def test_get_peak_parameters_no_peak(self, repeat):
utils.np.random.seed(1234 * repeat)
img, p, cutoff, s_noise = utils.create_test_image(peak=False)
with pytest.raises(utils.LargeNoiseError):
utils.get_peak_parameters(img)
def test_get_peak_parameters_images(self, file, params):
img = imageio.imread(file, as_gray=True)
h, a, x0, y0, sx, sy, theta, cutoff = params
p_fit = utils.get_peak_parameters(img)
h_f, a_f, x0_f, y0_f, sx_f, sy_f, theta_f, cutoff_f = p_fit
s_noise = 1 # representative for the noise of most images
rel = max(s_noise / cutoff, 1e-2)
assert x0_f == approx(x0, rel=1e-3)
assert y0_f == approx(y0, rel=1e-3)
assert h_f == approx(h, rel=rel, abs=2 * s_noise) # rather inaccurate
assert a_f == approx(a, rel=3 * a / cutoff * rel,
abs=2 * s_noise) # inaccurate if a >> cutoff
assert sx_f == approx(sx, rel=0.1)
assert sy_f == approx(sy, rel=0.1)
if sx == approx(sy, rel=0.05):
assert theta_f == approx(theta, abs=1)
elif sx == approx(sy, rel=0.1):
assert theta_f == approx(theta, abs=0.2)
else:
assert theta_f == approx(theta, abs=0.05)
assert cutoff_f == approx(cutoff, rel=rel,
abs=3 * s_noise) # inaccurate due to filter
def test_get_peak_parameters(self, repeat):
utils.np.random.seed(1234 * repeat)
# ensure strong enough signal without large eccentricity
h, a, x0, y0, sx, sy, theta, img_gauss, cutoff, s_noise = [0] * 10
while (cutoff - h) <= 4 * s_noise or eccentricity(sx, sy) > 0.95:
img_gauss, p, cutoff, s_noise = utils.create_test_image()
h, a, x0, y0, sx, sy, theta = p
p_fit = utils.get_peak_parameters(img_gauss)
h_f, a_f, x0_f, y0_f, sx_f, sy_f, theta_f, cutoff_f = p_fit
rel = max(s_noise / cutoff, 1e-2)
assert x0_f == approx(x0, rel=1e-3)
assert y0_f == approx(y0, rel=1e-3)
assert h_f == approx(h, rel=rel, abs=2 * s_noise) # rather inaccurate
assert a_f == approx(a, rel=3 * a / cutoff * rel,
abs=2 * s_noise) # inaccurate if a >> cutoff
assert sx_f == approx(sx, rel=0.1)
assert sy_f == approx(sy, rel=0.1)
if sx == approx(sy, rel=0.05):
assert theta_f == approx(theta, abs=1)
elif sx == approx(sy, rel=0.1):
assert theta_f == approx(theta, abs=0.2)
else:
assert theta_f == approx(theta, abs=0.05)
assert cutoff_f == approx(cutoff, rel=rel,
abs=3 * s_noise) # inaccurate due to filter
def __call__(self, data):
if self.key:
img = data.value.pop(self.key)
else:
img = data.value
if img is None:
return data
img = img.astype(np.float64)
try:
p_fit = utils.get_peak_parameters(img)
except utils.FittingError:
p_fit = None
if p_fit:
self.log_frames(data.timestamp, img, p_fit)
h, a, x0, y0, sx, sy, theta, cutoff = p_fit
d = {
"beam_on": True,
"mu_x": x0,
"mu_y": y0,
"sigma_x": sx,
"sigma_y": sy,
"rotation": theta,
"z_offset": h,
"amplitude": a,
"cutoff": cutoff
}
else:
d = {"beam_on": False}
if self.key:
data.value.update(d)
else:
data.value = d
return data
def test_get_peak_parameters_images_no_beam_raw(self, file):
img = np.load(file)
with pytest.raises((utils.LargeNoiseError, utils.SmallRegionError)):
utils.get_peak_parameters(img)
def test_get_peak_parameters_images_beam_raw(self, file):
img = np.load(file)
p_fit = utils.get_peak_parameters(img)
assert p_fit
def test_get_peak_parameters_images_no_beam(self, file):
img = imageio.imread(file, as_gray=True)
with pytest.raises((utils.LargeNoiseError, utils.SmallRegionError)):
utils.get_peak_parameters(img)