-
Notifications
You must be signed in to change notification settings - Fork 0
/
UG 2 reconstruct fluid carbonates.py
324 lines (241 loc) · 13 KB
/
UG 2 reconstruct fluid carbonates.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
# This code is used to:
# Model ambient water oxygen isotope compositions based on the late calcite data
# INPUT: UG Table S4.csv (carbonate data)
# OUTPUT: UG fluid model late calcite.csv (modeled compositions)
# >>>>>>>>>
# Import libraries
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from tqdm import tqdm
from functions import *
# Plot parameters
plt.rcParams.update({"font.size": 6})
plt.rcParams["figure.figsize"] = (8, 8)
plt.rcParams["patch.linewidth"] = 0.5
plt.rcParams['lines.linewidth'] = 0.5
plt.rcParams["savefig.dpi"] = 600
plt.rcParams["savefig.bbox"] = "tight"
plt.rcParams['savefig.transparent'] = False
plt.rcParams['mathtext.default'] = 'regular'
# Functions that make life easier
def a18_cc(T):
return np.exp((17.57 * 1000 / T - 29.13) / 1000) # Daeron et al. (2019) – calcite
# Alternative equations:
# 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)
# return np.exp((2.84 * 10**6 / T**2 - 2.96) / 1000) # Wostbrock et al. (2020) – calcite
# return np.exp((17.88 * 1000 / T - 31.14) / 1000) # Kim et al. (2007) – aragonite
# return 0.0201 * (1000 / T) + 0.9642 # Guo and Zhou (2019) – aragonite
def theta_cc(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))
# Alternative equations:
# return -1.39 / T + 0.5305 # Wostbrock et al. (2020) – calcite
# return 59.1047/T**2 + -1.4089/T + 0.5297 # Guo and Zhou (2019) – aragonite
# return -1.53 / T + 0.5305 # Wostbrock et al. (2020) – aragonite
def a17_cc(T):
return a18_cc(T)**theta_cc(T)
def d18O_cc(equilibrium_temperatures, d18Ow):
return a18_cc(equilibrium_temperatures) * (d18Ow+1000) - 1000
def d17O_cc(equilibrium_temperatures, d17Ow):
return a17_cc(equilibrium_temperatures) * (d17Ow+1000) - 1000
# Read calcite data from CSV file
df = pd.read_csv(os.path.join(sys.path[0], "UG Table S4.csv"))
# Filter data if needed
df = df[df["Type"] == "late"]
# Rename columns
df["Dp17O"] = df["Dp17O_AC"]
df["d18O"] = df["d18O_AC"]
df["d17O"] = df["d17O_AC"]
print(df)
# Plot parameters
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
# First plot and model
# Plot the samples
ax1.scatter(prime(df["d18O"]), Dp17O(df["d17O"], df["d18O"]),
marker="o", fc="#1455C0", ec="w", lw=0.5,
zorder=10, label="samples")
# Range of d18Ow and Dp17Ow values to consider in the model
d18Ow_min, d18Ow_max, d18Ow_step = -9, 3, 0.2
Dp17Ow_min, Dp17Ow_max, Dp17Ow_step = -50, 10, 0.5
# Temperature range for the equilibrium calculations
T_min, T_max = 0, 300
equilibrium_temperatures = np.arange(T_min, T_max + 1, 1) + 273.15
model_length = ((d18Ow_max-d18Ow_min)/d18Ow_step) * ((Dp17Ow_max-Dp17Ow_min)/Dp17Ow_step)
print(f"Modeling {model_length:.0f} fluids")
# Create an empty dataframe to store the modeled values
modeldf = pd.DataFrame(columns=["d18Ow", "d17Ow", "Dp17Ow", "sum_distance",
"avg_temperature", "min_temperature", "max_temperature"])
with tqdm(total=model_length) as pbar:
for d18Ow in np.arange(d18Ow_min, d18Ow_max, d18Ow_step):
for Dp17Ow in np.arange(Dp17Ow_min, Dp17Ow_max, Dp17Ow_step):
d17Ow = d17O(d18Ow, Dp17Ow)
# Calculate the equilibrium points
d18O_mineral = d18O_cc(equilibrium_temperatures, d18Ow)
d17O_mineral = d17O_cc(equilibrium_temperatures, d17Ow)
mineral_equilibrium = np.array([d18O_mineral, Dp17O(
d17O_mineral, d18O_mineral), equilibrium_temperatures]).T
ax1.plot(prime(mineral_equilibrium[:, 0]), mineral_equilibrium[:, 1],
ls="solid", color="grey", alpha=0.3, zorder=-1,
label="quartz equilibrium")
ax1.scatter(prime(d18Ow), Dp17O(d17Ow, d18Ow),
marker="d", fc="w", ec="k",
label=f"model fluids ($\\mathit{{N}}$ = {model_length:.0f})")
data = []
for i, row in df.iterrows():
A = np.array([row["d18O"], row["Dp17O"]])
distances = np.linalg.norm(mineral_equilibrium[:, :2] - A, axis=1)
mindist = np.min(distances)
closest_index = np.argmin(distances)
closest_point = mineral_equilibrium[closest_index]
tempera = closest_point[2]
ax1.plot([prime(A[0]), prime(closest_point[0])], [A[1], closest_point[1]],
color="#63A615", ls="-", linewidth=0.4, alpha=0.3,
label="distance to closest equi. point")
data.append({"distances": mindist, "temperatures": tempera})
modeldfa = pd.DataFrame(data)
modeldf = modeldf.dropna(axis=1, how='all')
modeldf = pd.concat([modeldf, pd.DataFrame({"d17Ow": [np.round(d17Ow, 4)],
"d18Ow": [d18Ow],
"Dp17Ow": [Dp17Ow],
"sum_distance": [np.round(np.sum(modeldfa["distances"]), 4)],
"avg_temperature": [np.round(np.mean(modeldfa["temperatures"])-273.15, 1)],
"min_temperature": [np.min(modeldfa["temperatures"])-273.15],
"max_temperature": [np.max(modeldfa["temperatures"])-273.15]}
)
], ignore_index=True)
modeldfa = []
pbar.update(1)
print("Modeling complete")
handles, labels = ax1.get_legend_handles_labels()
by_label = dict(zip(labels, handles))
ax1.legend(by_label.values(), by_label.keys(), loc="upper right")
# Plot axes
ax1.text(0.02, 0.98, "a", fontsize=14, fontweight="bold",
va="top", ha="left", transform=ax1.transAxes)
ax1.set_xlabel("$\delta\prime^{18}$O (‰, VSMOW)")
ax1.set_ylabel("$\Delta\prime^{17}$O (ppm)")
ax1.set_ylim(-155, 105)
ax1.set_xlim(-25, 55)
print("Plot 1 complete")
# Second plot
ax2.scatter(modeldf["sum_distance"], modeldf["avg_temperature"],
marker="d", fc="w", ec="k", label=f"model fluids ($\\mathit{{N}}$ = {model_length:.0f})")
# Define the cut-off values
T_cut_lower = 33
T_cut_upper = 46
Dist_cut = modeldf['sum_distance'].quantile(0.10)
# Display the cut-off values
ax2.axhline(y=T_cut_upper, color="#EC0016", linestyle="-", zorder=3, label = "cut-off")
ax2.axhline(y=T_cut_lower, color="#EC0016", linestyle="-", zorder=3)
ax2.axvline(x=Dist_cut, color="#EC0016", linestyle="-", zorder=3)
xmin, xmax = ax2.get_xlim()
ax2.text(xmax, T_cut_upper, str(T_cut_upper)+" °C",
color="#EC0016", va="bottom", ha="right",
bbox=dict(fc="w", pad=0.1, ec="none", alpha=0.8))
ax2.text(xmax, T_cut_lower, str(T_cut_lower)+" °C",
color="#EC0016", va="top", ha="right",
bbox=dict(fc="w", pad=0.1, ec="none", alpha=0.8))
# Filter the modeled fluids
modeldf = modeldf[modeldf["sum_distance"] <= Dist_cut]
modeldf = modeldf[modeldf["avg_temperature"] >= T_cut_lower]
modeldf = modeldf[modeldf["avg_temperature"] <= T_cut_upper]
ax2.scatter((modeldf["sum_distance"]), modeldf["avg_temperature"],
marker="d", fc="#EC0016", ec="#EC0016", label=f"best-fit fluids ($\\mathit{{N}}$ = {modeldf.shape[0]:.0f})")
ax2.legend(loc="upper right")
ax2.set_xlabel("sum of distances")
ax2.set_ylabel("average temperature (°C)")
ax2.text(0.02, 0.98, "b", fontsize=14, fontweight="bold",
va="top", ha="left", transform=ax2.transAxes,
bbox=dict(fc="w", pad=0.1, ec="none", alpha=0.8))
print("Plot 2 complete")
# Third plot
ax3.scatter(prime(df["d18O"]), Dp17O(df["d17O"], df["d18O"]),
marker="o", fc="#1455C0", ec="w", lw=0.5,
zorder=10, label="samples")
# Rectangle for the fluid range considered in the model
ax3.add_patch(Rectangle((d18Ow_min, Dp17Ow_min), d18Ow_max-d18Ow_min,
(Dp17Ow_max-Dp17Ow_min),
fc="#DDDED6", ec=None, zorder=-10))
for _, row in modeldf.iterrows():
ax3.scatter(prime(row["d18Ow"]), Dp17O(row["d17Ow"], row["d18Ow"]),
marker="d", fc="#EC0016", ec="#EC0016", label="best-fit fluids", zorder=2)
# Plot the equilibrium line
d18O_mineral = d18O_cc(equilibrium_temperatures, row["d18Ow"])
d17O_mineral = d17O_cc(equilibrium_temperatures, row["d17Ow"])
ax3.scatter(prime(d18O_mineral), Dp17O(d17O_mineral, d18O_mineral),
s=0.5, marker="o", fc = "k", ec="none", alpha=0.3,
label=f"equilibrium ({np.min(equilibrium_temperatures-273.15):.0f}–{np.max(equilibrium_temperatures-273.15):.0f} °C)")
confidence_ellipse(prime(modeldf["d18Ow"]), modeldf["Dp17Ow"], ax3,
ec="k", zorder=10, label="$\pm$1$\sigma$ CI, modeled fluids")
handles, labels = ax3.get_legend_handles_labels()
by_label = dict(zip(labels, handles))
ax3.legend(by_label.values(), by_label.keys(), loc="upper right")
ax3.set_xlabel("$\delta\prime^{18}$O (‰, VSMOW)")
ax3.set_ylabel("$\Delta\prime^{17}$O (ppm)")
ax3.text(0.02, 0.98, "c", fontsize=14, fontweight="bold",
va="top", ha="left", transform=ax3.transAxes,
bbox=dict(fc="w", pad=0.1, ec="none", alpha=0.8))
ax3.set_ylim(-155, 105)
ax3.set_xlim(-25, 55)
print("Plot 3 complete")
# Fourth plot
ax4.scatter(prime(df["d18O"]), Dp17O(df["d17O"], df["d18O"]),
marker="o", fc="#1455C0", ec="w", lw=0.5,
zorder=10, label="samples")
# Rectangle for the fluid range considered in the model
ax4.add_patch(Rectangle((d18Ow_min, Dp17Ow_min), d18Ow_max-d18Ow_min,
(Dp17Ow_max-Dp17Ow_min),
fc="#DDDED6", ec=None, zorder=-10))
# model water
mean_d18Ow = modeldf["d18Ow"].mean()
sd_d18Ow = modeldf["d18Ow"].std()
mean_d17Ow = np.mean(modeldf["d17Ow"])
mean_Dp17Ow = np.mean(modeldf["Dp17Ow"])
sd_Dp17Ow = np.std(modeldf["Dp17Ow"])
ax4.errorbar(prime(mean_d18Ow), mean_Dp17Ow,
xerr=sd_d18Ow,
yerr=sd_Dp17Ow,
ecolor="k", marker="d", mfc="w", mec="k", ls="none",
label="modeled fluid")
confidence_ellipse(prime(modeldf["d18Ow"]), modeldf["Dp17Ow"], ax4,
ec="k", zorder=10, label="$\pm$1$\sigma$ CI, modeled fluids")
ax4.text(0.05, 0.2,
"modeled best-fit fluid composition:\n" +
f"$\delta^{{18}}$O: {mean_d18Ow:.1f}$\pm${sd_d18Ow:.1f}‰\n$\Delta\prime^{{17}}$O: {mean_Dp17Ow:.0f}$\pm${sd_Dp17Ow:.0f} ppm",
color="k", ha="left", va="top", transform=ax4.transAxes)
equilibrium_temperatures = np.arange(T_min, T_max+1, 1) + 273.15
d18O_mineral = d18O_cc(equilibrium_temperatures, mean_d18Ow)
d17O_mineral = d17O_cc(equilibrium_temperatures, mean_d17Ow)
ax4.plot(prime(d18O_mineral), Dp17O(d17O_mineral, d18O_mineral),
":", lw=0.5, zorder=1, color="k",
label=f"equilibrium ({np.min(equilibrium_temperatures-273.15):.0f}–{np.max(equilibrium_temperatures-273.15):.0f} °C)")
equilibrium_temperatures = np.arange(10, 81, 1) + 273.15
d18O_mineral = d18O_cc(equilibrium_temperatures, mean_d18Ow)
d17O_mineral = d17O_cc(equilibrium_temperatures, mean_d17Ow)
ax4.plot(prime(d18O_mineral), Dp17O(d17O_mineral, d18O_mineral),
"-", lw=2, mec="white", zorder=1, color="k",
label=f"equilibrium ({np.min(equilibrium_temperatures-273.15):.0f}–{np.max(equilibrium_temperatures-273.15):.0f} °C)")
ax4.set_xlabel("$\delta\prime^{18}$O (‰, VSMOW)")
ax4.set_ylabel("$\Delta\prime^{17}$O (ppm)")
ax4.set_ylim(-155, 105)
ax4.set_xlim(-25, 55)
ax4.legend(loc="upper right")
ax4.text(0.02, 0.98, "d", fontsize=14, fontweight="bold",
va="top", ha="left", transform=ax4.transAxes)
print("Plot 4 complete")
plt.savefig(os.path.join(sys.path[0], "UG Figure S6.png"))
print("Figure saved")
modeldf.to_csv(os.path.join(sys.path[0], "UG fluid model late calcite.csv"), index=False)
print("Modeled fluid values exported to CSV")