def test_gaussian():
p, data = generate_gaussian()
p0 = guess_gaussian(data)
p1 = fit_gaussian(data, p0)
print('p:', p)
print('p0:', p0)
print('p1:', p1)
def test_gaussian(self):
from pycoldatom.functions.fitclassical import generate_gaussian, fit_gaussian, guess_gaussian, fit_gaussian_result
p, data = generate_gaussian(p0=[1.0, 40, 60, 15, 10, 0.1])
# p, data = generate_gaussian(p0=[1.0, 4, 6, 5, 3, 0.1], size=(10, 10))
data = add_noise(data)
# mask = np.random.random(data.shape)<0.1
mask = np.zeros_like(data)
mask[30:50, 50:70] = 1
data = np.ma.array(data, mask=mask)
p0 = guess_gaussian(data)
p1 = fit_gaussian(data)
# print('p:', p)
# print('p0:', p0)
# print('p1:', p1)
result, err = fit_gaussian_result(data)
# print(result)
np.testing.assert_allclose(p, p1, rtol=1e-2)
def test_gaussian(self):
from pycoldatom.functions.fitclassical import generate_gaussian, fit_gaussian, guess_gaussian, fit_gaussian_result
p, data = generate_gaussian(p0=[1.0, 40, 60, 15, 10, 0.1])
# p, data = generate_gaussian(p0=[1.0, 4, 6, 5, 3, 0.1], size=(10, 10))
data = add_noise(data)
# mask = np.random.random(data.shape)<0.1
mask = np.zeros_like(data)
mask[30:50, 50:70] = 1
data = np.ma.array(data, mask=mask)
p0 = guess_gaussian(data)
p1 = fit_gaussian(data)
# print('p:', p)
# print('p0:', p0)
# print('p1:', p1)
result, err = fit_gaussian_result(data)
# print(result)
np.testing.assert_allclose(p, p1, rtol=1e-2)