def test_pullingoutguess():
g = 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(g, Mie.calc_holo)
s = Sphere(center=[.567e-5, .567e-5, 15e-6], n=1.59 + 1e-4j, r=8.5e-7)
assert_equal(s.n, model.scatterer.guess.n)
assert_equal(s.r, model.scatterer.guess.r)
assert_equal(s.center, model.scatterer.guess.center)
g = 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=1.59 + 1e-4j)
model = Model(g, Mie.calc_holo)
s = Sphere(center=[.567e-5, .567e-5, 15e-6], n=1.59 + 1e-4j, r=8.5e-7)
assert_equal(s.n, model.scatterer.guess.n)
assert_equal(s.r, model.scatterer.guess.r)
assert_equal(s.center, model.scatterer.guess.center)
def test_next_model():
exampleresult = FitResult(parameters={
'center[1]': 31.367170884695756, 'r': 0.6465280831465722,
'center[0]': 32.24150087110443,
'center[2]': 35.1651561654966,
'alpha': 0.7176299231169572,
'n': 1.580122175314896},
scatterer=Sphere(n=1.580122175314896, r=0.6465280831465722,
center=[32.24150087110443, 31.367170884695756, 35.1651561654966]),
chisq=0.0001810513851216454, rsq=0.9727020197282801,
converged=True, time=5.179728031158447,
model=Model(scatterer=ParameterizedObject(obj=
Sphere(n=Parameter(guess=1.59, limit=[1.4, 1.7], name='n'),
r=Parameter(guess=0.65, limit=[0.6, 0.7], name='r'),
center=[Parameter(guess=32.110424836601304, limit=[2, 40], name='center[0]'),
Parameter(guess=31.56683986928105, limit=[4, 40], name='center[1]'),
Parameter(guess=33, limit=[5, 45], name='center[2]')])),
theory=Mie.calc_holo, alpha=Parameter(guess=0.6, limit=[0.1, 1], name='alpha'),
constraints=[]), minimizer = None, minimization_details = None)
gold = Model(scatterer=ParameterizedObject(obj=Sphere(
n=Parameter(guess=1.580122175314896, limit=[1.4, 1.7], name='n'),
r=Parameter(guess=0.6465280831465722, limit=[0.6, 0.7], name='r'),
center=[Parameter(guess=32.24150087110443, limit=[2, 40], name='center[0]'),
Parameter(guess=31.367170884695756, limit=[4, 40], name='center[1]'),
Parameter(guess=35.1651561654966, limit=[5, 45], name='center[2]')])),
theory=Mie.calc_holo, alpha=Parameter(guess=0.7176299231169572, limit=[0.1, 1], name='alpha'),
constraints=[])
assert_obj_close(gold, exampleresult.next_model())
def test_Get_Alpha():
#checking get_alpha function
sc = Spheres([Sphere(n = 1.58, r = par(0.5e-6), center = np.array([10., 10., 20.])),
Sphere(n = 1.58, r = par(0.5e-6), center = np.array([9., 11., 21.]))])
model = Model(sc, Mie.calc_holo, alpha = par(.7,[.6,1]))
sc = Spheres([Sphere(n = 1.58, r = par(0.5e-6), center = np.array([10., 10., 20.])),
Sphere(n = 1.58, r = par(0.5e-6), center = np.array([9., 11., 21.]))])
model2 = Model(sc, Mie.calc_holo)
assert_equal(model.get_alpha(model.parameters).guess, 0.7)
assert_equal(model.get_alpha(model.parameters).name, 'alpha')
assert_equal(model2.get_alpha(model2.parameters), 1.0)
def test_dda_fit():
s = Sphere(n=1.59, r=.2, center=(5, 5, 5))
o = Optics(wavelen=.66, index=1.33, pixel_scale=.1)
schema = ImageSchema(optics=o, shape=100)
h = Mie.calc_holo(s, schema)
def make_scatterer(r, x, y, z):
local_s = Sphere(r=r, center=(x, y, z))
return Scatterer(local_s.indicators, n=s.n)
parameters = [
par(.18, [.1, .3], name='r', step=.1),
par(5, [4, 6], 'x'),
par(5, [4, 6], 'y'),
par(5.2, [4, 6], 'z')
]
p = Parametrization(make_scatterer, parameters)
model = Model(p, DDA.calc_holo)
res = fit(model, h)
assert_parameters_allclose(res.parameters,
dict([('r', 0.2003609439787491),
('x', 5.0128083665603995),
('y', 5.0125252883133617),
('z', 4.9775097284878775)]),
rtol=1e-3)
def test_fit_mie_single():
holo = normalize(get_example_data('image0001.yaml'))
parameters = [
Parameter(name='x', guess=.567e-5, limit=[0.0, 1e-5]),
Parameter(name='y', guess=.576e-5, limit=[0, 1e-5]),
Parameter(name='z', guess=15e-6, limit=[1e-5, 2e-5]),
Parameter(name='n', guess=1.59, limit=[1, 2]),
Parameter(name='r', guess=8.5e-7, limit=[1e-8, 1e-5])
]
def make_scatterer(x, y, z, r, n):
return Sphere(n=n + 1e-4j, r=r, center=(x, y, z))
thry = Mie(False)
model = Model(Parametrization(make_scatterer, parameters),
thry.calc_holo,
alpha=Parameter(name='alpha', guess=.6, limit=[.1, 1]))
assert_raises(InvalidMinimizer, fit, model, holo, minimizer=Sphere)
result = fit(model, holo)
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
def test_serialization():
par_s = Sphere(center=(par(.567e-5, [0, 1e-5]), par(.576e-6, [0, 1e-5]),
par(15e-6, [1e-5, 2e-5])),
r=par(8.5e-7, [1e-8, 1e-5]),
n=par(1.59, [1, 2]))
alpha = par(.6, [.1, 1], 'alpha')
schema = ImageSchema(shape=100,
spacing=.1151e-6,
optics=Optics(.66e-6, 1.33))
model = Model(par_s, Mie.calc_holo, alpha=alpha)
holo = Mie.calc_holo(model.scatterer.guess, schema, model.alpha.guess)
result = fit(model, holo)
temp = tempfile.NamedTemporaryFile()
save(temp, result)
temp.flush()
temp.seek(0)
loaded = load(temp)
assert_obj_close(result, loaded, context='serialized_result')
def test_layered():
s = Sphere(n = (1,2), r = (1, 2), center = (2, 2, 2))
sch = ImageSchema((10, 10), .2, Optics(.66, 1, (1, 0)))
hs = Mie.calc_holo(s, sch)
guess = hp.scattering.scatterer.sphere.LayeredSphere((1,2), (par(1.01), par(.99)), (2, 2, 2))
model = Model(guess, Mie.calc_holo)
res = fit(model, hs)
assert_allclose(res.scatterer.t, (1, 1), rtol = 1e-12)
def test_fit_multisphere_noisydimer_slow():
optics = Optics(wavelen=658e-9, polarization = [0., 1.0],
divergence = 0., pixel_scale = [0.345e-6, 0.345e-6],
index = 1.334)
holo = normalize(get_example_data('image0002.yaml'))
# Now construct the model, and fit
parameters = [Parameter(name = 'x0', guess = 1.64155e-5,
limit = [0, 1e-4]),
Parameter(1.7247e-5, [0, 1e-4], 'y0'),
Parameter(20.582e-6, [0, 1e-4], 'z0'),
Parameter(.6856e-6, [1e-8, 1e-4], 'r0'),
Parameter(1.6026, [1, 2], 'nr0'),
Parameter(1.758e-5, [0, 1e-4], 'x1'),
Parameter(1.753e-5, [0, 1e-4], 'y1'),
Parameter(21.2698e-6, [1e-8, 1e-4], 'z1'),
Parameter(.695e-6, [1e-8, 1e-4], 'r1'),
Parameter(1.6026, [1, 2], 'nr1')]
def make_scatterer(x0, x1, y0, y1, z0, z1, r0, r1, nr0, nr1):
s = Spheres([
Sphere(center = (x0, y0, z0), r=r0, n = nr0+1e-5j),
Sphere(center = (x1, y1, z1), r=r1, n = nr1+1e-5j)])
return s
# initial guess
#s1 = Sphere(n=1.6026+1e-5j, r = .6856e-6,
# center=(1.64155e-05, 1.7247e-05, 20.582e-6))
#s2 = Sphere(n=1.6026+1e-5j, r = .695e-6,
# center=(1.758e-05, 1.753e-05, 21.2698e-6))
#sc = Spheres([s1, s2])
#alpha = 0.99
#lb1 = Sphere(1+1e-5j, 1e-8, 0, 0, 0)
#ub1 = Sphere(2+1e-5j, 1e-5, 1e-4, 1e-4, 1e-4)
#step1 = Sphere(1e-4+1e-4j, 1e-8, 0, 0, 0)
#lb = Spheres([lb1, lb1]), .1
#ub = Spheres([ub1, ub1]), 1
#step = Spheres([step1, step1]), 0
model = Model(parameters, Multisphere, make_scatterer=make_scatterer, alpha
= Parameter(.99, [.1, 1.0], 'alpha'))
result = fit(model, holo)
print result.scatterer
gold = np.array([1.642e-5, 1.725e-5, 2.058e-5, 1e-5, 1.603, 6.857e-7,
1.758e-5, 1.753e-5, 2.127e-5, 1e-5, 1.603,
6.964e-7])
gold_alpha = 1.0
assert_parameters_allclose(result.scatterer, gold, rtol=1e-2)
# TODO: This test fails, alpha comes back as .9899..., where did
# the gold come from?
assert_approx_equal(result.alpha, gold_alpha, significant=2)
def test_fit_complex_parameter():
'''
Test that complex parameters are handled correctly when fit.
'''
# use a Sphere with complex n
# a fake scattering model
def scat_func(sph, schema, scaling=None):
# TODO: scaling kwarg required, seems like a silly kluge
def silly_function(theta):
return theta * sph.r + sph.n.real * theta**2 + 2. * sph.n.imag
#import pdb
#pdb.set_trace()
return Marray(
np.array([
silly_function(theta)
for theta, phi in schema.positions_theta_phi()
]), **schema._dict)
# generate data
schema = Schema(positions=Angles(np.linspace(0., np.pi / 2., 6)))
ref_sph = Sphere(r=1.5, n=0.4 + 0.8j)
data = scat_func(ref_sph, schema)
# varying both real and imaginary parts
par_s = Sphere(r=par(1.49),
n=ComplexParameter(real=par(0.405), imag=par(0.81)))
model = Model(par_s, scat_func)
result = fit(model, data)
assert_allclose(result.scatterer.r, ref_sph.r)
assert_allclose(result.scatterer.n.real, ref_sph.n.real)
assert_allclose(result.scatterer.n.imag, ref_sph.n.imag)
# varying just the real part
par_s2 = Sphere(r=par(1.49), n=ComplexParameter(real=par(0.405), imag=0.8))
model2 = Model(par_s2, scat_func)
result2 = fit(model2, data)
assert_allclose(result2.scatterer.r, ref_sph.r)
assert_allclose(result2.scatterer.n.real, ref_sph.n.real)
assert_allclose(result2.scatterer.n.imag, ref_sph.n.imag)
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_integer_correctness():
# we keep having bugs where the fitter doesn't
schema = ImageSchema(shape = 100, spacing = .1,
optics = Optics(wavelen = .660, index = 1.33, polarization = (1, 0)))
s = Sphere(center = (10.2, 9.8, 10.3), r = .5, n = 1.58)
holo = Mie.calc_holo(s, schema)
par_s = Sphere(center = (par(guess = 10, limit = [5,15]), par(10, [5, 15]), par(10, [5, 15])),
r = .5, n = 1.58)
model = Model(par_s, Mie.calc_holo, alpha = par(.6, [.1, 1]))
result = fit(model, holo)
assert_allclose(result.scatterer.center, [10.2, 9.8, 10.3])
def test_Tying():
#tied parameters
n1 = par(1.59)
sc = Spheres([
Sphere(n=n1, r=par(0.5e-6), center=np.array([10., 10., 20.])),
Sphere(n=n1, r=par(0.5e-6), center=np.array([9., 11., 21.]))
])
model = Model(sc, Mie.calc_holo, alpha=par(.7, [.6, 1]))
assert_equal(model.parameters[0].guess, 1.59)
assert_equal(model.parameters[1].guess, 5e-7)
assert_equal(len(model.parameters), 4)
def test_n():
sph = Sphere(par(.5), 1.6, (5,5,5))
sch = ImageSchema(shape=[100, 100], spacing=[0.1, 0.1],
optics=Optics(wavelen=0.66,
index=1.33,
polarization=[1, 0],
divergence=0.0),
origin=[0.0, 0.0, 0.0])
model = Model(sph, Mie.calc_holo, alpha=1)
holo = Mie.calc_holo(model.scatterer.guess, sch)
coster = CostComputer(holo, model, random_subset=.1)
assert_allclose(coster.flattened_difference({'n' : .5}), 0)
def test_constraint():
sch = ImageSchema(100)
with warnings.catch_warnings():
# TODO: we should really only supress overlap warnings here,
# but I am too lazy to figure it out right now, and I don't
# think we are likely to hit warnings here that won't get
# caught elsewhere -tgd 2013-12-01
warnings.simplefilter("ignore")
spheres = Spheres([Sphere(r=.5, center=(0,0,0)),
Sphere(r=.5, center=(0,0,par(.2)))])
model = Model(spheres, Multisphere.calc_holo, constraints=limit_overlaps())
coster = CostComputer(sch, model)
cost = coster._calc({'1:Sphere.center[2]' : .2})
assert_equal(cost, np.ones_like(sch)*np.inf)
def test_ComplexPar():
#complex parameter
def makeScatterer(n):
n**2
return fake_sph
parm = Parametrization(
makeScatterer,
[ComplexParameter(real=par(1.58), imag=par(.001), name='n')])
model = Model(parm, Mie.calc_holo, alpha=par(.7, [.6, 1]))
assert_equal(model.parameters[0].name, 'n.real')
assert_equal(model.parameters[1].name, 'n.imag')
def test_fit_single_openopt():
holo = normalize(get_example_data('image0001.yaml'))
s = Sphere(center = (par(guess=.567e-5, limit=[.4e-5,.6e-5]),
par(.567e-5, (.4e-5, .6e-5)), par(15e-6, (1.3e-5, 1.8e-5))),
r = par(8.5e-7, (5e-7, 1e-6)),
n = ComplexParameter(par(1.59, (1.5,1.8)), 1e-4j))
model = Model(s, Mie(False).calc_holo, alpha = par(.6, [.1,1]))
try:
minimizer = OpenOpt('scipy_slsqp')
except ImportError:
raise SkipTest
result = fit(model, holo, minimizer = minimizer)
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)
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_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-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_Get_Alpha():
#checking get_alpha function
sc = Spheres([
Sphere(n=1.58, r=par(0.5e-6), center=np.array([10., 10., 20.])),
Sphere(n=1.58, r=par(0.5e-6), center=np.array([9., 11., 21.]))
])
model = Model(sc, Mie.calc_holo, alpha=par(.7, [.6, 1]))
sc = Spheres([
Sphere(n=1.58, r=par(0.5e-6), center=np.array([10., 10., 20.])),
Sphere(n=1.58, r=par(0.5e-6), center=np.array([9., 11., 21.]))
])
model2 = Model(sc, Mie.calc_holo)
assert_equal(model.get_alpha(model.parameters).guess, 0.7)
assert_equal(model.get_alpha(model.parameters).name, 'alpha')
assert_equal(model2.get_alpha(model2.parameters), 1.0)
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_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_model_guess():
ps = Sphere(n=par(1.59, [1.5,1.7]), r = .5, center=(5,5,5))
m = Model(ps, Mie)
assert_obj_close(m.scatterer.guess, Sphere(n=1.59, r=0.5, center=[5, 5, 5]))