def setUp(self):
self.holo = get_example_data('image0001')
self.holograms = [
get_example_data('image0001'),
get_example_data('image0002')
]
self.tempdir = tempfile.mkdtemp()
def test_AlphaModelholo_likelihood():
holo = get_example_data('image0001')
s = Sphere(
prior.Gaussian(.5, .1), prior.Gaussian(1.6, .1),
(prior.Gaussian(5, 1), prior.Gaussian(5, 1), prior.Gaussian(5, 1)))
model = AlphaModel(s, alpha=prior.Gaussian(.7, .1), noise_sd=.01)
assert_pickle_roundtrip(model)
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_reconstruction():
im = get_example_data('image0003')
rec = propagate(im, 4e-6)
verify(rec, 'recon_single')
rec = propagate(im, [4e-6, 7e-6, 10e-6])
verify(rec, 'recon_multiple')
def test_image_io():
holo = get_example_data('image0001.yaml')
t = tempfile.mkdtemp()
filename = os.path.join(t, 'image0001.tif')
save(filename, holo)
l = load(filename)
assert_obj_close(l, holo)
# check that it defaults to saving as tif
filename = os.path.join(t, 'image0002')
save_image(filename, holo)
l = load(filename + '.tif')
assert_obj_close(l, holo)
# check saving 16 bit
filename = os.path.join(t, 'image0003')
save_image(filename, holo, scaling=None, depth=16)
l = load(filename + '.tif')
assert_obj_close(l, holo)
# test that yaml save works corretly with a string instead of a file
filename = os.path.join(t, 'image0001.yaml')
save(filename, holo)
loaded = load(filename)
assert_obj_close(loaded, holo)
f = get_example_data_path('image0001.yaml')
spacing = .1
optics = Optics(.66, 1.33, (1, 0))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
h = load(f, spacing=spacing, optics=optics)
assert_obj_close(h.optics, optics)
assert_equal(h.spacing, spacing)
assert_equal(len(w), 1)
assert "Overriding spacing and optics of loaded yaml" in w[-1].message
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
h = load(f, optics=optics)
assert_obj_close(h.optics, optics)
assert_equal(h.spacing, holo.spacing)
assert_equal(len(w), 1)
assert ("WARNING: overriding optics of loaded yaml without "
"overriding spacing, this is probably incorrect."
in w[-1].message)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
h = load(f, spacing=spacing)
assert_obj_close(h.optics, holo.optics)
assert_equal(h.spacing, spacing)
assert_equal(len(w), 1)
assert ("WARNING: overriding spacing of loaded yaml without "
"overriding optics, this is probably incorrect."
in w[-1].message)
shutil.rmtree(t)
def test_propagate_0_distance():
im = get_example_data('image0003')
rec = propagate(im, 0)
# propagating no distance should leave the image unchanged
assert_obj_close(im, rec)
rec = propagate(im, [0, 3e-6])
verify(rec, 'recon_multiple_with_0')
def test_subset_tempering():
holo = normalize(get_example_data('image0001'))
scat = Sphere(r=0.65e-6,n=1.58,center=[5.5e-6,5.8e-6,14e-6])
mod = AlphaModel(scat,noise_sd=.1, alpha=prior.Gaussian(0.7,0.1))
with warnings.catch_warnings():
warnings.simplefilter('ignore')
inf = sample.tempered_sample(mod, holo, nwalkers=4, samples=10, stages=1, stage_len=10, threads=None, seed=40)
assert_obj_close(inf.MAP,gold_alpha, rtol=1e-3)
def test_emcee():
holo = get_example_data('image0001.yaml')
s = Sphere(
prior.Gaussian(.5, .1), prior.Gaussian(1.6, .1),
(prior.Gaussian(5, 1), prior.Gaussian(5, 1), prior.Gaussian(5, 1)))
model = AlphaModel(s, Mie, alpha=prior.Gaussian(.7, .1), noise_sd=.01)
emcee = Emcee(model, holo)
assert_pickle_roundtrip(emcee)
def test_subset_tempering():
holo = normalize(get_example_data('image0001'))
scat = Sphere(r=0.65e-6,n=1.58,center=[5.5e-6,5.8e-6,14e-6])
mod = AlphaModel(scat,noise_sd=.1, alpha=prior.Gaussian(0.7,0.1))
with warnings.catch_warnings():
warnings.simplefilter('ignore')
inf = sample.tempered_sample(mod, holo, nwalkers=4, samples=10, stages=1, stage_len=10, threads=None, seed=40)
assert_obj_close(inf.MAP,gold_alpha, rtol=1e-3)
def test_ExactModelholo_likelihood():
holo = get_example_data('image0001')
sphere_center = (prior.Gaussian(5, 1), prior.Gaussian(5, 1),
prior.Gaussian(5, 1))
s = Sphere(n=prior.Gaussian(1.6, .1),
r=prior.Gaussian(.5, .1),
center=sphere_center)
model = ExactModel(s, noise_sd=.01)
assert_pickle_roundtrip(model)
def test_PerfectLensModelholo_likelihood():
holo = get_example_data('image0001')
sphere_center = (prior.Gaussian(5, 1), prior.Gaussian(5, 1),
prior.Gaussian(5, 1))
s = Sphere(n=prior.Gaussian(1.6, .1),
r=prior.Gaussian(.5, .1),
center=sphere_center)
model = PerfectLensModel(s, noise_sd=0.01, lens_angle=0.8)
assert_pickle_roundtrip(model)
def test_fft_ifft_2d_are_inverses(self):
xarray = get_example_data('image0001')
forward = fft(xarray)
backward = ifft(forward)
data_is_same = np.allclose(xarray.values,
backward.values,
atol=1e-13,
rtol=1e-13)
self.assertTrue(data_is_same)
def test_show():
d = get_example_data('image0001')
try:
hp.show(d)
except RuntimeError:
# this occurs on travis since there is no display
raise SkipTest()
with warnings.catch_warnings():
warnings.simplefilter('ignore'), (DeprecationWarning, UserWarning)
plt.savefig(tempfile.TemporaryFile(suffix='.pdf'))
def test_show():
d = get_example_data('image0001')
try:
hp.show(d)
except RuntimeError:
# this occurs on travis since there is no display
raise SkipTest()
with warnings.catch_warnings():
warnings.simplefilter('ignore'), (DeprecationWarning, UserWarning)
plt.savefig(tempfile.TemporaryFile(suffix='.pdf'))
def test_subset_tempering():
np.random.seed(40)
holo = normalize(get_example_data('image0001.yaml'))
scat = Sphere(r=0.65e-6,n=1.58,center=[5.5e-6,5.8e-6,14e-6])
mod = AlphaModel(scat,Mie,noise_sd=.1,alpha=prior.Gaussian(0.7,0.1))
inf=mcmc.subset_tempering(mod,holo,final_len=10,nwalkers=4,stages=1,stage_len=10,threads=None, verbose=False,seed=40)
assert_obj_close(inf.most_probable_values(),gold_alpha)
assert_equal(inf.n_steps,gold_nsteps)
assert_obj_close(inf.acceptance_fraction,gold_frac)
assert_obj_close(float(inf.data_frame(burn_in=6)[1:2].alpha),gold_alpha)
def test_image_io():
holo = get_example_data('image0001.yaml')
t = tempfile.mkdtemp()
filename = os.path.join(t, 'image0001.tif')
save(filename, holo)
l = load(filename)
assert_obj_close(l, holo)
# check that it defaults to saving as tif
filename = os.path.join(t, 'image0002')
save_image(filename, holo)
l = load(filename+'.tif')
assert_obj_close(l, holo)
# test that yaml save works corretly with a string instead of a file
filename = os.path.join(t, 'image0001.yaml')
save(filename, holo)
loaded = load(filename)
assert_obj_close(loaded, holo)
f = get_example_data_path('image0001.yaml')
spacing = .1
optics = Optics(.66, 1.33, (1,0))
with warnings.catch_warnings(record =True) as w:
warnings.simplefilter('always')
h = load(f, spacing = spacing, optics = optics)
assert_obj_close(h.optics, optics)
assert_equal(h.spacing, spacing)
assert_equal(len(w), 1)
assert "Overriding spacing and optics of loaded yaml" in w[-1].message
with warnings.catch_warnings(record =True) as w:
warnings.simplefilter('always')
h = load(f, optics = optics)
assert_obj_close(h.optics, optics)
assert_equal(h.spacing, holo.spacing)
assert_equal(len(w), 1)
assert ("WARNING: overriding optics of loaded yaml without "
"overriding spacing, this is probably incorrect." in
w[-1].message)
with warnings.catch_warnings(record =True) as w:
warnings.simplefilter('always')
h = load(f, spacing = spacing)
assert_obj_close(h.optics, holo.optics)
assert_equal(h.spacing, spacing)
assert_equal(len(w), 1)
assert ("WARNING: overriding spacing of loaded yaml without "
"overriding optics, this is probably incorrect." in
w[-1].message)
shutil.rmtree(t)
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_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_alpha_subset_tempering(self):
holo = normalize(get_example_data('image0001'))
scat = Sphere(r=0.65e-6, n=1.58, center=[5.5e-6, 5.8e-6, 14e-6])
mod = AlphaModel(scat, noise_sd=.1, alpha=prior.Gaussian(0.7, 0.1))
strat = TemperedStrategy(nwalkers=4, nsamples=10, stages=1,
stage_len=10, parallel=None, seed=40)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
inference_result = strat.sample(mod, holo)
desired_alpha = np.array([0.650348])
assert_allclose(inference_result._parameters, desired_alpha, rtol=5e-3)
def test_subset_data_fit_result_stores_model(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)
self.assertEqual(model, fitted.model)
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_perfectlens_subset_tempering(self):
data = normalize(get_example_data('image0001'))
scatterer = Sphere(r=0.65e-6, n=1.58, center=[5.5e-6, 5.8e-6, 14e-6])
model = PerfectLensModel(
scatterer, noise_sd=.1, lens_angle=prior.Gaussian(0.7, 0.1))
strat = TemperedStrategy(nwalkers=4, nsamples=10, stages=1,
stage_len=10, parallel=None, seed=40)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
inference_result = strat.sample(model, data)
desired_lens_angle = np.array([0.7197887])
is_ok = np.allclose(
inference_result._parameters, desired_lens_angle, rtol=1e-3)
self.assertTrue(is_ok)
def test_returns_close_values(self):
model = self._make_model()
holo = normalize(get_example_data('image0001'))
np.random.seed(40)
result = NmpfitStrategy(npixels=1000).fit(model, holo)
# TODO: figure out if it is a problem that alpha is frequently
# coming out wrong in the 3rd decimal place.
self.assertAlmostEqual(result.parameters['alpha'],
gold_alpha,
places=3)
# 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)
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_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'))
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_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.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'))
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_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_fit_mie_single():
holo = normalize(get_example_data('image0001'))
parameters = [
Uniform(0, 1e-5, name='x', guess=.567e-5),
Uniform(0, 1e-5, name='y', guess=.576e-5),
Uniform(1e-5, 2e-5, name='z', guess=15e-6),
Uniform(1, 2, name='n', guess=1.59),
Uniform(1e-8, 1e-5, name='r', guess=8.5e-7)
]
def make_scatterer(parlist):
return Sphere(n=parlist[3], r=parlist[4], center=parlist[0:3])
thry = Mie(False)
model = AlphaModel(make_scatterer(parameters),
theory=thry,
alpha=Uniform(.1, 1, name='alpha', guess=.6))
result = NmpfitStrategy().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_image():
holo = get_example_data('image0001')
assert_pickle_roundtrip(holo)
def test_FoundLocation():
holo = get_example_data('image0001')
location = center_find(holo, threshold=.25)
assert_allclose(location, gold_location, atol=0.01)
def test_image():
holo = get_example_data('image0001.yaml')
assert_pickle_roundtrip(holo)
def test_ifft_of_xarray_returns_xarray(self):
xarray = get_example_data('image0001')
forward = fft(xarray)
backward = ifft(forward)
self.assertIsInstance(backward, xr.DataArray)
def test_fft_of_xarray_returns_xarray(self):
xarray = get_example_data('image0001')
after_fft = fft(xarray)
self.assertIsInstance(after_fft, xr.DataArray)
def test_hologram_io():
holo = normalize(get_example_data('image0001.yaml'))
assert_read_matches_write(holo)
def test_show():
d = get_example_data('image0001.yaml')
hp.show(d)
with warnings.catch_warnings():
warnings.simplefilter('ignore', DeprecationWarning)
plt.savefig(tempfile.TemporaryFile(suffix='.pdf'))
def test_show():
d = get_example_data('image0001.yaml')
hp.show(d)
plt.savefig(tempfile.TemporaryFile(suffix='.pdf'))
def test_AlphaModelholo_likelihood():
holo = get_example_data('image0001')
s = Sphere(prior.Gaussian(.5, .1), prior.Gaussian(1.6, .1),
(prior.Gaussian(5, 1), prior.Gaussian(5, 1), prior.Gaussian(5, 1)))
model = AlphaModel(s, alpha = prior.Gaussian(.7, .1), noise_sd=.01)
assert_pickle_roundtrip(model)
from numpy import linspace
import holopy as hp
from holopy.core import Optics
from holopy.propagation import propagate
from holopy.core.tests.common import get_example_data
from holopy.core import load
holo = get_example_data('image0001.yaml')
rec_vol = propagate(holo, linspace(4e-6, 10e-6, 7))
hp.show(rec_vol)
