def __init__(self,corefile=os.path.join(about_me()['cawadir'],'luts','cloud_core_meris.nc4'),used_ab=None):
self.about_me=about_me()
with Dataset(corefile,'r') as ncds:
#get the full lut
self.lut=np.array(ncds.variables['lut'][:],order='F')
self.jlut=np.array(ncds.variables['jlut'][:],order='F')
self.axes=tuple([np.array(ncds.variables[a][:]) for a in ncds.variables['lut'].dimensions[:-1]])
self.jaxes=tuple([np.array(ncds.variables[a][:]) for a in ncds.variables['jlut'].dimensions[:-1]])
self.ny_nx=np.array(ncds.variables['jaco'][:])
self.wb=ncds.getncattr('win_bnd').split(',')
self.ab=ncds.getncattr('abs_bnd').split(',')
self.cha={bb:
{kk:ncds.groups['cha'].groups[bb].getncattr(kk)
for kk in ('bwvl','cwvl')}
for bb in self.wb+self.ab}
if used_ab is None:
self.ab_idx=None
else:
self.ab=[used_ab,]
self.ab_idx=pos_in_list(ncds.getncattr('abs_bnd').split(','),used_ab)[0]
#generic forward
self._forward=lut2func.lut2func(self.lut,self.axes)
#generic jacobian
self._jacobian=lut2jacobian_lut.jlut2func({'lut':self.jlut
,'axes':self.jaxes
,'ny':self.ny_nx[0]
,'nx':self.ny_nx[1]})
#global predefinition of input for speed reasons
self.xaa=np.zeros(2)
self.par=np.zeros(5)
self.mes=np.zeros(len(self.wb)+len(self.ab))
#local predefine inp for speed
inp=np.zeros(7)
def forward(woo,par):
'''
Input:
woo: state (ctp,cot)
par: parameter (alb, sza, vza, azd, dwvl)
aot is aot at winband [0]
wvc is sqrt of wvc
Output:
normalized radiances at winbands and absbands
'''
inp[:2],inp[2:]=woo,par
if self.ab_idx is None:
return self._forward(inp)
else:
return self._forward(inp)[((0,1+self.ab_idx),)]
self.forward =forward
def jforward(woo,geo):
'''
as forward, but returns jacobian
output must be limited to the first three elements (the state and not the geometry)
'''
inp[:2],inp[2:]=woo,geo
if self.ab_idx is None:
return self._jacobian(inp)[:,:2]
else:
return self._jacobian(inp)[(0,1+self.ab_idx),:2]
self.jforward=jforward
#min_state
a=np.array([self.axes[i].min() for i in range(2)])
#max_state
b=np.array([self.axes[i].max() for i in range(2)])
self.inverter=oe.my_inverter(self.forward,a,b,jaco=self.jforward)
#finaly preset SE
sew=[0.0001 for i in self.wb]
sea=[0.0001 for i in self.ab]
self.SE=np.diag(sew+sea)
def __init__(self, ocean_lut=os.path.join('.', 'luts', 'ocean', 'ocean_core_meris.nc4')):
# sys.stderr.write('entering CawaTcwvOceanCore... \n')
libdir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'libs')
sys.path.append(libdir)
with Dataset(ocean_lut, 'r') as ncds:
# get the full lut
self.lut = np.array(ncds.variables['lut'][:], order='F')
self.jlut = np.array(ncds.variables['jlut'][:], order='F')
self.axes = tuple([np.array(ncds.variables[a][:]) for a in ncds.variables['lut'].dimensions[:-1]])
self.jaxes = tuple([np.array(ncds.variables[a][:]) for a in ncds.variables['jlut'].dimensions[:-1]])
self.ny_nx = np.array(ncds.variables['jaco'][:])
self.wb = ncds.getncattr('win_bnd').split(',')
self.ab = ncds.getncattr('abs_bnd').split(',')
# generic forward
self._forward = lut2func.lut2func(self.lut, self.axes)
# generic jacobian
self._jacobian = lut2jacobian_lut.jlut2func({'lut': self.jlut,
'axes': self.jaxes,
'ny': self.ny_nx[0],
'nx': self.ny_nx[1]})
# global predefinition of input for speed reasons
self.xaa = np.zeros(3)
self.par = np.zeros(3)
self.mes = np.zeros(len(self.wb) + len(self.ab))
# local predefine inp for speed
inp = np.zeros(6)
def forward(woo, geo):
"""
Input:
woo: state (wvc aot wsp)
geo: azi vie suz
aot is aot at winband [0]
wvc is sqrt of wvc
Output:
normalized radiances at winbands
-np.log(effective_transmission)/sqrt(amf) at absbands
effective_transmission= L_toa/L_0
with L_0 is normalized radiance without water vapor
"""
inp[:3], inp[3:] = woo, geo
return self._forward(inp)
self.forward = forward
def jforward(woo, geo):
"""
as forward, but returns jacobian
output must be limited to the first three elements (the state and not the geometry)
"""
inp[:3], inp[3:] = woo, geo
return self._jacobian(inp)[:, :3]
self.jforward = jforward
# min_state
a = np.array([self.axes[i].min() for i in range(3)])
# max_state
b = np.array([self.axes[i].max() for i in range(3)])
self.inverter = oe.my_inverter(self.forward, a, b, jaco=self.jforward)
# finaly preset SE
sew = [0.0001 for i in self.wb]
sea = [0.001 for i in self.ab]
self.SE = np.diag(sew + sea)
def __init__(self,
corefile=os.path.join(about_me()['cawadir'], 'luts',
'cloud_core_meris.nc4'),
used_ab=None):
self.about_me = about_me()
with Dataset(corefile, 'r') as ncds:
#get the full lut
self.lut = np.array(ncds.variables['lut'][:], order='F')
self.jlut = np.array(ncds.variables['jlut'][:], order='F')
self.axes = tuple([
np.array(ncds.variables[a][:])
for a in ncds.variables['lut'].dimensions[:-1]
])
self.jaxes = tuple([
np.array(ncds.variables[a][:])
for a in ncds.variables['jlut'].dimensions[:-1]
])
self.ny_nx = np.array(ncds.variables['jaco'][:])
self.wb = ncds.getncattr('win_bnd').split(',')
self.ab = ncds.getncattr('abs_bnd').split(',')
self.cha = {
bb: {
kk: ncds.groups['cha'].groups[bb].getncattr(kk)
for kk in ('bwvl', 'cwvl')
}
for bb in self.wb + self.ab
}
if used_ab is None:
self.ab_idx = None
else:
self.ab = [
used_ab,
]
self.ab_idx = pos_in_list(
ncds.getncattr('abs_bnd').split(','), used_ab)[0]
#generic forward
self._forward = lut2func.lut2func(self.lut, self.axes)
#generic jacobian
self._jacobian = lut2jacobian_lut.jlut2func({
'lut': self.jlut,
'axes': self.jaxes,
'ny': self.ny_nx[0],
'nx': self.ny_nx[1]
})
#global predefinition of input for speed reasons
self.xaa = np.zeros(2)
self.par = np.zeros(5)
self.mes = np.zeros(len(self.wb) + len(self.ab))
#local predefine inp for speed
inp = np.zeros(7)
def forward(woo, par):
'''
Input:
woo: state (ctp,cot)
par: parameter (alb, sza, vza, azd, dwvl)
aot is aot at winband [0]
wvc is sqrt of wvc
Output:
normalized radiances at winbands and absbands
'''
inp[:2], inp[2:] = woo, par
if self.ab_idx is None:
return self._forward(inp)
else:
return self._forward(inp)[((0, 1 + self.ab_idx), )]
self.forward = forward
def jforward(woo, geo):
'''
as forward, but returns jacobian
output must be limited to the first three elements (the state and not the geometry)
'''
inp[:2], inp[2:] = woo, geo
if self.ab_idx is None:
return self._jacobian(inp)[:, :2]
else:
return self._jacobian(inp)[(0, 1 + self.ab_idx), :2]
self.jforward = jforward
#min_state
a = np.array([self.axes[i].min() for i in range(2)])
#max_state
b = np.array([self.axes[i].max() for i in range(2)])
self.inverter = oe.my_inverter(self.forward, a, b, jaco=self.jforward)
#finaly preset SE
sew = [0.0001 for i in self.wb]
sea = [0.0001 for i in self.ab]
self.SE = np.diag(sew + sea)
def __init__(self,
ocean_lut=os.path.join('.', 'luts', 'ocean',
'ocean_core_meris.nc4')):
# sys.stderr.write('entering CawaTcwvOceanCore... \n')
libdir = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'libs')
sys.path.append(libdir)
with Dataset(ocean_lut, 'r') as ncds:
# get the full lut
self.lut = np.array(ncds.variables['lut'][:], order='F')
self.jlut = np.array(ncds.variables['jlut'][:], order='F')
self.axes = tuple([
np.array(ncds.variables[a][:])
for a in ncds.variables['lut'].dimensions[:-1]
])
self.jaxes = tuple([
np.array(ncds.variables[a][:])
for a in ncds.variables['jlut'].dimensions[:-1]
])
self.ny_nx = np.array(ncds.variables['jaco'][:])
self.wb = ncds.getncattr('win_bnd').split(',')
self.ab = ncds.getncattr('abs_bnd').split(',')
# generic forward
self._forward = lut2func.lut2func(self.lut, self.axes)
# generic jacobian
self._jacobian = lut2jacobian_lut.jlut2func({
'lut': self.jlut,
'axes': self.jaxes,
'ny': self.ny_nx[0],
'nx': self.ny_nx[1]
})
# global predefinition of input for speed reasons
self.xaa = np.zeros(3)
self.par = np.zeros(3)
self.mes = np.zeros(len(self.wb) + len(self.ab))
# local predefine inp for speed
inp = np.zeros(6)
def forward(woo, geo):
"""
Input:
woo: state (wvc aot wsp)
geo: azi vie suz
aot is aot at winband [0]
wvc is sqrt of wvc
Output:
normalized radiances at winbands
-np.log(effective_transmission)/sqrt(amf) at absbands
effective_transmission= L_toa/L_0
with L_0 is normalized radiance without water vapor
"""
inp[:3], inp[3:] = woo, geo
return self._forward(inp)
self.forward = forward
def jforward(woo, geo):
"""
as forward, but returns jacobian
output must be limited to the first three elements (the state and not the geometry)
"""
inp[:3], inp[3:] = woo, geo
return self._jacobian(inp)[:, :3]
self.jforward = jforward
# min_state
a = np.array([self.axes[i].min() for i in range(3)])
# max_state
b = np.array([self.axes[i].max() for i in range(3)])
self.inverter = oe.my_inverter(self.forward, a, b, jaco=self.jforward)
# finaly preset SE
sew = [0.0001 for i in self.wb]
sea = [0.001 for i in self.ab]
self.SE = np.diag(sew + sea)