def slicell(self,varname,X,Y,time=0,plot=False,**opts):
x,y,z,v,m=[[]]*5
data = opts.get('data',False)
dist = opts.get('dist',False)
extrap = opts.get('extrap',False)
varOnly = opts.get('retv',False)
maskLimit = opts.get('lmask',0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
X=np.array(X)
Y=np.array(Y)
if X.ndim>1: X=np.squeeze(X)
if Y.ndim>1: Y=np.squeeze(X)
if varname not in netcdf.varnames(self.name):
print(':: variable %s not found' % varname)
return x,y,z,v,m
if time>=self.TIME:
print('t = %d exceeds TIME dimension (%d)' % (time,self.TIME))
return x,y,z,v,m
if data is False: v=self.use(varname,SEARCHtime=time)
else: v=data
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname))
if v.ndim==3:
V=calc.griddata(x,y,v,X,Y,extrap=extrap,mask2d=m==0, keepMaskVal=maskLimit)
elif v.ndim==2:
V=calc.griddata(x,y,np.ma.masked_where(m==0,v),X,Y,extrap=extrap, keepMaskVal=maskLimit)
if varOnly: return V
# Z:
if v.ndim==3:
Z=self.path_s_levels(time,X,Y,rw=varname[0])
else: Z=0.*X
# X, Y, dist:
if dist:
Dist=calc.distance(X,Y)
if v.ndim==3:
Dist=np.tile(Dist,(v.shape[0],1))
else:
if v.ndim==3:
X=np.tile(X,(v.shape[0],1))
Y=np.tile(Y,(v.shape[0],1))
if dist:
return Dist,Z,V
else:
return X,Y,Z,V
def slicej(self,varname,ind,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicej')).split(',')
out=vis.Data()
out.label='slicej'
out.msg=self.check_slice(varname,t=time,j=ind)
if out.msg: return out
v=self.use(varname,SEARCHtime=time,eta_SEARCH=ind)
# add mask if not masked:
if not np.ma.isMA(v):
### m=self.grid.vars(ruvp=self.var_at(varname)[0],j=ind)[-1]
m=self.grid.vars(ruvp=self.vloc(varname)[0],j=ind)[-1]
if v.ndim==2: m=np.tile(m,(v.shape[0],1))
v=np.ma.masked_where(m==0,v)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='j=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.name,varname,'units')
except: pass
# coords:
if 'z' in coords and v.ndim==2:
###### out.z=self.s_levels(time=time,ruvpw=self.var_at(varname),j=ind)
### out.z=self.s_levels(time=time,loc=self.var_at(varname),j=ind)
out.z=self.s_levels(time=time,loc=self.vloc(varname),j=ind)
out.info['z']=dict(name='Depth',units='m')
if any([i in coords for i in 'xyd']):
### x,y,h,m=self.grid.vars(ruvp=self.var_at(varname)[0],j=ind)
x,y,h,m=self.grid.vars(ruvp=self.vloc(varname)[0],j=ind)
if 'd' in coords:
d=calc.distance(x,y)
if d[-1]-d[0]>1e4:
d=d/1000.
dunits='km'
else: dunits='m'
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
out.info['d']=dict(name='Distance',units=dunits)
if 'x' in coords:
if v.ndim==2: x=np.tile(x,(v.shape[0],1))
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
if 'y' in coords:
if v.ndim==2: y=np.tile(y,(v.shape[0],1))
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
######## if 't' in coords and self.hast(varname): out.t=self.time[time]
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if v.ndim==2:
out.extra=[vis.Data()]
if 'd' in coords: out.extra[0].x=out.d[0]
if 'x' in coords: out.extra[0].y=out.x[0]
if 'y' in coords: out.extra[0].x=out.y[0]
out.extra[0].v=-h
out.extra[0].config['d1.plot']='fill_between'
out.extra[0].config['d1.y0']=-h.max()-(h.max()-h.min())/20.
out.extra[0].label='bottom'
out.coordsReq=','.join(sorted(coords))
return out
def slicell(self,varname,X,Y,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicell')).split(',')
data = opts.get('data',False)
extrap = opts.get('extrap',False)
maskLimit = opts.get('lmask',0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
out=vis.Data()
out.label='slicell'
out.msg=self.check_slice(varname,t=time)
if out.msg: return out#None,aux
X=np.asarray(X)
Y=np.asarray(Y)
if X.ndim>1: X=np.squeeze(X)
if Y.ndim>1: Y=np.squeeze(X)
### x,y,h,m=self.grid.vars(ruvp=self.var_at(varname)[0])
x,y,h,m=self.grid.vars(ruvp=self.vloc(varname)[0])
if True: # extrat only portion of data needed:
i0,i1,j0,j1=calc.ij_limits(x, y, (X.min(),X.max()),(Y.min(),Y.max()), margin=1)
xi='%d:%d'%(i0,i1)
eta='%d:%d'%(j0,j1)
if data is False: V=self.use(varname,SEARCHtime=time,xi_SEARCH=xi,eta_SEARCH=eta)
else: v=data[...,j0:j1,i0:i1]
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
h=h[j0:j1,i0:i1]
m=m[j0:j1,i0:i1]
else:
if data is False: V=self.use(varname,SEARCHtime=time)
else: v=data
if V.ndim==3:
v=calc.griddata(x,y,V,X,Y,extrap=extrap,mask2d=m==0, keepMaskVal=maskLimit)
elif V.ndim==2:
v=calc.griddata(x,y,np.ma.masked_where(m==0,V),X,Y,extrap=extrap, keepMaskVal=maskLimit)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='path npts=%d'%X.size
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and V.ndim==3:
inds=dict(xi=(i0,i1),eta=(j0,j1))
######### out.z=self.path_s_levels(time,X,Y,rw=varname[0],inds=inds)
### out.z=self.path_s_levels(time,X,Y,rw=self.var_at(varname)[1],inds=inds)
###
####### out.z,zw=self.path_s_levels(time,X,Y,rw=False,inds=inds)
####### if self.vloc(varname)[1]=='w': out.z=zw
out.z=self.path_s_levels(time,X,Y,rw=self.vloc(varname)[1],inds=inds)
out.info['z']=dict(name='Depth',units='m')
if 'd' in coords:
d=calc.distance(X,Y)
if d[-1]-d[0]>1e4:
d=d/1000.
dunits='km'
else: dunits='m'
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
out.info['d']=dict(name='Distance',units=dunits)
if 'x' in coords:
if v.ndim==2: X=np.tile(X,(v.shape[0],1))
out.x=X
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
if 'y' in coords:
if v.ndim==2: Y=np.tile(Y,(v.shape[0],1))
out.y=Y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
####### if 't' in coords and self.hast(varname): out.t=self.time[time]
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if v.ndim==2: ################3 and not out.z is None: # zeta and bottom already calculated
out.extra=[vis.Data()]
if 'd' in coords: out.extra[0].x=out.d[0]
if 'x' in coords: out.extra[0].y=out.x[0]
if 'y' in coords: out.extra[0].x=out.y[0]
#### #h=-zw[0]
h = calc.griddata(x,y,h,X,Y,extrap=False)
out.extra[0].v=-h # bottom
out.extra[0].config['d1.plot']='fill_between'
out.extra[0].config['d1.y0']=-h.max()-(h.max()-h.min())/20.
out.extra[0].label='bottom'
out.coordsReq=','.join(sorted(coords))
return out
def slicei(self, varname, ind, time=0, dist=1, plot=False, **opts):
savename = False
if "savename" in opts.keys():
savename = opts["savename"]
d, x, y, z, v = [[]] * 5
if varname not in netcdf.varnames(self.name):
print ":: variable %s not found" % varname
if dist:
return d, z, v
else:
return x, y, z, v
isU = varname == "u"
isV = varname == "v"
if isU:
XI = self.XI_U
xi = "XI_U"
elif isV:
if hasattr(self, "XI_V"):
XI = self.XI_V
xi = "XI_V"
else: # this happens in current roms-agrif
XI = self.XI_RHO
xi = "XI_RHO"
else:
XI = self.XI_RHO
xi = "XI_RHO"
if ind >= XI:
print "j = %d exceeds %s dimension (%d)" % (ind, xi, XI)
x, y, h, m = self.grid.vars(ruvp=self.var_at(varname), i=ind)
karg = {"SEARCHtime": time, xi.lower(): ind}
v = self.use(varname, **karg)
if v.ndim == 2:
z = self.s_levels(time=time, ruvpw=self.var_at(varname), i=ind)
elif v.ndim == 1:
z = []
N = v.shape[0]
if dist:
d = calc.distance(x, y)
if plot:
pylab.figure()
if v.ndim == 2:
pylab.pcolor(np.tile(d, (N, 1)), z, np.ma.masked_where(np.tile(m, (N, 1)) == 0, v), shading="flat")
pylab.plot(d, -h)
pylab.colorbar(shrink=0.7)
elif v.ndim == 1:
pylab.plot(d, v)
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if v.ndim == 2:
return np.tile(d, (N, 1)), z, v # ,np.tile(m,(N,1))==0
elif v.ndim == 1:
return d, z, v # ,m==0
else:
if plot:
pylab.figure()
if v.ndim == 2:
pylab.pcolor(np.tile(y, (N, 1)), z, np.ma.masked_where(np.tile(m, (N, 1)) == 0, v), shading="flat")
pylab.colorbar(shrink=0.7)
pylab.plot(y, -h)
elif v.ndim == 1:
pylab.plot(y, v)
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if v.ndim == 2:
return np.tile(x, (N, 1)), np.tile(y, (N, 1)), z, v # ,np.tile(m,(N,1))==0
elif v.ndim == 1:
return x, y, z, v # ,m==0
def slicej(self,varname,ind,time=0,dist=1,plot=False,**opts):
savename=False
if 'savename' in opts.keys(): savename=opts['savename']
d,x,y,z,v=[[]]*5
if varname not in netcdf.varnames(self.name):
print(':: variable %s not found' % varname)
if dist: return d,z,v
else: return x,y,z,v
isU=varname=='u'
isV=varname=='v'
if isU:
if hasattr(self,'ETA_U'):
ETA= self.ETA_U
eta='ETA_U'
else: # this happens in current roms-agrif
ETA= self.ETA_RHO
eta='ETA_RHO'
elif isV:
ETA= self.ETA_V
eta='ETA_V'
else:
ETA= self.ETA_RHO
eta='ETA_RHO'
if ind >= ETA:
print('j = %d exceeds %s dimension (%d)' % (ind,eta,ETA))
if dist: return d,z,v
else: return x,y,z,v
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname),j=ind)
karg={'SEARCHtime':time,eta.lower(): ind}
v=self.use(varname,**karg)
if v.ndim==2:
z=self.s_levels(time=time,ruvpw=self.var_at(varname),j=ind)
elif v.ndim==1:
z=[]
N=v.shape[0]
if dist:
d=calc.distance(x,y)
if plot:
pylab.figure()
if v.ndim==2:
pylab.pcolor(np.tile(d,(N,1)),z,np.ma.masked_where(np.tile(m,(N,1))==0,v),shading='flat')
pylab.plot(d,-h)
pylab.colorbar(shrink=.7)
elif v.ndim==1:
pylab.plot(d,v)
if savename:
pylab.savefig(savename,dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if v.ndim==2:
return np.tile(d,(N,1)),z,v
elif v.ndim==1:
return d,z,v #np.ma.masked_where(m==0,v)
else:
if plot:
pylab.figure()
if v.ndim==2:
pylab.pcolor(tile(x,(N,1)),z,ma.masked_where(tile(m,(N,1))==0,v),shading='flat')
pylab.colorbar(shrink=.7)
pylab.plot(x,-h)
elif v.ndim==1:
pylab.plot(x,v)
if savename:
pylab.savefig(savename,dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if v.ndim==2:
m=np.tile(m,(N,1))==0
return np.tile(x,(N,1)),np.tile(y,(N,1)),z,v #np.ma.masked_where(m,v)
elif v.ndim==1:
return x,y,z,v #np.ma.masked_where(m==0,v)
def slicej(self, varname, ind, time=0, dist=1, plot=False, **opts):
savename = False
if 'savename' in opts.keys(): savename = opts['savename']
d, x, y, z, v = [[]] * 5
if varname not in netcdf.varnames(self.name):
print(':: variable %s not found' % varname)
if dist: return d, z, v
else: return x, y, z, v
isU = varname == 'u'
isV = varname == 'v'
if isU:
if hasattr(self, 'ETA_U'):
ETA = self.ETA_U
eta = 'ETA_U'
else: # this happens in current roms-agrif
ETA = self.ETA_RHO
eta = 'ETA_RHO'
elif isV:
ETA = self.ETA_V
eta = 'ETA_V'
else:
ETA = self.ETA_RHO
eta = 'ETA_RHO'
if ind >= ETA:
print('j = %d exceeds %s dimension (%d)' % (ind, eta, ETA))
if dist: return d, z, v
else: return x, y, z, v
x, y, h, m = self.grid.vars(ruvp=self.var_at(varname), j=ind)
karg = {'SEARCHtime': time, eta.lower(): ind}
v = self.use(varname, **karg)
if v.ndim == 2:
z = self.s_levels(time=time, ruvpw=self.var_at(varname), j=ind)
elif v.ndim == 1:
z = []
N = v.shape[0]
if dist:
d = calc.distance(x, y)
if plot:
pylab.figure()
if v.ndim == 2:
pylab.pcolor(np.tile(d, (N, 1)),
z,
np.ma.masked_where(
np.tile(m, (N, 1)) == 0, v),
shading='flat')
pylab.plot(d, -h)
pylab.colorbar(shrink=.7)
elif v.ndim == 1:
pylab.plot(d, v)
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if v.ndim == 2:
return np.tile(d, (N, 1)), z, v
elif v.ndim == 1:
return d, z, v #np.ma.masked_where(m==0,v)
else:
if plot:
pylab.figure()
if v.ndim == 2:
pylab.pcolor(tile(x, (N, 1)),
z,
ma.masked_where(tile(m, (N, 1)) == 0, v),
shading='flat')
pylab.colorbar(shrink=.7)
pylab.plot(x, -h)
elif v.ndim == 1:
pylab.plot(x, v)
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if v.ndim == 2:
m = np.tile(m, (N, 1)) == 0
return np.tile(x, (N, 1)), np.tile(
y, (N, 1)), z, v #np.ma.masked_where(m,v)
elif v.ndim == 1:
return x, y, z, v #np.ma.masked_where(m==0,v)
def slicell(self, varname, X, Y, time=0, plot=False, **opts):
x, y, z, v, m = [[]] * 5
data = opts.get('data', False)
dist = opts.get('dist', False)
extrap = opts.get('extrap', False)
varOnly = opts.get('retv', False)
maskLimit = opts.get('lmask',
0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
X = np.array(X)
Y = np.array(Y)
if X.ndim > 1: X = np.squeeze(X)
if Y.ndim > 1: Y = np.squeeze(X)
if varname not in netcdf.varnames(self.name):
print(':: variable %s not found' % varname)
return x, y, z, v, m
if time >= self.TIME:
print('t = %d exceeds TIME dimension (%d)' % (time, self.TIME))
return x, y, z, v, m
if data is False: v = self.use(varname, SEARCHtime=time)
else: v = data
x, y, h, m = self.grid.vars(ruvp=self.var_at(varname))
if v.ndim == 3:
V = calc.griddata(x,
y,
v,
X,
Y,
extrap=extrap,
mask2d=m == 0,
keepMaskVal=maskLimit)
elif v.ndim == 2:
V = calc.griddata(x,
y,
np.ma.masked_where(m == 0, v),
X,
Y,
extrap=extrap,
keepMaskVal=maskLimit)
if varOnly: return V
# Z:
if v.ndim == 3:
Z = self.path_s_levels(time, X, Y, rw=varname[0])
else:
Z = 0. * X
# X, Y, dist:
if dist:
Dist = calc.distance(X, Y)
if v.ndim == 3:
Dist = np.tile(Dist, (v.shape[0], 1))
else:
if v.ndim == 3:
X = np.tile(X, (v.shape[0], 1))
Y = np.tile(Y, (v.shape[0], 1))
if dist:
return Dist, Z, V
else:
return X, Y, Z, V
def slicell(self,varname,X,Y,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicell')).split(',')
data = opts.get('data',False)
extrap = opts.get('extrap',False)
maskLimit = opts.get('lmask',0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
out=Data()
out.msg=self.check_slice(varname,t=time)
if out.msg: return out#None,aux
X=np.asarray(X)
Y=np.asarray(Y)
if X.ndim>1: X=np.squeeze(X)
if Y.ndim>1: Y=np.squeeze(X)
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname)[0])
if True: # extrat only portion of data needed:
i0,i1,j0,j1=calc.ij_limits(x, y, (X.min(),X.max()),(Y.min(),Y.max()), margin=1)
xi='%d:%d'%(i0,i1)
eta='%d:%d'%(j0,j1)
if data is False: V=self.use(varname,SEARCHtime=time,xi_SEARCH=xi,eta_SEARCH=eta)
else: v=data[...,j0:j1,i0:i1]
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
#h=h[j0:j1,i0:i1] # not used
m=m[j0:j1,i0:i1]
else:
if data is False: V=self.use(varname,SEARCHtime=time)
else: v=data
if V.ndim==3:
v=calc.griddata(x,y,V,X,Y,extrap=extrap,mask2d=m==0, keepMaskVal=maskLimit)
elif V.ndim==2:
v=calc.griddata(x,y,np.ma.masked_where(m==0,V),X,Y,extrap=extrap, keepMaskVal=maskLimit)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='path npts=%d'%X.size
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and V.ndim==3:
inds=dict(xi=(i0,i1),eta=(j0,j1))
######### out.z=self.path_s_levels(time,X,Y,rw=varname[0],inds=inds)
out.z=self.path_s_levels(time,X,Y,rw=self.var_at(varname)[1],inds=inds)
if 'd' in coords:
d=calc.distance(X,Y)
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
if 'x' in coords:
if v.ndim==2: X=np.tile(X,(v.shape[0],1))
out.x=X
if 'y' in coords:
if v.ndim==2: Y=np.tile(Y,(v.shape[0],1))
out.y=Y
if 't' in coords and self.hast(varname): out.t=self.time[time]
out.coordsReq=','.join(sorted(coords))
return out
def data2roms(data,grd,sparams,**kargs):
'''
Interpolates data to roms 3D grid.
The dict data must contain the prognostic variables temp, salt, u,
v (3d) and ssh (zeta, 2d), as well as lon, lat (2d), depth (1d) and
time/date info: date (data date), date0 (reference date) and time
(difference between date and date0). The input data can be provided
by load_data.
Parameters
----------
data : dict with prognostic variables
grd : ROMS netcdf grid file
sparams : s-coordinates parameters, theta_s,theta_b, hc and NLevels
**kargs:
ij : axis for vertical interpolations (*i,j)
ij_ind : list of i or j index for vertical interpolation, all by
default (ij_ind=False)
horizAux : if True, the original data horizontally interpolated is
returned and can be used for next data2roms call with
this same karg
quiet : output messages flag (false by default)
'''
ij='j'
ij_ind=False
horizAux=False
quiet=False
if 'ij' in kargs.keys(): ij = kargs['ij']
if 'ij_ind' in kargs.keys(): ij_ind = kargs['ij_ind']
if 'horizAux' in kargs.keys(): horizAux = kargs['horizAux']
if 'quiet' in kargs.keys(): quiet = kargs['quiet']
if not quiet: print 'using grid %s' % grd
g=roms.Grid(grd)
xr,yr,hr,mr=g.vars('r')
xu,yu,hu,mu=g.vars('u')
xv,yv,hv,mv=g.vars('v')
ny,nx=hr.shape
nz=sparams[3]
NX=data['NX']
NY=data['NY']
NZ=data['NZ']
if not quiet: print 'calc s levels...'
sshr=calc.griddata(data['lon'],data['lat'],data['ssh'],xr,yr,extrap=True)
Zr = g.s_levels(sparams,sshr,hr,'r')
Zu = g.s_levels(sparams,sshr,hr,'u')
Zv = g.s_levels(sparams,sshr,hr,'v')
# interp horiz:
retHorizAux=horizAux is True
if horizAux in (True,False):
TEMP = np.ma.masked_all((NZ,ny,nx),data['temp'].dtype)
SALT = np.ma.masked_all((NZ,ny,nx),data['salt'].dtype)
U = np.ma.masked_all((NZ,ny,nx),data['u'].dtype)
V = np.ma.masked_all((NZ,ny,nx),data['v'].dtype)
if not quiet: print 'horizontal interpolation:'
for i in range(NZ):
if not quiet and i%10==0: print ' lev %d of %d' % (i,NZ)
#import pylab
#pylab.figure()
#pylab.pcolormesh(data['lon'],data['lat'],data['temp'][i,...])
try: TEMP[i,...] = calc.griddata(data['lon'],data['lat'],data['temp'][i,...],xr,yr,extrap=True)
except: pass
try: SALT[i,...] = calc.griddata(data['lon'],data['lat'],data['salt'][i,...],xr,yr,extrap=True)
except: pass
try: U[i,...] = calc.griddata(data['lon'],data['lat'],data['u'][i,...],xr,yr,extrap=True)
except: pass
try: V[i,...] = calc.griddata(data['lon'],data['lat'],data['v'][i,...],xr,yr,extrap=True)
except: pass
# rotate U,V:
if not quiet: print 'rotating U,V to grid angle'
angle=g.use('angle') # rad
U,V=calc.rot2d(U,V,angle)
U=rt.rho2uvp3d(U,'u')
V=rt.rho2uvp3d(V,'v')
horizAux={}
horizAux['TEMP'] = TEMP
horizAux['SALT'] = SALT
horizAux['U'] = U
horizAux['V'] = V
else:
TEMP = horizAux['TEMP']
SALT = horizAux['SALT']
U = horizAux['U']
V = horizAux['V']
# interp vert:
nxu=nx-1
nyv=ny-1
#> -----------------------------------------------------------------
useInd=not ij_ind is False
if ij_ind is False:
if ij=='j': ij_ind=range(ny)
elif ij=='i': ij_ind=range(nx)
else:
try: iter(ij_ind)
except: ij_ind=[ij_ind]
if ij=='j': ny=nyv=len(ij_ind)
elif ij=='i': nx=nxu=len(ij_ind)
# -----------------------------------------------------------------<
Temp = np.zeros((nz,ny ,nx ),data['temp'].dtype)
Salt = np.zeros((nz,ny ,nx ),data['salt'].dtype)
Uvel = np.zeros((nz,ny ,nxu),data['u'].dtype)
Vvel = np.zeros((nz,nyv,nx ),data['v'].dtype)
jslice=lambda x,ind: x[:,ind,:]
islice=lambda x,ind: x[:,:,ind]
ZZr = np.tile(data['depth'],(nx,ny,1)).T
ZZu = np.tile(data['depth'],(nxu,ny,1)).T
ZZv = np.tile(data['depth'],(nx,nyv,1)).T
if not useInd is False: #>------------------------------------------
if ij=='j':
slice=jslice
sshr=sshr[ij_ind,:]
hr =hr[ij_ind,:]
elif ij=='i':
slice=islice
sshr=sshr[:,ij_ind]
hr =hr[:,ij_ind]
Zr,Zu,Zv,TEMP,SALT,U,V=[slice(k,ij_ind) for k in [Zr,Zu,Zv,TEMP,SALT,U,V]]
# -----------------------------------------------------------------<
if useInd: # then store distances for a possible bry file
dtype=Temp.dtype
distr=np.zeros((nz,ny, nx ),dtype)
distu=np.zeros((nz,ny, nxu),dtype)
distv=np.zeros((nz,nyv,nx ),dtype)
if not quiet: print 'vertical interpolation:'
if ij=='j':
for j in range(ny):
if not quiet and (ny<10 or (ny>=10 and j%10==0)): print ' j=%3d of %3d' % (j,ny)
ind=ij_ind[j]
dr=np.tile(calc.distance(xr[ind,:],yr[ind,:]),(nz,1))
du=np.tile(calc.distance(xu[ind,:],yu[ind,:]),(nz,1))
Dr=np.tile(calc.distance(xr[ind,:],yr[ind,:]),(NZ,1))
Du=np.tile(calc.distance(xu[ind,:],yu[ind,:]),(NZ,1))
if useInd:
distr[:,j,:]=dr;
distu[:,j,:]=du;
Temp[:,j,:] = calc.griddata(Dr,ZZr[:,j,:],TEMP[:,j,:],dr,Zr[:,j,:],extrap=True)
Salt[:,j,:] = calc.griddata(Dr,ZZr[:,j,:],SALT[:,j,:],dr,Zr[:,j,:],extrap=True)
if 0 and j%10==0:
print Dr.shape, ZZr[:,j,:].shape
import pylab as pl
pl.figure(1)
pl.clf()
pl.pcolormesh(Dr,ZZr[:,j,:],SALT[:,j,:])
pl.colorbar()
clim=pl.gci().get_clim()
pl.figure(2)
pl.clf()
pl.pcolormesh(dr,Zr[:,j,:],Salt[:,j,:])
pl.clim(clim)
pl.colorbar()
raw_input()
Uvel[:,j,:] = calc.griddata(Du,ZZu[:,j,:],U[:,j,:], du,Zu[:,j,:],extrap=True)
if j<Vvel.shape[1]:
dv=np.tile(calc.distance(xv[ind,:],yv[ind,:]),(nz,1))
Dv=np.tile(calc.distance(xv[ind,:],yv[ind,:]),(NZ,1))
Vvel[:,j,:] = calc.griddata(Dv,ZZv[:,j,:],V[:,j,:], dv,Zv[:,j,:],extrap=True)
if useInd:
distv[:,j,:]=dv
if np.any(np.isnan(Temp[:,j,:])): print 'found nan in temp',j
if np.any(np.isnan(Salt[:,j,:])): print 'found nan in salt',j
if np.any(np.isnan(Uvel[:,j,:])): print 'found nan in u',j
if j<Vvel.shape[1] and np.any(np.isnan(Vvel[:,j,:])): print 'found nan in v',j
elif ij=='i':
for i in range(nx):
if not quiet and (nx<10 or (nx>=10 and i%10==0)): print ' i=%3d of %3d' % (i,nx)
ind=ij_ind[i]
dr=np.tile(calc.distance(xr[:,ind],yr[:,ind]),(nz,1))
dv=np.tile(calc.distance(xv[:,ind],yv[:,ind]),(nz,1))
Dr=np.tile(calc.distance(xr[:,ind],yr[:,ind]),(NZ,1))
Dv=np.tile(calc.distance(xv[:,ind],yv[:,ind]),(NZ,1))
if useInd:
distr[:,:,i]=dr;
distv[:,:,i]=dv;
Temp[:,:,i] = calc.griddata(Dr,ZZr[:,:,i],TEMP[:,:,i],dr,Zr[:,:,i],extrap=True)
Salt[:,:,i] = calc.griddata(Dr,ZZr[:,:,i],SALT[:,:,i],dr,Zr[:,:,i],extrap=True)
Vvel[:,:,i] = calc.griddata(Dv,ZZv[:,:,i],V[:,:,i], dv,Zv[:,:,i],extrap=True)
if i<Uvel.shape[2]:
du=np.tile(calc.distance(xu[:,ind],yu[:,ind]),(nz,1))
Du=np.tile(calc.distance(xu[:,ind],yu[:,ind]),(NZ,1))
Uvel[:,:,i] = calc.griddata(Du,ZZu[:,:,i],U[:,:,i], du,Zu[:,:,i],extrap=True)
if useInd:
distu[:,:,i]=du
# uv bar:
if not quiet: print 'calc uvbar'
if useInd is False:
ubar,vbar=rt.uvbar(Uvel,Vvel,sshr,hr,sparams)
else: #>------------------------------------------------------------
sshu=calc.griddata(data['lon'],data['lat'],data['ssh'],xu,yu,extrap=True)
sshv=calc.griddata(data['lon'],data['lat'],data['ssh'],xv,yv,extrap=True)
if ij=='j':
sshu=sshu[ij_ind,:]
sshv=sshv[ij_ind,:]
hu =hu[ij_ind,:]
hv =hv[ij_ind,:]
elif ij=='i':
sshu=sshu[:,ij_ind]
sshv=sshv[:,ij_ind]
hu =hu[:,ij_ind]
hv =hv[:,ij_ind]
ubar=rt.barotropic(Uvel,sshu,hu,sparams)
vbar=rt.barotropic(Vvel,sshv,hv,sparams)
# -----------------------------------------------------------------<
Vars=cb.odict()
Vars['temp'] = Temp
Vars['salt'] = Salt
Vars['u'] = Uvel
Vars['v'] = Vvel
Vars['zeta'] = sshr
Vars['ubar'] = ubar
Vars['vbar'] = vbar
Vars['date'] = data['date']
if not useInd is False: #>------------------------------------------
Vars['depth'] = Zr
Vars['depthu'] = Zu
Vars['depthv'] = Zv
Vars['dist'] = distr
Vars['distu'] = distu
Vars['distv'] = distv
# -----------------------------------------------------------------<
if retHorizAux: return Vars, horizAux
else: return Vars
def data2roms(data, grd, sparams, **kargs):
'''
Interpolates data to roms 3D grid.
The dict data must contain the prognostic variables temp, salt, u,
v (3d) and ssh (zeta, 2d), as well as lon, lat (2d), depth (1d) and
time/date info: date (data date), date0 (reference date) and time
(difference between date and date0). The input data can be provided
by load_data.
Parameters
----------
data : dict with prognostic variables
grd : ROMS netcdf grid file
sparams : s-coordinates parameters, theta_s,theta_b, hc and NLevels
**kargs:
ij : axis for vertical interpolations (*i,j)
ij_ind : list of i or j index for vertical interpolation, all by
default (ij_ind=False)
horizAux : if True, the original data horizontally interpolated is
returned and can be used for next data2roms call with
this same karg
quiet : output messages flag (false by default)
'''
ij = 'j'
ij_ind = False
horizAux = False
quiet = False
if 'ij' in kargs.keys(): ij = kargs['ij']
if 'ij_ind' in kargs.keys(): ij_ind = kargs['ij_ind']
if 'horizAux' in kargs.keys(): horizAux = kargs['horizAux']
if 'quiet' in kargs.keys(): quiet = kargs['quiet']
if not quiet: print 'using grid %s' % grd
g = roms.Grid(grd)
xr, yr, hr, mr = g.vars('r')
xu, yu, hu, mu = g.vars('u')
xv, yv, hv, mv = g.vars('v')
ny, nx = hr.shape
nz = sparams[3]
NX = data['NX']
NY = data['NY']
NZ = data['NZ']
if not quiet: print 'calc s levels...'
sshr = calc.griddata(data['lon'],
data['lat'],
data['ssh'],
xr,
yr,
extrap=True)
Zr = g.s_levels(sparams, sshr, hr, 'r')
Zu = g.s_levels(sparams, sshr, hr, 'u')
Zv = g.s_levels(sparams, sshr, hr, 'v')
# interp horiz:
retHorizAux = horizAux is True
if horizAux in (True, False):
TEMP = np.ma.masked_all((NZ, ny, nx), data['temp'].dtype)
SALT = np.ma.masked_all((NZ, ny, nx), data['salt'].dtype)
U = np.ma.masked_all((NZ, ny, nx), data['u'].dtype)
V = np.ma.masked_all((NZ, ny, nx), data['v'].dtype)
if not quiet: print 'horizontal interpolation:'
for i in range(NZ):
if not quiet and i % 10 == 0: print ' lev %d of %d' % (i, NZ)
#import pylab
#pylab.figure()
#pylab.pcolormesh(data['lon'],data['lat'],data['temp'][i,...])
try:
TEMP[i, ...] = calc.griddata(data['lon'],
data['lat'],
data['temp'][i, ...],
xr,
yr,
extrap=True)
except:
pass
try:
SALT[i, ...] = calc.griddata(data['lon'],
data['lat'],
data['salt'][i, ...],
xr,
yr,
extrap=True)
except:
pass
try:
U[i, ...] = calc.griddata(data['lon'],
data['lat'],
data['u'][i, ...],
xr,
yr,
extrap=True)
except:
pass
try:
V[i, ...] = calc.griddata(data['lon'],
data['lat'],
data['v'][i, ...],
xr,
yr,
extrap=True)
except:
pass
# rotate U,V:
if not quiet: print 'rotating U,V to grid angle'
angle = g.use('angle') # rad
U, V = calc.rot2d(U, V, angle)
U = rt.rho2uvp3d(U, 'u')
V = rt.rho2uvp3d(V, 'v')
horizAux = {}
horizAux['TEMP'] = TEMP
horizAux['SALT'] = SALT
horizAux['U'] = U
horizAux['V'] = V
else:
TEMP = horizAux['TEMP']
SALT = horizAux['SALT']
U = horizAux['U']
V = horizAux['V']
# interp vert:
nxu = nx - 1
nyv = ny - 1
#> -----------------------------------------------------------------
useInd = not ij_ind is False
if ij_ind is False:
if ij == 'j': ij_ind = range(ny)
elif ij == 'i': ij_ind = range(nx)
else:
try:
iter(ij_ind)
except:
ij_ind = [ij_ind]
if ij == 'j': ny = nyv = len(ij_ind)
elif ij == 'i': nx = nxu = len(ij_ind)
# -----------------------------------------------------------------<
Temp = np.zeros((nz, ny, nx), data['temp'].dtype)
Salt = np.zeros((nz, ny, nx), data['salt'].dtype)
Uvel = np.zeros((nz, ny, nxu), data['u'].dtype)
Vvel = np.zeros((nz, nyv, nx), data['v'].dtype)
jslice = lambda x, ind: x[:, ind, :]
islice = lambda x, ind: x[:, :, ind]
ZZr = np.tile(data['depth'], (nx, ny, 1)).T
ZZu = np.tile(data['depth'], (nxu, ny, 1)).T
ZZv = np.tile(data['depth'], (nx, nyv, 1)).T
if not useInd is False: #>------------------------------------------
if ij == 'j':
slice = jslice
sshr = sshr[ij_ind, :]
hr = hr[ij_ind, :]
elif ij == 'i':
slice = islice
sshr = sshr[:, ij_ind]
hr = hr[:, ij_ind]
Zr, Zu, Zv, TEMP, SALT, U, V = [
slice(k, ij_ind) for k in [Zr, Zu, Zv, TEMP, SALT, U, V]
]
# -----------------------------------------------------------------<
if useInd: # then store distances for a possible bry file
dtype = Temp.dtype
distr = np.zeros((nz, ny, nx), dtype)
distu = np.zeros((nz, ny, nxu), dtype)
distv = np.zeros((nz, nyv, nx), dtype)
if not quiet: print 'vertical interpolation:'
if ij == 'j':
for j in range(ny):
if not quiet and (ny < 10 or (ny >= 10 and j % 10 == 0)):
print ' j=%3d of %3d' % (j, ny)
ind = ij_ind[j]
dr = np.tile(calc.distance(xr[ind, :], yr[ind, :]), (nz, 1))
du = np.tile(calc.distance(xu[ind, :], yu[ind, :]), (nz, 1))
Dr = np.tile(calc.distance(xr[ind, :], yr[ind, :]), (NZ, 1))
Du = np.tile(calc.distance(xu[ind, :], yu[ind, :]), (NZ, 1))
if useInd:
distr[:, j, :] = dr
distu[:, j, :] = du
Temp[:, j, :] = calc.griddata(Dr,
ZZr[:, j, :],
TEMP[:, j, :],
dr,
Zr[:, j, :],
extrap=True)
Salt[:, j, :] = calc.griddata(Dr,
ZZr[:, j, :],
SALT[:, j, :],
dr,
Zr[:, j, :],
extrap=True)
if 0 and j % 10 == 0:
print Dr.shape, ZZr[:, j, :].shape
import pylab as pl
pl.figure(1)
pl.clf()
pl.pcolormesh(Dr, ZZr[:, j, :], SALT[:, j, :])
pl.colorbar()
clim = pl.gci().get_clim()
pl.figure(2)
pl.clf()
pl.pcolormesh(dr, Zr[:, j, :], Salt[:, j, :])
pl.clim(clim)
pl.colorbar()
raw_input()
Uvel[:, j, :] = calc.griddata(Du,
ZZu[:, j, :],
U[:, j, :],
du,
Zu[:, j, :],
extrap=True)
if j < Vvel.shape[1]:
dv = np.tile(calc.distance(xv[ind, :], yv[ind, :]), (nz, 1))
Dv = np.tile(calc.distance(xv[ind, :], yv[ind, :]), (NZ, 1))
Vvel[:, j, :] = calc.griddata(Dv,
ZZv[:, j, :],
V[:, j, :],
dv,
Zv[:, j, :],
extrap=True)
if useInd:
distv[:, j, :] = dv
if np.any(np.isnan(Temp[:, j, :])): print 'found nan in temp', j
if np.any(np.isnan(Salt[:, j, :])): print 'found nan in salt', j
if np.any(np.isnan(Uvel[:, j, :])): print 'found nan in u', j
if j < Vvel.shape[1] and np.any(np.isnan(Vvel[:, j, :])):
print 'found nan in v', j
elif ij == 'i':
for i in range(nx):
if not quiet and (nx < 10 or (nx >= 10 and i % 10 == 0)):
print ' i=%3d of %3d' % (i, nx)
ind = ij_ind[i]
dr = np.tile(calc.distance(xr[:, ind], yr[:, ind]), (nz, 1))
dv = np.tile(calc.distance(xv[:, ind], yv[:, ind]), (nz, 1))
Dr = np.tile(calc.distance(xr[:, ind], yr[:, ind]), (NZ, 1))
Dv = np.tile(calc.distance(xv[:, ind], yv[:, ind]), (NZ, 1))
if useInd:
distr[:, :, i] = dr
distv[:, :, i] = dv
Temp[:, :, i] = calc.griddata(Dr,
ZZr[:, :, i],
TEMP[:, :, i],
dr,
Zr[:, :, i],
extrap=True)
Salt[:, :, i] = calc.griddata(Dr,
ZZr[:, :, i],
SALT[:, :, i],
dr,
Zr[:, :, i],
extrap=True)
Vvel[:, :, i] = calc.griddata(Dv,
ZZv[:, :, i],
V[:, :, i],
dv,
Zv[:, :, i],
extrap=True)
if i < Uvel.shape[2]:
du = np.tile(calc.distance(xu[:, ind], yu[:, ind]), (nz, 1))
Du = np.tile(calc.distance(xu[:, ind], yu[:, ind]), (NZ, 1))
Uvel[:, :, i] = calc.griddata(Du,
ZZu[:, :, i],
U[:, :, i],
du,
Zu[:, :, i],
extrap=True)
if useInd:
distu[:, :, i] = du
# uv bar:
if not quiet: print 'calc uvbar'
if useInd is False:
ubar, vbar = rt.uvbar(Uvel, Vvel, sshr, hr, sparams)
else: #>------------------------------------------------------------
sshu = calc.griddata(data['lon'],
data['lat'],
data['ssh'],
xu,
yu,
extrap=True)
sshv = calc.griddata(data['lon'],
data['lat'],
data['ssh'],
xv,
yv,
extrap=True)
if ij == 'j':
sshu = sshu[ij_ind, :]
sshv = sshv[ij_ind, :]
hu = hu[ij_ind, :]
hv = hv[ij_ind, :]
elif ij == 'i':
sshu = sshu[:, ij_ind]
sshv = sshv[:, ij_ind]
hu = hu[:, ij_ind]
hv = hv[:, ij_ind]
ubar = rt.barotropic(Uvel, sshu, hu, sparams)
vbar = rt.barotropic(Vvel, sshv, hv, sparams)
# -----------------------------------------------------------------<
Vars = cb.odict()
Vars['temp'] = Temp
Vars['salt'] = Salt
Vars['u'] = Uvel
Vars['v'] = Vvel
Vars['zeta'] = sshr
Vars['ubar'] = ubar
Vars['vbar'] = vbar
Vars['date'] = data['date']
if not useInd is False: #>------------------------------------------
Vars['depth'] = Zr
Vars['depthu'] = Zu
Vars['depthv'] = Zv
Vars['dist'] = distr
Vars['distu'] = distu
Vars['distv'] = distv
# -----------------------------------------------------------------<
if retHorizAux: return Vars, horizAux
else: return Vars
def slicell(self,varname,X,Y,time=0,**opts):
# x,y,z,v,m=[[]]*5
######## plot= opts.get('plot',False)
######### ax = opts.get('ax',None)
coords=opts.get('coords',self._default_coords('slicell')).split(',')
data = opts.get('data',False)
# dist = opts.get('dist',False)
extrap = opts.get('extrap',False)
# varOnly = opts.get('retv',False)
maskLimit = opts.get('lmask',0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
out=Data()
out.msg=self.check_slice(varname,t=time)
if out.msg: return out#None,aux
X=np.asarray(X)
Y=np.asarray(Y)
if X.ndim>1: X=np.squeeze(X)
if Y.ndim>1: Y=np.squeeze(X)
# if varname not in netcdf.varnames(self.name):
# print ':: variable %s not found' % varname
# return x,y,z,v,m
#
# if time>=self.TIME:
# print 't = %d exceeds TIME dimension (%d)' % (time,self.TIME)
# return x,y,z,v,m
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname))
if True: # extrat only portion of data needed:
## print 'start', x.shape,y.shape,X.shape
i0,i1,j0,j1=calc.ij_limits(x, y, (X.min(),X.max()),(Y.min(),Y.max()), margin=1)
## print 'end'
xi='%d:%d'%(i0,i1)
eta='%d:%d'%(j0,j1)
if data is False: V=self.use(varname,SEARCHtime=time,xi_SEARCH=xi,eta_SEARCH=eta)
else: v=data[...,j0:j1,i0:i1]
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
h=h[j0:j1,i0:i1]
m=m[j0:j1,i0:i1]
else:
if data is False: V=self.use(varname,SEARCHtime=time)
else: v=data
if V.ndim==3:
v=calc.griddata(x,y,V,X,Y,extrap=extrap,mask2d=m==0, keepMaskVal=maskLimit)
elif V.ndim==2:
v=calc.griddata(x,y,np.ma.masked_where(m==0,V),X,Y,extrap=extrap, keepMaskVal=maskLimit)
# coords:
if 'z' in coords and V.ndim==3:
out.z=self.path_s_levels(time,X,Y,rw=varname[0])
if 'd' in coords:
d=calc.distance(X,Y)
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
if 'x' in coords:
if v.ndim==2: X=np.tile(X,(v.shape[0],1))
out.x=X
if 'y' in coords:
if v.ndim==2: Y=np.tile(Y,(v.shape[0],1))
out.y=Y
if 't' in coords: out.t=self.time[time]
# # X, Y, dist:
# if dist:
# Dist=calc.distance(X,Y)
# if v.ndim==3:
# Dist=np.tile(Dist,(v.shape[0],1))
# else:
# if v.ndim==3:
# X=np.tile(X,(v.shape[0],1))
# Y=np.tile(Y,(v.shape[0],1))
#
# if dist:
# return Dist,Z,V
# else:
# return X,Y,Z,V
#
# if plot:
# if not ax: ax=pl.gca()
# if v.ndim==2:
# if 'x' in coords:
# p=ax.pcolormesh(out.x,out.z,v)
### ax.plot(aux.x[0],-h)
# elif 'y' in coords:
# p=ax.pcolormesh(out.y,out.z,v)
### ax.plot(aux.y[0],-h)
# else:
# p=ax.pcolormesh(out.d,out.z,v)
#### ax.plot(aux.d[0],-h)
##
# pl.colorbar(p,shrink=.7)
## elif v.ndim==1:
# if 'x' in coords:
# ax.plot(out.x,v)
# elif 'y' in coords:
# ax.plot(out.y,v)
# else:
# ax.plot(out.d,v)
#
out.v=v
out.coordsReq=','.join(sorted(coords))
return out###v,aux
def slicej(self,varname,ind,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicej')).split(',')
out=Data()
out.msg=self.check_slice(varname,t=time,j=ind)
if out.msg: return out
v=self.use(varname,SEARCHtime=time,eta_SEARCH=ind)
# add mask if not masked:
if not np.ma.isMA(v):
m=self.grid.vars(ruvp=self.var_at(varname),j=ind)[-1]
if v.ndim==2: m=np.tile(m,(v.shape[0],1))
v=np.ma.masked_where(m==0,v)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='j=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.name,varname,'units')
except: pass
# coords:
if 'z' in coords and v.ndim==2:
out.z=self.s_levels(time=time,ruvpw=self.var_at(varname),j=ind)
out.info['z']=dict(name='Depth',units='m')
if any([i in coords for i in 'xyd']):
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname),j=ind)
if 'd' in coords:
d=calc.distance(x,y)
if d[-1]-d[0]>1e4:
d=d/1000.
dunits='km'
else: dunits='m'
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
out.info['d']=dict(name='Distance',units=dunits)
if 'x' in coords:
if v.ndim==2: x=np.tile(x,(v.shape[0],1))
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
if 'y' in coords:
if v.ndim==2: y=np.tile(y,(v.shape[0],1))
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
if 't' in coords: out.t=self.time[time]
if v.ndim==2:
out.extra=[Data()]
if 'd' in coords: out.extra[0].x=out.d[0]
if 'x' in coords: out.extra[0].y=out.x[0]
if 'y' in coords: out.extra[0].x=out.y[0]
out.extra[0].v=-h
out.extra[0].config['1d.plot']='fill_between'
out.extra[0].config['1d.y0']=-h.max()-(h.max()-h.min())/20.
out.extra[0].alias='bottom'
out.coordsReq=','.join(sorted(coords))
return out
def slicell(self,varname,X,Y,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicell')).split(',')
data = opts.get('data',False)
extrap = opts.get('extrap',False)
maskLimit = opts.get('lmask',0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
out=vis.Data()
out.label='slicell'
out.msg=self.check_slice(varname,t=time)
if out.msg: return out#None,aux
X=np.asarray(X)
Y=np.asarray(Y)
if X.ndim>1: X=np.squeeze(X)
if Y.ndim>1: Y=np.squeeze(X)
### x,y,h,m=self.grid.vars(ruvp=self.var_at(varname)[0])
x,y,h,m=self.grid.vars(ruvp=self.vloc(varname)[0])
if True: # extrat only portion of data needed:
i0,i1,j0,j1=calc.ij_limits(x, y, (X.min(),X.max()),(Y.min(),Y.max()), margin=1)
xi='%d:%d'%(i0,i1)
eta='%d:%d'%(j0,j1)
if data is False: V=self.use(varname,SEARCHtime=time,xi_SEARCH=xi,eta_SEARCH=eta)
else: v=data[...,j0:j1,i0:i1]
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
h=h[j0:j1,i0:i1]
m=m[j0:j1,i0:i1]
else:
if data is False: V=self.use(varname,SEARCHtime=time)
else: v=data
if V.ndim==3:
v=calc.griddata(x,y,V,X,Y,extrap=extrap,mask2d=m==0, keepMaskVal=maskLimit)
elif V.ndim==2:
v=calc.griddata(x,y,np.ma.masked_where(m==0,V),X,Y,extrap=extrap, keepMaskVal=maskLimit)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='path npts=%d'%X.size
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and V.ndim==3:
inds=dict(xi=(i0,i1),eta=(j0,j1))
######### out.z=self.path_s_levels(time,X,Y,rw=varname[0],inds=inds)
### out.z=self.path_s_levels(time,X,Y,rw=self.var_at(varname)[1],inds=inds)
###
####### out.z,zw=self.path_s_levels(time,X,Y,rw=False,inds=inds)
####### if self.vloc(varname)[1]=='w': out.z=zw
out.z=self.path_s_levels(time,X,Y,rw=self.vloc(varname)[1],inds=inds)
out.info['z']=dict(name='Depth',units='m')
if 'd' in coords:
d=calc.distance(X,Y)
if d[-1]-d[0]>1e4:
d=d/1000.
dunits='km'
else: dunits='m'
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
out.info['d']=dict(name='Distance',units=dunits)
if 'x' in coords:
if v.ndim==2: X=np.tile(X,(v.shape[0],1))
out.x=X
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
if 'y' in coords:
if v.ndim==2: Y=np.tile(Y,(v.shape[0],1))
out.y=Y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
####### if 't' in coords and self.hast(varname): out.t=self.time[time]
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if v.ndim==2: ################3 and not out.z is None: # zeta and bottom already calculated
out.extra=[vis.Data()]
if 'd' in coords: out.extra[0].x=out.d[0]
if 'x' in coords: out.extra[0].y=out.x[0]
if 'y' in coords: out.extra[0].x=out.y[0]
#### #h=-zw[0]
h = calc.griddata(x,y,h,X,Y,extrap=False)
out.extra[0].v=-h # bottom
out.extra[0].config['d1.plot']='fill_between'
out.extra[0].config['d1.y0']=-h.max()-(h.max()-h.min())/20.
out.extra[0].label='bottom'
out.coordsReq=','.join(sorted(coords))
return out
def slicell(self,varname,X,Y,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicell')).split(',')
data = opts.get('data',False)
extrap = opts.get('extrap',False)
maskLimit = opts.get('lmask',0.5) # points where interpolated mask are above
# this value are considered as mask!
# Most strict value is 0
out=Data()
out.msg=self.check_slice(varname,t=time)
if out.msg: return out#None,aux
X=np.asarray(X)
Y=np.asarray(Y)
if X.ndim>1: X=np.squeeze(X)
if Y.ndim>1: Y=np.squeeze(X)
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname)[0])
if True: # extrat only portion of data needed:
i0,i1,j0,j1=calc.ij_limits(x, y, (X.min(),X.max()),(Y.min(),Y.max()), margin=1)
xi='%d:%d'%(i0,i1)
eta='%d:%d'%(j0,j1)
if data is False: V=self.use(varname,SEARCHtime=time,xi_SEARCH=xi,eta_SEARCH=eta)
else: v=data[...,j0:j1,i0:i1]
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
#h=h[j0:j1,i0:i1] # not used
m=m[j0:j1,i0:i1]
else:
if data is False: V=self.use(varname,SEARCHtime=time)
else: v=data
if V.ndim==3:
v=calc.griddata(x,y,V,X,Y,extrap=extrap,mask2d=m==0, keepMaskVal=maskLimit)
elif V.ndim==2:
v=calc.griddata(x,y,np.ma.masked_where(m==0,V),X,Y,extrap=extrap, keepMaskVal=maskLimit)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='path npts=%d'%X.size
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and V.ndim==3:
inds=dict(xi=(i0,i1),eta=(j0,j1))
######### out.z=self.path_s_levels(time,X,Y,rw=varname[0],inds=inds)
out.z=self.path_s_levels(time,X,Y,rw=self.var_at(varname)[1],inds=inds)
if 'd' in coords:
d=calc.distance(X,Y)
if v.ndim==2: d=np.tile(d,(v.shape[0],1))
out.d=d
if 'x' in coords:
if v.ndim==2: X=np.tile(X,(v.shape[0],1))
out.x=X
if 'y' in coords:
if v.ndim==2: Y=np.tile(Y,(v.shape[0],1))
out.y=Y
if 't' in coords and self.hast(varname): out.t=self.time[time]
out.coordsReq=','.join(sorted(coords))
return out
