Added flag to BasicPSFPhotometry to preserve ID order

CHANGES.rst

@@ -21,7 +21,11 @@ Bug Fixes
 API changes
 ^^^^^^^^^^^
 
--
+- ``photutils.psf``
+
+  - Added ``preserve_id_order`` as input parameter to ``BasicPSFPhotometry``,
+    allowing for the return of source table in ``id`` rather than ``group_id``
+    order. [#746]
 
 Other Changes and Additions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

photutils/psf/photometry.py

@@ -90,6 +90,11 @@ class BasicPSFPhotometry:
         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``.
+    preserve_id_order : bool, optional
+        Flag indicating whether to present final output table in ascending
+        ID order. Default is ``False``, and thus sources may be presented
+        in differing order to any input as ``init_guesses``, with output
+        being arranged by ``group_id``.
 
     Notes
     -----
@@ -113,7 +118,8 @@ class BasicPSFPhotometry:
     """
 
     def __init__(self, group_maker, bkg_estimator, psf_model, fitshape,
-                 finder=None, fitter=LevMarLSQFitter(), aperture_radius=None):
+                 finder=None, fitter=LevMarLSQFitter(), aperture_radius=None,
+                 preserve_id_order=False):
         self.group_maker = group_maker
         self.bkg_estimator = bkg_estimator
         self.psf_model = psf_model
@@ -124,6 +130,7 @@ def __init__(self, group_maker, bkg_estimator, psf_model, fitshape,
         self._pars_to_set = None
         self._pars_to_output = None
         self._residual_image = None
+        self._preserve_id_order = preserve_id_order
 
     @property
     def fitshape(self):
@@ -292,6 +299,9 @@ def do_photometry(self, image, init_guesses=None):
         star_groups = star_groups.group_by('group_id')
         output_tab = hstack([star_groups, output_tab])
 
+        if self._preserve_id_order:
+            output_tab = output_tab.group_by('id')
+
         return output_tab
 
     def nstar(self, image, star_groups):

photutils/psf/tests/test_photometry.py

@@ -111,6 +111,14 @@ def make_psf_photometry_objs(std=1, sigma_psf=1):
 sources3['group_id'] = [1] * 2
 sources3['iter_detected'] = [1, 2]
 
+sigma_psfs.append(2)
+sources4 = Table()
+sources4['flux'] = [10000, 5000, 3000]
+sources4['x_mean'] = [20, 8, 13]
+sources4['y_mean'] = [9, 19, 21]
+sources4['x_stddev'] = [sigma_psfs[-1]] * len(sources4['x_mean'])
+sources4['y_stddev'] = [sigma_psfs[-1]] * len(sources4['y_mean'])
+
 
 @pytest.mark.skipif('not HAS_SCIPY')
 @pytest.mark.parametrize("sigma_psf, sources", [(sigma_psfs[2], sources3)])
@@ -518,6 +526,49 @@ def test_psf_boundary_gaussian():
     assert_allclose(f['flux_fit'], 0, atol=1e-8)
 
 
+@pytest.mark.xfail('not HAS_SCIPY')
+@pytest.mark.parametrize("sigma_psf, sources", [(sigma_psfs[0], sources4)])
+def test_psf_preserve_order(sigma_psf, sources):
+    """
+    Test the order preservation of BasicPSFPhotometry.
+    """
+
+    psf_model = IntegratedGaussianPRF(sigma=sigma_psf)
+
+    img_shape = (32, 32)
+    # generate image with read-out noise (Gaussian) and
+    # background noise (Poisson)
+    image = (make_gaussian_prf_sources_image(img_shape, sources) +
+             make_noise_image(img_shape, type='poisson', mean=6.,
+                              random_state=1) +
+             make_noise_image(img_shape, type='gaussian', mean=0.,
+                              stddev=2., random_state=1))
+
+    psf_model.sigma.fixed = False
+    mmm_bkg = MMMBackground()
+    daogroup = DAOGroup(crit_separation=8)
+    basic_phot = BasicPSFPhotometry(group_maker=daogroup, bkg_estimator=mmm_bkg,
+                                    psf_model=psf_model, fitshape=(11, 11))
+    phot_results = basic_phot(image, init_guesses=Table(names=['x_0', 'y_0', 'flux_0'],
+                                                        data=[[7.8, 20.5, 13.1],
+                                                              [19.4, 8.8, 21.4],
+                                                              [4500, 9000, 2950]]))
+    # If preserver ID order is off -- by default -- we get group as [1, 1, 2]
+    assert np.all(phot_results['group_id'] == [1, 1, 2])
+    assert np.all(phot_results['id'] == [1, 3, 2])
+
+    basic_phot = BasicPSFPhotometry(group_maker=daogroup, bkg_estimator=mmm_bkg,
+                                    psf_model=psf_model, fitshape=(11, 11),
+                                    preserve_id_order=True)
+    phot_results = basic_phot(image, init_guesses=Table(names=['x_0', 'y_0', 'flux_0'],
+                                                        data=[[7.8, 20.5, 13.1],
+                                                              [19.4, 8.8, 21.4],
+                                                              [4500, 9000, 2950]]))
+
+    assert np.all(phot_results['group_id'] == [1, 2, 1])
+    assert np.all(phot_results['id'] == [1, 2, 3])
+
+
 @pytest.mark.skipif('not HAS_SCIPY')
 def test_psf_photometry_gaussian():
     """