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OH2 6 triple oxygen.py
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OH2 6 triple oxygen.py
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# This code creates Figures 3 and 4 of the manuscript
# INPUT: OH2 Table S3.csv
# OUTPUT: OH2 Figure 4.png, OH2 Graphical Abstract.png
# >>>>>>>>>
# Import libraries
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from functions import *
# Plot parameters
plt.rcParams.update({'font.size': 7})
plt.rcParams['scatter.edgecolors'] = "k"
plt.rcParams['scatter.marker'] = "o"
plt.rcParams['lines.markersize'] = 5
plt.rcParams["lines.linewidth"] = 0.5
plt.rcParams["patch.linewidth"] = 0.5
plt.rcParams["figure.figsize"] = (4, 4)
plt.rcParams["savefig.dpi"] = 800
plt.rcParams["savefig.bbox"] = "tight"
plt.rcParams['savefig.transparent'] = False
plt.rcParams['mathtext.default'] = 'regular'
# Functions that make life easier
def a18cal(T):
# Hayles et al. (2018) - calcite
B_calcite = 7.027321E+14 / T**7 + -1.633009E+13 / T**6 + 1.463936E+11 / T**5 + -5.417531E+08 / T**4 + -4.495755E+05 / T**3 + 1.307870E+04 / T**2 + -5.393675E-01 / T + 1.331245E-04
B_water = -6.705843E+15 / T**7 + 1.333519E+14 / T**6 + -1.114055E+12 / T**5 + 5.090782E+09 / T**4 + -1.353889E+07 / T**3 + 2.143196E+04 / T**2 + 5.689300 / T + -7.839005E-03
return np.exp(B_calcite) / np.exp(B_water)
def theta_cal(T):
# Hayles et al. (2018) - calcite
K_calcite = 1.019124E+09 / T**5 + -2.117501E+07 / T**4 + 1.686453E+05 / T**3 + -5.784679E+02 / T**2 + 1.489666E-01 / T + 0.5304852
B_calcite = 7.027321E+14 / T**7 + -1.633009E+13 / T**6 + 1.463936E+11 / T**5 + -5.417531E+08 / T**4 + -4.495755E+05 / T**3 + 1.307870E+04 / T**2 + -5.393675E-01 / T + 1.331245E-04
K_water = 7.625734E+06 / T**5 + 1.216102E+06 / T**4 + -2.135774E+04 / T**3 + 1.323782E+02 / T**2 + -4.931630E-01 / T + 0.5306551
B_water = -6.705843E+15 / T**7 + 1.333519E+14 / T**6 + -1.114055E+12 / T**5 + 5.090782E+09 / T**4 + -1.353889E+07 / T**3 + 2.143196E+04 / T**2 + 5.689300 / T + -7.839005E-03
a18 = np.exp(B_calcite) / np.exp(B_water)
return K_calcite + (K_calcite-K_water) * (B_water / np.log(a18))
def a17cal(T):
return a18cal(T)**theta_cal(T)
def d18Ocal(T, d18Ow):
return a18cal(T) * (d18Ow+1000) - 1000
def d17Ocal(T, d18Ow):
return a17cal(T) * (d18Ow+1000) - 1000
def plot_calcite_equilibrium(Dp17Ow, d18Ow, Tmin, Tmax, ax, fluid_name="precipitating fluid", color="k"):
d17Ow = unprime(0.528 * prime(d18Ow) + Dp17Ow/1000)
ax.scatter(prime(d18Ow), Dp17O(d17Ow, d18Ow),
marker="D", fc=color, ec="k", zorder=3, label=fluid_name)
# equilibrium, entire T range
toInf = np.arange(0, 330, 1) + 273.15
d18O_mineral = d18Ocal(toInf, d18Ow)
d17O_mineral = d17Ocal(toInf, d17Ow)
mineral_equilibrium = np.array(
[d18O_mineral, Dp17O(d17O_mineral, d18O_mineral), toInf]).T
ax.plot(prime(mineral_equilibrium[:, 0]), mineral_equilibrium[:, 1],
":", c=color, zorder=3)
# equilibrium, highlight range
equilibrium_temperatures = np.arange(Tmin, Tmax, 0.5) + 273.15
colors = np.linspace(0, 1, len(equilibrium_temperatures))
d18O_mineral = d18Ocal(equilibrium_temperatures, d18Ow)
d17O_mineral = d17Ocal(equilibrium_temperatures, d17Ow)
mineral_equilibrium = np.array([d18O_mineral, Dp17O(
d17O_mineral, d18O_mineral), equilibrium_temperatures]).T
ax.scatter(prime(mineral_equilibrium[:, 0]), mineral_equilibrium[:, 1],
marker=".", c=colors, cmap='coolwarm', zorder=3)
# equilibrium, highlight range, marker every 10 °C
equilibrium_temperatures = np.arange(Tmin, Tmax+1, 10) + 273.15
d18O_mineral = d18Ocal(equilibrium_temperatures, d18Ow)
d17O_mineral = d17Ocal(equilibrium_temperatures, d17Ow)
mineral_equilibrium = np.array([d18O_mineral, Dp17O(
d17O_mineral, d18O_mineral), equilibrium_temperatures]).T
ax.scatter(prime(mineral_equilibrium[:, 0]), mineral_equilibrium[:, 1],
s=15, marker="o", fc="white", ec=color, zorder=3, label="calcite equilibrium (" + str(Tmin) + "–" + str(Tmax) + " °C)")
# Return equilibrium data as a dataframe
equilibrium_df = pd.DataFrame(mineral_equilibrium)
equilibrium_df[2] = equilibrium_df[2]-273.15
equilibrium_df = equilibrium_df.rename(
columns={0: 'd18O', 1: 'Dp17O', 2: 'temperature'})
return equilibrium_df
def a18OH(T=273.15+22, eq="Z20-X3LYP"):
if (eq == "Z20-X3LYP"):
e18_H2O_OH = (-4.4573 + (10.3255 * 10**3) /
(T) + (-0.5976 * 10**6) / (T)**2)
elif (eq == "Z20-MP2"):
e18_H2O_OH = (-4.0771 + (9.8350 * 10**3) /
(T) + (-0.8729 * 10**6) / (T)**2)
elif (eq == "BH21_original"):
e18_H2O_OH = -0.034 * (T-273.15) + 43.4
elif (eq == "BH21"):
e18_H2O_OH = -0.035 * (T-273.15) + 40.1
return e18_H2O_OH / 1000 + 1
def a17OH(T = 273.15+22, eq = "Z20-X3LYP", theta = 0.530):
return a18OH(T, eq)**theta
def calculate_OH(d18O_CO2, Dp17O_CO2, d18O_precipitate, Dp17O_precipitate):
d18O_OH = (d18O_precipitate - 2/3 * d18O_CO2) * 3
d17O_OH = (d17O(d18O_precipitate, Dp17O_precipitate) - 2/3 * d17O(d18O_CO2, Dp17O_CO2)) * 3
return d18O_OH, Dp17O(d17O_OH, d18O_OH)
monte_carlo_iterations = 10**3
# Read in TILDAS data
df = pd.read_csv(os.path.join(sys.path[0], "OH2 Table S3.csv"))
# Isotope composition of CO2 gas
d18O_CO2 = df.loc[df['SampleName'] == 'KoelnRefCO2-2', 'd18O_CO2'].iloc[0]
d18O_CO2_err = df.loc[df['SampleName'] == 'KoelnRefCO2-2', 'd18O_error'].iloc[0] #/ np.sqrt(df.loc[df['SampleName'] == 'KoelnRefCO2-2', 'Replicates'].iloc[0])
Dp17O_CO2 = df.loc[df['SampleName'] == 'KoelnRefCO2-2', 'Dp17O_CO2'].iloc[0]
Dp17O_CO2_err = df.loc[df['SampleName'] == 'KoelnRefCO2-2', 'Dp17O_error'].iloc[0]
print(f"\nThe composition of the tank CO2 is: d18O = {d18O_CO2:.2f}(±{d18O_CO2_err:.2f})‰, ∆'17O = {Dp17O_CO2:.0f}(±{Dp17O_CO2_err:.0f}) ppm")
df = df[~df['SampleName'].str.contains('Koeln')]
# Isotope composition of the water
d18O_water = -7.92
d18O_water_err = 0.71
Dp17O_water = 25
Dp17O_water_err = 5
print(f"\nThe composition of the water is: d18O = {d18O_water:.2f}(±{d18O_water_err:.2f})‰, ∆'17O = {Dp17O_water:.0f}(±{Dp17O_water_err:.0f}) ppm")
# Average of the precipitates
d18O_precipitate = df['d18O_AC'].mean()
d18O_precipitate_err = df['d18O_AC'].std()
Dp17O_precipitate = df['Dp17O_AC'].mean()
Dp17O_precipitate_err = df['Dp17O_AC'].std()
print(f"\nThe mean composition of the precipitates is: d18O = {d18O_precipitate:.2f}(±{d18O_precipitate_err:.2f})‰, ∆'17O = {Dp17O_precipitate:.0f}(±{Dp17O_precipitate_err:.0f}) ppm")
# Calculate the composition of the KIE OH- using Monte Carlo simulations
d18O_OH_lst = []
Dp17O_OH_lst = []
d18O_CO2_lst = []
d18O_precipitate_lst = []
Dp17O_CO2_lst = []
Dp17O_precipitate_lst = []
for _ in range(monte_carlo_iterations):
sample_d18O_CO2 = np.random.normal(d18O_CO2, d18O_CO2_err)
sample_Dp17O_CO2 = np.random.normal(Dp17O_CO2, Dp17O_CO2_err)
sample_d18O_precipitate = np.random.normal(d18O_precipitate, d18O_precipitate_err)
sample_Dp17O_precipitate = np.random.normal(Dp17O_precipitate, Dp17O_precipitate_err)
result_d18O, result_Dp17O = calculate_OH(sample_d18O_CO2, sample_Dp17O_CO2, sample_d18O_precipitate, sample_Dp17O_precipitate)
d18O_OH_lst.append(result_d18O)
Dp17O_OH_lst.append(result_Dp17O)
d18O_CO2_lst.append(sample_d18O_CO2)
d18O_precipitate_lst.append(sample_d18O_precipitate)
Dp17O_CO2_lst.append(sample_Dp17O_CO2)
Dp17O_precipitate_lst.append(sample_Dp17O_precipitate)
d18O_OH = np.mean(d18O_OH_lst)
d18O_OH_err = np.std(d18O_OH_lst)
Dp17O_OH = np.mean(Dp17O_OH_lst)
Dp17O_OH_err = np.std(Dp17O_OH_lst)
print(f"\nThe calcualted composition of the OH- is: d18O = {d18O_OH:.2f}(±{d18O_OH_err:.2f})‰, Dp17O = {Dp17O_OH:.0f}(±{Dp17O_OH_err:.0f}) ppm")
# Calculate the effective H2O/OH- theta using Monte Carlo simulations
theta_effective_lst = []
for _ in range(monte_carlo_iterations):
sample_d18O_OH = np.random.normal(d18O_OH, d18O_OH_err)
sample_Dp17O_OH = np.random.normal(Dp17O_OH, Dp17O_OH_err)
sample_d18O_water = np.random.normal(d18O_water, d18O_water_err)
sample_Dp17O_water = np.random.normal(Dp17O_water, Dp17O_water_err)
result = calculate_theta(sample_d18O_OH, sample_Dp17O_OH, sample_d18O_water, sample_Dp17O_water)
theta_effective_lst.append(result)
theta_effective = np.round(np.mean(theta_effective_lst), 3)
theta_effective_err = np.round(np.std(theta_effective_lst), 3)
print(f"\nThe effective H2O/OH- theta is: {theta_effective}(±{theta_effective_err})")
# Calculate the OH-(KIE)/OH-(equilibrium) theta using Monte Carlo simulations
theta_eq_H2O_OH = 0.5296
e18_theoretical_model = "Z20-X3LYP" # Z20-X3LYP or Z20-MP2
d18O_OH_eq = B_from_a(a18OH(T=273.15+22, eq=e18_theoretical_model), d18O_water)
Dp17O_OH_eq = Dp17O(B_from_a(a17OH(T=273.15+22, eq=e18_theoretical_model, theta=theta_eq_H2O_OH), d17O(d18O_water, Dp17O_water)), d18O_OH_eq)
theta_kinetic_lst = []
for _ in range(monte_carlo_iterations):
sample_d18O_OH = np.random.normal(d18O_OH, d18O_OH_err)
sample_Dp17O_OH = np.random.normal(Dp17O_OH, Dp17O_OH_err)
sample_d18O_OH_eq = d18O_OH_eq
sample_Dp17O_OH_eq = Dp17O(B_from_a(a17OH(T = 273.15+22, eq = e18_theoretical_model, theta = theta_eq_H2O_OH), d17O(d18O_water, Dp17O_water)), sample_d18O_OH_eq)
result = calculate_theta(sample_d18O_OH, sample_Dp17O_OH, sample_d18O_OH_eq, sample_Dp17O_OH_eq)
theta_kinetic_lst.append(result)
theta_kinetic = np.round(np.mean(theta_kinetic_lst),3)
theta_kinetic_err = np.round(np.std(theta_kinetic_lst),3)
print(f"\nThe kinetic OH-/OH- theta is: {theta_kinetic}(±{theta_kinetic_err})")
fig, ax = plt.subplots()
# Monte Carlo results
# ax.scatter(apply_prime_to_list(d18O_OH_lst), Dp17O_OH_lst,
# marker=".", fc="#cacaca", alpha=0.1, zorder=-2)
# ax.scatter(apply_prime_to_list(d18O_CO2_lst), Dp17O_CO2_lst,
# marker=".", fc="#cacaca", alpha=0.1, zorder=-2)
# ax.scatter(apply_prime_to_list(d18O_precipitate_lst), Dp17O_precipitate_lst,
# marker=".", fc="#cacaca", alpha=0.1, zorder=-2)
# water
ax.scatter(prime(d18O_water), Dp17O_water,
marker="*", fc="k", ec="k", s = 50, label="H$_2$O")
ax.errorbar(prime(d18O_water), Dp17O_water, xerr=d18O_water_err, yerr=Dp17O_water_err,
fmt="None", ecolor="k", zorder=-1)
ax.text(prime(d18O_water)+2, Dp17O_water, "H$_2$O",
ha="left", va="center", color="k")
# KIE OH-
ax.scatter(prime(d18O_OH), Dp17O_OH,
marker="s", fc="k", ec="k", label="OH$^-$")
ax.errorbar(prime(d18O_OH), Dp17O_OH, xerr=d18O_OH_err, yerr=Dp17O_OH_err,
fmt="None", ecolor="k", zorder=-1)
ax.text(prime(d18O_OH)+2, Dp17O_OH,
r"OH$^{-}$ $\plus$ KIE",
ha="left", va="center", color="k")
# Line between effective OH- and water
ax.text((prime(d18O_OH) + prime(d18O_water))/2+25, (Dp17O_OH + Dp17O_water)/2,
r"$\theta_{H_2O/OH^-}^{effective}$ = " + f"{theta_effective} \n(±{theta_effective_err})",
ha="right", va="center", color="#EC0016")
ax.annotate("", xy=(prime(d18O_OH), Dp17O_OH), xycoords='data',
xytext=(prime(d18O_water), Dp17O_water), textcoords='data',
arrowprops=dict(arrowstyle="<|-|>", color="#EC0016", lw=1.5), zorder = -1)
# OH- equilibrium
ax.scatter(prime(d18O_OH_eq), Dp17O_OH_eq,
marker="s", fc="k")
ax.text(prime(d18O_OH_eq)-2, Dp17O_OH_eq,
r"OH$^{-}$",
ha="right", va="center", color="k")
print(f"\nThe difference in Dp17O between effective and equilibrium OH- is {Dp17O_OH-Dp17O_OH_eq:.0f} ppm")
# Line between equilibrium OH- and water
ax.text(0, -15,
r"$\theta_{H_2O/OH^-}^{equilibrium}$ = " + f"{theta_eq_H2O_OH}",
ha="right", va="center", color="#EC0016")
ax.annotate("", xy=(prime(d18O_OH_eq), Dp17O_OH_eq), xycoords='data',
xytext=(prime(d18O_water), Dp17O_water), textcoords='data',
arrowprops=dict(arrowstyle="<|-|>", color="#EC0016", lw=1.5), zorder = -1)
# Line between effective OH- equilibrium OH-
grahams_law = np.round((np.log((16+1)/(17+1)))/(np.log((16+1)/(18+1))),3)
print(f"\nThe KIE theta is: {theta_kinetic}. The expected value based on Graham's law is: {grahams_law}")
ax.text((prime(d18O_OH) + prime(d18O_OH_eq))/2+10, (Dp17O_OH + Dp17O_OH_eq)/2,
r"$\theta_{OH^-}^{KIE}$ = " + f"{theta_kinetic} \n(±{theta_kinetic_err})",
ha="right", va="top", color="#EC0016",
bbox=dict(fc='white', ec="None", alpha=0.8, pad=0.1))
ax.annotate("", xy=(prime(d18O_OH), Dp17O_OH), xycoords='data',
xytext=(prime(d18O_OH_eq), Dp17O_OH_eq), textcoords='data',
arrowprops=dict(arrowstyle="-|>", color="#EC0016", lw=1.5), zorder = -1)
# CO2
ax.scatter(prime(d18O_CO2), Dp17O_CO2,
marker="D", fc="k", ec="k", label="CO$_2$")
ax.errorbar(prime(d18O_CO2), Dp17O_CO2, xerr=d18O_CO2_err, yerr=Dp17O_CO2_err,
fmt="None", ecolor="k", zorder=-1)
ax.text(prime(d18O_CO2)-2, Dp17O_CO2,
"CO$_2$",
ha="right", va="center", color="k")
# Precipitate
ax.scatter(prime(d18O_precipitate), Dp17O_precipitate,
marker="o", c="#38342F", ec="k", label="precipitates")
ax.errorbar(prime(d18O_precipitate), Dp17O_precipitate, xerr=d18O_precipitate_err, yerr=Dp17O_precipitate_err,
fmt="None", ecolor="#38342F", zorder=-1)
ax.text(prime(d18O_precipitate), (Dp17O_precipitate+20), "witherite\nprecipitates",
ha="center", va="bottom", color="#38342F",
bbox=dict(fc='white', ec="None", alpha=0.8, pad=0.1))
# BaCO3 in equilibrium
d18OBaCO3 = d18Ocal(22+273.15, d18O_water)
d17OBaCO3 = d17Ocal(22+273.15, d17O(d18O_water, Dp17O_water))
ax.scatter(prime(d18OBaCO3), Dp17O(d17OBaCO3, d18OBaCO3),
marker="o", c="w", ec = "k", label="equilibrium carbonate")
ax.text(prime(d18OBaCO3), Dp17O(d17OBaCO3, d18OBaCO3)+10, "equilibrium\ncarbonate",
ha="center", va="bottom", color="k")
# Mixing curve between KIE OH- and CO2
mixdf = mix_d17O(d18O_OH, D17O_A=Dp17O_OH, d18O_B=d18O_CO2, D17O_B=Dp17O_CO2)
ax.plot(prime(mixdf["mix_d18O"]), mixdf["mix_Dp17O"],
color="#282D37", lw=.5, ls=":", zorder=-2)
ax.set_ylabel("$\Delta\prime^{17}$O (ppm)")
ax.set_xlabel("$\delta\prime^{18}$O (‰, VSMOW)")
# Save figure
plt.savefig(os.path.join(sys.path[0], "OH2 Figure 4.png"))
plt.close("all")
# Create abstract graphics
plt.rcParams["figure.figsize"] = (13/2, 5/2)
plt.rcParams.update({'font.size': 14})
plt.rcParams["lines.linewidth"] = 1 # error bar width
plt.rcParams["patch.linewidth"] = 1 # marker edge width
fig, ax = plt.subplots()
fig.patch.set_facecolor('#1455C0')
ax.set_facecolor('#1455C0')
# change axis colors to white
ax.spines['bottom'].set_color('w')
ax.spines['top'].set_color('w')
ax.spines['left'].set_color('w')
ax.spines['right'].set_color('w')
ax.xaxis.label.set_color('w')
ax.yaxis.label.set_color('w')
ax.tick_params(axis='x', colors='w')
ax.tick_params(axis='y', colors='w')
# water
ax.scatter(prime(d18O_water), Dp17O_water,
fc="w", ec="#1455C0", marker="o", s=50, zorder=3)
ax.text(prime(d18O_water), Dp17O_water-15,
"H$_2$O",
ha="center", va="top", color="w")
# KIE OH-
ax.scatter(prime(d18O_OH), Dp17O_OH,
fc="w", ec="#1455C0", marker="o", s=50, zorder=3)
ax.text(prime(d18O_OH), Dp17O_OH+15,
r"OH$^{-}$ + KIE",
ha="center", va="bottom", color="w")
# Line between effective OH- and water
ax.text((prime(d18O_OH) + prime(d18O_water))/2+13, (Dp17O_OH + Dp17O_water)/2+25,
r"$\theta_{H_2O/OH^-}^{effective}$ = " + f"{theta_effective}",
ha="right", va="center", color="w",
fontsize=12)
ax.annotate("", xy=(prime(d18O_OH), Dp17O_OH), xycoords='data',
xytext=(prime(d18O_water), Dp17O_water), textcoords='data',
arrowprops=dict(arrowstyle="<|-|>", color="w", lw=1.5), zorder = -1)
# OH- equilibrium
ax.scatter(prime(d18O_OH_eq), Dp17O_OH_eq,
fc="w", ec="#1455C0", marker="o", s=50, zorder=3)
ax.text(prime(d18O_OH_eq), Dp17O_OH_eq-15,
r"OH$^{-}$",
ha="center", va="top", color="w")
# Line between equilibrium OH- and water
ax.text((prime(d18O_OH_eq) + prime(d18O_water))/2, (Dp17O_OH_eq + Dp17O_water)/2-20,
r"$\theta_{H_2O/OH^-}^{equilibrium}$ = " + f"{theta_eq_H2O_OH}",
ha="center", va="top", color="w",
fontsize=12)
ax.annotate("", xy=(prime(d18O_OH_eq), Dp17O_OH_eq), xycoords='data',
xytext=(prime(d18O_water), Dp17O_water), textcoords='data',
arrowprops=dict(arrowstyle="<|-|>", color="w", lw=1.5), zorder = -1)
# Line between effective OH- equilibrium OH-
grahams_law = np.round((np.log((16+1)/(17+1)))/(np.log((16+1)/(18+1))),3)
ax.text((prime(d18O_OH) + prime(d18O_OH_eq))/2, (Dp17O_OH + Dp17O_OH_eq)/2-30,
r"$\theta_{OH^-}^{KIE}$ = " + f"{theta_kinetic}",
ha="right", va="center", color="w",
fontsize=12)
ax.annotate("", xy=(prime(d18O_OH), Dp17O_OH), xycoords='data',
xytext=(prime(d18O_OH_eq), Dp17O_OH_eq), textcoords='data',
arrowprops=dict(arrowstyle="-|>", color="w", lw=1.5), zorder = -1)
# Axis parameters
ax.set_ylabel("$\Delta\prime^{17}$O")
ax.set_xlabel("$\delta\prime^{18}$O")
ax.set_ylim(-90, 290)
ax.set_xlim(-53, -5)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
# Save figure
plt.savefig(os.path.join(sys.path[0], "OH2 Graphical Abstract.png"))
plt.close("all")