def path_s_levels(self,time,x,y,rw='r',**kargs):
inds=kargs.get('inds',None)
xr,yr,h,m=self.grid.vars()
zeta=self.use('zeta',SEARCHtime=time)
if inds:
i0,i1=inds['xi']
j0,j1=inds['eta']
xr=xr[j0:j1,i0:i1]
yr=yr[j0:j1,i0:i1]
h=h[j0:j1,i0:i1]
m=m[j0:j1,i0:i1]
zeta= zeta[j0:j1,i0:i1]
if np.ma.isMA(zeta): h=np.ma.masked_where(zeta.mask,h)
else:# probably a clm/ini file. Mask maskc point (pygridgen complex grid):
if 'maskc' in netcdf.varnames(self.grid.nc):
mc=self.grid.use('maskc')
if inds: mc=mc[j0:j1,i0:i1]
h=np.ma.masked_where(mc==0,h)
zeta=np.ma.masked_where(mc==0,zeta)
h = calc.griddata(xr,yr,h,x,y,extrap=False)
zeta = calc.griddata(xr,yr,zeta,x,y,extrap=False)
z=rt.s_levels(h,zeta,self.s_params,rw=rw)
z=np.squeeze(z)
return np.ma.masked_where(np.isnan(z),z)
def s_levels(self,
sparams,
zeta=0,
h=False,
ruvpw='r',
i=False,
j=False,
k=False):
ruvpw = ruvpw[0]
isW = ruvpw == 'w'
if h is False:
h = self.h
try:
zeta.shape == h.shape
except:
zeta = np.tile(zeta, h.shape).astype(h.dtype)
if h.ndim == 2:
h = rt.rho2uvp(h, ruvpw)
zeta = rt.rho2uvp(zeta, ruvpw)
zr, zw = rt.s_levels(h, zeta, sparams)
if isW: z = zw
else: z = zr
if k is False: k = slice(None)
if j is False: j = slice(None)
if i is False: i = slice(None)
return np.squeeze(z[k, j, i])
def s_levels(self,sparams,zeta=0,h=False,loc='rr',i=False,j=False,k=False):
try:
hLoc,vLoc=loc
except:
hLoc,vLoc=loc,'r'
#### ruvpw=ruvpw[0]
#### isW=ruvpw=='w'
if h is False:
h=self.h
try:
zeta.shape==h.shape
except:
zeta=np.tile(zeta,h.shape).astype(h.dtype)
if h.ndim==2:
h=rt.rho2uvp(h,hLoc)
zeta=rt.rho2uvp(zeta,hLoc)
z=rt.s_levels(h,zeta,sparams,rw=vLoc)
### zr,zw=rt.s_levels(h,zeta,sparams)
### if isW:z=zw
### if vLoc=='w': z=zw
### else: z=zr
if k is False: k=slice(None)
if j is False: j=slice(None)
if i is False: i=slice(None)
return np.squeeze(z[k,j,i])
def s_levels(self,
time,
ruvpw='r',
i=False,
j=False,
k=False,
extrapZeta=False):
ruvpw = ruvpw[0]
h = self.grid.h
zeta = self.use('zeta', SEARCHtime=time)
if extrapZeta:
if not calc.ismarray(zeta):
zeta = np.ma.masked_where(self.grid.mask == 0, zeta)
zeta = calc.mask_extrap(self.grid.lon, self.grid.lat, zeta)
h = rt.rho2uvp(h, ruvpw)
zeta = rt.rho2uvp(zeta, ruvpw)
z = rt.s_levels(h, zeta, self.s_params, rw=ruvpw)
if k is False: k = slice(None)
if j is False: j = slice(None)
if i is False: i = slice(None)
return z[k, j, i]
def s_levels(self,time,loc='rr',i=False,j=False,k=False,extrapZeta=False):
## ruvpw=ruvpw[0]
try:
hLoc,vLoc=loc
except:
hLoc,vLoc=loc,'r'
h=self.grid.h
zeta=self.use('zeta',SEARCHtime=time)
if extrapZeta:
if not calc.ismarray(zeta): zeta=np.ma.masked_where(self.grid.mask==0,zeta)
zeta=calc.mask_extrap(self.grid.lon,self.grid.lat,zeta)
h=rt.rho2uvp(h,hLoc) ##########ruvpw)
zeta=rt.rho2uvp(zeta,hLoc) ###########ruvpw)
z=rt.s_levels(h,zeta,self.s_params,rw=vLoc) ##########ruvpw)
if k is False: k=slice(None)
if j is False: j=slice(None)
if i is False: i=slice(None)
return z[k,j,i]
def path_s_levels(self,time,x,y,rw='r',**kargs):
inds=kargs.get('inds',None)
xr,yr,h,m=self.grid.vars()
zeta=self.use('zeta',SEARCHtime=time)
if inds:
i0,i1=inds['xi']
j0,j1=inds['eta']
xr=xr[j0:j1,i0:i1]
yr=yr[j0:j1,i0:i1]
h=h[j0:j1,i0:i1]
m=m[j0:j1,i0:i1]
zeta= zeta[j0:j1,i0:i1]
if np.ma.isMA(zeta): h=np.ma.masked_where(zeta.mask,h)
else:# probably a clm/ini file. Mask maskc point (pygridgen complex grid):
if 'maskc' in netcdf.varnames(self.grid.nc):
mc=self.grid.use('maskc')
if inds: mc=mc[j0:j1,i0:i1]
h=np.ma.masked_where(mc==0,h)
zeta=np.ma.masked_where(mc==0,zeta)
h = calc.griddata(xr,yr,h,x,y,extrap=False)
zeta = calc.griddata(xr,yr,zeta,x,y,extrap=False)
z=rt.s_levels(h,zeta,self.s_params,rw=rw)
z=np.squeeze(z)
return np.ma.masked_where(np.isnan(z),z)
def s_levels(self,sparams,zeta=0,h=False,loc='rr',i=False,j=False,k=False):
try:
hLoc,vLoc=loc
except:
hLoc,vLoc=loc,'r'
#### ruvpw=ruvpw[0]
#### isW=ruvpw=='w'
if h is False:
h=self.h
try:
zeta.shape==h.shape
except:
zeta=np.tile(zeta,h.shape).astype(h.dtype)
if h.ndim==2:
h=rt.rho2uvp(h,hLoc)
zeta=rt.rho2uvp(zeta,hLoc)
z=rt.s_levels(h,zeta,sparams,rw=vLoc)
### zr,zw=rt.s_levels(h,zeta,sparams)
### if isW:z=zw
### if vLoc=='w': z=zw
### else: z=zr
if k is False: k=slice(None)
if j is False: j=slice(None)
if i is False: i=slice(None)
return np.squeeze(z[k,j,i])
def s_levels(self,sparams,zeta=0,h=False,ruvpw='r',i=False,j=False,k=False):
ruvpw=ruvpw[0]
isW=ruvpw=='w'
if h is False:
h=self.h
try:
zeta.shape==h.shape
except:
zeta=np.tile(zeta,h.shape).astype(h.dtype)
if h.ndim==2:
h=rt.rho2uvp(h,ruvpw)
zeta=rt.rho2uvp(zeta,ruvpw)
zr,zw=rt.s_levels(h,zeta,sparams)
if isW:z=zw
else: z=zr
if k is False: k=slice(None)
if j is False: j=slice(None)
if i is False: i=slice(None)
return np.squeeze(z[k,j,i])
def path_s_levels(self,time,x,y,rw='r'):
xr,yr,h,m=self.grid.vars()
zeta=self.use('zeta',SEARCHtime=time)
h = calc.griddata(xr,yr,h,x,y,extrap=False)
zeta = calc.griddata(xr,yr,zeta,x,y,extrap=False)
z=rt.s_levels(h,zeta,self.s_params,rw=rw)
z=np.squeeze(z)
return np.ma.masked_where(np.isnan(z),z)
def s_levels(self, itime, isw=False):
tts, ttb, hc, N = self.s_params
zeta = self.use('zeta', ftime=itime)
zr, zw = rt.s_levels(self.h, zeta, hc, tts, ttb, N)
if isw: z = zw
else: z = zr
return squeeze(z)
def s_levels(self,itime,isw=False):
tts,ttb,hc,N=self.s_params
zeta=self.use('zeta',ftime=itime)
zr,zw=rt.s_levels(self.h,zeta,hc,tts,ttb,N)
if isw:z=zw
else: z=zr
return squeeze(z)
def path_s_levels(self, time, x, y, rw='r'):
xr, yr, h, m = self.grid.vars()
zeta = self.use('zeta', SEARCHtime=time)
if np.ma.isMA(zeta): h = np.ma.masked_where(zeta.mask, h)
else: # probably a clm/ini file. Mask maskc point (pygridgen complex grid):
if 'maskc' in netcdf.varnames(self.grid.name):
mc = self.grid.use('maskc')
h = np.ma.masked_where(mc == 0, h)
zeta = np.ma.masked_where(mc == 0, zeta)
h = calc.griddata(xr, yr, h, x, y, extrap=False)
zeta = calc.griddata(xr, yr, zeta, x, y, extrap=False)
z = rt.s_levels(h, zeta, self.s_params, rw=rw)
z = np.squeeze(z)
return np.ma.masked_where(np.isnan(z), z)
def path_s_levels(self,time,x,y,rw='r'):
xr,yr,h,m=self.grid.vars()
zeta=self.use('zeta',SEARCHtime=time)
if np.ma.isMA(zeta): h=np.ma.masked_where(zeta.mask,h)
else:# probably a clm/ini file. Mask maskc point (pygridgen complex grid):
if 'maskc' in netcdf.varnames(self.grid.name):
mc=self.grid.use('maskc')
h=np.ma.masked_where(mc==0,h)
zeta=np.ma.masked_where(mc==0,zeta)
h = calc.griddata(xr,yr,h,x,y,extrap=False)
zeta = calc.griddata(xr,yr,zeta,x,y,extrap=False)
z=rt.s_levels(h,zeta,self.s_params,rw=rw)
z=np.squeeze(z)
return np.ma.masked_where(np.isnan(z),z)
def s_levels(self, time, ruvpw="r", i=False, j=False, k=False, extrapZeta=False):
ruvpw = ruvpw[0]
h = self.grid.h
zeta = self.use("zeta", SEARCHtime=time)
if extrapZeta:
if not calc.ismarray(zeta):
zeta = np.ma.masked_where(self.grid.mask == 0, zeta)
zeta = calc.mask_extrap(self.grid.lon, self.grid.lat, zeta)
h = rt.rho2uvp(h, ruvpw)
zeta = rt.rho2uvp(zeta, ruvpw)
z = rt.s_levels(h, zeta, self.s_params, rw=ruvpw)
if k is False:
k = slice(None)
if j is False:
j = slice(None)
if i is False:
i = slice(None)
return z[k, j, i]
def time_series(self, varname, x, y, times=False, depth=False, **opts):
t, z, v = [[]] * 3
RetVarOnly = False
savename = False
retMsg = False
nearest = True # UNIQUE case Currently
plot = False
if 'retvar' in opts.keys(): RetVarOnly = opts['retvar']
if 'savename' in opts.keys(): savename = opts['savename']
if 'msg' in opts.keys(): retMsg = opts['msg']
if 'nearest' in opts.keys(): nearest = opts['msg']
if 'plot' in opts.keys(): plot = opts['msg']
if varname not in netcdf.varnames(self.name):
msg = ':: variable %s not found' % varname
if retMsg: return t, z, v, msg
else:
print(msg)
return t, z, v
isW = varname == 'w'
hasZ = self.hasz(varname)
if not depth is False and hasZ:
if isW and depth >= self.S_W:
msg = 'k = %d exceeds S_W dimension (%d)' % (depth, self.S_W)
if retMsg: return t, z, v, msg
else:
print(msg)
return t, z, v
elif depth >= self.S_RHO:
msg = 'k = %d exceeds S_RHO dimension (%d)' % (depth,
self.S_RHO)
if retMsg: return t, z, v, msg
else:
print(msg)
return t, z, v
if times is False:
times = 0, len(self.tdays)
if self.hast(varname) and times[-1] > self.TIME:
msg = 't = %d exceeds TIME dimension (%d)' % (times[-1], self.TIME)
if retMsg: return t, z, v, msg
else:
print(msg)
return t, z, v
lon, lat, hr, mr = self.grid.vars(ruvp=self.var_at(varname))
dist = (lon - x)**2 + (lat - y)**2
i, j = np.where(dist == dist.min())
i, j = i[0], j[0]
v = self.use(varname,
xiSEARCH=j,
etaSEARCH=i,
SEARCHtime=range(times[0], times[-1])).T
# time:
t = self.tdays[range(times[0], times[-1])]
t = t + 0. * v
# depth:
if hasZ:
h = self.grid.h[i, j]
zeta = self.use('zeta',
xiSEARCH=j,
etaSEARCH=i,
SEARCHtime=range(times[0], times[-1]))
h = h + 0 * zeta
z = rt.s_levels(h, zeta, self.s_params, rw=varname)
z = np.squeeze(z)
if not depth is False:
if depth >= 0:
t = t[0, :]
z = z[depth, :]
v = v[depth, :]
else:
t0, z0 = t, z
t = t[0, :]
z = depth + 0. * t
v = calc.griddata(t0,
z0,
v,
t,
z,
extrap=False,
norm_xy=True)
else: # not hasZ
z = 0. * t
if plot:
pass # TODO
if RetVarOnly:
return v
else:
if retMsg: return t, z, v, ''
else: return t, z, v
def time_series(self,varname,x,y,times=None,depth=None,**opts):
######## plot= opts.get('plot',False)
######## ax = opts.get('ax',None)
coords=opts.get('coords',self._default_coords('sliceiso')).split(',')
if times is None: times=range(0,self.time.size)
out=Data()
out.msg=self.check_slice(varname,t=np.max(times),k=depth)
if out.msg: return out###None,aux
## t,z,v=[[]]*3
#
# RetVarOnly=False
# savename=False
# retMsg=False
# nearest=True # UNIQUE case Currently
# plot=False
#
# if 'retvar' in opts.keys(): RetVarOnly = opts['retvar']
# if 'savename' in opts.keys(): savename = opts['savename']
# if 'msg' in opts.keys(): retMsg = opts['msg']
# if 'nearest' in opts.keys(): nearest = opts['msg']
# if 'plot' in opts.keys(): plot = opts['msg']
#
#
# if varname not in netcdf.varnames(self.name):
# msg=':: variable %s not found' % varname
# if retMsg: return t,z,v,msg
# else:
# print msg
# return t,z,v
#
# isW=varname=='w'
# hasZ=self.hasz(varname)
#
# if not depth is False and hasZ :
# if isW and depth >= self.S_W:
# msg='k = %d exceeds S_W dimension (%d)' % (depth,self.S_W)
# if retMsg: return t,z,v,msg
# else:
# print msg
# return t,z,v
#
# elif depth >= self.S_RHO:
# msg='k = %d exceeds S_RHO dimension (%d)' % (depth,self.S_RHO)
# if retMsg: return t,z,v,msg
# else:
# print msg
# return t,z,v
#
# if times is False:
# times=0,len(self.tdays)
#
# if self.hast(varname) and times[-1]>self.TIME:
# msg='t = %d exceeds TIME dimension (%d)' % (times[-1],self.TIME)
# if retMsg: return t,z,v,msg
# else:
# print msg
# return t,z,v
#
# find nearest point:
lon,lat,hr,mr=self.grid.vars(ruvp=self.var_at(varname))
dist=(lon-x)**2+(lat-y)**2
i,j=np.where(dist==dist.min())
i,j=i[0],j[0]
if depth>=0: arg={'s_SEARCH':depth}
else: arg={}
v=self.use(varname,xiSEARCH=j,etaSEARCH=i,SEARCHtime=times,**arg).T
# calculate depths:
if self.hasz(varname):
h=self.grid.h[i,j]
zeta=self.use('zeta',xiSEARCH=j,etaSEARCH=i,SEARCHtime=times)
h=h+0*zeta
z=rt.s_levels(h,zeta,self.s_params,rw=varname)
z=np.squeeze(z)
# depth slice:
if not depth is None and self.hasz(varname) and depth<0:
if v.ndim==2:
from matplotlib.dates import date2num
t=np.tile(date2num(self.time[times]),(v.shape[0],1))
v=calc.griddata(t,z,v,t[0],depth+np.zeros(t[0].shape),
extrap=opts.get('extrap',False),norm_xy=opts.get('norm_xy',False))
# norm_xy True may be needed!
# extrap also may be needed cos the 1st and last value may be masked!
else: # one time only
v=np.interp(depth,z,v,left=np.nan,right=np.nan)
v=np.ma.masked_where(np.isnan(v),v)
# coords
if 't' in coords:
if v.ndim==2:
out.t=np.tile(self.time[times],(v.shape[0],1))
from matplotlib.dates import date2num
out.tnum=np.tile(date2num(self.time[times]),(v.shape[0],1))
else: out.t=self.time[times]
if 'z' in coords and self.hasz(varname):
if not depth is None:
if depth>=0: out.z=z[depth,...]
else: out.z=depth+0*v
else: out.z=z
if 'x' in coords: out.x=lon[i,j]
if 'y' in coords: out.y=lat[i,j]
## if plot and v.ndim:
## if not ax:
## ax=pl.gca()
## opts['ax']=ax
##
## if v.ndim==2:
## p=ax.pcolormesh(out.tnum,out.z,v)
## #ax.xaxis_date()
## pl.colorbar(p,shrink=.7)
## elif v.size>1:
## if out.t.size>1: # time series:
## ax.plot(out.t,v)
## else: # vertical profile:
## ax.plot(v,out.z)
out.v=v
out.coordsReq=','.join(sorted(coords))
return out##v,aux
def time_series(self,varname,x,y,times=None,depth=None,**opts):
coords=opts.get('coords',self._default_coords('time_series')).split(',')
if times is None: times=range(0,self.time.size)
# depth or s_level: check is is float or if is negative!
isDepth=False
if not depth is None:
if calc.isiterable(depth): depth=np.asarray(depth)
if calc.isarray(depth):
isDepth=np.any(depth<0) or depth.kind!='i'
else: isDepth=depth<0 or np.asarray(depth).dtype.kind!='i'
out=Data()
if not depth is None and not isDepth:
out.msg=self.check_slice(varname,t=np.max(times),k=depth)
else:
out.msg=self.check_slice(varname,t=np.max(times))
if out.msg: return out
# find nearest point:
lon,lat,hr,mr=self.grid.vars(ruvp=self.var_at(varname))
dist=(lon-x)**2+(lat-y)**2
i,j=np.where(dist==dist.min())
i,j=i[0],j[0]
if not depth is None and not isDepth: arg={'s_SEARCH':depth}
else: arg={}
v=self.use(varname,xiSEARCH=j,etaSEARCH=i,SEARCHtime=times,**arg).T
# calculate depths:
if self.hasz(varname):
h=self.grid.h[i,j]
zeta=self.use('zeta',xiSEARCH=j,etaSEARCH=i,SEARCHtime=times)
h=h+0*zeta
#### z=rt.s_levels(h,zeta,self.s_params,rw=varname)
z=rt.s_levels(h,zeta,self.s_params,rw=self.var_at(varname)[1])
z=np.squeeze(z)
# depth slice:
if isDepth and self.hasz(varname):
if v.ndim==2:
# could use calc.griddata, but better use slicez cos data at
# different times may be independent!
if 0:
from matplotlib.dates import date2num
t=np.tile(date2num(self.time[times]),(v.shape[0],1))
v=calc.griddata(t,z,v,t[0],depth+np.zeros(t[0].shape),
extrap=opts.get('extrap',False),norm_xy=opts.get('norm_xy',False))
# norm_xy True may be needed!
# extrap also may be needed cos the 1st and last value may be masked!
else:
nt=len(times)
land_mask=np.ones((nt,1),dtype=v.dtype) # needed for slicez... not used here!
v,vm=rt.slicez(v[...,np.newaxis],land_mask,
self.grid.h[i,j]*np.ones((nt,1),dtype=v.dtype), # bottom depth
zeta[:,np.newaxis],self.s_params,depth,spline=opts.get('spline',True))
v=np.ma.masked_where(vm,v)
v=v[...,0]
else: # one time only
v=np.interp(depth,z,v,left=np.nan,right=np.nan)
v=np.ma.masked_where(np.isnan(v),v)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='time series'
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords
if 't' in coords and self.hast(varname):
if v.ndim==2:
out.t=np.tile(self.time[times],(v.shape[0],1))
from matplotlib.dates import date2num
out.tnum=np.tile(date2num(self.time[times]),(v.shape[0],1))
else: out.t=self.time[times]
out.info['t']['name']='Time'
out.info['tnum']=dict(name='Time',units=self.var_as['time']['units'])
if 'z' in coords and self.hasz(varname):
if not depth is None:
if not isDepth: out.z=z[depth,...]
else: out.z=depth+0*v
else: out.z=z
out.info['z']=dict(name='Depth',units='m')
if 'x' in coords:
out.x=lon[i,j]
if self.grid.is_spherical:
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='X-position',units='km')
if 'y' in coords:
out.y=lat[i,j]
if self.grid.is_spherical:
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Y-position',units='km')
out.coordsReq=','.join(sorted(coords))
return out
def time_series(self,varname,x,y,times=False,depth=False,**opts):
t,z,v=[[]]*3
RetVarOnly=False
savename=False
retMsg=False
nearest=True # UNIQUE case Currently
plot=False
if 'retvar' in opts.keys(): RetVarOnly = opts['retvar']
if 'savename' in opts.keys(): savename = opts['savename']
if 'msg' in opts.keys(): retMsg = opts['msg']
if 'nearest' in opts.keys(): nearest = opts['msg']
if 'plot' in opts.keys(): plot = opts['msg']
if varname not in netcdf.varnames(self.name):
msg=':: variable %s not found' % varname
if retMsg: return t,z,v,msg
else:
print(msg)
return t,z,v
isW=varname=='w'
hasZ=self.hasz(varname)
if not depth is False and hasZ :
if isW and depth >= self.S_W:
msg='k = %d exceeds S_W dimension (%d)' % (depth,self.S_W)
if retMsg: return t,z,v,msg
else:
print(msg)
return t,z,v
elif depth >= self.S_RHO:
msg='k = %d exceeds S_RHO dimension (%d)' % (depth,self.S_RHO)
if retMsg: return t,z,v,msg
else:
print(msg)
return t,z,v
if times is False:
times=0,len(self.tdays)
if self.hast(varname) and times[-1]>self.TIME:
msg='t = %d exceeds TIME dimension (%d)' % (times[-1],self.TIME)
if retMsg: return t,z,v,msg
else:
print(msg)
return t,z,v
lon,lat,hr,mr=self.grid.vars(ruvp=self.var_at(varname))
dist=(lon-x)**2+(lat-y)**2
i,j=np.where(dist==dist.min())
i,j=i[0],j[0]
v=self.use(varname,xiSEARCH=j,etaSEARCH=i,SEARCHtime=range(times[0],times[-1])).T
# time:
t=self.tdays[range(times[0],times[-1])]
t=t+0.*v
# depth:
if hasZ:
h=self.grid.h[i,j]
zeta=self.use('zeta',xiSEARCH=j,etaSEARCH=i,SEARCHtime=range(times[0],times[-1]))
h=h+0*zeta
z=rt.s_levels(h,zeta,self.s_params,rw=varname)
z=np.squeeze(z)
if not depth is False:
if depth>=0:
t=t[0,:]
z=z[depth,:]
v=v[depth,:]
else:
t0,z0=t,z
t=t[0,:]
z=depth+0.*t
v=calc.griddata(t0,z0,v,t,z,extrap=False,norm_xy=True)
else: # not hasZ
z=0.*t
if plot:
pass # TODO
if RetVarOnly:
return v
else:
if retMsg: return t,z,v,''
else: return t,z,v
def time_series(self,varname,x,y,times=None,depth=None,**opts):
coords=opts.get('coords',self._default_coords('time_series')).split(',')
if times is None: times=range(0,self.time.size)
# depth or s_level: check is is float or if is negative!
isDepth=False
if not depth is None:
if calc.isiterable(depth): depth=np.asarray(depth)
if calc.isarray(depth):
isDepth=np.any(depth<0) or depth.kind!='i'
else: isDepth=depth<0 or np.asarray(depth).dtype.kind!='i'
out=Data()
if not depth is None and not isDepth:
out.msg=self.check_slice(varname,t=np.max(times),k=depth)
else:
out.msg=self.check_slice(varname,t=np.max(times))
if out.msg: return out
# find nearest point:
lon,lat,hr,mr=self.grid.vars(ruvp=self.var_at(varname))
dist=(lon-x)**2+(lat-y)**2
i,j=np.where(dist==dist.min())
i,j=i[0],j[0]
if not depth is None and not isDepth: arg={'s_SEARCH':depth}
else: arg={}
v=self.use(varname,xiSEARCH=j,etaSEARCH=i,SEARCHtime=times,**arg).T
# calculate depths:
if self.hasz(varname):
h=self.grid.h[i,j]
zeta=self.use('zeta',xiSEARCH=j,etaSEARCH=i,SEARCHtime=times)
h=h+0*zeta
#### z=rt.s_levels(h,zeta,self.s_params,rw=varname)
z=rt.s_levels(h,zeta,self.s_params,rw=self.var_at(varname)[1])
z=np.squeeze(z)
# depth slice:
if isDepth and self.hasz(varname):
if v.ndim==2:
# could use calc.griddata, but better use slicez cos data at
# different times may be independent!
if 0:
from matplotlib.dates import date2num
t=np.tile(date2num(self.time[times]),(v.shape[0],1))
v=calc.griddata(t,z,v,t[0],depth+np.zeros(t[0].shape),
extrap=opts.get('extrap',False),norm_xy=opts.get('norm_xy',False))
# norm_xy True may be needed!
# extrap also may be needed cos the 1st and last value may be masked!
else:
nt=len(times)
land_mask=np.ones((nt,1),dtype=v.dtype) # needed for slicez... not used here!
v,vm=rt.slicez(v[...,np.newaxis],land_mask,
self.grid.h[i,j]*np.ones((nt,1),dtype=v.dtype), # bottom depth
zeta[:,np.newaxis],self.s_params,depth,spline=opts.get('spline',True))
v=np.ma.masked_where(vm,v)
v=v[...,0]
else: # one time only
v=np.interp(depth,z,v,left=np.nan,right=np.nan)
v=np.ma.masked_where(np.isnan(v),v)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='time series'
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords
if 't' in coords and self.hast(varname):
if v.ndim==2:
out.t=np.tile(self.time[times],(v.shape[0],1))
from matplotlib.dates import date2num
out.tnum=np.tile(date2num(self.time[times]),(v.shape[0],1))
else: out.t=self.time[times]
out.info['t']['name']='Time'
out.info['tnum']=dict(name='Time',units=self.var_as['time']['units'])
if 'z' in coords and self.hasz(varname):
if not depth is None:
if not isDepth: out.z=z[depth,...]
else: out.z=depth+0*v
else: out.z=z
out.info['z']=dict(name='Depth',units='m')
if 'x' in coords:
out.x=lon[i,j]
if self.grid.is_spherical:
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x#/1000.
out.info['x']=dict(name='X-position',units='m')
if 'y' in coords:
out.y=lat[i,j]
if self.grid.is_spherical:
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y#/1000.
out.info['y']=dict(name='Y-position',units='m')
out.coordsReq=','.join(sorted(coords))
return out
