def extract(self,year,level=3,version=2,lims=False):
a=SMOSdownload(self.path)
p=a.destination_folder(year,level,version,gen=False)
files=glob.glob(os.path.join(p,'*.nc'))
files.sort()
res=OrderedDict()
for f in files:
print(' -- extracting from %s'%f)
if f==files[0]:
lon=netcdf.use(f,'longitude')
lat=netcdf.use(f,'latitude')
if lims:
i0,i1,j0,j1=calc.ij_limits(lon,lat,lims[:2],lims[2:],margin=2)
ii='%d:%d'%(i0,i1+1)
jj='%d:%d'%(j0,j1+1)
lon=netcdf.use(f,'longitude',longitude=ii)
lat=netcdf.use(f,'latitude',latitude=jj)
else: ii,jj=':',':'
date0=netcdf.nctime(f,'date_start')[0]
date1=netcdf.nctime(f,'date_stop')[0]
date=netcdf.nctime(f,'time')[0]
sss=netcdf.use(f,'sss',longitude=ii,latitude=jj)
res[date]=date0,date1,sss
return lon,lat,res
def extract(self, year, level=3, version=2, lims=False):
a = SMOSdownload(self.path)
p = a.destination_folder(year, level, version, gen=False)
files = glob.glob(os.path.join(p, '*.nc'))
files.sort()
res = OrderedDict()
for f in files:
print(' -- extracting from %s' % f)
if f == files[0]:
lon = netcdf.use(f, 'longitude')
lat = netcdf.use(f, 'latitude')
if lims:
i0, i1, j0, j1 = calc.ij_limits(lon,
lat,
lims[:2],
lims[2:],
margin=2)
ii = '%d:%d' % (i0, i1 + 1)
jj = '%d:%d' % (j0, j1 + 1)
lon = netcdf.use(f, 'longitude', longitude=ii)
lat = netcdf.use(f, 'latitude', latitude=jj)
else:
ii, jj = ':', ':'
date0 = netcdf.nctime(f, 'date_start')[0]
date1 = netcdf.nctime(f, 'date_stop')[0]
date = netcdf.nctime(f, 'time')[0]
sss = netcdf.use(f, 'sss', longitude=ii, latitude=jj)
res[date] = date0, date1, sss
return lon, lat, res
def make_frc(frcname,grd,date0,date1):
g=roms.Grid(grd)
if not os.path.isfile(frcname):
# create file:
gen_frc(frcname,grd)
else:
last=netcdf.nctime(frcname,'wind_time',wind_time=-1)
print '-->found file %s with last time %s'%(frcname,last.isoformat())
date0=last+datetime.timedelta(1/4.)
dates=[date0]
while dates[-1]<date1:
dates+=[dates[-1]+datetime.timedelta(1/4.)]
n=-1
for d in dates:
n+=1
if d==dates[0]:
x,y,u,v,ij=read_wind(grd,d,ij=False)
else:
u,v=read_wind(grd,d,ij)
U=calc.griddata(x,y,u,g.lon,g.lat)
V=calc.griddata(x,y,v,g.lon,g.lat)
# rotate wind,
print ' --> rot U,V'
angle=g.use('angle')
U,V=calc.rot2d(U,V,angle)
print ' filling %s'%d.isoformat(' ')
fill_frc(frcname,d,U,V)
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 make_frc(frcname, grd, date0, date1):
g = roms.Grid(grd)
if not os.path.isfile(frcname):
# create file:
gen_frc(frcname, grd)
else:
last = netcdf.nctime(frcname, 'wind_time', wind_time=-1)
print '-->found file %s with last time %s' % (frcname,
last.isoformat())
date0 = last + datetime.timedelta(1.) # daily
dates = [date0]
while dates[-1] < date1:
dates += [dates[-1] + datetime.timedelta(1.)] # daily
n = -1
for d in dates:
n += 1
if d == dates[0]:
x, y, u, v, ij = read_wind(grd, d, ij=False)
else:
u, v = read_wind(grd, d, ij)
U = calc.griddata(x, y, u, g.lon, g.lat, extrap=True)
V = calc.griddata(x, y, v, g.lon, g.lat, extrap=True)
# rotate wind,
print ' --> rot U,V'
angle = g.use('angle')
U, V = calc.rot2d(U, V, angle)
print ' filling %s' % d.isoformat(' ')
fill_frc(frcname, d, U, V)
def source(date):
src=glob.glob('%d/aoscat_%d_*.nc'%(date.year,date.year))
src.sort()
# cant return all files cos MFDataset wont work with files
# without unlimited dimension !
for s in src:
t=netcdf.nctime(s,'time')
if date>=t[0] and date<=t[-1]: return s
def source(date):
src = glob.glob('%d/aoscat_%d_*.nc' % (date.year, date.year))
src.sort()
# cant return all files cos MFDataset wont work with files
# without unlimited dimension !
for s in src:
t = netcdf.nctime(s, 'time')
if date >= t[0] and date <= t[-1]: return s
def read_wind(grd, date, ij=False):
f = source(date)
print('-- reading from %s' % f)
time = netcdf.nctime(f, 'time')
if 0:
try:
i = np.where(time == date)[0][0]
except:
return 'date %s not found' % date.isoformat(' ')
else:
i = 0
returnXY = False
if ij is False:
returnXY = True
lon = netcdf.use(f, 'longitude') # -180..180
lat = netcdf.use(f, 'latitude')
g = roms.Grid(grd)
xl0 = np.asarray((g.lon.min(), g.lon.max()))
xl = np.asarray((g.lon.min(), g.lon.max()))
if np.any(xl > 180) or np.any(xl < -180):
print('ERROR: grid is supposed to be -180<x<180')
print(
'Can be implemented with mpl_toolkits.basemap.shiftgrid ... TODO'
)
print('(http://matplotlib.org/basemap/api/basemap_api.html)')
return
yl = g.lat.min(), g.lat.max()
ij = calc.ij_limits(lon, lat, xl, yl, margin=1)
i0, i1, j0, j1 = ij
u = netcdf.use(f,
'eastward_wind',
longitude='%d:%d' % (i0, i1),
latitude='%d:%d' % (j0, j1),
time=i)
v = netcdf.use(f,
'northward_wind',
longitude='%d:%d' % (i0, i1),
latitude='%d:%d' % (j0, j1),
time=i)
if returnXY:
lon = netcdf.use(f,
'longitude',
longitude='%d:%d' % (i0, i1),
latitude='%d:%d' % (j0, j1))
lat = netcdf.use(f,
'latitude',
longitude='%d:%d' % (i0, i1),
latitude='%d:%d' % (j0, j1))
lon, lat = np.meshgrid(lon, lat)
#if np.all(xl0<0): lon=lon-360 # this may be wrong ... if xl is near 0, lon ay have pos and neg values !!! fix this one day ...
return lon, lat, u, v, ij
else:
return u, v
def make_blk_wrf(wrfpath,grd,bulk,date0=False,date1=False,**kargs):
'''
see make_blk_interim
'''
quiet = kargs.get('quiet',0)
create = kargs.get('create',1)
model = kargs.get('model','roms') # or roms-agrif
wrffiles=kargs.get('wrffiles','wrfout*')
dt = kargs.get('dt',6)
proj = kargs.get('proj','auto')
data=load_blkdata_wrf(wrfpath,wrffiles,date0,date1,quiet)
if not len(data): return
g=roms.Grid(grd)
if proj=='auto': kargs['proj']=g.get_projection()
q=gennc.GenBlk(bulk,grd,**kargs)
if create:
# about original data, run data2romsblk once to test for x_original:
tmp=data2romsblk(data[list(data.keys())[0]],g,**kargs)
if 'x_original' in tmp: original=tmp['x_original'].shape
else: original=False
q.create(model,original)
for d in data:
# be sure time increases. Note that in load_blkdata_wrf
# we checked if time increases in the dataset... not if dates are higher
# then previous dates in file
ntimes=netcdf.fdim(bulk,'time')
if ntimes:
tin=netcdf.nctime(bulk,'time')
if tin.size and (d-tin[-1])<datetime.timedelta(hours=dt-0.1):
print('-> not including %s'%d.isoformat())
continue
if model=='roms':
if not quiet: print(' converting units:'),
conv_units(data[d],model,quiet)
D=data2romsblk(data[d],g,**kargs)
D['date']=d
if not quiet: print(' =>filling date=%s' % d.isoformat(' '))
q.fill(D,quiet=quiet)
def make_blk_wrf(wrfpath, grd, bulk, date0=False, date1=False, **kargs):
"""
see make_blk_interim
"""
quiet = kargs.get("quiet", 0)
create = kargs.get("create", 1)
model = kargs.get("model", "roms") # or roms-agrif
wrffiles = kargs.get("wrffiles", "wrfout*")
dt = kargs.get("dt", 6)
data = load_blkdata_wrf(wrfpath, wrffiles, date0, date1, quiet)
if not len(data):
return
q = gennc.GenBlk(bulk, grd, **kargs)
if create:
# about original data, run data2romsblk once to test for x_original:
tmp = data2romsblk(data[data.keys()[0]], grd, **kargs)
if "x_original" in tmp.keys():
original = tmp["x_original"].shape
else:
original = False
q.create(model, original)
for d in data.keys():
# be sure time increases. Note that in load_blkdata_wrf
# we checked if time increases in the dataset... not if dates are higher
# then previous dates in file
ntimes = netcdf.fdim(bulk, "time")
if ntimes:
tin = netcdf.nctime(bulk, "time")
if tin.size and (d - tin[-1]) < datetime.timedelta(hours=dt - 0.1):
print "-> not including %s" % d.isoformat()
continue
if model == "roms":
if not quiet:
print " converting units:",
conv_units(data[d], model, quiet)
D = data2romsblk(data[d], grd, **kargs)
D["date"] = d
if not quiet:
print " =>filling date=%s" % d.isoformat(" ")
q.fill(D, quiet=quiet)
def make_blk_wrf(wrfpath, grd, bulk, date0=False, date1=False, **kargs):
'''
see make_blk_interim
'''
quiet = kargs.get('quiet', 0)
create = kargs.get('create', 1)
model = kargs.get('model', 'roms') # or roms-agrif
wrffiles = kargs.get('wrffiles', 'wrfout*')
dt = kargs.get('dt', 6)
proj = kargs.get('proj', 'auto')
data = load_blkdata_wrf(wrfpath, wrffiles, date0, date1, quiet)
if not len(data): return
g = roms.Grid(grd)
if proj == 'auto': kargs['proj'] = g.get_projection()
q = gennc.GenBlk(bulk, grd, **kargs)
if create:
# about original data, run data2romsblk once to test for x_original:
tmp = data2romsblk(data[list(data.keys())[0]], g, **kargs)
if 'x_original' in tmp: original = tmp['x_original'].shape
else: original = False
q.create(model, original)
for d in data:
# be sure time increases. Note that in load_blkdata_wrf
# we checked if time increases in the dataset... not if dates are higher
# then previous dates in file
ntimes = netcdf.fdim(bulk, 'time')
if ntimes:
tin = netcdf.nctime(bulk, 'time')
if tin.size and (d - tin[-1]) < datetime.timedelta(hours=dt - 0.1):
print('-> not including %s' % d.isoformat())
continue
if model == 'roms':
if not quiet: print(' converting units:'),
conv_units(data[d], model, quiet)
D = data2romsblk(data[d], g, **kargs)
D['date'] = d
if not quiet: print(' =>filling date=%s' % d.isoformat(' '))
q.fill(D, quiet=quiet)
def __update_wind(fname, datapath, source, **kargs):
if source == 'quikscat':
new_wind_info = 'wind from quikscat'
from okean.datasets import quikscat
a = quikscat.WINDData(datapath)
elif source == 'blended':
new_wind_info = 'wind from myocean blended'
from okean.datasets import blended_wind
a = blended_wind.WINDData(datapath)
time = netcdf.nctime(fname, 'time')
date0 = dts.next_date(time[0], -1)
date1 = dts.next_date(time[-1], +2)
data = a.data(date0, date1)
update_wind(fname, data, new_wind_info, **kargs)
def __update_wind(fname,datapath,source,**kargs):
if source=='quikscat':
new_wind_info='wind from quikscat'
from okean.datasets import quikscat
a=quikscat.WINDData(datapath)
elif source=='blended':
new_wind_info='wind from myocean blended'
from okean.datasets import blended_wind
a=blended_wind.WINDData(datapath)
time=netcdf.nctime(fname,'time')
date0=dts.next_date(time[0],-1)
date1=dts.next_date(time[-1],+2)
data=a.data(date0,date1)
update_wind(fname,data,new_wind_info,**kargs)
def extract(self, year, type, lims=False, date=False):
a = OSTIAdownload(self.path)
p = a.destination_folder(year, type) #########level,version,gen=False)
files = glob.glob(os.path.join(p, '*.nc'))
files.sort()
res = OrderedDict()
c = -1
for f in files:
c += 1
print(' -- extracting from %s' % f)
if c == 0:
lon = netcdf.use(f, 'lon')
lat = netcdf.use(f, 'lat')
if lims:
i0, i1, j0, j1 = calc.ij_limits(lon,
lat,
lims[:2],
lims[2:],
margin=2)
ii = '%d:%d' % (i0, i1 + 1)
jj = '%d:%d' % (j0, j1 + 1)
lon = netcdf.use(f, 'lon', lon=ii)
lat = netcdf.use(f, 'lat', lat=jj)
else:
ii, jj = ':', ':'
date = netcdf.nctime(f, 'time')[0]
u = netcdf.use(f, 'analysed_sst', lon=ii, lat=jj)
if c == 0:
sst = np.ma.zeros((len(files), ) + u.shape, u.dtype)
time = np.zeros(len(files), datetime.datetime)
sst[c] = u
time[c] = date
### date0=netcdf.nctime(f,'date_start')[0]
### date1=netcdf.nctime(f,'date_stop')[0]
# res[date]=sst
##### return lon,lat,res
return time, lon, lat, sst
def make_frc(frcname, grd, date0, date1):
g = roms.Grid(grd)
if not os.path.isfile(frcname):
# create file:
gen_frc(frcname, grd)
else:
last = netcdf.nctime(frcname, 'wind_time', wind_time=-1)
print('-->found file %s with last time %s' %
(frcname, last.isoformat()))
date0 = last + datetime.timedelta(1 / 4.) # 6h data
dates = [date0]
while dates[-1] < date1:
dates += [dates[-1] + datetime.timedelta(1 / 4.)] # 6h data
n = -1
ij = False
for d in dates:
n += 1
if ij is False:
tmp = read_wind(grd, d, ij=False)
if isinstance(tmp, basestring):
continue # some error, like date not found!
else:
x, y, u, v, ij = tmp
else:
tmp = read_wind(grd, d, ij)
if isinstance(tmp, basestring): continue
else: u, v = tmp
U = calc.griddata(x, y, u, g.lon, g.lat, extrap=True)
V = calc.griddata(x, y, v, g.lon, g.lat, extrap=True)
# rotate wind,
print(' --> rot U,V')
angle = g.use('angle')
U, V = calc.rot2d(U, V, angle)
print(' filling %s' % d.isoformat(' '))
fill_frc(frcname, d, U, V)
def read_wind(grd,date,ij=False):
f=source(date)
print '-- reading from %s'%f
time=netcdf.nctime(f,'time')
if 0:
try:
i=np.where(time==date)[0][0]
except:
return 'date %s not found'%date.isoformat(' ')
else: i=0
returnXY=False
if ij is False:
returnXY=True
lon=netcdf.use(f,'longitude') # -180..180
lat=netcdf.use(f,'latitude')
g=roms.Grid(grd)
xl0=np.asarray((g.lon.min(),g.lon.max()))
xl=np.asarray((g.lon.min(),g.lon.max()))
if np.any(xl>180) or np.any(xl<-180):
print 'ERROR: grid is supposed to be -180<x<180'
print 'Can be implemented with mpl_toolkits.basemap.shiftgrid ... TODO'
print '(http://matplotlib.org/basemap/api/basemap_api.html)'
return
yl=g.lat.min(),g.lat.max()
ij=calc.ij_limits(lon,lat,xl,yl,margin=1)
i0,i1,j0,j1=ij
u=netcdf.use(f,'eastward_wind',longitude='%d:%d'%(i0,i1),latitude='%d:%d'%(j0,j1),time=i)
v=netcdf.use(f,'northward_wind',longitude='%d:%d'%(i0,i1),latitude='%d:%d'%(j0,j1),time=i)
if returnXY:
lon=netcdf.use(f,'longitude',longitude='%d:%d'%(i0,i1),latitude='%d:%d'%(j0,j1))
lat=netcdf.use(f,'latitude',longitude='%d:%d'%(i0,i1),latitude='%d:%d'%(j0,j1))
lon,lat=np.meshgrid(lon,lat)
#if np.all(xl0<0): lon=lon-360 # this may be wrong ... if xl is near 0, lon ay have pos and neg values !!! fix this one day ...
return lon,lat,u,v, ij
else: return u,v
def make_frc(frcname,grd,date0,date1):
g=roms.Grid(grd)
if not os.path.isfile(frcname):
# create file:
gen_frc(frcname,grd)
else:
last=netcdf.nctime(frcname,'wind_time',wind_time=-1)
print '-->found file %s with last time %s'%(frcname,last.isoformat())
date0=last+datetime.timedelta(1/4.) # 6h data
dates=[date0]
while dates[-1]<date1:
dates+=[dates[-1]+datetime.timedelta(1/4.)] # 6h data
n=-1
ij=False
for d in dates:
n+=1
if ij is False:
tmp=read_wind(grd,d,ij=False)
if isinstance(tmp,basestring): continue # some error, like date not found!
else: x,y,u,v,ij=tmp
else:
tmp=read_wind(grd,d,ij)
if isinstance(tmp,basestring): continue
else: u,v=tmp
U=calc.griddata(x,y,u,g.lon,g.lat,extrap=True)
V=calc.griddata(x,y,v,g.lon,g.lat,extrap=True)
# rotate wind,
print ' --> rot U,V'
angle=g.use('angle')
U,V=calc.rot2d(U,V,angle)
print ' filling %s'%d.isoformat(' ')
fill_frc(frcname,d,U,V)
def extract(self,year,type,lims=False,date=False):
a=OSTIAdownload(self.path)
p=a.destination_folder(year,type)#########level,version,gen=False)
files=glob.glob(os.path.join(p,'*.nc'))
files.sort()
res=OrderedDict()
c=-1
for f in files:
c+=1
print(' -- extracting from %s'%f)
if c==0:
lon=netcdf.use(f,'lon')
lat=netcdf.use(f,'lat')
if lims:
i0,i1,j0,j1=calc.ij_limits(lon,lat,lims[:2],lims[2:],margin=2)
ii='%d:%d'%(i0,i1+1)
jj='%d:%d'%(j0,j1+1)
lon=netcdf.use(f,'lon',lon=ii)
lat=netcdf.use(f,'lat',lat=jj)
else: ii,jj=':',':'
date=netcdf.nctime(f,'time')[0]
u=netcdf.use(f,'analysed_sst',lon=ii,lat=jj)
if c==0:
sst=np.ma.zeros((len(files),)+u.shape,u.dtype)
time=np.zeros(len(files),datetime.datetime)
sst[c]=u
time[c]=date
### date0=netcdf.nctime(f,'date_start')[0]
### date1=netcdf.nctime(f,'date_stop')[0]
# res[date]=sst
##### return lon,lat,res
return time,lon,lat,sst
def read_wind(grd,date,ij=False):
f=source(date)
print '-- reading from %s'%f
time=netcdf.nctime(f,'time')
try:
i=np.where(time==date)[0][0]
except:
return 'date %s not found'%d.isoformat(' ')
returnXY=False
if ij is False:
returnXY=True
lon=netcdf.use(f,'lon')
lat=netcdf.use(f,'lat')
g=roms.Grid(grd)
xl0=np.asarray((g.lon.min(),g.lon.max()))
xl=np.asarray((g.lon.min(),g.lon.max()))
if np.all(xl<0): xl=xl+360
elif np.any(xl<0) and np.any(xl>0):
print 'ERROR: zero crossing not implemented !!!'
print 'can be done with mpl_toolkits.basemap.shiftgrid ... TODO'
print '(http://matplotlib.org/basemap/api/basemap_api.html)'
return
yl=g.lat.min(),g.lat.max()
ij=calc.ij_limits(lon,lat,xl,yl,margin=1)
i0,i1,j0,j1=ij
u=netcdf.use(f,'uwnd',lon='%d:%d'%(i0,i1),lat='%d:%d'%(j0,j1),time=i)
v=netcdf.use(f,'vwnd',lon='%d:%d'%(i0,i1),lat='%d:%d'%(j0,j1),time=i)
if returnXY:
lon=netcdf.use(f,'lon',lon='%d:%d'%(i0,i1),lat='%d:%d'%(j0,j1))
lat=netcdf.use(f,'lat',lon='%d:%d'%(i0,i1),lat='%d:%d'%(j0,j1))
lon,lat=np.meshgrid(lon,lat)
if np.all(xl0<0): lon=lon-360 # this may be wrong ... if xl is near 0, lon ay have pos and neg values !!! fix this one day ...
return lon,lat,u,v, ij
else: return u,v
def plt_wind(conf, plconf, date, FA='a', nest=0, **kargs):
err = ''
fig = False
info = ''
ifig = kargs.get('ifig', 0)
day = kargs.get('day', 0)
quiet = kargs.get('quiet', 0)
time = day
date = dateu.parse_date(date)
# find input files:
args = {'cf': conf, 'date': date, 'FA': FA, 'nest': nest}
atm = opt.nameof('in', 'blk', **args)
grd = opt.nameof('in', 'grd', **args)
if not os.path.isfile(atm):
err = 'ATM file not found (%s)' % atm
return err, fig, info
if not os.path.isfile(grd):
err = 'Grid file not found (%s)' % grd
return err, fig, info
Data, err = opt.get_plconf(plconf, 'WIND')
dcurr = Data['dcurr'][ifig]
lcurr = Data['lcurr'][ifig]
scurr = Data['scurr'][ifig]
clim = Data['clim'][ifig]
tind = Data['time'][ifig]
x = netcdf.use(grd, 'lon_rho')
y = netcdf.use(grd, 'lat_rho')
wtime = netcdf.nctime(atm, 'time')
cnd = (wtime >= date + datetime.timedelta(days=day)) & (
date < date + datetime.timedelta(days=day + 1))
u = netcdf.use(atm, 'Uwind', time=cnd)
v = netcdf.use(atm, 'Uwind', time=cnd)
if tind == 'dailyMean':
u = u.mean(0)
v = v.mean(0)
sdate = wtime[cnd][
0] # for title... 1st day 00h is expected to be 1st date,
# or model should not run!
else: # tind of some day, ex: tind 0 from forec day 3
u = u[tind]
v = v[tind]
sdate = wtime[cnd][tind]
if day > len(u) - 1:
err = 'Invalid day %d (max=%d)' % (day, len(u) - 1)
return err, fig, info
# plot grid:
proj, fig, ax = plt_grid(plconf, grd, ifig)
# no mask on land:
mask = np.zeros(u.shape, 'bool')
mask[::dcurr[0], ::dcurr[1]] = True
xm, ym = proj(x, y)
s = np.sqrt(u**2 + v**2)
q = pl.quiver(xm[mask],
ym[mask],
u[mask],
v[mask],
s[mask],
scale=scurr,
zorder=100)
pl.clim(clim[0], clim[1])
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:
cb.set_label(r'Wind Speed [m s$^{\rm{-1}}$]')
def add_currkey(handle):
pos = Data['kcurrpos'][ifig]
if pos:
pl.quiverkey(handle,
pos[0],
pos[1],
lcurr,
'%s m/s' % str(lcurr),
labelpos='S',
coordinates='axes')
add_colorbar(q)
add_currkey(q)
# tilte:
Title, err = opt.get_plconf(plconf, 'AXES', 'title')
if Title[ifig]:
simpleTitle = 1
rdate = date.strftime('%d-%m-%Y')
title = 'wind %s %s %d' % (rdate, FA, day)
if simpleTitle: # simpler version of title:
if FA == 'f': # forecast date:
rdate = dateu.next_date(date, day)
rdate = rdate.strftime('%d-%m-%Y')
title = 'wind %s' % (rdate)
if FA == 'f':
title = title + ' (forec)'
pl.title(title)
# 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),
sdate.strftime("%d %b %Y"),
#pl.text(proj.xmax*.62, proj.ymax*.93,sdate.strftime("%d %b %Y"),
fontdict={
'size': 13,
'family': 'monospace'
},
ha='center')
# change date format if tind is not daily mean, ie, add hour, etc
if FA == 'f':
s = 'Pronostico desde %s' % date.strftime("%d %b %Y")
pl.text(
proj.xmax * .8,
proj.ymax * (-.15),
s, ##this is outside
#pl.text(proj.xmax-W/2., proj.ymin+1.1*H,s, ##this is in the proj (inside)
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 frc2gnome(fname,frc,grd,xylim=False,dates=False,ij=(1,1),**kargs):
'''
Creates GNOME wind file
kargs:
t[u,v]var
t[u,v]dim
x[y,ang]var
Ex:
.frc2gnome(out,frc,grd,ij=(10,10),dates=dates,**{'tdim':'Time'})
'''
deta,dxi=ij
tvar='time'
uvar='Uwind'
vvar='Vwind'
#tvar='bulk_time'
#uvar='uwnd'
#vvar='vwnd'
tdim='time'
#tdim='bulk_time'
xdim='xi_rho'
ydim='eta_rho'
xvar='lon_rho'
yvar='lat_rho'
angvar='angle'
if 'tvar' in kargs.keys(): tvar=kargs['tvar']
if 'uvar' in kargs.keys(): uvar=kargs['uvar']
if 'vvar' in kargs.keys(): vvar=kargs['vvar']
if 'tdim' in kargs.keys(): tdim=kargs['tdim']
if 'xdim' in kargs.keys(): xdim=kargs['xdim']
if 'ydim' in kargs.keys(): ydim=kargs['ydim']
if 'xvar' in kargs.keys(): xvar=kargs['xvar']
if 'yvar' in kargs.keys(): yvar=kargs['yvar']
if 'angvar' in kargs.keys(): angvar=kargs['angvar']
dims=netcdf.fdim(grd)
xi,eta=dims[xdim],dims[ydim]
xi0,eta0=xi,eta
ncg=netcdf.ncopen(grd)
nc0=netcdf.ncopen(frc)
try:
t=netcdf.nctime(nc0,tvar)
except:
t=netcdf.use(nc0,tvar)
t=netcdf.num2date(t,'days since %d-01-01' % year0)
time=netcdf.date2num(t,tunits)
x0=netcdf.use(grd,xvar)
y0=netcdf.use(grd,yvar)
if x0.ndim==1: x0,y0=np.meshgrid(x0,y0)
if angvar:
ang=netcdf.use(grd,angvar)
if not xylim is False:
xlim=xylim[:2]
ylim=xylim[2:]
i1,i2,j1,j2=calc.ij_limits(x0,y0,xlim,ylim)
xi=i2-i1
eta=j2-j1
else:
i1,i2=0,xi
j1,j2=0,eta
XI ='%d:%d:%d' %(i1,i2,dxi)
ETA ='%d:%d:%d' %(j1,j2,deta)
xi=len(range(i1,i2,dxi))
eta=len(range(j1,j2,deta))
# create file:
create_wind(fname,xi,eta)
nc=netcdf.ncopen(fname,'a')
x=x0[j1:j2:deta,i1:i2:dxi]
y=y0[j1:j2:deta,i1:i2:dxi]
nc.vars['lon'][:]=x
nc.vars['lat'][:]=y
if angvar: ang=ang[j1:j2:deta,i1:i2:dxi]
n=-1
for it in range(len(time)):
if not dates is False:
d0,d1=dates
if t[it]<d0 or t[it]>=d1: continue
n+=1
u=netcdf.use(nc0,uvar,**{xdim:XI,ydim:ETA,tdim:it})
v=netcdf.use(nc0,vvar,**{xdim:XI,ydim:ETA,tdim:it})
# rotate uv:
if angvar:
print 'rotating ...'
u,v=calc.rot2d(u,v,-ang)
nc.vars['time'][n]=time[it]
print 'filling uv',n,t[it]
nc.vars['air_u'][n,...]=u
nc.vars['air_v'][n,...]=v
nc.close()
nc0.close()
ncg.close()
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 roms2swan_wind(frc,date0,date1,fname='swan_wind.dat',**kargs):
tname='wind_time'
uname='Uwind'
vname='Vwind'
grd=False # needed if wind is 1d
dt=1 # hours
path=''
if 'tname' in kargs.keys(): tname=kargs['tname']
if 'uname' in kargs.keys(): uname=kargs['uname']
if 'vname' in kargs.keys(): vname=kargs['vname']
if 'grd' in kargs.keys(): grd =kargs['grd']
if 'dt' in kargs.keys(): dt =kargs['dt']
if 'path' in kargs.keys(): path =kargs['path']
print 'wind: loading time ...'
time=netcdf.nctime(frc,tname)
#time=np.load('tfile')
#cond=(time>=date0)&(time<=date1)
cond=(time>=date0)&(time<=date1+datetime.timedelta(days=1)) # add one day at the end, just to avoid the "repeating last"
time=time[cond]
d=np.diff(pl.date2num(time))
print 'current max and min dt = %6.2f %6.2f hrs = %6.2f %6.2f mins'%(d.max()*24, d.min()*24, d.max()*24*60, d.min()*24*60)
# time=time[::dt]
# d=np.diff(pl.date2num(time))
# print ' final max and min dt = %6.2f %6.2f hrs = %6.2f %6.2f mins'%(d.max()*24, d.min()*24, d.max()*24*60, d.min()*24*60)
print 'wind: loading u ...'
u,nc=netcdf.var(frc,uname)
print 'wind: loading v ...'
v,nc=netcdf.var(nc,uname)
# u=u[cond,...][::dt,...]
# v=v[cond,...][::dt,...]
u=u[cond,...]
v=v[cond,...]
nc.close()
if u.ndim==1:
if not grd:
print 'must provide grd karg!'
return
nt=u.size
eta=netcdf.fdim(grd)['eta_rho']
xi=netcdf.fdim(grd)['xi_rho']
else:
nt,eta,xi=u.shape
# array may be too big, so do this later (for each it)
#
# u=np.tile(u[:,np.newaxis,np.newaxis],(1,eta,xi))
# v=np.tile(v[:,np.newaxis,np.newaxis],(1,eta,xi))
i=open(fname,'w')
times=[]
time0=time[0]-datetime.timedelta(hours=dt)
ITs=[]
for it in range(nt):
time0=time0+datetime.timedelta(hours=dt)
if time0>date1: break
if time0>time[-1]:
print 'Warning : repeating last ...', it
times+=[time0]
d=np.abs(time-time0)
it=np.where(d==d.min())[0][0]
ITs+=[it]
if it%100==0: print 'saving u %s %s'%(fname,time[it].isoformat(' '))
if u[it,...].ndim==0:
U=np.tile(u[it,...],(eta,xi)).flatten()
else:
U=u[it,...].flatten()
[i.write('%8.4f\n'%uu) for uu in U]
for it in ITs:
if it%100==0: print 'saving v %s %s'%(fname,time[it].isoformat(' '))
if v[it,...].ndim==0:
V=np.tile(v[it,...],(eta,xi)).flatten()
else:
V=v[it,...].flatten()
[i.write('%8.4f\n'%vv) for vv in V]
times=np.asarray(times)
t0iso=times[0].strftime('%Y%m%d.%H%M%S')
t1iso=times[-1].strftime('%Y%m%d.%H%M%S')
dt=times[1]-times[0]
dth=dt.days*24. + dt.seconds/60.**2
print ' -- created swan wind file %s\n'%fname
# add to swan INPUT:
print '\n'
print 'INPGRID WIND CURVILINEAR 0 0 %d %d EXC 9.999000e+003 &'%(xi-1,eta-1)
print ' NONSTATIONARY %s %.2f HR %s'%(t0iso,dth,t1iso)
print 'READINP WIND 1 \'%s\' 4 0 FREE '%(os.path.join(path,fname))
print '\n'
import matplotlib.pyplot as plt from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU # <markdowncell> # Lets use some ROMS-ESPRESSO output and load info from a glider: # <codecell> froms='http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2009_da/his' fglider='http://tds.marine.rutgers.edu/thredds/dodsC/cool/glider/mab/Gridded/20121025T000000_20121105T000000_maracoos_ru23.nc' fglider='http://tds.marine.rutgers.edu:8080/thredds/dodsC/cool/glider/all/ru23-20121025T1944.ncCFMA.nc3.nc' x=netcdf.use(fglider,'longitude') y=netcdf.use(fglider,'latitude') t=netcdf.nctime(fglider,'time') a=glider.RomsGlider(froms,x,y,t) a.plot() # <markdowncell> # Extract and plot the glider data # <codecell> z=netcdf.use(fglider,'depth') v=netcdf.use(fglider,'temperature') # <codecell>
def frc2gnome(fname, frc, grd, xylim=False, dates=False, ij=(1, 1), **kargs):
'''
Creates GNOME wind file
kargs:
t[u,v]var
t[u,v]dim
x[y,ang]var
Ex:
.frc2gnome(out,frc,grd,ij=(10,10),dates=dates,**{'tdim':'Time'})
'''
deta, dxi = ij
tvar = 'time'
uvar = 'Uwind'
vvar = 'Vwind'
#tvar='bulk_time'
#uvar='uwnd'
#vvar='vwnd'
tdim = 'time'
#tdim='bulk_time'
xdim = 'xi_rho'
ydim = 'eta_rho'
xvar = 'lon_rho'
yvar = 'lat_rho'
angvar = 'angle'
if 'tvar' in kargs.keys(): tvar = kargs['tvar']
if 'uvar' in kargs.keys(): uvar = kargs['uvar']
if 'vvar' in kargs.keys(): vvar = kargs['vvar']
if 'tdim' in kargs.keys(): tdim = kargs['tdim']
if 'xdim' in kargs.keys(): xdim = kargs['xdim']
if 'ydim' in kargs.keys(): ydim = kargs['ydim']
if 'xvar' in kargs.keys(): xvar = kargs['xvar']
if 'yvar' in kargs.keys(): yvar = kargs['yvar']
if 'angvar' in kargs.keys(): angvar = kargs['angvar']
dims = netcdf.fdim(grd)
xi, eta = dims[xdim], dims[ydim]
xi0, eta0 = xi, eta
ncg = netcdf.ncopen(grd)
nc0 = netcdf.ncopen(frc)
try:
t = netcdf.nctime(nc0, tvar)
except:
t = netcdf.use(nc0, tvar)
t = netcdf.num2date(t, 'days since %d-01-01' % year0)
time = netcdf.date2num(t, tunits)
x0 = netcdf.use(grd, xvar)
y0 = netcdf.use(grd, yvar)
if x0.ndim == 1: x0, y0 = np.meshgrid(x0, y0)
if angvar:
ang = netcdf.use(grd, angvar)
if not xylim is False:
xlim = xylim[:2]
ylim = xylim[2:]
i1, i2, j1, j2 = calc.ij_limits(x0, y0, xlim, ylim)
xi = i2 - i1
eta = j2 - j1
else:
i1, i2 = 0, xi
j1, j2 = 0, eta
XI = '%d:%d:%d' % (i1, i2, dxi)
ETA = '%d:%d:%d' % (j1, j2, deta)
xi = len(range(i1, i2, dxi))
eta = len(range(j1, j2, deta))
# create file:
create_wind(fname, xi, eta)
nc = netcdf.ncopen(fname, 'a')
x = x0[j1:j2:deta, i1:i2:dxi]
y = y0[j1:j2:deta, i1:i2:dxi]
nc.vars['lon'][:] = x
nc.vars['lat'][:] = y
if angvar: ang = ang[j1:j2:deta, i1:i2:dxi]
n = -1
for it in range(len(time)):
if not dates is False:
d0, d1 = dates
if t[it] < d0 or t[it] >= d1: continue
n += 1
u = netcdf.use(nc0, uvar, **{xdim: XI, ydim: ETA, tdim: it})
v = netcdf.use(nc0, vvar, **{xdim: XI, ydim: ETA, tdim: it})
# rotate uv:
if angvar:
print('rotating ...')
u, v = calc.rot2d(u, v, -ang)
nc.vars['time'][n] = time[it]
print('filling uv', n, t[it])
nc.vars['air_u'][n, ...] = u
nc.vars['air_v'][n, ...] = v
nc.close()
nc0.close()
ncg.close()
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 load_data(f,quiet=0,**kargs):
'''
Loads prognostic variables (temp,salt,u,v,ubar,vbar,zeta) from
netcdf file or opendap server. Also loads lon,lat, depth, and time.
If f is a file, it must include the 1d variables lon,lat and depth;
the 2d variable ssh (zeta) and the 3d variables temp, salt, u and v;
ie, each file must contain data for a simgle time. The file must also
contain the variable time.
If f is a opendap address, it must contain also all these variables
or the ones defined in the input karg settings (DataAccess object)
To deal with the case of variables in different files/opendap addresses,
f can also be a dictionary with keys the variables and values the files
or opendap addresses. In this case, the keys must be:
- temp
- salt
- u
- v
- ssh
- misc, for lon, lat, depth, time and dimensions
or xy for lon,lat and x,ydim; z for depth and zdim, time for time
The output data (dict) is suitable to be used by data2roms, which
interpolates the data to ROMS 3d grid.
Also outputs an error/status string.
kargs:
inds, dict with dimension names/values (where time dim can be integer
or datetime)
settings, DataAccess object
extra, extra misc vars to load [(outKey0,fileVar0),...]
t_units, units of variable time, by default the att units is used
'''
sett=DataAccess()
inds={}
extra=[]
t_units=[]
if 'settings' in kargs.keys(): sett = kargs['settings']
if 'inds' in kargs.keys(): inds = kargs['inds']
if 'extra' in kargs.keys(): extra = kargs['extra']
if 't_units' in kargs.keys(): t_units = kargs['t_units']
res={}
msg=''
if not isinstance(f,dict) and not f.startswith('http') and not isfile(f):
msg='file not found %s' % f
if not quiet: print msg
return res, msg
# load nc files:
if not isinstance(f,dict):
f={'temp':f,'salt':f,'u':f,'v':f,'ssh':f,'misc':f}
if not f.has_key('xy'): f['xy'] = f['misc']
if not f.has_key('z'): f['z'] = f['misc']
if not f.has_key('time'): f['time'] = f['misc']
filesUsed=[]
ncUsed=[]
for i in f.keys():
if not quiet: print '(%s) loading from %s' % (i.ljust(5),f[i])
if i=='temp':
if f[i] in filesUsed: ncTemp=ncUsed[filesUsed.index(f[i])]
else:
ncTemp=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncTemp]
elif i=='salt':
if f[i] in filesUsed: ncSalt=ncUsed[filesUsed.index(f[i])]
else:
ncSalt=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncSalt]
elif i=='u':
if f[i] in filesUsed: ncU=ncUsed[filesUsed.index(f[i])]
else:
ncU=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncU]
elif i=='v':
if f[i] in filesUsed: ncV=ncUsed[filesUsed.index(f[i])]
else:
ncV=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncV]
elif i=='ssh':
if f[i] in filesUsed: ncSsh=ncUsed[filesUsed.index(f[i])]
else:
ncSsh=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncSsh]
elif i=='xy':
if f[i] in filesUsed: ncXy=ncUsed[filesUsed.index(f[i])]
else:
ncXy=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncXy]
elif i=='z':
if f[i] in filesUsed: ncZ=ncUsed[filesUsed.index(f[i])]
else:
ncZ=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncZ]
elif i=='time':
if f[i] in filesUsed: ncTime=ncUsed[filesUsed.index(f[i])]
else:
ncTime=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncTime]
elif i=='misc':
if f[i] in filesUsed: ncMisc=ncUsed[filesUsed.index(f[i])]
else:
ncMisc=netcdf.ncopen(f[i])
filesUsed+=[f[i]]
ncUsed+=[ncMisc]
# load dims:
if not quiet: print ' loading dims...'
dimsXy=netcdf.fdim(ncXy)
dimsZ =netcdf.fdim(ncZ)
res['NX']=dimsXy[sett.xdim]
res['NY']=dimsXy[sett.ydim]
###if sett.z_name:
if sett.zdim:
res['NZ']=dimsZ[sett.zdim]
else:
res['NZ']=1
# about horizontal inds:
if inds.has_key(sett.xdim) and len(inds[sett.xdim])==2 and not isinstance(inds[sett.xdim],basestring):
if not quiet: print ' calc horizontal inds...'
xlim=inds[sett.xdim]
ylim=inds[sett.ydim]
inds.pop(sett.xdim)
inds.pop(sett.ydim)
lon=netcdf.use(ncXy,sett.x_name,**inds)
if np.any(lon>360): lon=np.mod(lon,360.)
lat=netcdf.use(ncXy,sett.y_name,**inds)
i0,i1,j0,j1=calc.ij_limits(lon,lat,xlim,ylim,margin=3)
inds[sett.xdim]='%d:%d' % (i0,i1)
inds[sett.ydim]='%d:%d' % (j0,j1)
if not quiet: print ' loading lon, lat, depth...'
res['lon'] = netcdf.use(ncXy,sett.x_name,**inds)
if np.any(res['lon']>360): res['lon']=np.mod(res['lon'],360.)
res['lat'] = netcdf.use(ncXy,sett.y_name,**inds)
if sett.z_name:
res['depth'] = -netcdf.use(ncZ,sett.z_name,**inds)
else: res['depth']=False
if res['lon'].size!=res['lat'].size:
res['lon'],res['lat']=np.meshgrid(res['lon'],res['lat'])
# needed for griddata, later
# update nx,ny:
if inds.has_key(sett.xdim):
res['NY'],res['NX']=res['lon'].shape
# extra misc vars:
if len(extra):
for outKey,fileVar in extra:
if not quiet: print ' loading extra misc... %s %s' % (outKey,fileVar)
res[outKey]=netcdf.use(ncMisc,fileVar,**inds)
# time:
# file may have one or several times. If several, time dim must be given
# with kargs inds!
if not quiet: print ' loading time...'
if t_units:
times=netcdf.use(ncTime,sett.time_name)
times=netcdftime.num2date(times,t_units)
else:
times=netcdf.nctime(ncTime,sett.time_name)
if inds.has_key(sett.tdim):
try: tind=dts.parse_date(inds[sett.tdim])
except: tind=inds[sett.tdim] # is an integer, for instance
if isinstance(tind,datetime.datetime):
tind,=np.where(times==tind)
if tind.size:
tind=tind[0]
inds[sett.tdim]=tind # update inds to extract other variables
else:
Msg='date not found'
msg+='\n'+Msg
return res,msg+' ERROR'
date=times[tind]
if not quiet: print ' tind, date= %d %s' % (tind,date.isoformat(' '))
elif times.size==1:
date=times[0]
if not quiet: print ' date= %s' % date.isoformat(' ')
else: # must provide tind as input!!
Msg='several dates in file... provice tind!'
msg+='\n'+Msg
return res,msg+' ERROR'
res['date'] = date
empty3d=np.zeros([res['NZ'],res['NY'],res['NX']])
empty2d=np.zeros([res['NY'],res['NX']])
if 'temp' in f.keys():
if not quiet: print ' loading temp...'
if sett.temp_name in ncTemp.varnames: res['temp'] = netcdf.use(ncTemp,sett.temp_name,**inds)
else:
Msg='var %s not found' % 'temp'
msg+='\n'+Msg
if not quiet: print Msg
res['temp']=empty3d
if 'salt' in f.keys():
if not quiet: print ' loading salt...'
if sett.salt_name in ncSalt.varnames: res['salt'] = netcdf.use(ncSalt,sett.salt_name,**inds)
else:
Msg='var %s not found' % 'salt'
msg+='\n'+Msg
if not quiet: print Msg
res['salt']=empty3d
if 'u' in f.keys():
if not quiet: print ' loading u...'
if sett.u_name in ncU.varnames: res['u'] = netcdf.use(ncU,sett.u_name,**inds)
else:
Msg='var %s not found' % 'u'
msg+='\n'+Msg
if not quiet: print Msg
res['u']=empty3d
if 'v' in f.keys():
if not quiet: print ' loading v...'
if sett.v_name in ncV.varnames: res['v'] = netcdf.use(ncV,sett.v_name,**inds)
else:
Msg='var %s not found' % 'v'
msg+='\n'+Msg
if not quiet: print Msg
res['v']=empty3d
if 'ssh' in f.keys():
if not quiet: print ' loading ssh...'
if sett.ssh_name in ncSsh.varnames: res['ssh'] = netcdf.use(ncSsh,sett.ssh_name,**inds)
else:
Msg='var %s not found' % 'ssh'
msg+='\n'+Msg
if not quiet: print Msg
res['ssh']=empty2d
for nc in ncUsed:
try: nc.close()
except: pass
return res, msg
def update_wind(self, data, keepOld=True, quiet=0):
# store old at first iteration
nc = netcdf.Pync(self.filename, 'w')
time = netcdf.nctime(self.filename, 'time')
tind, = np.where(time == data['date'])
if tind.size: tind = tind[0]
else:
if not quiet:
print('cannot find time=%s' % data['date'].isoformat(' '))
return
if 'Uwind' in nc.varnames: # roms
names = ('Uwind', 'uwnd'), ('Vwind',
'vwnd'), 'sustr', 'svstr', 'wspd'
else: # roms-agrif
names = 'uwnd', 'vwnd', 'sustr', 'svstr', 'wspd'
# store old wind data:
if False and tind == 0: # not using for now.... too slow!
# TODO: remove keepOld form argument !?
if 'x_wind' in data.keys():
if not quiet: print('adding x_wind, y_wind')
nc.add_dim('x_wind_original', data['x_wind'].shape[1])
nc.add_dim('y_wind_original', data['x_wind'].shape[0])
nc.add_var('x_wind_original', data['x_wind'].dtype,
('y_wind_original', 'x_wind_original'))
nc.add_var('y_wind_original', data['y_wind'].dtype,
('y_wind_original', 'x_wind_original'))
nc.vars['x_wind_original'][...] = data['x_wind']
nc.vars['y_wind_original'][...] = data['y_wind']
for i in names:
if isinstance(i, basestring): filev, datav = i, i
else: filev, datav = i
newName = filev + '_old'
newType = nc.vars[filev].dtype()
newDims = nc.vars[filev].dims.keys()
if not quiet: print(' creating var %s' % newName)
nc.add_var(newName, newType, newDims)
if not quiet: print(' filling var %s' % newName)
nc.vars[newName][...] = nc.vars[filev][...]
# also store old original data, if present:
orig = datav + '_original'
if orig in nc.varnames:
newName = orig + '_old'
newType = nc.vars[orig].dtype()
newDims = nc.vars[orig].dims.keys()
if not quiet: print(' creating var %s' % newName)
nc.add_var(newName, newType, newDims)
if not quiet: print(' filling var %s' % newName)
nc.vars[newName][...] = nc.vars[orig][...]
# store new wind data:
for i in names:
if isinstance(i, basestring): filev, datav = i, i
else: filev, datav = i
if not quiet:
print(' %s (%s) min=%8.3f max=%8.3f' %
(filev.ljust(7), datav.ljust(7), data[datav].min(),
data[datav].max()))
nc.vars[filev][tind, ...] = data[datav]
# also store new wind original data, if present:
orig = datav + '_original'
if not orig in data.keys(): continue
if not quiet:
print(' %s min=%8.3f max=%8.3f' %
(orig.ljust(7 + 9), data[orig].min(), data[orig].max()))
nc.vars[orig][tind, ...] = data[datav]
# adding global atts:
if 'attr' in data.keys():
for i in data['attr'].keys():
nc.add_att(i, data['attr'][i])
nc.close()
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 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 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 his2gnome(fname,his,grd=False,nomask=False,gshhsMask=True,xylim=False,dates=False,ij=(1,1)):
'''
Creates GNOME wind file
Ex:
his2gnome(out,his,grd,dates=dates,ij=(2,2))
if gshhsMask, the high res mask file mask_gshhs.npy will be created at 1st usage.
Mask is based on high (h) resolution gshhs data which must be available (env variable
GSHHS_MASK must be set).
'''
if not grd: grd=his
deta,dxi=ij
dims=netcdf.fdim(his)
xi,eta=dims['xi_rho'],dims['eta_rho']
xi0,eta0=xi,eta
nc0=netcdf.ncopen(his)
time=netcdf.nctime(nc0,'ocean_time')
# for roms agrif:
#t=netcdf.use(nc0,'scrum_time')
#time=netcdf.num2date(t,'seconds since %d-01-01' % year0)
x0=netcdf.use(grd,'lon_rho')
y0=netcdf.use(grd,'lat_rho')
ang=netcdf.use(grd,'angle')
if not xylim is False:
xlim=xylim[:2]
ylim=xylim[2:]
i1,i2,j1,j2=calc.ij_limits(x0,y0,xlim,ylim)
print i1,i2,j1,j2
xi=i2-i1
eta=j2-j1
else:
i1,i2=0,xi
j1,j2=0,eta
XI ='%d:%d:%d' %(i1,i2,dxi)
ETA ='%d:%d:%d' %(j1,j2,deta)
xi=len(range(i1,i2,dxi))
eta=len(range(j1,j2,deta))
# create file:
create_uv(fname,xi,eta)
nc=netcdf.ncopen(fname,'a')
for v0,v in ('lon_rho','lon'),('lat_rho','lat'),('mask_rho','mask'),('h','depth'):
print 'filling %s with %s' % (v,v0)
nc.vars[v][:]=netcdf.use(grd,v0,xi_rho=XI,eta_rho=ETA)
if nomask:
print 'NO MASK !!!'
nc.vars['mask'][:]=1
if gshhsMask:
try:
mask=np.load('mask_gshhs.npy')
except:
mask=1+0*netcdf.use(nc0,'mask_rho',xi_rho=XI,eta_rho=ETA)
mask=mask.astype('bool')
x=netcdf.use(grd,'lon_rho',xi_rho=XI,eta_rho=ETA)
y=netcdf.use(grd,'lat_rho',xi_rho=XI,eta_rho=ETA)
from okean import gshhs
axis=x.min(),x.max(),y.min(),y.max()
g=gshhs.gshhs(axis, resolution='h',area_thresh=0., max_level=2,clip=True)
for lon, lat, level in zip(g.lon, g.lat, g.level):
if level == 1: # land
print 'mask ',lon.shape
i=calc.inpolygon(x,y,lon,lat)
mask=mask & ~i
mask.dump('mask_gshhs.npy')
nc.vars['mask'][:]=mask
x=x0[j1:j2:deta,i1:i2:dxi]
y=y0[j1:j2:deta,i1:i2:dxi]
ang=ang[j1:j2:deta,i1:i2:dxi]
n=-1
for it in range(len(time)):
if not dates is False:
d0,d1=dates
if time[it]<d0 or time[it]>=d1: continue
n+=1
U=np.zeros((eta0,xi0),'f')
V=np.zeros((eta0,xi0),'f')
nc.vars['time'][n]=netcdf.date2num(time[it],tunits)
# for roms agrif:
#u=netcdf.use(nc0,'u',time=it,s_rho=-1)
#v=netcdf.use(nc0,'v',time=it,s_rho=-1)
u=netcdf.use(nc0,'u',ocean_time=it,s_rho=-1)
v=netcdf.use(nc0,'v',ocean_time=it,s_rho=-1)
# mask extrap:
print 'mask extrap...'
u=calc.mask_extrap(x0,y0,np.ma.masked_where(u==0,u))
v=calc.mask_extrap(x0,y0,np.ma.masked_where(v==0,v))
U[:,1:-1]=0.5*(u[:,:-1]+u[:,1:])
U[:,0]=u[:,0]
U[:,-1]=u[:,-1]
V[1:-1,:]=0.5*(v[:-1.:]+v[1:,:])
V[0,:]=v[0,:]
V[-1,:]=v[-1,:]
U=U[j1:j2,i1:i2]
V=V[j1:j2,i1:i2]
U=U[j1:j2:deta,i1:i2:dxi]
V=V[j1:j2:deta,i1:i2:dxi]
# rotate uv:
print 'rotating ...'
U,V=calc.rot2d(U,V,-ang)
print 'filling uv', n, time[it]
nc.vars['u'][n,...]=U
nc.vars['v'][n,...]=V
nc.close()
nc0.close()
def interim_file_data(files,quiet=False):
'''
ECMWF ERA INTERIM data for ROMS
To be used with data obtained from the new server:
http://apps.ecmwf.int/datasets/
and not the old one (http://data-portal.ecmwf.int/data/d/interim_daily/)
To deal with data from old server use module interim_past
=> forecast vars:
time=00h, 12h
step=3,6,9,12 --> n forec steps=4
needed (suggestion):
- Surface net solar radiation (ssr)
- Surface thermal radiation (str)
- Total precipitation (tp)
others:
- Surface thermal radiation downwards (strd)
- Evaporation (e)
- ...
=>analysis+forec vars: (interim analysis starts every 6h; forecast starts every 12h !!)
time=00h,6h,12h,18h
step=0,3,9 (3 and 9 are for the forecast 00h and 12h)
needed (suggestion):
- Surface pressure (sp)
- Total cloud cover (tcc)
- 10 metre U wind component (v10u or u10)
- 10 metre V wind component (v10v or v10)
- 2 metre temperature (v2t or t2m)
- 2 metre dewpoint temperature (v2d or d2m)
Accumulated vars (rad SW, LW and precipitation are converted to averages
by acum2avg
'''
# some variables may have different names!
Vars={}
Vars['v10u']='v10u','u10'
Vars['v10v']='v10v','v10'
Vars['v2t']='v2t','t2m'
Vars['v2d']='v2d','d2m'
def find_v(name):
if name in Vars.keys():
for v in Vars[name]:
if varfile(v): return v
else: return name
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
def check_var_type(var):
# new interim dataserver provides forec+analysis vars with extra dim
# 'type', 0 or 1
if var.ndim==4:
if not quiet: print(' dealing with var type... '),
v=np.zeros(var.shape[1:],var.dtype)
v[::2]=var[0,::2,...]
v[1::2]=var[1,1::2,...]
var=v
if not quiet: print('done.')
return var
out={}
# time:
# all times from analysis file, except last ind which will be
# the last time of forecast file
aFile=varfile(find_v('v2t')) # air temp, for instance
fFile=varfile(find_v('ssr')) # sw rad, for instance
if not quiet: print(' reading "analysis" time from file %s' % aFile)
aTime=netcdf.nctime(aFile,'time')
aTime.sort() # analysis+forecast files may not have time sorted!!
if not quiet: print(' reading "forecast" time from file %s' % fFile)
fTime=netcdf.nctime(fFile,'time')
fTime.sort() # this one should be sorted...
time=np.append(aTime,fTime[-1])
out['time']=time
# calc number of forecast steps stored,nforec (used by accum2avg)
if [fTime[i].hour for i in range(8)]==range(3,22,3)+[0]: nforec=4
elif [fTime[i].hour for i in range(4)]==range(6,19,6)+[0]: nforec=2
else:
if not quiet: print('INTERIM WRONG TIME: cannot n forec steps')
return
if not quiet: print(' ==> n forecast steps = %d' % nforec)
# x,y:
if not quiet: print(' reading x,y from file %s' % files[0])
x=netcdf.use(files[0],'longitude')
y=netcdf.use(files[0],'latitude')
x[x>180]=x[x>180]-360
if x.ndim==1 and y.ndim==1:
x,y=np.meshgrid(x,y)
# tair [K-->C]
if not quiet: print(' --> T air')
vname=find_v('v2t')
f=varfile(vname)
# time may not be monotonically increasing !!
# when using mix of analysis and forecast variables and steps
sortInds=np.argsort(netcdf.use(f,'time'))
tair=netcdf.use(f,vname,time=sortInds)-273.15
tair=check_var_type(tair)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
tair=fill_tend(tair)
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from T dew)')
vname=find_v('v2d')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
Td=netcdf.use(f,vname,time=sortInds)-273.15
Td=check_var_type(Td)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend... (T dew)')
Td=fill_tend(Td)
T=tair
rhum=air_sea.relative_humidity(T,Td)
## rhum=((112-0.1*T+Td)/(112+0.9*T))**8
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname=find_v('sp')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
pres=netcdf.use(f,vname,time=sortInds)
pres=check_var_type(pres)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
pres=fill_tend(pres)
out['pres']=Data(x,y,pres,'Pa')
# P rate [m --> cm day-1]
if not quiet: print(' --> P rate')
vname=find_v('tp')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
prate=netcdf.use(f,vname,time=sortInds)
prate=check_var_type(prate)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
prate=accum2avg(prate,nforec)
conv= 100*86400 # from m s-1 --> cm day-1
#conv= 100*86400/1000. # from kg m-2 s-1 --> cm day-1
prate=prate*conv # cm day-1
if not quiet: print(' fill_t0...')
prate=fill_t0(prate)
prate[prate<0]=0
out['prate']=Data(x,y,prate,'cm day-1')
# Net shortwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net shortwave flux')
vname=find_v('ssr')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
sw_net=netcdf.use(f,vname,time=sortInds)
sw_net=check_var_type(sw_net)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
sw_net=accum2avg(sw_net,nforec)
if not quiet: print(' fill_t0...')
sw_net=fill_t0(sw_net)
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net longwave flux')
vname=find_v('str')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
lw_net=netcdf.use(f,vname,time=sortInds)*-1 # let us consider positive upward (*-1)
lw_net=check_var_type(lw_net)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_net=accum2avg(lw_net,nforec)
if not quiet: print(' fill_t0...')
lw_net=fill_t0(lw_net)
out['radlw']=Data(x,y,lw_net,'W m-2',info='positive upward')
# longwave down:
# can be obtained from clouds!!
if not quiet: print(' --> Down longwave flux')
vname=find_v('strd')
f=varfile(vname)
if f:
sortInds=np.argsort(netcdf.use(f,'time'))
lw_down=netcdf.use(f,vname,time=sortInds)*-1 # let us consider positive upward (*-1)
lw_down=check_var_type(lw_down)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_down=accum2avg(lw_down,nforec)
if not quiet: print(' fill_t0...')
lw_down=fill_t0(lw_down)
out['dlwrf']=Data(x,y,lw_down,'W m-2',info='negative... downward')
else: print('down long wave CANNOT BE USED')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
vname=find_v('v10u')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
uwnd=netcdf.use(f,vname,time=sortInds)
uwnd=check_var_type(uwnd)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
uwnd=fill_tend(uwnd)
vname=find_v('v10v')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
vwnd=netcdf.use(f,vname,time=sortInds)
vwnd=check_var_type(vwnd)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
vwnd=fill_tend(vwnd)
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')
vname=find_v('tcc')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
clouds=netcdf.use(f,vname,time=sortInds)
clouds=check_var_type(clouds)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
clouds=fill_tend(clouds)
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
return out
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 his2gnome(fname,
his,
grd=False,
nomask=False,
gshhsMask=True,
xylim=False,
dates=False,
ij=(1, 1)):
'''
Creates GNOME wind file
Ex:
his2gnome(out,his,grd,dates=dates,ij=(2,2))
if gshhsMask, the high res mask file mask_gshhs.npy will be created at 1st usage.
Mask is based on high (h) resolution gshhs data which must be available (env variable
GSHHS_MASK must be set).
'''
if not grd: grd = his
deta, dxi = ij
dims = netcdf.fdim(his)
xi, eta = dims['xi_rho'], dims['eta_rho']
xi0, eta0 = xi, eta
nc0 = netcdf.ncopen(his)
time = netcdf.nctime(nc0, 'ocean_time')
# for roms agrif:
#t=netcdf.use(nc0,'scrum_time')
#time=netcdf.num2date(t,'seconds since %d-01-01' % year0)
x0 = netcdf.use(grd, 'lon_rho')
y0 = netcdf.use(grd, 'lat_rho')
ang = netcdf.use(grd, 'angle')
if not xylim is False:
xlim = xylim[:2]
ylim = xylim[2:]
i1, i2, j1, j2 = calc.ij_limits(x0, y0, xlim, ylim)
print(i1, i2, j1, j2)
xi = i2 - i1
eta = j2 - j1
else:
i1, i2 = 0, xi
j1, j2 = 0, eta
XI = '%d:%d:%d' % (i1, i2, dxi)
ETA = '%d:%d:%d' % (j1, j2, deta)
xi = len(range(i1, i2, dxi))
eta = len(range(j1, j2, deta))
# create file:
create_uv(fname, xi, eta)
nc = netcdf.ncopen(fname, 'a')
for v0, v in ('lon_rho', 'lon'), ('lat_rho', 'lat'), ('mask_rho',
'mask'), ('h',
'depth'):
print('filling %s with %s' % (v, v0))
nc.vars[v][:] = netcdf.use(grd, v0, xi_rho=XI, eta_rho=ETA)
if nomask:
print('NO MASK !!!')
nc.vars['mask'][:] = 1
if gshhsMask:
try:
mask = np.load('mask_gshhs.npy')
except:
mask = 1 + 0 * netcdf.use(nc0, 'mask_rho', xi_rho=XI, eta_rho=ETA)
mask = mask.astype('bool')
x = netcdf.use(grd, 'lon_rho', xi_rho=XI, eta_rho=ETA)
y = netcdf.use(grd, 'lat_rho', xi_rho=XI, eta_rho=ETA)
from okean import gshhs
axis = x.min(), x.max(), y.min(), y.max()
g = gshhs.gshhs(axis,
resolution='h',
area_thresh=0.,
max_level=2,
clip=True)
for lon, lat, level in zip(g.lon, g.lat, g.level):
if level == 1: # land
print('mask ', lon.shape)
i = calc.inpolygon(x, y, lon, lat)
mask = mask & ~i
mask.dump('mask_gshhs.npy')
nc.vars['mask'][:] = mask
x = x0[j1:j2:deta, i1:i2:dxi]
y = y0[j1:j2:deta, i1:i2:dxi]
ang = ang[j1:j2:deta, i1:i2:dxi]
n = -1
for it in range(len(time)):
if not dates is False:
d0, d1 = dates
if time[it] < d0 or time[it] >= d1: continue
n += 1
U = np.zeros((eta0, xi0), 'f')
V = np.zeros((eta0, xi0), 'f')
nc.vars['time'][n] = netcdf.date2num(time[it], tunits)
# for roms agrif:
#u=netcdf.use(nc0,'u',time=it,s_rho=-1)
#v=netcdf.use(nc0,'v',time=it,s_rho=-1)
u = netcdf.use(nc0, 'u', ocean_time=it, s_rho=-1)
v = netcdf.use(nc0, 'v', ocean_time=it, s_rho=-1)
# mask extrap:
print('mask extrap...')
u = calc.mask_extrap(x0, y0, np.ma.masked_where(u == 0, u))
v = calc.mask_extrap(x0, y0, np.ma.masked_where(v == 0, v))
U[:, 1:-1] = 0.5 * (u[:, :-1] + u[:, 1:])
U[:, 0] = u[:, 0]
U[:, -1] = u[:, -1]
V[1:-1, :] = 0.5 * (v[:-1.:] + v[1:, :])
V[0, :] = v[0, :]
V[-1, :] = v[-1, :]
U = U[j1:j2, i1:i2]
V = V[j1:j2, i1:i2]
U = U[j1:j2:deta, i1:i2:dxi]
V = V[j1:j2:deta, i1:i2:dxi]
# rotate uv:
print('rotating ...')
U, V = calc.rot2d(U, V, -ang)
print('filling uv', n, time[it])
nc.vars['u'][n, ...] = U
nc.vars['v'][n, ...] = V
nc.close()
nc0.close()
def plt_wind(conf,plconf,date,FA='a',nest=0,**kargs):
err = ''
fig = False
info = ''
ifig = kargs.get('ifig',0)
day = kargs.get('day',0)
quiet = kargs.get('quiet',0)
time=day
date=dateu.parse_date(date)
# find input files:
args={'cf':conf,'date':date,'FA':FA,'nest':nest}
atm = opt.nameof('in','blk',**args)
grd = opt.nameof('in','grd',**args)
if not os.path.isfile(atm):
err='ATM file not found (%s)' % atm
return err,fig,info
if not os.path.isfile(grd):
err='Grid file not found (%s)' % grd
return err,fig,info
Data,err = opt.get_plconf(plconf,'WIND')
dcurr=Data['dcurr'][ifig]
lcurr=Data['lcurr'][ifig]
scurr=Data['scurr'][ifig]
clim =Data['clim'][ifig]
tind = Data['time'][ifig]
x=netcdf.use(grd,'lon_rho')
y=netcdf.use(grd,'lat_rho')
wtime=netcdf.nctime(atm,'time')
cnd=(wtime>=date+datetime.timedelta(days=day))&(date<date+datetime.timedelta(days=day+1))
u=netcdf.use(atm,'Uwind',time=cnd)
v=netcdf.use(atm,'Uwind',time=cnd)
if tind=='dailyMean':
u=u.mean(0)
v=v.mean(0)
sdate=wtime[cnd][0] # for title... 1st day 00h is expected to be 1st date,
# or model should not run!
else: # tind of some day, ex: tind 0 from forec day 3
u=u[tind]
v=v[tind]
sdate=wtime[cnd][tind]
if day>len(u)-1:
err='Invalid day %d (max=%d)' % (day,len(u)-1)
return err,fig,info
# plot grid:
proj,fig,ax= plt_grid(plconf,grd,ifig)
# no mask on land:
mask=np.zeros(u.shape,'bool')
mask[::dcurr[0],::dcurr[1]]=True
xm, ym = proj(x,y)
s=np.sqrt(u**2+v**2)
q=pl.quiver(xm[mask],ym[mask],u[mask],v[mask],s[mask],scale=scurr,zorder=100)
pl.clim(clim[0],clim[1])
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:
cb.set_label(r'Wind Speed [m s$^{\rm{-1}}$]')
def add_currkey(handle):
pos=Data['kcurrpos'][ifig]
if pos:
pl.quiverkey(handle, pos[0], pos[1], lcurr, '%s m/s' % str(lcurr),labelpos='S',
coordinates='axes')
add_colorbar(q)
add_currkey(q)
# tilte:
Title,err=opt.get_plconf(plconf,'AXES','title')
if Title[ifig]:
simpleTitle=1
rdate=date.strftime('%d-%m-%Y')
title='wind %s %s %d' % (rdate,FA,day)
if simpleTitle: # simpler version of title:
if FA=='f': # forecast date:
rdate=dateu.next_date(date,day);
rdate=rdate.strftime('%d-%m-%Y')
title='wind %s' % (rdate)
if FA=='f':
title=title+' (forec)'
pl.title(title)
# 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),sdate.strftime("%d %b %Y"),
#pl.text(proj.xmax*.62, proj.ymax*.93,sdate.strftime("%d %b %Y"),
fontdict={'size':13,'family':'monospace'},ha='center')
# change date format if tind is not daily mean, ie, add hour, etc
if FA=='f':
s='Pronostico desde %s' % date.strftime("%d %b %Y")
pl.text(proj.xmax*.8, proj.ymax*(-.15),s, ##this is outside
#pl.text(proj.xmax-W/2., proj.ymin+1.1*H,s, ##this is in the proj (inside)
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 roms2swan_wind(frc, date0, date1, fname='swan_wind.dat', **kargs):
tname = 'wind_time'
uname = 'Uwind'
vname = 'Vwind'
grd = False # needed if wind is 1d
dt = 1 # hours
path = ''
if 'tname' in kargs.keys(): tname = kargs['tname']
if 'uname' in kargs.keys(): uname = kargs['uname']
if 'vname' in kargs.keys(): vname = kargs['vname']
if 'grd' in kargs.keys(): grd = kargs['grd']
if 'dt' in kargs.keys(): dt = kargs['dt']
if 'path' in kargs.keys(): path = kargs['path']
print 'wind: loading time ...'
time = netcdf.nctime(frc, tname)
#time=np.load('tfile')
#cond=(time>=date0)&(time<=date1)
cond = (time >= date0) & (
time <= date1 + datetime.timedelta(days=1)
) # add one day at the end, just to avoid the "repeating last"
time = time[cond]
d = np.diff(pl.date2num(time))
print 'current max and min dt = %6.2f %6.2f hrs = %6.2f %6.2f mins' % (
d.max() * 24, d.min() * 24, d.max() * 24 * 60, d.min() * 24 * 60)
# time=time[::dt]
# d=np.diff(pl.date2num(time))
# print ' final max and min dt = %6.2f %6.2f hrs = %6.2f %6.2f mins'%(d.max()*24, d.min()*24, d.max()*24*60, d.min()*24*60)
print 'wind: loading u ...'
nc = netcdf.ncopen(frc)
u = netcdf.var(nc, uname)
print 'wind: loading v ...'
v = netcdf.var(nc, uname)
# u=u[cond,...][::dt,...]
# v=v[cond,...][::dt,...]
u = u[cond, ...]
v = v[cond, ...]
nc.close()
if u.ndim == 1:
if not grd:
print 'must provide grd karg!'
return
nt = u.size
eta = netcdf.fdim(grd)['eta_rho']
xi = netcdf.fdim(grd)['xi_rho']
else:
nt, eta, xi = u.shape
# array may be too big, so do this later (for each it)
#
# u=np.tile(u[:,np.newaxis,np.newaxis],(1,eta,xi))
# v=np.tile(v[:,np.newaxis,np.newaxis],(1,eta,xi))
i = open(fname, 'w')
times = []
time0 = time[0] - datetime.timedelta(hours=dt)
ITs = []
for it in range(nt):
time0 = time0 + datetime.timedelta(hours=dt)
if time0 > date1: break
if time0 > time[-1]:
print 'Warning : repeating last ...', it
times += [time0]
d = np.abs(time - time0)
it = np.where(d == d.min())[0][0]
ITs += [it]
if it % 100 == 0:
print 'saving u %s %s' % (fname, time[it].isoformat(' '))
if u[it, ...].ndim == 0:
U = np.tile(u[it, ...], (eta, xi)).flatten()
else:
U = u[it, ...].flatten()
[i.write('%8.4f\n' % uu) for uu in U]
for it in ITs:
if it % 100 == 0:
print 'saving v %s %s' % (fname, time[it].isoformat(' '))
if v[it, ...].ndim == 0:
V = np.tile(v[it, ...], (eta, xi)).flatten()
else:
V = v[it, ...].flatten()
[i.write('%8.4f\n' % vv) for vv in V]
times = np.asarray(times)
t0iso = times[0].strftime('%Y%m%d.%H%M%S')
t1iso = times[-1].strftime('%Y%m%d.%H%M%S')
dt = times[1] - times[0]
dth = dt.days * 24. + dt.seconds / 60.**2
print ' -- created swan wind file %s\n' % fname
# add to swan INPUT:
print '\n'
print 'INPGRID WIND CURVILINEAR 0 0 %d %d EXC 9.999000e+003 &' % (xi - 1,
eta - 1)
print ' NONSTATIONARY %s %.2f HR %s' % (t0iso, dth, t1iso)
print 'READINP WIND 1 \'%s\' 4 0 FREE ' % (os.path.join(path, fname))
print '\n'
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 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(self,data,keepOld=True,quiet=0):
# store old at first iteration
nc=netcdf.Pync(self.filename,'w')
time=netcdf.nctime(self.filename,'time')
tind,=np.where(time==data['date'])
if tind.size: tind=tind[0]
else:
if not quiet: print('cannot find time=%s' % data['date'].isoformat(' '))
return
if 'Uwind' in nc.varnames: # roms
names=('Uwind','uwnd'),('Vwind','vwnd'),'sustr','svstr','wspd'
else: # roms-agrif
names='uwnd','vwnd','sustr','svstr','wspd'
# store old wind data:
if False and tind==0: # not using for now.... too slow!
# TODO: remove keepOld form argument !?
if 'x_wind' in data.keys():
if not quiet: print('adding x_wind, y_wind')
nc.add_dim('x_wind_original',data['x_wind'].shape[1])
nc.add_dim('y_wind_original',data['x_wind'].shape[0])
nc.add_var('x_wind_original',data['x_wind'].dtype,
('y_wind_original','x_wind_original'))
nc.add_var('y_wind_original',data['y_wind'].dtype,
('y_wind_original','x_wind_original'))
nc.vars['x_wind_original'][...]=data['x_wind']
nc.vars['y_wind_original'][...]=data['y_wind']
for i in names:
if isinstance(i,basestring): filev,datav=i,i
else: filev,datav=i
newName = filev+'_old'
newType = nc.vars[filev].dtype()
newDims = nc.vars[filev].dims.keys()
if not quiet: print(' creating var %s' % newName)
nc.add_var(newName,newType,newDims)
if not quiet: print(' filling var %s' % newName)
nc.vars[newName][...]=nc.vars[filev][...]
# also store old original data, if present:
orig=datav+'_original'
if orig in nc.varnames:
newName = orig+'_old'
newType = nc.vars[orig].dtype()
newDims = nc.vars[orig].dims.keys()
if not quiet: print(' creating var %s' % newName)
nc.add_var(newName,newType,newDims)
if not quiet: print(' filling var %s' % newName)
nc.vars[newName][...]=nc.vars[orig][...]
# store new wind data:
for i in names:
if isinstance(i,basestring): filev,datav=i,i
else: filev,datav=i
if not quiet: print(' %s (%s) min=%8.3f max=%8.3f' % (filev.ljust(7),datav.ljust(7),
data[datav].min(),data[datav].max()))
nc.vars[filev][tind,...]=data[datav]
# also store new wind original data, if present:
orig=datav+'_original'
if not orig in data.keys(): continue
if not quiet: print(' %s min=%8.3f max=%8.3f' % (orig.ljust(7+9),
data[orig].min(),data[orig].max()))
nc.vars[orig][tind,...]=data[datav]
# adding global atts:
if 'attr' in data.keys():
for i in data['attr'].keys(): nc.add_att(i,data['attr'][i])
nc.close()
def interim_file_data(files, quiet=False):
'''
ECMWF ERA INTERIM data for ROMS
To be used with data obtained from the new server:
http://apps.ecmwf.int/datasets/
and not the old one (http://data-portal.ecmwf.int/data/d/interim_daily/)
To deal with data from old server use module interim_past
=> forecast vars:
time=00h, 12h
step=3,6,9,12 --> n forec steps=4
needed (suggestion):
- Surface net solar radiation (ssr)
- Surface thermal radiation (str)
- Total precipitation (tp)
others:
- Surface thermal radiation downwards (strd)
- Evaporation (e)
- ...
=>analysis+forec vars: (interim analysis starts every 6h; forecast starts every 12h !!)
time=00h,6h,12h,18h
step=0,3,9 (3 and 9 are for the forecast 00h and 12h)
needed (suggestion):
- Surface pressure (sp)
- Total cloud cover (tcc)
- 10 metre U wind component (v10u or u10)
- 10 metre V wind component (v10v or v10)
- 2 metre temperature (v2t or t2m)
- 2 metre dewpoint temperature (v2d or d2m)
Accumulated vars (rad SW, LW and precipitation are converted to averages
by acum2avg
'''
# some variables may have different names!
Vars = {}
Vars['v10u'] = 'v10u', 'u10'
Vars['v10v'] = 'v10v', 'v10'
Vars['v2t'] = 'v2t', 't2m'
Vars['v2d'] = 'v2d', 'd2m'
def find_v(name):
if name in Vars.keys():
for v in Vars[name]:
if varfile(v): return v
else: return name
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
def check_var_type(var):
# new interim dataserver provides forec+analysis vars with extra dim
# 'type', 0 or 1
if var.ndim == 4:
if not quiet: print(' dealing with var type... '),
v = np.zeros(var.shape[1:], var.dtype)
v[::2] = var[0, ::2, ...]
v[1::2] = var[1, 1::2, ...]
var = v
if not quiet: print('done.')
return var
out = {}
# time:
# all times from analysis file, except last ind which will be
# the last time of forecast file
aFile = varfile(find_v('v2t')) # air temp, for instance
fFile = varfile(find_v('ssr')) # sw rad, for instance
if not quiet: print(' reading "analysis" time from file %s' % aFile)
aTime = netcdf.nctime(aFile, 'time')
aTime.sort() # analysis+forecast files may not have time sorted!!
if not quiet: print(' reading "forecast" time from file %s' % fFile)
fTime = netcdf.nctime(fFile, 'time')
fTime.sort() # this one should be sorted...
time = np.append(aTime, fTime[-1])
out['time'] = time
# calc number of forecast steps stored,nforec (used by accum2avg)
if [fTime[i].hour for i in range(8)] == range(3, 22, 3) + [0]: nforec = 4
elif [fTime[i].hour for i in range(4)] == range(6, 19, 6) + [0]: nforec = 2
else:
if not quiet: print('INTERIM WRONG TIME: cannot n forec steps')
return
if not quiet: print(' ==> n forecast steps = %d' % nforec)
# x,y:
if not quiet: print(' reading x,y from file %s' % files[0])
x = netcdf.use(files[0], 'longitude')
y = netcdf.use(files[0], 'latitude')
x[x > 180] = x[x > 180] - 360
if x.ndim == 1 and y.ndim == 1:
x, y = np.meshgrid(x, y)
# tair [K-->C]
if not quiet: print(' --> T air')
vname = find_v('v2t')
f = varfile(vname)
# time may not be monotonically increasing !!
# when using mix of analysis and forecast variables and steps
sortInds = np.argsort(netcdf.use(f, 'time'))
tair = netcdf.use(f, vname, time=sortInds) - 273.15
tair = check_var_type(tair)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
tair = fill_tend(tair)
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from T dew)')
vname = find_v('v2d')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
Td = netcdf.use(f, vname, time=sortInds) - 273.15
Td = check_var_type(Td)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend... (T dew)')
Td = fill_tend(Td)
T = tair
rhum = relative_humidity(T, Td)
## rhum=((112-0.1*T+Td)/(112+0.9*T))**8
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname = find_v('sp')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
pres = netcdf.use(f, vname, time=sortInds)
pres = check_var_type(pres)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
pres = fill_tend(pres)
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [m --> cm day-1]
if not quiet: print(' --> P rate')
vname = find_v('tp')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
prate = netcdf.use(f, vname, time=sortInds)
prate = check_var_type(prate)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
prate = accum2avg(prate, nforec)
conv = 100 * 86400 # from m s-1 --> cm day-1
#conv= 100*86400/1000. # from kg m-2 s-1 --> cm day-1
prate = prate * conv # cm day-1
if not quiet: print(' fill_t0...')
prate = fill_t0(prate)
prate[prate < 0] = 0
out['prate'] = Data(x, y, prate, 'cm day-1')
# Net shortwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net shortwave flux')
vname = find_v('ssr')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
sw_net = netcdf.use(f, vname, time=sortInds)
sw_net = check_var_type(sw_net)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
sw_net = accum2avg(sw_net, nforec)
if not quiet: print(' fill_t0...')
sw_net = fill_t0(sw_net)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net longwave flux')
vname = find_v('str')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
lw_net = netcdf.use(
f, vname, time=sortInds) * -1 # let us consider positive upward (*-1)
lw_net = check_var_type(lw_net)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_net = accum2avg(lw_net, nforec)
if not quiet: print(' fill_t0...')
lw_net = fill_t0(lw_net)
out['radlw'] = Data(x, y, lw_net, 'W m-2', info='positive upward')
# longwave down:
# can be obtained from clouds!!
if not quiet: print(' --> Down longwave flux')
vname = find_v('strd')
f = varfile(vname)
if f:
sortInds = np.argsort(netcdf.use(f, 'time'))
lw_down = netcdf.use(
f, vname,
time=sortInds) * -1 # let us consider positive upward (*-1)
lw_down = check_var_type(lw_down)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_down = accum2avg(lw_down, nforec)
if not quiet: print(' fill_t0...')
lw_down = fill_t0(lw_down)
out['dlwrf'] = Data(x,
y,
lw_down,
'W m-2',
info='negative... downward')
else:
print('down long wave CANNOT BE USED')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
vname = find_v('v10u')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
uwnd = netcdf.use(f, vname, time=sortInds)
uwnd = check_var_type(uwnd)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
uwnd = fill_tend(uwnd)
vname = find_v('v10v')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
vwnd = netcdf.use(f, vname, time=sortInds)
vwnd = check_var_type(vwnd)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
vwnd = fill_tend(vwnd)
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')
vname = find_v('tcc')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
clouds = netcdf.use(f, vname, time=sortInds)
clouds = check_var_type(clouds)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
clouds = fill_tend(clouds)
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out
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 load_data(f, quiet=0, **kargs):
'''
Loads prognostic variables (temp,salt,u,v,ubar,vbar,zeta) from
netcdf file or opendap server. Also loads lon,lat, depth, and time.
If f is a file, it must include the 1d variables lon,lat and depth;
the 2d variable ssh (zeta) and the 3d variables temp, salt, u and v;
ie, each file must contain data for a simgle time. The file must also
contain the variable time.
If f is a opendap address, it must contain also all these variables
or the ones defined in the input karg settings (DataAccess object)
To deal with the case of variables in different files/opendap addresses,
f can also be a dictionary with keys the variables and values the files
or opendap addresses. In this case, the keys must be:
- temp
- salt
- u
- v
- ssh
- misc, for lon, lat, depth, time and dimensions
or xy for lon,lat and x,ydim; z for depth and zdim, time for time
The output data (dict) is suitable to be used by data2roms, which
interpolates the data to ROMS 3d grid.
Also outputs an error/status string.
kargs:
inds, dict with dimension names/values (where time dim can be integer
or datetime)
settings, DataAccess object
extra, extra misc vars to load [(outKey0,fileVar0),...]
t_units, units of variable time, by default the att units is used
'''
sett = DataAccess()
inds = {}
extra = []
t_units = []
if 'settings' in kargs.keys(): sett = kargs['settings']
if 'inds' in kargs.keys(): inds = kargs['inds']
if 'extra' in kargs.keys(): extra = kargs['extra']
if 't_units' in kargs.keys(): t_units = kargs['t_units']
res = {}
msg = ''
if not isinstance(f, dict) and not f.startswith('http') and not isfile(f):
msg = 'file not found %s' % f
if not quiet: print msg
return res, msg
# load nc files:
if not isinstance(f, dict):
f = {'temp': f, 'salt': f, 'u': f, 'v': f, 'ssh': f, 'misc': f}
if not f.has_key('xy'): f['xy'] = f['misc']
if not f.has_key('z'): f['z'] = f['misc']
if not f.has_key('time'): f['time'] = f['misc']
filesUsed = []
ncUsed = []
for i in f.keys():
if not quiet: print '(%s) loading from %s' % (i.ljust(5), f[i])
if i == 'temp':
if f[i] in filesUsed: ncTemp = ncUsed[filesUsed.index(f[i])]
else:
ncTemp = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncTemp]
elif i == 'salt':
if f[i] in filesUsed: ncSalt = ncUsed[filesUsed.index(f[i])]
else:
ncSalt = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncSalt]
elif i == 'u':
if f[i] in filesUsed: ncU = ncUsed[filesUsed.index(f[i])]
else:
ncU = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncU]
elif i == 'v':
if f[i] in filesUsed: ncV = ncUsed[filesUsed.index(f[i])]
else:
ncV = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncV]
elif i == 'ssh':
if f[i] in filesUsed: ncSsh = ncUsed[filesUsed.index(f[i])]
else:
ncSsh = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncSsh]
elif i == 'xy':
if f[i] in filesUsed: ncXy = ncUsed[filesUsed.index(f[i])]
else:
ncXy = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncXy]
elif i == 'z':
if f[i] in filesUsed: ncZ = ncUsed[filesUsed.index(f[i])]
else:
ncZ = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncZ]
elif i == 'time':
if f[i] in filesUsed: ncTime = ncUsed[filesUsed.index(f[i])]
else:
ncTime = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncTime]
elif i == 'misc':
if f[i] in filesUsed: ncMisc = ncUsed[filesUsed.index(f[i])]
else:
ncMisc = netcdf.ncopen(f[i])
filesUsed += [f[i]]
ncUsed += [ncMisc]
# load dims:
if not quiet: print ' loading dims...'
dimsXy = netcdf.fdim(ncXy)
dimsZ = netcdf.fdim(ncZ)
res['NX'] = dimsXy[sett.xdim]
res['NY'] = dimsXy[sett.ydim]
###if sett.z_name:
if sett.zdim:
res['NZ'] = dimsZ[sett.zdim]
else:
res['NZ'] = 1
# about horizontal inds:
if inds.has_key(
sett.xdim) and len(inds[sett.xdim]) == 2 and not isinstance(
inds[sett.xdim], basestring):
if not quiet: print ' calc horizontal inds...'
xlim = inds[sett.xdim]
ylim = inds[sett.ydim]
inds.pop(sett.xdim)
inds.pop(sett.ydim)
lon = netcdf.use(ncXy, sett.x_name, **inds)
if np.any(lon > 360): lon = np.mod(lon, 360.)
lat = netcdf.use(ncXy, sett.y_name, **inds)
i0, i1, j0, j1 = calc.ij_limits(lon, lat, xlim, ylim, margin=3)
inds[sett.xdim] = '%d:%d' % (i0, i1)
inds[sett.ydim] = '%d:%d' % (j0, j1)
if not quiet: print ' loading lon, lat, depth...'
res['lon'] = netcdf.use(ncXy, sett.x_name, **inds)
if np.any(res['lon'] > 360): res['lon'] = np.mod(res['lon'], 360.)
res['lat'] = netcdf.use(ncXy, sett.y_name, **inds)
if sett.z_name:
res['depth'] = -netcdf.use(ncZ, sett.z_name, **inds)
else:
res['depth'] = False
if res['lon'].size != res['lat'].size:
res['lon'], res['lat'] = np.meshgrid(res['lon'], res['lat'])
# needed for griddata, later
# update nx,ny:
if inds.has_key(sett.xdim):
res['NY'], res['NX'] = res['lon'].shape
# extra misc vars:
if len(extra):
for outKey, fileVar in extra:
if not quiet:
print ' loading extra misc... %s %s' % (outKey, fileVar)
res[outKey] = netcdf.use(ncMisc, fileVar, **inds)
# time:
# file may have one or several times. If several, time dim must be given
# with kargs inds!
# but file may also have no time dim or time name !
if sett.time_name:
if not quiet: print ' loading time...'
if t_units:
times = netcdf.use(ncTime, sett.time_name)
times = netcdf.num2date(times, t_units)
else:
times = netcdf.nctime(ncTime, sett.time_name)
if inds.has_key(sett.tdim):
try:
tind = dts.parse_date(inds[sett.tdim])
except:
tind = inds[sett.tdim] # is an integer, for instance
if isinstance(tind, datetime.datetime):
tind, = np.where(times == tind)
if tind.size:
tind = tind[0]
inds[sett.
tdim] = tind # update inds to extract other variables
else:
Msg = 'date not found'
msg += '\n' + Msg
return res, msg + ' ERROR'
date = times[tind]
try:
len(date)
ndates = True
except:
ndates = False
if ndates:
if not quiet:
print ' tind, date= len=%d: %d to %d, %s to %s' % (
len(date), tind[0], tind[-1], date[0].isoformat(' '),
date[-1].isoformat(' '))
else:
if not quiet:
print ' tind, date= %d %s' % (tind, date.isoformat(' '))
elif times.size == 1:
date = times[0]
if not quiet: print ' date= %s' % date.isoformat(' ')
else: # must provide tind as input!!
Msg = 'several dates in file... provice tind!'
msg += '\n' + Msg
return res, msg + ' ERROR'
res['date'] = date
else:
if not quiet: print ' warning: not using time !!'
res['date'] = 0
empty3d = np.zeros([res['NZ'], res['NY'], res['NX']])
empty2d = np.zeros([res['NY'], res['NX']])
if 'temp' in f.keys():
if not quiet: print ' loading temp...'
if sett.temp_name in ncTemp.varnames:
res['temp'] = netcdf.use(ncTemp, sett.temp_name, **inds)
else:
Msg = 'var %s not found' % 'temp'
msg += '\n' + Msg
if not quiet: print Msg
res['temp'] = empty3d
if 'salt' in f.keys():
if not quiet: print ' loading salt...'
if sett.salt_name in ncSalt.varnames:
res['salt'] = netcdf.use(ncSalt, sett.salt_name, **inds)
else:
Msg = 'var %s not found' % 'salt'
msg += '\n' + Msg
if not quiet: print Msg
res['salt'] = empty3d
if 'u' in f.keys():
if not quiet: print ' loading u...'
if sett.u_name in ncU.varnames:
res['u'] = netcdf.use(ncU, sett.u_name, **inds)
else:
Msg = 'var %s not found' % 'u'
msg += '\n' + Msg
if not quiet: print Msg
res['u'] = empty3d
if 'v' in f.keys():
if not quiet: print ' loading v...'
if sett.v_name in ncV.varnames:
res['v'] = netcdf.use(ncV, sett.v_name, **inds)
else:
Msg = 'var %s not found' % 'v'
msg += '\n' + Msg
if not quiet: print Msg
res['v'] = empty3d
if 'ssh' in f.keys():
if not quiet: print ' loading ssh...'
if sett.ssh_name in ncSsh.varnames:
res['ssh'] = netcdf.use(ncSsh, sett.ssh_name, **inds)
else:
Msg = 'var %s not found' % 'ssh'
msg += '\n' + Msg
if not quiet: print Msg
res['ssh'] = empty2d
for nc in ncUsed:
try:
nc.close()
except:
pass
return res, msg
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 era5_file_data(files, quiet=False):
'''
ECMWF ERA5 data for ROMS
variables:
# radiation:
msdwlwrf - mean_surface_downward_long_wave_radiation_flux
#msdwswrf - mean_surface_downward_short_wave_radiation_flux -- not needed
msnlwrf - mean_surface_net_long_wave_radiation_flux
msnswrf - mean_surface_net_short_wave_radiation_flux
# rain:
mtpr - mean_total_precipitation_rate
# wind:
u10 - 10m_u_component_of_wind
v10 - 10m_v_component_of_wind
# temp:
t2m - 2m_temperature
d2m - 2m_dewpoint_temperature (for relative humidity)
# pres:
sp - surface_pressure
# clouds:
tcc - 'total_cloud_cover
'''
### # some variables may have different names!
### Vars={}
### Vars['v10u']='v10u','u10'
### Vars['v10v']='v10v','v10'
### Vars['v2t']='v2t','t2m'
### Vars['v2d']='v2d','d2m'
###
#### def find_v(name):
### if name in Vars.keys():
### for v in Vars[name]:
### if varfile(v): return v
### else: return name
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
##3 def check_var_type(var):
### # new interim dataserver provides forec+analysis vars with extra dim
### # 'type', 0 or 1
### if var.ndim==4:
### if not quiet: print(' dealing with var type... '),
### v=np.zeros(var.shape[1:],var.dtype)
### v[::2]=var[0,::2,...]
### v[1::2]=var[1,1::2,...]
### var=v
### if not quiet: print('done.')
###
### return var
out = {}
# time:
File = varfile('t2m') # air temp, for instance
if not quiet: print(' reading time from file %s' % File)
time = netcdf.nctime(File, 'time')
# check if last time at 00h:
if time[-1].hour != 0:
dateEnd = datetime.datetime(time[-1].year, time[-1].month,
time[-1].day) + datetime.timedelta(days=1)
time = np.append(time, dateEnd)
else:
dateEnd = 0
out['time'] = time
### # all times from analysis file, except last ind which will be
###### # the last time of forecast file
### aFile=varfile(find_v('v2t')) # air temp, for instance
###### fFile=varfile(find_v('ssr')) # sw rad, for instance
### if not quiet: print(' reading "analysis" time from file %s' % aFile)
### aTime=netcdf.nctime(aFile,'time')
### aTime.sort() # analysis+forecast files may not have time sorted!!
### if not quiet: print(' reading "forecast" time from file %s' % fFile)
### fTime=netcdf.nctime(fFile,'time')
### fTime.sort() # this one should be sorted...
### time=np.append(aTime,fTime[-1])
### out['time']=time
##
# # calc number of forecast steps stored,nforec (used by accum2avg)
# if [fTime[i].hour for i in range(8)]==range(3,22,3)+[0]: nforec=4
# elif [fTime[i].hour for i in range(4)]==range(6,19,6)+[0]: nforec=2
# else:
# if not quiet: print('INTERIM WRONG TIME: cannot n forec steps')
# return
#
### if not quiet: print(' ==> n forecast steps = %d' % nforec)
# x,y:
if not quiet: print(' reading x,y from file %s' % File)
x = netcdf.use(File, 'longitude')
y = netcdf.use(File, 'latitude')
x[x > 180] = x[x > 180] - 360
if x.ndim == 1 and y.ndim == 1:
x, y = np.meshgrid(x, y)
# tair [K-->C]
if not quiet: print(' --> T air')
vname = 't2m'
f = varfile(vname)
tair = netcdf.use(f, vname) - 273.15
if dateEnd:
if not quiet: print(' fill_tend...')
tair = fill_tend(tair)
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from T dew)')
vname = 'd2m'
f = varfile(vname)
Td = netcdf.use(f, vname) - 273.15
if dateEnd:
if not quiet: print(' fill_tend... (T dew)')
Td = fill_tend(Td)
T = tair
rhum = air_sea.relative_humidity(T, Td)
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname = 'sp'
f = varfile(vname)
pres = netcdf.use(f, vname)
if dateEnd:
if not quiet: print(' fill_tend...')
pres = fill_tend(pres)
out['pres'] = Data(x, y, pres, 'Pa')
def avg_fix_time(v, DT):
'''Fix data to right time in avg rate fields (ie, prev half hour to now)
See:
https://confluence.ecmwf.int/display/CKB/ERA5+data+documentation#ERA5datadocumentation-Meanratesandaccumulations
'''
DTstep = 1
a = DTstep / 2.
b = DT - a
u = np.zeros_like(v)
u[:-1] = (v[:-1] * b + v[1:] * a) / (a + b)
# last one lost, use prev value (from 30 min before)
u[-1] = v[-1]
return u
DT = (time[1] - time[0]).total_seconds() / 3600. # hours
# P rate [kg m-2 s-1 --> cm day-1]
if not quiet: print(' --> P rate')
vname = 'mtpr'
f = varfile(vname)
prate = netcdf.use(f, vname)
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
prate = avg_fix_time(prate, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
prate = fill_tend(prate)
conv = 100 * 86400 / 1000. # from kg m-2 s-1 --> cm day-1
prate = prate * conv # cm day-1
prate[prate < 0] = 0
out['prate'] = Data(x, y, prate, 'cm day-1')
# dt=(TIMe[1]-time[0]).total_seconds()/3600.
# if not quiet: print(' accum to avg at correct time - DTstep=1h - DT=%.2f h'%DT)
# prate2=accum2avg(prate,DT)
# prate2=prate2*1000
# return prate,prate2
# prate=check_var_type(prate)
# if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
# if not quiet: print(' accum2avg...')
# prate=accum2avg(prate,nforec)
# conv= 100*86400 # from m s-1 --> cm day-1
# #conv= 100*86400/1000. # from kg m-2 s-1 --> cm day-1
# prate=prate*conv # cm day-1
# if not quiet: print(' fill_t0...')
# prate=fill_t0(prate)
# prate[prate<0]=0
# out['prate']=Data(x,y,prate,'cm day-1')
#
# return out
# Net shortwave flux [W m-2 --> W m-2]
if not quiet: print(' --> Net shortwave flux')
vname = 'msnswrf'
f = varfile(vname)
sw_net = netcdf.use(f, vname)
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
sw_net = avg_fix_time(sw_net, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
sw_net = fill_tend(sw_net)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2 --> W m-2]
if not quiet: print(' --> Net longwave flux')
vname = 'msnlwrf'
f = varfile(vname)
lw_net = netcdf.use(f, vname) * -1 # positive upward (*-1)
# here vars have roms-agrif signs --> radlw is positive upward!
# conversion to ROMS is done in surface.py
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
lw_net = avg_fix_time(lw_net, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
lw_net = fill_tend(lw_net)
out['radlw'] = Data(x, y, lw_net, 'W m-2', info='positive upward')
# longwave down:
if not quiet: print(' --> Down longwave flux')
vname = 'msdwlwrf'
f = varfile(vname)
lw_down = netcdf.use(f, vname) * -1 # positive upward (*-1)
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
lw_down = avg_fix_time(lw_down, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
lw_down = fill_tend(lw_down)
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')
vname = 'u10'
f = varfile(vname)
uwnd = netcdf.use(f, vname)
vname = 'v10'
f = varfile(vname)
vwnd = netcdf.use(f, vname)
if dateEnd:
if not quiet: print(' fill_tend...')
uwnd = fill_tend(uwnd)
vwnd = fill_tend(vwnd)
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
# speed and stress:
if 0:
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['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--1]:
if not quiet: print(' --> Cloud cover')
vname = 'tcc'
f = varfile(vname)
clouds = netcdf.use(f, vname)
if dateEnd:
if not quiet: print(' fill_tend...')
clouds = fill_tend(clouds)
out['cloud'] = Data(x, y, clouds, 'fraction (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 'missind 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 'missind 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:
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 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)
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 'missind 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
if t[-1]<t1:
dt=t1-t[-1]
dt=dt.days*24+dt.seconds/3600. # hours
print 'missind 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
return var
out={}
# time:
if 0:
time=netcdf.nctime(files['cc'],'time')
# files have diff units !! so, cannot load all times at once!
# thse result will use only units of 1st file!!
else:
time=load_time(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:
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