def test_optical_depth(test_od):
# Trap the guess
if not 0 <= test_od <= 2:
return 9999999
od = OpticalDepth(dust_profile, hydro.column_density, fs.extinction,
test_od)
dust_info = (od.total, dust_ssa, dust_pf)
model = Atmosphere(rayleigh_info, dust_info)
od_holder = np.zeros(n_wavelengths)
for wav_index in range(n_wavelengths):
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = \
disort.disort(USRANG, USRTAU, IBCND, ONLYFL, PRNT, PLANK, LAMBER,
DELTAMPLUS, DO_PSEUDO_SPHERE,
model.optical_depth[:, wav_index],
model.single_scattering_albedo[:, wav_index],
model.legendre_moments[:, :, wav_index],
TEMPER, WVNMLO, WVNMHI,
UTAU, UMU0, PHI0, UMU, PHI, FBEAM, FISOT,
ALBEDO, BTEMP, TTEMP, TEMIS, EARTH_RADIUS, H_LYR, RHOQ, RHOU,
RHO_ACCURATE, BEMST, EMUST, ACCUR, HEADER, RFLDIR,
RFLDN, FLUP, DFDT, UAVG, UU, ALBMED, TRNMED)
od_holder[wav_index] = uu[0, 0, 0]
return np.sum((od_holder - rfl)**2)
def myModel(guess, pixel):
dust_od = guess[0]
ice_od = guess[1]
if dust_od <= 0 or ice_od <= 0:
return 99999
# The column will change everytime I use a different OD
dust_column = Column(dust, lay, dust_conrath, np.array([1]),
np.array([dust_od]))
ice_column = Column(ice, lay, ice_profile, np.array([2]),
np.array([ice_od]))
# The phase functions change as columns change
dust_phase = TabularLegendreCoefficients(
dust_column,
dust_phase_function.array,
particle_sizes=dust_phase_radii.array,
wavelengths=dust_phase_wavs.array)
ice_phase = TabularLegendreCoefficients(ice_column, ice_coeff.array)
# Make the model
model = ModelAtmosphere()
dust_info = (dust_column.hyperspectral_total_optical_depths,
dust_column.hyperspectral_scattering_optical_depths,
dust_phase.coefficients)
ice_info = (ice_column.hyperspectral_total_optical_depths,
ice_column.hyperspectral_scattering_optical_depths,
ice_phase.coefficients)
# Add everything to the model
model.add_constituent(dust_info)
model.add_constituent(ice_info)
model.add_constituent(rayleigh_info)
# Once everything is in the model, compute the model
model.compute_model()
optical_depths = model.hyperspectral_total_optical_depths
ssa = model.hyperspectral_total_single_scattering_albedos
polynomial_moments = model.hyperspectral_legendre_moments
test_run = np.zeros(len(wavs))
for w in range(len(wavs)):
# The 0s here are just me testing this on a single pixel
junk, junk, junk, junk, junk, uu, junk, junk = disort.disort(
usrang, usrtau, ibcnd, onlyfl, prnt, plank, lamber, deltamplus,
do_pseudo_sphere, optical_depths[:, w], ssa[:, w],
polynomial_moments[:, :, w], temperatures, low_wavenumber[w],
high_wavenumber[w], utau, umu0[pixel], phi0, umu[pixel],
phi[pixel], fbeam, fisot, albedo, surface_temp, top_temp,
top_emissivity, planet_radius, h_lyr, hapke.rhoq, hapke.rhou,
hapke.rho_accurate[:, pixel], hapke.bemst, hapke.emust, accur,
header, direct_beam_flux, diffuse_down_flux, diffuse_up_flux,
flux_divergence, mean_intensity, intensity[:, :, pixel],
albedo_medium, transmissivity_medium)
test_run[w] = uu[0, 0] # Just the the TOA I/F value
return test_run
def fit_ssa(guess, wav_index: int):
# Trap the guess
if not 0 <= guess <= 1:
return 9999999
test_cext = np.copy(cext)
test_csca = np.copy(csca)
# BECAUSE I'm using NN, just set all of the coefficients to be my guess
# test_csca[:, wav_index] = guess * test_cext[:, wav_index]
test_csca = guess * test_cext
fs = ForwardScattering(test_csca, test_cext, fsp_psizes, fsp_wavs,
pgrad, wavelengths, wave_ref)
fs.make_nn_properties()
od = OpticalDepth(dust_profile, hydro.column_density, fs.extinction,
pixel_od[pixel_index])
tlc = TabularLegendreCoefficients(phsfn, pf_psizes, pf_wavs, pgrad,
wavelengths)
tlc.make_nn_phase_function()
dust_info = (od.total, fs.single_scattering_albedo, tlc.phase_function)
model = Atmosphere(rayleigh_info, dust_info)
oa = OutputArrays(cp.n_polar, cp.n_user_levels, cp.n_azimuth)
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = \
disort.disort(ob.user_angles, ob.user_optical_depths,
ob.incidence_beam_conditions, ob.only_fluxes,
mb.print_variables, te.thermal_emission,
lamb[wav_index].lambertian,
mb.delta_m_plus, mb.do_pseudo_sphere,
model.optical_depth[:, wav_index],
model.single_scattering_albedo[:, wav_index],
model.legendre_moments[:, :, wav_index],
hydro.temperature, spectral.low_wavenumber,
spectral.high_wavenumber, ulv.optical_depth_output,
angles.mu0[pixel_index], angles.phi0[pixel_index],
angles.mu[pixel_index], angles.phi[pixel_index],
flux.beam_flux, flux.isotropic_flux,
lamb[wav_index].albedo, te.bottom_temperature,
te.top_temperature, te.top_emissivity,
mb.radius, hydro.scale_height, lamb[wav_index].rhoq,
lamb[wav_index].rhou, lamb[wav_index].rho_accurate,
lamb[wav_index].bemst, lamb[wav_index].emust,
mb.accuracy, mb.header, oa.direct_beam_flux,
oa.diffuse_down_flux, oa.diffuse_up_flux,
oa.flux_divergence, oa.mean_intensity, oa.intensity,
oa.albedo_medium, oa.transmissivity_medium)
return (uu[0, 0, 0] - reflectance[pixel_index, wav_index])**2
RHOQ = sfc.rhoq
BEMST = sfc.bemst
EMUST = sfc.emust
RHO_ACCURATE = sfc.rho_accurate
# Run the model
import disort
test_run = np.zeros(pixel_wavelengths.shape)
for ind in range(pixel_wavelengths.size):
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = \
disort.disort(USRANG, USRTAU, IBCND, ONLYFL, PRNT, PLANK, LAMBER,
DELTAMPLUS, DO_PSEUDO_SPHERE, DTAUC[:, ind], SSALB[:, ind],
PMOM[:, :, ind], TEMPER, WVNMLO, WVNMHI,
UTAU, UMU0, PHI0, UMU, PHI, FBEAM, FISOT,
ALBEDO, BTEMP, TTEMP, TEMIS, EARTH_RADIUS, H_LYR, RHOQ, RHOU,
RHO_ACCURATE, BEMST, EMUST, ACCUR, HEADER, RFLDIR,
RFLDN, FLUP, DFDT, UAVG, UU, ALBMED, TRNMED)
test_run[ind] = uu[0, 0, 0]
rfl = np.array([0.116, 0.108, 0.084, 0.094, 0.092])
def test_optical_depth(test_od):
# Trap the guess
if not 0 <= test_od <= 2:
return 9999999
od = OpticalDepth(dust_profile, hydro.column_density, fs.extinction,
#hapke = Hapke(size, obs, control, boundary, albedo)
#hapke = HapkeHG2(size, obs, control, boundary, albedo, w=0.12, asym=0.75, frac=0.9, b0=1, hh=0.04, n_mug=200)
hapke = HapkeHG2Roughness(size,
obs,
control,
boundary,
albedo,
w=0.12,
asym=0.75,
frac=0.5,
b0=1,
hh=0.04,
n_mug=200,
roughness=0.5)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run the model
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = disort.disort(
usrang, usrtau, ibcnd, onlyfl, prnt, plank, lamber, deltamplus,
do_pseudo_sphere, optical_depths, ssa, polynomial_moments, temperatures,
low_wavenumber, high_wavenumber, utau, umu0, phi0, umu, phi, fbeam, fisot,
albedo, surface_temp, top_temp, top_emissivity, planet_radius, h_lyr,
hapke.rhoq, hapke.rhou, hapke.rho_accurate, hapke.bemst, hapke.emust,
accur, header, direct_beam_flux, diffuse_down_flux, diffuse_up_flux,
flux_divergence, mean_intensity, intensity, albedo_medium,
transmissivity_medium)
print(uu[0, :20, 0]) # shape: (1, 81, 1)
lamber = hapke.lambertian
rhou = hapke.rhou
rhoq = hapke.rhoq
bemst = hapke.bemst
emust = hapke.emust
rho_accurate = hapke.rho_accurate
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# I guess I have no idea where to put this still
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
utau = np.zeros(n_user_levels)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run the model
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = \
disort.disort(usrang, usrtau, ibcnd, onlyfl, prnt, plank, lamber,
deltamplus, dopseudosphere, optical_depths, ssa,
polynomial_moments, temperatures, low_wavenumber,
high_wavenumber, utau, mu0, phi0, mu, phi, fbeam, fisot,
albedo, btemp, ttemp, temis, radius, h_lyr, rhoq, rhou,
rho_accurate, bemst, emust, accur, header, rfldir,
rfldn, flup, dfdt, uavg, uu, albmed, trnmed)
print(uu[0, 0, 0]) # shape: (1, 81, 1)
# This gives 0.027767317
# disort_multi gives ??? I'm unsure how to test this in d_m
# I'm running
# testInput.txt is: 1, 0.5, 10, 30, 50, 40, 20, 1, 0, 0
# And dust_phsfn has 65 moments at 1.5 micron (size) and 9.3 microns(wavelength)
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = disort.disort(
usrang,
usrtau,
ibcnd,
onlyfl,
prnt,
plank,
lamber,
deltamplus,
do_pseudo_sphere,
dtauc,
ssalb,
pmom,
temper,
wvnmlo,
wvnmhi,
utau,
umu0,
phi0,
umu,
phi,
fbeam,
fisot,
albedo,
btemp,
ttemp,
temis,
earth_radius,
h_lyr,
rhoq,
rhou,
rho_accurate,
bemst,
emust,
accur,
header,
rfldir,
rfldn,
flup,
dfdt,
uavg,
uu,
albmed,
trnmed,
maxcly=nlyr,
maxmom=nmom,
maxcmu=nstr,
maxumu=numu,
maxphi=nphi,
maxulv=ntau)
def fit_ssa(guess, wav_index: int):
# Trap the guess
if not 0 <= guess <= 1:
return 9999999
# Take care of surface variables
albedo = hapke[wav_index].albedo
lamber = hapke[wav_index].lambertian
rhou = hapke[wav_index].rhou
rhoq = hapke[wav_index].rhoq
bemst = hapke[wav_index].bemst
emust = hapke[wav_index].emust
rho_accurate = hapke[wav_index].rho_accurate
cext = dust_file.array['primary'].data[:, :, 0]
csca = dust_file.array['primary'].data[:, :, 1]
csca[:, 0:2] = guess * cext[:, 0:2]
c_ext = ForwardScatteringProperty(
cext,
particle_size_grid=sizes,
wavelength_grid=wavs)
c_sca = ForwardScatteringProperty(
csca,
particle_size_grid=sizes,
wavelength_grid=wavs)
dust_properties = ForwardScatteringPropertyCollection()
dust_properties.add_property(c_ext, 'c_extinction')
dust_properties.add_property(c_sca, 'c_scattering')
# Use the Curiosity OD
dust_col = Column(dust_properties, model_eos,
conrath_profile.profile, p_sizes,
short_wav[pixel_index, :], 0.88, pixel_od[pixel_index],
dust_phsfn)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Make the model
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
model = ModelAtmosphere()
# Make tuples of (dtauc, ssalb, pmom) for each constituent
dust_info = (
dust_col.total_optical_depth, dust_col.scattering_optical_depth,
dust_col.scattering_optical_depth * dust_col.phase_function)
rayleigh_info = (
rco2.scattering_optical_depths, rco2.scattering_optical_depths,
rco2.phase_function) # This works since scat OD = total OD
# Add dust and Rayleigh scattering to the model
model.add_constituent(dust_info)
model.add_constituent(rayleigh_info)
model.compute_model()
optical_depths = model.hyperspectral_total_optical_depths[:, wav_index]
ssa = model.hyperspectral_total_single_scattering_albedos[:, wav_index]
polynomial_moments = model.hyperspectral_legendre_moments[:, :, wav_index]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Make the output arrays
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
oa = OutputArrays(cp)
albmed = oa.albedo_medium
flup = oa.diffuse_up_flux
rfldn = oa.diffuse_down_flux
rfldir = oa.direct_beam_flux
dfdt = oa.flux_divergence
uu = oa.intensity
uavg = oa.mean_intensity
trnmed = oa.transmissivity_medium
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Optical depth output structure
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
utau = UserLevel(cp, mb).optical_depth_output
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run the model
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
rfldir, rfldn, flup, dfdt, uavg, uu, albmed, trnmed = \
disort.disort(usrang, usrtau, ibcnd, onlyfl, prnt, plank, lamber,
deltamplus, dopseudosphere, optical_depths, ssa,
polynomial_moments, temperatures, low_wavenumber,
high_wavenumber, utau,
mu0[pixel_index],
phi0[pixel_index],
mu[pixel_index],
phi[pixel_index], fbeam, fisot,
albedo, btemp, ttemp, temis, radius, h_lyr, rhoq, rhou,
rho_accurate, bemst, emust, accur, header, rfldir,
rfldn, flup, dfdt, uavg, uu, albmed, trnmed)
return (uu[0, 0, 0] - reflectance[pixel_index, wav_index])**2
