def load_proj(self):
# load and parse attrs needed for basemap projection
self.proj_info={}
for i in ['proj4string','basemap_opts']:
if i in netcdf.fatt(self.nc):
self.proj_info[i]=netcdf.fatt(self.nc,i)
else:
self.proj_info[i]=''
o={}
if self.proj_info['basemap_opts']: # parse it
for i in self.proj_info['basemap_opts'].split():
name,val=i[1:].split('=')
try:
val=float(val)
except: pass
o[name]=val
self.proj_info['basemap_opts0']=o
# Now let ys make sure the projection is well centered (may not be
# at grid creation):
# - build a set of reasonable options; user can always change them
# after. Let us consider only the lcc and merc projections, and use
# lcc by default if none is provided at grid atts
if o: prj=o['projection']
else: prj='lcc'
if self.spherical:
xlim=self.lon.min(),self.lon.max()
ylim=self.lat.min(),self.lat.max()
dlon=xlim[1]-xlim[0]
dlat=ylim[1]-ylim[0]
if prj=='lcc':
lon_0=0.5*(xlim[0]+xlim[1])
lat_0=0.5*(ylim[0]+ylim[1])
lat_1=lat_0-dlat/4.
lat_2=lat_0+dlat/4.
W=110*np.cos(lat_0*np.pi/180)*(xlim[1]-xlim[0])*1e3
H=110*(ylim[1]-ylim[0])*1e3
W*=1.2
H*=1.2
o=dict(resolution='i',projection=prj,
lat_1=lat_1,lat_2=lat_2,lat_0=lat_0,lon_0=lon_0,
width=W,height=H)
elif prj=='merc':
dlon_=dlon_/10
dlat_=dlat_/10
o=dict(resolution='c',projection=prj,
llcrnrlon=xlim[0]-dlon_, llcrnrlat=ylim[0]-dlat_,
urcrnrlon=xlim[1]+dlon_, urcrnrlat=ylim[1]+dlat_,
lon_0=0.5*(xlim[0]+xlim[1]),
lat_0=0.5*(ylim[0]+ylim[1]))
self.proj_info['basemap_opts']=o
def load_proj(self):
# load and parse attrs needed for basemap projection
self.proj_info={}
for i in ['proj4string','basemap_opts']:
if i in netcdf.fatt(self.nc):
self.proj_info[i]=netcdf.fatt(self.nc,i)
else:
self.proj_info[i]=''
o={}
if self.proj_info['basemap_opts']: # parse it
for i in self.proj_info['basemap_opts'].split():
name,val=i[1:].split('=')
try:
val=float(val)
except: pass
o[name]=val
self.proj_info['basemap_opts0']=o
# Now let ys make sure the projection is well centered (may not be
# at grid creation):
# - build a set of reasonable options; user can always change them
# after. Let us consider only the lcc and merc projections, and use
# lcc by default if none is provided at grid atts
if o: prj=o['projection']
else: prj='lcc'
if self.spherical:
xlim=self.lon.min(),self.lon.max()
ylim=self.lat.min(),self.lat.max()
dlon=xlim[1]-xlim[0]
dlat=ylim[1]-ylim[0]
if prj=='lcc':
lon_0=0.5*(xlim[0]+xlim[1])
lat_0=0.5*(ylim[0]+ylim[1])
lat_1=lat_0-dlat/4.
lat_2=lat_0+dlat/4.
W=110*np.cos(lat_0*np.pi/180)*(xlim[1]-xlim[0])*1e3
H=110*(ylim[1]-ylim[0])*1e3
W*=1.2
H*=1.2
o=dict(resolution='i',projection=prj,
lat_1=lat_1,lat_2=lat_2,lat_0=lat_0,lon_0=lon_0,
width=W,height=H)
elif prj=='merc':
dlon_=dlon_/10
dlat_=dlat_/10
o=dict(resolution='c',projection=prj,
llcrnrlon=xlim[0]-dlon_, llcrnrlat=ylim[0]-dlat_,
urcrnrlon=xlim[1]+dlon_, urcrnrlat=ylim[1]+dlat_,
lon_0=0.5*(xlim[0]+xlim[1]),
lat_0=0.5*(ylim[0]+ylim[1]))
self.proj_info['basemap_opts']=o
def load_grid(self,grd=False):
ats=netcdf.fatt(self.nc)
if not grd:
if 'grd_file' in ats: grd=ats['grd_file'].value
if grd and not grd.startswith('http') and not os.path.isfile(grd): grd=False
if grd: self.grid=Grid(grd,self.quiet)
else:
try:
self.grid=Grid(self.name)
except:
self.grid=False
def load_grid(self,grd=False):
ats=netcdf.fatt(self.name)
if not grd:
if 'grd_file' in ats.keys(): grd=ats['grd_file'].value
if grd and not os.path.isfile(grd): grd=False
if grd: self.grid=Grid(grd,self.quiet)
else:
try:
self.grid=Grid(self.name)
except:
self.grid=False
def load_atts(self):
atts=netcdf.fatt(self.nc)
self.atts={}
self.atts.update(atts)
def load_atts(self):
atts=netcdf.fatt(self.name)
self.atts={}
for k in atts.keys(): self.atts[k]=atts[k].value
def load_atts(self):
atts = netcdf.fatt(self.name)
self.atts = {}
for k in atts.keys():
self.atts[k] = atts[k].value
def update_wind(fname, data, new_wind_info, **kargs):
'''
new_wind_info will be added to fname as global attribute
(ex: 'new wind from database xxx')
'''
quiet = False
Margin = 4 # for griddata and for original data to keep
grd = False
keepOriginal = 2
for k in kargs.keys():
if k == 'quiet': quiet = kargs[k]
elif k == 'margin': Margin = kargs[k]
elif k == 'grid': grd = kargs[k]
elif k.lower().startswith('keepor'): keepOriginal = kargs[k]
if not grd: grd = netcdf.fatt(fname, 'grd_file')
g = roms.Grid(grd)
x0 = data['x']
y0 = data['y']
if x0.ndim == 1: x0, y0 = np.meshgrid(x0, y0)
dates = data.keys()[:]
dates.remove('x')
dates.remove('y')
dates = np.array(dates)
# interp in time:
time = netcdf.nctime(fname, 'time')
cond = False
tind = -1
fob = gennc.GenBlk(fname, grd)
for t in time:
newWind = {}
tind += 1
I, = np.where(dates == t)
if I.size:
I = I[0]
uv = data[dates[I]]
if not quiet: print(t, dates[I])
else:
i1, = np.where(dates > t)
i0, = np.where(dates < t)
if i0.size and i1.size:
i1 = i1[0]
i0 = i0[-1]
d0 = t - dates[i0]
d1 = dates[i1] - t
d0 = d0.days + d0.seconds / 86400.
d1 = d1.days + d1.seconds / 86400.
uv = (data[dates[i0]] * d1 + data[dates[i1]] * d0) / (d0 + d1)
if not quiet: print(t, dates[i0], dates[i1], d0, d1)
elif not i1.size:
uv = data[dates[-1]]
if not quiet: print(t, dates[-1])
elif not i0.size:
uv = data[dates[0]]
if not quiet: print(t, dates[0])
# interp to grid:
if cond is False:
cond, inds = rt.grid_vicinity(grd,
x0,
y0,
margin=Margin,
rect=True,
retinds=True)
i1, i2, j1, j2 = inds
if not quiet: print(' --> inter uv %s' % t.isoformat(' '))
u = calc.griddata(x0[cond],
y0[cond],
uv.real[cond],
g.lon,
g.lat,
extrap=True)
v = calc.griddata(x0[cond],
y0[cond],
uv.imag[cond],
g.lon,
g.lat,
extrap=True)
# rotate wind, calc stress:
if not quiet: print(' --> rot U,V wind and U,V wind stress')
wspd = np.sqrt(u**2 + v**2)
sustr, svstr = air_sea.wind_stress(u, v)
angle = g.use('angle')
uwnd, vwnd = calc.rot2d(u, v, angle)
sustr, svstr = calc.rot2d(sustr, svstr, angle)
sustr = rt.rho2uvp(sustr, 'u')
svstr = rt.rho2uvp(svstr, 'v')
# update wind data:
newWind['date'] = t
newWind['uwnd'] = uwnd
newWind['vwnd'] = vwnd
newWind['sustr'] = sustr
newWind['svstr'] = svstr
newWind['wspd'] = wspd
# original xy:
if tind == 0:
newWind['attr'] = {'new_wind_info': new_wind_info}
if not quiet: print(' --> original xy')
if keepOriginal == 1:
newWind['x_wind'] = x0
newWind['y_wind'] = y0
elif keepOriginal == 2:
newWind['x_wind'] = x0[j1:j2, i1:i2]
newWind['y_wind'] = y0[j1:j2, i1:i2]
# add to file:
if not quiet: print(' --> adding to file')
fob.update_wind(newWind, quiet=quiet)
if not quiet: print('')
def update_wind_blended2(fname, datapaths, **kargs):
'''
In days without blended data will try to use quikscat data
'''
from okean.datasets import quikscat
from okean.datasets import blended_wind
a = blended_wind.WINDData(datapaths[0])
b = quikscat.WINDData(datapaths[1])
time = netcdf.nctime(fname, 'time')
date0 = dts.next_date(time[0], -1)
date1 = dts.next_date(time[-1], +2)
data = a.data(date0, date1)
# limit are... otherwise, quikscat interp will be very slow!
grd = netcdf.fatt(fname, 'grd_file')
import os
if not os.path.isfile(grd): grd = kargs['grd']
cond, inds = rt.grid_vicinity(grd,
data['x'],
data['y'],
margin=5,
rect=True,
retinds=True)
i1, i2, j1, j2 = inds
for d in data.keys():
if d == 'x': data[d] = data[d][i1:i2]
elif d == 'y': data[d] = data[d][j1:j2]
else: data[d] = data[d][j1:j2, i1:i2]
# check for missing days:
time0 = data.keys()
x0 = data['x']
y0 = data['y']
x0, y0 = np.meshgrid(x0, y0)
time0.remove('x')
time0.remove('y')
out = OrderedDict()
out['x'] = x0
out['y'] = y0
info = ''
qs_ij_limits_done = False
for d in dts.drange(date0, date1):
found = 0
for t in time0:
if (t.year, t.month, t.day) == (d.year, d.month, d.day):
print('==> blended : ', t)
out[t] = data[t]
found = 1
if not found: # use quikscat:
print('==> quikscat : ', d.strftime('%Y-%m-%d'))
tmp = b.data(d, dts.next_date(d))
if not tmp.has_key('x'): continue
x, y = tmp['x'], tmp['y']
x, y = np.meshgrid(x, y)
# reduce qs data:
if not qs_ij_limits_done:
i1, i2, j1, j2 = calc.ij_limits(x, y,
[x0.min(), x0.max()],
[y0.min(), y0.max()])
qs_ij_limits_done = True
x = x[j1:j2, i1:i2]
y = y[j1:j2, i1:i2]
tmp[tmp.keys()[0]] = tmp[tmp.keys()[0]][j1:j2, i1:i2]
print(' griddata u')
u = calc.griddata(x, y, tmp[tmp.keys()[0]].real, x0, y0)
print(' griddata v')
v = calc.griddata(x, y, tmp[tmp.keys()[0]].imag, x0, y0)
out[tmp.keys()[0]] = u + 1.j * v
info += '#' + d.strftime('%Y%m%d')
new_wind_info = 'blended+quikscat at days: ' + info
update_wind(fname, out, new_wind_info, **kargs)
def update_wind(fname, data, new_wind_info, **kargs):
"""
new_wind_info will be added to fname as global attribute
(ex: 'new wind from database xxx')
"""
quiet = False
Margin = 4 # for griddata and for original data to keep
grd = False
keepOriginal = 2
for k in kargs.keys():
if k == "quiet":
quiet = kargs[k]
elif k == "margin":
Margin = kargs[k]
elif k == "grid":
grd = kargs[k]
elif k.lower().startswith("keepor"):
keepOriginal = kargs[k]
if not grd:
grd = netcdf.fatt(fname, "grd_file")
g = roms.Grid(grd)
x0 = data["x"]
y0 = data["y"]
if x0.ndim == 1:
x0, y0 = np.meshgrid(x0, y0)
dates = data.keys()[:]
dates.remove("x")
dates.remove("y")
dates = np.array(dates)
# interp in time:
time = netcdf.nctime(fname, "time")
cond = False
tind = -1
fob = gennc.GenBlk(fname, grd)
for t in time:
newWind = {}
tind += 1
I, = np.where(dates == t)
if I.size:
I = I[0]
uv = data[dates[I]]
if not quiet:
print t, dates[I]
else:
i1, = np.where(dates > t)
i0, = np.where(dates < t)
if i0.size and i1.size:
i1 = i1[0]
i0 = i0[-1]
d0 = t - dates[i0]
d1 = dates[i1] - t
d0 = d0.days + d0.seconds / 86400.0
d1 = d1.days + d1.seconds / 86400.0
uv = (data[dates[i0]] * d1 + data[dates[i1]] * d0) / (d0 + d1)
if not quiet:
print t, dates[i0], dates[i1], d0, d1
elif not i1.size:
uv = data[dates[-1]]
if not quiet:
print t, dates[-1]
elif not i0.size:
uv = data[dates[0]]
if not quiet:
print t, dates[0]
# interp to grid:
if cond is False:
cond, inds = rt.grid_vicinity(grd, x0, y0, margin=Margin, rect=True, retinds=True)
i1, i2, j1, j2 = inds
if not quiet:
print " --> inter uv %s" % t.isoformat(" ")
u = calc.griddata(x0[cond], y0[cond], uv.real[cond], g.lon, g.lat, extrap=True)
v = calc.griddata(x0[cond], y0[cond], uv.imag[cond], g.lon, g.lat, extrap=True)
# rotate wind, calc stress:
if not quiet:
print " --> rot U,V wind and U,V wind stress"
wspd = np.sqrt(u ** 2 + v ** 2)
sustr, svstr = air_sea.wind_stress(u, v)
angle = g.use("angle")
uwnd, vwnd = calc.rot2d(u, v, angle)
sustr, svstr = calc.rot2d(sustr, svstr, angle)
sustr = rt.rho2uvp(sustr, "u")
svstr = rt.rho2uvp(svstr, "v")
# update wind data:
newWind["date"] = t
newWind["uwnd"] = uwnd
newWind["vwnd"] = vwnd
newWind["sustr"] = sustr
newWind["svstr"] = svstr
newWind["wspd"] = wspd
# original xy:
if tind == 0:
newWind["attr"] = {"new_wind_info": new_wind_info}
if not quiet:
print " --> original xy"
if keepOriginal == 1:
newWind["x_wind"] = x0
newWind["y_wind"] = y0
elif keepOriginal == 2:
newWind["x_wind"] = x0[j1:j2, i1:i2]
newWind["y_wind"] = y0[j1:j2, i1:i2]
# add to file:
if not quiet:
print " --> adding to file"
fob.update_wind(newWind, quiet=quiet)
if not quiet:
print ""
def update_wind_blended2(fname, datapaths, **kargs):
"""
In days without blended data will try to use quikscat data
"""
from okean.datasets import quikscat
from okean.datasets import blended_wind
a = blended_wind.WINDData(datapaths[0])
b = quikscat.WINDData(datapaths[1])
time = netcdf.nctime(fname, "time")
date0 = dts.next_date(time[0], -1)
date1 = dts.next_date(time[-1], +2)
data = a.data(date0, date1)
# limit are... otherwise, quikscat interp will be very slow!
grd = netcdf.fatt(fname, "grd_file")
import os
if not os.path.isfile(grd):
grd = kargs["grd"]
cond, inds = rt.grid_vicinity(grd, data["x"], data["y"], margin=5, rect=True, retinds=True)
i1, i2, j1, j2 = inds
for d in data.keys():
if d == "x":
data[d] = data[d][i1:i2]
elif d == "y":
data[d] = data[d][j1:j2]
else:
data[d] = data[d][j1:j2, i1:i2]
# check for missing days:
time0 = data.keys()
x0 = data["x"]
y0 = data["y"]
x0, y0 = np.meshgrid(x0, y0)
time0.remove("x")
time0.remove("y")
out = cb.odict()
out["x"] = x0
out["y"] = y0
info = ""
qs_ij_limits_done = False
for d in dts.drange(date0, date1):
found = 0
for t in time0:
if (t.year, t.month, t.day) == (d.year, d.month, d.day):
print "==> blended : ", t
out[t] = data[t]
found = 1
if not found: # use quikscat:
print "==> quikscat : ", d.strftime("%Y-%m-%d")
tmp = b.data(d, dts.next_date(d))
if not tmp.has_key("x"):
continue
x, y = tmp["x"], tmp["y"]
x, y = np.meshgrid(x, y)
# reduce qs data:
if not qs_ij_limits_done:
i1, i2, j1, j2 = calc.ij_limits(x, y, [x0.min(), x0.max()], [y0.min(), y0.max()])
qs_ij_limits_done = True
x = x[j1:j2, i1:i2]
y = y[j1:j2, i1:i2]
tmp[tmp.keys()[0]] = tmp[tmp.keys()[0]][j1:j2, i1:i2]
print " griddata u"
u = calc.griddata(x, y, tmp[tmp.keys()[0]].real, x0, y0)
print " griddata v"
v = calc.griddata(x, y, tmp[tmp.keys()[0]].imag, x0, y0)
out[tmp.keys()[0]] = u + 1.0j * v
info += "#" + d.strftime("%Y%m%d")
new_wind_info = "blended+quikscat at days: " + info
update_wind(fname, out, new_wind_info, **kargs)
def update_wind(fname,data,new_wind_info,**kargs):
'''
new_wind_info will be added to fname as global attribute
(ex: 'new wind from database xxx')
'''
quiet = False
Margin = 4 # for griddata and for original data to keep
grd = False
keepOriginal = 2
for k in kargs.keys():
if k=='quiet': quiet=kargs[k]
elif k=='margin': Margin=kargs[k]
elif k=='grid': grd=kargs[k]
elif k.lower().startswith('keepor'): keepOriginal=kargs[k]
if not grd: grd=netcdf.fatt(fname,'grd_file')
g=roms.Grid(grd)
x0=data['x']
y0=data['y']
if x0.ndim==1: x0,y0=np.meshgrid(x0,y0)
dates=data.keys()[:]
dates.remove('x')
dates.remove('y')
dates=np.array(dates)
# interp in time:
time=netcdf.nctime(fname,'time')
cond=False
tind=-1
fob=gennc.GenBlk(fname,grd)
for t in time:
newWind={}
tind+=1
I,=np.where(dates==t)
if I.size:
I=I[0]
uv=data[dates[I]]
if not quiet: print(t,dates[I])
else:
i1,=np.where(dates>t)
i0,=np.where(dates<t)
if i0.size and i1.size:
i1=i1[0]
i0=i0[-1]
d0=t-dates[i0]
d1=dates[i1]-t
d0=d0.days+d0.seconds/86400.
d1=d1.days+d1.seconds/86400.
uv=(data[dates[i0]]*d1+data[dates[i1]]*d0)/(d0+d1)
if not quiet: print(t,dates[i0],dates[i1], d0,d1)
elif not i1.size:
uv=data[dates[-1]]
if not quiet: print(t,dates[-1])
elif not i0.size:
uv=data[dates[0]]
if not quiet: print(t,dates[0])
# interp to grid:
if cond is False:
cond,inds=rt.grid_vicinity(grd,x0,y0,margin=Margin,rect=True,retinds=True)
i1,i2,j1,j2=inds
if not quiet: print(' --> inter uv %s' % t.isoformat(' '))
u=calc.griddata(x0[cond],y0[cond],uv.real[cond],g.lon,g.lat,extrap=True)
v=calc.griddata(x0[cond],y0[cond],uv.imag[cond],g.lon,g.lat,extrap=True)
# rotate wind, calc stress:
if not quiet: print(' --> rot U,V wind and U,V wind stress')
wspd=np.sqrt(u**2+v**2)
sustr,svstr=air_sea.wind_stress(u,v)
angle=g.use('angle')
uwnd,vwnd=calc.rot2d(u,v,angle)
sustr,svstr=calc.rot2d(sustr,svstr,angle)
sustr=rt.rho2uvp(sustr,'u')
svstr=rt.rho2uvp(svstr,'v')
# update wind data:
newWind['date'] = t
newWind['uwnd'] = uwnd
newWind['vwnd'] = vwnd
newWind['sustr'] = sustr
newWind['svstr'] = svstr
newWind['wspd'] = wspd
# original xy:
if tind==0:
newWind['attr']={'new_wind_info':new_wind_info}
if not quiet: print(' --> original xy')
if keepOriginal==1:
newWind['x_wind']=x0
newWind['y_wind']=y0
elif keepOriginal==2:
newWind['x_wind']=x0[j1:j2,i1:i2]
newWind['y_wind']=y0[j1:j2,i1:i2]
# add to file:
if not quiet: print(' --> adding to file')
fob.update_wind(newWind,quiet=quiet)
if not quiet: print('')
def update_wind_blended2(fname,datapaths,**kargs):
'''
In days without blended data will try to use quikscat data
'''
from okean.datasets import quikscat
from okean.datasets import blended_wind
a=blended_wind.WINDData(datapaths[0])
b=quikscat.WINDData(datapaths[1])
time=netcdf.nctime(fname,'time')
date0=dts.next_date(time[0],-1)
date1=dts.next_date(time[-1],+2)
data=a.data(date0,date1)
# limit are... otherwise, quikscat interp will be very slow!
grd=netcdf.fatt(fname,'grd_file')
import os
if not os.path.isfile(grd): grd=kargs['grd']
cond,inds=rt.grid_vicinity(grd,data['x'],data['y'],margin=5,rect=True,retinds=True)
i1,i2,j1,j2=inds
for d in data.keys():
if d == 'x': data[d]=data[d][i1:i2]
elif d == 'y': data[d]=data[d][j1:j2]
else: data[d]=data[d][j1:j2,i1:i2]
# check for missing days:
time0=data.keys()
x0=data['x']
y0=data['y']
x0,y0=np.meshgrid(x0,y0)
time0.remove('x')
time0.remove('y')
out=OrderedDict()
out['x']=x0
out['y']=y0
info=''
qs_ij_limits_done=False
for d in dts.drange(date0,date1):
found=0
for t in time0:
if (t.year,t.month,t.day)==(d.year,d.month,d.day):
print('==> blended : ',t)
out[t]=data[t]
found=1
if not found: # use quikscat:
print('==> quikscat : ',d.strftime('%Y-%m-%d'))
tmp= b.data(d,dts.next_date(d))
if not tmp.has_key('x'): continue
x,y=tmp['x'],tmp['y']
x,y=np.meshgrid(x,y)
# reduce qs data:
if not qs_ij_limits_done:
i1,i2,j1,j2=calc.ij_limits(x,y,[x0.min(),x0.max()],[y0.min(),y0.max()])
qs_ij_limits_done=True
x=x[j1:j2,i1:i2]
y=y[j1:j2,i1:i2]
tmp[tmp.keys()[0]]=tmp[tmp.keys()[0]][j1:j2,i1:i2]
print(' griddata u')
u=calc.griddata(x,y,tmp[tmp.keys()[0]].real,x0,y0)
print(' griddata v')
v=calc.griddata(x,y,tmp[tmp.keys()[0]].imag,x0,y0)
out[tmp.keys()[0]]=u+1.j*v
info+='#'+d.strftime('%Y%m%d')
new_wind_info='blended+quikscat at days: '+info
update_wind(fname,out,new_wind_info,**kargs)
def load_atts(self):
atts=netcdf.fatt(self.nc)
self.atts={}
self.atts.update(atts)
