def getPSfromSigmas(
sigmas,
sigmaorder=default_sigmaorder,
dummyvals=False,
salttype='nom',
):
"""
From the sigmas of the shape returned in getsimgas(), get a list of power
supplies for those sigma values.
"""
CO2s, H2s, CH4s, nATPs, k_corr, Ts, pHs = sigmas
E = Enc(name='En',
pH=pHs,
CO2origin='HTHeatingSalts',
T=Ts,
workoutID=True)
E = Qunc.get_salty_Enc(E, salttype)
E.composition['CO2(aq)'].activity = E.composition['CO2(aq)'].activity.n + (
CO2s * E.composition['CO2(aq)'].activity.s)
E.composition['H2(aq)'].activity = E.composition['H2(aq)'].activity.n + (
H2s * E.composition['H2(aq)'].activity.s)
E.composition[
'Methane(aq)'].activity = E.composition['Methane(aq)'].activity.n + (
CH4s * E.composition['Methane(aq)'].activity.s)
# H2O errors not supported yet
E.composition['H2O(l)'].activity = E.composition['H2O(l)'].activity.n
return getPS(E, nATPs, k_corr)
def get_maintenances(T, pH, E=None, TOM=None, nATP=1.0):
"""
Get the four maintenance updates at T and pH.
This may take time to run the first time, it will be quicker if you have
the database set up in the TOM directory. You can also contact me for a
copy of the full database.
"""
PT = 1
if T < 375:
if nATP == 1.0:
PT = tem.MaintenanceRange_nATPs(Trange=[int(T)],
mCH4=3e-8,
Tlst=False,
fraction=False,
Perform=False,
dbpath='../TOM/allMtestc')[0][0]
elif nATP == 0.5 or nATP == 0.25:
PT = tem.MaintenanceRange_nATPs(Trange=[int(T)],
mCH4=3e-8,
Tlst=False,
fraction=False,
Perform=False,
dbpath='../TOM/allMtestc')[1][0]
if PT < 1e-20:
#correct up for the v low estimates at near 0C
PT = 1e-12
elif nATP > 1.5:
PT = tem.MaintenanceRange_nATPs(
Trange=[int(T)],
mCH4=3e-8,
Tlst=False,
fraction=False,
Perform=False,
dbpath='../../NutMEG-Implementations/TOM/allMtestc')[2][0]
# make a TOM
if E == None or TOM == None:
E = Enc('EncT')
TOM = getE_TOM(E)
# make an Enceladus
TE = es.applications.theory_estimates(TOM, E)
TE.loc.change_T(T)
TE.org.get_ESynth(AA=True)
td = TE.temperature_defenses(T)
Ti = td['TijhuisAnaerobe']
# L10.append(td['Lever10pc'])
L2 = td['Lever2pc']
return PT, Ti, L2
def maintenancemesh(Trange, pHrange, nATP=1.0):
"""Get a meshgrid of maintenance powers."""
E = Enc('EncT')
TOM = getE_TOM(E)
HC = np.ndarray((len(Trange), len(pHrange)))
Ti = np.ndarray((len(Trange), len(pHrange)))
L = np.ndarray((len(Trange), len(pHrange)))
B = np.ndarray((len(Trange), len(pHrange)))
yindex = 0
for T in Trange:
xindex = 0
for pH in pHrange:
MPs = get_maintenances(T, pH, E, TOM, nATP=nATP)
HC[yindex][xindex] = MPs[0]
Ti[yindex][xindex] = MPs[1]
L[yindex][xindex] = MPs[2]
B[yindex][xindex] = 1e-18
xindex += 1
yindex += 1
return HC, Ti, L, B
mpl.rcParams['mathtext.fontset'] = 'cm'
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'] = 14
mpl.rc('axes', unicode_minus=False)
mpl.rcParams['hatch.linewidth'] = 2.0
# print(mpl.rcParams.keys()) # to see what can be changed
mpl.rcParams['font.size'] = 14
fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(5, 8))
Ts = range(273, 473) #range(273,373)
GM1, GATP1 = [], []
GM100, GATP100 = [], []
for T in range(273, 473):
E = Enc('Enceladus', T=T, depth=0.)
E.env.P = 1e5
TOMobj = TOM(E, paramchange={'Basal': 1e-15, 'Tdef': 'None'})
GM1.append(TOMobj.respiration.net_pathway.std_molar_gibbs / 1000)
GATP1.append(TOMobj.respiration.ATP_production.std_molar_gibbs / 1000)
E = Enc('Enceladus', T=T, depth=1.)
E.env.P = 1e7
TOMobj = TOM(E, paramchange={'Basal': 1e-15, 'Tdef': 'None'})
GM100.append(TOMobj.respiration.net_pathway.std_molar_gibbs / 1000)
GATP100.append(TOMobj.respiration.ATP_production.std_molar_gibbs / 1000)
axs[0].set_title('Methanogenesis')
axs[0].plot(Ts, GM1, color='b', label='1 bar')
def getEncEnergetics(
ocean_pH=8.,
T=273.15,
nATP=1.0,
k_corr=0.,
CO2origin='pH', # change to HTHeating later
output=gEE_default_output,
quotienttype='salty_endmember'):
"""
Get the requested parameters in output at the selected parameters of
ocean_pH, T, nATP, and k_corr.
"""
outputbools = boolify_output(output)
outputdict = {}
E = Enc(name='En', pH=ocean_pH, CO2origin=CO2origin, T=T, workoutID=False)
if outputbools['Composition']:
_concs = {}
for species, rx in E.composition.items():
_concs[species] = np.array([[
rx.activity.n, rx.activity.n + rx.activity.s,
rx.activity.n - rx.activity.s
]])
outputdict['Composition'] = _concs
if outputbools['CO2Tiger']:
CO2T = E.get_tigerstripe_CO2(logform=True)
CO2Tiger = 10**CO2T.n
CO2Tiger_up = 10**(CO2T.n + CO2T.s)
CO2Tiger_down = 10**(CO2T.n - CO2T.s)
outputdict['CO2Tiger'] = np.array(
[[CO2Tiger, CO2Tiger_do, CO2Tiger_up]])
if outputbools['Gibbs_Methanogenesis'] or outputbools[
'PowerSupply'] or outputbools['ATPGibbs'] or outputbools[
'max_k'] or outputbools['Quotient']:
# need to do the energetic calculations. This is outsourced to the
# QuotientUncertainties class.
Qdict = {} # dictionary outputs from Qunc
if quotienttype == 'allunc':
Qdict = Qunc.Q_allunc(E, nATP, k_corr)
elif quotienttype == 'salty_endmember':
Qdict = Qunc.Q_salty_endmember(E, nATP, k_corr)
elif quotienttype == 'salty_nominal':
Qdict = Qunc.Q_salty(E, nATP, k_corr, 'nom')
elif quotienttype == 'salty_high':
Qdict = Qunc.Q_salty(E, nATP, k_corr, 'high')
elif quotienttype == 'salty_low':
Qdict = Qunc.Q_salty(E, nATP, k_corr, 'low')
else:
raise ValueError(
'unrecognised quotienttype, how do you want to consider salt effects?'
)
if outputbools['Gibbs_Methanogenesis']:
outputdict['Gibbs_Methanogenesis'] = Qdict['Gibbs_Methanogenesis']
if outputbools['Quotient']:
outputdict['Quotient'] = Qdict['Quotient']
if outputbools['PowerSupply']:
outputdict['PowerSupply'] = Qdict['PowerSupply']
if outputbools['ATPGibbs']:
outputdict['ATPGibbs'] = Qdict['ATPGibbs']
if outputbools['max_k']:
outputdict['max_k'] = Qdict['max_k']
return outputdict
R.composition['H2(aq)'].activity = VenusDrop.getgasconc('H2(aq)', 0.8*R.env.P,
R.env.T, P_bar=R.env.P)
R.composition['CO2(aq)'].activity = VenusDrop.getgasconc('CO2(aq)', 0.2*R.env.P,
R.env.T, P_bar=R.env.P)
return R
Ts=range(273,373)
ks, kTs, met = [],[], []
kTs30, kTs10 = [], []
CO2_30, CO2_10, CO2_18, CO2_500 = [],[],[],[]
H2_30, H2_10, H2_18, H2_500 = [],[],[],[]
for T in Ts:
E = Enc('Enceladus', T=T, nominals=True)
E.env.P=182000
E = concupdate(E)
TOMobj = TOM(E, paramchange={'Basal':1e-15, 'Tdef':'None'}, fromdata=False)
ks.append( TOMobj.respiration.net_pathway.rate_constant_RTP)
met.append(TOMobj.max_metabolic_rate)
kTs.append(TOMobj.respiration.net_pathway.rate_constant_env)
for r, mr in TOMobj.respiration.net_pathway.reactants.items():
if r.name=='CO2(aq)':
CO2_18.append(r.activity)
elif r.name=='H2(aq)':
H2_18.append(r.activity)
E.env.P = 300000
E = concupdate(E)
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'] = 14
mpl.rcParams['lines.linewidth'] = 3
mpl.rc('axes', unicode_minus=False)
mpl.rcParams['hatch.linewidth'] = 2.0
# print(mpl.rcParams.keys()) # to see what can be changed
mpl.rcParams['font.size'] = 14
fig, axs = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
Ts = range(273, 473)
GATP, worseGATP, minGATP, betterGATP, maxGATP = [], [], [], [], []
for T in Ts:
E = Enc('Enceladus', T=T, depth=0)
TOMobj = TOM(E, paramchange={'Basal': 1e-15, 'Tdef': 'None'})
GATP.append(TOMobj.respiration.G_P / 1000)
# TOMobj.respiration.build_ATP_reaction([0.0002, 0.004, 0.0056, 7.])#[0.00001,0.0004,0.05,7.])
worseGATP.append(TOMobj.respiration.G_P * 0.5 / 1000)
minGATP.append(TOMobj.respiration.G_P * 0.25 / 1000)
# TOMobj.respiration.build_ATP_reaction([0.002,0.022,0.0007,7.])#[0.001,0.04,0.0005,7.])
betterGATP.append(TOMobj.respiration.G_P * 1.5 / 1000)
maxGATP.append(TOMobj.respiration.G_P * 2.0 / 1000)
axs.set_title('ATP Production')