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 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 slicez(self, varname, ind, time=0, plot=False, **opts):
x, y, z, v = [[]] * 4
savename = False
retMsg = False
surf_nans = True
if "savename" in opts.keys():
savename = opts["savename"]
if "msg" in opts.keys():
retMsg = opts["msg"]
if "surf_nans" in opts.keys():
surf_nans = opts["surf_nans"]
if varname not in netcdf.varnames(self.name):
msg = ":: variable %s not found" % varname
if retMsg:
return x, y, z, v, msg
else:
print msg
return x, y, z, v
if self.hast(varname) and time >= self.TIME:
msg = "t = %d exceeds TIME dimension (%d)" % (time, self.TIME)
if retMsg:
return x, y, z, v, msg
else:
print msg
return x, y, z, v
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)
if len(v.shape) == 2: # no vertical comp
if retMsg:
return x, y, ind + np.zeros(v.shape), np.ma.masked_where(m == 0, v), ""
else:
return x, y, ind + np.zeros(v.shape), np.ma.masked_where(m == 0, v)
v, mask = rt.slicez(v, m, h, zeta, self.s_params, ind, surf_nans)
v = np.ma.masked_where(mask, v)
if plot:
p = self.grid.plot(bathy=None, **opts)
xm, ym = p(x, y)
pch = pylab.pcolor(xm, ym, v, shading="flat")
pylab.colorbar(pch, shrink=0.7)
pylab.axis([p.xmin, p.xmax, p.ymin, p.ymax])
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if retMsg:
return x, y, ind + np.zeros(v.shape), v, ""
else:
return x, y, ind + np.zeros(v.shape), 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 slicez(self, varname, ind, time=0, plot=False, **opts):
x, y, z, v = [[]] * 4
savename = False
retMsg = False
surf_nans = True
if 'savename' in opts.keys(): savename = opts['savename']
if 'msg' in opts.keys(): retMsg = opts['msg']
if 'surf_nans' in opts.keys(): surf_nans = opts['surf_nans']
if varname not in netcdf.varnames(self.name):
msg = ':: variable %s not found' % varname
if retMsg: return x, y, z, v, msg
else:
print(msg)
return x, y, z, v
if self.hast(varname) and time >= self.TIME:
msg = 't = %d exceeds TIME dimension (%d)' % (time, self.TIME)
if retMsg: return x, y, z, v, msg
else:
print(msg)
return x, y, z, v
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)
if len(v.shape) == 2: # no vertical comp
if retMsg:
return x, y, ind + np.zeros(v.shape), np.ma.masked_where(
m == 0, v), ''
else:
return x, y, ind + np.zeros(v.shape), np.ma.masked_where(
m == 0, v)
v, mask = rt.slicez(v, m, h, zeta, self.s_params, ind, surf_nans)
v = np.ma.masked_where(mask, v)
if plot:
p = self.grid.plot(bathy=None, **opts)
xm, ym = p(x, y)
pch = pylab.pcolor(xm, ym, v, shading='flat')
pylab.colorbar(pch, shrink=.7)
pylab.axis([p.xmin, p.xmax, p.ymin, p.ymax])
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if retMsg: return x, y, ind + np.zeros(v.shape), v, ''
else: return x, y, ind + np.zeros(v.shape), v
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 check_slice(self,varname,**dims):
msg=''
# check varname:
if varname not in netcdf.varnames(self.nc):
return ':: variable %s not found' % varname
isU=varname in ('u','ubar')
isV=varname in ('v','vbar')
isW=varname=='w'
for dim,ind in dims.items():
# check time:
if dim=='t':
if self.hast(varname) and ind>=self.TIME:
msg='t = %d exceeds TIME dimension (%d)' % (ind,self.TIME)
# check dim k:
elif dim=='k':
if self.hasz(varname):
if isW: s='S_W'
else: s='S_RHO'
indMax=getattr(self,s)
if ind >= indMax: msg='k = %d exceeds %s dimension (%d)' % (ind,s,indMax)
# check dim i:
elif dim=='i':
xi='XI_RHO'
if isU: xi='XI_U'
elif isV and hasattr(self,'XI_V'): xi='XI_V' # needed for roms-agrif
indMax=getattr(self,xi)
if ind >= indMax: msg='i = %d exceeds %s dimension (%d)' % (ind,xi,indMax)
# check dim j:
elif dim=='j':
eta='ETA_RHO'
if isU and hasattr(self,'ETA_U'): eta='ETA_U' # needed for roms-agrif
elif isV: eta='ETA_V'
indMax=getattr(self,eta)
if ind >= indMax: msg='j = %d exceeds %s dimension (%d)' % (ind,eta,indMax)
return msg
def check_slice(self,varname,**dims):
msg=''
# check varname:
if varname not in netcdf.varnames(self.nc):
return ':: variable %s not found' % varname
isU=varname in ('u','ubar')
isV=varname in ('v','vbar')
isW=varname=='w'
for dim,ind in dims.items():
# check time:
if dim=='t':
if 't' in self.vaxes(varname) and ind>=self.TIME:
msg='t = %d exceeds TIME dimension (%d)' % (ind,self.TIME)
# check dim k:
elif dim=='k':
if 'z' in self.vaxes(varname):
if isW: s='S_W'
else: s='S_RHO'
indMax=getattr(self,s)
if ind >= indMax: msg='k = %d exceeds %s dimension (%d)' % (ind,s,indMax)
# check dim i:
elif dim=='i':
xi='XI_RHO'
if isU: xi='XI_U'
elif isV and hasattr(self,'XI_V'): xi='XI_V' # needed for roms-agrif
indMax=getattr(self,xi)
if ind >= indMax: msg='i = %d exceeds %s dimension (%d)' % (ind,xi,indMax)
# check dim j:
elif dim=='j':
eta='ETA_RHO'
if isU and hasattr(self,'ETA_U'): eta='ETA_U' # needed for roms-agrif
elif isV: eta='ETA_V'
indMax=getattr(self,eta)
if ind >= indMax: msg='j = %d exceeds %s dimension (%d)' % (ind,eta,indMax)
return msg
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.is_masked(zeta):
# h=np.ma.masked_where(zeta.mask,h)
# comment above in order to keep right depth inside the domain
# on masked regions
pass
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)
# better interpolate the gaps, otherwise s_levels will use default
# values for zeta and h:
X=np.arange(h.size)
if np.ma.is_masked(h): h=np.interp(X,X[~h.mask],h[~h.mask])
if np.ma.is_masked(zeta): zeta=np.interp(X,X[~zeta.mask],zeta[~zeta.mask])
### return h,zeta,self.s_params,rw
return rt.s_levels(h,zeta,self.s_params,rw=rw)
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.is_masked(zeta):
# h=np.ma.masked_where(zeta.mask,h)
# comment above in order to keep right depth inside the domain
# on masked regions
pass
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)
# better interpolate the gaps, otherwise s_levels will use default
# values for zeta and h:
X=np.arange(h.size)
if np.ma.is_masked(h): h=np.interp(X,X[~h.mask],h[~h.mask])
if np.ma.is_masked(zeta): zeta=np.interp(X,X[~zeta.mask],zeta[~zeta.mask])
### return h,zeta,self.s_params,rw
return rt.s_levels(h,zeta,self.s_params,rw=rw)
def cordex_file_data(f,lims=False,quiet=False):
'''
CORDEX data for ROMS
No accumulated variables are considered
'''
out={}
# time, x, y:
if not quiet: print(' reading time,x,y')
out['time']=netcdf.nctime(f,'time')
x=netcdf.use(f,'lon')
y=netcdf.use(f,'lat')
x[x>180]=x[x>180]-360
if x.ndim==1 and y.ndim==1:
x,y=np.meshgrid(x,y)
if np.ma.isMA(x): x=x.data
if np.ma.isMA(y): y=y.data
if lims:
from okean import calc
xlim,ylim=lims
i1,i2,j1,j2=calc.ij_limits(x,y,xlim,ylim,margin=3)
else:
i0=0
j0=0
j1,i1=x.shape
I=range(i1,i2)
J=range(j1,j2)
x=x[j1:j2,i1:i2]
y=y[j1:j2,i1:i2]
# tair [K-->C]
if not quiet: print(' --> T air')
vname='tair'
tair=netcdf.use(f,vname,lon=I,lat=J)-273.15
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from specific humidity)')
vname='humid'
q=netcdf.use(f,vname,lon=I,lat=J) # specific humidity
rhum=q/air_sea.qsat(tair)
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname='press'
pres=netcdf.use(f,vname,lon=I,lat=J)
out['pres']=Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
vname='rain'
prate=netcdf.use(f,vname,lon=I,lat=J)
prate=prate*86400*100/1000.
prate[prate<0]=0
out['prate']=Data(x,y,prate,'cm/d')
# Net shortwave flux [W m-2]
if not quiet: print(' --> Net shortwave flux')
if not quiet: print(' SW down')
sw_down=netcdf.use(f,'sw_down',lon=I,lat=J)
if not quiet: print(' SW up')
sw_up=netcdf.use(f,'sw_up',lon=I,lat=J)
sw_net=sw_down-sw_up
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print(' --> Net longwave flux')
if not quiet: print(' LW down')
lw_down=netcdf.use(f,'lw_down',lon=I,lat=J)
if not quiet: print(' LW up')
lw_up=netcdf.use(f,'lw_up',lon=I,lat=J)
lw_net=lw_down-lw_up
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
# downward lw:
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='negative... downward')
# signs convention is better explained in wrf.py
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
uwnd=netcdf.use(f,'u',lon=I,lat=J)
vwnd=netcdf.use(f,'v',lon=I,lat=J)
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
if 'clouds' in netcdf.varnames(f):
clouds=netcdf.use(f,'clouds',lon=I,lat=J)/100.
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
else:
print('==> clouds not present!')
return fill_extremes(out,quiet)
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
def cfsr_file_data(files,quiet=False):
'''
Returns bulk data from one CFRS files
'''
def load_time(f):
time=np.array((),datetime.datetime)
ff=glob.glob(f)
ff.sort()
for f in ff: time=np.append(time,netcdf.nctime(f,'time'))
return time
def load_time_main(f):
time=load_time(f)
# I want 0,6,12,... after 2006 results may be 3,9,15, ...
if time[0].hour in [3,9,15,21]: time=time+datetime.timedelta(hours=3)
# for 2011 1st time is not 0!
if time[0].hour==6: time=np.hstack((time[0].replace(hour=0),time))
return time
def fix_time(t,var,t0,t1):
# convert 1h, 7h, ... to 0h, 6h, ...
if t[0].hour in [1,7,13,19]: # not all! sp analysis starts at 0, 6,...!
print(' 1,7,... to 0,6,...')
var=(var[1:]*5+var[:-1]*1)/6.
t=t[1:]-datetime.timedelta(hours=1)
elif t[0].hour in [3,9,15,21]:
print(' 3,9,... to 0,6,...')
var=(var[1:]*3+var[:-1]*3)/6.
t=t[1:]-datetime.timedelta(hours=3)
cond=(t>=t0)&(t<=t1)
t=t[cond]
var=var[cond]
if t[0]>t0:
dt=t[0]-t0
dt=dt.days*24+dt.seconds/3600. # hours
print('missing data at start: %.2d h missing --> repeating 1st data'%dt)
v=np.zeros((var.shape[0]+1,)+var.shape[1:],var.dtype)
v[1:]=var
v[0]=var[0]
var=v
t_=np.zeros((t.shape[0]+1,)+t.shape[1:],t.dtype)
t_[1:]=t
t_[0]=t0
t=t_
if t[-1]<t1:
dt=t1-t[-1]
dt=dt.days*24+dt.seconds/3600. # hours
print('missing data at end: %.2d h missing --> repeating last data'%dt)
v=np.zeros((var.shape[0]+1,)+var.shape[1:],var.dtype)
v[:-1]=var
v[-1]=var[-1]
var=v
t_=np.zeros((t.shape[0]+1,)+t.shape[1:],t.dtype)
t_[:-1]=t
t_[-1]=t1
t=t_
return var,t
out={}
# time:
if 0:
time=netcdf.nctime(files['cc'],'time')
# files have diff units !! so, cannot load all times at once!
# these result will use only units of 1st file!!
else:
time=load_time_main(files['cc'])
out['time']=time
# T air [K->C]
if not quiet: print(' --> T air')
f=files['st']
tair=netcdf.use(f,'TMP_L103')
tair=tair-273.15
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!',)
tair,tfix=fix_time(ttmp,tair,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['tair']=Data(x,y,tair,'C')
# R humidity [%-->0--1]
if not quiet: print(' --> R humidity')
f=files['rh']
rhum=netcdf.use(f,'R_H_L103')
rhum=rhum/100.
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'), # should use end=' ' for python3 print continuation
rhum,tfix=fix_time(ttmp,rhum,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
f=files['sp']
pres=netcdf.use(f,'PRES_L1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
pres,tfix=fix_time(ttmp,pres,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['pres']=Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
f=files['pr']
if 'PRATE_L1' in netcdf.varnames(f):
prate=netcdf.use(f,'PRATE_L1')
else:
prate=netcdf.use(f,'PRATE_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# Conversion kg m^-2 s^-1 to cm/day
prate=prate*86400*100/1000.
prate=np.where(abs(prate)<1.e-4,0,prate)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
prate,tfix=fix_time(ttmp,prate,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['prate']=Data(x,y,prate,'cm/d')
# Net shortwave flux [W/m^2]
if not quiet: print(' --> Net shortwave flux')
if not quiet: print(' SW down')
f=files['rad']
sw_down=netcdf.use(f,'DSWRF_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
if not quiet: print(' SW up')
sw_up=netcdf.use(f,'USWRF_L1_Avg_1')
sw_net=sw_down-sw_up
sw_net=np.where(sw_net<1.e-10,0,sw_net)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
sw_net,tfix=fix_time(ttmp,sw_net,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W/m^2]
if not quiet: print(' --> Net longwave flux')
if not quiet: print(' LW down')
f=files['rad']
lw_down=netcdf.use(f,'DLWRF_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
if not quiet: print(' LW up')
lw_up=netcdf.use(f,'ULWRF_L1_Avg_1')
lw_net=lw_down-lw_up
lw_net=np.where(np.abs(lw_net)<1.e-10,0,lw_net)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
lw_net,tfix1=fix_time(ttmp,lw_net,time[0],time[-1])
lw_down,tfix2=fix_time(ttmp,lw_down,time[0],time[-1])
if tfix1.size==tfix2.size==time.size and np.all((tfix1==time)&(tfix2==time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
# ROMS (agrif, used to be!) convention: positive upward
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
# downward lw:
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='negative... downward')
# signs convention is better explained in wrf.py
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
f=files['uv']
uwnd=netcdf.use(f,'U_GRD_L103')
vwnd=netcdf.use(f,'V_GRD_L103')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
uwnd,tfix1=fix_time(ttmp,uwnd,time[0],time[-1])
vwnd,tfix2=fix_time(ttmp,vwnd,time[0],time[-1])
if tfix1.size==tfix2.size==time.size and np.all((tfix1==time)&(tfix2==time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
#
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
f=files['cc']
if 'T_CDC_L200' in netcdf.varnames(f):
clouds=netcdf.use(f,'T_CDC_L200')
else:
clouds=netcdf.use(f,'T_CDC_L200_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
clouds=clouds/100.
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
clouds,tfix=fix_time(ttmp,clouds,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
# rhum has different resolution (0.5, just like dew point!)
# so, i can edit surface.py or just interpolate here rhum to
# other vars resolution:
if out['rhum'].data.shape!=out['uwnd'].data.shape:
from okean import calc
print('rhum shape differs!! --> interp:')
nt,ny,nx=out['uwnd'].data.shape
x,y=out['uwnd'].x,out['uwnd'].y
rhum=np.zeros((nt,ny,nx), out['rhum'].data.dtype)
for it in range(nt):
if it%100==0: print(' %d of %d'%(it,nt))
rhum[it]=calc.griddata(out['rhum'].x,out['rhum'].y,out['rhum'].data[it],x,y)
out['rhum']=Data(x,y,rhum,'0--1')
return out
def cfsr_file_data(files, quiet=False):
'''
Returns bulk data from one CFRS files
'''
def load_time(f):
time = np.array((), datetime.datetime)
ff = glob.glob(f)
ff.sort()
for f in ff:
time = np.append(time, netcdf.nctime(f, 'time'))
return time
def load_time_main(f):
time = load_time(f)
# I want 0,6,12,... after 2006 results may be 3,9,15, ...
if time[0].hour in [3, 9, 15, 21]:
time = time + datetime.timedelta(hours=3)
# for 2011 1st time is not 0!
if time[0].hour == 6: time = np.hstack((time[0].replace(hour=0), time))
return time
def fix_time(t, var, t0, t1):
# convert 1h, 7h, ... to 0h, 6h, ...
if t[0].hour in [1, 7, 13,
19]: # not all! sp analysis starts at 0, 6,...!
print(' 1,7,... to 0,6,...')
var = (var[1:] * 5 + var[:-1] * 1) / 6.
t = t[1:] - datetime.timedelta(hours=1)
elif t[0].hour in [3, 9, 15, 21]:
print(' 3,9,... to 0,6,...')
var = (var[1:] * 3 + var[:-1] * 3) / 6.
t = t[1:] - datetime.timedelta(hours=3)
cond = (t >= t0) & (t <= t1)
t = t[cond]
var = var[cond]
if t[0] > t0:
dt = t[0] - t0
dt = dt.days * 24 + dt.seconds / 3600. # hours
print(
'missing data at start: %.2d h missing --> repeating 1st data'
% dt)
v = np.zeros((var.shape[0] + 1, ) + var.shape[1:], var.dtype)
v[1:] = var
v[0] = var[0]
var = v
t_ = np.zeros((t.shape[0] + 1, ) + t.shape[1:], t.dtype)
t_[1:] = t
t_[0] = t0
t = t_
if t[-1] < t1:
dt = t1 - t[-1]
dt = dt.days * 24 + dt.seconds / 3600. # hours
print(
'missing data at end: %.2d h missing --> repeating last data' %
dt)
v = np.zeros((var.shape[0] + 1, ) + var.shape[1:], var.dtype)
v[:-1] = var
v[-1] = var[-1]
var = v
t_ = np.zeros((t.shape[0] + 1, ) + t.shape[1:], t.dtype)
t_[:-1] = t
t_[-1] = t1
t = t_
return var, t
out = {}
# time:
if 0:
time = netcdf.nctime(files['cc'], 'time')
# files have diff units !! so, cannot load all times at once!
# these result will use only units of 1st file!!
else:
time = load_time_main(files['cc'])
out['time'] = time
# T air [K->C]
if not quiet: print(' --> T air')
f = files['st']
tair = netcdf.use(f, 'TMP_L103')
tair = tair - 273.15
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!', )
tair, tfix = fix_time(ttmp, tair, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['tair'] = Data(x, y, tair, 'C')
# R humidity [%-->0--1]
if not quiet: print(' --> R humidity')
f = files['rh']
rhum = netcdf.use(f, 'R_H_L103')
rhum = rhum / 100.
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'
), # should use end=' ' for python3 print continuation
rhum, tfix = fix_time(ttmp, rhum, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
f = files['sp']
pres = netcdf.use(f, 'PRES_L1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
pres, tfix = fix_time(ttmp, pres, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
f = files['pr']
if 'PRATE_L1' in netcdf.varnames(f):
prate = netcdf.use(f, 'PRATE_L1')
else:
prate = netcdf.use(f, 'PRATE_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# Conversion kg m^-2 s^-1 to cm/day
prate = prate * 86400 * 100 / 1000.
prate = np.where(abs(prate) < 1.e-4, 0, prate)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
prate, tfix = fix_time(ttmp, prate, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['prate'] = Data(x, y, prate, 'cm/d')
# Net shortwave flux [W/m^2]
if not quiet: print(' --> Net shortwave flux')
if not quiet: print(' SW down')
f = files['rad']
sw_down = netcdf.use(f, 'DSWRF_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
if not quiet: print(' SW up')
sw_up = netcdf.use(f, 'USWRF_L1_Avg_1')
sw_net = sw_down - sw_up
sw_net = np.where(sw_net < 1.e-10, 0, sw_net)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
sw_net, tfix = fix_time(ttmp, sw_net, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W/m^2]
if not quiet: print(' --> Net longwave flux')
if not quiet: print(' LW down')
f = files['rad']
lw_down = netcdf.use(f, 'DLWRF_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
if not quiet: print(' LW up')
lw_up = netcdf.use(f, 'ULWRF_L1_Avg_1')
lw_net = lw_down - lw_up
lw_net = np.where(np.abs(lw_net) < 1.e-10, 0, lw_net)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
lw_net, tfix1 = fix_time(ttmp, lw_net, time[0], time[-1])
lw_down, tfix2 = fix_time(ttmp, lw_down, time[0], time[-1])
if tfix1.size == tfix2.size == time.size and np.all((tfix1 == time)
& (tfix2 == time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
# ROMS (agrif, used to be!) convention: positive upward
out['radlw'] = Data(x, y, -lw_net, 'W m-2', info='positive upward')
# downward lw:
out['dlwrf'] = Data(x, y, -lw_down, 'W m-2', info='negative... downward')
# signs convention is better explained in wrf.py
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
f = files['uv']
uwnd = netcdf.use(f, 'U_GRD_L103')
vwnd = netcdf.use(f, 'V_GRD_L103')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
uwnd, tfix1 = fix_time(ttmp, uwnd, time[0], time[-1])
vwnd, tfix2 = fix_time(ttmp, vwnd, time[0], time[-1])
if tfix1.size == tfix2.size == time.size and np.all((tfix1 == time)
& (tfix2 == time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
#
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2 + vwnd**2)
taux, tauy = air_sea.wind_stress(uwnd, vwnd)
out['wspd'] = Data(x, y, speed, 'm s-1')
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
out['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
f = files['cc']
if 'T_CDC_L200' in netcdf.varnames(f):
clouds = netcdf.use(f, 'T_CDC_L200')
else:
clouds = netcdf.use(f, 'T_CDC_L200_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
clouds = clouds / 100.
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
clouds, tfix = fix_time(ttmp, clouds, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
# rhum has different resolution (0.5, just like dew point!)
# so, i can edit surface.py or just interpolate here rhum to
# other vars resolution:
if out['rhum'].data.shape != out['uwnd'].data.shape:
from okean import calc
print('rhum shape differs!! --> interp:')
nt, ny, nx = out['uwnd'].data.shape
x, y = out['uwnd'].x, out['uwnd'].y
rhum = np.zeros((nt, ny, nx), out['rhum'].data.dtype)
for it in range(nt):
if it % 100 == 0: print(' %d of %d' % (it, nt))
rhum[it] = calc.griddata(out['rhum'].x, out['rhum'].y,
out['rhum'].data[it], x, y)
out['rhum'] = Data(x, y, rhum, '0--1')
return out
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print ' time ok'
else:
print ' time differs !!!!',
pres, tfix = fix_time(ttmp, pres, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print ' ...fixed!'
else:
print 'time is NOT OK. Please check !!'
return
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print ' --> P rate'
f = files['pr']
if 'PRATE_L1' in netcdf.varnames(f):
prate = netcdf.use(f, 'PRATE_L1')
else:
prate = netcdf.use(f, 'PRATE_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# Conversion kg m^-2 s^-1 to cm/day
prate = prate * 86400 * 100 / 1000.
prate = np.where(abs(prate) < 1.e-4, 0, prate)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print ' time ok'
else:
print ' time differs !!!!',
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 wrf_file_data(file, quiet=False):
'''
WRF data for ROMS
'''
out = {}
# time:
if not quiet: print ' --> get time'
time = read_time(file)
out['time'] = time
# lon,lat:
if not quiet: print ' --> reading x,y'
x = netcdf.use(file, 'XLONG', **{'0': 0})
y = netcdf.use(file, 'XLAT', **{'0': 0})
# tair [K-->C]
if not quiet: print ' --> T air'
tair = netcdf.use(file, 'T2') - 273.15
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [kg/kg --> 0--1]
if not quiet: print ' --> R humidity from QV at 2m'
wv = netcdf.use(file, 'Q2') # water vapor mixing ratio at 2m
rhum = wv / air_sea.qsat(tair)
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print ' --> Surface pressure'
pres = netcdf.use(file, 'PSFC')
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [mm --> cm day-1]
if not quiet: print ' --> P rate (rainc+rainnc)'
rainc = netcdf.use(file, 'RAINC')
rainnc = netcdf.use(file, 'RAINNC')
prate = rainc + rainnc
if not quiet: print ' accum2avg...'
prate = accum2avg(prate, dt=time[1] - time[0]) # mm s-1
conv = 0.1 * 86400 # from mm s-1 --> cm day-1
prate = prate * conv # cm day-1
prate[prate < 0] = 0 # interpolation errors may result in negative rain!
out['prate'] = Data(x, y, prate, 'cm day-1')
# LW, SW, latent, sensible signs:
# positive (downward flux, heating) or negative (upward flux, cooling)
#https://www.myroms.org/forum/viewtopic.php?f=1&t=2621
# Net shortwave flux [W m-2]
if not quiet: print ' --> Net shortwave flux'
sw_down = netcdf.use(file, 'SWDOWN')
albedo = netcdf.use(file, 'ALBEDO')
sw_net = sw_down * (1 - albedo)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print ' --> Net longwave flux'
lw_down = netcdf.use(file, 'GLW') # positive
# sst needed:
if not quiet: print ' --> SST for LW up'
sst = netcdf.use(file, 'SST') # K
lw_net = air_sea.lwhf(sst, lw_down) # positive down
# here vars have roms-agrif signs --> radlw is positive upward!
#conversion to ROMS is done in surface.py
out['radlw'] = Data(x, y, -lw_net, 'W m-2', info='positive upward')
out['dlwrf'] = Data(x, y, -lw_down, 'W m-2', info='positive upward')
# U and V wind speed 10m
if not quiet: print ' --> U and V wind'
uwnd = netcdf.use(file, 'U10')
vwnd = netcdf.use(file, 'V10')
if not quiet: print ' --> calc wind speed and stress'
speed = np.sqrt(uwnd**2 + vwnd**2)
taux, tauy = air_sea.wind_stress(uwnd, vwnd)
out['wspd'] = Data(x, y, speed, 'm s-1')
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
out['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--1]:
if not quiet:
print ' --> Cloud cover for LONGWAVE. Use LONGWAVE_OUT instead...'
if 'CLDFRA' in netcdf.varnames(file):
clouds = netcdf.use(file, 'CLDFRA').sum(-3)
clouds = np.where(clouds > 1, 1, clouds)
else:
if not quiet: print 'CLDFRA not found!! Using SST and air_sea.clouds'
sst = netcdf.use(f, 'SST')
clouds = air_sea.clouds(lw_net, sst, tair, rhum, Wtype='net')
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out
def wrf_file_data(file,quiet=False):
'''
WRF data for ROMS
'''
out={}
# time:
if not quiet: print ' --> get time'
time=read_time(file)
out['time']=time
# lon,lat:
if not quiet: print ' --> reading x,y'
x=netcdf.use(file,'XLONG',**{'0': 0})
y=netcdf.use(file,'XLAT',**{'0': 0})
# tair [K-->C]
if not quiet: print ' --> T air'
tair=netcdf.use(file,'T2')-273.15
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [kg/kg --> 0--1]
if not quiet: print ' --> R humidity from QV at 2m'
wv=netcdf.use(file,'Q2') # water vapor mixing ratio at 2m
rhum=wv/air_sea.qsat(tair)
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print ' --> Surface pressure'
pres=netcdf.use(file,'PSFC')
out['pres']=Data(x,y,pres,'Pa')
# P rate [mm --> cm day-1]
if not quiet: print ' --> P rate (rainc+rainnc)'
rainc = netcdf.use(file,'RAINC')
rainnc = netcdf.use(file,'RAINNC')
prate=rainc+rainnc
if not quiet: print ' accum2avg...'
prate=accum2avg(prate,dt=time[1]-time[0]) # mm s-1
conv= 0.1*86400 # from mm s-1 --> cm day-1
prate=prate*conv # cm day-1
prate[prate<0]=0 # interpolation errors may result in negative rain!
out['prate']=Data(x,y,prate,'cm day-1')
# LW, SW, latent, sensible signs:
# positive (downward flux, heating) or negative (upward flux, cooling)
#https://www.myroms.org/forum/viewtopic.php?f=1&t=2621
# Net shortwave flux [W m-2]
if not quiet: print ' --> Net shortwave flux'
sw_down=netcdf.use(file,'SWDOWN')
albedo=netcdf.use(file,'ALBEDO')
sw_net=sw_down*(1-albedo)
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print ' --> Net longwave flux'
lw_down=netcdf.use(file,'GLW') # positive
# sst needed:
if not quiet: print ' --> SST for LW up'
sst=netcdf.use(file,'SST') # K
lw_net = air_sea.lwhf(sst,lw_down) # positive down
# here vars have roms-agrif signs --> radlw is positive upward!
#conversion to ROMS is done in surface.py
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='positive upward')
# U and V wind speed 10m
if not quiet: print ' --> U and V wind'
uwnd=netcdf.use(file,'U10')
vwnd=netcdf.use(file,'V10')
if not quiet: print ' --> calc wind speed and stress'
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--1]:
if not quiet: print ' --> Cloud cover for LONGWAVE. Use LONGWAVE_OUT instead...'
if 'CLDFRA' in netcdf.varnames(file):
clouds=netcdf.use(file,'CLDFRA').sum(-3)
clouds=np.where(clouds>1,1,clouds)
else:
if not quiet: print 'CLDFRA not found!! Using SST and air_sea.clouds'
sst=netcdf.use(f,'SST')
clouds=air_sea.clouds(lw_net,sst,tair,rhum,Wtype='net')
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
return out
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 get(self, what, **kargs):
time = False
day = False
quiet = True
keys = kargs.keys()
if 'time' in keys: time = kargs['time']
if 'day' in keys: day = kargs['day']
if 'quiet' in keys: quiet = kargs['quiet']
if what == 'wind_ts':
if 'xi' in kargs.keys() and 'eta' in kargs.keys():
xi = kargs['xi']
eta = kargs['eta']
elif 'lon' in kargs.keys() and 'lat' in kargs.keys():
d = (self.grid.lon - kargs['lon'])**2 + (self.grid.lat -
kargs['lat'])**2
i, j = np.where(d == d.min())
eta = i[0]
xi = j[0]
u = self.use('uwnd', xi_rho=xi, eta_rho=eta)
v = self.use('vwnd', xi_rho=xi, eta_rho=eta)
ang = self.grid.angle[eta, xi] * np.ones(u.shape)
u, v = calc.rot2d(u, v, -ang)
tdays = self.tdays
if not day is False:
ndays = self.tdays[-1] - self.tdays[0]
n = int(86400 / self.dt)
u = u[day * n:(day + 1) * n]
v = v[day * n:(day + 1) * n]
tdays = tdays[day * n:(day + 1) * n]
return tdays, u, v
elif what == 'wind':
if 'uwnd' in netcdf.varnames(self.name):
u = self.use('uwnd')
v = self.use('vwnd')
else:
u = self.use('Uwind')
v = self.use('Vwind')
if time == 'mean' and u.ndim == 3:
u = u.mean(0)
v = v.mean(0)
elif time == 'dailyMean' and u.ndim == 3:
ndays = self.tdays[-1] - self.tdays[0]
n = int(86400 / self.dt)
if not day is False:
if day == -1: day = ndays - 1
# this is wrong if there is data missing!
u = u[day * n:(day + 1) * n, ...].mean(0)
v = v[day * n:(day + 1) * n, ...].mean(0)
else:
U = range(ndays)
V = range(ndays)
for i in range(ndays):
U[i] = u[i * n:(i + 1) * n, ...].mean(0)
V[i] = v[i * n:(i + 1) * n, ...].mean(0)
u = U
v = V
elif not time is False and isinstance(time, int) and u.ndim == 3:
u = u[time, ...]
v = v[time, ...]
if isinstance(u, list):
for i in range(len(u)):
u[i], v[i] = calc.rot2d(u[i], v[i], -self.grid.angle)
else:
if u.ndim == 3:
for i in range(u.shape[0]):
u[i, ...], v[i,
...] = calc.rot2d(u[i, ...], v[i, ...],
-self.grid.angle)
elif u.ndim == 2:
u, v = calc.rot2d(u, v, -self.grid.angle)
if self.grid:
x = self.grid.lon
y = self.grid.lat
m = self.grid.mask
return x, y, u, v, m
else:
return u, v
elif what == 'data':
out = {}
args = {}
if not time is False:
args['bulk_time'] = time
if not quiet:
print('loading Blk data time=', time)
print('* * * * * * * * * * * *.')
if not quiet: print('*'),
out['tair'] = self.use('tair', **args)
if not quiet: print('*'),
out['rhum'] = self.use('rhum', **args)
if not quiet: print('*'),
out['pres'] = self.use('pres', **args)
if not quiet: print('*'),
out['prate'] = self.use('prate', **args)
if not quiet: print('*'),
out['radsw'] = self.use('radsw', **args)
if not quiet: print('*'),
out['radlw'] = self.use('radlw', **args)
if not quiet: print('*'),
out['dlwrf'] = self.use('dlwrf', **args)
if not quiet: print('*'),
out['uwnd'] = self.use('uwnd', **args)
if not quiet: print('*'),
out['vwnd'] = self.use('vwnd', **args)
if not quiet: print('*'),
out['sustr'] = self.use('sustr', **args)
if not quiet: print('*'),
out['svstr'] = self.use('svstr', **args)
if not quiet: print('*.')
out['wspd'] = self.use('wspd', **args)
blktime = self.use('bulk_time', **args)
t0 = self.atts['t0']
return out, blktime, t0
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
def plt_hslice(conf, plconf, date, FA='a', nest=0, **kargs):
err = ''
fig = False
info = {}
type = 'avg'
var = 'temp'
slice = 'z'
ind = -10
time = -1
currents = False
dcurr = (3, 3)
scurr = 3
lcurr = 0.2
ifig = 0
# closefig = True
clim = False
quiet = False
outStoragePath = False
cmap = None
norm = None
useBar = True # currents are barotropic for 2D vars (like zeta)
keys = kargs.keys()
if 'type' in keys: type = kargs['type']
if 'var' in keys: var = kargs['var']
if 'slice' in keys: slice = kargs['slice']
if 'ind' in keys: ind = kargs['ind']
if 'time' in keys: time = kargs['time']
if 'currents' in keys: currents = kargs['currents']
if 'dcurr' in keys: dcurr = kargs['dcurr']
if 'scurr' in keys: scurr = kargs['scurr']
if 'lcurr' in keys: lcurr = kargs['lcurr']
if 'ifig' in keys: ifig = kargs['ifig']
if 'closefig' in keys: closefig = kargs['closefig']
if 'clim' in keys: clim = kargs['clim']
if 'quiet' in keys: quiet = kargs['quiet']
if 'ostorage' in keys: outStoragePath = kargs['ostorage']
if 'cmap' in keys: cmap = kargs['cmap']
if 'usebar' in keys: useBar = kargs['usebar']
if 'norm' in keys: norm = kargs['norm']
date = dateu.parse_date(date)
# find input files:
args = {
'cf': conf,
'date': date,
'FA': FA,
'nest': nest,
'ostorage': outStoragePath
}
his = opt.nameof('out', type, **args)
clm = opt.nameof('in', 'clm', **args)
grd = opt.nameof('in', 'grd', **args)
if not os.path.isfile(his):
err = 'Main file not found (%s)' % his
return err, fig, info
if not os.path.isfile(grd):
err = 'Grid file not found (%s)' % grd
return err, fig, info
r = roms.His(his, grd)
# plot grid:
proj, fig, ax = plt_grid(plconf, grd, ifig)
def add_colorbar(handle, **args):
ax = pl.gca()
Data, err = opt.get_plconf(plconf, 'AXES')
cbpos = Data['cbpos'][ifig]
cbbgpos = Data['cbbgpos'][ifig]
cbbgc = Data['cbbgcolor'][ifig]
cbbga = Data['cbbgalpha'][ifig]
cblab = Data['cblabel'][ifig]
# colorbar bg axes:
if cbbgpos:
rec = pl.axes((cbpos[0] - cbpos[2] * cbbgpos[0],
cbpos[1] - cbbgpos[2] * cbpos[3], cbpos[2] *
(1 + cbbgpos[0] + cbbgpos[1]), cbpos[3] *
(1 + cbbgpos[2] + cbbgpos[3])),
axisbg=cbbgc,
frameon=1)
rec.patch.set_alpha(cbbga)
rec.set_xticks([])
rec.set_yticks([])
for k in rec.axes.spines.keys():
rec.axes.spines[k].set_color(cbbgc)
rec.axes.spines[k].set_alpha(cbbga)
# colorbar:
if cbpos:
cbax = fig.add_axes(cbpos)
if cbpos[2] > cbpos[3]: orient = 'horizontal'
else: orient = 'vertical'
cb = pl.colorbar(handle,
cax=cbax,
orientation=orient,
drawedges=0,
**args)
pl.axes(ax)
# colorbar label:
if cblab:
Data, err = opt.get_plconf(plconf, 'HSLICES')
varnames = Data['varnames'][ifig].split(',')
vnames = Data['vnames'][ifig].split(',')
lab = ''
for i in range(len(varnames)):
if varnames[i].strip() == var:
lab = vnames[i].strip()
break
if lab:
if r.hasz(var):
if slice == 'k':
if ind == 0: lab = 'Bottom ' + lab
elif ind in (-1, 'surface'): lab = 'Surface ' + lab
elif slice == 'z':
lab = lab + ' ' + str(ind) + 'm'
cb.set_label(lab)
def add_currkey(handle):
Data, err = opt.get_plconf(plconf, 'HSLICES')
pos = Data['kcurrpos'][ifig]
if pos:
pl.quiverkey(handle,
pos[0],
pos[1],
lcurr,
'%s m/s' % str(lcurr),
labelpos='S',
coordinates='axes')
# hslice:
if var:
if slice == 'k': metodo = r.slicek
elif slice == 'z': metodo = r.slicez
x, y, z, v = metodo(var, ind, time, plot=False)
x, y = proj(x, y)
# cmap:
if isinstance(cmap, basestring):
try:
cmap = pl.cm.cmap_d[cmap]
except:
try:
from okean import pl_tools
cmap = pl_tools.cm.cmap_d[cmap]
except:
cmap = pl.cm.jet
# original data from clm
if slice == 'k' and ind in (
-1, ) and var + '_original' in netcdf.varnames(clm):
tcurr = r.datetime[time]
x_o = netcdf.use(clm, 'x_original')
y_o = netcdf.use(clm, 'y_original')
x_o, y_o = proj(x_o, y_o)
v_o = netcdf.use(clm, 'y_original')
t_o = netcdf.nctime(clm, 'clim_time')
# average to current time:
i0, = np.where(t_o <= tcurr)[-1]
i1, = np.where(t_o > tcurr)[0]
v_o0 = netcdf.use(clm, var + '_original', time=i0)
v_o1 = netcdf.use(clm, var + '_original', time=i1)
# avg:
a = tcurr - t_o[i0]
b = t_o[i1] - tcurr
a = a.days * 86400 + a.seconds
b = b.days * 86400 + b.seconds
if a == 0: v_o = v_o0
elif b == 0: v_o = v_o1
else: v_o = (v_o0 * b + v_o1 * a) / (a + b)
pch = pl.pcolormesh(x_o, y_o, v_o, shading='flat', cmap=cmap)
if clim: pl.clim(clim[0], clim[1])
if norm == 'log':
from matplotlib.colors import LogNorm
Norm = LogNorm(vmin=clim[0], vmax=clim[1])
else:
Norm = None
# change hypoxia colorbar/cmap
if var == 'dye_01':
HypoxiaLim = 135
from okean import pl_tools
cmap = pl_tools.ucmaps().gen_oxygen(
v=(0, HypoxiaLim, 300.)) # default is 0,135,300 !!
pch = pl.pcolormesh(x, y, v, shading='flat', cmap=cmap, norm=Norm)
if clim: pl.clim(clim[0], clim[1])
# hypoxia:
if var == 'dye_01' and ind == 0 and ifig == 0:
cond = v < 135.
cond = v < HypoxiaLim
cond = (v < HypoxiaLim) & (r.grid.h > 5)
pm = r.grid.use('pm')
pn = r.grid.use('pn')
A = (1 / pm[cond] * 1 / pn[cond] / 1e6).sum()
x_, y_ = proj(-98, 29.5)
pl.text(x_,
y_,
'Hypoxia area = %.0f km$^2$' % A,
color='r',
fontweight='bold',
fontname='monospace',
bbox=dict(edgecolor='none', facecolor='white', alpha=0.8))
# hypoxia.
# colorbar:
if norm == 'log':
tks = 10**np.linspace(np.log10(clim[0]), np.log10(clim[1]), 4)
opts = {'ticks': tks, 'format': '%.2f'}
else:
opts = {'ticks': None}
add_colorbar(pch, **opts)
if currents:
if (var and r.hasz(var)) or not useBar: uvind = ind
else: uvind = 'bar'
x, y, z, u, v = r.sliceuv(uvind, time)
xm, ym = proj(x, y)
mm = np.zeros(x.shape, 'bool')
mm[::dcurr[0], ::dcurr[1]] = True
Data, err = opt.get_plconf(plconf, 'HSLICES')
wcurr = Data['wcurr'][ifig]
acurr = Data['acurr'][ifig]
qvopts = {'units': 'x', 'scale': scurr, 'width': wcurr, 'alpha': acurr}
if var:
q = pl.quiver(xm[mm], ym[mm], u[mm], v[mm], **qvopts)
else:
s = np.sqrt(u**2 + v**2)
q = pl.quiver(xm[mm], ym[mm], u[mm], v[mm], s[mm], **qvopts)
if clim: pl.clim(clim[0], clim[1])
add_colorbar(q)
add_currkey(q)
# store some info that may be required later
info['hasz'] = False
if var and r.hasz(var): info['hasz'] = True
# logo:
if ifig == 0:
im = os.path.join(os.path.dirname(__file__), 'logo_INOCAR.png')
i = pl.imread(im)
h, w = i.shape[:2]
rx = .12
W = (proj.xmax - proj.xmin) * rx
H = W * h / w
l = proj.xmax
#pl.fill([proj.xmax-W, proj.xmax, proj.xmax, proj.xmax-W],
# [proj.ymin, proj.ymin, proj.ymin+2.8*H, proj.ymin+2.8*H],
# '#500000',alpha=0.25,ec='none')
ax.imshow(i,
extent=(proj.xmax * .98 - W, proj.xmax * .98,
proj.ymin + H * .1, proj.ymin + H * 1.1),
zorder=1e3)
#pl.text(proj.xmax-W/2., proj.ymin+2.2*H,'OOF',
# fontdict={'size':14,'family':'serif'},
# color='#500000',ha='center',weight='bold')
pl.text(proj.xmax * .8,
proj.ymax * (-.1),
r.datetime[time].strftime("%d %b %Y"),
fontdict={
'size': 11,
'family': 'monospace'
},
ha='center')
if FA == 'f':
s = 'Pronostico desde %s' % r.datetime[0].strftime("%d %b %Y")
pl.text(
proj.xmax * .8,
proj.ymax * (-.15),
s,
#pl.text(proj.xmax-W/2., proj.ymin+1.1*H,s,
fontdict={'fontsize': 10},
ha='center')
# logo.
# lims change in some mpl versions !!
pl.gca().axis([proj.xmin, proj.xmax, proj.ymin, proj.ymax])
return err, fig, info
def get(self,what,**kargs):
time=False
day=False
quiet=True
keys=kargs.keys()
if 'time' in keys: time = kargs['time']
if 'day' in keys: day = kargs['day']
if 'quiet' in keys: quiet = kargs['quiet']
if what=='wind_ts':
if 'xi' in kargs.keys() and 'eta' in kargs.keys():
xi=kargs['xi']
eta=kargs['eta']
elif 'lon' in kargs.keys() and 'lat' in kargs.keys():
d=(self.grid.lon-kargs['lon'])**2+(self.grid.lat-kargs['lat'])**2
i,j=np.where(d==d.min())
eta=i[0]
xi=j[0]
u=self.use('uwnd',xi_rho=xi,eta_rho=eta)
v=self.use('vwnd',xi_rho=xi,eta_rho=eta)
ang=self.grid.angle[eta,xi]*np.ones(u.shape)
u,v=calc.rot2d(u,v,-ang)
tdays=self.tdays
if not day is False:
ndays=self.tdays[-1]-self.tdays[0]
n=int(86400/self.dt)
u=u[day*n:(day+1)*n]
v=v[day*n:(day+1)*n]
tdays=tdays[day*n:(day+1)*n]
return tdays,u,v
elif what=='wind':
if 'uwnd' in netcdf.varnames(self.name):
u=self.use('uwnd')
v=self.use('vwnd')
else:
u=self.use('Uwind')
v=self.use('Vwind')
if time=='mean' and u.ndim==3:
u=u.mean(0)
v=v.mean(0)
elif time=='dailyMean' and u.ndim==3:
ndays=self.tdays[-1]-self.tdays[0]
n=int(86400/self.dt)
if not day is False:
if day==-1: day=ndays-1
# this is wrong if there is data missing!
u=u[day*n:(day+1)*n,...].mean(0)
v=v[day*n:(day+1)*n,...].mean(0)
else:
U=range(ndays)
V=range(ndays)
for i in range(ndays):
U[i]=u[i*n:(i+1)*n,...].mean(0)
V[i]=v[i*n:(i+1)*n,...].mean(0)
u=U
v=V
elif not time is False and isinstance(time,int) and u.ndim==3:
u=u[time,...]
v=v[time,...]
if isinstance(u,list):
for i in range(len(u)):
u[i],v[i]=calc.rot2d(u[i],v[i],-self.grid.angle)
else:
if u.ndim==3:
for i in range(u.shape[0]):
u[i,...],v[i,...]=calc.rot2d(u[i,...],v[i,...],-self.grid.angle)
elif u.ndim==2:
u,v=calc.rot2d(u,v,-self.grid.angle)
if self.grid:
x=self.grid.lon
y=self.grid.lat
m=self.grid.mask
return x,y,u,v,m
else:
return u,v
elif what=='data':
out={}
args={}
if not time is False:
args['bulk_time']=time
if not quiet:
print('loading Blk data time=',time)
print('* * * * * * * * * * * *.')
if not quiet: print('*'),
out['tair'] = self.use('tair', **args)
if not quiet: print('*'),
out['rhum'] = self.use('rhum', **args)
if not quiet: print('*'),
out['pres'] = self.use('pres', **args)
if not quiet: print('*'),
out['prate'] = self.use('prate', **args)
if not quiet: print('*'),
out['radsw'] = self.use('radsw', **args)
if not quiet: print('*'),
out['radlw'] = self.use('radlw', **args)
if not quiet: print('*'),
out['dlwrf'] = self.use('dlwrf', **args)
if not quiet: print('*'),
out['uwnd'] = self.use('uwnd', **args)
if not quiet: print('*'),
out['vwnd'] = self.use('vwnd', **args)
if not quiet: print('*'),
out['sustr'] = self.use('sustr', **args)
if not quiet: print('*'),
out['svstr'] = self.use('svstr', **args)
if not quiet: print('*.')
out['wspd'] = self.use('wspd', **args)
blktime=self.use('bulk_time',**args)
t0=self.atts['t0']
return out,blktime,t0
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
if ttmp.size==time.size and np.all(ttmp==time): print ' time ok'
else:
print ' time differs !!!!',
pres,tfix=fix_time(ttmp,pres,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print ' ...fixed!'
else:
print 'time is NOT OK. Please check !!'
return
out['pres']=Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print ' --> P rate'
f=files['pr']
if 'PRATE_L1' in netcdf.varnames(f):
prate=netcdf.use(f,'PRATE_L1')
else:
prate=netcdf.use(f,'PRATE_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# Conversion kg m^-2 s^-1 to cm/day
prate=prate*86400*100/1000.
prate=np.where(abs(prate)<1.e-4,0,prate)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print ' time ok'
else:
print ' time differs !!!!',
prate,tfix=fix_time(ttmp,prate,time[0],time[-1])
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 slicek(self,varname,ind,time=0,plot=False,**opts):
x,y,z,v=[[]]*4
RetVarOnly=False
savename=False
retMsg=False
if ind in ('s','surf','surface'): ind=-1
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 varname not in netcdf.varnames(self.name):
msg=':: variable %s not found' % varname
if retMsg: return x,y,z,v,msg
else:
print(msg)
return x,y,z,v
isW=varname=='w'
hasZ=self.hasz(varname)
if hasZ:
if isW and ind >= self.S_W:
msg='k = %d exceeds S_W dimension (%d)' % (ind,self.S_W)
if retMsg: return x,y,z,v,msg
else:
print(msg)
return x,y,z,v
elif ind >= self.S_RHO:
msg='k = %d exceeds S_RHO dimension (%d)' % (ind,self.S_RHO)
if retMsg: return x,y,z,v,msg
else:
print(msg)
return x,y,z,v
if self.hast(varname) and time>=self.TIME:
msg='t = %d exceeds TIME dimension (%d)' % (time,self.TIME)
if retMsg: return x,y,z,v,msg
else:
print(msg)
return x,y,z,v
if isW: v=self.use(varname,SEARCHtime=time,s_w=ind)
else: v=self.use(varname,SEARCHtime=time,s_rho=ind)
x,y,h,m=self.grid.vars(ruvp=self.var_at(varname))
if hasZ:
z=self.s_levels(time,k=ind,ruvpw=self.var_at(varname))
else:
z=self.use('zeta',SEARCHtime=time)
z=rt.rho2uvp(z,varname)
if not np.ma.isMA(v): # roms agrif ...
v=np.ma.masked_where(m==0,v)
if plot:
p=self.grid.plot(bathy=None,**opts)
xm, ym = p(x,y)
pch=pylab.pcolor(xm,ym,v,shading='flat')
pylab.colorbar(pch,shrink=.7)
pylab.axis([p.xmin, p.xmax, p.ymin, p.ymax])
if savename:
pylab.savefig(savename,dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if RetVarOnly:
return v
else:
if retMsg: return x,y,z,v,''
else: return x,y,z,v
def get(self, what, **kargs):
time = False
day = False
quiet = True
keys = kargs.keys()
if "time" in keys:
time = kargs["time"]
if "day" in keys:
day = kargs["day"]
if "quiet" in keys:
quiet = kargs["quiet"]
if what == "wind_ts":
if "xi" in kargs.keys() and "eta" in kargs.keys():
xi = kargs["xi"]
eta = kargs["eta"]
elif "lon" in kargs.keys() and "lat" in kargs.keys():
d = (self.grid.lon - kargs["lon"]) ** 2 + (self.grid.lat - kargs["lat"]) ** 2
i, j = np.where(d == d.min())
eta = i[0]
xi = j[0]
u = self.use("uwnd", xi_rho=xi, eta_rho=eta)
v = self.use("vwnd", xi_rho=xi, eta_rho=eta)
ang = self.grid.angle[eta, xi] * np.ones(u.shape)
u, v = calc.rot2d(u, v, -ang)
tdays = self.tdays
if not day is False:
ndays = self.tdays[-1] - self.tdays[0]
n = int(86400 / self.dt)
u = u[day * n : (day + 1) * n]
v = v[day * n : (day + 1) * n]
tdays = tdays[day * n : (day + 1) * n]
return tdays, u, v
elif what == "wind":
if "uwnd" in netcdf.varnames(self.name):
u = self.use("uwnd")
v = self.use("vwnd")
else:
u = self.use("Uwind")
v = self.use("Vwind")
if time == "mean" and u.ndim == 3:
u = u.mean(0)
v = v.mean(0)
elif time == "dailyMean" and u.ndim == 3:
ndays = self.tdays[-1] - self.tdays[0]
n = int(86400 / self.dt)
if not day is False:
if day == -1:
day = ndays - 1
# this is wrong if there is data missing!
u = u[day * n : (day + 1) * n, ...].mean(0)
v = v[day * n : (day + 1) * n, ...].mean(0)
else:
U = range(ndays)
V = range(ndays)
for i in range(ndays):
U[i] = u[i * n : (i + 1) * n, ...].mean(0)
V[i] = v[i * n : (i + 1) * n, ...].mean(0)
u = U
v = V
elif not time is False and isinstance(time, int) and u.ndim == 3:
u = u[time, ...]
v = v[time, ...]
if isinstance(u, list):
for i in range(len(u)):
u[i], v[i] = calc.rot2d(u[i], v[i], -self.grid.angle)
else:
if u.ndim == 3:
for i in range(u.shape[0]):
u[i, ...], v[i, ...] = calc.rot2d(u[i, ...], v[i, ...], -self.grid.angle)
elif u.ndim == 2:
u, v = calc.rot2d(u, v, -self.grid.angle)
if self.grid:
x = self.grid.lon
y = self.grid.lat
m = self.grid.mask
return x, y, u, v, m
else:
return u, v
elif what == "data":
out = {}
args = {}
if not time is False:
args["bulk_time"] = time
if not quiet:
print "loading Blk data time=", time
print "* * * * * * * * * * * *."
if not quiet:
print "*",
out["tair"] = self.use("tair", **args)
if not quiet:
print "*",
out["rhum"] = self.use("rhum", **args)
if not quiet:
print "*",
out["pres"] = self.use("pres", **args)
if not quiet:
print "*",
out["prate"] = self.use("prate", **args)
if not quiet:
print "*",
out["radsw"] = self.use("radsw", **args)
if not quiet:
print "*",
out["radlw"] = self.use("radlw", **args)
if not quiet:
print "*",
out["dlwrf"] = self.use("dlwrf", **args)
if not quiet:
print "*",
out["uwnd"] = self.use("uwnd", **args)
if not quiet:
print "*",
out["vwnd"] = self.use("vwnd", **args)
if not quiet:
print "*",
out["sustr"] = self.use("sustr", **args)
if not quiet:
print "*",
out["svstr"] = self.use("svstr", **args)
if not quiet:
print "*."
out["wspd"] = self.use("wspd", **args)
blktime = self.use("bulk_time", **args)
t0 = self.atts["t0"]
return out, blktime, t0
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 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 slicek(self, varname, ind, time=0, plot=False, **opts):
x, y, z, v = [[]] * 4
RetVarOnly = False
savename = False
retMsg = False
if ind in ('s', 'surf', 'surface'): ind = -1
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 varname not in netcdf.varnames(self.name):
msg = ':: variable %s not found' % varname
if retMsg: return x, y, z, v, msg
else:
print(msg)
return x, y, z, v
isW = varname == 'w'
hasZ = self.hasz(varname)
if hasZ:
if isW and ind >= self.S_W:
msg = 'k = %d exceeds S_W dimension (%d)' % (ind, self.S_W)
if retMsg: return x, y, z, v, msg
else:
print(msg)
return x, y, z, v
elif ind >= self.S_RHO:
msg = 'k = %d exceeds S_RHO dimension (%d)' % (ind, self.S_RHO)
if retMsg: return x, y, z, v, msg
else:
print(msg)
return x, y, z, v
if self.hast(varname) and time >= self.TIME:
msg = 't = %d exceeds TIME dimension (%d)' % (time, self.TIME)
if retMsg: return x, y, z, v, msg
else:
print(msg)
return x, y, z, v
if isW: v = self.use(varname, SEARCHtime=time, s_w=ind)
else: v = self.use(varname, SEARCHtime=time, s_rho=ind)
x, y, h, m = self.grid.vars(ruvp=self.var_at(varname))
if hasZ:
z = self.s_levels(time, k=ind, ruvpw=self.var_at(varname))
else:
z = self.use('zeta', SEARCHtime=time)
z = rt.rho2uvp(z, varname)
if not np.ma.isMA(v): # roms agrif ...
v = np.ma.masked_where(m == 0, v)
if plot:
p = self.grid.plot(bathy=None, **opts)
xm, ym = p(x, y)
pch = pylab.pcolor(xm, ym, v, shading='flat')
pylab.colorbar(pch, shrink=.7)
pylab.axis([p.xmin, p.xmax, p.ymin, p.ymax])
if savename:
pylab.savefig(savename, dpi=pylab.gcf().dpi)
pylab.close(pylab.gcf())
if RetVarOnly:
return v
else:
if retMsg: return x, y, z, v, ''
else: return x, y, z, v
def plt_hslice(conf,plconf,date,FA='a',nest=0,**kargs):
err=''
fig=False
info={}
type = 'avg'
var = 'temp'
slice = 'z'
ind = -10
time = -1
currents = False
dcurr = (3,3)
scurr = 3
lcurr = 0.2
ifig = 0
# closefig = True
clim = False
quiet = False
outStoragePath=False
cmap = None
norm = None
useBar = True # currents are barotropic for 2D vars (like zeta)
keys=kargs.keys()
if 'type' in keys: type = kargs['type']
if 'var' in keys: var = kargs['var']
if 'slice' in keys: slice = kargs['slice']
if 'ind' in keys: ind = kargs['ind']
if 'time' in keys: time = kargs['time']
if 'currents' in keys: currents = kargs['currents']
if 'dcurr' in keys: dcurr = kargs['dcurr']
if 'scurr' in keys: scurr = kargs['scurr']
if 'lcurr' in keys: lcurr = kargs['lcurr']
if 'ifig' in keys: ifig = kargs['ifig']
if 'closefig' in keys: closefig = kargs['closefig']
if 'clim' in keys: clim = kargs['clim']
if 'quiet' in keys: quiet = kargs['quiet']
if 'ostorage' in keys: outStoragePath = kargs['ostorage']
if 'cmap' in keys: cmap = kargs['cmap']
if 'usebar' in keys: useBar = kargs['usebar']
if 'norm' in keys: norm = kargs['norm']
date=dateu.parse_date(date)
# find input files:
args={'cf':conf,'date':date,'FA':FA,'nest':nest,'ostorage':outStoragePath}
his = opt.nameof('out',type,**args)
clm = opt.nameof('in','clm',**args)
grd = opt.nameof('in','grd',**args)
if not os.path.isfile(his):
err='Main file not found (%s)' % his
return err,fig,info
if not os.path.isfile(grd):
err='Grid file not found (%s)' % grd
return err,fig,info
r=roms.His(his,grd)
# plot grid:
proj,fig, ax = plt_grid(plconf,grd,ifig)
def add_colorbar(handle,**args):
ax=pl.gca()
Data,err = opt.get_plconf(plconf,'AXES')
cbpos = Data['cbpos'][ifig]
cbbgpos = Data['cbbgpos'][ifig]
cbbgc = Data['cbbgcolor'][ifig]
cbbga = Data['cbbgalpha'][ifig]
cblab = Data['cblabel'][ifig]
# colorbar bg axes:
if cbbgpos:
rec=pl.axes((cbpos[0]-cbpos[2]*cbbgpos[0],cbpos[1]-cbbgpos[2]*cbpos[3],
cbpos[2]*(1+cbbgpos[0]+cbbgpos[1]),cbpos[3]*(1+cbbgpos[2]+cbbgpos[3])),
axisbg=cbbgc,frameon=1)
rec.patch.set_alpha(cbbga)
rec.set_xticks([])
rec.set_yticks([])
for k in rec.axes.spines.keys():
rec.axes.spines[k].set_color(cbbgc)
rec.axes.spines[k].set_alpha(cbbga)
# colorbar:
if cbpos:
cbax=fig.add_axes(cbpos)
if cbpos[2]>cbpos[3]: orient='horizontal'
else: orient='vertical'
cb=pl.colorbar(handle,cax=cbax,orientation=orient,drawedges=0,**args)
pl.axes(ax)
# colorbar label:
if cblab:
Data,err = opt.get_plconf(plconf,'HSLICES')
varnames=Data['varnames'][ifig].split(',')
vnames=Data['vnames'][ifig].split(',')
lab=''
for i in range(len(varnames)):
if varnames[i].strip()==var:
lab=vnames[i].strip()
break
if lab:
if r.hasz(var):
if slice=='k':
if ind==0: lab = 'Bottom '+lab
elif ind in (-1,'surface'): lab = 'Surface '+lab
elif slice=='z':
lab=lab+' '+str(ind)+'m'
cb.set_label(lab)
def add_currkey(handle):
Data,err = opt.get_plconf(plconf,'HSLICES')
pos=Data['kcurrpos'][ifig]
if pos:
pl.quiverkey(handle, pos[0], pos[1], lcurr, '%s m/s' % str(lcurr),labelpos='S',
coordinates='axes')
# hslice:
if var:
if slice=='k': metodo=r.slicek
elif slice=='z': metodo=r.slicez
x,y,z,v=metodo(var,ind,time,plot=False)
x,y=proj(x,y)
# cmap:
if isinstance(cmap,basestring):
try:cmap=pl.cm.cmap_d[cmap]
except:
try:
from okean import pl_tools
cmap=pl_tools.cm.cmap_d[cmap]
except: cmap=pl.cm.jet
# original data from clm
if slice=='k' and ind in (-1,) and var+'_original' in netcdf.varnames(clm):
tcurr= r.datetime[time]
x_o=netcdf.use(clm,'x_original')
y_o=netcdf.use(clm,'y_original')
x_o,y_o=proj(x_o,y_o)
v_o=netcdf.use(clm,'y_original')
t_o=netcdf.nctime(clm,'clim_time')
# average to current time:
i0,=np.where(t_o<=tcurr)[-1]
i1,=np.where(t_o>tcurr)[0]
v_o0=netcdf.use(clm,var+'_original',time=i0)
v_o1=netcdf.use(clm,var+'_original',time=i1)
# avg:
a=tcurr-t_o[i0]
b=t_o[i1]-tcurr
a=a.days*86400+a.seconds
b=b.days*86400+b.seconds
if a==0: v_o=v_o0
elif b==0: v_o=v_o1
else: v_o=(v_o0*b+v_o1*a)/(a+b)
pch=pl.pcolormesh(x_o,y_o,v_o,shading='flat',cmap=cmap)
if clim: pl.clim(clim[0],clim[1])
if norm=='log':
from matplotlib.colors import LogNorm
Norm=LogNorm(vmin=clim[0],vmax=clim[1])
else: Norm=None
# change hypoxia colorbar/cmap
if var=='dye_01':
HypoxiaLim=135
from okean import pl_tools
cmap=pl_tools.ucmaps().gen_oxygen(v=(0,HypoxiaLim,300.)) # default is 0,135,300 !!
pch=pl.pcolormesh(x,y,v,shading='flat',cmap=cmap, norm=Norm)
if clim: pl.clim(clim[0],clim[1])
# hypoxia:
if var=='dye_01' and ind==0 and ifig==0:
cond=v<135.
cond=v<HypoxiaLim
cond=(v<HypoxiaLim)&(r.grid.h>5)
pm=r.grid.use('pm')
pn=r.grid.use('pn')
A=(1/pm[cond]*1/pn[cond]/1e6).sum()
x_,y_=proj(-98,29.5)
pl.text(x_,y_,'Hypoxia area = %.0f km$^2$' % A,color='r',
fontweight='bold',fontname='monospace',
bbox=dict(edgecolor='none',facecolor='white', alpha=0.8))
# hypoxia.
# colorbar:
if norm=='log':
tks=10**np.linspace(np.log10(clim[0]),np.log10(clim[1]),4)
opts={'ticks':tks,'format':'%.2f'}
else: opts={'ticks':None}
add_colorbar(pch,**opts)
if currents:
if (var and r.hasz(var)) or not useBar: uvind=ind
else: uvind='bar'
x,y,z,u,v=r.sliceuv(uvind,time)
xm, ym = proj(x,y)
mm=np.zeros(x.shape,'bool')
mm[::dcurr[0],::dcurr[1]]=True
Data,err = opt.get_plconf(plconf,'HSLICES')
wcurr=Data['wcurr'][ifig]
acurr=Data['acurr'][ifig]
qvopts={'units':'x','scale':scurr,'width':wcurr,'alpha':acurr}
if var:
q=pl.quiver(xm[mm],ym[mm],u[mm],v[mm],**qvopts)
else:
s=np.sqrt(u**2+v**2)
q=pl.quiver(xm[mm],ym[mm],u[mm],v[mm],s[mm],**qvopts)
if clim: pl.clim(clim[0],clim[1])
add_colorbar(q)
add_currkey(q)
# store some info that may be required later
info['hasz']=False
if var and r.hasz(var): info['hasz']=True
# logo:
if ifig==0:
im=os.path.join(os.path.dirname(__file__),'logo_INOCAR.png')
i=pl.imread(im)
h,w=i.shape[:2]
rx=.12
W=(proj.xmax- proj.xmin)*rx
H=W*h/w
l=proj.xmax
#pl.fill([proj.xmax-W, proj.xmax, proj.xmax, proj.xmax-W],
# [proj.ymin, proj.ymin, proj.ymin+2.8*H, proj.ymin+2.8*H],
# '#500000',alpha=0.25,ec='none')
ax.imshow(i,extent=(proj.xmax*.98-W,proj.xmax*.98, proj.ymin+H*.1, proj.ymin+H*1.1),zorder=1e3)
#pl.text(proj.xmax-W/2., proj.ymin+2.2*H,'OOF',
# fontdict={'size':14,'family':'serif'},
# color='#500000',ha='center',weight='bold')
pl.text(proj.xmax*.8, proj.ymax*(-.1),r.datetime[time].strftime("%d %b %Y"),
fontdict={'size':11,'family':'monospace'},ha='center')
if FA=='f':
s='Pronostico desde %s' % r.datetime[0].strftime("%d %b %Y")
pl.text(proj.xmax*.8, proj.ymax*(-.15),s,
#pl.text(proj.xmax-W/2., proj.ymin+1.1*H,s,
fontdict={'fontsize':10},ha='center')
# logo.
# lims change in some mpl versions !!
pl.gca().axis([proj.xmin, proj.xmax, proj.ymin, proj.ymax])
return err, fig, info
