mhvk · Apr 18, 2021 · Apr 15, 2021 · Apr 15, 2021 · Apr 15, 2021 · Apr 15, 2021
diff --git a/astropy/config/configuration.py b/astropy/config/configuration.py
@@ -362,8 +362,9 @@ def reload(self):
 
         Returns
         -------
-        val
+        val : object
             The new value loaded from the configuration file.
+
         """
         self.set(self.defaultvalue)
         baseobj = get_config(self.module, True, rootname=self.rootname)
@@ -406,14 +407,15 @@ def __call__(self):
 
         Returns
         -------
-        val
+        val : object
             This item's value, with a type determined by the ``cfgtype``
             attribute.
 
         Raises
         ------
         TypeError
             If the configuration value as stored is not this item's type.
+
         """
         def section_name(section):
             if section == '':
@@ -599,7 +601,7 @@ def generate_config(pkgname='astropy', filename=None, verbose=False):
     ----------
     pkgname : str or None
         The package for which to retrieve the configuration object.
-    filename : str or file object or None
+    filename : str or file-like or None
         If None, the default configuration path is taken from `get_config`.
 
     """

diff --git a/astropy/constants/constant.py b/astropy/constants/constant.py
@@ -14,9 +14,9 @@
 
 
 class ConstantMeta(type):
-    """Metaclass for the :class:`Constant`. The primary purpose of this is to
-    wrap the double-underscore methods of :class:`Quantity` which is the
-    superclass of :class:`Constant`.
+    """Metaclass for `~astropy.constants.Constant`. The primary purpose of this
+    is to wrap the double-underscore methods of `~astropy.units.Quantity`
+    which is the superclass of `~astropy.constants.Constant`.
 
     In particular this wraps the operator overloads such as `__add__` to
     prevent their use with constants such as ``e`` from being used in

diff --git a/astropy/constants/utils.py b/astropy/constants/utils.py
@@ -54,7 +54,7 @@ def _set_c(codata, iaudata, module, not_in_module_only=True, doclines=None,
         If ``True``, constants that are already in the namespace
         of ``module`` will not be modified.
 
-    doclines : list or `None`
+    doclines : list or None
         If a list is given, this list will be modified in-place to include
         documentation of modified constants. This can be used to update
         docstring of ``module``.

@@ -3,7 +3,7 @@
 
 from .core import *  # noqa
 from .kernels import *  # noqa
-from .utils import discretize_model  # noqa
+from .utils import discretize_model, KernelSizeError  # noqa
 
 from .convolve import convolve, convolve_fft, interpolate_replace_nans, convolve_models, convolve_models_fft  # noqa
 

@@ -160,7 +160,7 @@ def convolve(array, kernel, boundary='fill', fill_value=0.,
 
     Parameters
     ----------
-    array : `~astropy.nddata.NDData` or `numpy.ndarray` or array_like
+    array : `~astropy.nddata.NDData` or array-like
         The array to convolve. This should be a 1, 2, or 3-dimensional array
         or a list or a set of nested lists representing a 1, 2, or
         3-dimensional array.  If an `~astropy.nddata.NDData`, the ``mask`` of
@@ -196,7 +196,7 @@ def convolve(array, kernel, boundary='fill', fill_value=0.,
     preserve_nan : bool
         After performing convolution, should pixels that were originally NaN
         again become NaN?
-    mask : `None` or `numpy.ndarray`
+    mask : None or ndarray
         A "mask" array.  Shape must match ``array``, and anything that is masked
         (i.e., not 0/`False`) will be set to NaN for the convolution.  If
         `None`, no masking will be performed unless ``array`` is a masked array.
@@ -491,7 +491,7 @@ def convolve_fft(array, kernel, boundary='fill', fill_value=0.,
         numerical value (default zero, see ``fill_value``) prior to
         convolution.  Note that if the kernel has a sum equal to zero, NaN
         interpolation is not possible and will raise an exception.
-    normalize_kernel : function or boolean, optional
+    normalize_kernel : callable or boolean, optional
         If specified, this is the function to divide kernel by to normalize it.
         e.g., ``normalize_kernel=np.sum`` means that kernel will be modified to be:
         ``kernel = kernel / np.sum(kernel)``.  If True, defaults to
@@ -503,7 +503,7 @@ def convolve_fft(array, kernel, boundary='fill', fill_value=0.,
     preserve_nan : bool
         After performing convolution, should pixels that were originally NaN
         again become NaN?
-    mask : `None` or `numpy.ndarray`
+    mask : None or ndarray
         A "mask" array.  Shape must match ``array``, and anything that is masked
         (i.e., not 0/`False`) will be set to NaN for the convolution.  If
         `None`, no masking will be performed unless ``array`` is a masked array.
@@ -538,20 +538,20 @@ def convolve_fft(array, kernel, boundary='fill', fill_value=0.,
     allow_huge : bool, optional
         Allow huge arrays in the FFT?  If False, will raise an exception if the
         array or kernel size is >1 GB.
-    fftn : functions, optional
+    fftn : callable, optional
         The fft function.  Can be overridden to use your own ffts,
-        e.g. an fftw3 wrapper or scipy's fftn, ``fft=scipy.fft.fftn``.
-    ifftn : functions, optional
+        e.g. an fftw3 wrapper or scipy's fftn, ``fft=scipy.fftpack.fftn``.
+    ifftn : callable, optional
         The inverse fft function. Can be overridden the same way ``fttn``.
-    complex_dtype : numpy.complex, optional
+    complex_dtype : complex type, optional
         Which complex dtype to use.  `numpy` has a range of options, from 64 to
         256.
 
     Raises
     ------
     ValueError:
-        If the array is bigger than 1 GB after padding, will raise this exception
-        unless ``allow_huge`` is True
+        If the array is bigger than 1 GB after padding, will raise this
+        exception unless ``allow_huge`` is True
 
     See Also
     --------
@@ -903,7 +903,7 @@ def convolve_models(model, kernel, mode='convolve_fft', **kwargs):
 
     Returns
     -------
-    default : CompoundModel
+    default : `~astropy.modeling.core.CompoundModel`
         Convolved model
     """
 

@@ -34,7 +34,7 @@ class Kernel:
 
     Parameters
     ----------
-    array : `~numpy.ndarray`
+    array : ndarray
         Kernel array.
     """
     _separable = False
@@ -189,9 +189,10 @@ class Kernel1D(Kernel):
     ----------
     model : `~astropy.modeling.FittableModel`
         Model to be evaluated.
-    x_size : odd int, optional
-        Size of the kernel array. Default = 8 * width.
-    array : `~numpy.ndarray`
+    x_size : int or None, optional
+        Size of the kernel array. Default = ⌊8*width+1⌋.
+        Only used if ``array`` is None.
+    array : ndarray or None, optional
         Kernel array.
     width : number
         Width of the filter kernel.
@@ -248,12 +249,14 @@ class Kernel2D(Kernel):
     ----------
     model : `~astropy.modeling.FittableModel`
         Model to be evaluated.
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * width.
-    y_size : odd int, optional
-        Size in y direction of the kernel array. Default = 8 * width.
-    array : `~numpy.ndarray`
-        Kernel array.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*width + 1⌋.
+        Only used if ``array`` is None.
+    y_size : int, optional
+        Size in y direction of the kernel array. Default = ⌊8*width + 1⌋.
+        Only used if ``array`` is None,
+    array : ndarray or None, optional
+        Kernel array. Default is None.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -319,9 +322,9 @@ def kernel_arithmetics(kernel, value, operation):
     Parameters
     ----------
     kernel : `astropy.convolution.Kernel`
-        Kernel instance
-    value : kernel, float or int
-        Value to operate with
+        Kernel instance.
+    value : `astropy.convolution.Kernel`, float, or int
+        Value to operate with.
     operation : {'add', 'sub', 'mul'}
         One of the following operations:
             * 'add'

@@ -5,7 +5,7 @@
 import numpy as np
 
 from .core import Kernel1D, Kernel2D, Kernel
-from .utils import has_even_axis, raise_even_kernel_exception
+from .utils import has_even_axis, raise_even_kernel_exception, KernelSizeError
 from astropy.modeling import models
 from astropy.modeling.core import Fittable1DModel, Fittable2DModel
 from astropy.utils.decorators import deprecated
@@ -36,8 +36,8 @@ class Gaussian1DKernel(Kernel1D):
     ----------
     stddev : number
         Standard deviation of the Gaussian kernel.
-    x_size : odd int, optional
-        Size of the kernel array. Default = 8 * stddev
+    x_size : int, optional
+        Size of the kernel array. Default = ⌊8*stddev+1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -101,13 +101,13 @@ class Gaussian2DKernel(Kernel2D):
         Standard deviation of the Gaussian in x before rotating by theta.
     y_stddev : float
         Standard deviation of the Gaussian in y before rotating by theta.
-    theta : float or :class:`~astropy.units.Quantity`
+    theta : float or `~astropy.units.Quantity`
         Rotation angle. If passed as a float, it is assumed to be in radians.
         The rotation angle increases counterclockwise.
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * stddev.
-    y_size : odd int, optional
-        Size in y direction of the kernel array. Default = 8 * stddev.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*stddev + 1⌋.
+    y_size : int, optional
+        Size in y direction of the kernel array. Default = ⌊8*stddev + 1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -563,8 +563,8 @@ class RickerWavelet1DKernel(Kernel1D):
     width : number
         Width of the filter kernel, defined as the standard deviation
         of the Gaussian function from which it is derived.
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * width.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -639,10 +639,10 @@ class RickerWavelet2DKernel(Kernel2D):
     width : number
         Width of the filter kernel, defined as the standard deviation
         of the Gaussian function from which it is derived.
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * width.
-    y_size : odd int, optional
-        Size in y direction of the kernel array. Default = 8 * width.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
+    y_size : int, optional
+        Size in y direction of the kernel array. Default = ⌊8*width +1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -703,10 +703,10 @@ class AiryDisk2DKernel(Kernel2D):
     ----------
     radius : float
         The radius of the Airy disk kernel (radius of the first zero).
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * radius.
-    y_size : odd int, optional
-        Size in y direction of the kernel array. Default = 8 * radius.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*radius + 1⌋.
+    y_size : int, optional
+        Size in y direction of the kernel array. Default = ⌊8*radius + 1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -767,10 +767,10 @@ class Moffat2DKernel(Kernel2D):
         Core width of the Moffat model.
     alpha : float
         Power index of the Moffat model.
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * radius.
-    y_size : odd int, optional
-        Size in y direction of the kernel array. Default = 8 * radius.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*radius + 1⌋.
+    y_size : int, optional
+        Size in y direction of the kernel array. Default = ⌊8*radius + 1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -832,8 +832,9 @@ class Model1DKernel(Kernel1D):
     ----------
     model : `~astropy.modeling.Fittable1DModel`
         Kernel response function model
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * width.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
+        Must be odd.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -896,10 +897,11 @@ class Model2DKernel(Kernel2D):
     ----------
     model : `~astropy.modeling.Fittable2DModel`
         Kernel response function model
-    x_size : odd int, optional
-        Size in x direction of the kernel array. Default = 8 * width.
-    y_size : odd int, optional
-        Size in y direction of the kernel array. Default = 8 * width.
+    x_size : int, optional
+        Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
+        Must be odd.
+    y_size : int, optional
+        Size in y direction of the kernel array. Default = ⌊8*width +1⌋.
     mode : str, optional
         One of the following discretization modes:
             * 'center' (default)
@@ -977,7 +979,7 @@ class CustomKernel(Kernel):
     ------
     TypeError
         If array is not a list or array.
-    KernelSizeError
+    `~astropy.convolution.KernelSizeError`
         If array size is even.
 
     See also

@@ -4,7 +4,7 @@
 
 from astropy.modeling.core import FittableModel, custom_model
 
-__all__ = ['discretize_model']
+__all__ = ['discretize_model', 'KernelSizeError']
 
 
 class DiscretizationError(Exception):