def assert_allclose_structured(x, y):
"""Assert that two structured arrays are close.
Compares floats relatively and everything else exactly.
"""
assert x.dtype == y.dtype
for name in x.dtype.names:
if np.issubdtype(x.dtype[name], float):
assert_allclose(x[name], y[name])
else:
assert_equal(x[name], y[name])
# If we have a FITS reader, read in the necessary test images
if not NO_FITS:
image_data = getdata(IMAGE_FNAME)
image_refback = getdata(BACKIMAGE_FNAME)
# -----------------------------------------------------------------------------
# Test versus Source Extractor results
@pytest.mark.skipif(NO_FITS, reason="no FITS reader")
def test_vs_sextractor():
"""Test behavior of sep versus sextractor.
Note: we turn deblending off for this test. This is because the
deblending algorithm uses a random number generator. Since the sequence
of random numbers is not the same between sextractor and sep or between
different platforms, object member pixels (and even the number of objects)
can differ when deblending is on.
# Try to import any FITS reader
try:
from fitsio import read as getdata
HAVE_FITS = True
NEED_BYTESWAP = False
except:
try:
from astropy.io.fits import getdata
HAVE_FITS = True
except:
HAVE_FITS = False
CONDENSED = True
if HAVE_FITS:
rawdata = getdata(join("data", "image.fits")) # original is 256 x 256
data = np.tile(rawdata, (4, 4))
print("test image shape:", data.shape)
print("test image dtype:", data.dtype)
t0 = time.time()
bkg = sep.Background(data) # estimate background
t1 = time.time()
print("measure background: {0:6.2f} ms".format((t1-t0) * 1.e3))
t0 = time.time()
bkg.subfrom(data) # subtract it
t1 = time.time()
print("subtract background: {0:6.2f} ms".format((t1-t0) * 1.e3))
# Try to import any FITS reader
try:
from fitsio import read as getdata
HAVE_FITS = True
NEED_BYTESWAP = False
except ImportError:
try:
from astropy.io.fits import getdata
HAVE_FITS = True
except ImportError:
HAVE_FITS = False
CONDENSED = True
if HAVE_FITS:
rawdata = getdata(join("data", "image.fits")) # original is 256 x 256
data = np.tile(rawdata, (4, 4))
print("test image shape:", data.shape)
print("test image dtype:", data.dtype)
t0 = time.time()
bkg = sep.Background(data) # estimate background
t1 = time.time()
print("measure background: {0:6.2f} ms".format((t1 - t0) * 1.e3))
t0 = time.time()
bkg.subfrom(data) # subtract it
t1 = time.time()
print("subtract background: {0:6.2f} ms".format((t1 - t0) * 1.e3))
