def test_scatterer_io():
s = Sphere()
assert_read_matches_write(s)
s1 = Sphere(1.59, .5, [1, 1, 2])
s2 = Sphere(1.59, .5, [1, 3, 2])
sc = Spheres([s1, s2])
assert_read_matches_write(sc)
def test_dataarray_attr(self):
result = generate_fit_result()
samples = xr.DataArray([[1, 2, 3], [4, 5, 6]],
dims=['dim1', 'dim2'],
coords={
'dim1': ['left', 'right'],
'dim2': ['r', 'b', 'g']
})
result.add_attr({'samples': samples})
assert_read_matches_write(result)
def test_fit_superposition():
"""
Fit Mie superposition to a calculated hologram from two spheres
"""
# Make a test hologram
optics = Optics(wavelen=6.58e-07,
index=1.33,
polarization=[0.0, 1.0],
divergence=0,
spacing=None,
train=None,
mag=None,
pixel_scale=[2 * 2.302e-07, 2 * 2.302e-07])
s1 = Sphere(n=1.5891 + 1e-4j, r=.65e-6, center=(1.56e-05, 1.44e-05, 15e-6))
s2 = Sphere(n=1.5891 + 1e-4j, r=.65e-6, center=(3.42e-05, 3.17e-05, 10e-6))
sc = Spheres([s1, s2])
alpha = .629
theory = Mie(optics, 100)
holo = theory.calc_holo(sc, alpha)
# Now construct the model, and fit
parameters = [
Parameter(name='x0', guess=1.6e-5, limit=[0, 1e-4]),
Parameter('y0', 1.4e-5, [0, 1e-4]),
Parameter('z0', 15.5e-6, [0, 1e-4]),
Parameter('r0', .65e-6, [0.6e-6, 0.7e-6]),
Parameter('nr', 1.5891, [1, 2]),
Parameter('x1', 3.5e-5, [0, 1e-4]),
Parameter('y1', 3.2e-5, [0, 1e-4]),
Parameter('z1', 10.5e-6, [0, 1e-4]),
Parameter('r1', .65e-6, [0.6e-6, 0.7e-6])
]
def make_scatterer(x0, x1, y0, y1, z0, z1, r0, r1, nr):
s = Spheres([
Sphere(center=(x0, y0, z0), r=r0, n=nr + 1e-4j),
Sphere(center=(x1, y1, z1), r=r1, n=nr + 1e-4j)
])
return s
model = Model(parameters,
Mie,
make_scatterer=make_scatterer,
alpha=Parameter('alpha', .63, [.5, 0.8]))
result = fit(model, holo)
assert_obj_close(result.scatterer, sc)
assert_approx_equal(result.alpha, alpha, significant=4)
assert_equal(result.model, model)
assert_read_matches_write(result)
def test_subset_data_fit_result_is_saveable(self):
model = self._make_model()
holo = get_example_data('image0001')
np.random.seed(40)
fitter = NmpfitStrategy(npixels=100, maxiter=1)
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
# ignore not-converged warnings since we only do 2 iterations
fitted = fitter.fit(model, holo)
result = fitted # was fix_flat(fitted)
assert_read_matches_write(result)
def test_fit_mie_par_scatterer():
holo = normalize(get_example_data('image0001'))
s = Sphere(center=(Uniform(0, 1e-5, guess=.567e-5),
Uniform(0, 1e-5, .567e-5), Uniform(1e-5, 2e-5)),
r=Uniform(1e-8, 1e-5, 8.5e-7),
n=ComplexPrior(Uniform(1, 2, 1.59), 1e-4))
thry = Mie(False)
model = AlphaModel(s, theory=thry, alpha=Uniform(.1, 1, .6))
result = fix_flat(NmpfitStrategy().fit(model, holo))
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
# TODO: see if we can get this back to 3 sig figs correct alpha
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_mie_par_scatterer():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center = (par(guess=.567e-5, limit=[0,1e-5]),
par(.567e-5, (0, 1e-5)), par(15e-6, (1e-5, 2e-5))),
r = par(8.5e-7, (1e-8, 1e-5)),
n = ComplexParameter(par(1.59, (1,2)), 1e-4j))
thry = Mie(False)
model = Model(s, thry.calc_holo, alpha = par(.6, [.1,1]))
result = fit(model, holo)
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
# TODO: see if we can get this back to 3 sig figs correct alpha
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_mie_par_scatterer():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center = (par(guess=.567e-5, limit=[0,1e-5]),
par(.567e-5, (0, 1e-5)), par(15e-6, (1e-5, 2e-5))),
r = par(8.5e-7, (1e-8, 1e-5)),
n = ComplexParameter(par(1.59, (1,2)), 1e-4j))
thry = Mie(False)
model = Model(s, thry.calc_holo, alpha = par(.6, [.1,1]))
result = fit(model, holo)
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
# TODO: see if we can get this back to 3 sig figs correct alpha
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_superposition():
"""
Fit Mie superposition to a calculated hologram from two spheres
"""
# Make a test hologram
optics = Optics(wavelen=6.58e-07, index=1.33, polarization=[0.0, 1.0],
divergence=0, spacing=None, train=None, mag=None,
pixel_scale=[2*2.302e-07, 2*2.302e-07])
s1 = Sphere(n=1.5891+1e-4j, r = .65e-6,
center=(1.56e-05, 1.44e-05, 15e-6))
s2 = Sphere(n=1.5891+1e-4j, r = .65e-6,
center=(3.42e-05, 3.17e-05, 10e-6))
sc = Spheres([s1, s2])
alpha = .629
theory = Mie(optics, 100)
holo = theory.calc_holo(sc, alpha)
# Now construct the model, and fit
parameters = [Parameter(name = 'x0', guess = 1.6e-5, limit = [0, 1e-4]),
Parameter('y0', 1.4e-5, [0, 1e-4]),
Parameter('z0', 15.5e-6, [0, 1e-4]),
Parameter('r0', .65e-6, [0.6e-6, 0.7e-6]),
Parameter('nr', 1.5891, [1, 2]),
Parameter('x1', 3.5e-5, [0, 1e-4]),
Parameter('y1', 3.2e-5, [0, 1e-4]),
Parameter('z1', 10.5e-6, [0, 1e-4]),
Parameter('r1', .65e-6, [0.6e-6, 0.7e-6])]
def make_scatterer(x0, x1, y0, y1, z0, z1, r0, r1, nr):
s = Spheres([
Sphere(center = (x0, y0, z0), r=r0, n = nr+1e-4j),
Sphere(center = (x1, y1, z1), r=r1, n = nr+1e-4j)])
return s
model = Model(parameters, Mie, make_scatterer=make_scatterer, alpha =
Parameter('alpha', .63, [.5, 0.8]))
result = fit(model, holo)
assert_obj_close(result.scatterer, sc)
assert_approx_equal(result.alpha, alpha, significant=4)
assert_equal(result.model, model)
assert_read_matches_write(result)
def test_fit_random_subset():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center = (par(guess=.567e-5, limit=[0,1e-5]),
par(.567e-5, (0, 1e-5)), par(15e-6, (1e-5, 2e-5))),
r = par(8.5e-7, (1e-8, 1e-5)), n = ComplexParameter(par(1.59, (1,2)),1e-4))
model = Model(s, Mie(False).calc_holo, alpha = par(.6, [.1,1]))
np.random.seed(40)
result = fit(model, holo, random_subset=.1)
# TODO: this tolerance has to be rather large to pass, we should
# probably track down if this is a sign of a problem
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-2)
# TODO: figure out if it is a problem that alpha is frequently coming out
# wrong in the 3rd decimal place.
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_random_subset():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center = (par(guess=.567e-5, limit=[0,1e-5]),
par(.567e-5, (0, 1e-5)), par(15e-6, (1e-5, 2e-5))),
r = par(8.5e-7, (1e-8, 1e-5)), n = ComplexParameter(par(1.59, (1,2)),1e-4j))
model = Model(s, Mie.calc_holo, alpha = par(.6, [.1,1]))
np.random.seed(40)
result = fit(model, holo, random_subset=.1)
# we have to use a relatively loose tolerance here because the random
# selection occasionally causes the fit to be a bit worse
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
# TODO: figure out if it is a problem that alpha is frequently coming out
# wrong in the 3rd decimal place.
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_random_subset():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center = (par(guess=.567e-5, limit=[0,1e-5]),
par(.567e-5, (0, 1e-5)), par(15e-6, (1e-5, 2e-5))),
r = par(8.5e-7, (1e-8, 1e-5)), n = ComplexParameter(par(1.59, (1,2)),1e-4j))
model = Model(s, Mie.calc_holo, alpha = par(.6, [.1,1]))
np.random.seed(40)
result = fit(model, holo, use_random_fraction=.1)
# we have to use a relatively loose tolerance here because the random
# selection occasionally causes the fit to be a bit worse
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-2)
# TODO: figure out if it is a problem that alpha is frequently coming out
# wrong in the 3rd decimal place.
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=2)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_random_subset():
holo = normalize(get_example_data('image0001'))
s = Sphere(center=(Uniform(0, 1e-5, guess=.567e-5),
Uniform(0, 1e-5, .567e-5), Uniform(1e-5, 2e-5)),
r=Uniform(1e-8, 1e-5, 8.5e-7),
n=ComplexPrior(Uniform(1, 2, 1.59), 1e-4))
model = AlphaModel(s, theory=Mie(False), alpha=Uniform(.1, 1, .6))
np.random.seed(40)
result = fix_flat(NmpfitStrategy(npixels=1000).fit(model, holo))
# TODO: this tolerance has to be rather large to pass, we should
# probably track down if this is a sign of a problem
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-2)
# TODO: figure out if it is a problem that alpha is frequently coming out
# wrong in the 3rd decimal place.
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_random_subset():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center=(par(guess=.567e-5, limit=[0, 1e-5]),
par(.567e-5, (0, 1e-5)), par(15e-6, (1e-5, 2e-5))),
r=par(8.5e-7, (1e-8, 1e-5)),
n=ComplexParameter(par(1.59, (1, 2)), 1e-4))
model = Model(s, Mie(False).calc_holo, alpha=par(.6, [.1, 1]))
np.random.seed(40)
result = fit(model, holo, random_subset=.1)
# TODO: this tolerance has to be rather large to pass, we should
# probably track down if this is a sign of a problem
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-2)
# TODO: figure out if it is a problem that alpha is frequently coming out
# wrong in the 3rd decimal place.
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_hologram_io():
holo = normalize(get_example_data('image0001.yaml'))
assert_read_matches_write(holo)
def test_io():
model = ExactModel(Sphere(1), calc_holo)
assert_read_matches_write(model)
model = ExactModel(Sphere(1), calc_holo, theory=Mie(False))
assert_read_matches_write(model)
def test_io():
model = Model(Sphere(par(1)), Mie.calc_holo)
assert_read_matches_write(model)
model = Model(Sphere(par(1)), Mie(False).calc_holo)
assert_read_matches_write(model)
def test_marray_io():
d = Marray(np.random.random((10, 10)))
assert_read_matches_write(d)
def test_hologram_io(self):
assert_read_matches_write(normalize(self.holo))
def test_hologram_io():
holo = normalize(get_example_data('image0001.yaml'))
assert_read_matches_write(holo)
def test_io():
model = Model(Sphere(par(1)), calc_holo)
assert_read_matches_write(model)
model = Model(Sphere(par(1)), calc_holo, theory=Mie(False))
assert_read_matches_write(model)
def test_marray_io():
d = Marray(np.random.random((10, 10)))
assert_read_matches_write(d)
def test_io():
model = Model(Sphere(par(1)), Mie.calc_holo)
assert_read_matches_write(model)
model = Model(Sphere(par(1)), Mie(False).calc_holo)
assert_read_matches_write(model)
def test_base_io(self):
result = FitResult(DATA, MODEL, CmaStrategy(), 10,
{'intervals': INTERVALS})
assert_read_matches_write(result)
def test_saved_calculations(self):
result = generate_fit_result()
result.best_fit
result.max_lnprob
assert_read_matches_write(result)
