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_naming():
#parameterizing with fixed params
def makeScatterer(n,m):
n**2+m
return fake_sph
parm = Parametrization(makeScatterer, [par(limit=4),par(2, [1,5])])
assert_equal(parm._fixed_params,{None: 4})
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_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_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_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, 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_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, 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_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_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_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, 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, 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_multidim():
s = Sphere(n = {'r':par(0,[-1,1]),'g':par(0,0),'b':prior.Gaussian(0,1),'a':0}, r = xr.DataArray([prior.Gaussian(0,1),par(0,[-1,1]),par(0,0),0], dims='alph',coords={'alph':['a','b','c','d']}), center=[par(0,[-1,1]),par(0,0),0])
par_s = ParameterizedObject(s)
params = {'n_r':3,'n_g':4,'n_b':5,'n_a':6,'r_a':7,'r_b':8,'r_c':9,'r_d':10,'center[0]':7,'center[1]':8,'center[2]':9}
out_s = Sphere(n={'r':3,'g':0,'b':5,'a':0}, r = xr.DataArray([7,8,0,0], dims='alph',coords={'alph':['a','b','c','d']}), center=[7,0,0])
assert_obj_close(par_s.make_from(params), out_s)
m = Model(s, np.sum)
m._use_parameter(OrderedDict([('r',par(0,[-1,1])),('g',par(0,0)),('b',prior.Gaussian(0,1)),('a',0)]),'letters')
m._use_parameter(xr.DataArray([prior.Gaussian(0,1),par(0,[-1,1]),par(0,0),0],dims='numbers',coords={'numbers':['one','two','three','four']}),'count')
expected_params = [par(0,[-1,1],'letters_r'),par(0,0,'letters_g'),prior.Gaussian(0,1,'letters_b'),prior.Gaussian(0,1,'count_one'),par(0,[-1,1],'count_two'), par(0,0,'count_three')]
assert_equal(m.parameters[-6:], expected_params)
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_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_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_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_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, use_random_fraction=.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_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_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, OrderedDict([('r',
0.2003609439787491), ('x', 5.0128083665603995), ('y', 5.0125252883133617),
('z', 4.9775097284878775)]), rtol=1e-3)
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, 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, 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_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.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, 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-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_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_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_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]))
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]))