def test_fg_creation_custom_hdu(self, tmpdir, hdu):
tmpdir, flist = tmpdir
fg = FitsFileGroup(location=str(tmpdir / 'custom_hdu'), ext=hdu)
assert_is_instance(fg, FitsFileGroup)
assert_equal(len(fg), 10)
assert_equal(sorted(fg.files), sorted(flist['custom_hdu']))
for k in ('object', 'exptime', 'observer', 'filter'):
assert_in(k, fg.summary.colnames)
for i in fg.summary:
assert_equal(i['object'], 'bias')
def test_simbad_catalog_get_simbad(self):
# must be free of fluxdata
s = self.cat.get_simbad()
assert_is_instance(s, Simbad.__class__)
for i in s.get_votable_fields():
assert_not_in('fluxdata', i)
# must contain flux data
s = self.cat.get_simbad(band='V')
assert_is_instance(s, Simbad.__class__)
assert_in('fluxdata(V)', s.get_votable_fields())
def test_extract_header_nosip(self):
header = fits.Header.fromstring(_base_header + _wcs_no_sip, sep='\n')
h, wcs = extract_header_wcs(header)
assert_is_instance(wcs, WCS)
assert_equal(wcs.wcs.ctype[0], 'RA---TAN')
assert_equal(wcs.wcs.ctype[1], 'DEC--TAN')
assert_is_instance(h, fits.Header)
for i in _comon_wcs_keys:
assert_not_in(f'{i}1', h.keys())
assert_not_in(f'{i}2', h.keys())
assert_in('DATE-OBS', h.keys())
assert_false(h is header)
assert_not_equal(h, header)
def test_assert_in(self):
assert_in(1, [1, 2, 3, 4, 5])
assert_in('a', 'abc')
with pytest.raises(AssertionError):
assert_in('d', 'abc')
with pytest.raises(AssertionError):
assert_in(1, [2, 3, 4])
def test_logger_remove_handler():
mylog = logger.getChild('testing')
msg = 'Some error happend here.'
logs = []
lh = log_to_list(mylog, logs)
mylog.setLevel('DEBUG')
mylog.error(msg)
assert_is_instance(lh, ListHandler)
assert_in(lh, mylog.handlers)
mylog.removeHandler(lh)
assert_not_in(lh, mylog.handlers)
assert_equal(logs[0], msg)
assert_equal(lh.log_list[0], msg)
assert_equal(lh.log_list, logs)
def test_chunk_yielder_uncertainty(self):
n = 100
d = np.random.random((100, 100)).astype(np.float64)
u = np.random.random((100, 100)).astype(np.float64)
l = [FrameData(d, uncertainty=u, unit='adu') for i in range(n)]
# simple sum with uncertainties
comb = ImCombiner(max_memory=2e6, dtype=np.float64)
comb._load_images(l)
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='sum'):
i += 1
for k, un in zip(chunk, unct):
assert_in(k.shape, ((7, 100), (2, 100)))
assert_almost_equal(k, d[slc])
assert_almost_equal(un, u[slc])
assert_is_instance(un, np.ma.MaskedArray)
assert_equal(i, 15)
# if a single uncertainty is empty, disable it
logs = []
lh = log_to_list(logger, logs, False)
level = logger.getEffectiveLevel()
logger.setLevel('DEBUG')
l[5].uncertainty = None
comb = ImCombiner(max_memory=2e6, dtype=np.float64)
comb._load_images(l)
i = 0
for chunk, unct, slc in comb._chunk_yielder(method='sum'):
i += 1
for k in chunk:
assert_in(k.shape, ((7, 100), (2, 100)))
assert_almost_equal(k, d[slc])
assert_equal(unct, None)
assert_equal(i, 15)
assert_in(
'One or more frames have empty uncertainty. '
'Some features are disabled.', logs)
logs.clear()
logger.setLevel(level)
logger.removeHandler(lh)
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_framemeta_get(self):
a = FrameMeta({'A': 1, 'b': 'test', 'TesT': 'AaA'})
res = a.get('test')
assert_equal(res, 'AaA')
assert_in('test', a)
assert_in('TEST', a)
def test_raise_north_angle():
with pytest.raises(ValueError) as exc:
wcs_from_coords(0, 0, 0, 0, 0, 'not a direction')
assert_in('invalid value for north', str(exc.value))
def test_invalid_op():
frame1 = FrameData(np.zeros((10, 10)), unit='')
frame2 = FrameData(np.zeros((10, 10)), unit='')
with pytest.raises(ValueError) as exc:
imarith(frame1, frame2, 'not an op')
assert_in('not supported', str(exc.value))
