def test_tileshape_adjustment_10():
sig_shape = (122, 455)
tile_shape = (8, 1)
base_shape = (2, 1)
excluded_coords = np.array([(121, ), (454, )])
excluded_pixels = sparse.COO(coords=excluded_coords,
shape=sig_shape,
data=True)
corr = CorrectionSet(excluded_pixels=excluded_pixels)
adjusted = corr.adjust_tileshape(tile_shape=tile_shape,
sig_shape=sig_shape,
base_shape=base_shape)
assert adjusted == (8, 3)
_validate(excluded_coords=excluded_coords,
adjusted=adjusted,
sig_shape=sig_shape)
def test_tileshape_adjustment_7():
sig_shape = (123, 456)
tile_shape = (14, 42)
base_shape = (7, 1)
excluded_coords = np.array([(14, ), (42, )])
excluded_pixels = sparse.COO(coords=excluded_coords,
shape=sig_shape,
data=True)
corr = CorrectionSet(excluded_pixels=excluded_pixels)
adjusted = corr.adjust_tileshape(tile_shape=tile_shape,
sig_shape=sig_shape,
base_shape=base_shape)
assert adjusted == (21, 41)
_validate(excluded_coords=excluded_coords,
adjusted=adjusted,
sig_shape=sig_shape)
def test_tileshape_adjustment_6_3():
sig_shape = (123, 456)
tile_shape = (1, 1)
base_shape = (1, 1)
excluded_coords = np.array([range(123), range(0, 246, 2)])
excluded_pixels = sparse.COO(coords=excluded_coords,
shape=sig_shape,
data=True)
corr = CorrectionSet(excluded_pixels=excluded_pixels)
adjusted = corr.adjust_tileshape(tile_shape=tile_shape,
sig_shape=sig_shape,
base_shape=base_shape)
assert adjusted == (123, 256)
_validate(excluded_coords=excluded_coords,
adjusted=adjusted,
sig_shape=sig_shape)
def test_tileshape_adjustment_6_1():
sig_shape = (123, 456)
tile_shape = (122, 1)
base_shape = (1, 1)
excluded_coords = np.array([range(123), np.zeros(123, dtype=int)])
excluded_pixels = sparse.COO(coords=excluded_coords,
shape=sig_shape,
data=True)
corr = CorrectionSet(excluded_pixels=excluded_pixels)
adjusted = corr.adjust_tileshape(tile_shape=tile_shape,
sig_shape=sig_shape,
base_shape=base_shape)
print(adjusted)
assert adjusted == (123, 2)
_validate(excluded_coords=excluded_coords,
adjusted=adjusted,
sig_shape=sig_shape)
def test_tileshape_adjustment_8():
sig_shape = (1014, 1024)
tile_shape = (1, 1)
base_shape = (1, 1)
# These magic numbers are "worst case" to produce collisions
# 2*3*4*5*6*7
excluded_coords = np.array([(720, 210, 306), (120, 210, 210)])
excluded_pixels = sparse.COO(coords=excluded_coords,
shape=sig_shape,
data=True)
corr = CorrectionSet(excluded_pixels=excluded_pixels)
adjusted = corr.adjust_tileshape(tile_shape=tile_shape,
sig_shape=sig_shape,
base_shape=base_shape)
print(adjusted)
assert adjusted != (1014, 1024)
_validate(excluded_coords=excluded_coords,
adjusted=adjusted,
sig_shape=sig_shape)
def test_tileshape_adjustment_fuzz():
for n in range(10):
sig_shape = (np.random.randint(1, 2**12), np.random.randint(1, 2**12))
print("Sig shape", sig_shape)
tile_shape = (1, 1)
base_shape = (1, 1)
size = max(1, max(sig_shape) // 10)
excluded_coords = np.vstack([
np.random.randint(0, sig_shape[0], size=size),
np.random.randint(0, sig_shape[1], size=size),
])
print("excluded_coords", excluded_coords.shape, excluded_coords)
excluded_pixels = sparse.COO(coords=excluded_coords,
shape=sig_shape,
data=True)
corr = CorrectionSet(excluded_pixels=excluded_pixels, allow_empty=True)
adjusted = corr.adjust_tileshape(tile_shape=tile_shape,
sig_shape=sig_shape,
base_shape=base_shape)
print(adjusted)
_validate(excluded_coords=excluded_coords,
adjusted=adjusted,
sig_shape=sig_shape)