avg = netCDF4.Dataset(roms.rundir + roms.run_name + '_avg.nc')
lm, km, im, jm = avg.variables['temp'].shape
print "\n\n Computing run average .... may take a while.......\n\n"
if zlev == 0:
print "Computing average for AVG 1\n\n"
umean = avg.variables['u'][1800::10,-1,...].mean(axis=0)
vmean = avg.variables['v'][1800::10,-1,...].mean(axis=0)
umean = griddata(lonu.ravel(), latu.ravel(), umean.ravel(), lon, lat)
vmean = griddata(lonv.ravel(), latv.ravel(), vmean.ravel(), lon, lat)
else:
zu = get_depths(avg, grd, 0, 'u')
zv = get_depths(avg, grd, 0, 'v')
u = np.zeros( (km, im, jm-1) )
v = np.zeros( (km, im-1, jm) )
umean = np.zeros( (im, jm-1) )
vmean = np.zeros( (im-1, jm) )
count = 0
print "\nComputing average for AVG 1"
for l in np.arange(1800, lm, 10):
print " Day %s of %s" %(l, lm)
count += 1
u += avg.variables['u'][l,...]
v += avg.variables['v'][l,...]
u = u / count
latv = grdfile.variables['lat_v'][:]
lonu = grdfile.variables['lon_u'][:]
lonv = grdfile.variables['lon_v'][:]
h = grdfile.variables['h'][:]
print ' \n' + '==> ' + ' READING CHOSEN ROMS OUTPUT NETCDF FILE ...\n' + ' '
outfile = netCDF4.Dataset(roms.rundir + roms.run_name + '_' + filetype + '.nc')
TSbc = []; TSnbuc = []; TN = []; TE = [];
for l in days:
U = outfile.variables['u'][l,...]
V = outfile.variables['v'][l,...]
print " DAY = " + str(l+1)
#km, im, jm = T.shape
zu = get_depths(outfile,grdfile,l,'u')
zv = get_depths(outfile,grdfile,l,'v')
f = latv[:,0]
fsec = near(f,niN)
xN = lonv[0,:]
zN = np.squeeze( zv[:, fsec, :] )
vN = np.squeeze( V[: ,fsec , :] )
fsec = near(f,niS)
xS = lonv[0,:]
zS = np.squeeze( zv[:, fsec, :] )
vS = np.squeeze( V[: ,fsec , :] )
f = lonu[0, :]
fsec = near(f,njE)
print ' \n' + '==> ' + ' READING CHOSEN ROMS OUTPUT NETCDF FILE ...\n' + ' '
outfile = netCDF4.Dataset(roms.rundir + roms.run_name + '_' + filetype + '.nc')
# assigning some variables from ROMS output file
if PLOT == 4:
ZETA = outfile.variables['zeta'][l, ...]
UBAR = outfile.variables['ubar'][l, ...]
VBAR = outfile.variables['vbar'][l, ...]
else:
T = outfile.variables['temp'][l, ...]
S = outfile.variables['salt'][l, ...]
U = outfile.variables['u'][l, ...]
V = outfile.variables['v'][l, ...]
print ' \n' + '==> ' + ' GETTING DEPTHS OF S-LEVELS ...\n' + ' '
km, im, jm = T.shape
zt = get_depths(outfile, grdfile, l, 'temp')
zu = get_depths(outfile, grdfile, l, 'u')
zv = get_depths(outfile, grdfile, l, 'v')
if PLOT == 1 or PLOT == 4:
# setting up map projection
m = Basemap(projection='cyl',
llcrnrlat=latplot[0],
urcrnrlat=latplot[1],
llcrnrlon=lonplot[0],
urcrnrlon=lonplot[1],
lat_ts=0,
resolution='i')
if PLOT == 1:
zmax = -1000 # treshold depth to compute volume transport lonlim = (-40, -36) # latlim = (-17, -14) # NBUC's flow is going to be an average of these meridians # OE2 data - from ROMS diag RUN ################################################### # I'm doing time-average too (4 days) oe2file = '/Users/rsoutelino/rsoutelino/myroms/leste2_run/leste2geo_avg.nc' stabday = 4 # day in which mean KE stabilized ncfile = nc.Dataset(oe2file) grd = nc.Dataset(oe2file[:-6] + 'grd.nc') lon = ncfile.variables['lon_v'][:] lat = ncfile.variables['lat_v'][:] v = ncfile.variables['v'][1:stabday,...]; v = v.mean(axis=0) v = np.ma.masked_where(v > 10, v) z = get_depths(ncfile, grd, stabday-1, 'v') k, i, j = v.shape # slicing sections and computing transports i1, i2, j1, j2 = slice(lon, lat, lonlim, latlim) v = np.squeeze( v[:, j1:j2, i1:i2] ); v = v.mean(axis=1) lon = np.squeeze( lon[j1:j2, i1:i2] ); lon = lon.mean(axis=0) z = np.squeeze( z[:, j1:j2, i1:i2] ); z = z.mean(axis=1) lon.shape = ( 1, np.size(lon) ) lon = lon.repeat(k, axis=0) v = np.ma.masked_where(z < zmax, v) Tv = transp(lon, z, v) if Tv > 0:
print "Computing average for AVG 2\n\n"
umean2 = avg2.variables['u'][::10, -1, ...].mean(axis=0)
vmean2 = avg2.variables['v'][::10, -1, ...].mean(axis=0)
print "Computing average for AVG 3\n\n"
umean3 = avg3.variables['u'][::10, -1, ...].mean(axis=0)
vmean3 = avg3.variables['v'][::10, -1, ...].mean(axis=0)
umean = (umean1 + umean2 + umean3) / 3
vmean = (vmean1 + vmean2 + vmean3) / 3
umean = griddata(lonu.ravel(), latu.ravel(), umean.ravel(), lon, lat)
vmean = griddata(lonv.ravel(), latv.ravel(), vmean.ravel(), lon, lat)
else:
zu = get_depths(avg1, grd, 0, 'u')
zv = get_depths(avg1, grd, 0, 'v')
umean1 = np.zeros((km, im, jm - 1))
vmean1 = np.zeros((km, im - 1, jm))
umean2 = np.zeros((km, im, jm - 1))
vmean2 = np.zeros((km, im - 1, jm))
umean3 = np.zeros((km, im, jm - 1))
vmean3 = np.zeros((km, im - 1, jm))
umean = np.zeros((im, jm - 1))
vmean = np.zeros((im - 1, jm))
count = 0
print "\nComputing average for AVG 1"
for l in np.arange(1800, lm1, 10):
print " Day %s of %s" % (l, lm1)
count += 1
print ' \n' + '==> ' + ' COMPUTING TIME-AVERAGE ...\n' + ' '
T=0; U=0; V=0; W=0;
for l in days:
T = T + outfile.variables['temp'][l,...]
U = U + outfile.variables['u'][l,...]
V = V + outfile.variables['v'][l,...]
#W = W + outfile.variables['w'][l,:-1,...]
T = T / len(days); U = U / len(days); V = V / len(days);
W = W / len(days);
km, im, jm = T.shape
print ' \n' + '==> ' + ' GETTING DEPTHS OF S-LEVELS ...\n' + ' '
zt = get_depths(outfile,grdfile,0,'temp')
zu = get_depths(outfile,grdfile,0,'u')
zv = get_depths(outfile,grdfile,0,'v')
# horizontal ==========================================
print ' \n' + '==> ' + ' INTERPOLATING FROM S --> Z ...\n' + ' '
u = 0*lonu; v = 0*lonv; t = 0*lon; s = 0*lon; w = 0*lon
for a in range (0, im):
for b in range(0, jm):
t[a,b] = np.interp(-nk, zt[:, a, b], T[:, a, b] )
#s[a,b] = np.interp(-nk, zt[:, a, b], S[:, a, b] )
#w[a,b] = np.interp(-nk, zt[:, a, b], W[:, a, b] )
for a in range (0, im):
lonlim = (-40, -36) # latlim = (-17, -14) # NBUC's flow is going to be an average of these meridians # OE2 data - from ROMS diag RUN ################################################### # I'm doing time-average too (4 days) oe2file = '/Users/rsoutelino/rsoutelino/myroms/leste2_run/leste2geo_avg.nc' stabday = 4 # day in which mean KE stabilized ncfile = nc.Dataset(oe2file) grd = nc.Dataset(oe2file[:-6] + 'grd.nc') lon = ncfile.variables['lon_v'][:] lat = ncfile.variables['lat_v'][:] v = ncfile.variables['v'][1:stabday, ...] v = v.mean(axis=0) v = np.ma.masked_where(v > 10, v) z = get_depths(ncfile, grd, stabday - 1, 'v') k, i, j = v.shape # slicing sections and computing transports i1, i2, j1, j2 = slice(lon, lat, lonlim, latlim) v = np.squeeze(v[:, j1:j2, i1:i2]) v = v.mean(axis=1) lon = np.squeeze(lon[j1:j2, i1:i2]) lon = lon.mean(axis=0) z = np.squeeze(z[:, j1:j2, i1:i2]) z = z.mean(axis=1) lon.shape = (1, np.size(lon)) lon = lon.repeat(k, axis=0) v = np.ma.masked_where(z < zmax, v)
lonu = oe2nc.variables['lon_u'][:] latu = oe2nc.variables['lat_u'][:] lonv = oe2nc.variables['lon_v'][:] latv = oe2nc.variables['lat_v'][:] u = oe2nc.variables['u'][stabday-1,] v = oe2nc.variables['v'][stabday-1,...] # masking the arrays u = np.ma.masked_where(u > 10, u) v = np.ma.masked_where(v > 10, v) ku, iu, ju = u.shape kv, iv, jv = v.shape zu = get_depths(oe2nc, grd, stabday-1, 'u') zv = get_depths(oe2nc, grd, stabday-1, 'v') # SLICING THE SECTIONS and COMPUTING TRANSPORTS # South boundary vS = np.squeeze( v[:, 2, :] ) lonS = np.squeeze( lonv[2, :] ) zS = np.squeeze( zv[:, 2, :] ) lonS.shape = ( 1, np.size(lonS) ) lonS = lonS.repeat(ku, axis=0) vSt = np.ma.masked_where(zS < zmax, vS) dx = np.diff(lonS, axis=1) * 111 * 1000 aux = dx[:,0]; aux.shape = (np.size(aux), 1) dx = np.concatenate( (dx, aux), axis=1)
lonu = grd.variables['lon_u'][:]
lonv = grd.variables['lon_v'][:]
h = grd.variables['h'][:]
print ' \n' + '==> ' + ' READING CHOSEN ROMS OUTPUT NETCDF FILE ...\n' + ' '
avg = netCDF4.Dataset(roms.rundir + roms.run_name + '_avg.nc')
lm, km, im, jm = avg.variables['temp'].shape
tmp = avg.variables['temp'][0, 0, ...]
lon, lat, tmp = subset(lonr, latr, tmp, lims[0], lims[1], lims[2], lims[3])
# AVG ===============================================================
zu = get_depths(avg, grd, 0, 'u')
zv = get_depths(avg, grd, 0, 'v')
zt = get_depths(avg, grd, 0, 't')
times = np.arange(1800, lm, 10)
U = np.zeros((times.size, lon.shape[0], lon.shape[1]), dtype=np.float64)
V = np.zeros((times.size, lon.shape[0], lon.shape[1]), dtype=np.float64)
T = np.zeros((times.size, lon.shape[0], lon.shape[1]), dtype=np.float64)
print "\n\n Slicing %s eddy, at %s m, in AVG.... may take a while.......\n\n" % (
eddy, zlev)
if zlev == 0:
count = 0
for l in times:
print "AVG - step %s of %s, at %s m" % (l, times[-1], zlev)
lonu = oe2nc.variables['lon_u'][:] latu = oe2nc.variables['lat_u'][:] lonv = oe2nc.variables['lon_v'][:] latv = oe2nc.variables['lat_v'][:] u = oe2nc.variables['u'][stabday - 1, ] v = oe2nc.variables['v'][stabday - 1, ...] # masking the arrays u = np.ma.masked_where(u > 10, u) v = np.ma.masked_where(v > 10, v) ku, iu, ju = u.shape kv, iv, jv = v.shape zu = get_depths(oe2nc, grd, stabday - 1, 'u') zv = get_depths(oe2nc, grd, stabday - 1, 'v') # SLICING THE SECTIONS and COMPUTING TRANSPORTS # South boundary vS = np.squeeze(v[:, 2, :]) lonS = np.squeeze(lonv[2, :]) zS = np.squeeze(zv[:, 2, :]) lonS.shape = (1, np.size(lonS)) lonS = lonS.repeat(ku, axis=0) vSt = np.ma.masked_where(zS < zmax, vS) dx = np.diff(lonS, axis=1) * 111 * 1000 aux = dx[:, 0] aux.shape = (np.size(aux), 1)
avg3 = netCDF4.Dataset(roms.rundir + roms.run_name + '_avg-3.nc')
lm1, km, im, jm = avg1.variables['temp'].shape
lm2, km, im, jm = avg2.variables['temp'].shape
lm3, km, im, jm = avg3.variables['temp'].shape
tmp = avg1.variables['temp'][0,0,...]
lon, lat, tmp = subset(lonr, latr, tmp, lims[0], lims[1], lims[2], lims[3])
# AVG1 output ===============================================================
zu = get_depths(avg1, grd, 0, 'u')
zv = get_depths(avg1, grd, 0, 'v')
zt = get_depths(avg1, grd, 0, 't')
times = np.arange(1800, lm1, 10)
U1 = np.zeros((times.size, lon.shape[0], lon.shape[1]), dtype=np.float64)
V1 = np.zeros((times.size, lon.shape[0], lon.shape[1]), dtype=np.float64)
T1 = np.zeros((times.size, lon.shape[0], lon.shape[1]), dtype=np.float64)
print "\n\n Slicing %s eddy, at %s m, in AVG-1.... may take a while.......\n\n" %(eddy, zlev)
if zlev == 0:
count = 0
for l in times:
print "AVG-1 - step %s of %s, at %s m" %(l, times[-1], zlev)
print ' \n' + '==> ' + ' COMPUTING TIME-AVERAGE ...\n' + ' '
T=0; S=0; U=0; V=0; W=0;
for l in days:
T = T + outfile.variables['temp'][l,...]
S = S + outfile.variables['salt'][l,...]
U = U + outfile.variables['u'][l,...]
V = V + outfile.variables['v'][l,...]
T = T / len(days); S = S / len(days);
U = U / len(days); V = V / len(days);
km, im, jm = T.shape
print ' \n' + '==> ' + ' GETTING DEPTHS OF S-LEVELS ...\n' + ' '
zt = get_depths(outfile,grdfile,days[0],'temp')
zu = get_depths(outfile,grdfile,days[0],'u')
zv = get_depths(outfile,grdfile,days[0],'v')
# vertical ==============================================
f = latv[:,0]
fsec = near(f,niN)
xN = lonv[0,:]
zN = np.squeeze( zv[:, fsec, :] )
vN = np.squeeze( V[: ,fsec , :] )
tN = np.squeeze( T[: ,fsec , :] )
sN = np.squeeze( S[: ,fsec , :] )
ztN = np.squeeze( zt[: ,fsec , :] )