def test_coadd_background_matching_with_nan(self, reproject_function):
# Test out the background matching when NaN values are present. We do
# this by using three arrays with the same footprint but with different
# parts masked.
array1 = self.array.copy() + random.uniform(-3, 3)
array2 = self.array.copy() + random.uniform(-3, 3)
array3 = self.array.copy() + random.uniform(-3, 3)
array1[:, 122:] = np.nan
array2[:, :50] = np.nan
array2[:, 266:] = np.nan
array3[:, :199] = np.nan
input_data = [(array1, self.wcs), (array2, self.wcs),
(array3, self.wcs)]
array, footprint = reproject_and_coadd(
input_data,
self.wcs,
shape_out=self.array.shape,
combine_function='mean',
reproject_function=reproject_function,
match_background=True)
# The absolute values of the two arrays will be offset since any
# solution that reproduces the offsets between images is valid
assert_allclose(array - np.mean(array),
self.array - np.mean(self.array),
atol=ATOL)
def test_coadd_background_matching(self, reproject_function):
# Test out the background matching
input_data = self._get_tiles(self._overlapping_views)
for array, wcs in input_data:
array += random.uniform(-3, 3)
# First check that without background matching the arrays don't match
array, footprint = reproject_and_coadd(
input_data,
self.wcs,
shape_out=self.array.shape,
combine_function='mean',
reproject_function=reproject_function)
assert not np.allclose(array, self.array, atol=ATOL)
# Now check that once the backgrounds are matched the values agree
array, footprint = reproject_and_coadd(
input_data,
self.wcs,
shape_out=self.array.shape,
combine_function='mean',
reproject_function=reproject_function,
match_background=True)
# The absolute values of the two arrays will be offset since any
# solution that reproduces the offsets between images is valid
assert_allclose(array - np.mean(array),
self.array - np.mean(self.array),
atol=ATOL)
def test_coadd_with_overlap(self, reproject_function):
# Here we make the input tiles overlapping. We can only check the
# mean, not the sum.
input_data = self._get_tiles(self._overlapping_views)
array, footprint = reproject_and_coadd(
input_data,
self.wcs,
shape_out=self.array.shape,
combine_function='mean',
reproject_function=reproject_function)
assert_allclose(array, self.array, atol=ATOL)
def test_coadd_solar_map():
# This is a test that exercises a lot of different parts of the mosaicking
# code. The idea is to take three solar images from different viewpoints
# and combine them into a single one. This uses weight maps that are not
# uniform and also include NaN values.
# The reference image was generated for sunpy 3.0.1 - it will not work with
# previous versions due to the bug that https://github.com/sunpy/sunpy/pull/5381
# fixes.
pytest.importorskip('sunpy', minversion='3.0.1')
from sunpy.map import Map, all_coordinates_from_map
# Load in three images from different viewpoints around the Sun
filenames = ['secchi_l0_a.fits', 'aia_171_level1.fits', 'secchi_l0_b.fits']
maps = [Map(os.path.join(DATA, f)) for f in filenames]
# Produce weight maps that are centered on the solar disk and go to zero at the edges
coordinates = tuple(map(all_coordinates_from_map, maps))
input_weights = [
coord.transform_to("heliocentric").z.value for coord in coordinates
]
input_weights = [(w / np.nanmax(w))**4 for w in input_weights]
shape_out = [90, 180]
wcs_out = WCS(Header.fromstring(HEADER_SOLAR_OUT, sep='\n'))
scales = [1 / 6, 1, 1 / 6]
input_data = tuple(
(a.data * scale, a.wcs) for (a, scale) in zip(maps, scales))
array, footprint = reproject_and_coadd(input_data,
wcs_out,
shape_out,
input_weights=input_weights,
reproject_function=reproject_interp,
match_background=True)
header_out = wcs_out.to_header()
# 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
# data files and remove this section.
for key in ('MJDREFF', 'MJDREFI', 'MJDREF', 'MJD-OBS'):
header_out.pop(key, None)
return array_footprint_to_hdulist(array, footprint, header_out)
def test_coadd_no_overlap(self, combine_function, reproject_function):
# Make sure that if all tiles are exactly non-overlapping, and
# we use 'sum' or 'mean', we get the exact input array back.
input_data = self._get_tiles(self._nonoverlapping_views)
input_data = [(self.array, self.wcs)]
array, footprint = reproject_and_coadd(
input_data,
self.wcs,
shape_out=self.array.shape,
combine_function=combine_function,
reproject_function=reproject_function)
assert_allclose(array, self.array, atol=ATOL)
assert_allclose(footprint, 1, atol=ATOL)
def test_coadd_with_weights(self, tmpdir, reproject_function, mode):
# Make sure that things work properly when specifying weights
array1 = self.array + 1
array2 = self.array - 1
weight1 = np.cumsum(np.ones_like(self.array), axis=1) - 1
weight2 = weight1[:, ::-1]
input_data = [(array1, self.wcs), (array2, self.wcs)]
if mode == 'arrays':
input_weights = [weight1, weight2]
elif mode == 'filenames':
filename1 = tmpdir.join('weight1.fits').strpath
filename2 = tmpdir.join('weight2.fits').strpath
fits.writeto(filename1, weight1)
fits.writeto(filename2, weight2)
input_weights = [filename1, filename2]
elif mode == 'hdus':
hdu1 = fits.ImageHDU(weight1)
hdu2 = fits.ImageHDU(weight2)
input_weights = [hdu1, hdu2]
array, footprint = reproject_and_coadd(
input_data,
self.wcs,
shape_out=self.array.shape,
combine_function='mean',
input_weights=input_weights,
reproject_function=reproject_function,
match_background=False)
expected = self.array + (2 * (weight1 / weight1.max()) - 1)
assert_allclose(array, expected, atol=ATOL)