Minor changes to the wording of the description of some PSF specifica…

…tions.

docs/psf_spec/background_estimator.rst

@@ -4,7 +4,9 @@ BackgroundEstimator
 Existing code documented at
 https://photutils.readthedocs.io/en/stable/api/photutils.background.BackgroundBase.html
 - while the ``__call__`` function has no docstring, the ``calc_background``
-function is the actual block API. The documentation is therefore unlikely to change.
+function is the actual block API. This function is used significantly through `photutils`
+and should therefore be relatively stable to call changes; documentation is therefore 
+unlikely to change.
 
 Routine to estimate the background level of images and provide background subtraction. 
 Can either be applied across an entire image or applied in a two-dimensional grid, at

docs/psf_spec/finder.rst

@@ -27,8 +27,8 @@ Example Usage
 
 StarFinder currently implements two methods: DAOFind and IRAFFind. For example, daofind
 can be run to find objects with FWHM of approximately 3 pixels with a peak 5-sigma above
-the background:
-::
+the background::
+
     from photutils import DAOStarFinder
     daofind = DAOStarFinder(fwhm=3.0, threshold=5.*std)
     sources = daofind(data)

docs/psf_spec/group_maker.rst

@@ -1,13 +1,15 @@
 GroupMaker
 ==========
 
-Documented as the `__call__` method of ``GroupStarsBase`` - see
+Documented as the ``__call__`` method of ``GroupStarsBase`` - see
 https://photutils.readthedocs.io/en/stable/api/photutils.psf.groupstars.GroupStarsBase.html
-It'll be substantial work to re-design the photometry loops if this is changed
-in a backwards-incompatible manner, but of course that's possible if there's a
-good reason for it.
+API may potentially change if group_stars is updated to scene_maker extending PSF fitting to
+non-point source objects; however, it is likely that the fundamental inputs and outputs
+remain at least functionally similar to those shown here. Large changes to the GroupMaker
+call may require significant changes to the PSF Photometry fitting routines (e.g.,
+``IterativelySubtractedPSFPhotometry``).
 
-An object which groups stars within some critical separation, returning potentially
+A function which groups stars within some critical separation, returning potentially
 overlapping sources with an additonal column indicating their common group members.
 Subclasses of ``GroupStarsBase`` may require further input parameters, such as 
 ``crit_separation`` required for ``DAOGroup``.
@@ -48,15 +50,15 @@ starlist : `~astropy.table.Table`
 Returns
 """""""
 
-`~np.array` with the IDs of all stars with distance less than ``crit_separation`` to ``star``.
+`~numpy.array` with the IDs of all stars with distance less than ``crit_separation`` to ``star``.
 
 
 Example Usage
 -------------
 
 Here we create a ``DAOGroup`` list of overlapping sources, then find all sources within 3 pixels
-of the first source in the list.
-::
+of the first source in the list.::
+
     from photutils.psf.groupstars import DAOGroup
     group = DAOGroup(starlist, crit_separation=3)
     stargroups = group.group_stars(starlist)

docs/psf_spec/index.rst

@@ -14,6 +14,7 @@ Blocks
 .. toctree::
     :maxdepth: 1
 
+    block_template
     background_estimator
     block_template
     culler_and_ender
@@ -24,3 +25,4 @@ Blocks
     psf_model
     scene_maker
     single_object_model
+

docs/psf_spec/psf_model.rst

@@ -1,22 +1,19 @@
 PsfModel
 ========
 
-The PSF models in the current ``photutils.psf`` are not explicitly
-defined as a specific class, but any 2D model (inputs: x,y, output: flux) can
-be considered a PSF Model.  The `~photutils.psf.PRFAdapter` class is the
-clearest application specific example, however, and demonstrates the required
-convention for *names* of the PSF model's parameters.  Hopefully that class can
-be used *directly* as this block, as it is meant to wrap any arbitrary other
-models to make it compatible with the machinery.
-
 The model for the PSF -- integrated over discrete pixels (see `discussion 
-<https://github.com/astropy/photutils/blob/master/docs/psf.rst#terminology>'_ 
+<https://github.com/astropy/photutils/blob/master/docs/psf.rst#terminology>`_ 
 on terminology for more details) -- on which we should evaluate the parameters
-of the source. The main input is a 2D image, with the model being represented
-by a ``FittableImageModel``, returning ``flux``, ``x_0``, and ``y_0``, the
-overall source flux and centroid position. Individual models will require 
-additional parameters, such as the ``IntegratedGaussianPRF`` which additionally
-requiring ``sigma`` as a parameter that describes the underlying Gaussian PSF.
+of the source. The main input is a 2D image, with the model returning ``flux``,
+``x_0``, and ``y_0``, the overall source flux and centroid position. PSF models
+in the current ``photutils.psf`` are not explicitly defined as a specific class,
+but any 2D model (inputs: x,y, output: flux) can be considered a PSF Model.
+
+The `~photutils.psf.PRFAdapter` class allows for the wrapping of any arbitrary 
+other models to make it compatible with the machinery. Some individual models 
+will require additional parameters, such as the ``IntegratedGaussianPRF`` which 
+additionally requiring ``sigma`` as a parameter that describes the underlying 
+Gaussian PSF.
 
 Parameters
 ----------
@@ -75,8 +72,8 @@ and with the flux level specified.
 Example Usage
 -------------
 Here we create a discrete Gaussian PSF with standard deviation of 2 pixels, and 
-fit for centroid and overall flux level of a source.
-::
+fit for centroid and overall flux level of a source.::
+
     from photutils.psf import IntegratedGaussianPRF
     psf_model = IntegratedGaussianPRF(sigma=2)
     model_image = psf_model.evaluate(x, y, flux, x_0, y_0, sigma=2)