def load(self, par, surf=False, mldint=False, fullint=False, noneg=False):
self.params.append(par)
key = self.pa[par][0]
self.par = par
vd = self.vd
t1 = self.t1
t2 = self.t2
i1 = self.i1
i2 = self.i2
j1 = self.j1
j2 = self.j2
k1 = self.k1
k2 = self.k2
pref = self.datadir + "BEC.gx3.22.pi.cv2.Ar.daily."
def readfield(par):
for y in [2003, 2004, 2005, 2006]:
for m in np.arange(1, 13):
n = pycdf.CDF(pref + "%04i-%02i.nc" % (y, m))
tmpfld = cnv(n.var(self.pa[par][0])[:],
self.gr)[..., i1:i2, j1:j2]
try:
fld = np.concatenate((fld, tmpfld), axis=0)
except UnboundLocalError:
fld = tmpfld
return fld * self.pa[par][1]
def condload(parlist):
for par in (parlist):
if not hasattr(self, par):
self.load(par)
if mldint:
na = np.newaxis
exists = False
if hasattr(self, par): exists = True
condload(('mldk', par))
if self.mldk.ndim == 1:
self.mldk = self.mldk[:, na, na]
self.__dict__[par] = self.__dict__[par][:, :, na, na]
elif self.mldk.ndim == 2:
self.mldk = self.mldk[:, na]
self.__dict__[par] = self.__dict__[par][:, :, na]
fld = self.mldk * 0
for m in np.unique(self.mldk):
t, i, j = np.nonzero(self.mldk == m)
fld[t, i,
j] = np.sum(self.__dict__[par][t, :m, i, j] * self.dzt[:m],
axis=1)
self.__dict__[par[:-1] + 'm'] = fld
if not exists: del self.__dict__[par]
return
if par == 'o2ar':
condload(('o2ct', 'o2st', 'arst', 'arct'))
self.o2ar = ((self.o2ct / self.o2st) /
(self.arct / self.arst)) * 100 - 100
return
elif par == 'o2st':
condload(('temp', 'salt', 'o2ss'))
self.o2st = sw.satO2(self.salt, self.temp)
self.o2st[:, 0] = self.o2ss[:]
return
elif par == 'arst':
condload(('temp', 'salt', 'arss'))
self.arst = sw.satAr(self.salt, self.temp)
self.arst[:, 0] = self.arss[:]
return
elif par == 'ssht':
self.__dict__['ssht'] = self.dxu * 0
return
elif par == 'icef':
pref = (self.datadir +
"/BEC.gx3.22.pi.cv2.Ar.daily.IFRAC.1999-2006.nc")
n = pycdf.CDF(pref)
self.icef = cnv(n.var('IFRAC')[t1:t2, ...], self.gr)[:, i1:, j1:j2]
self.icef[self.icef > 1] = np.nan
return
elif par == 'dens':
condload(('temp', 'salt'))
self.dens = sw.dens0(self.salt, self.temp)
return
elif par == 'nwnd':
self.load('nwndsqr')
self.nwnd = np.sqrt(self.nwndsqr)
del self.nwndsqr
return
elif par == 'mldk':
condload(('mldp', ))
self.mldk = np.ceil(np.interp(self.mldp, self.zlev, np.arange(25)))
return
elif par == 'nn10':
if not hasattr(self, 'ncpm'):
self.load('ncpo', mldint=True)
self.nn10 = self.ncpm.copy() * np.nan
for t in np.arange(10, self.nn10.shape[0]):
self.nn10[t, ...] = np.mean(self.ncpm[t - 10:t, ...], axis=0)
return
elif par == 'wwfl':
condload(('o2ar', 'dens', 'mldp', 'nwnd', 'icef'))
self.wwfl = pv2fl(self.o2ar,
self.temp,
self.mldp,
self.nwnd,
wtlen=60,
o2st=self.o2st,
dens=self.dens)
ii2 = min(i2, 26)
if i1 < 26:
self.wwfl[:, i1:ii2, :] = ((1 - self.icef[:, i1:ii2, :]) *
self.wwfl[:, i1:ii2, :])
"""
nulmat = np.zeros( (self.t1-self.tw,self.k2-self.k1,
self.i2-self.i1,self.j2-self.j1) )
self.t1 = self.tw
self.load('nwnd')
#self.wwfl = self.o2ar.copy()*0
self.wwfl = pv2fl(np.concatenate((nulmat,self.o2ar),axis=0),
np.concatenate((nulmat,self.temp),axis=0),
np.concatenate((nulmat[:,0,:,:],
self.mldp),axis=0),
self.nwnd,
wtlen=60,
o2st=np.concatenate((nulmat,
self.o2st),axis=0),
dens=np.concatenate((nulmat,
self.dens),axis=0))
#self.wwfl=self.wwfl[t1:t2,...]
self.t1 = t1
"""
return
elif par == 'crwwfl':
condload(('wwfl', ))
return
elif par == 'nrm10':
condload(('wwfl', 'nn10'))
self.nrm10 = nodimb(self.wwfl, self.nn10)
return
elif par == 'wpv':
condload(('wwfl', ))
self.wpv = pv2wpv(self.temp, self.mldp, self.nwnd, dens=self.dens)
return
elif par == 'chlo':
condload(('spChl', 'diatChl', 'diazChl'))
self.chlo = self.spChl + self.diatChl + self.diazChl
del self.spChl, self.diatChl, self.diazChl
return
elif par == 'poco':
condload(('spC', 'diatC', 'diazC'))
self.poco = self.spC + self.diatC + self.diazC
del self.spC, self.diatC, self.diazC
return
elif 'z_t' in self.vd[key][1]:
fld = readfield(par)[t1:t2, k1:k2, ...]
else:
fld = readfield(par)[t1:t2, ...]
if 'z_t' in vd[key][1] and surf:
exists = False
if hasattr(self, par): exists = True
condload(('mldk', par))
self.__dict__[par[:-1] + 'm'] = self.__dict__[par][:, 0, ...]
if not exists: del self.__dict__[par]
return
elif 'z_t' in vd[key][1] and fullint:
""" Integrate over each column """
na = np.newaxis
exists = False
if not hasattr(self, par): exists = True
condload((par, 'mldp', 'ssht'))
fld = self.__dict__[par] * self.dz[na, :, na, na]
if noneg: fld[fld < 0] = 0
fld[:,
0, :, :] = fld[:,
0, :, :] * (self.ssht + self.dz[0]) / self.dz[0]
fld = np.nansum(fld, axis=1)
if exists: del self.__dict__[par]
par = par[:-1] + 'f'
print par + ": ", fld.shape
fld[fld < -1e9] = np.nan
fld[fld > 1e9] = np.nan
self.__dict__[par] = fld
def load(self, par, surf=False, mldint=False, fullint=False, noneg=False):
self.fields.append(par)
def scale(fld):
return (fld * self.slope[par]).astype(np.float32)
if par == 'dens':
self.load('temp', surf, mldint, fullint)
self.load('salt', surf, mldint, fullint)
self.dens = sw.dens0(self.salt, self.temp)
return
elif par == 'o2ar':
self.condload(('o2ct', 'o2st', 'arct', 'arst'))
self.o2ar = ((self.o2ct / self.o2st) /
(self.arct / self.arst) - 1) * 100
return
elif par == 'd003':
self.condload(('mldp', 'dens'))
mld = self.mldp.copy() * 0
for k in np.arange(len(self.zlev)):
mld[(self.dens[:, k, ...] - self.dens[:, 2, ...]) < 0.03] = k
for k in np.arange(len(self.zlev), 0, -1) - 1:
mld[mld == k] = self.zlev[k] - self.dz[k] / 2.0
self.d003 = mld
return
elif par == 'depth':
return
elif par == 'mldk':
self.condload(('mldp', ))
self.mldk = np.ceil(np.interp(self.mldp, self.zlev, np.arange(50)))
return
elif par == 'wwfl':
self.condload(('o2ar', 'dens', 'nwnd', 'mldp'))
del self.o2ct, self.arct, self.arst
self.wwfl = pv2fl(self.o2ar,
self.temp,
self.mldp,
self.nwnd,
wtlen=60,
o2st=self.o2st,
dens=self.dens)
return
elif par == 'crwwfl':
self.fields[-1] = 'wwfl'
tm = len(self.tvec)
tvec = self.tvec
self.tvec = np.arange(self.tvec.min() - 60, self.tvec.min())
self.load('crwnd')
self.tvec = tvec
self.condload(('nwnd', 'o2ar', 'dens', 'mldp'))
self.nwnd = np.concatenate((self.crwnd, self.nwnd), axis=0)
print self.nwnd.shape
fillmat = np.zeros((self.crwnd.shape[0], ) + self.o2ar.shape[1:])
for v in ['o2ar', 'temp', 'o2st', 'dens', 'mldp']:
self.__dict__[v] = np.concatenate(
(self.__dict__[v][tm - 60:, ...], self.__dict__[v]),
axis=0)
try:
del self.o2ct, self.arct, self.arst, self.crwnd
except AttributeError:
pass
print self.nwnd.shape, self.o2ar.shape
self.wwfl = pv2fl(self.o2ar,
self.temp,
self.mldp,
self.nwnd,
wtlen=60,
o2st=self.o2st,
dens=self.dens)
for v in ['o2ar', 'temp', 'mldp', 'o2st', 'dens', 'wwfl', 'nwnd']:
self.__dict__[v] = self.__dict__[v][60:, ...]
return
elif par == 'nn10':
if not hasattr(self, 'ncpm'):
self.load('ncpo', mldint=True)
self.nn10 = self.ncpm.copy() * np.nan
for t in np.arange(10, self.nn10.shape[0]):
self.nn10[t, ...] = np.mean(self.ncpm[t - 10:t, ...], axis=0)
return
elif par == 'nnlg':
if not hasattr(self, 'ncpm'):
self.load('ncpo', mldint=True)
self.condload(('mldp', 'wpv', 'temp'))
self.nnlg = self.ncpm.copy()
self.nnlg[self.nnlg > 1e6] = 0
self.nnlg[self.nnlg < -1e6] = 0
self.cnt = self.ncpm[:] * 0
wlg = np.floor((self.mldp / self.wpv) / np.log(2))
wlg[wlg > 60] = 60
for t in np.arange(60, self.ncpm.shape[0]):
for b in np.arange(60):
msk = wlg[t, :, :] > b
self.nnlg[t,
msk] = self.nnlg[t, msk] + self.ncpm[t - b, msk]
self.cnt[t, msk] = self.cnt[t, msk] + 1
self.nnlg = self.nnlg / self.cnt
return
elif par == 'nrm10':
self.condload(('crwwfl', 'nn10'))
self.nrm10 = nodimb(self.wwfl, self.nn10)
return
elif par == 'wpv':
self.condload(('wwfl', ))
self.wpv = pv2wpv(self.temp, self.mldp, self.nwnd, dens=self.dens)
return
elif par == 'crwpv':
tm = len(self.tvec)
tvec = self.tvec
self.tvec = np.arange(self.tvec.min() - 61, self.tvec.min())
self.load('crwnd')
self.tvec = tvec
self.condload(('nwnd', 'o2ar', 'dens', 'mldp'))
self.nwnd = np.concatenate((self.crwnd, self.nwnd), axis=0)
fillmat = np.zeros((self.crwnd.shape[0], ) + self.o2ar.shape[1:])
for v in ['temp', 'dens', 'mldp']:
print v, " before: ", self.__dict__[v].shape
self.__dict__[v] = np.concatenate(
(self.__dict__[v][tm - 60:, ...], self.__dict__[v]),
axis=0)
print v, " after: ", self.__dict__[v].shape
self.wpv = pv2wpv(self.temp, self.mldp, self.nwnd, dens=self.dens)
for v in ['temp', 'mldp', 'dens', 'wpv', 'nwnd']:
self.__dict__[v] = self.__dict__[v][60:, ...]
return
elif par == 'crwnd':
self.fields[-1] = 'nwnd'
cr = winds.CORE2()
cr.load(self.tvec.min(), self.tvec.max())
self.crwnd = np.zeros(self.tvec.shape + self.llon.shape)
for tpos in np.arange(len(self.tvec)):
try:
self.crwnd[tpos, :, :] = self.reproject(
cr, cr.nwnd[tpos, :, :])
except IndexError:
self.crwnd[tpos, :, :] = self.reproject(
cr, cr.nwnd[-1, :, :])
return
key = self.keypref + self.pa[par]
self.par = par
vd = self.vd
t1 = self.t1
t2 = self.t2
i1 = self.i1
i2 = self.i2
j1 = self.j1
j2 = self.j2
k1 = self.k1
k2 = self.k2
if not t1: t1 = 0
if not t2: t2 = self.tvec.shape[0]
n = netcdf_file(vd[key][2])
if par == 'nwnd':
wnfld = self.scl(scale(n.variables[vd[key][0]][:]))
print wnfld.shape
fld = self.gmt.field(wnfld[t1:t2, ...])[:, j1:j2, i1:i2]
fld[fld < -1e9] = np.nan
fld[fld > 1e9] = np.nan
self.__dict__[par] = fld.astype(np.float32)
return
if 'zt_ocean' in vd[key][1].dimensions and mldint:
""" Integrate down to MLD """
na = np.newaxis
exists = False
if not hasattr(self, par): exists = True
self.condload((
par,
'mldp',
))
fld = grid.mldint(self.__dict__[par], self.mldp, self.dz)
if exists: del self.__dict__[par]
par = par[:-1] + 'm'
self.fields[-1] = par
elif 'zt_ocean' in vd[key][1].dimensions and fullint:
""" Integrate over each column """
na = np.newaxis
exists = False
if not hasattr(self, par): exists = True
self.condload((par, 'mldp', 'ssht'))
fld = self.__dict__[par] * self.dz[na, :, na, na]
if noneg: fld[fld < 0] = 0
fld[:,
0, :, :] = fld[:,
0, :, :] * (self.ssht + self.dz[0]) / self.dz[0]
fld = np.nansum(fld, axis=1)
if exists: del self.__dict__[par]
par = par[:-1] + 'f'
self.fields[-1] = par
elif 'zt_ocean' in vd[key][1].dimensions and surf:
fld = self.gmt.field(
scale(n.variables[vd[key][0]][t1:t2, 0, j1:j2, :]))
elif 'zt_ocean' in vd[key][1].dimensions:
fld = self.gmt.field(
scale(n.variables[vd[key][0]][t1:t2, k1:k2, j1:j2, :]))
else:
fld = self.gmt.field(
scale(n.variables[vd[key][0]][t1:t2, j1:j2, :]))
print par + ": ", fld.shape
fld[fld < -1e9] = np.nan
fld[fld > 1e9] = np.nan
self.__dict__[par] = fld[..., self.i1:self.i2]
def load(self, par, surf=False,mldint=False,fullint=False,noneg=False):
self.params.append(par)
key = self.pa[par][0]
self.par=par; vd=self.vd;
t1 = self.t1; t2 = self.t2
i1 = self.i1; i2 = self.i2
j1 = self.j1; j2 = self.j2
k1 = self.k1; k2 = self.k2
pref = self.datadir + "BEC.gx3.22.pi.cv2.Ar.daily."
def readfield(par):
for y in [2003,2004,2005,2006]:
for m in np.arange(1,13):
n = pycdf.CDF(pref + "%04i-%02i.nc" % (y,m) )
tmpfld = cnv(n.var(self.pa[par][0])[:],
self.gr)[...,i1:i2,j1:j2]
try:
fld = np.concatenate((fld,tmpfld),axis=0)
except UnboundLocalError:
fld = tmpfld
return fld * self.pa[par][1]
def condload(parlist):
for par in (parlist):
if not hasattr(self,par):
self.load(par)
if mldint:
na = np.newaxis
exists = False
if hasattr(self,par): exists = True
condload( ('mldk',par) )
if self.mldk.ndim == 1:
self.mldk = self.mldk[:,na,na]
self.__dict__[par] = self.__dict__[par][:,:,na,na]
elif self.mldk.ndim == 2:
self.mldk = self.mldk[:,na]
self.__dict__[par] = self.__dict__[par][:,:,na]
fld = self.mldk * 0
for m in np.unique(self.mldk):
t,i,j = np.nonzero(self.mldk == m)
fld[t,i,j] = np.sum(self.__dict__[par][t,:m,i,j] *
self.dzt[:m], axis=1)
self.__dict__[par[:-1] + 'm'] = fld
if not exists: del self.__dict__[par]
return
if par == 'o2ar':
condload( ('o2ct','o2st','arst','arct') )
self.o2ar = ( (self.o2ct/self.o2st)/
(self.arct/self.arst) ) * 100 - 100
return
elif par == 'o2st':
condload( ('temp','salt','o2ss') )
self.o2st = sw.satO2(self.salt,self.temp)
self.o2st[:,0] = self.o2ss[:]
return
elif par == 'arst':
condload( ('temp','salt','arss') )
self.arst = sw.satAr(self.salt,self.temp)
self.arst[:,0] = self.arss[:]
return
elif par == 'ssht':
self.__dict__['ssht'] = self.dxu * 0
return
elif par == 'icef':
pref = (self.datadir +
"/BEC.gx3.22.pi.cv2.Ar.daily.IFRAC.1999-2006.nc")
n = pycdf.CDF(pref)
self.icef = cnv(n.var('IFRAC')[t1:t2,...], self.gr)[:,i1:,j1:j2]
self.icef[self.icef>1] = np.nan
return
elif par == 'dens':
condload( ('temp','salt') )
self.dens = sw.dens0(self.salt,self.temp)
return
elif par == 'nwnd':
self.load('nwndsqr')
self.nwnd = np.sqrt(self.nwndsqr)
del self.nwndsqr
return
elif par == 'mldk':
condload( ('mldp',) )
self.mldk = np.ceil(np.interp(self.mldp,
self.zlev, np.arange(25)) )
return
elif par == 'nn10':
if not hasattr(self,'ncpm'):
self.load('ncpo',mldint=True)
self.nn10 =self.ncpm.copy() * np.nan
for t in np.arange(10,self.nn10.shape[0]):
self.nn10[t,...]=np.mean(self.ncpm[t-10:t,...],axis=0)
return
elif par == 'wwfl':
condload( ('o2ar','dens','mldp','nwnd','icef') )
self.wwfl = pv2fl(self.o2ar,
self.temp,
self.mldp,
self.nwnd,
wtlen=60,
o2st=self.o2st,
dens=self.dens)
ii2= min(i2,26)
if i1<26:
self.wwfl[:,i1:ii2,:] = ( (1- self.icef[:,i1:ii2,:]) *
self.wwfl[:,i1:ii2,:] )
"""
nulmat = np.zeros( (self.t1-self.tw,self.k2-self.k1,
self.i2-self.i1,self.j2-self.j1) )
self.t1 = self.tw
self.load('nwnd')
#self.wwfl = self.o2ar.copy()*0
self.wwfl = pv2fl(np.concatenate((nulmat,self.o2ar),axis=0),
np.concatenate((nulmat,self.temp),axis=0),
np.concatenate((nulmat[:,0,:,:],
self.mldp),axis=0),
self.nwnd,
wtlen=60,
o2st=np.concatenate((nulmat,
self.o2st),axis=0),
dens=np.concatenate((nulmat,
self.dens),axis=0))
#self.wwfl=self.wwfl[t1:t2,...]
self.t1 = t1
"""
return
elif par == 'crwwfl':
condload( ('wwfl',) )
return
elif par == 'nrm10':
condload( ('wwfl','nn10') )
self.nrm10 = nodimb(self.wwfl,self.nn10)
return
elif par == 'wpv':
condload( ('wwfl',) )
self.wpv = pv2wpv(self.temp,self.mldp,self.nwnd,dens=self.dens)
return
elif par == 'chlo':
condload( ('spChl','diatChl','diazChl') )
self.chlo = self.spChl + self.diatChl + self.diazChl
del self.spChl, self.diatChl, self.diazChl
return
elif par == 'poco':
condload( ('spC','diatC','diazC') )
self.poco = self.spC + self.diatC + self.diazC
del self.spC, self.diatC, self.diazC
return
elif 'z_t' in self.vd[key][1]:
fld = readfield(par)[t1:t2,k1:k2,...]
else:
fld = readfield(par)[t1:t2,...]
if 'z_t' in vd[key][1] and surf:
exists = False
if hasattr(self,par): exists = True
condload( ('mldk',par) )
self.__dict__[par[:-1] + 'm'] = self.__dict__[par][:,0,...]
if not exists: del self.__dict__[par]
return
elif 'z_t' in vd[key][1] and fullint:
""" Integrate over each column """
na = np.newaxis
exists = False
if not hasattr(self,par): exists = True
condload( (par,'mldp','ssht') )
fld = self.__dict__[par] * self.dz[na,:,na,na]
if noneg: fld[fld<0] = 0
fld[:,0,:,:] = fld[:,0,:,:] * (self.ssht+self.dz[0]) / self.dz[0]
fld = np.nansum(fld,axis=1)
if exists: del self.__dict__[par]
par = par[:-1] + 'f'
print par + ": ",fld.shape
fld[fld <-1e9] = np.nan
fld[fld > 1e9] = np.nan
self.__dict__[par] = fld
def load(self, par, surf=False,mldint=False,fullint=False,noneg=False):
self.fields.append(par)
def scale(fld):
return (fld*self.slope[par]).astype(np.float32)
if par == 'dens':
self.load('temp',surf,mldint,fullint)
self.load('salt',surf,mldint,fullint)
self.dens = sw.dens0(self.salt, self.temp)
return
elif par == 'o2ar':
self.condload( ('o2ct','o2st','arct','arst') )
self.o2ar = ( (self.o2ct/self.o2st) /
(self.arct/self.arst) - 1 ) * 100
return
elif par == 'd003':
self.condload( ('mldp','dens') )
mld = self.mldp.copy() * 0
for k in np.arange(len(self.zlev)):
mld[(self.dens[:,k,...]-self.dens[:,2,...])<0.03] = k
for k in np.arange(len(self.zlev),0,-1)-1:
mld[mld==k] = self.zlev[k]-self.dz[k]/2.0
self.d003 = mld
return
elif par == 'depth':
return
elif par == 'mldk':
self.condload( ('mldp',) )
self.mldk = np.ceil(np.interp(self.mldp,
self.zlev, np.arange(50)) )
return
elif par == 'wwfl':
self.condload( ('o2ar','dens','nwnd','mldp') )
del self.o2ct, self.arct, self.arst
self.wwfl = pv2fl(self.o2ar, self.temp, self.mldp, self.nwnd,
wtlen=60,o2st=self.o2st, dens=self.dens)
return
elif par == 'crwwfl':
self.fields[-1] = 'wwfl'
tm = len(self.tvec)
tvec = self.tvec
self.tvec = np.arange(self.tvec.min()-60,self.tvec.min())
self.load('crwnd')
self.tvec = tvec
self.condload( ('nwnd','o2ar','dens','mldp') )
self.nwnd = np.concatenate((self.crwnd,self.nwnd),axis=0)
print self.nwnd.shape
fillmat = np.zeros( (self.crwnd.shape[0],) + self.o2ar.shape[1:] )
for v in ['o2ar','temp','o2st','dens','mldp']:
self.__dict__[v] = np.concatenate(
(self.__dict__[v][tm-60:,...],self.__dict__[v]), axis=0)
try:
del self.o2ct, self.arct, self.arst,self.crwnd
except AttributeError:
pass
print self.nwnd.shape,self.o2ar.shape
self.wwfl = pv2fl(self.o2ar, self.temp, self.mldp, self.nwnd,
wtlen=60,o2st=self.o2st, dens=self.dens)
for v in ['o2ar','temp','mldp','o2st','dens','wwfl','nwnd']:
self.__dict__[v] = self.__dict__[v][60:,...]
return
elif par == 'nn10':
if not hasattr(self,'ncpm'):
self.load('ncpo',mldint=True)
self.nn10 =self.ncpm.copy() * np.nan
for t in np.arange(10,self.nn10.shape[0]):
self.nn10[t,...]=np.mean(self.ncpm[t-10:t,...],axis=0)
return
elif par == 'nnlg':
if not hasattr(self,'ncpm'):
self.load('ncpo',mldint=True)
self.condload( ('mldp','wpv','temp') )
self.nnlg = self.ncpm.copy()
self.nnlg[self.nnlg>1e6] = 0
self.nnlg[self.nnlg<-1e6] = 0
self.cnt = self.ncpm[:] * 0
wlg = np.floor((self.mldp/self.wpv)/np.log(2))
wlg[wlg>60] = 60
for t in np.arange(60,self.ncpm.shape[0]):
for b in np.arange(60):
msk = wlg[t,:,:]>b
self.nnlg[t,msk] = self.nnlg[t,msk] + self.ncpm[t-b,msk]
self.cnt[t,msk] = self.cnt[t,msk] + 1
self.nnlg = self.nnlg/self.cnt
return
elif par == 'nrm10':
self.condload( ('crwwfl','nn10') )
self.nrm10 = nodimb(self.wwfl,self.nn10)
return
elif par == 'wpv':
self.condload( ('wwfl',) )
self.wpv = pv2wpv(self.temp,self.mldp,self.nwnd,dens=self.dens)
return
elif par == 'crwpv':
tm = len(self.tvec)
tvec = self.tvec
self.tvec = np.arange(self.tvec.min()-61,self.tvec.min())
self.load('crwnd')
self.tvec = tvec
self.condload( ('nwnd','o2ar','dens','mldp') )
self.nwnd = np.concatenate((self.crwnd,self.nwnd),axis=0)
fillmat = np.zeros( (self.crwnd.shape[0],) + self.o2ar.shape[1:] )
for v in ['temp','dens','mldp']:
print v, " before: ",self.__dict__[v].shape
self.__dict__[v] = np.concatenate(
(self.__dict__[v][tm-60:,...],self.__dict__[v]), axis=0)
print v, " after: ",self.__dict__[v].shape
self.wpv = pv2wpv(self.temp,self.mldp,self.nwnd,dens=self.dens)
for v in ['temp','mldp','dens','wpv','nwnd']:
self.__dict__[v] = self.__dict__[v][60:,...]
return
elif par == 'crwnd':
self.fields[-1] = 'nwnd'
cr = winds.CORE2()
cr.load(self.tvec.min(), self.tvec.max())
self.crwnd = np.zeros(self.tvec.shape + self.llon.shape)
for tpos in np.arange(len(self.tvec)):
try:
self.crwnd[tpos,:,:] = self.reproject(cr, cr.nwnd[tpos,:,:])
except IndexError:
self.crwnd[tpos,:,:] = self.reproject(cr, cr.nwnd[-1,:,:])
return
key = self.keypref + self.pa[par]
self.par=par; vd=self.vd;
t1 = self.t1; t2 = self.t2
i1 = self.i1; i2 = self.i2
j1 = self.j1; j2 = self.j2
k1 = self.k1; k2 = self.k2
if not t1: t1 = 0
if not t2: t2 = self.tvec.shape[0]
n = netcdf_file(vd[key][2])
if par == 'nwnd':
wnfld = self.scl(scale(n.variables[vd[key][0]][:]))
print wnfld.shape
fld = self.gmt.field(wnfld[t1:t2,...])[:,j1:j2,i1:i2]
fld[fld <-1e9] = np.nan
fld[fld > 1e9] = np.nan
self.__dict__[par] = fld.astype(np.float32)
return
if 'zt_ocean' in vd[key][1].dimensions and mldint:
""" Integrate down to MLD """
na = np.newaxis
exists = False
if not hasattr(self,par): exists = True
self.condload( (par,'mldp',) )
fld = grid.mldint(self.__dict__[par],self.mldp,self.dz)
if exists: del self.__dict__[par]
par = par[:-1] + 'm'
self.fields[-1] = par
elif 'zt_ocean' in vd[key][1].dimensions and fullint:
""" Integrate over each column """
na = np.newaxis
exists = False
if not hasattr(self,par): exists = True
self.condload( (par,'mldp','ssht') )
fld = self.__dict__[par] * self.dz[na,:,na,na]
if noneg: fld[fld<0] = 0
fld[:,0,:,:] = fld[:,0,:,:] * (self.ssht+self.dz[0]) / self.dz[0]
fld = np.nansum(fld,axis=1)
if exists: del self.__dict__[par]
par = par[:-1] + 'f'
self.fields[-1] = par
elif 'zt_ocean' in vd[key][1].dimensions and surf:
fld = self.gmt.field(scale(n.variables[vd[key][0]][t1:t2,0, j1:j2,:]))
elif 'zt_ocean' in vd[key][1].dimensions:
fld = self.gmt.field(scale(n.variables[vd[key][0]][t1:t2,k1:k2,j1:j2,:]))
else:
fld = self.gmt.field(scale(n.variables[vd[key][0]][t1:t2, j1:j2,:]))
print par + ": ", fld.shape
fld[fld <-1e9] = np.nan
fld[fld > 1e9] = np.nan
self.__dict__[par] = fld[...,self.i1:self.i2]
