def test_basics():
"Check the basic functionality of the profiling"
r = np.linspace(2, 6, 300)
sigma = 0.1
model = dd_gauss
y = model(r, mean=3, width=0.2) + whitegaussnoise(r, sigma, seed=1)
profuq = profile_analysis(model, y, r, samples=3, noiselvl=sigma)
x, pdf = profuq['mean'].pardist()
mean_mean = x[np.argmax(pdf)]
x, pdf = profuq['width'].pardist()
width_mean = x[np.argmax(pdf)]
assert np.allclose([mean_mean, width_mean], [3, 0.2], rtol=1e-2)
def test_specific_parameters():
"Check that parameters can be specified via keywords"
r = np.linspace(2, 6, 300)
sigma = 0.1
model = deepcopy(dd_gauss)
y = model(r, mean=3, width=0.2) + whitegaussnoise(r, sigma, seed=1)
profuq = profile_analysis(model,
y,
r,
samples=3,
noiselvl=sigma,
parameters='mean')
assert 'mean' in profuq.keys() and not 'width' in profuq.keys()
def test_types():
# ======================================================================
"Check that the correct data types are returned"
r = np.linspace(2, 6, 300)
sigma = 0.1
model = deepcopy(dd_gauss)
model.addlinear('scale', par0=1)
y = model(r, mean=3, width=0.2, scale=1) + whitegaussnoise(
r, sigma, seed=1)
profuq = profile_analysis(model, y, r, samples=3, noiselvl=sigma)
assert isinstance(profuq['mean'],
UQResult) and profuq['scale'] is None and isinstance(
profuq, dict)
def test_grids():
"Check that grids can be specified"
r = np.linspace(2, 6, 300)
sigma = 0.1
model = dd_gauss
y = model(r, mean=3, width=0.2) + whitegaussnoise(r, sigma, seed=1)
grid = {'mean': np.linspace(3, 5, 3)}
profuq = profile_analysis(model,
y,
r,
parameters='mean',
grids=grid,
noiselvl=sigma)
x = profuq['mean'].profile['x']
assert np.allclose(x, grid['mean'])
def test_globalmodel():
"Check the profiling works with multiple datasets"
r = np.linspace(2, 6, 300)
sigma = 0.1
modelA = deepcopy(dd_gauss)
modelB = deepcopy(dd_gauss)
model = merge(modelA, modelB)
model = link(model,
mean=['mean_1', 'mean_2'],
width=['width_1', 'width_2'])
y = model(r, r, mean=3, width=0.2, scale_1=1, scale_2=1)
y[0] += whitegaussnoise(r, sigma, seed=1)
y[1] += whitegaussnoise(r, sigma, seed=1)
profuq = profile_analysis(model, y, r, r, samples=3, noiselvl=sigma)
x, pdf = profuq['mean'].pardist()
mean_mean = x[np.argmax(pdf)]
x, pdf = profuq['width'].pardist()
width_mean = x[np.argmax(pdf)]
assert np.allclose([mean_mean, width_mean], [3, 0.2], rtol=1e-2)