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SK 1 scale data.py
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SK 1 scale data.py
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# The following code is used to scale the TILDAS data
# based on the accepted values for the reference gases
# INPUT: SK Table S-2.csv
# OUTPUT: SK Figure S2a–c.png, SK Figure S3.png, SK Table S-3 part-1.csv
# >>>>>>>>>
# Import libraries
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Import functions
from functions import *
# Plot parameters
plt.rcParams["legend.loc"] = "best"
plt.rcParams.update({'font.size': 7})
plt.rcParams['scatter.edgecolors'] = "k"
plt.rcParams['scatter.marker'] = "o"
plt.rcParams["lines.linewidth"] = 0.5
plt.rcParams["patch.linewidth"] = 0.5
plt.rcParams["figure.figsize"] = (9, 4)
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 print_info(df, d18O_col, Dp17O_col, sample_name):
gas_subset = df[df["SampleName"].str.contains(sample_name)].copy()
d18O_mean = gas_subset[d18O_col].mean()
d18O_std = gas_subset[d18O_col].std()
Dp17O_mean = gas_subset[Dp17O_col].mean()
Dp17O_std = gas_subset[Dp17O_col].std()
N_gas = len(gas_subset)
print(f"{sample_name}, N = {N_gas}, d18O = {d18O_mean:.3f}(±{d18O_std:.3f})‰, ∆'17O = {Dp17O_mean:.0f}(±{Dp17O_std:.0f}) ppm", end="")
# This function applies the acid fractionation factor based on the mineralogy
def applyAFF(d18O_CO2, d17O_CO2, mineral):
# Acid fractionation correction
if mineral == "calcite":
alpha = 1.01025
elif mineral == "aragonite":
alpha = 1.01063
elif mineral == "dolomite":
# alpha = np.exp(11.03/1000) # Sharma and Clayton (1965)
alpha = np.mean([1.01178, 1.01186]) # Rosenbaum and Sheppard (1986)
d18O_AC = (d18O_CO2 + 1000) / alpha - 1000
d17O_AC = (d17O_CO2 + 1000) / (alpha ** 0.523) - 1000
Dp17O_AC = Dp17O(d17O_AC, d18O_AC)
return d18O_AC, d17O_AC, Dp17O_AC
# This function scales the data based on the accepted values of the light and heavy reference gases
# The scaling is done for each measurement period separately
def scaleData(df, project):
df["dateTimeMeasured"] = pd.to_datetime(df["dateTimeMeasured"])
# Perform the scaling for each meausrement period separately
df_samples = pd.DataFrame()
grouped = df.groupby("measurementPeriod")
if grouped.ngroups == 1:
SuppFig = [""]
else:
SuppFig = ["a","b","c","d"]
FigNum = 0
for period, group in grouped:
print(f"\nMeasurement period {period}:")
print_info(group, "d18O", "Dp17O", "light"); print("\t<--- unscaled")
print_info(group, "d18O", "Dp17O", "heavy"); print("\t<--- unscaled")
# Do the scaling here, based on the accepted values of the light and heavy reference gases
# Measured CO2 values
heavy_d18O_measured = group[group["SampleName"].str.contains("heavy")]["d18O"].mean()
heavy_d17O_measured = group[group["SampleName"].str.contains("heavy")]["d17O"].mean()
light_d18O_measured = group[group["SampleName"].str.contains("light")]["d18O"].mean()
light_d17O_measured = group[group["SampleName"].str.contains("light")]["d17O"].mean()
# Accepted CO2 values - values calculated in OH2 scale reference.py
heavy_d18O_accepted = 76.820
heavy_Dp17O_accepted = -213
heavy_d17O_accepted = unprime(heavy_Dp17O_accepted/1000 + 0.528 * prime(heavy_d18O_accepted))
light_d18O_accepted = -1.509
light_Dp17O_accepted = -141
light_d17O_accepted = unprime(light_Dp17O_accepted/1000 + 0.528 * prime(light_d18O_accepted))
# Calculate the scaling factors
slope_d18O = (light_d18O_accepted - heavy_d18O_accepted) / (light_d18O_measured - heavy_d18O_measured)
intercept_d18O = heavy_d18O_accepted - slope_d18O * heavy_d18O_measured
slope_d17O = (light_d17O_accepted - heavy_d17O_accepted) / (light_d17O_measured - heavy_d17O_measured)
intercept_d17O = heavy_d17O_accepted - slope_d17O * heavy_d17O_measured
# Scale the measured values
group["d18O_scaled"] = slope_d18O*group['d18O']+intercept_d18O
group["d17O_scaled"] = slope_d17O*group['d17O']+intercept_d17O
group["Dp17O_scaled"] = Dp17O(group["d17O_scaled"], group["d18O_scaled"])
# Print out the scaled values for the carbonate standards for each measurement period
standards = ["DH11", "NBS18", "IAEA603"]
for standard in standards:
if standard in group["SampleName"].values:
only_standard = group[group["SampleName"].str.contains(standard)].copy()
only_standard[["d18O_AC", "d17O_AC", "Dp17O_AC"]] = only_standard.apply(lambda x: applyAFF(x["d18O_scaled"], x["d17O_scaled"], "calcite"), axis=1, result_type="expand")
print_info(only_standard, "d18O_AC", "Dp17O_AC", standard); print("\t<--- scaled + AFF")
# Assign colors and markers to samples
categories = group["SampleName"].unique()
markers = dict(zip(categories, ["o", "s", "D", "v", "^",
"<", ">", "p", "P", "*",
"o", "s", "D", "v", "^",
"<", ">", "p", "P", "*"]))
colors = dict(zip(categories, plt.cm.tab20(np.linspace(0, 1, 20))))
# Figure: unscaled Dp17O vs time
fig, ax = plt.subplots()
for cat in categories:
data = group[group["SampleName"] == cat]
ax.scatter(data["dateTimeMeasured"], data["Dp17O"],
marker=markers[cat], fc=colors[cat], label=cat)
if np.isnan(data["Dp17OError"]).any() == False:
plt.errorbar(group["dateTimeMeasured"], group["Dp17O"],
yerr=group["Dp17OError"],
fmt="none", color="#cacaca", zorder=0)
plt.legend(loc='upper right', bbox_to_anchor=(1.18, 1))
plt.title(f"Measurement period: {period}")
# Axis properties
ax.set_ylabel("$\Delta\prime^{17}$O (ppm, unscaled CO$_2$)")
ax.set_xlabel("Measurement date")
ax.text(0.02, 0.98, "(" + SuppFig[FigNum] + ")", size=10, ha="left", va="top",
transform=ax.transAxes)
plt.savefig(os.path.join(sys.path[0], f"{project} Figure S2{SuppFig[FigNum]}"))
plt.close()
# Exclude the standards from the exported dataframe
group = group[~group["SampleName"].str.contains("heavy|light|NBS|DH11|IAEA")]
df_samples = pd.concat([df_samples, group])
FigNum += 1
return df_samples
# This function averages the scaled data from multiple measurement periods
def average_data(df):
# Calculate the mean values from the replicate measurements
df = df.loc[:, ["SampleName", "d18O_scaled", "d17O_scaled", "Dp17O_scaled"]]
df_mean = df.groupby('SampleName').mean().reset_index()
df_mean = df_mean.rename(columns={'d18O_scaled': 'd18O_CO2', 'd17O_scaled': 'd17O_CO2', 'Dp17O_scaled': 'Dp17O_CO2'})
# Calculate the standard deviation from the replicate measurements
df_std = df.groupby('SampleName').std().reset_index()
df_std = df_std.rename(columns={'d18O_scaled': 'd18O_error', 'd17O_scaled': 'd17O_error', 'Dp17O_scaled': 'Dp17O_error'})
dfMerged = df_mean.merge(df_std, on='SampleName')
dfMerged['Replicates'] = df.groupby('SampleName').size().reset_index(name='counts')['counts']
df = dfMerged
return df
# Here we go!
# Scale the data
df = scaleData(pd.read_csv(os.path.join(sys.path[0], "SK Table S-2.csv")), "SK")
# Average the data
df_avg = average_data(df)
# Apply acid fractionation factor
df_avg[["d18O_AC", "d17O_AC", "Dp17O_AC"]] = df_avg.apply(lambda x: applyAFF(x["d18O_CO2"], x["d17O_CO2"], "aragonite"), axis=1, result_type="expand")
# Print the average values
# print("\nAll sample replicates averaged:")
# print(df_avg.round({"Dp17O_CO2": 0, "Dp17O_error": 0, "Dp17O_AC": 0}).round(3))
# Create Figure S2
fig, (ax1, ax2) = plt.subplots(1, 2)
# Assign colors and markers to samples
df.sort_values(by="SampleName", inplace=True)
categories = df["SampleName"].unique()
markers = dict(zip(categories, ["o", "s", "D", "^", "v", "X", "P", "*", "o",
"s", "D", "^", "v", "X", "P", "*", "o", "s", "D", "^", "v", "X", "P", "*"]))
colors = dict(zip(categories, plt.cm.tab20(
np.linspace(0, 1, len(categories)))))
# Subplot A
for cat in categories:
data = df[df["SampleName"] == cat]
ax1.scatter(prime(data["d18O_scaled"]), data["Dp17O_scaled"],
marker=markers[cat], fc=colors[cat], label=cat)
ax1.errorbar(prime(df["d18O_scaled"]), df["Dp17O_scaled"],
yerr=df["Dp17OError"], xerr=df["d18OError"],
fmt="none", color="#cacaca", zorder=0)
# Axis properties
ylim = ax1.get_ylim()
xlim = ax1.get_xlim()
ax1.set_ylabel("$\Delta\prime^{17}$O (ppm, CO$_2$)")
ax1.set_xlabel("$\delta\prime^{18}$O (‰, VSMOW, CO$_2$)")
ax1.text(0.02, 0.98, "(a)", size=10, ha="left", va="top",
transform=ax1.transAxes)
# Subplot B
for cat in categories:
data = df_avg[df_avg["SampleName"] == cat]
ax2.scatter(prime(data["d18O_CO2"]), data["Dp17O_CO2"],
marker=markers[cat], fc=colors[cat], label=cat)
ax2.errorbar(prime(df_avg["d18O_CO2"]), df_avg["Dp17O_CO2"],
yerr=df_avg["Dp17O_error"], xerr=df_avg["d18O_error"],
fmt="none", color="#cacaca", zorder=0)
ax2.legend(loc='upper right', bbox_to_anchor=(1.35, 1))
# Axis properties
ax2.set_ylim(ylim)
ax2.set_xlim(xlim)
ax2.set_ylabel("$\Delta\prime^{17}$O (ppm, CO$_2$)")
ax2.set_xlabel("$\delta\prime^{18}$O (‰, VSMOW, CO$_2$)")
ax2.text(0.02, 0.98, "(b)", size=10, ha="left", va="top",
transform=ax2.transAxes)
plt.savefig(os.path.join(sys.path[0], "SK Figure S3"))
plt.close()
# Print some values for the manuscript
print(f"\nAverage error for Dp17O: {df_avg['Dp17O_error'].mean():.0f} ppm")
print(f"Average error for d18O: {df_avg['d18O_error'].mean():.1f} ppm")
# Add the coral datapoint from Passey et al. (2014) to the dataset
# Normalization between the Passey et al. (2014) and Wostbrock et al. (2020) reference frames
d18O_CO2_Passey = unprime(35.902)
Dp17O_CO2_Passey = -129
d17O_CO2_Passey = d17O(d18O_CO2_Passey, Dp17O_CO2_Passey)
alpha = 1/0.9919498 # empirical fractionation, from Huth et al. (2022)
d18O_AC_Passey = (d18O_CO2_Passey + 1000) / alpha - 1000
d17O_AC_Passey = (d17O_CO2_Passey + 1000) / (alpha ** 0.5234) - 1000
Dp17O_AC_Passey = Dp17O(d17O_AC_Passey, d18O_AC_Passey)
# Account for the difference in the acid fractionation factors
# between calcite and aragonite, only for d18O
d18O_AC_Passey += A_from_a(1.008541, d18O_CO2_Passey) - A_from_a(1.008146, d18O_CO2_Passey)
d17O_AC_Passey = d17O(d18O_AC_Passey, Dp17O_AC_Passey)
Passey_data = {"SampleName": "JBC03",
"d18O_CO2": d18O_CO2_Passey,
"d17O_CO2": d17O_CO2_Passey,
"Dp17O_CO2": Dp17O_CO2_Passey,
"d18O_error": 0.6,
"d17O_error": np.nan,
"Dp17O_error": 13,
"Replicates": 3,
"d18O_AC": d18O_AC_Passey,
"d17O_AC": d17O_AC_Passey,
"Dp17O_AC": Dp17O_AC_Passey,
}
Passey_data_df = pd.DataFrame([Passey_data])
df_avg = pd.concat([df_avg, Passey_data_df], ignore_index=True)
# Export CSV
df_avg.to_csv(os.path.join(sys.path[0], "SK Table S-3 part-1.csv"), index=False)