def test_log_posterior():
warnings.simplefilter('ignore', UserWarning)
spectrum=Spectrum(500, reflectance = 0.5, sigma_r = 0.1)
theta = (0.5, 0, 0)
sample = Sample(500, 200, 200, 1.5, 1)
post = log_posterior(theta, spectrum, sample, seed=2)
assert_approx_equal(post, -6.418847855673672)
def test_calc_max_like():
warnings.simplefilter('ignore', UserWarning)
sample = Sample([450, 500, 550],
particle_radius=119,
thickness=120,
particle_index=1.59,
matrix_index=1)
theta = (0.55, 0, 0
) #these are the default starting values for lmfit calculation
spect = calc_model_spect(sample, theta, seed=2)
max_like_vals = find_max_like(spect, sample, seed=2)
assert_almost_equal(max_like_vals, (0.55, 0.0, 0.0, 0.0, 0))
def test_find_max_like():
wavelength = [450, 500, 550, 600]
wavelength_ind = find_close_indices(wavelength_sigma,
sc.Quantity(wavelength, 'nm'))
sigma_test = sigma[np.array(wavelength_ind)]
sample = Sample(wavelength, particle_index=1.59, matrix_index=1)
theta = (0.55, 120, 120, 0.02, 0, 0.02, 0
) #these are the default starting values for lmfit calculation
spect = calc_model_spect(sample,
theta, (sigma_test, sigma_test),
ntrajectories,
nevents,
seed=2)
theta_range = {
'min_phi': 0.34,
'max_phi': 0.74,
'min_radius': 70,
'max_radius': 160,
'min_thickness': 1,
'max_thickness': 1000,
'min_l0_r': 0,
'max_l0_r': 1,
'min_l1_r': -1,
'max_l1_r': 1,
'min_l0_t': 0,
'max_l0_t': 1,
'min_l1_t': -1,
'max_l1_t': 1
}
theta_guess = {
'phi': 0.55,
'radius': 120,
'thickness': 120,
'l0_r': 0.02,
'l1_r': 0,
'l0_t': 0.02,
'l1_t': 0
}
max_like_vals = find_max_like(spect,
sample,
theta_guess=theta_guess,
theta_range=theta_range,
sigma=(sigma_test, sigma_test),
ntrajectories=ntrajectories,
nevents=nevents,
seed=2)
assert_almost_equal(max_like_vals, theta)
def test_calc_model_spect():
wavelength = [500]
sample = Sample(wavelength, 1.5, 1)
theta = (0.5, 100, 200, 0, 0, 0, 0)
wavelength_ind = find_close_indices(wavelength_sigma,
sc.Quantity(wavelength, 'nm'))
sigma_test = sigma[np.array(wavelength_ind)]
assert_frame_equal(
calc_model_spect(sample, theta, (sigma_test, sigma_test),
ntrajectories, nevents, 2),
Spectrum(500,
reflectance=0.828595524325,
sigma_r=0.0193369922424,
transmittance=0.171404475675,
sigma_t=0.0193369922424))
def test_run_mcmc():
spectrum = Spectrum([500, 550, 600, 650],
reflectance=[0.5, 0.5, 0.5, 0.5],
transmittance=[0.5, 0.5, 0.5, 0.5],
sigma_r=[0.1, 0.1, 0.1, 0.1],
sigma_t=[0.1, 0.1, 0.1, 0.1])
sample = Sample([500, 550, 600, 650], 1.5, 1)
theta_guess = {
'phi': 0.5,
'radius': 150,
'thickness': 120,
'l0_r': 0,
'l1_r': 0,
'l0_t': 0,
'l1_t': 0
}
theta_range = {
'min_phi': 0.35,
'max_phi': 0.73,
'min_radius': 70,
'max_radius': 201,
'min_thickness': 1,
'max_thickness': 1000,
'min_l0_r': 0,
'max_l0_r': 1,
'min_l1_r': -1,
'max_l1_r': 1,
'min_l0_t': 0,
'max_l0_t': 1,
'min_l1_t': -1,
'max_l1_t': 1
}
# Test that run_mcmc runs correctly
run_mcmc(spectrum,
sample,
nwalkers=14,
nsteps=1,
theta_guess=theta_guess,
theta_range=theta_range,
seed=3)
def test_log_posterior():
wavelength = [500]
spectrum = Spectrum(wavelength, reflectance=0.5, sigma_r=0.1)
sample = Sample(wavelength, 1.5, 1)
theta_range = {
'min_phi': 0.35,
'max_phi': 0.74,
'min_radius': 70,
'max_radius': 201,
'min_thickness': 1,
'max_thickness': 1000
}
wavelength_ind = find_close_indices(wavelength_sigma,
sc.Quantity(wavelength, 'nm'))
sigma_test = sigma[np.array(wavelength_ind)]
# When parameters are within prior range
theta1 = (0.5, 200, 200, 0, 0)
post1 = log_posterior(theta1,
spectrum,
sample,
theta_range=theta_range,
sigma=(sigma_test, sigma_test),
ntrajectories=ntrajectories,
nevents=nevents,
seed=2)
assert_approx_equal(post1, -6.0413752765269875)
# When parameters are not within prior range
theta2 = (0.3, 200, 200, 0, 0)
post2 = log_posterior(theta2,
spectrum,
sample,
theta_range=theta_range,
sigma=(sigma_test, sigma_test),
ntrajectories=ntrajectories,
nevents=nevents,
seed=2)
assert_approx_equal(post2, -1e100)
def test_run_structcol():
# Test that structcol package is imported and reflectance calculation is correct
import structcol as sc
from infer_structcol.run_structcol import calc_refl_trans
from infer_structcol.main import Sample
wavelength = np.array([400., 500.])
particle_radius = 150.
thickness = 100.
particle_index = np.array([1.40, 1.41])
matrix_index = np.array([1.0, 1.0])
volume_fraction = 0.5
incident_angle = 0.0
medium_index = np.array([1.0, 1.0])
spectrum = calc_refl_trans(volume_fraction,
Sample(wavelength, particle_radius, thickness,
particle_index, matrix_index,
medium_index, incident_angle),
seed=1)
outarray = np.array([0.8531, 0.7529])
assert_almost_equal(spectrum.reflectance, outarray, decimal=5)
def test_run_structcol():
# Test that structcol package is imported and reflectance calculation is correct
import structcol as sc
wavelength = np.array([400., 500.])
particle_radius = 150.
thickness = 100.
particle_index = np.array([1.40, 1.41])
matrix_index = np.array([1.0, 1.0])
volume_fraction = 0.5
incident_angle = 0.0
medium_index = np.array([1.0, 1.0])
front_index = np.array([1.0, 1.0])
back_index = np.array([1.0, 1.0])
wavelength_ind = find_close_indices(wavelength_sigma,
sc.Quantity(wavelength, 'nm'))
sigma_test = sigma[np.array(wavelength_ind)]
refl, trans = calc_refl_trans(volume_fraction,
particle_radius,
thickness,
Sample(wavelength, particle_index,
matrix_index, medium_index,
front_index, back_index,
incident_angle),
ntrajectories,
nevents,
seed=1)
spectrum = Spectrum(wavelength,
reflectance=refl,
transmittance=trans,
sigma_r=sigma_test,
sigma_t=sigma_test)
outarray = np.array([0.84576, 0.74796])
assert_almost_equal(spectrum.reflectance, outarray, decimal=5)
def test_calc_model_spect():
sample = Sample(500, 100, 200, 1.5, 1)
theta = (0.5, 0, 0, 0, 0)
assert_frame_equal(calc_model_spect(sample, theta, 2), Spectrum(500, reflectance = 0.813006364656, sigma_r = 0.0226350830535,
transmittance =0.186993635344, sigma_t = 0.0226350830535))
def test_check_wavelength():
spect = Spectrum([300,500], reflectance = [0.5,0.5], sigma_r = [0.1,0.1])
samp = Sample([300,500], 1.5, 1.3)
assert_equal(check_wavelength(spect, samp), samp.wavelength)
def test_Sample():
# Test if Sample object is created correctly
samp = Sample([300,500], 1.5, 1.3)
assert_equal(samp.particle_index, np.array([1.5, 1.5]))
def test_run_mcmc():
spectrum = Spectrum(500, reflectance=0.5, sigma_r=0.1)
sample = Sample(500, 200, 200, 1.5, 1)
theta = (0.5, 0, 0)
# Test that run_mcmc runs correctly
run_mcmc(spectrum, sample, nwalkers=6, nsteps=1, theta=theta, seed=2)