def get_salty_Enc(E, salttype, CO2unc=0.0, H2Ounc=0.0):
"""Get a salty, nominal or less-salty enceladus with the activities as ufloats.
"""
QE = deepcopy(E)#nc(name='En', pH=ocean_pH, CO2origin='pH', T=T, workoutID=False)
CO2salt, H2Osalt = Enc.get_CO2_from_HTHeating(QE.env.T, QE.ocean_pH, salts=True)
CO2salt273, H2Osalt273 = Enc.get_CO2_from_HTHeating(273.15, QE.ocean_pH, salts=True)
aCO2, aH20, aH2, aCH4 = 0,0,0,0
if salttype == 'nom':
aCO2 = CO2salt[0]
aH2O = H2Osalt[0]
aH2 = QE.calc_mol_H2(CO2salt273[0])
aCH4 = QE.calc_mol_CH4(CO2salt273[0])
elif salttype =='high':
aCO2 = CO2salt[1]
aH2O = H2Osalt[1]
aH2 = QE.calc_mol_H2(CO2salt273[1])
aCH4 = QE.calc_mol_CH4(CO2salt273[1])
elif salttype == 'low':
aCO2 = CO2salt[2]
aH2O = H2Osalt[2]
aH2 = QE.calc_mol_H2(CO2salt273[2])
aCH4 = QE.calc_mol_CH4(CO2salt273[2])
else:
raise ValueError('Unknown salt type sent to Q_salty')
QE.composition['CO2(aq)'].activity = uf(aCO2, aCO2*CO2unc)
QE.composition['H2(aq)'].activity = aH2
QE.composition['Methane(aq)'].activity = aCH4
QE.composition['H2O(l)'].activity = uf(aH2O, aH2O*H2Ounc)
return QE
def Q_salty_endmember(E, nATP, k_corr):
"""
Get growth parameters for a methanogenesis quotient considering the widest
endmemebers possible. So, variation across the salt content of the ocean
affecting CO2. The 'up' bounds are for a salty ocean with maximised reactants
and minimised products.
The 'low' bounds are for a less salty ocean with maximised products and
minimized reactants.
"""
QE = deepcopy(E)#nc(name='En', pH=ocean_pH, CO2origin='pH', T=T, workoutID=False)
CO2salt, H2Osalt = Enc.get_CO2_from_HTHeating(QE.env.T, QE.ocean_pH, salts=True)
CO2salt273, H2Osalt273 = Enc.get_CO2_from_HTHeating(273.15, QE.ocean_pH, salts=True)
######### Nominal parameters ##########
QE.composition['CO2(aq)'].activity = CO2salt[0]
QE.composition['H2(aq)'].activity = E.calc_mol_H2(CO2salt273[0]).n
QE.composition['Methane(aq)'].activity = E.calc_mol_CH4(CO2salt273[0]).n
QE.composition['H2O(l)'].activity = H2Osalt[0]
# quotient, gibbs, ATPgibbs, PowerSupply, max_k
Q, G, GP, PS, mk = MGparams(QE, k_corr=k_corr, nATP=nATP)
######### min products, max reactants #########
QE.composition['CO2(aq)'].activity = (CO2salt[1])
H2 = E.calc_mol_H2(CO2salt273[1])
QE.composition['H2(aq)'].activity = (H2.n + H2.s)
CH4 = E.calc_mol_CH4(CO2salt273[1])
QE.composition['Methane(aq)'].activity = (CH4.n - CH4.s)
QE.composition['H2O(l)'].activity = (H2Osalt[1])
Q_up, G_up, GP_up, PS_up, mk_up = MGparams(QE, k_corr=k_corr, nATP=nATP)
######### max products, min reactants #########
QE.composition['CO2(aq)'].activity = (CO2salt[2])
H2 = E.calc_mol_H2(CO2salt273[2])
QE.composition['H2(aq)'].activity = (H2.n - H2.s)
CH4 = E.calc_mol_CH4(CO2salt273[2])
QE.composition['Methane(aq)'].activity = (CH4.n + CH4.s)
QE.composition['H2O(l)'].activity = (H2Osalt[2])
Q_do, G_do, GP_do, PS_do, mk_do = MGparams(QE, k_corr=k_corr, nATP=nATP)
return {'Gibbs_Methanogenesis' : np.array([[G, G_do, G_up]]),
'Quotient' : np.array([[Q, Q_do, Q_up]]),
'PowerSupply' : np.array([[PS, PS_do, PS_up]]),
'ATPGibbs': np.array([[GP, GP_do, GP_up]]),
'max_k' : np.array([[mk, mk_do, mk_up]])}
def default_org():
rtr = Enceladus('EncT', nominals=True, T=298)
# make an organism, for this example, use the preset methanogen.
org = TOM(rtr)
org.maintenance.Tdef = 'None'
org.maintenance.pHdef = 'FluxPerm'
return org
def fluxplot():
# make a reactor object
rtr = Enceladus('EncT', nominals=True, T=298)
# make an organism, for this example, use the preset methanogen.
org = TOM(rtr)
org.maintenance.Tdef='None'
org.maintenance.pHdef='FluxPerm'
def_vals = pH_flux_power(org, -1e-3, 1e-10, 1e-10, org.base_volume)
high_phi = pH_flux_power(org, -1e-1, 1e-10, 1e-10, org.base_volume)
high_perms = pH_flux_power(org, -1e-3, 1e-5, 1e-5, org.base_volume)
bigger = pH_flux_power(org, -1e-3, 1e-10, 1e-10, org.base_volume*100)
fig, ax = plt.subplots(figsize=(8,4.5), ncols=2)
cols = ['k', 'tab:blue', 'cyan', 'tab:purple']
alphas = [1, 0.8, 0.4, 0.6]
ax[0].plot(def_vals['pH'], def_vals['fluxH'], c=cols[0],
label=r'NutMEG default values: $\bar{P}_H = 10^{-10},\ \bar{P}_{OH}=10^{-10},\ \Delta\Psi = -10^{-3},\ v = 3.44\times10^{-18}$', alpha=alphas[0])
ax[0].plot(def_vals['pH'], def_vals['fluxOH'], c=cols[0], ls='dashed', alpha=alphas[0])
ax[1].plot(def_vals['pH'], def_vals['pow'], c=cols[0], alpha=alphas[0])
ax[0].plot(high_phi['pH'], high_phi['fluxH'], c=cols[1],
label=r'Increase potential: $\bar{P}_H = 10^{-10},\ \bar{P}_{OH}=10^{-10},\ \Delta\Psi = -0.1,\ v = 3.44\times10^{-18}$', alpha=alphas[1])
ax[0].plot(high_phi['pH'], high_phi['fluxOH'], c=cols[1], ls='dashed', alpha=alphas[1])
ax[1].plot(high_phi['pH'], high_phi['pow'], c=cols[1], alpha=alphas[1])
ax[0].plot(high_perms['pH'], high_perms['fluxH'], c=cols[2],
label=r'Increase permeabilities: $\bar{P}_H = 10^{-5},\ \bar{P}_{OH}=10^{-5},\ \Delta\Psi = -10^{-3},\ v = 3.44\times10^{-18}$', alpha=alphas[2])
ax[0].plot(high_perms['pH'], high_perms['fluxOH'], c=cols[2], ls='dashed', alpha=alphas[2])
ax[1].plot(high_perms['pH'], high_perms['pow'], c=cols[2], alpha=alphas[2])
ax[0].plot(bigger['pH'], bigger['fluxH'], c=cols[3],
label=r'Cell size: $\bar{P}_H = 10^{-10},\ \bar{P}_{OH}=10^{-10},\ \Delta\Psi = -10^{-3},\ v = 3.44\times10^{-16}$', alpha=alphas[3])
ax[0].plot(bigger['pH'], bigger['fluxOH'], c=cols[3], ls='dashed', alpha=alphas[3])
ax[1].plot(bigger['pH'], bigger['pow'], c=cols[3], ls='dashed', alpha=alphas[3])
ax[0].set_title('Goldman-style fluxes for H$^{+}$ and OH$^{-}$')
ax[1].set_title('Corresponding power demands')
ax[0].set_ylabel('Flux of ions [mol (s cell$^{-1}$)]')
ax[1].set_ylabel('Power demand [W cell$^{-1}$]')
for a in ax:
a.set_yscale('log')
a.set_xlabel('external pH')
fig.subplots_adjust(wspace=0.25, top=0.65, right=0.995, left=0.1)
ax[0].legend(bbox_to_anchor=(0., 1.12, 2.25, 1.4), loc=3,
ncol=1, mode="expand", borderaxespad=0.)
# plt.show()
plt.savefig('fluxplot.pdf')
def add_maintenance_lines(ax, colors=['tab:orange', 'k', 'y', 'c']):
T1 = range(273, 375)
PT = tem.MaintenanceRange_nATPs(
Trange=T1,
mCH4=3e-8,
Tlst=False,
fraction=False,
Perform=False,
dbpath='../../NutMEG-Implementations/TOM/allMtestc')
ax.fill_between(T1,
np.log10(PT[1]),
np.log10(PT[2]),
color=colors[0],
alpha=0.6)
T2 = range(273, 400)
# make a TOM and an Enceladus
_Enc = Enceladus('EncT')
TOM = getE_TOM(_Enc)
TE = es.applications.theory_estimates(TOM, _Enc)
Ti, L10, L2 = [], [], []
for t in T2:
TE.loc.change_T(t)
TE.org.get_ESynth(AA=True) # update the synthesis energy
td = TE.temperature_defenses(t)
Ti.append(td['TijhuisAnaerobe'])
L10.append(td['Lever10pc'])
L2.append(td['Lever2pc'])
Ti = np.array(Ti)
ax.fill_between(T2,
np.log10(Ti - (0.32 * Ti)),
np.log10(Ti + (0.32 * Ti)),
color=colors[1],
alpha=0.4,
lw=3.)
ax.fill_between(T2,
np.log10(L10),
np.log10(L2),
color=colors[2],
alpha=0.4)
ax.fill_between([T2[0], T2[-1]], [-18, -18], [-21, -21],
color=colors[3],
alpha=0.4)
return ax
def add_pH_maintenance_lines(ax,
T,
pHrange=[7, 12],
colors=['tab:orange', 'k', 'g', 'c']):
PT = tem.MaintenanceRange_nATPs(
Trange=[T],
mCH4=3e-8,
Tlst=False,
fraction=False,
Perform=False,
dbpath='../../NutMEG-Implementations/TOM/allMtestc')
# pH is only 5--9 as that's as far as the data goes
ax.fill_between([5, 9],
np.log10(PT[1]),
np.log10(PT[2]),
color=colors[0],
alpha=0.4)
_Enc = Enceladus('EncT')
TOM = getE_TOM(_Enc)
TE = es.applications.theory_estimates(TOM, _Enc)
Ti, L10, L2 = [], [], []
td = TE.temperature_defenses(T)
Ti.append(td['Tijhuis'])
L10.append(td['Lever10pc'])
L2.append(td['Lever2pc'])
ax.fill_between(pHrange,
np.log10(Ti),
np.log10(Ti),
color=colors[1],
alpha=0.8,
lw=3.)
ax.fill_between(pHrange,
np.log10(L10),
np.log10(L2),
color=colors[2],
alpha=0.4)
ax.fill_between(pHrange, [-18, -18], [-21, -21],
color=colors[3],
alpha=0.4)
return ax
def pow_perm():
# make a reactor object
rtr = Enceladus('EncT', nominals=True, T=298)
# make an organism, for this example, use the preset methanogen.
org = TOM(rtr)
org.maintenance.Tdef='None'
org.maintenance.pHdef='FluxPerm'
Perm4 = pH_flux_power(org, 1e-3, 1e-6, 1e-6, org.base_volume)
Perm8 = pH_flux_power(org, 1e-3, 1e-11, 1e-11, org.base_volume)
org.locale.env.T = 400
Perm4_350 = pH_flux_power(org, 1e-3, 1e-6, 1e-6, org.base_volume)
Perm8_350 = pH_flux_power(org, 1e-3, 1e-11, 1e-11, org.base_volume)
fig, ax = plt.subplots(figsize=(6,3.25), ncols=2, sharey=True)
ax[0].fill_between(Perm4['pH'], Perm4['pow'], Perm8['pow'])
ax[0].set_title('Variable Permeability')
ax[1].fill_between(Perm8['pH'], Perm8_350['pow'], Perm8['pow'])
ax[1].fill_between(Perm4['pH'], Perm4_350['pow'], Perm4['pow'], facecolor='g')
ax[1].set_title('Variable Temperature')
for a in ax:
a.set_yscale('log')
a.set_xlabel('pH')
a.set_xlim(0,14)
a.set_ylim(1e-27,1e-9)
a.grid(b=True, which='major', color='#666666', linestyle='-', alpha=0.8)
ax[0].set_ylabel('Power Demand [W cell$^{-1}$]')
ax[0].text(10.,10**(-13.25), r'$\bar{P} = 10^{-6}$', c='C0', rotation=40)
ax[0].text(10.,10**(-20.5), r'$\bar{P} = 10^{-11}$', c='C0', rotation=40)
ax[1].text(10.,10**(-13), r'$\bar{P} = 10^{-6}$', c='g', rotation=40)
ax[1].text(10.,10**(-20.5), r'$\bar{P} = 10^{-11}$', c='C0', rotation=40)
fig.subplots_adjust(wspace=0., right=0.995, top=0.905, bottom=0.14)
# plt.show()
plt.savefig('pH_pow_perm.pdf')
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
def complex_fluxes():
rtr = Enceladus('EncT', nominals=True, T=298)
# make an organism, for this example, use the preset methanogen.
org = TOM(rtr)
org.maintenance.Tdef='None'
org.maintenance.pHdef='FluxPerm'
phidicts, phidicts2, pHdicts, pHdicts2 = [], [], [], []
phis= [1, 1e-1, -1e-3, -1e-1, -1]
pHints= [5,6,7,8,9]
nomcols = plt.get_cmap('coolwarm', len(phis)+1)
phicmaplist = [nomcols(i) for i in range(nomcols.N)][1:]
cmap = clr.LinearSegmentedColormap.from_list(
'Custom cmap', phicmaplist, len(phis)+1)
nomcols2 = plt.get_cmap('PRGn', len(pHints)+3)
pHcmaplist = [nomcols2(i) for i in range(nomcols2.N)][1:-2]
pHcmaplist = ['#984ea3', '#fb9a99', '#fdcdac', '#b2df8a', '#33a02c']
cmap2 = clr.LinearSegmentedColormap.from_list(
'Custom cmap2', pHcmaplist, len(pHints)+1)
for i, phi in enumerate(phis):
phidicts.append(pH_flux_power(org, phi, 1e-10, 1e-10, org.base_volume))
phidicts2.append(pH_flux_power(org, phi, 1e-10, 1e-13, org.base_volume))
for i, pHint in enumerate(pHints):
org.pH_interior = pHint
pHdicts.append(pH_flux_power(org, -1e-3, 1e-10, 1e-10, org.base_volume))
pHdicts2.append(pH_flux_power(org, -1e-3, 1e-10, 1e-13, org.base_volume))
fig, ax = plt.subplots(figsize=(7,8), ncols=2, nrows=3)
for i in range(len(phidicts)):
ax[0][0].plot(phidicts[i]['pH'], phidicts[i]['fluxH'], c=phicmaplist[i], linewidth=3)
ax[0][0].plot(phidicts[i]['pH'], phidicts[i]['fluxOH'], ls='dashed', c=phicmaplist[i], linewidth=3)
ax[1][0].plot(phidicts[i]['pH'], phidicts[i]['pow'], c=phicmaplist[i], linewidth=3)
ax[2][0].plot(phidicts2[i]['pH'], phidicts2[i]['pow'], c=phicmaplist[i], linewidth=3)
for i in range(len(pHdicts)):
ax[0][1].plot(pHdicts[i]['pH'], pHdicts[i]['fluxH'], c=pHcmaplist[i], linewidth=3)
ax[0][1].plot(pHdicts[i]['pH'], pHdicts[i]['fluxOH'], ls='dashed', c=pHcmaplist[i], linewidth=3)
ax[1][1].plot(pHdicts[i]['pH'], pHdicts[i]['pow'], c=pHcmaplist[i], linewidth=3)
ax[2][1].plot(pHdicts2[i]['pH'], pHdicts2[i]['pow'], c=pHcmaplist[i], linewidth=3)
for ai in ax:
for a in ai:
a.set_yscale('log')
a.set_xlim(0,14)
a.get_xaxis().set_ticks([1,3,5,7,9,11,13])
a.grid(b=True, which='major', color='#666666', linestyle='-', alpha=0.8)
for a in ax[-1]:
a.set_xlabel('external pH')
a.set_ylim(1e-27,1e-12)
for a in ax[1]:
a.set_ylim(1e-27,1e-12)
a.get_xaxis().set_ticklabels([])
for a in ax[0]:
a.set_ylim(1e-31, 1e-16)
a.get_xaxis().set_ticklabels([])
for a in [ax[0][1], ax[1][1], ax[2][1]]:
a.get_yaxis().set_ticklabels([])
ax[0][0].set_ylabel('Flux of ions [mol (s cell$^{-1}$)]')
ax[1][0].set_ylabel('Power demand [W cell$^{-1}$]')
ax[2][0].set_ylabel('Power demand [W cell$^{-1}$]')
ax[0][0].text(0.42,0.9,'(a)', horizontalalignment='center', verticalalignment='center', transform=ax[0][0].transAxes)
ax[0][1].text(0.42,0.9, '(b)', horizontalalignment='center', verticalalignment='center', transform=ax[0][1].transAxes)
ax[1][0].text(0.42,0.9, '(c)', horizontalalignment='center', verticalalignment='center', transform=ax[1][0].transAxes)
ax[1][1].text(0.42,0.9, '(d)', horizontalalignment='center', verticalalignment='center', transform=ax[1][1].transAxes)
ax[2][0].text(0.42,0.9, '(e)', horizontalalignment='center', verticalalignment='center', transform=ax[2][0].transAxes)
ax[2][1].text(0.42,0.9, '(f)', horizontalalignment='center', verticalalignment='center', transform=ax[2][1].transAxes)
cbaxes = fig.add_axes([0.125, 0.91, 0.425, 0.02])
cbaxes2 = fig.add_axes([0.57, 0.91, 0.425, 0.02])
# plt.colorbar(clr.BoundaryNorm(phis, nomcols.N), cax=cbaxes, label='phi', orientation='horizontal', pad=0.5, aspect=7)
mpl.colorbar.ColorbarBase(cbaxes, cmap=cmap, norm=clr.BoundaryNorm([0.5,1.5,2.5,3.5,4.5], nomcols.N-1),
spacing='proportional', ticks=[0.5,1.5,2.5,3.5,4.5], boundaries=[0,1,2,3,4,5], format='%1i', orientation='horizontal')
cbaxes.set_xticklabels(['1','10$^{-1}$', '$-10^{-3}$', '$-10^{-1}$', '-1'])
cbaxes.set_xlabel('Membrane potential [V]')
cbaxes.xaxis.set_label_position('top')
cbaxes.xaxis.tick_top()
mpl.colorbar.ColorbarBase(cbaxes2, cmap=cmap2, norm=clr.BoundaryNorm(pHints, nomcols2.N-3),
spacing='proportional', ticks=pHints, boundaries=[4.5,5.5,6.5,7.5,8.5,9.5], format='%1i', orientation='horizontal')
cbaxes2.set_xlabel('Internal pH')
cbaxes2.xaxis.set_label_position('top')
cbaxes2.xaxis.tick_top()
# cmappable = ScalarMappable(norm=Normalize(0,1), cmap=cmapper.r2a())
# fig.colorbar(clr.BoundaryNorm(pHints, nomcols.N), cax=cbaxes2, label='pH', orientation='horizontal', pad=0.5, aspect=7)
fig.subplots_adjust(wspace=0.05, hspace=0.07, right=0.995, left=0.125, bottom=0.062, top=0.9)
plt.savefig('complex_fluxes.pdf')
sys.path.append(os.path.dirname(__file__) + '../../NutMEG')
import ThesisSetup
import NutMEG as nm
from NutMEG.reactor.saved_systems.Enceladus import Enceladus
from NutMEG.culture.saved_organisms.TypicalOptimalMethanogen import TypicalOptimalMethanogen as TOM
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
from uncertainties import ufloat as uf
Ts = range(270, 400)
# make a reactor object
rtr = Enceladus('EncT')
# make an organism, for this example, use the preset methanogen.
org = TOM(rtr)
# org.dry_mass=29e-18 # set volume to 1 cubic micron.
TE = nm.apps_theory_estimates(org, rtr)
Tijhuis, TijhuisAerobe, TijhuisAnaerobe = [], [], []
Lever10pc, Lever2pc, Lever1AA = [], [], [] # in-built energy
Lever10pc_ces, Lever2pc_ces, Lever1AA_ces = [], [], [] # constant energy
for i, T in enumerate(Ts):
TE.loc.change_T(T)
TE.org.get_ESynth(AA=True) # update the synthesis energy
td = TE.temperature_defenses(T)
Tijhuis.append(uf(td['Tijhuis'], 0.29 * td['Tijhuis']))
TijhuisAerobe.append(uf(td['TijhuisAerobe'], 0.41 * td['TijhuisAerobe']))