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atmo_solver_test.py
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atmo_solver_test.py
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#/usr/bin/env python
import sys
sys.path.insert(0, 'python')
import numpy as np
from glob import glob
import cPickle as pkl
from scipy import optimize
from fastDiff import fastDiff
from multi_process import parmap
class solving_atmo_paras(object):
'''
A simple implementation of dark dense vegitation method for the restieval of prior aod.
'''
def __init__(self,
boa, toa,
sza, vza,
saa, vaa,
aod_prior,
tcwv_prior,
tco3_prior,
elevation,
aod_unc,
tcwv_unc,
tco3_unc,
boa_unc,
Hx, Hy,
mask,
full_res,
aero_res,
emulators,
band_indexs,
band_wavelength,
pix_res = 10.,
gamma = 0.5,
alpha = -1.6,# from nasa modis climatology
subsample = 1,
subsample_start = 0
):
self.boa = boa
self.toa = toa
self.sza = np.cos(sza*np.pi/180.)
self.vza = np.cos(vza*np.pi/180.)
self.saa = np.cos(saa*np.pi/180.)
self.vaa = np.cos(vaa*np.pi/180.)
if self.sza.ndim == 3:
self.sza, self.saa = self.sza[0], self.saa[0]
self.raa = np.cos((self.saa - self.vaa)*np.pi/180.)
self.aod_prior = aod_prior
self.tcwv_prior = tcwv_prior
self.tco3_prior = tco3_prior
self.ele = elevation
self.aod_unc = aod_unc
self.tcwv_unc = tcwv_unc
self.tco3_unc = tco3_unc
self.boa_unc = boa_unc
self.Hx, self.Hy = Hx, Hy
self.mask = mask
self.full_res = full_res
self.aero_res = aero_res
self.emus = np.array(emulators)
self.band_indexs = band_indexs
self.gamma = gamma
self.alpha = alpha
self.band_weights = (np.array(band_wavelength)/1000.)**self.alpha
self.band_weights = self.band_weights / self.band_weights.sum()
self.pix_res = pix_res
self.subsample = subsample
self.subsample_start = subsample_start
self.b_m_pixs = (self.aero_res/500.)**2
def _pre_process(self,):
self.block_size = int(np.ceil(1. * self.aero_res / self.pix_res))
self.num_blocks_x = int(np.ceil(self.full_res[0]/(self.block_size)))
self.num_blocks_y = int(np.ceil(self.full_res[1]/(self.block_size)))
#self.mask = self.mask.reshape(self.num_blocks, self.block_size, \
# self.num_blocks, self.block_size).astype(int).sum(axis=(3,1))
#self.mask = (self.mask/((1.*self.block_size)**2)) >= 0.5
try:
#import pdb; pdb.set_trace()
zero_aod = np.zeros((self.num_blocks_x, self.num_blocks_y))
zero_tcwv = np.zeros((self.num_blocks_x, self.num_blocks_y))
zero_tco3 = np.zeros((self.num_blocks_x, self.num_blocks_y))
self.control_variables = np.zeros((self.boa.shape[0], 7, self.num_blocks_x, self.num_blocks_y))
if self.vza.ndim == 2:
for i, parameter in enumerate([self.sza, self.vza, self.raa, zero_aod, zero_tcwv, zero_tco3, self.ele]):
if parameter.shape != (self.num_blocks_x, self.num_blocks_y):
self.control_variables[:, i, :, :] = self._block_resample(parameter)
else:
self.control_variables[:, i, :, :] = parameter
elif self.vza.ndim == 3:
for j in range(len(self.vza)):
for i, parameter in enumerate([self.sza, self.vza[j], self.raa[j], zero_aod, zero_tcwv, zero_tco3, self.ele]):
if parameter.shape != (self.num_blocks_x, self.num_blocks_y):
self.control_variables[j, i, :, :] = self._block_resample(parameter)
else:
self.control_variables[j, i, :, :] = parameter
else:
raise IOError('Angles should be 2D arrays.')
except:
raise IOError('Check the shape of input angles and elevation.')
try:
self.prior_uncs = np.zeros((3, self.num_blocks_x * self.num_blocks_y))
for i, parameter in enumerate([self.aod_unc, self.tcwv_unc, self.tco3_unc]):
if parameter.shape != (self.num_blocks_x, self.num_blocks_y):
self.prior_uncs[i] = self._block_resample(parameter).ravel()
else:
self.prior_uncs[i] = parameter.ravel()
except:
raise IOError('Check the shape of input uncertainties.')
try:
self.priors = np.zeros((3, self.num_blocks_x * self.num_blocks_y))
for i, parameter in enumerate([self.aod_prior, self.tcwv_prior, self.tco3_prior]):
if parameter.shape != (self.num_blocks_x, self.num_blocks_y):
self.priors[i] = self._block_resample(parameter).ravel()
else:
self.priors[i] = parameter.ravel()
except:
raise IOError('Check the shape of input uncertainties.')
self.resample_hx = (1. * self.Hx / self.full_res[0] * self.num_blocks_x).astype(int)
self.resample_hy = (1. * self.Hy / self.full_res[1] * self.num_blocks_y).astype(int)
self.xap_emus = self.emus[0][self.band_indexs]
self.xbp_emus = self.emus[1][self.band_indexs]
self.xcp_emus = self.emus[2][self.band_indexs]
self.up_bounds = self.xap_emus[0].inputs[:,3:6].max(axis=0)
self.bot_bounds = self.xap_emus[0].inputs[:,3:6].min(axis=0)
self.bot_bounds[0] = 0.001
#self.uncs = self.uncs [:, self.resample_hx, self.resample_hx]
#self.priors = self.priors[:, self.resample_hx, self.resample_hx]
y = np.zeros((self.num_blocks_x, self.num_blocks_y))
self.diff = fastDiff(y,axis=(0,),gamma = self.gamma)
def _block_resample(self, parameter):
hx = np.repeat(range(self.num_blocks_x), self.num_blocks_y)
hy = np.tile (range(self.num_blocks_y), self.num_blocks_x)
x_size, y_size = parameter.shape
resample_x = (1.* hx / self.num_blocks_x*x_size).astype(int)
resample_y = (1.* hy / self.num_blocks_y*y_size).astype(int)
resampled_parameter = parameter[resample_x, resample_y].reshape(self.num_blocks_x, self.num_blocks_y)
return resampled_parameter
def _helper(self, inp):
H, dH = inp[0].predict(inp[1].T, do_unc=False)
H, dH = np.array(H).reshape(self.num_blocks_x, self.num_blocks_y), \
np.array(dH)[:,3:6].reshape(self.num_blocks_x, self.num_blocks_y, 3)
return np.hstack([H[self.resample_hx, self.resample_hy][..., None], dH[self.resample_hx, self.resample_hy, :]])
def _obs_cost(self, p, is_full = True):
p = np.array(p).reshape(3, -1)
X = self.control_variables.reshape(self.boa.shape[0], 7, -1)
X[:, 3:6, :] = np.array(p)
xap_H, xbp_H, xcp_H = [], [], []
xap_dH, xbp_dH, xcp_dH = [], [], []
for i in range(len(self.xap_emus)):
H, dH = self.xap_emus[i].predict(X[i].T, do_unc=False)
H, dH = np.array(H).reshape(self.num_blocks_x, self.num_blocks_y), \
np.array(dH)[:,3:6].reshape(self.num_blocks_x, self.num_blocks_y, 3)
xap_H. append(H [self.resample_hx,self.resample_hy])
xap_dH.append(dH[self.resample_hx,self.resample_hy,:])
H, dH = self.xbp_emus[i].predict(X[i].T, do_unc=False)
H, dH = np.array(H).reshape(self.num_blocks_x, self.num_blocks_y), \
np.array(dH)[:,3:6].reshape(self.num_blocks_x, self.num_blocks_y, 3)
xbp_H. append(H [self.resample_hx,self.resample_hy])
xbp_dH.append(dH[self.resample_hx,self.resample_hy,:])
H, dH = self.xcp_emus[i].predict(X[i].T, do_unc=False)
H, dH = np.array(H).reshape(self.num_blocks_x, self.num_blocks_y), \
np.array(dH)[:,3:6].reshape(self.num_blocks_x, self.num_blocks_y, 3)
xcp_H. append(H [self.resample_hx,self.resample_hy])
xcp_dH.append(dH[self.resample_hx,self.resample_hy,:])
#import pdb;pdb.set_trace()
xap_H, xbp_H, xcp_H = np.array(xap_H), np.array(xbp_H), np.array(xcp_H)
xap_dH, xbp_dH, xcp_dH = np.array(xap_dH), np.array(xbp_dH), np.array(xcp_dH)
y = xap_H * self.toa - xbp_H
sur_ref = y / (1 + xcp_H * y)
diff = sur_ref - self.boa
full_J = np.nansum(0.5 * self.band_weights[...,None] * (diff)**2 / self.boa_unc**2, axis=0)
J = np.zeros(self.full_res)
J[self.Hx, self.Hy] = full_J
J = np.nansum(J.reshape(self.num_blocks_x, self.block_size, \
self.num_blocks_y, self.block_size).sum(axis=(3,1))*self.mask)
dH = -1 * (-self.toa[...,None] * xap_dH + xcp_dH * (xbp_H[...,None] - xap_H[...,None] * self.toa[...,None])**2 + \
xbp_dH) /(self.toa[...,None] * xap_H[...,None] * xcp_H[...,None] - xbp_H[...,None] * xcp_H[...,None] + 1)**2
full_dJ = [ self.band_weights[...,None] * dH[:,:,i] * diff / (self.boa_unc**2) for i in range(3)]
if is_full:
dJ = np.nansum(np.array(full_dJ), axis=(1,))
J_ = np.zeros((3,) + self.full_res)
J_[:, self.Hx, self.Hy] = dJ
J_ = np.nansum(J_.reshape(3, self.num_blocks_x, self.block_size, \
self.num_blocks_y, self.block_size), axis=(4,2))
J_[:, ~self.mask] = 0
J_ = J_.reshape(3, -1)
else:
J_ = np.nansum(np.array(full_dJ), axis=(1, 2))
return J, J_
def _obs_cost_test(self, p, is_full = True):
p = np.array(p).reshape(3, -1)
X = self.control_variables.reshape(self.boa.shape[0], 7, -1)
X[:, 3:6, :] = np.array(p)
xap_H, xbp_H, xcp_H = [], [], []
xap_dH, xbp_dH, xcp_dH = [], [], []
emus = list(self.xap_emus) + list(self.xbp_emus) + list(self.xcp_emus)
Xs = list(X) + list(X) + list(X)
inps = zip(emus, Xs)
ret = np.array(parmap(self._helper, inps))
xap_H, xbp_H, xcp_H = ret[:, :, 0] .reshape(3, self.boa.shape[0], len(self.resample_hx))
xap_dH, xbp_dH, xcp_dH = ret[:, :, 1:].reshape(3, self.boa.shape[0], len(self.resample_hx), 3)
y = xap_H * self.toa - xbp_H
sur_ref = y / (1 + xcp_H * y)
diff = sur_ref - self.boa
full_J = np.nansum(0.5 * self.band_weights[...,None] * (diff)**2 / self.boa_unc**2, axis=0)
J = np.zeros(self.full_res)
J[self.Hx, self.Hy] = full_J
J = np.nansum(J.reshape(self.num_blocks_x, self.block_size, \
self.num_blocks_y, self.block_size).sum(axis=(3,1))*self.mask)
#dH = -1 * (-self.toa[...,None] * xap_dH + xcp_dH * (xbp_H[...,None] - xap_H[...,None] * self.toa[...,None])**2 + \
# xbp_dH) /(self.toa[...,None] * xap_H[...,None] * xcp_H[...,None] - xbp_H[...,None] * xcp_H[...,None] + 1)**2
dH = -1 * (-self.toa[...,None] * xap_dH - \
2 * self.toa[...,None] * xap_H[...,None] * xbp_H[...,None] * xcp_dH + \
self.toa[...,None]**2 * xap_H[...,None]**2 * xcp_dH + \
xbp_dH + \
xbp_H[...,None]**2 * xcp_dH) / \
(self.toa[...,None] * xap_H[...,None] * xcp_H[...,None] - \
xbp_H[...,None] * xcp_H[...,None] + 1)**2
full_dJ = [ self.band_weights[...,None] * dH[:,:,i] * diff / (self.boa_unc**2) for i in range(3)]
if is_full:
dJ = np.nansum(np.array(full_dJ), axis=(1,))
J_ = np.zeros((3,) + self.full_res)
J_[:, self.Hx, self.Hy] = dJ
J_ = np.nansum(J_.reshape(3, self.num_blocks_x, self.block_size, \
self.num_blocks_y, self.block_size), axis=(4,2))
J_[:, ~self.mask] = 0
J_ = J_.reshape(3, -1)
else:
J_ = np.nansum(np.array(full_dJ), axis=(1, 2))
return J, J_
def _smooth_cost(self, p, is_full=True):
p = np.array(p).reshape(3, -1)
aod, tcwv, tco3 = np.array(p).reshape(3, self.num_blocks_x, self.num_blocks_y)
J_aod, J_aod_ = self.diff.cost_der_cost(aod, self.mask)
J_tcwv, J_tcwv_ = self.diff.cost_der_cost(tcwv, self.mask)
J_tco3, J_tco3_ = self.diff.cost_der_cost(tco3, self.mask)
J, full_dJ = J_aod + J_tcwv + J_tco3, np.array([J_aod_, J_tcwv_, J_tco3_])
if is_full:
J_ = np.array(full_dJ).reshape(3, -1)
else:
J_ = np.array(full_dJ).reshape(3, -1).sum(axis=(1,))
return J, J_
def _new_smooth_cost(self, p, is_full=True):
p = np.array(p).reshape(3, -1)
aod, tcwv, tco3 = np.array(p).reshape(3, self.num_blocks_x, self.num_blocks_y)
J_aod, J_aod_ = self._fit_smoothness(aod, self.mask, 1. / self.gamma)
J_tcwv, J_tcwv_ = self._fit_smoothness(tcwv, self.mask, 1. / self.gamma)
J_tco3, J_tco3_ = self._fit_smoothness(tco3, self.mask, 1. / self.gamma)
J, full_dJ = J_aod + J_tcwv + J_tco3, np.array([J_aod_, J_tcwv_, J_tco3_])
if is_full:
J_ = np.array(full_dJ).reshape(3, -1)
else:
J_ = np.nansum(np.array(full_dJ).reshape(3, -1), axis=(1,))
return J, J_
def _fit_smoothness (self, x, mask, sigma_model):
# Build up the 8-neighbours
hood = np.array ( [ x[:-2, :-2], x[:-2, 1:-1], x[ :-2, 2: ], \
x[ 1:-1,:-2], x[1:-1, 2:], \
x[ 2:,:-2], x[ 2:, 1:-1], x[ 2:, 2:] ] )
j_model = 0
der_j_model = np.zeros_like(x)
for i in [1,3,4,6]:
dif = hood[i,:,:] - x[1:-1,1:-1]
dif[~mask[1:-1,1:-1]] = 0
j_model = j_model + 0.5 * np.sum(dif **2)/sigma_model**2
der_j_model[1:-1,1:-1] = der_j_model[1:-1,1:-1] - dif/sigma_model**2
return j_model, 2 * der_j_model
def _prior_cost(self, p, is_full=True):
p = np.array(p).reshape(3, -1)
J = 0.5 * (p - self.priors)**2/self.prior_uncs**2
full_dJ = (p - self.priors)/self.prior_uncs**2
J [:, ~self.mask.ravel()] = 0
full_dJ[:, ~self.mask.ravel()] = 0
if is_full:
J_ = np.array(full_dJ)
else:
J_ = np.nansum(np.array(full_dJ), axis=(1,))
J = np.array(J).sum()
return J, J_
def _cost(self, p):
print '-------------------------------------------------------------------------------'
print 'Means: ', np.array(p).reshape(3, -1)[:, self.mask.ravel()].mean(axis=-1)
obs_J, obs_J_ = self._obs_cost_test(p)
prior_J, prior_J_ = self._prior_cost(p)
#smooth_J, smooth_J_ = self._smooth_cost(p)
smooth_J, smooth_J_ = self._new_smooth_cost(p)
J = obs_J/self.b_m_pixs + prior_J + smooth_J
J_ = (obs_J_/self.b_m_pixs + prior_J_ + smooth_J_).ravel()
print 'costs: ', [obs_J/self.b_m_pixs, prior_J, smooth_J]
print 'J_prime: ', ((obs_J_/self.b_m_pixs)[:,self.mask.ravel()] + prior_J_[:, self.mask.ravel()] + smooth_J_[:, self.mask.ravel()]).sum(axis=1)
print '-------------------------------------------------------------------------------'
return J, J_
def _pure_cost(self, p):
J, J_ = self._cost(p)
Jprime = J_
def _optimization(self,):
self._pre_process()
p0 = self.priors
bot = np.zeros_like(p0)
up = np.zeros_like(p0)
bot = np.ones(p0.shape) * self.bot_bounds[...,None]
up = np.ones(p0.shape) * self.up_bounds [...,None]
p0 = p0.ravel()
bot = bot.ravel()
up = up.ravel()
bounds = np.array([bot, up]).T
gamma = 0.01
p = p0.copy()
Js = []
ps = []
J, J_ = self._cost(p0)
Js.append(J)
ps.append(p0)
for i in range(100):
p -= gamma * J_
J, J_ = self._cost(p)
J += (((p < bot) | (p > up)).sum()) * 1e8
p[p < bot] = bot[p < bot]
p[p > up ] = up [p > up ]
#gamma = 1 * 2**(-i-1)
#gamma = np.nansum((p - ps[-1])*(J-Js[-1]))/np.nansum(J-Js[-1])**2
#gamma = (p1 - p) * (J_ - J1_)/(J_ - J1_)**2
#J, J_ = J1, J1_
#p = p1
Js.append(J)
ps.append(p.copy())
jj
'''
for i in range(20):
J, J_ = self._cost(p)
p -= J_*0.1
Js.append(J)
ps.append(p)
#if abs((J-J_old)).sum()/self.mask.sum() < 1e-10:
# return p
#elif max(abs(J_)) < 1e-5:
# return p
#else:
# J_old = J
#return p
jj
'''
#solved = optimize.root(self._cost, p0, jac=True, method= 'krylov')
#solved = optimize.fmin_tnc(self._cost, p0, approx_grad = 0, disp = 5, maxfun=500, bounds = bounds, fprime=None)
#psolve = optimize.fmin_l_bfgs_b(self._cost, p0, approx_grad = 0, iprint = 101, m=20,\
# maxiter=500, pgtol = 1e-5,factr=1e3, bounds = bounds,fprime=None)
#ijj
#return p
if __name__ == '__main__':
sza = np.ones((23,23))
vza = np.ones((23,23))
vaa = np.ones((23,23))
saa = np.ones((23,23))
ele = np.ones((61,61))
aod = np.ones((61,61))
tcwv = np.ones((61,61))
tco3 = np.ones((61,61))
aod_unc = np.ones((61,61))
tcwv_unc = np.ones((61,61))
tco3_unc = np.ones((61,61))
sza[:] = 30.
vza[:] = 10.
vaa[:] = 100.
saa[:] = 150.
ele[:] = 0.02
aod[:] = 0.45
tcwv[:] = 2.3
tco3[:] = 0.3
aod_unc[:] = 0.5
tcwv_unc[:] = 0.2
tco3_unc[:] = 0.2
toa = np.random.rand(6, 50000)
y = toa * 2.639794 - 0.038705
boa = y/(1+0.068196*y)
boa_unc = np.ones(50000) * 0.05
Hx = np.random.choice(10980, 50000)
Hy = np.random.choice(10980, 50000)
full_res = (10980, 10980)
aero_res = 3050
emus_dir = '/home/ucfafyi/DATA/Multiply/emus/'
sensor = 'msi'
xap_emu = glob(emus_dir + '/isotropic_%s_emulators_*_xap.pkl'%(sensor))[0]
xbp_emu = glob(emus_dir + '/isotropic_%s_emulators_*_xbp.pkl'%(sensor))[0]
xcp_emu = glob(emus_dir + '/isotropic_%s_emulators_*_xcp.pkl'%(sensor))[0]
f = lambda em: pkl.load(open(em, 'rb'))
emus = parmap(f, [xap_emu, xbp_emu, xcp_emu])
band_indexs = [1, 2, 3, 7, 11, 12]
band_wavelength = [469, 555, 645, 869, 1640, 2130]
mask = np.zeros((10980, 10980)).astype(bool)
mask[1, 1] = True
aero = solving_atmo_paras(boa, toa,
sza, vza,
saa, vaa,
aod,
tcwv,
tco3,
ele,
aod_unc,
tcwv_unc,
tco3_unc,
boa_unc,
Hx, Hy,
mask,
full_res,
aero_res,
emus,
band_indexs,
band_wavelength)
solved = aero._optimization()