/usr/lib/python3/dist-packages/photutils/psf/photometry.py is in python3-photutils 0.4-1.
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"""Module which provides classes to perform PSF Photometry"""
from __future__ import division
import numpy as np
import warnings
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.nddata.utils import overlap_slices
from astropy.stats import gaussian_sigma_to_fwhm, SigmaClip
from astropy.table import Table, Column, vstack, hstack
from astropy.utils import deprecated_renamed_argument
from astropy.utils.exceptions import AstropyUserWarning
from . import DAOGroup
from .funcs import subtract_psf, _extract_psf_fitting_names
from .models import get_grouped_psf_model
from ..aperture import CircularAperture, aperture_photometry
from ..background import MMMBackground
from ..detection import DAOStarFinder
__all__ = ['BasicPSFPhotometry', 'IterativelySubtractedPSFPhotometry',
'DAOPhotPSFPhotometry']
class BasicPSFPhotometry(object):
"""
This class implements a PSF photometry algorithm that can find
sources in an image, group overlapping sources into a single model,
fit the model to the sources, and subtracting the models from the
image. This is roughly equivalent to the DAOPHOT routines FIND,
GROUP, NSTAR, and SUBTRACT. This implementation allows a flexible
and customizable interface to perform photometry. For instance, one
is able to use different implementations for grouping and finding
sources by using ``group_maker`` and ``finder`` respectivelly. In
addition, sky background estimation is performed by
``bkg_estimator``.
Parameters
----------
group_maker : callable or `~photutils.psf.GroupStarsBase`
``group_maker`` should be able to decide whether a given star
overlaps with any other and label them as beloging to the same
group. ``group_maker`` receives as input an
`~astropy.table.Table` object with columns named as ``id``,
``x_0``, ``y_0``, in which ``x_0`` and ``y_0`` have the same
meaning of ``xcentroid`` and ``ycentroid``. This callable must
return an `~astropy.table.Table` with columns ``id``, ``x_0``,
``y_0``, and ``group_id``. The column ``group_id`` should cotain
integers starting from ``1`` that indicate which group a given
source belongs to. See, e.g., `~photutils.psf.DAOGroup`.
bkg_estimator : callable, instance of any `~photutils.BackgroundBase` subclass, or None
``bkg_estimator`` should be able to compute either a scalar
background or a 2D background of a given 2D image. See, e.g.,
`~photutils.background.MedianBackground`. If None, no
background subtraction is performed.
psf_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models in
this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other suitable 2D
model. This object needs to identify three parameters (position
of center in x and y coordinates and the flux) in order to set
them to suitable starting values for each fit. The names of
these parameters should be given as ``x_0``, ``y_0`` and
``flux``. `~photutils.psf.prepare_psf_model` can be used to
prepare any 2D model to match this assumption.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used
to collect the data to do the fitting. Can be an integer to be
the same along both axes. E.g., 5 is the same as (5, 5), which
means to fit only at the following relative pixel positions:
[-2, -1, 0, 1, 2]. Each element of ``fitshape`` must be an odd
number.
finder : callable or instance of any `~photutils.detection.StarFinderBase` subclasses or None
``finder`` should be able to identify stars, i.e. compute a
rough estimate of the centroids, in a given 2D image.
``finder`` receives as input a 2D image and returns an
`~astropy.table.Table` object which contains columns with names:
``id``, ``xcentroid``, ``ycentroid``, and ``flux``. In which
``id`` is an integer-valued column starting from ``1``,
``xcentroid`` and ``ycentroid`` are center position estimates of
the sources and ``flux`` contains flux estimates of the sources.
See, e.g., `~photutils.detection.DAOStarFinder`. If ``finder``
is ``None``, initial guesses for positions of objects must be
provided.
fitter : `~astropy.modeling.fitting.Fitter` instance
Fitter object used to compute the optimized centroid positions
and/or flux of the identified sources. See
`~astropy.modeling.fitting` for more details on fitters.
aperture_radius : float or None
The radius (in units of pixels) used to compute initial
estimates for the fluxes of sources. If ``None``, one FWHM will
be used if it can be determined from the ```psf_model``.
Notes
-----
Note that an ambiguity arises whenever ``finder`` and
``init_guesses`` (keyword argument for ``do_photometry``) are both
not ``None``. In this case, ``finder`` is ignored and initial
guesses are taken from ``init_guesses``. In addition, an warning is
raised to remaind the user about this behavior.
If there are problems with fitting large groups, change the
parameters of the grouping algorithm to reduce the number of sources
in each group or input a ``star_groups`` table that only includes
the groups that are relevant (e.g. manually remove all entries that
coincide with artifacts).
References
----------
[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222.
Available at: http://adsabs.harvard.edu/abs/1987PASP...99..191S
"""
def __init__(self, group_maker, bkg_estimator, psf_model, fitshape,
finder=None, fitter=LevMarLSQFitter(), aperture_radius=None):
self.group_maker = group_maker
self.bkg_estimator = bkg_estimator
self.psf_model = psf_model
self.fitter = fitter
self.fitshape = fitshape
self.finder = finder
self.aperture_radius = aperture_radius
self._pars_to_set = None
self._pars_to_output = None
self._residual_image = None
@property
def fitshape(self):
return self._fitshape
@fitshape.setter
def fitshape(self, value):
value = np.asarray(value)
# assume a lone value should mean both axes
if value.shape == ():
value = np.array((value, value))
if value.size == 2:
if np.all(value) > 0:
if np.all(value % 2) == 1:
self._fitshape = tuple(value)
else:
raise ValueError('fitshape must be odd integer-valued, '
'received fitshape = {}'.format(value))
else:
raise ValueError('fitshape must have positive elements, '
'received fitshape = {}'.format(value))
else:
raise ValueError('fitshape must have two dimensions, '
'received fitshape = {}'.format(value))
@property
def aperture_radius(self):
return self._aperture_radius
@aperture_radius.setter
def aperture_radius(self, value):
if isinstance(value, (int, float)) and value > 0:
self._aperture_radius = value
elif value is None:
self._aperture_radius = value
else:
raise ValueError('aperture_radius must be a real-valued '
'number, received aperture_radius = {}'
.format(value))
def get_residual_image(self):
"""
Returns an image that is the result of the subtraction between
the original image and the fitted sources.
Returns
-------
residual_image : 2D array-like, `~astropy.io.fits.ImageHDU`, `~astropy.io.fits.HDUList`
"""
return self._residual_image
@deprecated_renamed_argument('positions', 'init_guesses', '0.4')
def __call__(self, image, init_guesses=None):
"""
Performs PSF photometry. See `do_photometry` for more details
including the `__call__` signature.
"""
return self.do_photometry(image, init_guesses)
@deprecated_renamed_argument('positions', 'init_guesses', '0.4')
def do_photometry(self, image, init_guesses=None):
"""
Perform PSF photometry in ``image``.
This method assumes that ``psf_model`` has centroids and flux
parameters which will be fitted to the data provided in
``image``. A compound model, in fact a sum of ``psf_model``,
will be fitted to groups of stars automatically identified by
``group_maker``. Also, ``image`` is not assumed to be background
subtracted. If ``init_guesses`` are not ``None`` then this
method uses ``init_guesses`` as initial guesses for the
centroids. If the centroid positions are set as ``fixed`` in the
PSF model ``psf_model``, then the optimizer will only consider
the flux as a variable.
Parameters
----------
image : 2D array-like, `~astropy.io.fits.ImageHDU`, `~astropy.io.fits.HDUList`
Image to perform photometry.
init_guesses: `~astropy.table.Table`
Table which contains the initial guesses (estimates) for the
set of parameters. Columns 'x_0' and 'y_0' which represent
the positions (in pixel coordinates) for each object must be
present. 'flux_0' can also be provided to set initial
fluxes. If 'flux_0' is not provided, aperture photometry is
used to estimate initial values for the fluxes. Additional
columns of the form '<parametername>_0' will be used to set
the initial guess for any parameters of the ``psf_model``
model that are not fixed.
Returns
-------
output_tab : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process. Uncertainties on the fitted parameters
are reported as columns called ``<paramname>_unc`` provided
that the fitter object contains a dictionary called
``fit_info`` with the key ``param_cov``, which contains the
covariance matrix. If ``param_cov`` is not present,
uncertanties are not reported.
"""
if self.bkg_estimator is not None:
image = image - self.bkg_estimator(image)
if self.aperture_radius is None:
if hasattr(self.psf_model, 'fwhm'):
self.aperture_radius = self.psf_model.fwhm.value
elif hasattr(self.psf_model, 'sigma'):
self.aperture_radius = (self.psf_model.sigma.value *
gaussian_sigma_to_fwhm)
if init_guesses is not None:
# make sure the code does not modify user's input
init_guesses = init_guesses.copy()
if self.aperture_radius is None:
if 'flux_0' not in init_guesses.colnames:
raise ValueError('aperture_radius is None and could not '
'be determined by psf_model. Please, '
'either provided a value for '
'aperture_radius or define fwhm/sigma '
'at psf_model.')
if self.finder is not None:
warnings.warn('Both init_guesses and finder are different '
'than None, which is ambiguous. finder is '
'going to be ignored.', AstropyUserWarning)
if 'flux_0' not in init_guesses.colnames:
apertures = CircularAperture((init_guesses['x_0'],
init_guesses['y_0']),
r=self.aperture_radius)
init_guesses['flux_0'] = aperture_photometry(
image, apertures)['aperture_sum']
else:
if self.finder is None:
raise ValueError('Finder cannot be None if init_guesses are '
'not given.')
sources = self.finder(image)
if len(sources) > 0:
apertures = CircularAperture((sources['xcentroid'],
sources['ycentroid']),
r=self.aperture_radius)
sources['aperture_flux'] = aperture_photometry(
image, apertures)['aperture_sum']
init_guesses = Table(names=['x_0', 'y_0', 'flux_0'],
data=[sources['xcentroid'],
sources['ycentroid'],
sources['aperture_flux']])
self._define_fit_param_names()
for p0, param in self._pars_to_set.items():
if p0 not in init_guesses.colnames:
init_guesses[p0] = (len(init_guesses) *
[getattr(self.psf_model, param).value])
star_groups = self.group_maker(init_guesses)
output_tab, self._residual_image = self.nstar(image, star_groups)
star_groups = star_groups.group_by('group_id')
output_tab = hstack([star_groups, output_tab])
return output_tab
def nstar(self, image, star_groups):
"""
Fit, as appropriate, a compound or single model to the given
``star_groups``. Groups are fitted sequentially from the
smallest to the biggest. In each iteration, ``image`` is
subtracted by the previous fitted group.
Parameters
----------
image : numpy.ndarray
Background-subtracted image.
star_groups : `~astropy.table.Table`
This table must contain the following columns: ``id``,
``group_id``, ``x_0``, ``y_0``, ``flux_0``. ``x_0`` and
``y_0`` are initial estimates of the centroids and
``flux_0`` is an initial estimate of the flux. Additionally,
columns named as ``<param_name>_0`` are required if any
other parameter in the psf model is free (i.e., the
``fixed`` attribute of that parameter is ``False``).
Returns
-------
result_tab : `~astropy.table.Table`
Astropy table that contains photometry results.
image : numpy.ndarray
Residual image.
"""
result_tab = Table()
for param_tab_name in self._pars_to_output.keys():
result_tab.add_column(Column(name=param_tab_name))
unc_tab = Table()
for param, isfixed in self.psf_model.fixed.items():
if not isfixed:
unc_tab.add_column(Column(name=param + "_unc"))
y, x = np.indices(image.shape)
star_groups = star_groups.group_by('group_id')
for n in range(len(star_groups.groups)):
group_psf = get_grouped_psf_model(self.psf_model,
star_groups.groups[n],
self._pars_to_set)
usepixel = np.zeros_like(image, dtype=np.bool)
for row in star_groups.groups[n]:
usepixel[overlap_slices(large_array_shape=image.shape,
small_array_shape=self.fitshape,
position=(row['y_0'], row['x_0']),
mode='trim')[0]] = True
fit_model = self.fitter(group_psf, x[usepixel], y[usepixel],
image[usepixel])
param_table = self._model_params2table(fit_model,
len(star_groups.groups[n]))
result_tab = vstack([result_tab, param_table])
if 'param_cov' in self.fitter.fit_info.keys():
unc_tab = vstack([unc_tab,
self._get_uncertainties(
len(star_groups.groups[n]))])
try:
from astropy.nddata.utils import NoOverlapError
except ImportError:
raise ImportError("astropy 1.1 or greater is required in "
"order to use this class.")
# do not subtract if the fitting did not go well
try:
image = subtract_psf(image, self.psf_model, param_table,
subshape=self.fitshape)
except NoOverlapError:
pass
if 'param_cov' in self.fitter.fit_info.keys():
result_tab = hstack([result_tab, unc_tab])
return result_tab, image
def _define_fit_param_names(self):
"""
Convenience function to define mappings between the names of the
columns in the initial guess table (and the name of the fitted
parameters) and the actual name of the parameters in the model.
This method sets the following parameters on the ``self`` object:
* ``pars_to_set`` : Dict which maps the names of the parameters
initial guesses to the actual name of the parameter in the
model.
* ``pars_to_output`` : Dict which maps the names of the fitted
parameters to the actual name of the parameter in the model.
"""
xname, yname, fluxname = _extract_psf_fitting_names(self.psf_model)
self._pars_to_set = {'x_0': xname, 'y_0': yname, 'flux_0': fluxname}
self._pars_to_output = {'x_fit': xname, 'y_fit': yname,
'flux_fit': fluxname}
for p, isfixed in self.psf_model.fixed.items():
p0 = p + '_0'
pfit = p + '_fit'
if p not in (xname, yname, fluxname) and not isfixed:
self._pars_to_set[p0] = p
self._pars_to_output[pfit] = p
def _get_uncertainties(self, star_group_size):
"""
Retrieve uncertainties on fitted parameters from the fitter
object.
Parameters
----------
star_group_size : int
Number of stars in the given group.
Returns
-------
unc_tab : `~astropy.table.Table`
Table which contains uncertainties on the fitted parameters.
The uncertainties are reported as one standard deviation.
"""
unc_tab = Table()
for param_name in self.psf_model.param_names:
if not self.psf_model.fixed[param_name]:
unc_tab.add_column(Column(name=param_name + "_unc",
data=np.empty(star_group_size)))
if 'param_cov' in self.fitter.fit_info.keys():
if self.fitter.fit_info['param_cov'] is not None:
k = 0
n_fit_params = len(unc_tab.colnames)
for i in range(star_group_size):
unc_tab[i] = np.sqrt(np.diag(
self.fitter.fit_info['param_cov'])
)[k: k + n_fit_params]
k = k + n_fit_params
return unc_tab
def _model_params2table(self, fit_model, star_group_size):
"""
Place fitted parameters into an astropy table.
Parameters
----------
fit_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models
in this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other
suitable 2D model.
star_group_size : int
Number of stars in the given group.
Returns
-------
param_tab : `~astropy.table.Table`
Table that contains the fitted parameters.
"""
param_tab = Table()
for param_tab_name in self._pars_to_output.keys():
param_tab.add_column(Column(name=param_tab_name,
data=np.empty(star_group_size)))
if star_group_size > 1:
for i in range(star_group_size):
for param_tab_name, param_name in self._pars_to_output.items():
param_tab[param_tab_name][i] = getattr(fit_model,
param_name +
'_' + str(i)).value
else:
for param_tab_name, param_name in self._pars_to_output.items():
param_tab[param_tab_name] = getattr(fit_model, param_name).value
return param_tab
class IterativelySubtractedPSFPhotometry(BasicPSFPhotometry):
"""
This class implements an iterative algorithm to perform point spread
function photometry in crowded fields. This consists of applying a
loop of find sources, make groups, fit groups, subtract groups, and
then repeat until no more stars are detected or a given number of
iterations is reached.
Parameters
----------
group_maker : callable or `~photutils.psf.GroupStarsBase`
``group_maker`` should be able to decide whether a given star
overlaps with any other and label them as beloging to the same
group. ``group_maker`` receives as input an
`~astropy.table.Table` object with columns named as ``id``,
``x_0``, ``y_0``, in which ``x_0`` and ``y_0`` have the same
meaning of ``xcentroid`` and ``ycentroid``. This callable must
return an `~astropy.table.Table` with columns ``id``, ``x_0``,
``y_0``, and ``group_id``. The column ``group_id`` should cotain
integers starting from ``1`` that indicate which group a given
source belongs to. See, e.g., `~photutils.psf.DAOGroup`.
bkg_estimator : callable, instance of any `~photutils.BackgroundBase` subclass, or None
``bkg_estimator`` should be able to compute either a scalar
background or a 2D background of a given 2D image. See, e.g.,
`~photutils.background.MedianBackground`. If None, no
background subtraction is performed.
psf_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models in
this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other suitable 2D
model. This object needs to identify three parameters (position
of center in x and y coordinates and the flux) in order to set
them to suitable starting values for each fit. The names of
these parameters should be given as ``x_0``, ``y_0`` and
``flux``. `~photutils.psf.prepare_psf_model` can be used to
prepare any 2D model to match this assumption.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used
to collect the data to do the fitting. Can be an integer to be
the same along both axes. E.g., 5 is the same as (5, 5), which
means to fit only at the following relative pixel positions:
[-2, -1, 0, 1, 2]. Each element of ``fitshape`` must be an odd
number.
finder : callable or instance of any `~photutils.detection.StarFinderBase` subclasses
``finder`` should be able to identify stars, i.e. compute a
rough estimate of the centroids, in a given 2D image.
``finder`` receives as input a 2D image and returns an
`~astropy.table.Table` object which contains columns with names:
``id``, ``xcentroid``, ``ycentroid``, and ``flux``. In which
``id`` is an integer-valued column starting from ``1``,
``xcentroid`` and ``ycentroid`` are center position estimates of
the sources and ``flux`` contains flux estimates of the sources.
See, e.g., `~photutils.detection.DAOStarFinder` or
`~photutils.detection.IRAFStarFinder`.
fitter : `~astropy.modeling.fitting.Fitter` instance
Fitter object used to compute the optimized centroid positions
and/or flux of the identified sources. See
`~astropy.modeling.fitting` for more details on fitters.
aperture_radius : float
The radius (in units of pixels) used to compute initial
estimates for the fluxes of sources. If ``None``, one FWHM will
be used if it can be determined from the ```psf_model``.
niters : int or None
Number of iterations to perform of the loop FIND, GROUP,
SUBTRACT, NSTAR. If None, iterations will proceed until no more
stars remain. Note that in this case it is *possible* that the
loop will never end if the PSF has structure that causes
subtraction to create new sources infinitely.
Notes
-----
If there are problems with fitting large groups, change the
parameters of the grouping algorithm to reduce the number of sources
in each group or input a ``star_groups`` table that only includes
the groups that are relevant (e.g. manually remove all entries that
coincide with artifacts).
References
----------
[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222.
Available at: http://adsabs.harvard.edu/abs/1987PASP...99..191S
"""
def __init__(self, group_maker, bkg_estimator, psf_model, fitshape,
finder, fitter=LevMarLSQFitter(), niters=3,
aperture_radius=None):
super(IterativelySubtractedPSFPhotometry, self).__init__(
group_maker, bkg_estimator, psf_model, fitshape, finder, fitter,
aperture_radius)
self.niters = niters
@property
def niters(self):
return self._niters
@niters.setter
def niters(self, value):
if value is None:
self._niters = None
else:
try:
if value <= 0:
raise ValueError('niters must be positive.')
else:
self._niters = int(value)
except ValueError:
raise ValueError('niters must be None or an integer or '
'convertable into an integer.')
@property
def finder(self):
return self._finder
@finder.setter
def finder(self, value):
if value is None:
raise ValueError("finder cannot be None for "
"IterativelySubtractedPSFPhotometry - you may "
"want to use BasicPSFPhotometry. Please see the "
"Detection section on photutils documentation.")
else:
self._finder = value
@deprecated_renamed_argument('positions', 'init_guesses', '0.4')
def do_photometry(self, image, init_guesses=None):
"""
Perform PSF photometry in ``image``.
This method assumes that ``psf_model`` has centroids and flux
parameters which will be fitted to the data provided in
``image``. A compound model, in fact a sum of ``psf_model``,
will be fitted to groups of stars automatically identified by
``group_maker``. Also, ``image`` is not assumed to be background
subtracted. If ``init_guesses`` are not ``None`` then this
method uses ``init_guesses`` as initial guesses for the
centroids. If the centroid positions are set as ``fixed`` in the
PSF model ``psf_model``, then the optimizer will only consider
the flux as a variable.
Parameters
----------
image : 2D array-like, `~astropy.io.fits.ImageHDU`, `~astropy.io.fits.HDUList`
Image to perform photometry.
init_guesses: `~astropy.table.Table`
Table which contains the initial guesses (estimates) for the
set of parameters. Columns 'x_0' and 'y_0' which represent
the positions (in pixel coordinates) for each object must be
present. 'flux_0' can also be provided to set initial
fluxes. If 'flux_0' is not provided, aperture photometry is
used to estimate initial values for the fluxes. Additional
columns of the form '<parametername>_0' will be used to set
the initial guess for any parameters of the ``psf_model``
model that are not fixed.
Returns
-------
output_table : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process. Uncertainties on the fitted parameters
are reported as columns called ``<paramname>_unc`` provided
that the fitter object contains a dictionary called
``fit_info`` with the key ``param_cov``, which contains the
covariance matrix.
"""
if init_guesses is not None:
table = super(IterativelySubtractedPSFPhotometry,
self).do_photometry(image, init_guesses)
table['iter_detected'] = np.ones(table['x_fit'].shape,
dtype=np.int32)
# n_start = 2 because it starts in the second iteration
# since the first iteration is above
output_table = self._do_photometry(init_guesses.colnames,
n_start=2)
output_table = vstack([table, output_table])
else:
if self.bkg_estimator is not None:
self._residual_image = image - self.bkg_estimator(image)
if self.aperture_radius is None:
if hasattr(self.psf_model, 'fwhm'):
self.aperture_radius = self.psf_model.fwhm.value
elif hasattr(self.psf_model, 'sigma'):
self.aperture_radius = (self.psf_model.sigma.value *
gaussian_sigma_to_fwhm)
output_table = self._do_photometry(['x_0', 'y_0', 'flux_0'])
return output_table
def _do_photometry(self, param_tab, n_start=1):
"""
Helper function which performs the iterations of the photometry
process.
Parameters
----------
param_names : list
Names of the columns which represent the initial guesses.
For example, ['x_0', 'y_0', 'flux_0'], for intial guesses on
the center positions and the flux.
n_start : int
Integer representing the start index of the iteration. It
is 1 if init_guesses are None, and 2 otherwise.
Returns
-------
output_table : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process.
"""
output_table = Table()
self._define_fit_param_names()
for (init_parname, fit_parname) in zip(self._pars_to_set.keys(),
self._pars_to_output.keys()):
output_table.add_column(Column(name=init_parname))
output_table.add_column(Column(name=fit_parname))
sources = self.finder(self._residual_image)
n = n_start
while(len(sources) > 0 and
(self.niters is None or n <= self.niters)):
apertures = CircularAperture((sources['xcentroid'],
sources['ycentroid']),
r=self.aperture_radius)
sources['aperture_flux'] = aperture_photometry(
self._residual_image, apertures)['aperture_sum']
init_guess_tab = Table(names=['id', 'x_0', 'y_0', 'flux_0'],
data=[sources['id'], sources['xcentroid'],
sources['ycentroid'],
sources['aperture_flux']])
for param_tab_name, param_name in self._pars_to_set.items():
if param_tab_name not in (['x_0', 'y_0', 'flux_0']):
init_guess_tab.add_column(
Column(name=param_tab_name,
data=(getattr(self.psf_model,
param_name) *
np.ones(len(sources)))))
star_groups = self.group_maker(init_guess_tab)
table, self._residual_image = super(
IterativelySubtractedPSFPhotometry, self).nstar(
self._residual_image, star_groups)
star_groups = star_groups.group_by('group_id')
table = hstack([star_groups, table])
table['iter_detected'] = n*np.ones(table['x_fit'].shape,
dtype=np.int32)
output_table = vstack([output_table, table])
# do not warn if no sources are found beyond the first iteration
with warnings.catch_warnings():
warnings.simplefilter('ignore', AstropyUserWarning)
sources = self.finder(self._residual_image)
n += 1
return output_table
class DAOPhotPSFPhotometry(IterativelySubtractedPSFPhotometry):
"""
This class implements an iterative algorithm based on the DAOPHOT
algorithm presented by Stetson (1987) to perform point spread
function photometry in crowded fields. This consists of applying a
loop of find sources, make groups, fit groups, subtract groups, and
then repeat until no more stars are detected or a given number of
iterations is reached.
Basically, this classes uses
`~photutils.psf.IterativelySubtractedPSFPhotometry`, but with
grouping, finding, and background estimation routines defined a
priori. More precisely, this class uses `~photutils.psf.DAOGroup`
for grouping, `~photutils.detection.DAOStarFinder` for finding
sources, and `~photutils.background.MMMBackground` for background
estimation. Those classes are based on GROUP, FIND, and SKY routines
used in DAOPHOT, respectively.
The parameter ``crit_separation`` is associated with
`~photutils.psf.DAOGroup`. ``sigma_clip`` is associated with
`~photutils.background.MMMBackground`. ``threshold`` and ``fwhm``
are associated with `~photutils.detection.DAOStarFinder`.
Parameters from ``ratio`` to ``roundhi`` are also associated with
`~photutils.detection.DAOStarFinder`.
Parameters
----------
crit_separation : float or int
Distance, in units of pixels, such that any two stars separated
by less than this distance will be placed in the same group.
threshold : float
The absolute image value above which to select sources.
fwhm : float
The full-width half-maximum (FWHM) of the major axis of the
Gaussian kernel in units of pixels.
psf_model : `astropy.modeling.Fittable2DModel` instance
PSF or PRF model to fit the data. Could be one of the models in
this package like `~photutils.psf.sandbox.DiscretePRF`,
`~photutils.psf.IntegratedGaussianPRF`, or any other suitable 2D
model. This object needs to identify three parameters (position
of center in x and y coordinates and the flux) in order to set
them to suitable starting values for each fit. The names of
these parameters should be given as ``x_0``, ``y_0`` and
``flux``. `~photutils.psf.prepare_psf_model` can be used to
prepare any 2D model to match this assumption.
fitshape : int or length-2 array-like
Rectangular shape around the center of a star which will be used
to collect the data to do the fitting. Can be an integer to be
the same along both axes. E.g., 5 is the same as (5, 5), which
means to fit only at the following relative pixel positions:
[-2, -1, 0, 1, 2]. Each element of ``fitshape`` must be an odd
number.
sigma : float, optional
Number of standard deviations used to perform sigma clip with a
`astropy.stats.SigmaClip` object.
ratio : float, optional
The ratio of the minor to major axis standard deviations of the
Gaussian kernel. ``ratio`` must be strictly positive and less
than or equal to 1.0. The default is 1.0 (i.e., a circular
Gaussian kernel).
theta : float, optional
The position angle (in degrees) of the major axis of the
Gaussian kernel measured counter-clockwise from the positive x
axis.
sigma_radius : float, optional
The truncation radius of the Gaussian kernel in units of sigma
(standard deviation) [``1 sigma = FWHM /
(2.0*sqrt(2.0*log(2.0)))``].
sharplo : float, optional
The lower bound on sharpness for object detection.
sharphi : float, optional
The upper bound on sharpness for object detection.
roundlo : float, optional
The lower bound on roundess for object detection.
roundhi : float, optional
The upper bound on roundess for object detection.
fitter : `~astropy.modeling.fitting.Fitter` instance
Fitter object used to compute the optimized centroid positions
and/or flux of the identified sources. See
`~astropy.modeling.fitting` for more details on fitters.
niters : int or None
Number of iterations to perform of the loop FIND, GROUP,
SUBTRACT, NSTAR. If None, iterations will proceed until no more
stars remain. Note that in this case it is *possible* that the
loop will never end if the PSF has structure that causes
subtraction to create new sources infinitely.
aperture_radius : float
The radius (in units of pixels) used to compute initial
estimates for the fluxes of sources. If ``None``, one FWHM will
be used if it can be determined from the ```psf_model``.
Notes
-----
If there are problems with fitting large groups, change the
parameters of the grouping algorithm to reduce the number of sources
in each group or input a ``star_groups`` table that only includes
the groups that are relevant (e.g. manually remove all entries that
coincide with artifacts).
References
----------
[1] Stetson, Astronomical Society of the Pacific, Publications,
(ISSN 0004-6280), vol. 99, March 1987, p. 191-222.
Available at: http://adsabs.harvard.edu/abs/1987PASP...99..191S
"""
def __init__(self, crit_separation, threshold, fwhm, psf_model, fitshape,
sigma=3., ratio=1.0, theta=0.0, sigma_radius=1.5,
sharplo=0.2, sharphi=1.0, roundlo=-1.0, roundhi=1.0,
fitter=LevMarLSQFitter(),
niters=3, aperture_radius=None):
self.crit_separation = crit_separation
self.threshold = threshold
self.fwhm = fwhm
self.sigma = sigma
self.ratio = ratio
self.theta = theta
self.sigma_radius = sigma_radius
self.sharplo = sharplo
self.sharphi = sharphi
self.roundlo = roundlo
self.roundhi = roundhi
group_maker = DAOGroup(crit_separation=self.crit_separation)
bkg_estimator = MMMBackground(sigma_clip=SigmaClip(sigma=self.sigma))
finder = DAOStarFinder(threshold=self.threshold, fwhm=self.fwhm,
ratio=self.ratio, theta=self.theta,
sigma_radius=self.sigma_radius,
sharplo=self.sharplo, sharphi=self.sharphi,
roundlo=self.roundlo, roundhi=self.roundhi)
super(DAOPhotPSFPhotometry, self).__init__(
group_maker=group_maker, bkg_estimator=bkg_estimator,
psf_model=psf_model, fitshape=fitshape, finder=finder,
fitter=fitter, niters=niters, aperture_radius=aperture_radius)
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