def test_image_loading_fitshdu(self, disk_cache):
n = 10
d = np.ones((10, 10))
l = [fits.PrimaryHDU(d) for i in range(n)]
logs = []
lh = log_to_list(logger, logs, full_record=True)
comb = ImCombiner(use_disk_cache=disk_cache)
comb._load_images(l)
# check if the logging is properly being emitted.
log = [
i for i in logs if i.msg == 'The images to combine are not '
'FrameData. Some features may be disabled.'
]
assert_equal(len(log), 1)
assert_equal(log[0].levelname, 'WARNING')
logger.removeHandler(lh)
assert_equal(len(comb._images), n)
assert_is_none(comb._buffer)
for i, v in enumerate(comb._images):
assert_is_instance(v, FrameData)
if disk_cache:
assert_true(v._memmapping)
comb._clear()
# must start empty
assert_equal(len(comb._images), 0)
assert_is_none(comb._buffer)
def test_extract_header_nowcs(self):
header = fits.Header.fromstring(_base_header, sep='\n')
h, wcs = extract_header_wcs(header)
assert_is_none(wcs)
assert_is_instance(h, fits.Header)
assert_equal(h, header)
assert_false(h is header)
def test_image_loading_fitsfile(self, tmpdir, disk_cache):
tmp = tmpdir.strpath
n = 10
d = np.ones((10, 10))
l = [os.path.join(tmp, f'fits_test{i}') for i in range(n)]
for f in l:
fits.PrimaryHDU(d).writeto(f)
logs = []
lh = log_to_list(logger, logs, full_record=True)
comb = ImCombiner(use_disk_cache=disk_cache)
comb._load_images(l)
# check if the logging is properly being emitted.
log = [
i for i in logs if i.msg == 'The images to combine are not '
'FrameData. Some features may be disabled.'
]
assert_equal(len(log), 1)
assert_equal(log[0].levelname, 'WARNING')
assert_equal(len(comb._images), n)
assert_is_none(comb._buffer)
for i, v in enumerate(comb._images):
assert_is_instance(v, FrameData)
if disk_cache:
assert_true(v._memmapping)
comb._clear()
# must start empty
assert_equal(len(comb._images), 0)
assert_is_none(comb._buffer)
def test_extract_invalid_wcs_header(self):
# It should no raise, just return empty wcs
# No header change too
header = fits.Header.fromstring(_base_header + _invalid_wcs, sep='\n')
del header['']
h, wcs = extract_header_wcs(header)
assert_is_none(wcs)
assert_is_instance(h, fits.Header)
assert_equal(h, header)
assert_false(h is header)
def test_image_loading_framedata(self, tmpdir, disk_cache):
tmp = tmpdir.strpath
n = 10
d = np.ones((10, 10))
l = [
FrameData(d,
unit='adu',
uncertainty=d,
cache_folder=tmp,
cache_filename=f'test{i}') for i in range(n)
]
comb = ImCombiner(use_disk_cache=disk_cache)
# must start empty
assert_equal(len(comb._images), 0)
assert_is_none(comb._buffer)
comb._load_images(l)
assert_equal(len(comb._images), n)
assert_is_none(comb._buffer)
for i, v in enumerate(comb._images):
fil = os.path.join(tmp, f'test{i}')
assert_is_instance(v, FrameData)
if disk_cache:
assert_true(v._memmapping)
# assert_path_exists(fil+'_copy_copy.data')
# assert_path_exists(fil+'_copy_copy.unct')
# assert_path_exists(fil+'_copy_copy.mask')
comb._clear()
# must start empty
assert_equal(len(comb._images), 0)
assert_is_none(comb._buffer)
# ensure tmp files cleaned
if disk_cache:
for i in range(n):
fil = os.path.join(tmp, f'test{i}')
assert_path_not_exists(fil + '_copy_copy.data')
assert_path_not_exists(fil + '_copy_copy.unct')
assert_path_not_exists(fil + '_copy_copy.mask')
def test_qfloat_unit_property_none():
# Check None and dimensionless_unscaled
c = DummyClass(None)
assert_is_none(c._unit)
assert_equal(c.unit, units.dimensionless_unscaled)
def test_chunk_yielder_f32(self):
# using float32, the number of chunks are almost halved
n = 100
d = np.random.random((100, 100)).astype(np.float64)
l = [FrameData(d, unit='adu') for i in range(n)]
# data size = 4 000 000 = 4 bytes * 100 * 100 * 100
# mask size = 1 000 000 = 1 bytes * 100 * 100 * 100
# total size = 5 000 000
comb = ImCombiner(max_memory=1e6, dtype=np.float32)
comb._load_images(l)
logs = []
lh = log_to_list(logger, logs, False)
level = logger.getEffectiveLevel()
logger.setLevel('DEBUG')
# for median, tot_size=5*4.5=22.5
# xstep = 4, so n_chuks=25
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='median'):
i += 1
for k in chunk:
assert_equal(k.shape, (4, 100))
assert_almost_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 25)
assert_in('Splitting the images into 25 chunks.', logs)
logs.clear()
# for mean and sum, tot_size=5*3=15
# xstep = 6, so n_chunks=16+1
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='mean'):
i += 1
for k in chunk:
assert_in(k.shape, [(6, 100), (4, 100)])
assert_almost_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 17)
assert_in('Splitting the images into 17 chunks.', logs)
logs.clear()
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='sum'):
i += 1
for k in chunk:
assert_in(k.shape, [(6, 100), (4, 100)])
assert_almost_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 17)
assert_in('Splitting the images into 17 chunks.', logs)
logs.clear()
# this should not split into chunks
comb = ImCombiner(max_memory=1e8, dtype=np.float32)
comb._load_images(l)
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='median'):
i += 1
for k in chunk:
assert_equal(k.shape, (100, 100))
assert_almost_equal(k, d)
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 1)
assert_equal(len(logs), 0)
logs.clear()
# this should split in 300 chunks!
# total_size = 4.5*5e6=22.5e6 = 225 chunks
# x_step = 1
# y_step = 45
comb = ImCombiner(max_memory=1e5, dtype=np.float32)
comb._load_images(l)
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='median'):
i += 1
for k in chunk:
assert_in(k.shape, ((1, 45), (1, 10)))
assert_almost_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 300)
assert_in('Splitting the images into 300 chunks.', logs)
logs.clear()
logger.setLevel(level)
logger.removeHandler(lh)
def test_chunk_yielder_f64(self):
n = 100
d = np.random.random((100, 100)).astype(np.float64)
l = [FrameData(d, unit='adu') for i in range(n)]
# data size = 8 000 000 = 8 bytes * 100 * 100 * 100
# mask size = 1 000 000 = 1 bytes * 100 * 100 * 100
# total size = 9 000 000
comb = ImCombiner(max_memory=1e6, dtype=np.float64)
comb._load_images(l)
logs = []
lh = log_to_list(logger, logs, False)
level = logger.getEffectiveLevel()
logger.setLevel('DEBUG')
# for median, tot_size=9*4.5=41
# xstep = 2, so n_chuks=50
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='median'):
i += 1
for k in chunk:
assert_equal(k.shape, (2, 100))
assert_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 50)
assert_in('Splitting the images into 50 chunks.', logs)
logs.clear()
# for mean and sum, tot_size=9*3=27
# xstep = 3, so n_chunks=33+1
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='mean'):
i += 1
for k in chunk:
assert_in(k.shape, [(3, 100), (1, 100)])
assert_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 34)
assert_in('Splitting the images into 34 chunks.', logs)
logs.clear()
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='sum'):
i += 1
for k in chunk:
assert_in(k.shape, [(3, 100), (1, 100)])
assert_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 34)
assert_in('Splitting the images into 34 chunks.', logs)
logs.clear()
# this should not split into chunks
comb = ImCombiner(max_memory=1e8)
comb._load_images(l)
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='median'):
i += 1
for k in chunk:
assert_equal(k.shape, (100, 100))
assert_equal(k, d)
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 1)
assert_equal(len(logs), 0)
logs.clear()
# this should split in 400 chunks!
comb = ImCombiner(max_memory=1e5)
comb._load_images(l)
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='median'):
i += 1
for k in chunk:
assert_equal(k.shape, (1, 25))
assert_equal(k, d[slc])
assert_is_none(unct)
assert_is_instance(k, np.ma.MaskedArray)
assert_equal(i, 400)
assert_in('Splitting the images into 400 chunks.', logs)
logs.clear()
logger.setLevel(level)
logger.removeHandler(lh)
def test_assert_is_none(self):
a = None
assert_is_none(a)
with pytest.raises(AssertionError):
assert_is_none(1)
def test_no_uncertainty(self):
ccd = self.ccd
ccd.uncertainty = None
f = _extract_ccddata(ccd)
assert_is_none(f['uncertainty'])
def test_no_mask(self):
ccd = self.ccd
ccd.mask = None
f = _extract_ccddata(ccd)
assert_is_none(f['mask'])