def _test(self):
# pull proper data and coefs
coefs = self.coefs[modeltype]
data = self.data[modeltype]
# add noise
if snr:
if fittype == "ls":
amp = coefs[0]
noise_data = (amp / snr) * RNG.normal(size=data.shape)
noisy_data = data + noise_data
# this is a heuristic and should be replaced with a better
# reasone parameter
rtol = 1 / snr
elif fittype == "mle":
noisy_data = RNG.poisson(data)
rtol = 1 / coefs[0]
else:
noisy_data = data
rtol = 1e-7
# build object to test
test_g = Gauss2D(noisy_data)
# speficify guesses
if not guess:
guess_coefs = None
else:
guess_coefs = coefs.copy()
# grab optimized coefs
test_coefs = test_g.optimize_params(guess_params=guess_coefs,
modeltype=modeltype,
fittype=fittype)
# do the actual test
assert_allclose(test_coefs, coefs, rtol=rtol)
def _test(self):
area = self.gauss.area()
model = self.gauss.fit_model
numeric_area = model.sum()
assert_allclose(area, numeric_area, err_msg="Failed with GT params")
assert_allclose(
Gauss2D(model).area(guess_params=self.gauss._popt),
numeric_area,
err_msg="Failed when optimizing params",
)
def setUp(self):
# make x, y ranges
self.x = np.arange(64)
self.y = np.arange(128)
# make meshgrid
self.xx, self.yy = np.meshgrid(self.x, self.y)
self.noisy_data = tif.imread(
os.path.join(os.path.dirname(__file__), "..", "fixtures",
"noisy_data.tif"))
self.raw_data = tif.imread(
os.path.join(os.path.dirname(__file__), "..", "fixtures",
"raw_data.tif"))
self.myg = Gauss2D(self.noisy_data)
def setUp(self):
"""Basic set up"""
data = np.zeros((2001, 2001))
self.gauss = Gauss2D(data)
