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)