperture.ApertureMask` or list of \
`~photutils.aperture.ApertureMask`
A mask for the aperture. If the aperture is scalar then
a single `~photutils.aperture.ApertureMask` is returned,
otherwise a list of `~photutils.aperture.ApertureMask` is
returned.
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
[docs]
def do_photometry(self, data, error=None, mask=None, method='exact',
subpixels=5):
"""
Perform aperture photometry on the input data.
Parameters
----------
data : array_like or `~astropy.units.Quantity` instance
The 2D array on which to perform photometry. ``data`` should
be background subtracted.
error : array_like or `~astropy.units.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
`~photutils.utils.calc_total_error`) . ``error`` must have
the same shape as the input ``data``.
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.
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.
subpixels : int, 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_sums : `~numpy.ndarray` or `~astropy.units.Quantity`
The sums within each aperture.
aperture_sum_errs : `~numpy.ndarray` or `~astropy.units.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.
"""
data = np.asanyarray(data)
if data.ndim != 2:
raise ValueError('data must be a 2D array.')
if error is not None:
error = np.asanyarray(error)
if error.shape != data.shape:
raise ValueError('error and data must have the same shape.')
# check Quantity inputs
unit = {getattr(arr, 'unit', None) for arr in (data, error)
if arr is not None}
if len(unit) > 1:
raise ValueError('If data or error has units, then they both must '
'have the same units.')
# strip data and error units for performance
unit = unit.pop()
if unit is not None:
unit = data.unit
data = data.value
if error is not None:
error = error.value
apermasks = self.to_mask(method=method, subpixels=subpixels)
if self.isscalar:
apermasks = (apermasks,)
aperture_sums = []
aperture_sum_errs = []
for apermask in apermasks:
(slc_large,
aper_weights,
pixel_mask) = apermask._get_overlap_cutouts(data.shape, mask=mask)
# no overlap of the aperture with the data
if slc_large is None:
aperture_sums.append(np.nan)
aperture_sum_errs.append(np.nan)
continue
with warnings.catch_warnings():
# ignore multiplication with non-finite data values
warnings.simplefilter('ignore', RuntimeWarning)
values = (data[slc_large] * aper_weights)[pixel_mask]
aperture_sums.append(values.sum())
if error is not None:
variance = (error[slc_large]**2 * aper_weights)[pixel_mask]
aperture_sum_errs.append(np.sqrt(variance.sum()))
aperture_sums = np.array(aperture_sums)
aperture_sum_errs = np.array(aperture_sum_errs)
# apply units
if unit is not None:
aperture_sums <<= unit
aperture_sum_errs <<= unit
return aperture_sums, aperture_sum_errs
@staticmethod
def _make_annulus_path(patch_inner, patch_outer):
"""
Define a matplotlib annulus path from two patches.
This preserves the cubic Bezier curves (CURVE4) of the aperture
paths.
"""
import matplotlib.path as mpath
path_inner = patch_inner.get_path()
transform_inner = patch_inner.get_transform()
path_inner = transform_inner.transform_path(path_inner)
path_outer = patch_outer.get_path()
transform_outer = patch_outer.get_transform()
path_outer = transform_outer.transform_path(path_outer)
verts_inner = path_inner.vertices[:-1][::-1]
verts_inner = np.concatenate((verts_inner, [verts_inner[-1]]))
verts = np.vstack((path_outer.vertices, verts_inner))
codes = np.hstack((path_outer.codes, path_inner.codes))
return mpath.Path(verts, codes)
def _define_patch_params(self, origin=(0, 0), **kwargs):
"""
Define the aperture patch position and set any default
matplotlib patch keywords (e.g., ``fill=False``).
Parameters
----------
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.Patch`.
Returns
-------
xy_positions : `~numpy.ndarray`
The aperture patch positions.
patch_params : dict
Any keyword arguments accepted by
`matplotlib.patches.Patch`.
"""
xy_positions = deepcopy(self._positions)
xy_positions[:, 0] -= origin[0]
xy_positions[:, 1] -= origin[1]
patch_params = self._default_patch_properties.copy()
patch_params.update(kwargs)
return xy_positions, patch_params
@abc.abstractmethod
def _to_patch(self, origin=(0, 0), **kwargs):
"""
Return a `~matplotlib.patches.Patch` for the aperture.
Parameters
----------
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.Patch`.
Returns
-------
patch : `~matplotlib.patches.Patch` or list of \
`~matplotlib.patches.Patch`
A patch for the aperture. If the aperture is scalar then a
single `~matplotlib.patches.Patch` is returned, otherwise a
list of `~matplotlib.patches.Patch` is returned.
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
[docs]
def plot(self, ax=None, origin=(0, 0), **kwargs):
"""
Plot the aperture on a matplotlib `~matplotlib.axes.Axes`
instance.
Parameters
----------
ax : `matplotlib.axes.Axes` or `None`, optional
The matplotlib axes on which to plot. If `None`, then the
current `~matplotlib.axes.Axes` instance is used.
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.Patch`.
Returns
-------
patch : list of `~matplotlib.patches.Patch`
A list of matplotlib patches for the plotted aperture. The
patches can be used, for example, when adding a plot legend.
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
patches = self._to_patch(origin=origin, **kwargs)
if self.isscalar:
patches = (patches,)
for patch in patches:
ax.add_patch(patch)
return patches
def _to_sky_params(self, wcs):
"""
Convert the pixel aperture parameters to those for a sky
aperture.
Parameters
----------
wcs : WCS object
A world coordinate system (WCS) transformation that
supports the `astropy shared interface for WCS
<https://docs.astropy.org/en/stable/wcs/wcsapi.html>`_
(e.g., `astropy.wcs.WCS`, `gwcs.wcs.WCS`).
Returns
-------
sky_params : dict
A dictionary of parameters for an equivalent sky aperture.
"""
sky_params = {}
xpos, ypos = np.transpose(self.positions)
sky_params['positions'] = skypos = wcs.pixel_to_world(xpos, ypos)
# Aperture objects require scalar shape parameters (e.g.,
# radius, a, b, theta, etc.), therefore we must calculate the
# pixel scale and angle at only a single sky position, which
# we take as the first aperture position. For apertures with
# multiple positions used with a WCS that contains distortions
# (e.g., a spatially-dependent pixel scale), this may lead to
# unexpected results (e.g., results that are dependent of the
# order of the positions). There is no good way to fix this with
# the current Aperture API allowing multiple positions.
if not self.isscalar:
skypos = skypos[0]
_, pixscale, angle = _pixel_scale_angle_at_skycoord(skypos, wcs)
for param in self._params:
value = getattr(self, param)
if param == 'positions':
continue
if param == 'theta':
# photutils aperture sky angles are defined as the PA of
# the semimajor axis (i.e., relative to the WCS latitude
# axis). region sky angles are defined relative to the WCS
# longitude axis.
value = (value * u.rad) - angle.to(u.rad)
else:
value = (value * u.pix * pixscale).to(u.arcsec)
sky_params[param] = value
return sky_params
[docs]
@abc.abstractmethod
def to_sky(self, wcs):
"""
Convert the aperture to a `SkyAperture` object defined in
celestial coordinates.
Parameters
----------
wcs : WCS object
A world coordinate system (WCS) transformation that
supports the `astropy shared interface for WCS
<https://docs.astropy.org/en/stable/wcs/wcsapi.html>`_
(e.g., `astropy.wcs.WCS`, `gwcs.wcs.WCS`).
Returns
-------
aperture : `SkyAperture` object
A `SkyAperture` object.
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
[docs]
class SkyAperture(Aperture):
"""
Abstract base class for all apertures defined in celestial
coordinates.
"""
def _to_pixel_params(self, wcs):
"""
Convert the sky aperture parameters to those for a pixel
aperture.
Parameters
----------
wcs : WCS object
A world coordinate system (WCS) transformation that
supports the `astropy shared interface for WCS
<https://docs.astropy.org/en/stable/wcs/wcsapi.html>`_
(e.g., `astropy.wcs.WCS`, `gwcs.wcs.WCS`).
Returns
-------
pixel_params : dict
A dictionary of parameters for an equivalent pixel aperture.
"""
pixel_params = {}
xpos, ypos = wcs.world_to_pixel(self.positions)
pixel_params['positions'] = np.transpose((xpos, ypos))
# Aperture objects require scalar shape parameters (e.g.,
# radius, a, b, theta, etc.), therefore we must calculate the
# pixel scale and angle at only a single sky position, which
# we take as the first aperture position. For apertures with
# multiple positions used with a WCS that contains distortions
# (e.g., a spatially-dependent pixel scale), this may lead to
# unexpected results (e.g., results that are dependent of the
# order of the positions). There is no good way to fix this with
# the current Aperture API allowing multiple positions.
skypos = self.positions if self.isscalar else self.positions[0]
_, pixscale, angle = _pixel_scale_angle_at_skycoord(skypos, wcs)
for param in self._params:
value = getattr(self, param)
if param == 'positions':
continue
if param == 'theta':
# photutils aperture sky angles are defined as the PA of
# the semimajor axis (i.e., relative to the WCS latitude
# axis). region sky angles are defined relative to the WCS
# longitude axis.
value = (value + angle).to(u.radian).value
elif value.unit.physical_type == 'angle':
value = (value / pixscale).to(u.pixel).value
else:
value = value.value
pixel_params[param] = value
return pixel_params
[docs]
@abc.abstractmethod
def to_pixel(self, wcs):
"""
Convert the aperture to a `PixelAperture` object defined in
pixel coordinates.
Parameters
----------
wcs : WCS object
A world coordinate system (WCS) transformation that
supports the `astropy shared interface for WCS
<https://docs.astropy.org/en/stable/wcs/wcsapi.html>`_
(e.g., `astropy.wcs.WCS`, `gwcs.wcs.WCS`).
Returns
-------
aperture : `PixelAperture` object
A `PixelAperture` object.
"""
raise NotImplementedError('Needs to be implemented in a subclass.')
def _aperture_metadata(aperture, index=''):
"""
Return a dictionary of aperture metadata.
Parameters
----------
aperture : `Aperture`
An aperture object.
index : str, optional
A string that will be prepended to each metadata key.
Returns
-------
meta : dict
A dictionary of aperture metadata
"""
params = aperture._params
meta = {}
for param in params:
if param != 'positions':
meta[f'aperture{index}'] = aperture.__class__.__name__
meta[f'aperture{index}_{param}'] = getattr(aperture, param)
return meta
