Make Gaussian kernel default for adaptive resampling

While this kernel introduces some slight blurring of the output image,
it significantly enhances the anti-aliasing properties that this
algorithm touts.

docs/celestial.rst

@@ -205,14 +205,14 @@ kernel---see below.)
 
 The kernel used for interpolation and averaging can be controlled with a set of
 options. The ``kernel`` argument can be set to 'hann' or 'gaussian' to set the
-function being used. The Hann window is the default, and the Gaussian window
-improves anti-aliasing and photometric accuracy (or flux conservation, when the
-flux-conserving mode is enabled) at the cost of blurring the output image by a
-few pixels. The ``kernel_width`` argument sets the width of the Gaussian
-kernel, in pixels, and is ignored for the Hann window. This width is measured
-between the Gaussian's :math:`\pm 1 \sigma` points. The default value is 1.3
-for the Gaussian, chosen to minimize blurring without compromising accuracy.
-Lower values may introduce photometric errors or leave input pixels
+function being used. The Gaussian window is the default, as it provides better
+anti-aliasing and photometric accuracy (or flux conservation, when the
+flux-conserving mode is enabled), though at the cost of blurring the output
+image by a few pixels. The ``kernel_width`` argument sets the width of the
+Gaussian kernel, in pixels, and is ignored for the Hann window. This width is
+measured between the Gaussian's :math:`\pm 1 \sigma` points. The default value
+is 1.3 for the Gaussian, chosen to minimize blurring without compromising
+accuracy. Lower values may introduce photometric errors or leave input pixels
 under-sampled, while larger values may improve anti-aliasing behavior but will
 increase blurring of the output image. Since the Gaussian function has infinite
 extent, it must be truncated. This is done by sampling within a region of

reproject/adaptive/core.py

@@ -34,7 +34,7 @@ def __call__(self, pixel_out):
 def _reproject_adaptive_2d(array, wcs_in, wcs_out, shape_out,
                            return_footprint=True, center_jacobian=False,
                            roundtrip_coords=True, conserve_flux=False,
-                           kernel='Hann', kernel_width=1.3,
+                           kernel='gaussian', kernel_width=1.3,
                            sample_region_width=4):
     """
     Reproject celestial slices from an n-d array from one WCS to another
@@ -105,7 +105,8 @@ def _reproject_adaptive_2d(array, wcs_in, wcs_out, shape_out,
 
     transformer = CoordinateTransformer(wcs_in, wcs_out, roundtrip_coords)
     map_coordinates(array_in, array_out, transformer, out_of_range_nan=True,
-                    center_jacobian=center_jacobian, conserve_flux=conserve_flux,
+                    center_jacobian=center_jacobian,
+                    conserve_flux=conserve_flux,
                     kernel=kernel, kernel_width=kernel_width,
                     sample_region_width=sample_region_width)
 

reproject/adaptive/deforest.pyx

@@ -177,8 +177,8 @@ KERNELS['gaussian'] = 1
 def map_coordinates(double[:,:] source, double[:,:] target, Ci, int max_samples_width=-1,
                     int conserve_flux=False, int progress=False, int singularities_nan=False,
                     int x_cyclic=False, int y_cyclic=False, int out_of_range_nan=False,
-                    bint center_jacobian=False, str kernel='Hann', double kernel_width=1.3,
-                    double sample_region_width=4):
+                    bint center_jacobian=False, str kernel='gaussian',
+                    double kernel_width=1.3, double sample_region_width=4):
     cdef int kernel_flag
     try:
         kernel_flag = KERNELS[kernel.lower()]

reproject/adaptive/high_level.py

@@ -9,7 +9,8 @@
 def reproject_adaptive(input_data, output_projection, shape_out=None, hdu_in=0,
                        return_footprint=True, center_jacobian=False,
                        roundtrip_coords=True, conserve_flux=False,
-                       kernel='Hann', kernel_width=1.3, sample_region_width=4):
+                       kernel='gaussian', kernel_width=1.3,
+                       sample_region_width=4):
     """
     Reproject a 2D array from one WCS to another using the DeForest (2004)
     adaptive, anti-aliased resampling algorithm, with optional flux

reproject/adaptive/tests/test_core.py

@@ -51,7 +51,8 @@ def test_reproject_adaptive_2d(wcsapi, center_jacobian, roundtrip_coords):
     array_out, footprint_out = reproject_adaptive(
             (data_in, wcs_in), wcs_out, shape_out=(60, 60),
             center_jacobian=center_jacobian,
-            roundtrip_coords=roundtrip_coords)
+            roundtrip_coords=roundtrip_coords,
+            kernel='hann')
 
     # Check that surface brightness is conserved in the unrotated case
     assert_allclose(np.nansum(data_in), np.nansum(array_out) * (256 / 60) ** 2, rtol=0.1)
@@ -92,7 +93,8 @@ def test_reproject_adaptive_2d_rotated(center_jacobian, roundtrip_coords):
     array_out, footprint_out = reproject_adaptive(
             (data_in, wcs_in), wcs_out, shape_out=(60, 60),
             center_jacobian=center_jacobian,
-            roundtrip_coords=roundtrip_coords)
+            roundtrip_coords=roundtrip_coords,
+            kernel='hann')
 
     # ASTROPY_LT_40: astropy v4.0 introduced new default header keywords,
     # once we support only astropy 4.0 and later we can update the reference
@@ -352,7 +354,7 @@ def test_reproject_adaptive_roundtrip(file_format):
     data, wcs, target_wcs = prepare_test_data(file_format)
 
     output, footprint = reproject_adaptive((data, wcs), target_wcs, (128, 128),
-                                           center_jacobian=True)
+                                           center_jacobian=True, kernel='hann')
 
     header_out = target_wcs.to_header()
 
@@ -378,7 +380,7 @@ def test_reproject_adaptive_uncentered_jacobian():
     data, wcs, target_wcs = prepare_test_data('fits')
 
     output, footprint = reproject_adaptive((data, wcs), target_wcs, (128, 128),
-                                           center_jacobian=False)
+                                           center_jacobian=False, kernel='hann')
 
     header_out = target_wcs.to_header()