def main():
ap = ArgumentParser()
ap.add_argument('--port',
type=int,
default=8888,
help='Port. [%(default)s]')
args = ap.parse_args()
# initialize isow as the mean of a fixed range
specwave = analysis.loadmat_single('../data/specwave2.mat').ravel()
calspec = analysis.loadmat_single('../data/calspecw2.mat').ravel()
isoind1, isoind2 = np.searchsorted(specwave, (0.5, 1.25))
MODEL.set_isow(calspec[isoind1:isoind2].mean())
# load spectrum data
SPECTRA['file1'] = analysis.loadmat_single('../data/kjs.mat')
SPECTRA['file2'] = analysis.loadmat_single('../data/kjm.mat')
SPECTRA['file3'] = analysis.loadmat_single('../data/kjb.mat')
for k in SPECTRA.keys():
SPECTRA[k] = analysis.preprocess_traj(SPECTRA[k], 0.405, 2.451, 0.301)
K['file2'] = analysis.MasterHapke1_PP(MODEL, SPECTRA['file2'], .1, .3, .46,
0, 63)
app = MplWebApp([(r'/', DemoHandler)], debug=True)
app.listen(args.port)
print('Starting UI server at http://%s:%s/' % (gethostname(), args.port))
tornado.ioloop.IOLoop.current().start()
def initialize(self, phase_fn='legendre', scatter_type='lambertian',
thetai=0, thetae=0, n1=0,
specwave_file='', calspec_file='', **kwargs):
plt.close('all') # hack!
# HACK: use defaults if some/all files aren't provided
specwave_file = specwave_file or '../data/specwave2.mat'
calspec_file = calspec_file or '../data/calspecw2.mat'
self.Bg = True if 'Bg' in kwargs else False
Hu = True if 'HuApprox' in kwargs else False
self.global_thetai, self.global_thetae = np.deg2rad([float(thetai), float(thetae)])
self.hapke_scalar = HapkeModel(self.global_thetai, self.global_thetae, float(n1), self.Bg, phase_fn, scatter_type, Hu)
self.spectra = {}
self.n1 = float(n1)
for key in kwargs:
if 'file' in key:
#Checks if the infile variable has a string -- sanity check if not all ten files are uploaded
#self.spectra has the number of grain files included in the process
if not kwargs[key] == '':
self.spectra[key] = analysis.loadmat_single(kwargs[key])
# data to be filled in (later) for each grain size
self.pp_spectra = {}
self.ks = {}
self.guesses = {}
self.scat_eff_grain = {}
if self.hapke_scalar.needs_isow:
# store the calibration spectrum
specwave = analysis.loadmat_single(specwave_file).ravel()
calspec = analysis.loadmat_single(calspec_file).ravel()
self.calspec = np.column_stack((specwave, calspec))
# plot the loaded spectra
num_plots = 2 if self.hapke_scalar.needs_isow else 1
fig = Figure(figsize=(9, 4), frameon=False, tight_layout=True)
ax1 = fig.add_subplot(1, num_plots, 1)
#Adding plots for files uploaded - can upload maximum of 10 files including the three default
for k in self.spectra:
ax1.plot(*self.spectra[k].T, label=k)
ax1.set_xlabel('Wavelength ($\mu{}m)$')
ax1.set_ylabel('Reflectance')
ax1.legend(fontsize='small', loc='best')
ax1.set_title('Input VNIR spectra')
if self.hapke_scalar.needs_isow:
ax2 = fig.add_subplot(1, num_plots, 2)
ax2.plot(specwave, calspec, 'k-')
ax2.set_title('Calibrated standard')
ax2.set_xlabel('Wavelength ($\mu{}m)$')
# return html for a plot of the inputs + calibration
return 'Initialization complete.', None, [fig]
def phase_solver(self, phaseAngleCount, fit_order, minScale, maxScale, minOffset, maxOffset, maxfun = 1000,
spts=30, diff_step = 0.0001, funtol = 0.00000000000001, xtol= 0.00000000000001, **kwargs ):
plt.close('all') # hack!
k = self.ks['global']
#Input: grain size, phase angle
self.phases = {}
#Total no of grain sizes == no of pp_spectras
no_grain_sizes = len(self.pp_spectra.keys())
phaseGrainList = {}
phase_bcsd = {}
for i in range(no_grain_sizes):
phaseGrainList[i] = []
ii = str(i)
if self.Bg:
bcsd = float(kwargs['p_b_'+ii]), float(kwargs['p_c_'+ii]), float(kwargs['p_s_'+ii]), float(kwargs['p_d_'+ii]), float(kwargs['p_b0_'+ii]), float(kwargs['p_h_'+ii])
lb_bcsd = float(kwargs['plb_b_'+ii]), float(kwargs['plb_c_'+ii]), float(kwargs['plb_s_'+ii]), float(kwargs['plb_d_'+ii]), float(kwargs['plb_b0_'+ii]), float(kwargs['plb_h_'+ii])
ub_bcsd = float(kwargs['pub_b_'+ii]), float(kwargs['pub_c_'+ii]), float(kwargs['pub_s_'+ii]), float(kwargs['pub_d_'+ii]), float(kwargs['pub_b0_'+ii]), float(kwargs['pub_h_'+ii])
else:
bcsd = float(kwargs['p_b_'+ii]), float(kwargs['p_c_'+ii]), float(kwargs['p_s_'+ii]), float(kwargs['p_d_'+ii])
lb_bcsd = float(kwargs['plb_b_'+ii]), float(kwargs['plb_c_'+ii]), float(kwargs['plb_s_'+ii]), float(kwargs['plb_d_'+ii])
ub_bcsd = float(kwargs['pub_b_'+ii]), float(kwargs['pub_c_'+ii]), float(kwargs['pub_s_'+ii]), float(kwargs['pub_d_'+ii])
phase_bcsd[i] = bcsd, lb_bcsd, ub_bcsd
for j in range(int(phaseAngleCount)):
id = '_%s_%s' % (i,j)
data = analysis.loadmat_single(kwargs['filepfile'+id])
phaseAngle = PhaseAngleObj(i, kwargs['pfile_i'+id], kwargs['pfile_e'+id], data)
phaseGrainList[i].append(phaseAngle)
phaseGrainList[i].sort(key=lambda x: (x.incident_angle, x.emission_angle))
#This program will use data from multiple viewing geometries to calculate
#phase function parameters for a sample where k and n for i=30, e=0 is already
#known.
#This program downsamples the data and then uses a
#minimization routine to find the best wavelength dependent b and c
#coefficients for the phase function by minimizing the difference between
#the calculated and observed data for multiple viewing geometries and
#multiple grain sizes simultaneously.
ffs = self.global_ff
lstart2 = self.sskk_lstart
lend2 = self.sskk_lend
lamdiff = self.sskk_lamdiff
low, high, UV = self.pp_bounds
vislam, visn = self.vislam, self.visn
wavelength = self.pp_spectra['file2'][:,0]
params = (lstart2, lend2, low, UV, lamdiff, float(minScale), float(maxScale), float(minOffset), float(maxOffset), int(maxfun), float(funtol), float(xtol), int(spts), float(diff_step),
vislam, visn, wavelength, k, int(fit_order), int(phaseAngleCount), phaseGrainList, phase_bcsd, ffs, self.hapke_vector_isow)
plt_data, allbest = analysis.solve_phase(self.phases, params)
self.phase_best_soln, self.phase_bscale, self.phase_boffset, self.phase_k, self.phase_wave, self.phase_n = allbest
figures = [plt.figure(i) for i in plt.get_fignums()]
self.phase = plt_data
return 'Phase Solved ', 'psolve', figures
def add_mir_data(self, mirk_file='', mirv_file='', repk_file='', adjType=3):
mirk_file = mirk_file or '../data/bytMIRk.mat'
mirv_file = mirv_file or '../data/bytMIRv.mat'
k = analysis.loadmat_single(repk_file)[:,1] if repk_file else self.ks['global']
self.ks['global'] = k # if from file we over-write it otherwise this is redundant.
wave = self.pp_spectra['file2'][:,0]
plt.close('all') # hack!
pltData, kset = analysis.MasterKcombine(mirk_file, mirv_file, wave, k, adjType)
self.vnirv, self.vnirk, self.fullv, self.fullk = kset
figures = [plt.figure(i) for i in plt.get_fignums()]
self.mirdata = pltData
return 'Combined MIR + VNIR k: ', 'mirdata', figures
def main():
ap = ArgumentParser()
ap.add_argument('--port', type=int, default=8888, help='Port. [%(default)s]')
args = ap.parse_args()
# initialize isow as the mean of a fixed range
specwave = analysis.loadmat_single('../data/specwave2.mat').ravel()
calspec = analysis.loadmat_single('../data/calspecw2.mat').ravel()
isoind1, isoind2 = np.searchsorted(specwave, (0.5, 1.25))
MODEL.set_isow(calspec[isoind1:isoind2].mean())
# load spectrum data
SPECTRA['file1'] = analysis.loadmat_single('../data/kjs.mat')
SPECTRA['file2'] = analysis.loadmat_single('../data/kjm.mat')
SPECTRA['file3'] = analysis.loadmat_single('../data/kjb.mat')
for k in SPECTRA.keys():
SPECTRA[k] = analysis.preprocess_traj(SPECTRA[k], 0.405, 2.451, 0.301)
K['file2'] = analysis.MasterHapke1_PP(MODEL, SPECTRA['file2'], .1, .3, .46, 0, 63)
app = MplWebApp([(r'/', DemoHandler)], debug=True)
app.listen(args.port)
print('Starting UI server at http://%s:%s/' % (gethostname(), args.port))
tornado.ioloop.IOLoop.current().start()
def add_mir_data(self,
mirk_file='',
mirv_file='',
repk_file='',
adjType=3):
mirk_file = mirk_file or '../data/bytMIRk.mat'
mirv_file = mirv_file or '../data/bytMIRv.mat'
k = analysis.loadmat_single(
repk_file)[:, 1] if repk_file else self.ks['global']
self.ks[
'global'] = k # if from file we over-write it otherwise this is redundant.
wave = self.pp_spectra['file2'][:, 0]
plt.close('all') # hack!
pltData, kset = analysis.MasterKcombine(mirk_file, mirv_file, wave, k,
adjType)
self.vnirv, self.vnirk, self.fullv, self.fullk = kset
figures = [plt.figure(i) for i in plt.get_fignums()]
self.mirdata = pltData
return 'Combined MIR + VNIR k: ', 'mirdata', figures
def phase_solver(self,
phaseAngleCount,
fit_order,
minScale,
maxScale,
minOffset,
maxOffset,
maxfun=1000,
spts=30,
diff_step=0.0001,
funtol=0.00000000000001,
xtol=0.00000000000001,
**kwargs):
plt.close('all') # hack!
k = self.ks['global']
#Input: grain size, phase angle
self.phases = {}
#Total no of grain sizes == no of pp_spectras
no_grain_sizes = len(self.pp_spectra.keys())
phaseGrainList = {}
phase_bcsd = {}
for i in range(no_grain_sizes):
phaseGrainList[i] = []
ii = str(i)
if self.Bg:
bcsd = float(kwargs['p_b_' + ii]), float(
kwargs['p_c_' + ii]), float(kwargs['p_s_' + ii]), float(
kwargs['p_d_' + ii]), float(
kwargs['p_b0_' + ii]), float(kwargs['p_h_' + ii])
lb_bcsd = float(kwargs['plb_b_' + ii]), float(
kwargs['plb_c_' + ii]), float(
kwargs['plb_s_' + ii]), float(
kwargs['plb_d_' + ii]), float(
kwargs['plb_b0_' + ii]), float(
kwargs['plb_h_' + ii])
ub_bcsd = float(kwargs['pub_b_' + ii]), float(
kwargs['pub_c_' + ii]), float(
kwargs['pub_s_' + ii]), float(
kwargs['pub_d_' + ii]), float(
kwargs['pub_b0_' + ii]), float(
kwargs['pub_h_' + ii])
else:
bcsd = float(kwargs['p_b_' + ii]), float(
kwargs['p_c_' + ii]), float(kwargs['p_s_' + ii]), float(
kwargs['p_d_' + ii])
lb_bcsd = float(kwargs['plb_b_' + ii]), float(
kwargs['plb_c_' + ii]), float(
kwargs['plb_s_' + ii]), float(kwargs['plb_d_' + ii])
ub_bcsd = float(kwargs['pub_b_' + ii]), float(
kwargs['pub_c_' + ii]), float(
kwargs['pub_s_' + ii]), float(kwargs['pub_d_' + ii])
phase_bcsd[i] = bcsd, lb_bcsd, ub_bcsd
for j in range(int(phaseAngleCount)):
id = '_%s_%s' % (i, j)
data = analysis.loadmat_single(kwargs['filepfile' + id])
phaseAngle = PhaseAngleObj(i, kwargs['pfile_i' + id],
kwargs['pfile_e' + id], data)
phaseGrainList[i].append(phaseAngle)
phaseGrainList[i].sort(
key=lambda x: (x.incident_angle, x.emission_angle))
#This program will use data from multiple viewing geometries to calculate
#phase function parameters for a sample where k and n for i=30, e=0 is already
#known.
#This program downsamples the data and then uses a
#minimization routine to find the best wavelength dependent b and c
#coefficients for the phase function by minimizing the difference between
#the calculated and observed data for multiple viewing geometries and
#multiple grain sizes simultaneously.
ffs = self.global_ff
lstart2 = self.sskk_lstart
lend2 = self.sskk_lend
lamdiff = self.sskk_lamdiff
low, high, UV = self.pp_bounds
vislam, visn = self.vislam, self.visn
wavelength = self.pp_spectra['file2'][:, 0]
params = (lstart2, lend2, low, UV, lamdiff, float(minScale),
float(maxScale), float(minOffset), float(maxOffset),
int(maxfun), float(funtol), float(xtol), int(spts),
float(diff_step), vislam, visn, wavelength, k,
int(fit_order), int(phaseAngleCount), phaseGrainList,
phase_bcsd, ffs, self.hapke_vector_isow)
plt_data, allbest = analysis.solve_phase(self.phases, params)
self.phase_best_soln, self.phase_bscale, self.phase_boffset, self.phase_k, self.phase_wave, self.phase_n = allbest
figures = [plt.figure(i) for i in plt.get_fignums()]
self.phase = plt_data
return 'Phase Solved ', 'psolve', figures
def initialize(self,
phase_fn='legendre',
scatter_type='lambertian',
thetai=0,
thetae=0,
n1=0,
specwave_file='',
calspec_file='',
**kwargs):
plt.close('all') # hack!
# HACK: use defaults if some/all files aren't provided
specwave_file = specwave_file or '../data/specwave2.mat'
calspec_file = calspec_file or '../data/calspecw2.mat'
self.Bg = True if 'Bg' in kwargs else False
Hu = True if 'HuApprox' in kwargs else False
self.global_thetai, self.global_thetae = np.deg2rad(
[float(thetai), float(thetae)])
self.hapke_scalar = HapkeModel(self.global_thetai, self.global_thetae,
float(n1), self.Bg, phase_fn,
scatter_type, Hu)
self.spectra = {}
self.n1 = float(n1)
for key in kwargs:
if 'file' in key:
#Checks if the infile variable has a string -- sanity check if not all ten files are uploaded
#self.spectra has the number of grain files included in the process
if not kwargs[key] == '':
self.spectra[key] = analysis.loadmat_single(kwargs[key])
# data to be filled in (later) for each grain size
self.pp_spectra = {}
self.ks = {}
self.guesses = {}
self.scat_eff_grain = {}
if self.hapke_scalar.needs_isow:
# store the calibration spectrum
specwave = analysis.loadmat_single(specwave_file).ravel()
calspec = analysis.loadmat_single(calspec_file).ravel()
self.calspec = np.column_stack((specwave, calspec))
# plot the loaded spectra
num_plots = 2 if self.hapke_scalar.needs_isow else 1
fig = Figure(figsize=(9, 4), frameon=False, tight_layout=True)
ax1 = fig.add_subplot(1, num_plots, 1)
#Adding plots for files uploaded - can upload maximum of 10 files including the three default
for k in self.spectra:
ax1.plot(*self.spectra[k].T, label=k)
ax1.set_xlabel('Wavelength ($\mu{}m)$')
ax1.set_ylabel('Reflectance')
ax1.legend(fontsize='small', loc='best')
ax1.set_title('Input VNIR spectra')
if self.hapke_scalar.needs_isow:
ax2 = fig.add_subplot(1, num_plots, 2)
ax2.plot(specwave, calspec, 'k-')
ax2.set_title('Calibrated standard')
ax2.set_xlabel('Wavelength ($\mu{}m)$')
# return html for a plot of the inputs + calibration
return 'Initialization complete.', None, [fig]
def main():
args = parse_args()
print('Preparing variables')
HapkeModel = get_hapke_model(phase_fn=args.phase_function,
scatter=args.scatter_type)
hapke = HapkeModel(np.deg2rad(args.thetai), np.deg2rad(args.thetae),
args.n1, args.Bg)
files = dict(sml=args.small_file, med=args.medium_file, big=args.large_file)
params = {}
for i, key in enumerate(('file1', 'file2', 'file3')):
params[key] = (args.b[i], args.c[i], args.ff[i], args.s[i], args.D[i])
if hapke.needs_isow:
# initialize isow as the mean of a fixed range
specwave = analysis.loadmat_single(args.specwave_file).ravel()
calspec = analysis.loadmat_single(args.calspec_file).ravel()
isoind1, isoind2 = np.searchsorted(specwave, (0.5, 1.25))
hapke.set_isow(calspec[isoind1:isoind2].mean())
# section 1
print('Running section 1')
spectra = {}
for key, infile in files.items():
traj = analysis.loadmat_single(infile)
spectra[key] = analysis.preprocess_traj(traj, args.low, args.high, args.UV)
# sections 2, 3, 4
ks = {}
for key, traj in spectra.items():
print('Running sections 2,3,4 (MasterHapke1_PP: %s)' % key)
ks[key] = analysis.MasterHapke1_PP(hapke, traj, *params[key],
debug_plots=args.debug_plots)
# section 5 isn't worth porting
print('Skipping section 5')
# section 6: iterative minimizations
print('Running section 6 (MasterHapke2_PP)')
if hapke.needs_isow:
specwave, calspec = analysis.prepare_spectrum(specwave, calspec, args.UV,
args.high)
hapke.set_isow(calspec)
# use the medium-grain k as an initial guess
k = ks['file2']
# XXX: this takes too long, skip it
# guesses = np.concatenate((args.b, args.c, args.s, args.D, k))
# lb = np.concatenate((args.lowb, args.lowc, args.lows, args.lowD,
# np.zeros_like(k) + args.lowk))
# ub = np.concatenate((args.upb, args.upc, args.ups, args.upD,
# np.zeros_like(k) + args.upk))
# solutions = analysis.MasterHapke2_PP(hapke, spectra, guesses, lb, ub,
# args.ff, tr_solver='lsmr', verbose=2)
# section 7/8: graphs the parameters from the previous section
if args.debug_plots:
# print 'Running sections 7,8 (plotting %d solutions)' % len(solutions)
# see HapkeEval1_PP.m
# plot initial guesses -> solved values for b, c, s, D, and k
# TODO
# see HapkeEval3_PP.m
# plot given reflectances (spectra) vs solved rcs
pass
# section 9: add in your MIR data
# If you do not have MIR data (not recommended), you can skip this step.
# If you do have MIR data, use the DISPERSION programs on the website to get
# k data for your sample through the MIR.
wave = spectra['file2'][:,0]
if all(os.path.exists(f) for f in (args.mir_dispersion_k,
args.mir_dispersion_v)):
print('Running section 9 (MasterKcombine)')
combined = analysis.MasterKcombine(args.mir_dispersion_k,
args.mir_dispersion_v, wave, k)
if args.debug_plots:
plt.figure()
plt.plot(10000/combined[:,0], combined[:,1])
plt.title('MasterKcombine')
else:
print('Skipping section 9 (MasterKcombine): MIR data not found')
combined = np.column_stack((10000/wave, k))
# section 10: singly subtractive Kramers Kronig calculation
print('Running section 10 (MasterSSKK)')
res = analysis.MasterSSKK(combined, args.n1, args.anchor)
if args.debug_plots:
fig, ax = plt.subplots()
ax.plot(10000/res[:,0], res[:,1])
ax.set_xlabel('Wavelength (um)')
ax.set_ylabel('n')
if args.debug_plots:
plt.show()
def main():
args = parse_args()
print('Preparing variables')
HapkeModel = get_hapke_model(phase_fn=args.phase_function,
scatter=args.scatter_type)
hapke = HapkeModel(np.deg2rad(args.thetai), np.deg2rad(args.thetae),
args.n1, args.Bg)
files = dict(sml=args.small_file,
med=args.medium_file,
big=args.large_file)
params = {}
for i, key in enumerate(('file1', 'file2', 'file3')):
params[key] = (args.b[i], args.c[i], args.ff[i], args.s[i], args.D[i])
if hapke.needs_isow:
# initialize isow as the mean of a fixed range
specwave = analysis.loadmat_single(args.specwave_file).ravel()
calspec = analysis.loadmat_single(args.calspec_file).ravel()
isoind1, isoind2 = np.searchsorted(specwave, (0.5, 1.25))
hapke.set_isow(calspec[isoind1:isoind2].mean())
# section 1
print('Running section 1')
spectra = {}
for key, infile in files.items():
traj = analysis.loadmat_single(infile)
spectra[key] = analysis.preprocess_traj(traj, args.low, args.high,
args.UV)
# sections 2, 3, 4
ks = {}
for key, traj in spectra.items():
print('Running sections 2,3,4 (MasterHapke1_PP: %s)' % key)
ks[key] = analysis.MasterHapke1_PP(hapke,
traj,
*params[key],
debug_plots=args.debug_plots)
# section 5 isn't worth porting
print('Skipping section 5')
# section 6: iterative minimizations
print('Running section 6 (MasterHapke2_PP)')
if hapke.needs_isow:
specwave, calspec = analysis.prepare_spectrum(specwave, calspec,
args.UV, args.high)
hapke.set_isow(calspec)
# use the medium-grain k as an initial guess
k = ks['file2']
# XXX: this takes too long, skip it
# guesses = np.concatenate((args.b, args.c, args.s, args.D, k))
# lb = np.concatenate((args.lowb, args.lowc, args.lows, args.lowD,
# np.zeros_like(k) + args.lowk))
# ub = np.concatenate((args.upb, args.upc, args.ups, args.upD,
# np.zeros_like(k) + args.upk))
# solutions = analysis.MasterHapke2_PP(hapke, spectra, guesses, lb, ub,
# args.ff, tr_solver='lsmr', verbose=2)
# section 7/8: graphs the parameters from the previous section
if args.debug_plots:
# print 'Running sections 7,8 (plotting %d solutions)' % len(solutions)
# see HapkeEval1_PP.m
# plot initial guesses -> solved values for b, c, s, D, and k
# TODO
# see HapkeEval3_PP.m
# plot given reflectances (spectra) vs solved rcs
pass
# section 9: add in your MIR data
# If you do not have MIR data (not recommended), you can skip this step.
# If you do have MIR data, use the DISPERSION programs on the website to get
# k data for your sample through the MIR.
wave = spectra['file2'][:, 0]
if all(
os.path.exists(f)
for f in (args.mir_dispersion_k, args.mir_dispersion_v)):
print('Running section 9 (MasterKcombine)')
combined = analysis.MasterKcombine(args.mir_dispersion_k,
args.mir_dispersion_v, wave, k)
if args.debug_plots:
plt.figure()
plt.plot(10000 / combined[:, 0], combined[:, 1])
plt.title('MasterKcombine')
else:
print('Skipping section 9 (MasterKcombine): MIR data not found')
combined = np.column_stack((10000 / wave, k))
# section 10: singly subtractive Kramers Kronig calculation
print('Running section 10 (MasterSSKK)')
res = analysis.MasterSSKK(combined, args.n1, args.anchor)
if args.debug_plots:
fig, ax = plt.subplots()
ax.plot(10000 / res[:, 0], res[:, 1])
ax.set_xlabel('Wavelength (um)')
ax.set_ylabel('n')
if args.debug_plots:
plt.show()
