def div_normalize(holo, bg, df, model):
if df is None:
df = np.zeros_like(holo)
if bg is not None:
imagetofit = normalize((holo-df)/(bg-df))
else:
imagetofit = normalize(holo)
return imagetofit
def div_normalize(holo, bg, df, model):
if df is None:
df = np.zeros_like(holo)
if bg is not None:
imagetofit = normalize((holo - df) / (bg - df))
else:
imagetofit = normalize(holo)
return imagetofit
def preprocess(holo, bg, df, model):
center = np.array(model.scatterer.guess.center[:2])/holo.spacing
try:
return normalize(subimage(holo/bg, center, size))
except IndexError:
if not recenter_at_edge:
raise
new_center = np.array(model.scatterer.guess.center[:2])
new_center -= np.clip(new_center-np.array(size)/2, -np.inf, 0)
new_center += np.clip(holo.shape[:2]-(new_center + np.array(size)/2), -np.inf, 0)
return normalize(subimage(holo/bg, new_center, size))
def preprocess(holo, bg, df, model):
center = np.array(model.scatterer.guess.center[:2]) / holo.spacing
try:
return normalize(subimage(holo / bg, center, size))
except IndexError:
if not recenter_at_edge:
raise
new_center = np.array(model.scatterer.guess.center[:2])
new_center -= np.clip(new_center - np.array(size) / 2, -np.inf, 0)
new_center += np.clip(
holo.shape[:2] - (new_center + np.array(size) / 2), -np.inf, 0)
return normalize(subimage(holo / bg, new_center, size))
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 load_bgdivide_crop_v2(
path, metadata, particle_position, bkg, dark, channel=RGB_CHANNEL,
size=HOLOGRAM_SIZE):
data = hp.load_image(path, channel=channel, **metadata)
data = bg_correct(data, bkg, dark)
data = subimage(data, particle_position[::-1], size)
data = normalize(data)
return data
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_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 load_bgdivide_crop(
path, metadata, particle_position, bg_prefix="bg", df_prefix=None,
channel=RGB_CHANNEL, size=HOLOGRAM_SIZE):
data = hp.load_image(path, channel=channel, **metadata)
bkg = load_bkg(path, bg_prefix, refimg=data)
dark = None # load_dark(path, df_prefix, refimg=data)
data = bg_correct(data, bkg, dark)
data = subimage(data, particle_position[::-1], size)
data = normalize(data)
return data
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_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_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_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_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_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_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 load_bgdivide_crop(path,
metadata,
particle_position,
bkg,
dark,
channel=RGB_CHANNEL,
size=HOLOGRAM_SIZE,
recenter=True):
data = hp.load_image(path, channel=channel, **metadata)
data = bg_correct(data, bkg, dark)
if recenter:
bbox = subimage(data, particle_position, size)
bbox_corner = np.array([bbox.x.min(), bbox.y.min()])
found_position = np.round(
center_find(bbox) + bbox_corner / metadata['spacing'])
data = subimage(data, found_position, size)
else:
data = subimage(data, particle_position, size)
data = normalize(data)
if recenter:
return data, found_position
return data, None
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 _load_data(self, name): # need metadata, particle_position!
data = hp.load_image(name, channel=RGB_CHANNEL, **self.metadata)
data = bg_correct(data, self._background, self._darkfield)
data = subimage(data, self.particle_position[::-1], HOLOGRAM_SIZE)
data = normalize(data)
return data
def load_gold_example_data():
return normalize(hp.load(hp.core.io.get_example_data_path('image0001.h5')))
def test_hologram_io():
holo = normalize(get_example_data('image0001.yaml'))
assert_read_matches_write(holo)
def test_hologram_io():
holo = normalize(get_example_data('image0001.yaml'))
assert_read_matches_write(holo)
def fit_multisphere(data_path,
a_p,
n_p,
z_guess,
theta_guess,
phi_guess,
fit_a=False):
px = cv2.imread(data_path).shape[0]
data_holo = hp.load_image(data_path,
spacing=mag,
medium_index=n_m,
illum_wavelen=wv,
illum_polarization=(1, 0),
channel=0)
data_holo = normalize(data_holo)
z_p = prior.Uniform(lower_bound=45,
upper_bound=100,
guess=z_guess,
name='z_p')
theta = prior.Uniform(lower_bound=0,
upper_bound=np.pi / 2,
guess=theta_guess,
name='theta')
phi = prior.Uniform(lower_bound=0,
upper_bound=np.pi,
guess=phi_guess,
name='phi')
'''
#idk why this doesn't work, maybe the fitter has some issue with numpy
center = (mag*px, mag*px, z_p)
delta = np.array([np.cos(phi)*np.cos(theta), np.sin(phi)*np.cos(theta), np.sin(theta)])
c1 = + 1.001*a_p*delta
c2 = - 1.001*a_p*delta
cluster = np.array([c1, c2])
s1 = Sphere(center = cluster[0], n = n_p, r = a_p)
s2 = Sphere(center = cluster[1], n = n_p, r = a_p)
'''
if fit_a:
a_1 = prior.Uniform(lower_bound=a_p * 0.8,
upper_bound=a_p * 1.2,
guess=a_p,
name='a_1')
a_2 = prior.Uniform(lower_bound=a_p * 0.8,
upper_bound=a_p * 1.2,
guess=a_p,
name='a_2')
else:
a_1 = a_p
a_2 = a_p
x1 = mag * px / 2 + a_1 * np.cos(phi) * np.cos(theta) * 1.001
x2 = mag * px / 2 - a_2 * np.cos(phi) * np.cos(theta) * 1.001
y1 = mag * px / 2 + a_1 * np.cos(theta) * np.sin(phi) * 1.001
y2 = mag * px / 2 + a_2 * np.cos(theta) * np.sin(phi) * 1.001
z1 = z_p + a_1 * np.sin(theta) * 1.001
z2 = z_p - a_2 * np.sin(theta) * 1.001
s1 = Sphere(center=[x1, y1, z1], n=n_p, r=a_1)
s2 = Sphere(center=[x2, y2, z2], n=n_p, r=a_2)
dimer = Spheres([s1, s2], warn=False)
model = ExactModel(scatterer=dimer,
calc_func=calc_holo,
noise_sd=None,
medium_index=n_m,
illum_wavelen=wv,
illum_polarization=(1, 0))
fit_result = hp.fit(data_holo, model)
return fit_result
def preprocess(holo, bg, df, model):
center = np.array(model.scatterer.guess.center[:2])/holo.spacing
return normalize(subimage(holo/bg, center, size))
'value': y
}, {
'parname': 'z',
'limited': [False, False],
'limits': [0, 0],
'step': 100e-9,
'mpmaxstep': 1e-6,
'value': z
}, {
'parname': 'scaling_alpha',
'limited': [True, True],
'limits': [0.0, 1.0],
'value': scaling_alpha
}]
holo = normalize(get_example_data('image0001'))
# define the residual function
def residfunct(p, fjac=None):
# nmpfit calls residfunct w/fjac as a kwarg, we ignore
sphere = Sphere(n=p[0] + n_particle_imag * 1j, r=p[1], center=p[2:5])
thry = Mie(False)
calculated = calc_holo(holo, sphere, scaling=p[5], theory=thry)
status = 0
derivates = holo - calculated
return ([status, get_values(flat(derivates))])
def test_hologram_io(self):
assert_read_matches_write(normalize(self.holo))
