def extract(self,varname,method='fast',nfast=1,quiet=1):
if not self.roms.hast(varname):
method,nfast='fast',1
tag=self.horiz_var_type(varname)
if tag in self.uinds.keys():
inds0=self.inds0[tag]
uinds=self.uinds[tag]
else:
inds0,uinds=self.locate(varname,quiet)
J,I,T = uinds.T
J0,I0,T0 = inds0.T
if not self.roms.hast(varname): no_time=True
else: no_time=False
if method=='fast': # faster but will download more data than needed!
II=range(I.min(),I.max()+1)
JJ=range(J.min(),J.max()+1)
TT=range(T[T>=0].min(),T.max()+1)
if not calc.isiterable(nfast) and nfast==1:
if not quiet: print('loading %s : ijt= %d %d %d'%(varname,len(II),len(JJ),len(TT)))
v=netcdf.use(self.nc,varname,xiSEARCH=II,etaSEARCH=JJ,ocean_time=TT)
else:
v=False
if not calc.isiterable(nfast):
nfast=min(nfast,len(TT)-1)
tmp=range(TT[0],TT[-1]+2,(TT[-1]-TT[0])/nfast)
if tmp[-1]<TT[-1]+1: tmp+=[TT[-1]+1]
else: tmp=nfast
for k in range(len(tmp)-1):
tt=range(tmp[k],tmp[k+1])
if not quiet: print('loading %s : ijt= %d %d %d (t %d to %d)'%(varname,len(II),len(JJ),len(tt), tt[0],tt[-1]))
vtmp=netcdf.use(self.nc,varname,xiSEARCH=II,etaSEARCH=JJ,ocean_time=tt)
if not v is False:
if vtmp.ndim<v.ndim: vtmp=vtmp[np.newaxis,:] # if len of last tt is 1 !
v=np.vstack((v,vtmp))
else: v=vtmp
if v.ndim>3:
V=np.ma.zeros((I.size,v.shape[1]),'f')
else:
V=np.ma.zeros(I.size,'f')
for i in range(I.size):
xi = I[i]-I.min()
eta = J[i]-J.min()
if T[i]>=0: tind = T[i]-T[T>=0].min()
else: tind=T[i] # negative means data ouside model time
if v.ndim==4:
V[i]=v[tind,:,eta,xi]
elif v.ndim==3:
V[i]=v[tind,eta,xi]
elif v.ndim==2:
V[i]=v[eta,xi]
else:
V=False
for i in range(len(I)):
if T[i]<0: continue
if not quiet: print('loading %s (%d of %d): ijt= %d %d %d'%(varname,i,len(I),I[i],J[i],T[i]))
v=netcdf.use(self.nc,varname,xiSEARCH=I[i],etaSEARCH=J[i],ocean_time=T[i])
if V is False:
if v.ndim>1: shape=(len(I),)+v.shape
else: shape=len(I)
V=np.ma.zeros(shape,'f')
V[i]=v
lon,lat=self.model_lon_lat(varname)
U=np.array(())
for i in range(len(self.t)):
xi = I0[i]
eta = J0[i]
tind = T0[i]
if tind<0: continue # data outside model time
# rotate cell before interp:
xp=np.asarray([lon[eta,xi],lon[eta,xi+1],lon[eta+1,xi+1],lon[eta+1,xi],self.x[i]])
yp=np.asarray([lat[eta,xi],lat[eta,xi+1],lat[eta+1,xi+1],lat[eta+1,xi],self.y[i]])
xp,yp=calc.rot2d(xp,yp,self.roms.grid.angle[eta,xi])
x=xp[:-1].min(),xp[-1],xp[:-1].max()
y=yp[:-1].min(),yp[-1],yp[:-1].max()
A = x[1]-x[0]
a = x[2]-x[1]
B = y[1]-y[0]
b = y[2]-y[1]
tcond=(T==tind)#|(tind<0)
tcond1=(T==tind+1)#|(tind+1<0)
j0=np.where((I==xi)&(J==eta)&tcond)[0][0]
j1=np.where((I==xi+1)&(J==eta)&tcond)[0][0]
j2=np.where((I==xi+1)&(J==eta+1)&tcond)[0][0]
j3=np.where((I==xi)&(J==eta+1)&tcond)[0][0]
u0=(V[j0]*a*b+V[j1]*A*b+V[j2]*A*B+V[j3]*a*B)/(a*b+A*b+A*B+a*B)
if not no_time:
dt0=self.t[i]-self.roms.time[tind]
dt1=self.roms.time[tind+1]-self.t[i]
dt0=dt0.days*86400+dt0.seconds
dt1=dt1.days*86400+dt1.seconds
k0=np.where((I==xi)&(J==eta)&tcond1)[0][0]
k1=np.where((I==xi+1)&(J==eta)&tcond1)[0][0]
k2=np.where((I==xi+1)&(J==eta+1)&tcond1)[0][0]
k3=np.where((I==xi)&(J==eta+1)&tcond1)[0][0]
u1=(V[k0]*a*b+V[k1]*A*b+V[k2]*A*B+V[k3]*a*B)/(a*b+A*b+A*B+a*B)
u=(u0*dt1+u1*dt0)/(dt1+dt0)
else:
u=u0
if not U.size:
U=np.ma.zeros((len(self.t),u.size),'f')
U=np.ma.masked_where(U==0,U)
U[i]=u
U=np.squeeze(U)
return U
def _slice(self,slc,varname,ind,it0,**opts):
## border=opts.get('border',1)
if slc=='ll': x,y=ind
o=[]
It,twarn=self.tinds(it0)
for c,i in enumerate(self):
meth=getattr(i,'slice'+slc)
if slc=='uv': o+=[meth(ind,It[c],**opts)]
elif slc=='ll':o+=[meth(varname,x,y,It[c],**opts)]
else: o+=[meth(varname,ind,It[c],**opts)]
#if msg[c]:
# if o[-1].msg: o[-1].msg+='\n'
# o[-1].msg+=msg[c]
# add filled border for next domain:
if slc in ['i','j','k','z','iso']:#,'uv']:
for i in range(len(self)-1):
xb,yb=self[i+1].grid.border()
o[i].extra+=[vis.Data(xb,yb)]
o[i].extra[-1].label='border grid %d'%(i+1)
#if i>0:
o[i].extra[-1].config['d1.plot']='fill'
o[i].extra[-1].config['d1_fill.options']['lw']=0
o[i].extra[-1].config['d1_fill.options']['facecolor']='w'
#o[i].extra[-1].config['plot.zorder']=1
#else:
# o[i].extra[-1].config['d1.plot']='plot'
elif slc=='uv': # for uv, better to mask arrows inside child domains
if not hasattr(self.grid,'ingrd'): self.grid._set_ingrid('p')
from functools import reduce
for c,i in enumerate(o):
if not i.v is None:
if len(self.grid[c].ingrd_p):
mask=reduce(lambda i,j: i&j,self.grid[c].ingrd_p)
mask=mask&(~i.v.mask)
i.nmask=mask # nested domains mask
i.v.mask=i.v.mask|i.nmask
else: i.nmask=None
# vfield settings:
if not o[0].v is None:
vfield=o[0].get_param('vfield')
if not vfield['options']['scale']:
vfield['options']={'units':'width','scale':10}
# use same vfield settings for all slices:
for i in o:
for k in vfield: i.config['vfield.'+k]=vfield[k]
# no key for slices[1:]:
for i in o[1:]:
i.config['vfield.key_XYU']=0,0,0
# field settings:
if not o[0].v is None:
field=o[0].get_param('field')
if field['clim'] is False:
if np.iscomplexobj(o[0].v):
field['clim']=np.abs(o[0].v).min(),np.abs(o[0].v).max()
else:
field['clim']=o[0].v.min(),o[0].v.max()
if field['clim'][0]==field['clim'][1]:
field['clim']=field['clim'][0]-1,field['clim'][0]+1
if field['cvals'] is False:
tk=ticks.loose_label_n(field['clim'][0],field['clim'][1],7)
field['cvals']=tk
# use same field settings for all slices:
for i in o:
for k in field: i.config['field.'+k]=field[k]
# also same settings for extras like bathy contours:
Lab=['bathy']
for lab in Lab:
for e in o[0].extra:
if e.label==lab and not e.v is None:
field=e.get_param('field')
if field['clim'] is False: field['clim']=e.v.min(),e.v.max()
if field['cvals'] is False or not calc.isiterable(field['cvals']):
# the isiterable here is because it can be an integer (nof fixed set)
tk=ticks.loose_label_n(field['clim'][0],field['clim'][1],3)
field['cvals']=tk
for i in o:
for e in i.extra:
if e.label==lab:
for k in field: e.config['field.'+k]=field[k]
# add border for all domains:
# xb,yb=self.grid.border()
# o[0].extra+=[vis.Data(x=xb,v=yb)]
borders=[]
for i in self:
xb,yb=i.grid.border()
c=vis.Data(x=xb,v=yb)
c.set_param(d1_line__options=dict(lw=0.5,color='k',ls='-'))
borders+=[c]
o[-1].extra+=borders
# set zorder:
c=1
for i in o:
c+=0.1
i.config['plot.zorder']=c
for j in i.extra:
c+=0.1
j.config['plot.zorder']=c
# place some stuff above continents:
z=o[0].config['proj.continents']['zorder']
z+=0.1
# vfield:
if slc=='uv':
for i in o: i.config['plot.zorder']=z
# place borders above continents:
for b in borders: b.config['plot.zorder']=z
return vis.MData(o,warnings=twarn)
def _slice(self,slc,varname,ind,it0,**opts):
## border=opts.get('border',1)
if slc=='ll': x,y=ind
o=[]
It,twarn=self.tinds(it0)
for c,i in enumerate(self):
meth=getattr(i,'slice'+slc)
if slc=='uv': o+=[meth(ind,It[c],**opts)]
elif slc=='ll':o+=[meth(varname,x,y,It[c],**opts)]
else: o+=[meth(varname,ind,It[c],**opts)]
#if msg[c]:
# if o[-1].msg: o[-1].msg+='\n'
# o[-1].msg+=msg[c]
# add filled border for next domain:
if slc in ['i','j','k','z','iso']:#,'uv']:
for i in range(len(self)-1):
xb,yb=self[i+1].grid.border()
o[i].extra+=[vis.Data(xb,yb)]
o[i].extra[-1].label='border grid %d'%(i+1)
#if i>0:
o[i].extra[-1].config['d1.plot']='fill'
o[i].extra[-1].config['d1_fill.options']['lw']=0
o[i].extra[-1].config['d1_fill.options']['facecolor']='w'
#o[i].extra[-1].config['plot.zorder']=1
#else:
# o[i].extra[-1].config['d1.plot']='plot'
elif slc=='uv': # for uv, better to mask arrows inside child domains
if not hasattr(self.grid,'ingrd'): self.grid._set_ingrid('p')
from functools import reduce
for c,i in enumerate(o):
if not i.v is None:
if len(self.grid[c].ingrd_p):
mask=reduce(lambda i,j: i&j,self.grid[c].ingrd_p)
mask=mask&(~i.v.mask)
i.nmask=mask # nested domains mask
i.v.mask=i.v.mask|i.nmask
else: i.nmask=None
# vfield settings:
if not o[0].v is None:
vfield=o[0].get_param('vfield')
if not vfield['options']['scale']:
vfield['options']={'units':'width','scale':10}
# use same vfield settings for all slices:
for i in o:
for k in vfield: i.config['vfield.'+k]=vfield[k]
# no key for slices[1:]:
for i in o[1:]:
i.config['vfield.key_XYU']=0,0,0
# field settings:
if not o[0].v is None:
field=o[0].get_param('field')
if field['clim'] is False:
if np.iscomplexobj(o[0].v):
field['clim']=np.abs(o[0].v).min(),np.abs(o[0].v).max()
else:
field['clim']=o[0].v.min(),o[0].v.max()
if field['clim'][0]==field['clim'][1]:
field['clim']=field['clim'][0]-1,field['clim'][0]+1
if field['cvals'] is False:
tk=ticks.loose_label_n(field['clim'][0],field['clim'][1],7)
field['cvals']=tk
# use same field settings for all slices:
for i in o:
for k in field: i.config['field.'+k]=field[k]
# also same settings for extras like bathy contours:
Lab=['bathy']
for lab in Lab:
for e in o[0].extra:
if e.label==lab and not e.v is None:
field=e.get_param('field')
if field['clim'] is False: field['clim']=e.v.min(),e.v.max()
if field['cvals'] is False or not calc.isiterable(field['cvals']):
# the isiterable here is because it can be an integer (nof fixed set)
tk=ticks.loose_label_n(field['clim'][0],field['clim'][1],3)
field['cvals']=tk
for i in o:
for e in i.extra:
if e.label==lab:
for k in field: e.config['field.'+k]=field[k]
# add border for all domains:
# xb,yb=self.grid.border()
# o[0].extra+=[vis.Data(x=xb,v=yb)]
borders=[]
for i in self:
xb,yb=i.grid.border()
c=vis.Data(x=xb,v=yb)
c.set_param(d1_line__options=dict(lw=0.5,color='k',ls='-'))
borders+=[c]
o[-1].extra+=borders
# set zorder:
c=1
for i in o:
c+=0.1
i.config['plot.zorder']=c
for j in i.extra:
c+=0.1
j.config['plot.zorder']=c
# place some stuff above continents:
z=o[0].config['proj.continents']['zorder']
z+=0.1
# vfield:
if slc=='uv':
for i in o: i.config['plot.zorder']=z
# place borders above continents:
for b in borders: b.config['plot.zorder']=z
return vis.MData(o,warnings=twarn)
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 if 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=vis.Data()
out.label='time_series'
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))
lon,lat,hr,mr=self.grid.vars(ruvp=self.vloc(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):
if 'z' in self.vaxes(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=rt.s_levels(h,zeta,self.s_params,rw=self.vloc(varname)[1])
z=np.squeeze(z)
# depth slice:
### if isDepth and self.hasz(varname):
if isDepth and 'z' in self.vaxes(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=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,
surface_masked=opts.get('surf_mask',True),
spline=opts.get('spline',True))[...,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 't' in coords and 't' in self.vaxes(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 'z' in coords and 'z' in self.vaxes(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.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.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=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 if 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=vis.Data()
out.label='time_series'
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))
lon,lat,hr,mr=self.grid.vars(ruvp=self.vloc(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):
if 'z' in self.vaxes(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=rt.s_levels(h,zeta,self.s_params,rw=self.vloc(varname)[1])
z=np.squeeze(z)
# depth slice:
### if isDepth and self.hasz(varname):
if isDepth and 'z' in self.vaxes(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=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,
surface_masked=opts.get('surf_mask',True),
spline=opts.get('spline',True))[...,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 't' in coords and 't' in self.vaxes(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 'z' in coords and 'z' in self.vaxes(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.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.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