def mocsy_3d_getOmA(tsal, ttemp, tdic, tta):
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] = 1
tzero = tpressure * 0
tsra_psu = tsra * 35 / 35.16504
ttera_is = gsw.t_from_CT(tsra, ttera, tzero)
response_tup = mocsy.mvars(temp=ttera_is,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tzero,
lat=tzero,
optcon='mol/m3',
optt='Tinsitu',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
OmegaAR = OmegaA.reshape(40, 898, 398)
return OmegaAR
def oned_moxy(tsal, ttemp, tdic, tta, pres_atm, depth_this):
size_box = np.shape(tdic)
size_0 = size_box[0]
size_1= size_box[1]
size_2 = size_box[2]
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] = pres_atm
tdepth = np.ravel(depth_this)
tzero = tpressure * 0
tsra_psu = tsra*35/35.16504
ttera_is = gsw.t_from_CT(tsra,ttera,tzero)
print('beginning mocsy')
response_tup = mocsy.mvars(temp=ttera_is, sal=tsra_psu, alk=ttara, dic=tdra,
sil=tzero, phos=tzero, patm=tpressure, depth=tdepth, lat=tzero,
optcon='mol/m3', optt='Tinsitu', optp='m',
optb = 'l10', optk1k2='m10', optkf = 'dg', optgas = 'Pinsitu')
pH,pco2,fco2,co2,hco3,co3,OmegaA,OmegaC,BetaD,DENis,p,Tis = response_tup
print('finished mocsy')
pHr = pH.reshape(size_0,size_1,size_2)
OmAr = OmegaA.reshape(size_0,size_1,size_2)
pco2r = pco2.reshape(size_0,size_1,size_2)
return pHr, OmAr, pco2r
def compute_potdens(ds, saltname='SALT', tempname='THETA'):
import gsw
""" compute the potential density
"""
# compute the Conservative Temperature from the model's potential temperature
temp = ds[tempname].transpose(*('time', 'k', 'face', 'j', 'i'))
salt = ds[tempname].transpose(*('time', 'k', 'face', 'j', 'i'))
CT = gsw.CT_from_pt(salt, temp)
z, lat = xr.broadcast(ds['Z'], ds['YC'])
z = z.transpose(*('k', 'face', 'j', 'i'))
lat = lat.transpose(*('k', 'face', 'j', 'i'))
# compute pressure from depth
p = gsw.p_from_z(z, lat)
# compute in-situ temperature
T = gsw.t_from_CT(salt, CT, p)
# compute potential density
rho = gsw.pot_rho_t_exact(salt, T, p, 0.)
# create new dataarray
darho = xr.full_like(temp, 0.)
darho = darho.load().chunk({'time': 1, 'face': 1})
darho.name = 'RHO'
darho.attrs['long_name'] = 'Potential Density ref at 0m'
darho.attrs['standard_name'] = 'RHO'
darho.attrs['units'] = 'kg/m3'
darho.values = rho
# filter special value
darho = darho.where(darho > 1000)
darho = darho.assign_coords(XC=ds['XC'], YC=ds['YC'], Z=ds['Z'])
return darho
def OmA_2D(grid,carp):
tsal = grid['vosaline'][0,0,:,:]
ttemp = grid['votemper'][0,0,:,:]
tdic = carp['dissolved_inorganic_carbon'][0,0,:,:]
tta = carp['total_alkalinity'][0,0,:,:]
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] =1
tzero = tpressure * 0
tsra_psu = tsra*35/35.16504
ttera_is = gsw.t_from_CT(tsra,ttera,tzero)
response_tup = mocsy.mvars(temp=ttera_is, sal=tsra_psu, alk=ttara, dic=tdra,
sil=tzero, phos=tzero, patm=tpressure, depth=tzero, lat=tzero,
optcon='mol/m3', optt='Tinsitu', optp='m',
optb = 'l10', optk1k2='m10', optkf = 'dg', optgas = 'Pinsitu')
pH,pco2,fco2,co2,hco3,co3,OmegaA,OmegaC,BetaD,DENis,p,Tis = response_tup
pHr = pH.reshape(898,398)
OmAr = OmegaA.reshape(898,398)
OmCr = OmegaC.reshape(898,398)
pco2r = pco2.reshape(898,398)
return pHr, OmAr, OmCr, pco2r
def tsappen_ct(p, **kwargs):
'''
add potential density contours to the current axis
'''
ax = kwargs.get('axis', plt.gca())
pref = kwargs.get('pref', 0)
levels = kwargs.get('levels', np.arange(20, 31))
colors = kwargs.get('colors', 'k')
keys = ['axis', 'pref', 'levels', 'colors']
for key in keys:
if key in kwargs:
kwargs.pop(key)
xlim = ax.get_xlim()
ylim = ax.get_ylim()
xax = np.arange(np.min(xlim) - 0.01, np.max(xlim) + 0.01, 0.01)
yax = np.arange(np.min(ylim) - 0.01, np.max(ylim) + 0.01, 0.01)
sa, ct = np.meshgrid(xax, yax)
# pden = sw.pden(x, y, pref, 0)-1000
t = gsw.t_from_CT(sa, ct, p)
pden = gsw.pot_rho_t_exact(sa, t, pref, 0) - 1000
c = plt.contour(sa, ct, pden, levels, colors=colors, **kwargs)
plt.clabel(c, fmt='%2.1f')
def potential_to_in_situ_temperature(dsPotTemp, dsSalin):
z = dsPotTemp.z.values
lat = numpy.maximum(dsPotTemp.lat.values, -80.)
lon = dsPotTemp.lon.values
if len(lat.shape) == 1:
lon, lat = numpy.meshgrid(lon, lat)
nz = len(z)
ny, nx = lat.shape
if 'time' in dsPotTemp.dims:
nt = dsPotTemp.sizes['time']
T = numpy.nan * numpy.ones((nt, nz, ny, nx))
for zIndex in range(nz):
pressure = gsw.p_from_z(z[zIndex], lat)
for tIndex in range(nt):
pt = dsPotTemp.temperature[tIndex, zIndex, :, :].values
salin = dsSalin.salinity[tIndex, zIndex, :, :].values
mask = numpy.logical_and(numpy.isfinite(pt),
numpy.isfinite(salin))
SA = gsw.SA_from_SP(salin[mask], pressure[mask], lon[mask],
lat[mask])
TSlice = T[tIndex, zIndex, :, :]
CT = gsw.CT_from_pt(SA, pt[mask])
TSlice[mask] = gsw.t_from_CT(SA, CT, pressure[mask])
T[tIndex, zIndex, :, :] = TSlice
else:
T = numpy.nan * numpy.ones((nz, ny, nx))
for zIndex in range(nz):
pressure = gsw.p_from_z(z[zIndex], lat)
pt = dsPotTemp.temperature[zIndex, :, :].values
salin = dsSalin.salinity[zIndex, :, :].values
mask = numpy.logical_and(numpy.isfinite(pt), numpy.isfinite(salin))
SA = gsw.SA_from_SP(salin[mask], pressure[mask], lon[mask],
lat[mask])
TSlice = T[zIndex, :, :]
CT = gsw.CT_from_pt(SA, pt[mask])
TSlice[mask] = gsw.t_from_CT(SA, CT, pressure[mask])
T[zIndex, :, :] = TSlice
dsTemp = dsPotTemp.drop('temperature')
dsTemp['temperature'] = (dsPotTemp.temperature.dims, T)
dsTemp['temperature'].attrs = dsPotTemp.temperature.attrs
return dsTemp
def potential_density(salt_PSU, temp_C, pres_db, lat, lon, pres_ref=0):
"""
Calculate density from glider measurements of salinity and temperature.
The Basestation calculates density from absolute salinity and potential
temperature. This function is a wrapper for this functionality, where
potential temperature and absolute salinity are calculated first.
Note that a reference pressure of 0 is used by default.
Parameters
----------
salt_PSU : array, dtype=float, shape=[n, ]
practical salinty
temp_C : array, dtype=float, shape=[n, ]
temperature in deg C
pres_db : array, dtype=float, shape=[n, ]
pressure in decibar
lat : array, dtype=float, shape=[n, ]
latitude in degrees north
lon : array, dtype=float, shape=[n, ]
longitude in degrees east
Returns
-------
potential_density : array, dtype=float, shape=[n, ]
Note
----
Using seawater.dens does not yield the same results as this function. We
get very close results to what the SeaGlider Basestation returns with this
function. The difference of this function with the basestation is on
average ~ 0.003 kg/m3
"""
try:
import gsw
salt_abs = gsw.SA_from_SP(salt_PSU, pres_db, lon, lat)
temp_pot = gsw.t_from_CT(salt_abs, temp_C, pres_db)
pot_dens = gsw.pot_rho_t_exact(salt_abs, temp_pot, pres_db, pres_ref)
except ImportError:
import seawater as sw
pot_dens = sw.pden(salt_PSU, temp_C, pres_db, pres_ref)
pot_dens = transfer_nc_attrs(
getframe(),
temp_C,
pot_dens,
'potential_density',
units='kg/m3',
comment='',
standard_name='potential_density',
)
return pot_dens
def OmA_3D(grid, carp):
tsal = grid['model_output']['SAL'][:, :, :]
ttemp = grid['model_output']['TEMP'][:, :, :]
tdic = carp['model_output']['DIC'][:, :, :]
tta = carp['model_output']['TA'][:, :, :]
test_LO = nc.Dataset(
'/results/forcing/LiveOcean/boundary_conditions/LiveOcean_v201905_y2018m01d01.nc'
)
zlevels = (test_LO['deptht'][:])
depths = np.zeros([40, 898, 398])
for j in range(0, 898):
for i in range(0, 398):
depths[:, j, i] = zlevels
tdepths = np.ravel(depths)
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] = 1
tzero = tpressure * 0
tsra_psu = tsra * 35 / 35.16504
ttera_is = gsw.t_from_CT(tsra, ttera, tzero)
response_tup = mocsy.mvars(temp=ttera_is,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tdepths,
lat=tzero,
optcon='mol/m3',
optt='Tinsitu',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
pHr = pH.reshape(40, 898, 398)
OmAr = OmegaA.reshape(40, 898, 398)
OmCr = OmegaC.reshape(40, 898, 398)
pco2r = pco2.reshape(40, 898, 398)
return pHr, OmAr, OmCr, pco2r
def OmA_3D(grid, carp):
tsal = grid['vosaline'][0, :, :, :]
ttemp = grid['votemper'][0, :, :, :]
tdic = carp['dissolved_inorganic_carbon'][0, :, :, :]
tta = carp['total_alkalinity'][0, :, :, :]
test_LO = nc.Dataset(
'/results/forcing/LiveOcean/boundary_conditions/LiveOcean_v201905_y2018m01d01.nc'
)
zlevels = (test_LO['deptht'][:])
depths = np.zeros([40, 898, 398])
for j in range(0, 898):
for i in range(0, 398):
depths[:, j, i] = zlevels
tdepths = np.ravel(depths)
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] = 1
tzero = tpressure * 0
tsra_psu = tsra * 35 / 35.16504
ttera_is = gsw.t_from_CT(tsra, ttera, tzero)
response_tup = mocsy.mvars(temp=ttera_is,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tdepths,
lat=tzero,
optcon='mol/m3',
optt='Tinsitu',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
BetaDr = BetaD.reshape(40, 898, 398)
return BetaDr
def oned_moxy(tsal, ttemp, tdic, tta, pres_atm, depth_this):
import sys
sys.path.append('/data/tjarniko/mocsy')
import mocsy
import numpy as np
import gsw
size_box = np.shape(tdic)
size_0 = size_box[0]
size_1 = size_box[1]
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
#tdepth = np.zeros_like(tsra)
tpressure[:] = pres_atm
tdepth = np.ravel(depth_this)
tzero = tpressure * 0
tsra_psu = tsra * 35 / 35.16504
ttera_is = gsw.t_from_CT(tsra, ttera, tzero)
response_tup = mocsy.mvars(temp=ttera_is,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tdepth,
lat=tzero,
optcon='mol/m3',
optt='Tinsitu',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
pHr = pH.reshape(size_0, size_1)
OmAr = OmegaA.reshape(size_0, size_1)
pco2r = pco2.reshape(size_0, size_1)
return pHr, OmAr, pco2r
def surface_maps(carp, grid, stns, ddmmmyy, rdir, humandate, dss_sig):
tsal = grid.variables['vosaline'][0, 0, :, :]
ttemp = grid.variables['votemper'][0, 0, :, :]
tdic = carp.variables['dissolved_inorganic_carbon'][0, 0, :, :]
tta = carp.variables['total_alkalinity'][0, 0, :, :]
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] = 1
tzero = tpressure * 0
tsra_psu = tsra * 35 / 35.16504
ttera_is = gsw.t_from_CT(tsra, ttera, tzero)
response_tup = mocsy.mvars(temp=ttera_is,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tzero,
lat=tzero,
optcon='mol/m3',
optt='Tinsitu',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
pHr = pH.reshape(898, 398)
OmA = OmegaA.reshape(898, 398)
surf_dat = [tsal, tdic, tta, ttemp, pHr, OmA]
vmins = [25, 1800, 1800, 5, 7.5, 0]
vmaxs = [32, 2200, 2200, 15, 8.5, 2]
msk = [0, 0, 0, 0, 1e20, 1e20]
cl = [
'salinity psu', 'DIC umol/kg', 'TA umol/kg', 'temp deg C', 'pH',
'Omega A'
]
t_cmap = [
cm.cm.haline, cm.cm.matter, cm.cm.matter, cm.cm.thermal, cm.cm.speed,
cm.cm.curl
]
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = \
plt.subplots(figsize=(20, 27) , nrows=2, ncols=3)
viz_tools.set_aspect(ax1)
viz_tools.set_aspect(ax2)
viz_tools.set_aspect(ax3)
viz_tools.set_aspect(ax4)
viz_tools.set_aspect(ax5)
viz_tools.set_aspect(ax6)
i = 0
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, msk[i])
tcmap = t_cmap[i]
mesh = ax1.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax1)
cbar.set_label(cl[i], fontsize=20)
i = 1
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, msk[i])
tcmap = t_cmap[i]
mesh = ax2.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax2)
cbar.set_label(cl[i], fontsize=20)
i = 2
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, msk[i])
tcmap = t_cmap[i]
mesh = ax3.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax3)
cbar.set_label(cl[i], fontsize=20)
i = 3
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, msk[i])
tcmap = t_cmap[i]
mesh = ax4.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax4)
cbar.set_label(cl[i], fontsize=20)
i = 4
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, msk[i])
tcmap = t_cmap[i]
mesh = ax5.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax5)
cbar.set_label(cl[i], fontsize=20)
i = 5
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, msk[i])
tcmap = t_cmap[i]
mesh = ax6.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax6)
cbar.set_label(cl[i], fontsize=20)
cols = []
xs = []
ys = []
stn_in = []
for s in stns:
col = stns[s]['color']
x = stns[s]['x']
y = stns[s]['y']
stn = stns[s]['code']
cols.append(col)
xs.append(x)
ys.append(y)
stn_in.append(stn)
for w in range(0, len(stns)):
pat = patches.Rectangle((xs[w], ys[w]),
20,
20,
linewidth=2,
edgecolor=cols[w],
facecolor='none')
ax1.add_patch(pat)
for w in range(0, len(cols)):
pat = patches.Rectangle((xs[w], ys[w]),
20,
20,
linewidth=2,
edgecolor=cols[w],
facecolor='none')
ax2.add_patch(pat)
for w in range(0, len(cols)):
pat = patches.Rectangle((xs[w], ys[w]),
20,
20,
linewidth=2,
edgecolor=cols[w],
facecolor='none')
ax3.add_patch(pat)
for w in range(0, len(cols)):
pat = patches.Rectangle((xs[w], ys[w]),
20,
20,
linewidth=2,
edgecolor=cols[w],
facecolor='none')
ax4.add_patch(pat)
for w in range(0, len(cols)):
pat = patches.Rectangle((xs[w], ys[w]),
20,
20,
linewidth=2,
edgecolor=cols[w],
facecolor='none')
ax5.add_patch(pat)
for w in range(0, len(cols)):
pat = patches.Rectangle((xs[w], ys[w]),
20,
20,
linewidth=2,
edgecolor=cols[w],
facecolor='none')
ax6.add_patch(pat)
for i in range(0, len(xs)):
ax1.text(xs[i] + 22, ys[i] + 3, stn_in[i], weight='bold', fontsize=20)
#tcmap.set_bad('white')
st = 'Salish Sea Carbonate Chemistry Map, ' + humandate
plt.suptitle(st, fontsize=20)
fname = rdir + f'{ddmmmyy}_map_' + dss_sig + '.png'
fig.savefig(fname)
plt.close()
def surface_buffer_maps(carp, grid, ddmmmyy, rdir, humandate, dss_sig):
#retrieve relevant data for mocsy calculation, calculate mocsy
tsal = grid.variables['vosaline'][0, 0, :, :]
ttemp = grid.variables['votemper'][0, 0, :, :]
tdic = carp.variables['dissolved_inorganic_carbon'][0, 0, :, :]
tta = carp.variables['total_alkalinity'][0, 0, :, :]
tsra = np.ravel(tsal)
ttera = np.ravel(ttemp)
ttara = np.ravel(tta) * 1e-3
tdra = np.ravel(tdic) * 1e-3
tzero = np.zeros_like(tsra)
tpressure = np.zeros_like(tsra)
tpressure[:] = 1
tzero = tpressure * 0
tsra_psu = tsra * 35 / 35.16504
ttera_is = gsw.t_from_CT(tsra, ttera, tzero)
response_tup = mocsy.mvars(temp=ttera_is,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tzero,
lat=tzero,
optcon='mol/m3',
optt='Tinsitu',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
#calculate borate and ohminus concentration
bicarb = hco3
carb = co3
#calculate borate, Uppstrom, 1974, looked up in mocsy
scl = tsra / 1.80655
borat = 0.000232 * scl / 10.811
hplus = 10**(-1 * pH)
borat2 = .0000119 * tsra
ohminus = ttara - bicarb - 2 * carb - borat
# - calculates quantities needed for Egleston's factors, and the factors themselves
#Khb is the acidity constant for boric acid - is this an appropriate ref?
# https://www2.chemistry.msu.edu/courses/cem262/aciddissconst.html
Khb = 5.81e-10
S = bicarb + 4 * (carb) + (hplus * borat) / (Khb + hplus) + hplus - ohminus
P = 2 * (carb) + bicarb
AlkC = bicarb + 2 * (carb)
DIC = co2 + bicarb + carb
#Alk = bicarb + 2*carb + borat - hplus + ohminus
g_dic = DIC - AlkC**2 / S
b_dic = (DIC * S - AlkC**2) / AlkC
w_dic = DIC - (AlkC * P) / bicarb
g_alk = (AlkC**2 - DIC * S) / AlkC
b_alk = (AlkC**2 / DIC) - S
w_alk = AlkC - (DIC * bicarb) / P
####
g_dicR = g_dic.reshape(898, 398) * 1000
b_dicR = b_dic.reshape(898, 398) * 1000
w_dicR = w_dic.reshape(898, 398) * -1000
g_alkR = g_alk.reshape(898, 398) * -1000
b_alkR = b_alk.reshape(898, 398) * -1000
w_alkR = w_alk.reshape(898, 398) * 1000
surf_dat = [g_dicR, b_dicR, w_dicR, g_alkR, b_alkR, w_alkR]
#ranges from nov 13,2014 hindcast.
vmins = [-0.7, -0.4, -0.1, -0.4, -0.4, -0.1]
vmaxs = [0.7, 1, 0.5, 1, 1, 0.4]
msk = [1.875e+23, 5e+23, 6e+23, 5e+23, 5e+23, 2e+23]
cl = ['$\gamma_{DIC}$', '$\\beta_{DIC}$', '-$\omega_{DIC}$',\
'$\gamma_{TA}$', '$\\beta_{TA}$', '-$\omega_{TA}$']
t_cmap = [cm.cm.oxy, cm.cm.oxy, cm.cm.oxy, cm.cm.oxy, cm.cm.oxy, cm.cm.oxy]
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = \
plt.subplots(figsize=(20, 27) , nrows=2, ncols=3)
viz_tools.set_aspect(ax1)
viz_tools.set_aspect(ax2)
viz_tools.set_aspect(ax3)
viz_tools.set_aspect(ax4)
viz_tools.set_aspect(ax5)
viz_tools.set_aspect(ax6)
i = 0
#'g_dicR',
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, 1.875e+23)
tcmap = t_cmap[i]
mesh = ax1.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax1)
cbar.set_label(cl[i], fontsize=20)
ax1.set_title('$CO_{2}$ with DIC', fontsize=22)
i = 1
#'b_dicR',
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, 5e+23)
tcmap = t_cmap[i]
mesh = ax2.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax2)
cbar.set_label(cl[i], fontsize=20)
ax2.set_title('pH with DIC', fontsize=22)
i = 2
#'-w_dicR',
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, 6e+23)
tcmap = t_cmap[i]
mesh = ax3.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax3)
cbar.set_label(cl[i], fontsize=20)
ax3.set_title('$\Omega$ with DIC', fontsize=22)
i = 3
#'-g_alkR',
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, 5e+23)
tcmap = t_cmap[i]
mesh = ax4.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax4)
cbar.set_label(cl[i], fontsize=20)
ax4.set_title('$CO_{2}$ with TA', fontsize=22)
i = 4
#'-b_alkR',
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, 5e+23)
tcmap = t_cmap[i]
mesh = ax5.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax5)
cbar.set_label(cl[i], fontsize=20)
ax5.set_title('pH with TA', fontsize=22)
i = 5
#'w_alkR'
tplt0 = surf_dat[i]
tplt = np.ma.masked_values(tplt0, 2e+23)
tcmap = t_cmap[i]
mesh = ax6.pcolormesh(tplt, cmap=tcmap, vmin=vmins[i], vmax=vmaxs[i])
cbar = fig.colorbar(mesh, ax=ax6)
cbar.set_label(cl[i], fontsize=20)
ax6.set_title('$\Omega$ with TA', fontsize=22)
#tcmap.set_bad('white')
st = 'Carbonate Chemistry Buffer Factors, ' + humandate
plt.suptitle(st, fontsize=20)
fname = rdir + f'{ddmmmyy}_buffmap_' + dss_sig + '.png'
fig.savefig(fname)
plt.close()
def run_task(self): # {{{
"""
Plots time-series output of properties in an ocean region.
"""
# Authors
# -------
# Xylar Asay-Davis
self.logger.info("\nPlotting TS diagram for {}"
"...".format(self.regionName))
register_custom_colormaps()
config = self.config
sectionName = self.sectionName
startYear = self.mpasClimatologyTask.startYear
endYear = self.mpasClimatologyTask.endYear
regionMaskSuffix = config.getExpression(sectionName,
'regionMaskSuffix')
regionMaskFile = get_region_mask(config,
'{}.geojson'.format(regionMaskSuffix))
fcAll = read_feature_collection(regionMaskFile)
fc = FeatureCollection()
for feature in fcAll.features:
if feature['properties']['name'] == self.regionName:
fc.add_feature(feature)
break
self.logger.info(' Make plots...')
groupLink = 'tsDiag' + self.regionGroup[0].lower() + \
self.regionGroup[1:].replace(' ', '')
nSubplots = 1 + len(self.obsDicts)
if self.controlConfig is not None:
nSubplots += 1
if nSubplots == 4:
nCols = 2
nRows = 2
else:
nCols = min(nSubplots, 3)
nRows = (nSubplots - 1) // 3 + 1
axisIndices = numpy.reshape(numpy.arange(nRows * nCols),
(nRows, nCols))[::-1, :].ravel()
titleFontSize = config.get('plot', 'titleFontSize')
axis_font = {'size': config.get('plot', 'axisFontSize')}
title_font = {
'size': titleFontSize,
'color': config.get('plot', 'titleFontColor'),
'weight': config.get('plot', 'titleFontWeight')
}
width = 3 + 4.5 * nCols
height = 2 + 4 * nRows
# noinspection PyTypeChecker
fig, axarray = plt.subplots(nrows=nRows,
ncols=nCols,
sharey=True,
figsize=(width, height))
if nSubplots == 1:
axarray = numpy.array(axarray)
if nRows == 1:
axarray = axarray.reshape((nRows, nCols))
T, S, zMid, volume, zmin, zmax = self._get_mpas_t_s(self.config)
mainRunName = config.get('runs', 'mainRunName')
plotFields = [{
'S': S,
'T': T,
'z': zMid,
'vol': volume,
'title': mainRunName
}]
if self.controlConfig is not None:
T, S, zMid, volume, _, _ = self._get_mpas_t_s(self.controlConfig)
controlRunName = self.controlConfig.get('runs', 'mainRunName')
plotFields.append({
'S': S,
'T': T,
'z': zMid,
'vol': volume,
'title': 'Control: {}'.format(controlRunName)
})
for obsName in self.obsDicts:
obsT, obsS, obsZ, obsVol = self._get_obs_t_s(
self.obsDicts[obsName])
plotFields.append({
'S': obsS,
'T': obsT,
'z': obsZ,
'vol': obsVol,
'title': obsName
})
Tbins = config.getExpression(sectionName, 'Tbins', usenumpyfunc=True)
Sbins = config.getExpression(sectionName, 'Sbins', usenumpyfunc=True)
normType = config.get(sectionName, 'normType')
PT, SP = numpy.meshgrid(Tbins, Sbins)
SA = gsw.SA_from_SP(SP, p=0., lon=0., lat=-75.)
CT = gsw.CT_from_t(SA, PT, p=0.)
neutralDensity = sigma0(SA, CT)
rhoInterval = config.getfloat(sectionName, 'rhoInterval')
contours = numpy.arange(24., 29. + rhoInterval, rhoInterval)
diagramType = config.get(sectionName, 'diagramType')
if diagramType not in ['volumetric', 'scatter']:
raise ValueError('Unexpected diagramType {}'.format(diagramType))
lastPanel = None
volMinMpas = None
volMaxMpas = None
for index in range(len(axisIndices)):
panelIndex = axisIndices[index]
row = nRows - 1 - index // nCols
col = numpy.mod(index, nCols)
if panelIndex >= nSubplots:
plt.delaxes(axarray[row, col])
continue
plt.sca(axarray[row, col])
T = plotFields[index]['T']
S = plotFields[index]['S']
z = plotFields[index]['z']
volume = plotFields[index]['vol']
title = plotFields[index]['title']
CS = plt.contour(SP,
PT,
neutralDensity,
contours,
linewidths=1.,
colors='k',
zorder=2)
plt.clabel(CS, fontsize=12, inline=1, fmt='%4.2f')
if diagramType == 'volumetric':
lastPanel, volMin, volMax = \
self._plot_volumetric_panel(T, S, volume)
if index == 0:
volMinMpas = volMin
volMaxMpas = volMax
if normType == 'linear':
norm = colors.Normalize(vmin=0., vmax=volMaxMpas)
elif normType == 'log':
if volMinMpas is None or volMaxMpas is None:
norm = None
else:
norm = colors.LogNorm(vmin=volMinMpas, vmax=volMaxMpas)
else:
raise ValueError(
'Unsupported normType {}'.format(normType))
if norm is not None:
lastPanel.set_norm(norm)
else:
lastPanel = self._plot_scatter_panel(T, S, z, zmin, zmax)
CTFreezing = freezing.CT_freezing(Sbins, 0, 1)
PTFreezing = gsw.t_from_CT(gsw.SA_from_SP(Sbins,
p=0.,
lon=0.,
lat=-75.),
CTFreezing,
p=0.)
plt.plot(Sbins,
PTFreezing,
linestyle='--',
linewidth=1.,
color='k')
plt.ylim([Tbins[0], Tbins[-1]])
plt.xlim([Sbins[0], Sbins[-1]])
plt.xlabel('Salinity (PSU)', **axis_font)
if col == 0:
plt.ylabel(r'Potential temperature ($^\circ$C)', **axis_font)
plt.title(title)
# do this before the inset because otherwise it moves the inset
# and cartopy doesn't play too well with tight_layout anyway
plt.tight_layout()
fig.subplots_adjust(right=0.91)
if nRows == 1:
fig.subplots_adjust(top=0.85)
else:
fig.subplots_adjust(top=0.88)
suptitle = 'T-S diagram for {} ({}, {:04d}-{:04d})\n' \
' {} m < z < {} m'.format(self.regionName, self.season,
startYear, endYear, zmin, zmax)
fig.text(0.5,
0.9,
suptitle,
horizontalalignment='center',
**title_font)
inset = add_inset(fig, fc, width=1.5, height=1.5)
# move the color bar down a little ot avoid the inset
pos0 = inset.get_position()
pos1 = axarray[-1, -1].get_position()
pad = 0.04
top = pos0.y0 - pad
height = top - pos1.y0
cbar_ax = fig.add_axes([0.92, pos1.y0, 0.02, height])
cbar = fig.colorbar(lastPanel, cax=cbar_ax)
if diagramType == 'volumetric':
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel(r'volume (m$^3$)', rotation=270)
else:
cbar.ax.set_ylabel('depth (m)', rotation=270)
outFileName = '{}/TS_diagram_{}_{}.png'.format(self.plotsDirectory,
self.prefix,
self.season)
savefig(outFileName, tight=False)
caption = 'Regional mean of {}'.format(suptitle)
write_image_xml(config=config,
filePrefix='TS_diagram_{}_{}'.format(
self.prefix, self.season),
componentName='Ocean',
componentSubdirectory='ocean',
galleryGroup='T-S Diagrams',
groupLink=groupLink,
gallery=self.regionGroup,
thumbnailDescription=self.regionName,
imageDescription=caption,
imageCaption=caption)
def profiles(carp, grid, stns):
'''
Take 2 daily datasets, carbon+ and grid, extract depth
profiles of sal,temp,DIC,TA,O2,pH,OmA and their standard deviations"
'''
w = carp
wp = grid
sal = wp.variables['vosaline'][0,:,:,:]
temp = wp.variables['votemper'][0,:,:,:]
DIC = w.variables['dissolved_inorganic_carbon'][0,:,:,:]
TA = w.variables['total_alkalinity'][0,:,:,:]
O2 = w.variables['dissolved_oxygen'][0,:,:,:]
prof_depth = w.variables['deptht'][:]
depth_broadcast = np.zeros([40,20,20])
for i in range(0,40):
depth_broadcast[i,:,:] = prof_depth[i]
nos = len(stns)
stn_list = []
sal_prof = np.zeros([nos,40])
temp_prof = np.zeros([nos,40])
DIC_prof = np.zeros([nos,40])
TA_prof = np.zeros([nos,40])
pH_prof = np.zeros([nos,40])
OmA_prof = np.zeros([nos,40])
O2_prof = np.zeros([nos,40])
sal_profSD = np.zeros([nos,40])
temp_profSD = np.zeros([nos,40])
DIC_profSD = np.zeros([nos,40])
TA_profSD = np.zeros([nos,40])
pH_profSD = np.zeros([nos,40])
OmA_profSD = np.zeros([nos,40])
O2_profSD = np.zeros([nos,40])
b = 0
for s in stns:
stn_list.append(s)
stn = stns[s]
#print('Calculating profiles for ' + stns[s]['fullname'])
ty = stns[s]['y']
tx = stns[s]['x']
ts = sal[:,ty:ty+20,tx:tx+20]
tte = temp[:,ty:ty+20,tx:tx+20]
td = DIC[:,ty:ty+20,tx:tx+20]
tta = TA[:,ty:ty+20,tx:tx+20]
to2 = O2[:,ty:ty+20,tx:tx+20]
tsr = ts.reshape(40,400)
tter = tte.reshape(40,400)
tdr = td.reshape(40,400)
ttar = tta.reshape(40,400)
tdepth = depth_broadcast.reshape(40,400)
to2r = to2.reshape(40,400)
tsr[tsr == 0] = np.ma.masked
tter[tter == 0] = np.ma.masked
tdr[tdr == 0] = np.ma.masked
ttar[ttar == 0] = np.ma.masked
sal_prof[b,:] = np.ma.MaskedArray.nanmean(tsr, axis = 1)
sal_profSD[b,:] = np.ma.MaskedArray.nanstd(tsr, axis = 1)
temp_prof[b,:] = np.ma.MaskedArray.nanmean(tter, axis = 1)
temp_profSD[b,:] = np.ma.MaskedArray.nanstd(tter, axis = 1)
DIC_prof[b,:] = np.ma.MaskedArray.nanmean(tdr, axis = 1)
DIC_profSD[b,:] = np.ma.MaskedArray.nanstd(tdr, axis = 1)
TA_prof[b,:] = np.ma.MaskedArray.nanmean(ttar, axis = 1)
TA_profSD[b,:] = np.ma.MaskedArray.nanstd(ttar, axis = 1)
O2_prof[b,:] = np.ma.MaskedArray.nanmean(to2r, axis = 1)
O2_profSD[b,:] = np.ma.MaskedArray.nanstd(to2r, axis = 1)
tsra = np.ravel(ts)
ttera = np.ravel(tte)
tdra = np.ravel(td) * 1e-3
ttara = np.ravel(tta) * 1e-3
tdepthra = np.ravel(tdepth)
tpressure = tdepthra
tpressure[:] =1
tzero = tdepthra * 0
tsra_psu = tsra*35/35.16504
ttera_is = gsw.t_from_CT(tsra,ttera,tdepthra)
response_tup = mocsy.mvars(temp=ttera_is, sal=tsra_psu, alk=ttara, dic=tdra,
sil=tzero, phos=tzero, patm=tpressure, depth=tdepthra, lat=tzero,
optcon='mol/m3', optt='Tinsitu', optp='m',
optb = 'l10', optk1k2='m10', optkf = 'dg', optgas = 'Pinsitu')
pH,pco2,fco2,co2,hco3,co3,OmegaA,OmegaC,BetaD,DENis,p,Tis = response_tup
pHra = pH.reshape(40,400)
OmegaAra = OmegaA.reshape(40,400)
pHra[pHra == 1.00000000e+20] = np.nan
OmegaAra[OmegaAra == 1.00000000e+20] = np.nan
pHra = np.ma.masked_invalid(pHra)
OmegaAra = np.ma.masked_invalid(OmegaAra)
pH_prof[b,:] = np.ma.MaskedArray.nanmean(pHra, axis = 1)
pH_profSD[b,:] = np.ma.MaskedArray.nanstd(pHra, axis = 1)
OmA_prof[b,:] = np.ma.MaskedArray.nanmean(OmegaAra, axis = 1)
OmA_profSD[b,:] = np.ma.MaskedArray.nanstd(OmegaAra, axis = 1)
#depth = w.variables['deptht'][:]
sal_prof[sal_prof == 0 ] = np.nan
temp_prof[temp_prof == 0 ] = np.nan
DIC_prof[DIC_prof == 0 ] = np.nan
TA_prof[TA_prof == 0 ] = np.nan
pH_prof[pH_prof == 0 ] = np.nan
OmA_prof[OmA_prof == 0 ] = np.nan
b = b+1
pars_profs = {'sal': sal_prof, 'sal_SD': sal_profSD,\
'temp': temp_prof, 'temp_SD': temp_profSD,\
'DIC': DIC_prof, 'DIC_SD' : DIC_profSD,\
'TA': TA_prof, 'TA_SD' : TA_profSD,\
'OmA': OmA_prof, 'OmA_SD': OmA_profSD,\
'pH': pH_prof, 'pH_SD': pH_profSD,\
'O2' : O2_prof, 'O2_SD': O2_profSD}
return pars_profs, stn_list, prof_depth
def point_value(carp, grid, stns):
'''
'''
w = carp
wp = grid
sal = wp.variables['vosaline'][0,:,:,:]
temp = wp.variables['votemper'][0,:,:,:]
DIC = w.variables['dissolved_inorganic_carbon'][0,:,:,:]
TA = w.variables['total_alkalinity'][0,:,:,:]
O2 = w.variables['dissolved_oxygen'][0,:,:,:]
depth = w.variables['deptht'][:]
depth_broadcast = np.zeros([40,20,20])
for i in range(0,40):
depth_broadcast[i,:,:] = depth[i]
depth_inds = [0,21,26]
dum = [0.0,0.0,0.0]
pt_depths = np.zeros_like(dum)
for i in range(0,len(pt_depths)):
di = depth_inds
pt_depths[i] = depth[depth_inds[i]]
nos = len(stns)
stn_list = []
sal_pt = np.zeros([nos,len(depth_inds)])
temp_pt = np.zeros([nos,len(depth_inds)])
DIC_pt = np.zeros([nos,len(depth_inds)])
TA_pt = np.zeros([nos,len(depth_inds)])
pH_pt = np.zeros([nos,len(depth_inds)])
OmA_pt = np.zeros([nos,len(depth_inds)])
O2_pt = np.zeros([nos,len(depth_inds)])
sal_ptSD = np.zeros([nos,len(depth_inds)])
temp_ptSD = np.zeros([nos,len(depth_inds)])
DIC_ptSD = np.zeros([nos,len(depth_inds)])
TA_ptSD = np.zeros([nos,len(depth_inds)])
pH_ptSD = np.zeros([nos,len(depth_inds)])
OmA_ptSD = np.zeros([nos,len(depth_inds)])
O2_ptSD = np.zeros([nos,len(depth_inds)])
b = 0
for s in stns:
stn_list.append(s)
#print('Calculating point values for ' + stns[s]['fullname'])
ty = stns[s]['y']
tx = stns[s]['x']
for d in range(0,len(depth_inds)):
dp = depth[depth_inds[d]]
ts = sal[d,ty:ty+20,tx:tx+20]
tte = temp[d,ty:ty+20,tx:tx+20]
td = DIC[d,ty:ty+20,tx:tx+20]
tta = TA[d,ty:ty+20,tx:tx+20]
to2 = O2[d,ty:ty+20,tx:tx+20]
tsr = ts.reshape(1,400)
tter = tte.reshape(1,400)
tdr = td.reshape(1,400)
ttar = tta.reshape(1,400)
tdepth = np.zeros_like(ttar)
tdepth[:] = dp
to2r = to2.reshape(1,400)
tsr[tsr == 0] = np.ma.masked
tter[tter == 0] = np.ma.masked
tdr[tdr == 0] = np.ma.masked
ttar[ttar == 0] = np.ma.masked
sal_pt[b,d] = np.ma.MaskedArray.nanmean(tsr, axis = 1)
sal_ptSD[b,d] = np.ma.MaskedArray.nanstd(tsr, axis = 1)
temp_pt[b,:] = np.ma.MaskedArray.nanmean(tter, axis = 1)
temp_ptSD[b,d] = np.ma.MaskedArray.nanstd(tter, axis = 1)
DIC_pt[b,d] = np.ma.MaskedArray.nanmean(tdr, axis = 1)
DIC_ptSD[b,d] = np.ma.MaskedArray.nanstd(tdr, axis = 1)
TA_pt[b,d] = np.ma.MaskedArray.nanmean(ttar, axis = 1)
TA_ptSD[b,d] = np.ma.MaskedArray.nanstd(ttar, axis = 1)
O2_pt[b,d] = np.ma.MaskedArray.nanmean(to2r, axis = 1)
O2_ptSD[b,d] = np.ma.MaskedArray.nanstd(to2r, axis = 1)
tsra = np.ravel(ts)
ttera = np.ravel(tte)
tdra = np.ravel(td) * 1e-3
ttara = np.ravel(tta) * 1e-3
tdepthra = np.ravel(tdepth)
tpressure = np.zeros_like(tdepthra)
tpressure[:] =1
tzero = tdepthra * 0
tsra_psu = tsra*35/35.16504
ttera_is = gsw.t_from_CT(tsra,ttera,tdepthra)
response_tup = mocsy.mvars(temp=ttera_is, sal=tsra_psu, alk=ttara, dic=tdra,
sil=tzero, phos=tzero, patm=tpressure, depth=tdepthra, lat=tzero,
optcon='mol/m3', optt='Tinsitu', optp='m',
optb = 'l10', optk1k2='m10', optkf = 'dg', optgas = 'Pinsitu')
pH,pco2,fco2,co2,hco3,co3,OmegaA,OmegaC,BetaD,DENis,p,Tis = response_tup
pHra = pH.reshape(1,400)
OmegaAra = OmegaA.reshape(1,400)
pHra[pHra == 1.00000000e+20] = np.nan
OmegaAra[OmegaAra == 1.00000000e+20] = np.nan
pHra = np.ma.masked_invalid(pHra)
OmegaAra = np.ma.masked_invalid(OmegaAra)
pH_pt[b,d] = np.ma.MaskedArray.nanmean(pHra, axis = 1)
pH_pt[b,d] = np.ma.MaskedArray.nanstd(pHra, axis = 1)
OmA_pt[b,d] = np.ma.MaskedArray.nanmean(OmegaAra, axis = 1)
OmA_pt[b,d] = np.ma.MaskedArray.nanstd(OmegaAra, axis = 1)
sal_pt[sal_pt == 0 ] = np.nan
temp_pt[temp_pt == 0 ] = np.nan
DIC_pt[DIC_pt == 0 ] = np.nan
TA_pt[TA_pt == 0 ] = np.nan
pH_pt[pH_pt == 0 ] = np.nan
OmA_pt[OmA_pt == 0 ] = np.nan
wb = b+1
pars_pts = {'sal': sal_pt, 'sal_SD': sal_ptSD,\
'temp': temp_pt, 'temp_SD': temp_ptSD,\
'DIC': DIC_pt, 'DIC_SD' : DIC_ptSD,\
'TA': TA_pt, 'TA_SD' : TA_ptSD,\
'OmA': OmA_pt, 'OmA_SD': OmA_ptSD,\
'pH': pH_pt, 'pH_SD': pH_ptSD,\
'O2' : O2_pt, 'O2_SD': O2_ptSD}
return pars_pts, pt_depths, stn_list
