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 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 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 calc_carb_dat(conc_DIC, conc_ALK, T):
if (conc_DIC < 1 / 100000) or (conc_ALK < 1 / 100000):
conc_DIC = 1e-6
with suppress_stdout_stderr():
carb_dat = np.dstack(
mocsy.mvars(temp=T,
sal=0,
alk=conc_ALK / 12,
dic=conc_DIC / 12,
sil=0,
phos=0,
patm=1.,
depth=1,
lat=0,
optcon='mol/m3',
optt='Tinsitu',
optp='db',
optb="u74",
optk1k2='l',
optkf="dg",
optgas='Pinsitu'))[0][0]
pH, pCO2 = operator.itemgetter(0, 1)(carb_dat)
if pH > 14:
pH = -999999999
if pCO2 > 1e6:
pCO2 = -999999999
return pCO2, pH
def find_DIC_corresp_to_pco2(tsal, ttemp, tpco2, tta, pres_atm, depth_this):
import numpy as np
import mocsy
steps = 10000
tsal_r = np.zeros([steps])
tsal_r[:] = tsal
ttemp_r = np.zeros([steps])
ttemp_r[:] = ttemp
tta_r = np.zeros([steps])
tta_r[:] = tta * 1e-3
tpres_r = np.zeros([steps])
tpres_r[:] = pres_atm
depth_r = np.zeros([steps])
depth_r[:] = depth_this
tzero = np.zeros([steps])
end_d = 2400
start_d = 600
intvl = (end_d - start_d) / steps
tdic_r = np.arange(start_d, end_d - 0.1, intvl) * 1e-3
response_tup = mocsy.mvars(temp=ttemp_r,
sal=tsal_r,
alk=tta_r,
dic=tdic_r,
sil=tzero,
phos=tzero,
patm=tpres_r,
depth=depth_r,
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
diffmat = pco2 - tpco2
idx, ans = find_nearest(diffmat, 0)
if ans > 2:
print('Danger, pco2 found >2 uatm from pco2 given')
# print(idx)
# print('difference between real pco2 and pco2 from calc. dic: ',ans)
# print('DIC found this way:', tdic_r[idx]*1e3)
fin_dic = tdic_r[idx] * 1e3
return fin_dic
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 oned_moxy(tsal, ttemp, tdic, tta, pres_atm, depth_this):
### GET pCO2 (and Omega, etc) GIVEN DIC, TA
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)
#convert cons. temperature to potential temperature
ttera_pot = gsw.pt_from_CT(tsra, ttera)
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_pot,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tdepth,
lat=tzero,
optcon='mol/m3',
optt='Tpot',
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 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 point_moxy(tsal, ttemp, tdic, tta, pres_atm, depth_this):
### GET pCO2 (and Omega, etc) GIVEN DIC, TA
import sys
sys.path.append('/data/tjarniko/mocsy')
import mocsy
import numpy as np
import gsw
tsra = (tsal)
ttera = (ttemp)
#convert cons. temperature to potential temperature
ttera_pot = gsw.pt_from_CT(tsra, ttera)
ttara = (tta) * 1e-3
tdra = (tdic) * 1e-3
tzero = 0
tpressure = pres_atm
tdepth = (depth_this)
tsra_psu = tsra * 35 / 35.16504
#ttera_is = gsw.t_from_CT(tsra,ttera,tzero)
response_tup = mocsy.mvars(temp=ttera_pot,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tdepth,
lat=tzero,
optcon='mol/m3',
optt='Tpot',
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
OmAr = OmegaA
pco2r = pco2
return pHr, OmAr, pco2r
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
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
#no need to convert these, they're already converted
#tsra_psu = tsra*35/35.16504
#ttera_is = gsw.t_from_CT(tsra,ttera,tzero)
response_tup = mocsy.mvars(temp=ttera,
sal=tsra,
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
return pH, OmegaA, pco2
def scalar_variables():
"""
Functions return 1-dimensional numpy arrays.
Scalar inputs return length-1 arrays.
DATA input: DIC and ALk in mol/kg, in situ temperature, pressure.
"""
return mocsy.mvars(temp=18,
sal=35,
alk=2300.e-6,
dic=2000.e-6,
sil=0,
phos=0,
patm=1,
depth=100,
lat=0,
optcon='mol/kg',
optt='Tinsitu',
optp='db',
optb='u74',
optk1k2='l',
optkf='dg',
optgas='Pinsitu')
def scalar_variables():
"""
Functions return 1-dimensional numpy arrays.
Scalar inputs return length-1 arrays.
DATA input: DIC and ALk in mol/kg, in situ temperature, pressure.
"""
return mocsy.mvars(temp=18,
sal=35,
alk=2300.e-6,
dic=2000.e-6,
sil=0,
phos=0,
patm=1,
depth=100,
lat=0,
optcon='mol/kg',
optt='Tinsitu',
optp='db',
optb='u74',
optk1k2='l',
optkf='dg',
optgas='Pinsitu')
def plume_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
response_tup = mocsy.mvars(temp=ttera,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tzero,
lat=tzero,
optcon='mol/m3',
optt='Tpot',
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 = [15, 500, 500, 5, 7.5, 0]
vmaxs = [30, 1800, 1800, 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=(17, 8.5) , 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)
y1 = 390
y2 = 460
x1 = 240
x2 = 398
i = 0
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax1.set_xticks([])
ax1.set_yticks([])
i = 1
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax2.set_xticks([])
ax2.set_yticks([])
i = 2
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax3.set_xticks([])
ax3.set_yticks([])
i = 3
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax4.set_xticks([])
ax4.set_yticks([])
i = 4
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax5.set_xticks([])
ax5.set_yticks([])
i = 5
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax6.set_xticks([])
ax6.set_yticks([])
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)
#tcmap.set_bad('white')
st = 'Fraser Plume Carbonate Chemistry, ' + humandate
plt.suptitle(st, fontsize=20)
fname = rdir + f'{ddmmmyy}_plume_' + dss_sig + '.png'
fig.savefig(fname)
#plt.show()
plt.close()
lat = np.ravel (lat)
# Make a flat array of 3D variables: t, s, alk, sio2 & po4
t = np.ravel (t.getValue())
s = np.ravel (s.getValue())
alk = np.ravel (alk.getValue())
po4 = np.ravel (po4.getValue())
sio2 = np.ravel (sio2.getValue())
# Flatten array, missing values filled with 0
# dic_array = np.ravel (dic.filled(0.))
dic = dic.filled(0.)
patm = t[:]*0 + 1
#
#-----------------------------------------------------------------------------
# Computed other carbonate system variables (pH, CO2*, HCO3-, CO3--, Omegas, R (BetaD), (& in-situ rho, pressure, & T)
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = (
mocsy.mvars (t, s, alk, dic.ravel(), sio2, po4, patm, depth, lat,
optcon='mol/m3', optt='Tpot', optp='m', optb="u74", optk1k2='l', optkf="dg", optgas="Pinsitu")
)
del (t)
del (s)
# del (alk)
# del (dic_array)
del (sio2)
del (po4)
#del (p)
del (depth)
del (lat)
del (patm)
#
#-----------------------------------------------------------------------------
# Make the variables have correct shape
pH = np.reshape (pH, dic.shape)
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 pHOmpco2_thres(carp, grid, ddmmmyy, rdir, humandate, dss_sig, pH_M, pCO2_M,
OmA_M, pH_T, pCO2_T, OmA_T):
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
response_tup = mocsy.mvars(temp=ttera,
sal=tsra_psu,
alk=ttara,
dic=tdra,
sil=tzero,
phos=tzero,
patm=tpressure,
depth=tzero,
lat=tzero,
optcon='mol/m3',
optt='Tpot',
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)
pco2R = pco2.reshape(898, 398)
#surf_dat = [tsal, tdic, tta, ttemp, pHr, OmA]
surf_dat = [pHr, OmA, pco2R]
print(OmA[20, 250])
print(pHr[20, 250])
print(pco2R[20, 250])
pH_20 = pH_T - pH_M
pH_MX = pH_M + (pH_20 * 5)
OmA_20 = OmA_T - OmA_M
OmA_MX = OmA_M + (OmA_20 * 5)
pCO2_20 = pCO2_T - pCO2_M
pCO2_MX = pCO2_M + (pCO2_20 * 5)
vmins = [pH_M, OmA_M, pCO2_M]
vmaxs = [pH_MX, OmA_MX, pCO2_MX]
msk = [1e20, 1e20, 1e20]
cl = ['pH', 'Omega A', 'pCO2']
t_cmap = [cm.cm.oxy, cm.cm.oxy, cm.cm.oxy]
fig, ((ax1, ax2, ax3)) = \
plt.subplots(figsize=(17, 8.5) , nrows=1, ncols=3)
viz_tools.set_aspect(ax1)
viz_tools.set_aspect(ax2)
viz_tools.set_aspect(ax3)
y1 = 0
y2 = 898
x1 = 0
x2 = 398
i = 0
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax1.set_xticks([])
ax1.set_yticks([])
i = 1
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax2.set_xticks([])
ax2.set_yticks([])
i = 2
tplt0 = surf_dat[i][y1:y2, x1:x2]
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)
ax3.set_xticks([])
ax3.set_yticks([])
st = 'pH, pCO2, Omega_A threshold plots, ' + humandate
plt.suptitle(st, fontsize=20)
fname = rdir + f'{ddmmmyy}_pHT_' + dss_sig + '.png'
fig.savefig(fname)
plt.show()
def atmospheric_exchange_DIC(spec, params, species, temperature, windspeed, Q,
flow_velocity, vol, width, length):
# [mol/m3]
try:
conc_DIC = max(spec[params.idic] * 1e-3 / vol, 0)
except (FloatingPointError):
conc_DIC = 0
try:
conc_ALK = max(spec[params.ialk] * 1e-3 / vol, 0)
except (FloatingPointError):
conc_ALK = 0
if conc_DIC < 999 and conc_ALK < 999 and conc_DIC > 0:
carb_dat = np.dstack(
mocsy.mvars(temp=temperature - params.tempcorrection,
sal=0,
alk=conc_ALK,
dic=conc_DIC,
sil=0,
phos=0,
patm=1.,
depth=1,
lat=0,
optcon='mol/m3',
optt='Tinsitu',
optp='db',
optb="u74",
optk1k2='l',
optkf="dg",
optgas='Pinsitu'))[0][0]
# [micro atmosphere]
pCO2 = operator.itemgetter(1)(carb_dat)
elif conc_DIC <= 0:
pCO2 = 0
else:
with suppress_stdout_stderr(
): # suppressing printed output of mocsy module when concentrations of either ALK or DIC are more than 1M
carb_dat = np.dstack(
mocsy.mvars(temp=temperature - params.tempcorrection,
sal=0,
alk=conc_ALK,
dic=conc_DIC,
sil=0,
phos=0,
patm=1.,
depth=1,
lat=0,
optcon='mol/m3',
optt='Tinsitu',
optp='db',
optb="u74",
optk1k2='l',
optkf="dg",
optgas='Pinsitu'))[0][0]
# [micro atmosphere]
pCO2 = operator.itemgetter(1)(carb_dat)
if pCO2 > 1e6:
pCO2 = 1e6
# [mol/m3]
CO2_conc = pCO2 * math.pow(10,
-6) * species[params.idic].get_val("kH") * 1000
# [cm s-1]
v = (flow_velocity * 1e5 / (365. * 86400.))
# [km]
length *= math.pow(10, -3)
# [km]
width *= math.pow(10, -3)
if width < 100 * math.pow(10, -3) and v > 0.0:
# [cm h-1]
k600 = (species[params.idic].get_val("exch_a1") +
species[params.idic].get_val("exch_b1") * v)
else:
# [cm h-1]
k600 = (species[params.idic].get_val("exch_a2") +
species[params.idic].get_val("exch_b2") * windspeed)
sc = 1911.1 - 118.11 * (temperature - params.tempcorrection) + 3.4527 * (
temperature - params.tempcorrection)**2 - 0.04132 * (
temperature - params.tempcorrection)**3
try:
k = k600 / ((600 / sc)**(-0.5))
except (FloatingPointError):
k = 1e-9
# [cm d-1]
k *= 24.
# [cm y-1]
k *= 365
# [m y-1]
k *= 0.01
#[mol/m3]
dCO2 = CO2_conc - params.CO2_eq
if params.lsensitivity == 1:
#[mol/m3]
dCO2 = CO2_conc - params.CO2_eq * params.co2_eq_factor
# [km y-1]
k *= math.pow(10, -3)
#[mol/km3]
dCO2 *= math.pow(10, 9)
#[Mmol/km3]
dCO2 *= math.pow(10, -6)
#exchange in Mmol/year
ex = dCO2 * k * length * width
return ex
# Flatten array, missing values filled with 0
# dic_array = np.ravel (dicd.filled(0.))
dicd = dicd.filled(0.0)
#
# -----------------------------------------------------------------------------
# Compute other carbonate system variables (pH, CO2*, HCO3-, CO3--, Omegas, R (BetaD), (& in-situ rho, pressure, & T)
print "Compute carbonate chemistry"
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = mocsy.mvars(
td,
sd,
alkd,
dicd.ravel(),
sio2d,
po4d,
patmd,
depth,
lat,
optcon="mol/kg",
optt="Tinsitu",
optp="m",
optb="u74",
optk1k2="l",
optkf="dg",
optgas="Pinsitu",
)
del (td)
del (sd)
# del (alkd)
# del (dic_array)
del (sio2d)
del (po4d)
# del (p)
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
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 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 find_DIC_corresp_to_pco2(tsal, ttemp, tpco2, tta, pres_atm, depth_this):
### given a pco2, salinity, temp, etc get DIC
import numpy as np
import mocsy
import gsw
steps = 10000
tsal_r = np.zeros([steps])
tsal_r[:] = tsal
#convert to psu
tsal_r_psu = tsal_r * 35 / 35.16504
ttemp_r = np.zeros([steps])
ttemp_r[:] = ttemp
#convert temperature to potential temperature
ttemp_r_pot = gsw.pt_from_CT(tsal_r, ttemp_r)
tta_r = np.zeros([steps])
tta_r[:] = tta * 1e-3
tpres_r = np.zeros([steps])
tpres_r[:] = pres_atm
depth_r = np.zeros([steps])
depth_r[:] = depth_this
tzero = np.zeros([steps])
tlat = np.zeros([steps])
tlat[:] = 50
end_d = 2400
start_d = 600
intvl = (end_d - start_d) / steps
tdic_r = np.arange(start_d, end_d - 0.1, intvl) * 1e-3
#change to take potential temperature
response_tup = mocsy.mvars(temp=ttemp_r_pot,
sal=tsal_r_psu,
alk=tta_r,
dic=tdic_r,
sil=tzero,
phos=tzero,
patm=tpres_r,
depth=depth_r,
lat=tzero,
optcon='mol/m3',
optt='Tpot',
optp='m',
optb='l10',
optk1k2='m10',
optkf='dg',
optgas='Pinsitu')
pH, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, DENis, p, Tis = response_tup
diffmat = pco2 - tpco2
idx, ans = find_nearest(diffmat, 0)
if ans > 2:
print('Danger, pco2 found >2 uatm from pco2 given')
# print(idx)
# print('difference between real pco2 and pco2 from calc. dic: ',ans)
# print('DIC found this way:', tdic_r[idx]*1e3)
fin_dic = tdic_r[idx] * 1e3
return fin_dic
BetaD = np.zeros((6, )).astype('float32')
rhoSW = np.zeros((6, )).astype('float32')
p = np.zeros((6, )).astype('float32')
tempis = np.zeros((6, )).astype('float32')
# values of derivatives w/ respect to 6 input variables and at 6 input points
ph_deriv = np.zeros((6 * 6, )).astype('float32')
pco2_deriv = np.zeros((6 * 6, )).astype('float32')
OmegaA_deriv = np.zeros((6 * 6, )).astype('float32')
ph_deriv = ph_deriv.reshape((6, 6), order='F')
pco2_deriv = pco2_deriv.reshape((6, 6), order='F')
OmegaA_deriv = OmegaA_deriv.reshape((6, 6), order='F')
# Run mocsy.vars()
# Notice that option names are all lowercase
ph, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, rhoSW, p, tempis = \
mocsy.mvars (temp, sal, alk, dic, sil, phos, Patm, depth, lat,
optcon='mol/kg', optt='Tinsitu', optp='db', optb='l10', optk1k2=optK1K2, optkf='dg')
# print mocsy results
# -------------------
print "pH pCO2 fCO2 CO2* HCO3- CO32- OmegaA OmegaC R Density Press Temperature"
for i in range(0, 6):
print ph[i], pco2[i], fco2[i], co2[i], hco3[i], co3[i], OmegaA[i], OmegaC[
i], BetaD[i], rhoSW[i], p[i], tempis[i]
# Compute automatic derivatives (using automatic differentiation)
ph_deriv, pco2_deriv, fco2_deriv, co2_deriv, hco3_deriv, co3_deriv, OmegaA_deriv, OmegaC_deriv = \
mocsy.mderivauto(temp, sal, alk, dic, sil, phos, Patm, depth, lat, # INPUT
optcon='mol/kg', optt='Tinsitu', optp='db', optb='l10', optk1k2=optK1K2, optkf='dg') # INPUT OPTIONS
# Compute buffer factors from Egleston
BetaD = np.zeros((6,)).astype('float32')
rhoSW = np.zeros((6,)).astype('float32')
p = np.zeros((6,)).astype('float32')
tempis = np.zeros((6,)).astype('float32')
# values of derivatives w/ respect to 6 input variables and at 6 input points
ph_deriv = np.zeros((6*6,)).astype('float32')
pco2_deriv = np.zeros((6*6,)).astype('float32')
OmegaA_deriv = np.zeros((6*6,)).astype('float32')
ph_deriv = ph_deriv.reshape ((6,6), order='F')
pco2_deriv = pco2_deriv.reshape ((6,6), order='F')
OmegaA_deriv = OmegaA_deriv.reshape ((6,6), order='F')
# Run mocsy.vars()
# Notice that option names are all lowercase
ph, pco2, fco2, co2, hco3, co3, OmegaA, OmegaC, BetaD, rhoSW, p, tempis = \
mocsy.mvars (temp, sal, alk, dic, sil, phos, Patm, depth, lat,
optcon='mol/kg', optt='Tinsitu', optp='db', optb='l10', optk1k2=optK1K2, optkf='dg')
# print mocsy results
# -------------------
print "pH pCO2 fCO2 CO2* HCO3- CO32- OmegaA OmegaC R Density Press Temperature"
for i in range (0, 6):
print ph[i], pco2[i], fco2[i], co2[i], hco3[i], co3[i], OmegaA[i], OmegaC[i], BetaD[i], rhoSW[i], p[i], tempis[i]
# Compute automatic derivatives (using automatic differentiation)
ph_deriv, pco2_deriv, fco2_deriv, co2_deriv, hco3_deriv, co3_deriv, OmegaA_deriv, OmegaC_deriv = \
mocsy.mderivauto(temp, sal, alk, dic, sil, phos, Patm, depth, lat, # INPUT
optcon='mol/kg', optt='Tinsitu', optp='db', optb='l10', optk1k2=optK1K2, optkf='dg') # INPUT OPTIONS
# Compute buffer factors from Egleston
