fna = f_list[t] + '/ocean_avg_0001.nc'
oceanfna = dir0 + fna
oceanfnd = dir0 + fnd
dsa = nc4.Dataset(oceanfna)
dsd = nc4.Dataset(oceanfnd)
if t == 0:
#get grid info from ocean
lonr = dsa['lon_rho'][:]
latr = dsa['lat_rho'][:]
lonr_s = lonr[1:-1, 1:-1]
latr_s = latr[1:-1, 1:-1]
maskr = dsa['mask_rho'][:]
b0 = efun.box_inds(-0.65, 0.0, 44.0, 46.0, oceanfna)
# lonu = dso['lon_u'][:]
# latu = dso['lat_u'][:]
# lonv = dso['lon_v'][:]
# latv = dso['lat_v'][:]
#plot background density
rho = dsa['rho'][:]
seafloor_rho = rho[0, 0, :, :].squeeze()
fig = plt.figure(figsize=(6, 6))
ax = plt.gca()
a_ll = [lonr.min(), 1.2, latr.min(), latr.max()]
rs = np.ma.masked_where(maskr == 0, seafloor_rho)
ax.pcolormesh(lonr, latr, rs, vmin=-30, vmax=30, cmap='RdBu', alpha=.5)
# loop over hours
tta = 0 # averages
for f_dir in f_list:
print(str(tta))
# get arrays for flux claculations from the averages
a_list = os.listdir(dir0 + f_dir)
a_list.sort()
a_list = [x for x in a_list if x[:9] == 'ocean_avg']
for ai in a_list:
fn = dir0 + f_dir + '/' + ai
ds = nc.Dataset(fn)
if tta == 0:
Ind = efun.box_inds(-1.3, 0.02, 44.85, 45.15, fn)
for vn in Ind.keys():
globals()[vn] = Ind[vn]
G, S, T = zrfun.get_basic_info(fn)
h = G['h'][latr0:latr1 + 1, lonr0:lonr1 + 1]
ny, nx = h.shape
tef_q_mouth = np.zeros((ns, nt, ny))
tef_q_plume = np.zeros((ns, nt, nx))
tef_q_shelf = np.zeros((ns, nt, ny))
tef_q_south = np.zeros((ns, nt, nx))
tef_qs_mouth = np.zeros((ns, nt, ny))
tef_qs_plume = np.zeros((ns, nt, nx))
tef_qs_shelf = np.zeros((ns, nt, ny))
# get volume-integrated quantities from the history files
h_list = os.listdir(dir0 + f_dir)
h_list.sort()
h_list = [x for x in h_list if x[:9] == 'ocean_his']
if tt == 0:
pass
else:
h_list = h_list[1:] # drop the zero hour for all but the first day
for hi in h_list:
fn = dir0 + f_dir + '/' + hi
ds = nc.Dataset(fn)
if tt == 0:
Ind = efun.box_inds(-0.6, 1.5, 44.5, 45.5, fn)
for vn in Ind.keys():
globals()[vn] = Ind[vn]
G, S, T = zrfun.get_basic_info(fn)
h = G['h'][latr0:latr1 + 1, lonr0:lonr1 + 1]
dx = G['DX'][latr0:latr1 + 1, lonr0:lonr1 + 1]
dy = G['DY'][latr0:latr1 + 1, lonr0:lonr1 + 1]
da = dx * dy
ny, nx = h.shape
tef_q_mouth0 = np.zeros((ns, nt, ny))
tef_q_plume = np.zeros((ns, nt, nx))
tef_q_shelf = np.zeros((ns, nt, ny))
tef_q_south = np.zeros((ns, nt, nx))
#get ocean background gridfn = '/pmr4/eab32/LiveOcean_ROMS/output/aestus1_base_ae1/f2017.01.01/ocean_his_0001.nc' ds = nc4.Dataset(gridfn) density0 = ds['rho'][0, zind, :, :].squeeze() ds.close() G = zrfun.get_basic_info(gridfn, only_G=True) lonr = G['lon_rho'][:] latr = G['lat_rho'][:] maskr = G['mask_rho'][:] maskuu = G['mask_u'][:] maskvv = G['mask_v'][:] lonr_s = lonr[1:-1, 1:-1] latr_s = latr[1:-1, 1:-1] b0 = efun.box_inds(-0.65, 0.0, 44.0, 46.0, gridfn) b1 = efun.box_inds(-0.2, 1.5, 44.8, 45.2, gridfn) D = pickle.load(open(data_fn, 'rb')) density = D['density_gf'][:] upd = D['u_prsgrd_gf'][:] vpd = D['v_prsgrd_gf'][:] uad = D['u_ageo_gf'][:] vad = D['v_ageo_gf'][:] nt, ny, nx = np.shape(density) maskr_s = maskr[1:-1, 1:-1] maskrr = np.reshape(maskr_s, (1, np.shape(maskr_s)[0], np.shape(maskr_s)[1])) mask0 = np.tile(maskrr, (nt, 1, 1))
alpha=0.7)
plt.colorbar(p)
ax1.axis(a_ll)
ax1.set_xlabel('Longitude', fontweight='bold')
ax1.set_ylabel('Latitude', fontweight='bold')
ax1.text(0.8,
0.9,
'Surface',
transform=ax1.transAxes,
horizontalalignment='center',
verticalalignment='center')
for lat in range(len(l)):
if l_list[lat] == 45.0:
bb = efun.box_inds(-0.65, 1.5, 44.0, 46.0, oceanfna)
else:
bb = efun.box_inds(-0.65, 0.0, 44.0, 46.0, oceanfna)
ax1.plot(lonr[l[lat], :bb['lonr1']],
latr[l[lat], :bb['lonr1']],
'k--',
linewidth=3.0)
#plot density crossection
ax = plt.subplot2grid((llen, 2), (lat, 1), colspan=1, rowspan=1)
rz = rho[:, l[lat], :]
xx, zz = np.meshgrid(lonvec[lat, :], zvec[:, lat, 0])
Nlist = np.linspace(vmin, vmax, 15)
ax.pcolormesh(xx,
zvec[:, lat, :],
def TEF_line(lon, t):
Ldir = Lfun.Lstart('aestus1', 'base')
Ldir['gtagex'] = Ldir['gtag'] + '_' + 'ae1'
Ldirroms = '/pmr4/eab32/LiveOcean_ROMS/'
dir0 = Ldirroms + 'output/' + Ldir['gtagex'] + '/'
f_list = os.listdir(dir0)
f_list.sort()
f_list = [x for x in f_list if x[0] == 'f']
if len(t) == 1:
f_list = f_list[t:]
if len(t) == 2:
f_list = f_list[t[0]:t[1] + 1] # control number of days
else:
print('t must be of length 1 or 2')
print('Defaulting to use whole f_list')
# number of time steps for averages and diagnostics
nt = len(f_list) * 24
# initialize result arrays for average files
# 0 and 1 mean ocean and river ends
# initialize intermediate results arrays for TEF quantities
sedges = np.linspace(0, 35, 1001) # original was 35*20 + 1
sbins = sedges[:-1] + np.diff(sedges) / 2
ns = len(sbins) # number of salinity bins
# loop over hours
tta = 0 # averages
for f_dir in f_list:
print(str(tta))
# get arrays for flux claculations from the averages
a_list = os.listdir(dir0 + f_dir)
a_list.sort()
a_list = [x for x in a_list if x[:9] == 'ocean_avg']
for ai in a_list:
fn = dir0 + f_dir + '/' + ai
ds = nc.Dataset(fn)
if tta == 0:
Ind = efun.box_inds(lon, 1.5, 44.9, 45.1, fn)
for vn in Ind.keys():
globals()[vn] = Ind[vn]
# print(vn)
G, S, T = zrfun.get_basic_info(fn)
h = G['h'][latr0:latr1 + 1, lonr0:lonr1 + 1]
ny, nx = h.shape
tef_q0 = np.zeros((ns, nt, ny))
tef_q1 = np.zeros((ns, nt, ny))
tef_qs0 = np.zeros((ns, nt, ny))
tef_qs1 = np.zeros((ns, nt, ny))
# getting fluxes of volume and salt
dq0 = ds['Huon'][0, :, latu0:latu1 + 1, lonu0].squeeze()
dq1 = ds['Huon'][0, :, latu0:latu1 + 1, lonu1 + 1].squeeze()
dqs0 = ds['Huon_salt'][0, :, latu0:latu1 + 1, lonu0].squeeze()
dqs1 = ds['Huon_salt'][0, :, latu0:latu1 + 1, lonu1 + 1].squeeze()
# then get the salinity averaged onto the u-grid on both open boundaries
s0 = (ds['salt'][0, :, latr0:latr1 + 1, lonr0 - 1].squeeze() +
ds['salt'][0, :, latr0:latr1 + 1, lonr0].squeeze()) / 2
s1 = (ds['salt'][0, :, latr0:latr1 + 1, lonr1].squeeze() +
ds['salt'][0, :, latr0:latr1 + 1, lonr1 + 1].squeeze()) / 2
# TEF variables
# which are also area integrals at ocean and river ends
# try:
for yy in range(ny):
s00 = s0[:, yy].squeeze()
dq00 = dq0[:, yy].squeeze()
dqs00 = dqs0[:, yy].squeeze()
s000 = s00[dq00.mask == False] # flattens the array
dq000 = dq00[dq00.mask == False]
dqs000 = dqs00[dqs00.mask == False]
inds = np.digitize(s000, sedges, right=True)
counter = 0
for ii in inds:
tef_q0[ii - 1, tta, yy] += dq000[counter]
tef_qs0[ii - 1, tta, yy] += dqs000[counter]
counter += 1
s11 = s1[:, yy].squeeze()
dq11 = dq1[:, yy].squeeze()
dqs11 = dqs1[:, yy].squeeze()
s111 = s11[s11.mask == False]
dq111 = dq11[dq11.mask == False]
dqs111 = dqs11[dqs11.mask == False]
inds = np.digitize(s111, sedges, right=True)
counter = 0
for ii in inds:
# at each time step these are vectors of hourly transport in
# salinity bins (centered at sbins)
tef_q1[ii - 1, tta, yy] += dq111[counter]
tef_qs1[ii - 1, tta, yy] += dqs111[counter]
counter += 1
ds.close()
tta += 1
#%% TEF processing
# first form tidal averages
tef_q0_lp = np.mean(tef_q0, axis=1)
tef_q1_lp = np.mean(tef_q1, axis=1)
tef_qs0_lp = np.mean(tef_qs0, axis=1)
tef_qs1_lp = np.mean(tef_qs1, axis=1)
# start by making the low-passed flux arrays sorted
# from high to low salinity
rq0 = np.flipud(tef_q0_lp)
rqs0 = np.flipud(tef_qs0_lp)
# then form the cumulative sum (the functon Q(s))
qcs = np.cumsum(rq0, axis=0)
# and find its maximum: this is Qin, and the salinity
# at which it occurs is the "dividing salinity" between
# inflow and outflow that we will use to calculate
# all TEF quantities
imax = np.argmax(qcs, axis=0)
Qin0 = np.zeros(ny)
QSin0 = np.zeros(ny)
Qout0 = np.zeros(ny)
QSout0 = np.zeros(ny)
for yy in range(ny):
Qin0[yy] = np.sum(rq0[:imax[yy], yy], axis=0)
Qout0[yy] = np.sum(rq0[imax[yy]:, yy], axis=0)
QSin0[yy] = np.sum(rqs0[:imax[yy], yy], axis=0)
QSout0[yy] = np.sum(rqs0[imax[yy]:, yy], axis=0)
# then fix masking so that the nan's from the low-pass are retained
nmask = np.isnan(tef_q0_lp[0, :])
Qin0[nmask] = np.nan
QSin0[nmask] = np.nan
Qout0[nmask] = np.nan
QSout0[nmask] = np.nan
# form derived quantities
Sin0 = QSin0 / Qin0
Sout0 = QSout0 / Qout0
# same steps for the river end
# OK in this case to do this the original way because
# it is all freshwater.
qin = tef_q1_lp.copy()
qout = tef_q1_lp.copy()
qsin = tef_qs1_lp.copy()
qsout = tef_qs1_lp.copy()
#
qin[tef_q1_lp > 0] = 0 # switch signs compared to open boundary 0
qout[tef_q1_lp < 0] = 0
qsin[tef_q1_lp > 0] = 0
qsout[tef_q1_lp < 0] = 0
#
Qin1 = qin.sum(axis=0)
Qout1 = qout.sum(axis=0)
QSin1 = qsin.sum(axis=0)
QSout1 = qsout.sum(axis=0)
#
#Sin1 = QSin1/Qin1
# Make a dictionary to return variables of interest efficiently
D = dict()
D_list = ['ny', 'Qin0', 'Qout0', 'Sin0', 'Sout0'] #,
#'Qin1']
for vn in D_list:
D[vn] = locals()[vn]
return D
#get grid info from ocean
G, S, T = zrfun.get_basic_info(oceanfnd)
lonu = G['lon_u'][:]
lonv = G['lon_v'][:]
lonr = G['lon_rho'][:]
latu = G['lat_u'][:]
latv = G['lat_v'][:]
latr = G['lat_rho'][:]
masku = G['mask_u'][:]
maskr = G['mask_rho'][:]
maskv = G['mask_v'][:]
a_ll = [-1.0, 0.02, latr.min(), latr.max()]
bb = efun.box_inds(-0.65, -0.1, 44.2, 45.8, oceanfna)
bu = np.array([[bb['lonu0'], bb['lonu1']], [bb['latu0'], bb['latu1']]])
bv = np.array([[bb['lonv0'], bb['lonv1']], [bb['latv0'], bb['latv1']]])
upmin = u_prsgrd[bu[0, 0]:bu[0, 1], bu[1, 0]:bu[1, 1]].min()
upmax = u_prsgrd[bu[0, 0]:bu[0, 1], bu[1, 0]:bu[1, 1]].max()
upm = np.max([np.abs(upmin), np.abs(upmax)]) * 10
vmin = v[bv[0, 0]:bv[0, 1], bv[1, 0]:bv[1, 1]].min()
vmax = v[bv[0, 0]:bv[0, 1], bv[1, 0]:bv[1, 1]].max()
vm = np.max([np.abs(vmin), np.abs(vmax)]) * 50
for t in range(ndays):
#identify file names for that day
fn_list = os.listdir(dir0 + f_list[t])
fnd = [x for x in fn_list if x[-11:-8] == 'dia']