def generate_interpol_to_index(xx):
return lambda x, xx=np.array(xx, order='F'
): wadamo_interpolators.linint2index(x, xx)
def generate_interpol_to_index(xx):
return lambda x, xx=np.array(xx,order='F'): wadamo_interpolators.linint2index(x,xx)
def _init_prs_tmp(self, data, poly=True, abscor=True):
'''
1. find
a) sourounding pressure level (prs)
b) sourounding temperatures (profiles)(prf)
2. generates the interpolating transmisson functions
3. index the derivative of th transmission function
4. generates the interpolating first guess functions
'''
# 0.)calc amf
data['amf'] = 1. / np.cos(np.deg2rad(data['suz'])) + 1. / np.cos(
np.deg2rad(data['vie']))
#prs_idx= np.interp(np.clip(data['prs'],self.min_prs,self.max_prs),self.prs,np.arange(self.nle))
prs_idx = wadamo_interpolators.linint2index(data['prs'], self.prs)
# 1a) pressure weights (need 2):
l_prs_idx = int(np.ceil(prs_idx))
u_prs_idx = int(l_prs_idx - 1) #( one level above)
l_prs_wgt = prs_idx - u_prs_idx
u_prs_wgt = 1. - l_prs_wgt
# 1b) find sourounding temps profile
lprf, ltmp = None, 0
uprf, utmp = None, 1000
for prf in self.prf:
dum = self.lut['ckd'][self.ab[0]][prf]['tmp'][l_prs_idx]*l_prs_wgt \
+self.lut['ckd'][self.ab[0]][prf]['tmp'][u_prs_idx]*u_prs_wgt
if dum < data['tmp'] and dum > ltmp:
ltmp = dum
lprf = prf
if dum > data['tmp'] and dum < utmp:
utmp = dum
uprf = prf
if uprf is not None and lprf is not None:
l_tmp_wgt = (utmp - data['tmp']) / (utmp - ltmp)
u_tmp_wgt = 1. - l_tmp_wgt
elif uprf is not None and lprf is None:
u_tmp_wgt = 1.
l_tmp_wgt = 0.
lprf = self.prf[0]
elif uprf is None and lprf is not None:
u_tmp_wgt = 0.
l_tmp_wgt = 1.
uprf = self.prf[0]
else:
print 'strange temperature:', data['tmp']
#ToDo raise something and exit !=0
#2. interpolating transoperator
def generate_wv2tr(ch):
#print 'cc',self.lut['cor'][ch]
if poly is True:
single_tro = self.single_tro_poly
else:
single_tro = self.single_tro_kdis
if abscor is True:
###########TODO: REMOVE, just TESTING
#self.lut['cor'][ch]=[1.,2.]
#self.lut['cor'][ch]=[0.,1.]
###########TODO: REMOVE, just TESTING
a, b = self.lut['cor'][ch]
#print a,b
else:
a, b = 0., 1.
ab = np.array((a, b), order='F')
def wv2tr(x):
out = x * 0.
for iprf, wprf in [[uprf, u_tmp_wgt], [lprf, l_tmp_wgt]]:
for iprs, wprs in [[u_prs_idx, u_prs_wgt],
[l_prs_idx, l_prs_wgt]]:
out += single_tro[ch][iprf][iprs](
x, data['amf']) * wprf * wprs
return wadamo_poly.explog(out, ab)
return wv2tr
self.tro = {ch: generate_wv2tr(ch) for ch in self.ab + self.wb}
#3. indexing transmission derivative d_trans__d_tcwv
if l_tmp_wgt > u_tmp_wgt:
mprf = lprf
else:
mprf = uprf
if l_prs_wgt > u_prs_wgt:
m_prs_idx = l_prs_idx
else:
m_prs_idx = u_prs_idx
def generate_d_tr__d_wv(ch):
def d_tr__d_wv(x):
#dum = self.single_tro_poly[ch][mprf][m_prs_idx](np.array((x*0.99,x*1.01)),data['amf'])
dum0 = self.single_tro_poly[ch][mprf][m_prs_idx](np.array(
x * 0.99), data['amf'])
dum1 = self.single_tro_poly[ch][mprf][m_prs_idx](np.array(
x * 1.01), data['amf'])
return (dum1 - dum0) / (0.02 * x)
return d_tr__d_wv
self.d_tro = {ch: generate_d_tr__d_wv(ch) for ch in self.ab} #+self.wb
#4. interpolating first guess-orator
def generate_tr2wv(ch):
def tr2wv(x, amf=data['amf']):
xx = np.array(np.log(x), order='F')
out = 0.
for iprf, wprf in [[uprf, u_tmp_wgt], [lprf, l_tmp_wgt]]:
for iprs, wprs in [[u_prs_idx, u_prs_wgt],
[l_prs_idx, l_prs_wgt]]:
par = np.array(self.lut['ckd'][ch][iprf]
['trn2wvc_coeff'][iprs, :],
order='F')
dum = wrap_trn2wvc_pol(xx, par)
#print 'dum',dum
out += dum * wprf * wprs
return out / amf
return tr2wv
self.fgu = {ch: generate_tr2wv(ch) for ch in self.ab}
def _init_prs_tmp(self, data, poly=True, abscor=True):
'''
1. find
a) sourounding pressure level (prs)
b) sourounding temperatures (profiles)(prf)
2. generates the interpolating transmisson functions
3. index the derivative of th transmission function
4. generates the interpolating first guess functions
'''
# 0.)calc amf
data['amf'] = 1. / np.cos(np.deg2rad(data['suz'])) + 1. / np.cos(
np.deg2rad(data['vie']))
#prs_idx= np.interp(np.clip(data['prs'],self.min_prs,self.max_prs),self.prs,np.arange(self.nle))
prs_idx = wadamo_interpolators.linint2index(data['prs'], self.prs)
# 1a) pressure weights (need 2):
l_prs_idx = int(np.ceil(prs_idx))
u_prs_idx = int(l_prs_idx - 1) #( one level above)
l_prs_wgt = prs_idx - u_prs_idx
u_prs_wgt = 1. - l_prs_wgt
# 1b) find sourounding temps profile
lprf, ltmp = None, 0
uprf, utmp = None, 1000
for prf in self.prf:
dum = self.lut['ckd'][self.ab[0]][prf]['tmp'][l_prs_idx]*l_prs_wgt \
+self.lut['ckd'][self.ab[0]][prf]['tmp'][u_prs_idx]*u_prs_wgt
if dum < data['tmp'] and dum > ltmp:
ltmp = dum
lprf = prf
if dum > data['tmp'] and dum < utmp:
utmp = dum
uprf = prf
if uprf is not None and lprf is not None:
l_tmp_wgt = (utmp - data['tmp']) / (utmp - ltmp)
u_tmp_wgt = 1. - l_tmp_wgt
elif uprf is not None and lprf is None:
u_tmp_wgt = 1.
l_tmp_wgt = 0.
lprf = self.prf[0]
elif uprf is None and lprf is not None:
u_tmp_wgt = 0.
l_tmp_wgt = 1.
uprf = self.prf[0]
else:
print 'strange temperature:', data['tmp']
#ToDo raise something and exit !=0
#2. interpolating transoperator
def generate_wv2tr(ch):
#print 'cc',self.lut['cor'][ch]
if poly is True:
single_tro = self.single_tro_poly
else:
single_tro = self.single_tro_kdis
if abscor is True:
###########TODO: REMOVE, just TESTING
#self.lut['cor'][ch]=[1.,2.]
#self.lut['cor'][ch]=[0.,1.]
###########TODO: REMOVE, just TESTING
a, b = self.lut['cor'][ch]
#print a,b
else:
a, b = 0., 1.
ab = np.array((a, b), order='F')
def wv2tr(x):
out = x * 0.
for iprf, wprf in [[uprf, u_tmp_wgt], [lprf, l_tmp_wgt]]:
for iprs, wprs in [[u_prs_idx, u_prs_wgt],
[l_prs_idx, l_prs_wgt]]:
out += single_tro[ch][iprf][iprs](
x, data['amf']) * wprf * wprs
return wadamo_poly.explog(out, ab)
return wv2tr
self.tro = {ch: generate_wv2tr(ch) for ch in self.ab + self.wb}
#3. indexing transmission derivative d_trans__d_tcwv
if l_tmp_wgt > u_tmp_wgt:
mprf = lprf
else:
mprf = uprf
if l_prs_wgt > u_prs_wgt:
m_prs_idx = l_prs_idx
else:
m_prs_idx = u_prs_idx
def generate_d_tr__d_wv(ch):
def d_tr__d_wv(x):
#dum = self.single_tro_poly[ch][mprf][m_prs_idx](np.array((x*0.99,x*1.01)),data['amf'])
dum0 = self.single_tro_poly[ch][mprf][m_prs_idx](np.array(
x * 0.99), data['amf'])
dum1 = self.single_tro_poly[ch][mprf][m_prs_idx](np.array(
x * 1.01), data['amf'])
return (dum1 - dum0) / (0.02 * x)
return d_tr__d_wv
self.d_tro = {ch: generate_d_tr__d_wv(ch) for ch in self.ab} #+self.wb
#4. interpolating first guess-orator
def generate_tr2wv(ch):
def tr2wv(x, amf=data['amf']):
xx = np.array(np.log(x), order='F')
out = 0.
for iprf, wprf in [[uprf, u_tmp_wgt], [lprf, l_tmp_wgt]]:
for iprs, wprs in [[u_prs_idx, u_prs_wgt],
[l_prs_idx, l_prs_wgt]]:
par = np.array(
self.lut['ckd'][ch][iprf]['trn2wvc_coeff']
[iprs, :],
order='F')
dum = wrap_trn2wvc_pol(xx, par)
#print 'dum',dum
out += dum * wprf * wprs
return out / amf
return tr2wv
self.fgu = {ch: generate_tr2wv(ch) for ch in self.ab}
