def fill_frc(fname, time, u, v):
nc = netcdf.Pync(fname, 'w')
tunits = netcdf.vatt(nc, 'wind_time', 'units')
time = netcdf.date2num(time, tunits)
tind = nc.dims['wind_time']
nc.vars['wind_time'][tind] = time
nc.vars['Uwind_ascat24'][tind, ...] = u
nc.vars['Vwind_ascat24'][tind, ...] = v
nc.close()
def fill_frc(fname,time,u,v): nc=netcdf.Pync(fname,'w') tunits=netcdf.vatt(nc,'wind_time','units') time=netcdf.date2num(time,tunits) tind=nc.dims['wind_time'] nc.vars['wind_time'][tind]=time nc.vars['Uwind_ascat24'][tind,...]=u nc.vars['Vwind_ascat24'][tind,...]=v nc.close()
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 sliceiso(self,varname,iso,time,**opts):
'''
Depths where variable, increasing/dec with depth, has some value.
Output is masked where surface is higher/lower than value or where all
water column is lower/higher than value.
'''
coords=opts.get('coords',self._default_coords('sliceiso')).split(',')
out=vis.Data()
out.label='slice_iso'
out.msg=self.check_slice(varname,t=time)
if out.msg: return out
### if not self.hasz(varname):
if not 'z' in self.vaxes(varname):
out.msg='a depth dependent variable is needed for sliceiso!'
return out
v=self.use(varname,SEARCHtime=time)
#### z=self.s_levels(time=time,ruvpw=self.var_at(varname))
### z=self.s_levels(time=time,loc=self.var_at(varname))
z=self.s_levels(time=time,loc=self.vloc(varname))
v=rt.depthof(v,z,iso)
out.v=v
#out.info['v']['name']=varname
out.info['v']['name']='depth'
if isinstance(iso,np.ndarray) and iso.ndim==2: siso='2d'
else: siso=str(iso)
try:
out.info['v']['slice']='%s (%s) iso=%s'%(varname,netcdf.vatt(self.nc,varname,'units'),siso)
except:
out.info['v']['slice']='%s iso=%s'%(varname,siso)
out.info['v']['units']='metre'
# coords:
if any([i in coords for i in 'xy']):
### x,y,h=self.grid.vars(ruvp=self.var_at(varname))[:3]
x,y,h=self.grid.vars(ruvp=self.vloc(varname))[:3]
if 'x' in coords:
if self.grid.spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 'z' in coords:
# makes no sense... v is the depth!
coords.remove('z')
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if out.v.ndim==2:
out.extra=[vis.Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.cmap']='k'
out.extra[0].label='bathy'
out.coordsReq=','.join(sorted(coords))
# about projection:
out.set_projection(self.grid.proj_info['basemap_opts'])
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 slicez(self,varname,ind,time=0,**opts):
surf_mask=opts.get('surf_mask',True)
spline=opts.get('spline',True)
coords=opts.get('coords',self._default_coords('slicez')).split(',')
out=vis.Data()
out.label='slicez'
out.msg=self.check_slice(varname,t=time)
if out.msg: return out
if not 'z' in self.vaxes(varname):
return self.slicek(varname,ind,time,**opts)
v=self.use(varname,SEARCHtime=time)
### 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])
zeta=self.use('zeta',SEARCHtime=time)
zeta=rt.rho2uvp(zeta,varname)
out.v=rt.slicez(v,m,h,zeta,self.s_params,ind,surf_mask,spline)
out.info['v']['name']=varname
if calc.isarray(ind):
out.info['v']['slice']='z= array %.2f to %.2f'%(ind.min(),ind.max())
else:
out.info['v']['slice']='z=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'x' in coords:
if self.grid.spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 'z' in coords:
out.z=ind+np.zeros(out.v.shape)
out.info['z']=dict(name='Depth',units='m')
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if out.v.ndim==2:
out.extra=[vis.Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.cmap']='k'
out.extra[0].label='bathy'
out.coordsReq=','.join(sorted(coords))
# about projection:
out.set_projection(self.grid.proj_info['basemap_opts'])
return out
def slicek(self,varname,ind,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicek')).split(',')
coords=['x','y','z','d']
out=Data()
out.msg=self.check_slice(varname,t=time,k=ind)
if out.msg: out
v=self.use(varname,SEARCHtime=time,s_SEARCH=ind)
# add mask if not masked:
if not np.ma.isMA(v):
m=self.grid.vars(ruvp=self.var_at(varname)[0])[-1]
v=np.ma.masked_where(m==0,v)
out.v=v
out.info['v']['name']=varname
if self.hasz(varname): out.info['v']['slice']='k=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and self.hasz(varname):
##### out.z=self.s_levels(time,k=ind,ruvpw=self.var_at(varname))
out.z=self.s_levels(time,k=ind,loc=self.var_at(varname))
out.info['z']=dict(name='Depth',units='m')
if any([i in coords for i in 'xy']):
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname)[0])
if 'x' in coords:
if self.grid.is_spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x #/1000.
out.info['x']=dict(name='Distance',units='m')
if 'y' in coords:
if self.grid.is_spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y#/1000.
out.info['y']=dict(name='Distance',units='m')
if 't' in coords and self.hast(varname): out.t=self.time[time]
if v.ndim==2:
out.extra=[Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.linecolors']='k'
out.extra[0].alias='bathy'
out.coordsReq=','.join(sorted(coords))
return out
def slicek(self,varname,ind,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicek')).split(',')
out=Data()
out.msg=self.check_slice(varname,t=time,k=ind)
if out.msg: out
v=self.use(varname,SEARCHtime=time,s_SEARCH=ind)
# add mask if not masked:
if not np.ma.isMA(v):
m=self.grid.vars(ruvp=self.var_at(varname))[-1]
v=np.ma.masked_where(m==0,v)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='k=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and self.hasz(varname):
out.z=self.s_levels(time,k=ind,ruvpw=self.var_at(varname))
out.info['z']=dict(name='Depth',units='m')
if any([i in coords for i in 'xy']):
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname))
if 'x' in coords:
if self.grid.is_spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.is_spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 't' in coords: out.t=self.time[time]
if v.ndim==2:
out.extra=[Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.linecolors']='k'
out.extra[0].alias='bathy'
out.coordsReq=','.join(sorted(coords))
return out
def slicez(self,varname,ind,time=0,**opts):
if not self.hasz(varname):
return self.slicek(varname,ind,time,**opts)
surf_nans=opts.get('surf_nans',True)
spline=opts.get('spline',True)
coords=opts.get('coords',self._default_coords('slicez')).split(',')
out=Data()
out.msg=self.check_slice(varname,t=time)
if out.msg: return out##None,au
v=self.use(varname,SEARCHtime=time)
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname))
zeta=self.use('zeta',SEARCHtime=time)
zeta=rt.rho2uvp(zeta,varname)
v,mask=rt.slicez(v,m,h,zeta,self.s_params,ind,surf_nans,spline)
v=np.ma.masked_where(mask,v)
out.v=v
out.info['v']['name']=varname
out.info['v']['slice']='z=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'x' in coords:
if self.grid.is_spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.is_spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 'z' in coords:
out.z=ind+np.zeros(v.shape)
out.info['z']=dict(name='Depth',units='m')
if 't' in coords: out.t=self.time[time]
if v.ndim==2:
out.extra=[Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.linecolors']='k'
out.extra[0].alias='bathy'
out.coordsReq=','.join(sorted(coords))
return out
def sliceiso(self,varname,iso,time,**opts):
'''
Depths where variable, increasing/dec with depth, has some value.
Output is masked where surface is higher/lower than value or where all
water column is lower/higher than value.
'''
coords=opts.get('coords',self._default_coords('sliceiso')).split(',')
out=Data()
out.msg=self.check_slice(varname,t=time)
if out.msg: return out
if not self.hasz(varname):
out.msg='a depth dependent variable is needed for sliceiso!'
return out
v=self.use(varname,SEARCHtime=time)
z=self.s_levels(time=time,ruvpw=self.var_at(varname))
v=rt.depthof(v,z,iso)
out.v=v
out.info['v']['name']=varname
if isinstance(iso,np.ndarray) and iso.ndim==2: siso='2d'
else: siso=str(iso)
out.info['v']['slice']='iso=%s'%siso
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if any([i in coords for i in 'xy']):
x,y,h=self.grid.vars(ruvp=self.var_at(varname))[:3]
if 'x' in coords:
if self.grid.is_spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.is_spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 'z' in coords:
# makes no sense... v is the depth!
coords.remove('z')
if 't' in coords: out.t=self.time[time]
if v.ndim==2:
out.extra=[Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.linecolors']='k'
out.extra[0].alias='bathy'
out.coordsReq=','.join(sorted(coords))
return out
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 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 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 slicez(self,varname,ind,time=0,**opts):
surf_mask=opts.get('surf_mask',True)
spline=opts.get('spline',True)
coords=opts.get('coords',self._default_coords('slicez')).split(',')
out=vis.Data()
out.label='slicez'
out.msg=self.check_slice(varname,t=time)
if out.msg: return out
if not 'z' in self.vaxes(varname):
return self.slicek(varname,ind,time,**opts)
v=self.use(varname,SEARCHtime=time)
### 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])
zeta=self.use('zeta',SEARCHtime=time)
zeta=rt.rho2uvp(zeta,varname)
out.v=rt.slicez(v,m,h,zeta,self.s_params,ind,surf_mask,spline)
out.info['v']['name']=varname
if calc.isarray(ind):
out.info['v']['slice']='z= array %.2f to %.2f'%(ind.min(),ind.max())
else:
out.info['v']['slice']='z=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'x' in coords:
if self.grid.spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 'z' in coords:
out.z=ind+np.zeros(out.v.shape)
out.info['z']=dict(name='Depth',units='m')
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if out.v.ndim==2:
out.extra=[vis.Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.cmap']='k'
out.extra[0].label='bathy'
out.coordsReq=','.join(sorted(coords))
# about projection:
out.set_projection(self.grid.proj_info['basemap_opts'])
return out
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=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 slicek(self,varname,ind,time=0,**opts):
coords=opts.get('coords',self._default_coords('slicek')).split(',')
out=vis.Data()
out.label='slicek'
out.msg=self.check_slice(varname,t=time,k=ind)
if out.msg: return out
v=self.use(varname,SEARCHtime=time,s_SEARCH=ind)
# add mask if not masked:
if not np.ma.isMA(v):
### m=self.grid.vars(ruvp=self.var_at(varname)[0])[-1]
m=self.grid.vars(ruvp=self.vloc(varname)[0])[-1]
v=np.ma.masked_where(m==0,v)
out.v=v
out.info['v']['name']=varname
##### if self.hasz(varname): out.info['v']['slice']='k=%d'%ind
if 'z' in self.vaxes(varname): out.info['v']['slice']='k=%d'%ind
try: out.info['v']['units']=netcdf.vatt(self.nc,varname,'units')
except: pass
# coords:
if 'z' in coords and 'z' in self.vaxes(varname):
out.z=self.s_levels(time,k=ind,loc=self.vloc(varname))
out.info['z']=dict(name='Depth',units='m')
if any([i in coords for i in 'xy']):
x,y,h,m=self.grid.vars(ruvp=self.vloc(varname)[0])
if 'x' in coords:
if self.grid.spherical:
out.x=x
out.info['x']=dict(name='Longitude',units=r'$\^o$E')
else:
out.x=x/1000.
out.info['x']=dict(name='Distance',units='km')
if 'y' in coords:
if self.grid.spherical:
out.y=y
out.info['y']=dict(name='Latitude',units=r'$\^o$N')
else:
out.y=y/1000.
out.info['y']=dict(name='Distance',units='km')
if 't' in coords and 't' in self.vaxes(varname): out.t=self.time[time]
if out.v.ndim==2: # always?
out.extra=[vis.Data()]
if 'x' in coords: out.extra[0].x=out.x
if 'y' in coords: out.extra[0].y=out.y
out.extra[0].v=h
if h.max()>1000: cvals=200.,1000.
elif h.max()>200: cvals=50.,100.,200.
else: cvals=3
out.extra[0].config['field.plot']='contour'
out.extra[0].config['field.cvals']=cvals
out.extra[0].config['field.cmap']='k'
out.extra[0].label='bathy'
out.coordsReq=','.join(sorted(coords))
# about projection:
out.set_projection(self.grid.proj_info['basemap_opts'])
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
