def get_ij_inds(grd,**kargs):
f=kargs.get('url','http://tds.hycom.org/thredds/dodsC/glb_analysis')
vlon=kargs.get('vlon','Longitude')
vlat=kargs.get('vlat','Latitude')
lon=kargs.get('lon',False)
lat=kargs.get('lat',False)
lon_add=kargs.get('lon_add',-360)
fsave=kargs.get('fsave','ijinds.pickle')
if lon is False:
lon=netcdf.use(f,vlon)
if np.any(lon>360): lon=np.mod(lon,360)
lon+=lon_add
if lat is False:
lat=netcdf.use(f,vlat)
rlon=netcdf.use(grd,'lon_rho')
rlat=netcdf.use(grd,'lat_rho')
xlim=rlon.min(),rlon.max()
ylim=rlat.min(),rlat.max()
from okean import calc
i1,i2,j1,j2=calc.ij_limits(lon,lat,xlim,ylim,margin=1)
i1=i1-2
i2=i2+2
j1=j1-2
j2=j2+2
if fsave:
np.asarray([ i1,i2,j1,j2]).dump(fsave)
print 'saved %s'%fsave
return np.asarray([ i1,i2,j1,j2])
def grid_vicinity(grid, x, y, margin=5, rect=False, retinds=False):
'''
Returns True for x,y points inside roms grid boundary plus margin.
Margin is the number of cells to add around the grid.
if rect is True returns True for all points in the smallest 2d xy grid
(usually a rectangle) around the grid.
In this case,margin is the rectangle margin, ie, in units of xy, not
in units of grid
if retinds, in the case of rect, the rectangle j1,j2 and i1,i2 are
also returned (cond,inds=grid_vicinity(....); i1,i2,j1,j2=inds)
mma, TAMU 2011
'''
xg = netcdf.use(grid, 'lon_rho')
yg = netcdf.use(grid, 'lat_rho')
xlim = xg.min(), xg.max()
ylim = yg.min(), yg.max()
if x.ndim == 1 and y.ndim == 1: x, y = np.meshgrid(x, y)
if rect:
out = np.zeros(x.shape, 'bool')
i1, i2, j1, j2 = calc.ij_limits(x, y, xlim, ylim, margin)
out[j1:j2, i1:i2] = 1
else:
from roms import Grid
g = Grid(grid)
xb, yb = g.border(margin=-margin)
out = calc.inpolygon(x, y, xb, yb)
if rect and retinds: return out, (i1, i2, j1, j2)
else: return out
def grid_vicinity(grid,x,y,margin=5,rect=False,retinds=False):
'''
Returns True for x,y points inside roms grid boundary plus margin.
Margin is the number of cells to add around the grid.
if rect is True returns True for all points in the smallest 2d xy grid
(usually a rectangle) around the grid.
In this case,margin is the rectangle margin, ie, in units of xy, not
in units of grid
if retinds, in the case of rect, the rectangle j1,j2 and i1,i2 are
also returned (cond,inds=grid_vicinity(....); i1,i2,j1,j2=inds)
mma, TAMU 2011
'''
xg=netcdf.use(grid,'lon_rho')
yg=netcdf.use(grid,'lat_rho')
xlim=xg.min(),xg.max()
ylim=yg.min(),yg.max()
if x.ndim==1 and y.ndim==1: x,y=np.meshgrid(x,y)
if rect:
out=np.zeros(x.shape,'bool')
i1,i2,j1,j2=calc.ij_limits(x,y,xlim,ylim,margin)
out[j1:j2,i1:i2]=1
else:
from roms import Grid
g=Grid(grid)
xb,yb=g.border(margin=-margin)
out=calc.inpolygon(x,y,xb,yb)
if rect and retinds: return out,(i1,i2,j1,j2)
else: return out
def get_ij_inds(grd, **kargs):
f = kargs.get('url', 'http://tds.hycom.org/thredds/dodsC/glb_analysis')
vlon = kargs.get('vlon', 'Longitude')
vlat = kargs.get('vlat', 'Latitude')
lon = kargs.get('lon', False)
lat = kargs.get('lat', False)
lon_add = kargs.get('lon_add', -360)
fsave = kargs.get('fsave', 'ijinds.pickle')
if lon is False:
lon = netcdf.use(f, vlon)
if np.any(lon > 360): lon = np.mod(lon, 360)
lon += lon_add
if lat is False:
lat = netcdf.use(f, vlat)
rlon = netcdf.use(grd, 'lon_rho')
rlat = netcdf.use(grd, 'lat_rho')
xlim = rlon.min(), rlon.max()
ylim = rlat.min(), rlat.max()
from okean import calc
i1, i2, j1, j2 = calc.ij_limits(lon, lat, xlim, ylim, margin=1)
i1 = i1 - 2
i2 = i2 + 2
j1 = j1 - 2
j2 = j2 + 2
if fsave:
np.asarray([i1, i2, j1, j2]).dump(fsave)
print 'saved %s' % fsave
return np.asarray([i1, i2, j1, j2])
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 get_lonlat(date,vname,dataset='GOM'): url=gen_url(date,vname,dataset) lon=netcdf.use(url,'Longitude') lat=netcdf.use(url,'Latitude') if dataset=='GLOBAL': lon=np.mod(lon,360)-360 return lon,lat
def get(xlim, ylim):
x = netcdf.use(f, 'lon')
y = netcdf.use(f, 'lat')
i0, i1, j0, j1 = calc.ij_limits(x, y, xlim, ylim)
ix = '%d:%d' % (i0, i1)
iy = '%d:%d' % (j0, j1)
x = netcdf.use(f, 'lon', lon=ix)
y = netcdf.use(f, 'lat', lat=iy)
z = netcdf.use(f, 'z', lon=ix, lat=iy)
x, y = np.meshgrid(x, y)
np.savez('etopo1_madeira.npz', x=x, y=y, z=z)
def roms2swan_grid(grd, exc=9999., label='swan_bathy'):
'''exc (EXCeption) = mask points
'''
x = netcdf.use(grd, 'lon_rho')
y = netcdf.use(grd, 'lat_rho')
m = netcdf.use(grd, 'mask_rho')
h = netcdf.use(grd, 'h')
h[m == 0] = exc
ny, nx = h.shape
fbot = label + '.bot'
fgrd = label + '.grd'
# depths file:
fb = open(fbot, 'w')
for i in range(nx):
for j in range(ny):
fb.write(' ')
fb.write('%12.8f' % h[j, i])
fb.write('\n')
fb.close()
print(' -- created swan depths file %s' % fbot)
# coords file:
#v=np.hstack((x.T.flatten(),y.T.flatten()))
v = np.hstack((x.flatten(), y.flatten()))
np.savetxt(fgrd, v, fmt='%12.6f')
print(' -- created swan computational grid file %s\n' % fgrd)
# add to swan INPUT:
print('\n')
print(
'CGRID CURVILINEAR %d %d EXC 9.999000e+003 9.999000e+003 CIRCLE 36 0.04 1.0 24'
% (nx - 1, ny - 1))
print('READGRID COORDINATES 1 \'%s\' 4 0 0 FREE ' % (fgrd))
print('INPGRID BOTTOM CURVILINEAR 0 0 %d %d EXC 9.999000e+003' %
(nx - 1, ny - 1))
print('READINP BOTTOM 1 \'%s\' 4 0 FREE ' % (fbot))
print('\n')
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 roms2swan_grid(grd,exc=9999.,label='swan_bathy'):
'''exc (EXCeption) = mask points
'''
x=netcdf.use(grd,'lon_rho')
y=netcdf.use(grd,'lat_rho')
m=netcdf.use(grd,'mask_rho')
h=netcdf.use(grd,'h')
h[m==0]=exc
ny,nx=h.shape
fbot=label+'.bot'
fgrd=label+'.grd'
# depths file:
fb=open(fbot,'w')
for i in range(nx):
for j in range(ny):
fb.write(' ')
fb.write('%12.8f'%h[j,i])
fb.write('\n')
fb.close()
print ' -- created swan depths file %s'%fbot
# coords file:
#v=np.hstack((x.T.flatten(),y.T.flatten()))
v=np.hstack((x.flatten(),y.flatten()))
np.savetxt(fgrd,v,fmt='%12.6f')
print ' -- created swan computational grid file %s\n'%fgrd
# add to swan INPUT:
print '\n'
print 'CGRID CURVILINEAR %d %d EXC 9.999000e+003 9.999000e+003 CIRCLE 36 0.04 1.0 24'%(nx-1,ny-1)
print 'READGRID COORDINATES 1 \'%s\' 4 0 0 FREE '%(fgrd)
print 'INPGRID BOTTOM CURVILINEAR 0 0 %d %d EXC 9.999000e+003'%(nx-1,ny-1)
print 'READINP BOTTOM 1 \'%s\' 4 0 FREE '%(fbot)
print '\n'
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')
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 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 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 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)
from okean import netcdf import numpy as np 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')
%matplotlib inline from okean.roms import glider from okean import netcdf import numpy as np import matplotlib.pyplot as plt # <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/20101025T1600_marcoos_ru22_active.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> vmin=30.0 vmax=36.0 z=netcdf.use(fglider,'depth')
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 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:
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=fix_time(ttmp,tair,time[0],time[-1])
print ' ...fixed!'
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 !!!!',
rhum=fix_time(ttmp,rhum,time[0],time[-1])
print ' ...fixed!'
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
def wrf_file_data(file,quiet=False):
'''
WRF data for ROMS
'''
out={}
# time:
if not quiet: print ' --> get time'
time=read_time(file)
out['time']=time
# lon,lat:
if not quiet: print ' --> reading x,y'
x=netcdf.use(file,'XLONG',**{'0': 0})
y=netcdf.use(file,'XLAT',**{'0': 0})
# tair [K-->C]
if not quiet: print ' --> T air'
tair=netcdf.use(file,'T2')-273.15
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [kg/kg --> 0--1]
if not quiet: print ' --> R humidity from QV at 2m'
wv=netcdf.use(file,'Q2') # water vapor mixing ratio at 2m
rhum=wv/air_sea.qsat(tair)
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print ' --> Surface pressure'
pres=netcdf.use(file,'PSFC')
out['pres']=Data(x,y,pres,'Pa')
# P rate [mm --> cm day-1]
if not quiet: print ' --> P rate (rainc+rainnc)'
rainc = netcdf.use(file,'RAINC')
rainnc = netcdf.use(file,'RAINNC')
prate=rainc+rainnc
if not quiet: print ' accum2avg...'
prate=accum2avg(prate,dt=time[1]-time[0]) # mm s-1
conv= 0.1*86400 # from mm s-1 --> cm day-1
prate=prate*conv # cm day-1
prate[prate<0]=0 # interpolation errors may result in negative rain!
out['prate']=Data(x,y,prate,'cm day-1')
# LW, SW, latent, sensible signs:
# positive (downward flux, heating) or negative (upward flux, cooling)
#https://www.myroms.org/forum/viewtopic.php?f=1&t=2621
# Net shortwave flux [W m-2]
if not quiet: print ' --> Net shortwave flux'
sw_down=netcdf.use(file,'SWDOWN')
albedo=netcdf.use(file,'ALBEDO')
sw_net=sw_down*(1-albedo)
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print ' --> Net longwave flux'
lw_down=netcdf.use(file,'GLW') # positive
# sst needed:
if not quiet: print ' --> SST for LW up'
sst=netcdf.use(file,'SST') # K
lw_net = air_sea.lwhf(sst,lw_down) # positive down
# here vars have roms-agrif signs --> radlw is positive upward!
#conversion to ROMS is done in surface.py
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='positive upward')
# U and V wind speed 10m
if not quiet: print ' --> U and V wind'
uwnd=netcdf.use(file,'U10')
vwnd=netcdf.use(file,'V10')
if not quiet: print ' --> calc wind speed and stress'
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--1]:
if not quiet: print ' --> Cloud cover for LONGWAVE. Use LONGWAVE_OUT instead...'
if 'CLDFRA' in netcdf.varnames(file):
clouds=netcdf.use(file,'CLDFRA').sum(-3)
clouds=np.where(clouds>1,1,clouds)
else:
if not quiet: print 'CLDFRA not found!! Using SST and air_sea.clouds'
sst=netcdf.use(f,'SST')
clouds=air_sea.clouds(lw_net,sst,tair,rhum,Wtype='net')
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
return out
def read_time(file): return parse_time(netcdf.use(file,'Times'))
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()
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 !!!!',
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 !!'
def use(self,varname,**kargs): return netcdf.use(self.nc,varname,**kargs)
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 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_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 ww3_specpoints(romsgrd, nseg=(5, 5), addxy=(.001, .001)):
gx = netcdf.use(romsgrd, 'lon_rho')
gy = netcdf.use(romsgrd, 'lat_rho')
mask = netcdf.use(romsgrd, 'mask_rho')
eta, xi = gx.shape
if 0: # use range (step=nseg)
spec_res = nseg
dxi, deta = spec_res
ix = range(0, xi, dxi) + [xi - 1]
iy = range(0, eta, deta) + [eta - 1]
else: # use linspace (nseg=n segments)
nNS, nEW = nseg
ix = np.round(np.linspace(0, xi - 1, nNS)).astype('i')
iy = np.round(np.linspace(0, eta - 1, nEW)).astype('i')
ix, iy = np.meshgrid(ix, iy)
ix = calc.var_border(ix)
iy = calc.var_border(iy)
# unsorted unique:
_, i = np.unique(ix + iy * 1j, return_index=True)
i = np.sort(i)
ix = ix[i]
iy = iy[i]
# add 1st:
ix = np.append(ix, ix[0])
iy = np.append(iy, iy[0])
# create segments:
segx = []
segy = []
for i in range(len(ix) - 1):
I = ix[i], ix[i + 1]
J = iy[i], iy[i + 1]
i0, i1 = np.min(I), np.max(I)
j0, j1 = np.min(J), np.max(J)
if i0 == i1: mseg = mask[j0:j1, i0]
else: mseg = mask[j0, i0:i1]
if 1:
# not use fully masked segments:
if np.all(mseg == 0):
print 'masked segment %d %d %d %d' % (i0, j0, i1, j1)
continue
else:
# not use if segment starts with mask:
if mseg.size and mseg[0] == 0:
print 'masked 1st point of segment %d %d %d %d' % (i0, j0, i1,
j1)
continue
segx += [[i0, i1]]
segy += [[j0, j1]]
# XY and IJ:
XY = []
IJ = []
for i in range(len(segx)):
i0, i1 = segx[i][0], segx[i][1]
j0, j1 = segy[i][0], segy[i][1]
IJ += [[i0, j0, i1, j1]]
XY += [[gx[j0, i0], gy[j0, i0], gx[j1, i1], gy[j1, i1]]]
IJ = np.asarray(IJ)
XY = np.asarray(XY)
# got to be sure the point is inside the comp grid avoiding the message:
# "** Error : Boundary point outside comp. grid"
if not addxy is False:
XY = in_comp_grid(XY, IJ, addxy)
return XY, IJ
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 ww3_specpoints(romsgrd,nseg=(5,5),addxy=(.001,.001)):
gx=netcdf.use(romsgrd,'lon_rho')
gy=netcdf.use(romsgrd,'lat_rho')
mask=netcdf.use(romsgrd,'mask_rho')
eta,xi=gx.shape
if 0: # use range (step=nseg)
spec_res=nseg
dxi,deta=spec_res
ix=range(0,xi,dxi)+[xi-1]
iy=range(0,eta,deta)+[eta-1]
else: # use linspace (nseg=n segments)
nNS,nEW=nseg
ix=np.round(np.linspace(0,xi-1,nNS)).astype('i')
iy=np.round(np.linspace(0,eta-1,nEW)).astype('i')
ix,iy=np.meshgrid(ix,iy)
ix=calc.var_border(ix)
iy=calc.var_border(iy)
# unsorted unique:
_,i=np.unique(ix+iy*1j,return_index=True)
i=np.sort(i)
ix=ix[i]
iy=iy[i]
# add 1st:
ix=np.append(ix,ix[0])
iy=np.append(iy,iy[0])
# create segments:
segx=[]
segy=[]
for i in range(len(ix)-1):
I=ix[i],ix[i+1]
J=iy[i],iy[i+1]
i0,i1=np.min(I),np.max(I)
j0,j1=np.min(J),np.max(J)
if i0==i1: mseg=mask[j0:j1,i0]
else: mseg=mask[j0,i0:i1]
if 1:
# not use fully masked segments:
if np.all(mseg==0):
print 'masked segment %d %d %d %d'%(i0,j0,i1,j1)
continue
else:
# not use if segment starts with mask:
if mseg.size and mseg[0]==0:
print 'masked 1st point of segment %d %d %d %d'%(i0,j0,i1,j1)
continue
segx+=[[i0,i1]]
segy+=[[j0,j1]]
# XY and IJ:
XY=[]
IJ=[]
for i in range(len(segx)):
i0,i1=segx[i][0],segx[i][1]
j0,j1=segy[i][0],segy[i][1]
IJ+=[[i0,j0,i1,j1]]
XY+=[[gx[j0,i0], gy[j0,i0], gx[j1,i1], gy[j1,i1]]]
IJ=np.asarray(IJ)
XY=np.asarray(XY)
# got to be sure the point is inside the comp grid avoiding the message:
# "** Error : Boundary point outside comp. grid"
if not addxy is False:
XY=in_comp_grid(XY,IJ,addxy)
return XY,IJ
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 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 read_time(file):
return parse_time(netcdf.use(file, 'Times'))
def narr_file_data(fname,xlim=False,ylim=False,quiet=False):
'''
Returns bulk data from one NARR file
'''
out={}
# loading grid:
if 0:
if not quiet: print(' reading lon,lat from file %s' % grd)
nc=netcdf.ncopen(grd)
x=nc.vars['East_longitude_0-360'][0,...]-360.
y=nc.vars['Latitude_-90_to_+90'][0,...] # time always 1 !!
nc.close()
else:
if not quiet: print(' reading lon,lat from file %s' % grdTxt)
x,y=load_grid()
#x=x-360.
x=-x
ny,nx=x.shape
if (xlim,ylim)==(False,False):i0,i1,j0,j1=0,nx,0,ny
else:
i0,i1,j0,j1=calc.ij_limits(x, y, xlim, ylim, margin=0)
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
try:
nc=netcdf.ncopen(fname)
except:
return {}
xx=str(i0)+':'+str(i1)
yy=str(j0)+':'+str(j1)
tdim=netcdf.fdim(nc,'time1')
if tdim!=1: print('WARNING: tdim !=1 !!!!!!')
# T surface [K->C]
if not quiet: print(' --> T air')
tair=netcdf.use(nc,'Temperature_surface',time1=0,x=xx,y=yy)
tair=tair-273.15
out['tair']=cb.Data(x,y,tair,'C')
# R humidity [% -> 0--1]
if not quiet: print(' --> R humidity')
rhum=netcdf.use(nc,'Relative_humidity',time1=0,x=xx,y=yy)
out['rhum']=cb.Data(x,y,rhum/100.,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
pres=netcdf.use(nc,'Pressure_surface',time1=0,x=xx,y=yy)
out['pres']=cb.Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
prate=netcdf.use(nc,'Precipitation_rate',time1=0,x=xx,y=yy)
prate=prate*86400*100/1000.
out['prate']=cb.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(nc,'Downward_shortwave_radiation_flux',time1=0,x=xx,y=yy)
if not quiet: print(' SW up')
sw_up=netcdf.use(nc,'Upward_short_wave_radiation_flux_surface',time1=0,x=xx,y=yy)
sw_net=sw_down-sw_up
out['radsw']=cb.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(nc,'Downward_longwave_radiation_flux',time1=0,x=xx,y=yy)
if not quiet: print(' LW up')
lw_up=netcdf.use(nc,'Upward_long_wave_radiation_flux_surface',time1=0,x=xx,y=yy)
lw_net=lw_down-lw_up
out['radlw']=cb.Data(x,y,-lw_net,'W m-2',info='positive upward')
# downward lw:
out['dlwrf']=cb.Data(x,y,-lw_down,'W m-2',info='negative... downward')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
# vertical dim is height_above_ground1: 10 and 30 m
uwnd=netcdf.use(nc,'u_wind_height_above_ground',height_above_ground1=0,time1=0,x=xx,y=yy)
vwnd=netcdf.use(nc,'v_wind_height_above_ground',height_above_ground1=0,time1=0,x=xx,y=yy)
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']=cb.Data(x,y,speed,'m s-1')
out['uwnd']=cb.Data(x,y,uwnd,'m s-1')
out['vwnd']=cb.Data(x,y,vwnd,'m s-1')
out['sustr']=cb.Data(x,y,taux,'Pa')
out['svstr']=cb.Data(x,y,tauy,'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
clouds=netcdf.use(nc,'Total_cloud_cover',time1=0,x=xx,y=yy)
out['cloud']=cb.Data(x,y,clouds/100.,'fraction (0--1)')
nc.close()
return out
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 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 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 use(self,varname,**kargs): return netcdf.use(self.name,varname,**kargs)
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 narr_file_data(fname, xlim=False, ylim=False, quiet=False):
'''
Returns bulk data from one NARR file
'''
out = {}
# loading grid:
if 0:
if not quiet: print ' reading lon,lat from file %s' % grd
nc = netcdf.ncopen(grd)
x = nc.vars['East_longitude_0-360'][0, ...] - 360.
y = nc.vars['Latitude_-90_to_+90'][0, ...] # time always 1 !!
nc.close()
else:
if not quiet: print ' reading lon,lat from file %s' % grdTxt
x, y = load_grid()
#x=x-360.
x = -x
ny, nx = x.shape
if (xlim, ylim) == (False, False): i0, i1, j0, j1 = 0, nx, 0, ny
else:
i0, i1, j0, j1 = calc.ij_limits(x, y, xlim, ylim, margin=0)
x = x[j0:j1, i0:i1]
y = y[j0:j1, i0:i1]
try:
nc = netcdf.ncopen(fname)
except:
return {}
xx = str(i0) + ':' + str(i1)
yy = str(j0) + ':' + str(j1)
tdim = netcdf.fdim(nc, 'time1')
if tdim != 1: print 'WARNING: tdim !=1 !!!!!!'
# T surface [K->C]
if not quiet: print ' --> T air'
tair = netcdf.use(nc, 'Temperature_surface', time1=0, x=xx, y=yy)
tair = tair - 273.15
out['tair'] = cb.Data(x, y, tair, 'C')
# R humidity [% -> 0--1]
if not quiet: print ' --> R humidity'
rhum = netcdf.use(nc, 'Relative_humidity', time1=0, x=xx, y=yy)
out['rhum'] = cb.Data(x, y, rhum / 100., '0--1')
# surface pressure [Pa]
if not quiet: print ' --> Surface pressure'
pres = netcdf.use(nc, 'Pressure_surface', time1=0, x=xx, y=yy)
out['pres'] = cb.Data(x, y, pres, 'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print ' --> P rate'
prate = netcdf.use(nc, 'Precipitation_rate', time1=0, x=xx, y=yy)
prate = prate * 86400 * 100 / 1000.
out['prate'] = cb.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(nc,
'Downward_shortwave_radiation_flux',
time1=0,
x=xx,
y=yy)
if not quiet: print ' SW up'
sw_up = netcdf.use(nc,
'Upward_short_wave_radiation_flux_surface',
time1=0,
x=xx,
y=yy)
sw_net = sw_down - sw_up
out['radsw'] = cb.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(nc,
'Downward_longwave_radiation_flux',
time1=0,
x=xx,
y=yy)
if not quiet: print ' LW up'
lw_up = netcdf.use(nc,
'Upward_long_wave_radiation_flux_surface',
time1=0,
x=xx,
y=yy)
lw_net = lw_down - lw_up
out['radlw'] = cb.Data(x, y, -lw_net, 'W m-2', info='positive upward')
# downward lw:
out['dlwrf'] = cb.Data(x,
y,
-lw_down,
'W m-2',
info='negative... downward')
# U and V wind speed 10m
if not quiet: print ' --> U and V wind'
# vertical dim is height_above_ground1: 10 and 30 m
uwnd = netcdf.use(nc,
'u_wind_height_above_ground',
height_above_ground1=0,
time1=0,
x=xx,
y=yy)
vwnd = netcdf.use(nc,
'v_wind_height_above_ground',
height_above_ground1=0,
time1=0,
x=xx,
y=yy)
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'] = cb.Data(x, y, speed, 'm s-1')
out['uwnd'] = cb.Data(x, y, uwnd, 'm s-1')
out['vwnd'] = cb.Data(x, y, vwnd, 'm s-1')
out['sustr'] = cb.Data(x, y, taux, 'Pa')
out['svstr'] = cb.Data(x, y, tauy, 'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print ' --> Cloud cover'
clouds = netcdf.use(nc, 'Total_cloud_cover', time1=0, x=xx, y=yy)
out['cloud'] = cb.Data(x, y, clouds / 100., 'fraction (0--1)')
nc.close()
return out
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 extract(self,varname,method='fast',nfast=1,quiet=1):
if not self.roms.hast(varname):
method,nfast='fast',1
tag=self.horiz_var_type(varname)
if tag in self.uinds.keys():
inds0=self.inds0[tag]
uinds=self.uinds[tag]
else:
inds0,uinds=self.locate(varname,quiet)
J,I,T = uinds.T
J0,I0,T0 = inds0.T
if not self.roms.hast(varname): no_time=True
else: no_time=False
if method=='fast': # faster but will download more data than needed!
II=range(I.min(),I.max()+1)
JJ=range(J.min(),J.max()+1)
TT=range(T[T>=0].min(),T.max()+1)
if not calc.isiterable(nfast) and nfast==1:
if not quiet: print('loading %s : ijt= %d %d %d'%(varname,len(II),len(JJ),len(TT)))
v=netcdf.use(self.nc,varname,xiSEARCH=II,etaSEARCH=JJ,ocean_time=TT)
else:
v=False
if not calc.isiterable(nfast):
nfast=min(nfast,len(TT)-1)
tmp=range(TT[0],TT[-1]+2,(TT[-1]-TT[0])/nfast)
if tmp[-1]<TT[-1]+1: tmp+=[TT[-1]+1]
else: tmp=nfast
for k in range(len(tmp)-1):
tt=range(tmp[k],tmp[k+1])
if not quiet: print('loading %s : ijt= %d %d %d (t %d to %d)'%(varname,len(II),len(JJ),len(tt), tt[0],tt[-1]))
vtmp=netcdf.use(self.nc,varname,xiSEARCH=II,etaSEARCH=JJ,ocean_time=tt)
if not v is False:
if vtmp.ndim<v.ndim: vtmp=vtmp[np.newaxis,:] # if len of last tt is 1 !
v=np.vstack((v,vtmp))
else: v=vtmp
if v.ndim>3:
V=np.ma.zeros((I.size,v.shape[1]),'f')
else:
V=np.ma.zeros(I.size,'f')
for i in range(I.size):
xi = I[i]-I.min()
eta = J[i]-J.min()
if T[i]>=0: tind = T[i]-T[T>=0].min()
else: tind=T[i] # negative means data ouside model time
if v.ndim==4:
V[i]=v[tind,:,eta,xi]
elif v.ndim==3:
V[i]=v[tind,eta,xi]
elif v.ndim==2:
V[i]=v[eta,xi]
else:
V=False
for i in range(len(I)):
if T[i]<0: continue
if not quiet: print('loading %s (%d of %d): ijt= %d %d %d'%(varname,i,len(I),I[i],J[i],T[i]))
v=netcdf.use(self.nc,varname,xiSEARCH=I[i],etaSEARCH=J[i],ocean_time=T[i])
if V is False:
if v.ndim>1: shape=(len(I),)+v.shape
else: shape=len(I)
V=np.ma.zeros(shape,'f')
V[i]=v
lon,lat=self.model_lon_lat(varname)
U=np.array(())
for i in range(len(self.t)):
xi = I0[i]
eta = J0[i]
tind = T0[i]
if tind<0: continue # data outside model time
# rotate cell before interp:
xp=np.asarray([lon[eta,xi],lon[eta,xi+1],lon[eta+1,xi+1],lon[eta+1,xi],self.x[i]])
yp=np.asarray([lat[eta,xi],lat[eta,xi+1],lat[eta+1,xi+1],lat[eta+1,xi],self.y[i]])
xp,yp=calc.rot2d(xp,yp,self.roms.grid.angle[eta,xi])
x=xp[:-1].min(),xp[-1],xp[:-1].max()
y=yp[:-1].min(),yp[-1],yp[:-1].max()
A = x[1]-x[0]
a = x[2]-x[1]
B = y[1]-y[0]
b = y[2]-y[1]
tcond=(T==tind)#|(tind<0)
tcond1=(T==tind+1)#|(tind+1<0)
j0=np.where((I==xi)&(J==eta)&tcond)[0][0]
j1=np.where((I==xi+1)&(J==eta)&tcond)[0][0]
j2=np.where((I==xi+1)&(J==eta+1)&tcond)[0][0]
j3=np.where((I==xi)&(J==eta+1)&tcond)[0][0]
u0=(V[j0]*a*b+V[j1]*A*b+V[j2]*A*B+V[j3]*a*B)/(a*b+A*b+A*B+a*B)
if not no_time:
dt0=self.t[i]-self.roms.time[tind]
dt1=self.roms.time[tind+1]-self.t[i]
dt0=dt0.days*86400+dt0.seconds
dt1=dt1.days*86400+dt1.seconds
k0=np.where((I==xi)&(J==eta)&tcond1)[0][0]
k1=np.where((I==xi+1)&(J==eta)&tcond1)[0][0]
k2=np.where((I==xi+1)&(J==eta+1)&tcond1)[0][0]
k3=np.where((I==xi)&(J==eta+1)&tcond1)[0][0]
u1=(V[k0]*a*b+V[k1]*A*b+V[k2]*A*B+V[k3]*a*B)/(a*b+A*b+A*B+a*B)
u=(u0*dt1+u1*dt0)/(dt1+dt0)
else:
u=u0
if not U.size:
U=np.ma.zeros((len(self.t),u.size),'f')
U=np.ma.masked_where(U==0,U)
U[i]=u
U=np.squeeze(U)
return U
def 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 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('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 wrf_file_data(file, quiet=False):
'''
WRF data for ROMS
'''
out = {}
# time:
if not quiet: print(' --> get time')
time = read_time(file)
out['time'] = time
# lon,lat:
if not quiet: print(' --> reading x,y')
x = netcdf.use(file, 'XLONG', Time=0) #**{'0': 0})
y = netcdf.use(file, 'XLAT', Time=0) #**{'0': 0})
# tair [K-->C]
if not quiet: print(' --> T air')
tair = netcdf.use(file, 'T2') - 273.15
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [kg/kg --> 0--1]
if not quiet: print(' --> R humidity from QV at 2m')
wv = netcdf.use(file, 'Q2') # water vapor mixing ratio at 2m
rhum = wv / air_sea.qsat(tair)
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
pres = netcdf.use(file, 'PSFC')
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [mm --> cm day-1]
if not quiet: print(' --> P rate (rainc+rainnc)')
rainc = netcdf.use(file, 'RAINC')
rainnc = netcdf.use(file, 'RAINNC')
prate = rainc + rainnc
if not quiet: print(' accum2avg...')
prate = accum2avg(prate, dt=time[1] - time[0]) # mm s-1
conv = 0.1 * 86400 # from mm s-1 --> cm day-1
prate = prate * conv # cm day-1
prate[prate < 0] = 0 # interpolation errors may result in negative rain!
out['prate'] = Data(x, y, prate, 'cm day-1')
# LW, SW, latent, sensible signs:
# positive (downward flux, heating) or negative (upward flux, cooling)
#https://www.myroms.org/forum/viewtopic.php?f=1&t=2621
# Net shortwave flux [W m-2]
if not quiet: print(' --> Net shortwave flux')
sw_down = netcdf.use(file, 'SWDOWN')
albedo = netcdf.use(file, 'ALBEDO')
sw_net = sw_down * (1 - albedo)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print(' --> Net longwave flux')
lw_down = netcdf.use(file, 'GLW') # positive
# sst needed:
if not quiet: print(' --> SST for LW up')
sst = netcdf.use(file, 'SST') # K
lw_net = air_sea.lwhf(sst, lw_down) # positive down
# here vars have roms-agrif signs --> radlw is positive upward!
#conversion to ROMS is done in surface.py
out['radlw'] = Data(x, y, -lw_net, 'W m-2', info='positive upward')
out['dlwrf'] = Data(x, y, -lw_down, 'W m-2', info='positive upward')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
uwnd = netcdf.use(file, 'U10')
vwnd = netcdf.use(file, 'V10')
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2 + vwnd**2)
taux, tauy = air_sea.wind_stress(uwnd, vwnd)
out['wspd'] = Data(x, y, speed, 'm s-1')
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
out['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--1]:
if not quiet:
print(' --> Cloud cover for LONGWAVE. Use LONGWAVE_OUT instead...')
if 0:
pass
# next code is wrong! If cloud cover is really needed, it needs to be calculated using wrfpost.
# See http://www2.mmm.ucar.edu/wrf/users/docs/user_guide/users_guide_chap8.html#_ARWpost_1
#
# if 'CLDFRA' in netcdf.varnames(file):
# clouds=netcdf.use(file,'CLDFRA').sum(-3)
# clouds=np.where(clouds>1,1,clouds)
else:
if not quiet:
print('CLDFRA not found!! Using SST and air_sea.cloud_fraction')
sst = netcdf.use(file, 'SST') - 273.15
clouds = air_sea.cloud_fraction(lw_net, sst, tair, rhum, Wtype='net')
clouds[clouds < 0] = 0
clouds[clouds > 1] = 1
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out
def use(self,varname,**kargs): return netcdf.use(self.nc,varname,**kargs) def hasz(self,v):