def test_smoke_datatypes(self):
self.check_skip()
SHAPE = (300, 300)
# simple "smoke" test to see if numba explodes
dummy_image = np.random.randint(0, 100, SHAPE).astype(np.uint8)
tp.locate(dummy_image, 5, engine=self.engine)
tp.locate(invert_image(dummy_image), 5, engine=self.engine)
# Check float types
dummy_image = np.random.rand(*SHAPE)
tp.locate(dummy_image, 5, engine=self.engine)
tp.locate(invert_image(dummy_image), 5, engine=self.engine)
def test_smoke_datatypes(self):
self.check_skip()
SHAPE = (300, 300)
# simple "smoke" test to see if numba explodes
dummy_image = np.random.randint(0, 100, SHAPE).astype(np.uint8)
tp.locate(dummy_image, 5, engine=self.engine)
tp.locate(invert_image(dummy_image), 5, engine=self.engine)
# Check float types
dummy_image = np.random.rand(*SHAPE)
tp.locate(dummy_image, 5, engine=self.engine)
tp.locate(invert_image(dummy_image), 5, engine=self.engine)
def setUpClass(cls):
super(TestReproducibility, cls).setUpClass()
# generate a new file
video = pims.ImageSequence(
os.path.join(path, 'video', 'image_sequence'))
actual = tp.batch(invert_image(video), diameter=9, minmass=240)
actual = tp.link_df(actual, search_range=5, memory=2)
actual.to_csv(reproduce_fn)
def test_oldmass_invert(self):
old_minmass = 2800000
im = draw_spots(self.shape, self.pos, self.size, bitdepth=12,
noise_level=500)
im = (im.max() - im + 10000)
new_minmass = self.minmass_v02_to_v04(im, old_minmass, invert=True)
f = tp.locate(invert_image(im), self.tp_diameter, minmass=new_minmass)
assert len(f) == self.N
def test_oldmass_invert(self):
old_minmass = 2800000
im = draw_spots(self.shape, self.pos, self.size, bitdepth=12,
noise_level=500)
im = (im.max() - im + 10000)
new_minmass = tp.minmass_version_change(im, old_minmass, invert=True,
smoothing_size=self.tp_diameter)
f = tp.locate(invert_image(im), self.tp_diameter, minmass=new_minmass)
assert len(f) == self.N
def setUpClass(cls):
super(TestReproducibility, cls).setUpClass()
npz = np.load(reproduce_fn)
cls.expected_find_raw = npz['arr_0']
cls.expected_find_bp = npz['arr_1']
cls.expected_refine = npz['arr_2']
cls.expected_locate = npz['arr_3']
cls.coords_link = npz['arr_4']
cls.expected_link = npz['arr_5']
cls.expected_link_memory = npz['arr_6']
cls.expected_characterize = npz['arr_7']
cls.v = TrackpyImageSequence(os.path.join(path, 'video',
'image_sequence', '*.png'))
cls.v0_inverted = invert_image(cls.v[0])
def setUpClass(cls):
super(TestReproducibility, cls).setUpClass()
npz = np.load(reproduce_fn)
cls.expected_find_raw = npz['arr_0']
cls.expected_find_bp = npz['arr_1']
cls.expected_refine = npz['arr_2']
cls.expected_locate = npz['arr_3']
cls.coords_link = npz['arr_4']
cls.expected_link = npz['arr_5']
cls.expected_link_memory = npz['arr_6']
cls.expected_characterize = npz['arr_7']
cls.v = pims.ImageSequence(os.path.join(path, 'video',
'image_sequence', '*.png'))
cls.v0_inverted = invert_image(cls.v[0])
