def get_monthly_average_2d(filePrefix, botIndices):
field, itrs, metadata = rdmds(filePrefix, rec=[0], returnmeta=True)
ny, nx = field.shape
# print nx, ny, nz
yearCount = metadata['nrecords'][0] // 12
dims = [12, nx, ny]
mask2D = botIndices == -1
xIndices, yIndices = numpy.meshgrid(numpy.arange(nx),
numpy.arange(ny),
indexing='ij')
monthlyClimatologies = numpy.ma.masked_all(dims)
for month in range(12):
first = True
for year in range(yearCount):
print('{:04d}-{:02d}'.format(year + 2005, month + 1))
recordIndex = year * 12 + month
field = rdmds(filePrefix, rec=[recordIndex])
field = field.transpose(1, 0)
field = numpy.ma.masked_array(field, mask=mask2D)
if first:
monthlyClimatologies[month, :, :] = field / float(yearCount)
first = False
else:
monthlyClimatologies[month, :, :] = \
monthlyClimatologies[month, :, :] + \
field/float(yearCount)
monthlyClimatologies = monthlyClimatologies.transpose(0, 2, 1)
return monthlyClimatologies
def get_dx_dy(folder):
DXC = rdmds(folder + "DXC")
DYC = rdmds(folder + "DYC")
DXG = rdmds(folder + "DXG")
DYG = rdmds(folder + "DYG")
DXC, DYC = rearrange_grid_spacing(DXC, DYC)
DXG, DYG = rearrange_grid_spacing(DXG, DYG)
return DXC, DYC, DXG, DYG
def make_TS_ds(sose_dir, records=None):
'''Reads in SothernOceanStateEstimate Temperatures and Salinities and returns them in a Xarray dataset.'''
#load grid data
print("load grid")
grid_path = os.path.join(sose_dir, 'grid.mat')
grid_raw = sio.loadmat(grid_path)
longitude = grid_raw["XC"][:, 0]
latitude = grid_raw['YC'][0, :]
depth = grid_raw['RC'].squeeze()
DRC = grid_raw['DRC'].squeeze()
maskC = np.flipud(np.rot90(grid_raw['maskCtrlC']))
DXC = np.flipud(np.rot90(grid_raw['DXC']))
DYC = np.flipud(np.rot90(grid_raw['DYC']))
Depth = np.flipud(np.rot90(grid_raw['Depth']))
#load temperature data
print("load temperature")
temp_path = os.path.join(sose_dir, 'THETA_mnthlyBar')
temp_raw = mds.rdmds(temp_path, 100, rec=records, fill_value=np.NaN)
#load salt data
print("load salt")
salt_path = os.path.join(sose_dir, 'SALT_mnthlyBar')
salt_raw = mds.rdmds(salt_path, 100, rec=records, fill_value=np.NaN)
#define array of datetime range
time = pd.period_range('2005-01', periods=len(temp_raw), freq='M')
time_stamp = pd.Timestamp('2005-01')
#construct Xarray dataset
print("construct Xarray dataset")
ds = xr.Dataset(
{
'temperature':
(['time', 'depth', 'latitude', 'longitude'], temp_raw),
'salinity': (['time', 'depth', 'latitude', 'longitude'], salt_raw),
'maskC': (['latitude', 'longitude', 'depth'], maskC),
'DRC': (['depth'], DRC),
'DXC': (['latitude', 'longitude'], DXC),
'DYC': (['latitude', 'longitude'], DYC),
'Depth': (['latitude', 'longitude'], Depth)
},
coords={
'longitude': (('longitude'), longitude),
'latitude': (('latitude'), latitude),
'depth': (('depth'), depth),
'time': (('time'), time),
'reference_time': time_stamp
})
print("done!")
return ds
def get_x_y(folder, do_rearrange=True):
XC = rdmds(folder + "XC")
XG = rdmds(folder + "XG")
YC = rdmds(folder + "YC")
YG = rdmds(folder + "YG")
if do_rearrange:
XC = rearrange(XC)
XG = rearrange(XG)
YC = rearrange(YC)
YG = rearrange(YG)
return XC, YC, XG, YG
# make sure correct location was chosen
print XC[iy0c:iy1c + 1, ixc:ixc + 2]
print YC[iy0c:iy1c + 1, ixc]
print DXC[iy0c:iy1c + 1, ixc]
# loop over hours
for hour in range(start, end + 1):
# file to save record to
savefile = savedir + "rec_{:010d}.npz".format(hour)
# if this step is required
if (not os.path.isfile(savefile)) or force_overwrite:
step = hour * 3600 / dt
print hour, step
# read variables
U = rdmds(diagdir + 'trsp_3d_set1', step, rec=0, lev=0)
V = rdmds(diagdir + 'trsp_3d_set1', step, rec=1, lev=0)
U, V = utils.rearrange_velocities(U, V)
h = rdmds(diagdir + 'state_2d_set1', step, rec=0)
h = utils.rearrange(h)
# save to file
np.savez(savefile,
h=h[iy0c:iy1c + 1, ixc:ixc + 2],
u=U[iy0c:iy1c + 1, ixc:ixc + 3],
v=V[iy0c:iy1c + 2, ixc:ixc + 2])
# run dir
rundir = basedir + 'run_' + name + '/'
# subdir with diagnostics
diagdir = rundir + 'diags/'
# location where diag files are saved
savedir = basedir + 'diag/' + name + '/section_{:04d}/'.format(int(lon))
# create output dir if it doesn't exist yet
if not os.path.isdir(savedir):
os.mkdir(savedir)
# get grid info
XC, YC, XG, YG = utils.get_x_y(rundir)
z = rdmds(rundir + "RC")[:, 0, 0]
z = z[utils.diag_lev]
# find index of nearest model point in rotated orientation
dist = (XC[0, :] - lon)**2
dist[np.isnan(dist)] = 999.
ix = np.argmin(dist)
# make sure correct location was chosen
print XC[0, ix]
# loop over hours
for hour in range(start, end + 1):
# file to save record to
# create output dir if it doesn't exist yet
if not os.path.isdir(savedir):
os.mkdir(savedir)
if not os.path.isdir(savedir + "ins_eke_map/"):
os.mkdir(savedir + "ins_eke_map/")
# save coordinates to file
np.savez(savedir + "eke_map/coord.npz", x=XC, y=YC)
# loop over hours
for hour in range(start, end + 1):
# if this step is required
if not os.path.isfile(savedir + 'ins_eke_map/rec_{0:010d}.npy'.format(hour)
) or force_overwrite:
# get time step
step = hour * 3600 / dt
print hour, step
# read velocities
U = rdmds(diagdir + 'trsp_3d_set1', step, rec=0, lev=level)
V = rdmds(diagdir + 'trsp_3d_set1', step, rec=1, lev=level)
U, V = utils.rearrange_velocities(U, V)
eke = (U**2 + V**2) / 2
# save KE
np.save(savedir + 'ins_eke_map/rec_{0:010d}.npy'.format(hour), eke)
def load_TH_diags(path, sim_type):
"""
Load temperature diagnostics for channel model.
path: where to look look for the data
sim_type: expt or cont
"""
TOTTTEND = xr.open_dataset('{0}/TOTTTEND_{1}.nc'.format(path, sim_type))
ADVx_TH = xr.open_dataset('{0}/ADVx_TH_{1}.nc'.format(path, sim_type))
ADVy_TH = xr.open_dataset('{0}/ADVy_TH_{1}.nc'.format(path, sim_type))
ADVr_TH = xr.open_dataset('{0}/ADVr_TH_{1}.nc'.format(path, sim_type))
DFxE_TH = xr.open_dataset('{0}/DFxE_TH_{1}.nc'.format(path, sim_type))
DFyE_TH = xr.open_dataset('{0}/DFyE_TH_{1}.nc'.format(path, sim_type))
DFrE_TH = xr.open_dataset('{0}/DFrE_TH_{1}.nc'.format(path, sim_type))
DFrI_TH = xr.open_dataset('{0}/DFrI_TH_{1}.nc'.format(path, sim_type))
TFLUX = xr.open_dataset('{0}/TFLUX_{1}.nc'.format(path, sim_type))
WTHMASS = xr.open_dataset('{0}/WTHMASS_{1}.nc'.format(path, sim_type))
TOTTTEND_clim = xr.open_dataset('{0}/TOTTTEND_{1}_clim.nc'.format(
path, sim_type))
ADVx_TH_clim = xr.open_dataset('{0}/ADVx_TH_{1}_clim.nc'.format(
path, sim_type))
ADVy_TH_clim = xr.open_dataset('{0}/ADVy_TH_{1}_clim.nc'.format(
path, sim_type))
ADVr_TH_clim = xr.open_dataset('{0}/ADVr_TH_{1}_clim.nc'.format(
path, sim_type))
DFxE_TH_clim = xr.open_dataset('{0}/DFxE_TH_{1}_clim.nc'.format(
path, sim_type))
DFyE_TH_clim = xr.open_dataset('{0}/DFyE_TH_{1}_clim.nc'.format(
path, sim_type))
DFrE_TH_clim = xr.open_dataset('{0}/DFrE_TH_{1}_clim.nc'.format(
path, sim_type))
DFrI_TH_clim = xr.open_dataset('{0}/DFrI_TH_{1}_clim.nc'.format(
path, sim_type))
TFLUX_clim = xr.open_dataset('{0}/TFLUX_{1}_clim.nc'.format(
path, sim_type))
WTHMASS_clim = xr.open_dataset('{0}/WTHMASS_{1}_clim.nc'.format(
path, sim_type))
RAC_data = mds.rdmds('{0}/40/RAC'.format(path))
DRF_data = np.squeeze(mds.rdmds('{0}/40/DRF'.format(path)))
hFacC_data = mds.rdmds('{0}/40/hFacC'.format(path))
hFacC_temp = xr.DataArray(data=hFacC_data,
coords={
'Z': TOTTTEND.Z,
'YC': TOTTTEND.YC,
'XC': TOTTTEND.XC
},
dims=['Z', 'YC', 'XC'])
hFacC = xr.Dataset(data_vars={'hFacC': hFacC_temp})
DRF_temp = xr.DataArray(data=DRF_data,
coords={'Z': TOTTTEND.Z},
dims=['Z'])
DRF = xr.Dataset(data_vars={'DRF': DRF_temp})
RAC_temp = xr.DataArray(data=RAC_data,
coords={
'YC': TOTTTEND.YC,
'XC': TOTTTEND.XC
},
dims=['YC', 'XC'])
RAC = xr.Dataset(data_vars={'RAC': RAC_temp})
# combine into one dataset
TH_diags = xr.merge([
TOTTTEND, ADVx_TH, ADVy_TH, ADVr_TH, DFxE_TH, DFyE_TH, DFrE_TH,
DFrI_TH, TFLUX, WTHMASS, TOTTTEND_clim, ADVx_TH_clim, ADVy_TH_clim,
ADVr_TH_clim, DFxE_TH_clim, DFyE_TH_clim, DFrE_TH_clim, DFrI_TH_clim,
TFLUX_clim, WTHMASS_clim, hFacC, DRF, RAC
])
return TH_diags
return a, a1, a5
def rearrangeuv(U, V):
U1 = np.reshape(U[:nx,:], (ny,nx))
V1 = np.reshape(V[:nx,:], (ny,nx))
U5 = np.reshape(V[nx:,:], (nx,ny))
V5 = np.roll(np.reshape(-U[nx:,:], (nx,ny)), -1, axis=1)
U = np.concatenate((U5[:,::-1].T, U1), axis=1)
V = np.concatenate((V5[:,::-1].T, V1), axis=1)
return U, V, U1, U5, V1, V5
rundir = '/nobackup1/joernc/patches/NA_2160/run_t1w1/'
diagdir = rundir + 'diags/'
# bathymetry
d = rdmds(rundir + 'Depth')
d[d==0] = np.nan
d, d1, d5 = rearrange(d)
# T, S
T = rdmds(diagdir + 'state_3d_set1', timestep, lev=level, rec=0)
S = rdmds(diagdir + 'state_3d_set1', timestep, lev=level, rec=1)
T[T==0] = np.nan
S[S==0] = np.nan
T, T1, T5 = rearrange(T)
S, S1, S5 = rearrange(S)
# U, V
U = rdmds(diagdir + 'trsp_3d_set1', timestep, lev=level, rec=0)
V = rdmds(diagdir + 'trsp_3d_set1', timestep, lev=level, rec=1)
U[U==0] = np.nan
rundir = basedir + 'run_' + name + '/'
# subdir with diagnostics
diagdir = rundir + 'diags/'
# location where diag files are saved
savedir = basedir + "diag/" + name \
+ "/mooring_{:04d}_{:04d}/".format(int(lon),int(lat))
# create output dir if it doesn't exist yet
if not os.path.isdir(savedir):
os.mkdir(savedir)
# get grid info
XC, YC, XG, YG = utils.get_x_y(rundir, do_rearrange=False)
z = rdmds(rundir + "RC")[:, 0, 0]
# save diag depths to file
np.save(savedir + "depths.npy", z[utils.diag_lev])
# find index of nearest model point (c, fx, fy) in original orientation
dist = (XC - lon)**2 + (YC - lat)**2
dist[np.isnan(dist)] = 999
iyc, ixc = np.unravel_index(np.argmin(dist), dist.shape)
dist = (XG - lon)**2 + (YC - lat)**2
dist[np.isnan(dist)] = 999
iyfx, ixfx = np.unravel_index(np.argmin(dist), dist.shape)
dist = (XC - lon)**2 + (YG - lat)**2
# phixyz=np.reshape(phi,(nx,nt,ny,nr))
# phixy=phixyz[:,:,:,0]
suff = "0000117000"
klev = 50
fldCode = 'T'
if len(sys.argv) > 1:
suff = "%10.10d" % (int(sys.argv[1]))
if len(sys.argv) > 2:
klev = (int(sys.argv[2]))
if len(sys.argv) > 3:
fldCode = sys.argv[3]
T = mds.rdmds("%s%s%s%s" % ('../run/', fldCode, '.', suff))
A = mds.rdmds('../run/RAZ')
Txy = np.reshape(T[klev, :, :], (ny, nt, nx))
# Txy=np.reshape(A,(ny,nt,nx))
plt.figure(1)
plt.clf()
plotxy(Txy)
phiBuf = np.zeros((ny, nx * nt))
phiXYave = np.zeros((nr))
Asum = np.sum(A)
for k in range(nr):
phiBuf = T[k, :, :] * A
phiSum = np.sum(phiBuf)
phiXYave[k] = phiSum / Asum
dist = (XC-lon)**2+(YC-lat0)**2
dist[np.isnan(dist)] = 999
iy0c, ixc = np.unravel_index(np.argmin(dist), dist.shape)
dist = (XC-lon)**2+(YC-lat1)**2
dist[np.isnan(dist)] = 999
iy1c, ixc = np.unravel_index(np.argmin(dist), dist.shape)
# make sure correct location was chosen
print XC[iy0c:iy1c+1,ixc]
print YC[iy0c:iy1c+1,ixc]
# loop over hours
for hour in range(start, end+1):
# file to save record to
savefile = savedir + "rec_{:010d}.npy".format(hour)
# if this step is required
if (not os.path.isfile(savefile)) or force_overwrite:
step = hour*3600/dt
print hour, step
# read variable
h = rdmds(diagdir + 'state_2d_set1', step, rec=0)
h = utils.rearrange(h)
# save to file
np.save(savefile, h[iy0c:iy1c+1,ixc])