This page was generated from docs/Examples/S6_S2_Corrections/S6_S2_Corrections_Nash_Jugo_Kleinsasser.ipynb. Interactive online version: Binder badge.

Python Notebook Download

Correcting SCSS for S6+ and SCAS for S2-

[30]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import PySulfSat as ss
import Thermobar as pt
pd.options.display.max_columns = None

Visualization

First, lets visualize for a linspace of delta QFM values, how different parameters change, using the S6/St ratios from Jugo et al. (2010)

[31]:

SCSS=1000
SCAS=5000
DeltaQFM_lin=np.linspace(-1, 3, 10001)
QFM_point1=DeltaQFM_lin==-1
QFM_point2=DeltaQFM_lin==1
QFM_point4=DeltaQFM_lin==2


df_Species=ss.calculate_S_Total_SCSS_SCAS(deltaQFM=DeltaQFM_lin,
            SCSS=SCSS, SCAS=SCAS, model='Jugo')

df_Species.head()

[31]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1000.079433 1000.0 0.079433 -1.0000 0.000079 1000 5000 1000.079433 6.295127e+07 0.079433 6.294627e+07
1 1000.079579 1000.0 0.079579 -0.9996 0.000080 1000 5000 1000.079579 6.283543e+07 0.079579 6.283043e+07
2 1000.079726 1000.0 0.079726 -0.9992 0.000080 1000 5000 1000.079726 6.271979e+07 0.079726 6.271479e+07
3 1000.079873 1000.0 0.079873 -0.9988 0.000080 1000 5000 1000.079873 6.260438e+07 0.079873 6.259938e+07
4 1000.080020 1000.0 0.080020 -0.9984 0.000080 1000 5000 1000.080020 6.248917e+07 0.080020 6.248417e+07 
[32]:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))
SCSS_Tot_excessS6=df_Species['S6 in SCSS_Tot']>df_Species['SCAS_6_ppm']

ax1.plot(df_Species['deltaQFM'].loc[~SCSS_Tot_excessS6],
         df_Species['SCSS_Tot'].loc[~SCSS_Tot_excessS6], '-m', label='SCSS Tot')

ax1.plot(df_Species['deltaQFM'].loc[SCSS_Tot_excessS6],
         df_Species['SCSS_Tot'].loc[SCSS_Tot_excessS6], ':m')

SCAS_Tot_excessS2=df_Species['S2 in SCAS_Tot']>df_Species['SCSS_2_ppm']

ax1.plot(df_Species['deltaQFM'].loc[~SCAS_Tot_excessS2],
         df_Species['SCAS_Tot'].loc[~SCAS_Tot_excessS2], '-k', label='SCAS_Tot')
ax1.plot(df_Species['deltaQFM'].loc[SCAS_Tot_excessS2],
         df_Species['SCAS_Tot'].loc[SCAS_Tot_excessS2], ':k')

ax1.plot([-1, 3], [SCSS, SCSS], '-m', lw=0.5, label='S2- sol limit')
ax1.plot([-1, 3], [SCAS, SCAS], '-k', lw=0.5, label='S6 sol limit')
ax1.plot([-1, 3], [SCAS+SCSS, SCAS+SCSS], '-g', lw=0.5, label='ST sol limit')
ax1.legend()
#ax1.legend()

maxS=SCSS+SCAS
maxX=df_Species['deltaQFM'].loc[df_Species['Total_S_ppm']==maxS]
ax2.plot(df_Species['deltaQFM'], df_Species['Total_S_ppm'], '-g', lw=2, label='Total S')

ax2.yaxis.set_tick_params(which='both', labelbottom=True)
ax2b=ax2.twinx()
ax2.plot(df_Species['deltaQFM'], df_Species['S2_Tot_ppm'], '-r', lw=0.5, label='S$^{2}$')
ax2.plot(df_Species['deltaQFM'], df_Species['S6_Tot_ppm'], '-', color='grey', lw=0.5, label='S$^{6}$')

ax2b.plot(df_Species['deltaQFM'], df_Species['S6St_Liq'], '-c', label='S$^{6}$/$_{T}$')
ax2.legend()
ax2b.plot([maxX, maxX], [0, 1], ':c')
ax1.set_ylim([0, 10000])
ax2.set_ylim([0, 10000])
ax2b.set_ylim([0, 1])
ax1.set_ylabel('S (ppm)')
ax2.set_ylabel('S (ppm)')
ax1.set_xlabel('ΔQFM')
ax2.set_xlabel('ΔQFM')
ax2b.set_ylabel('S$^{6+}$/S$_{T}$')
fig.tight_layout()
fig.savefig('SCSS_SCAS.png', dpi=300)
../../_images/Examples_S6_S2_Corrections_S6_S2_Corrections_Nash_Jugo_Kleinsasser_4_0.png 
[33]:

QFM2=ss.calculate_S_Total_SCSS_SCAS(deltaQFM=2,
            SCSS=SCSS, SCAS=SCAS, model='Jugo')
QFM2

[33]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 5062.946271 62.946271 5000.0 2 0.987567 1000 5000 80432.823472 5062.946271 79432.823472 62.946271 
[34]:

SCAS_Tot=ss.calculate_SCAS_Total (SCAS=5000, S6St_Liq=0.9876)
SCAS_Tot

[34]:

5062.778452814905

Now lets apply it to some Petrolog fractionation path

[35]:

df_out=ss.import_data('PetrologCalculations.xlsx', Petrolog=True)
df_out.head()

We have replaced all missing liquid oxides and strings with zeros.

[35]:
SiO2_Liq TiO2_Liq Al2O3_Liq FeOt_Liq MnO_Liq MgO_Liq CaO_Liq Na2O_Liq K2O_Liq P2O5_Liq H2O_Liq Fe3Fet_Liq Ni_Liq_ppm Cu_Liq_ppm SiO2_magma TiO2_magma Al2O3_magma Fe2O3_magma FeO_magma MnO_magma MgO_magma CaO_magma Na2O_magma K2O_magma P2O5_magma Cr2O3_magma Ni_magma Cu_magma Cr2O3_Liq Ni_Liq Cu_Liq SiO2_cumulate TiO2_cumulate Al2O3_cumulate Fe2O3_cumulate FeO_cumulate MnO_cumulate MgO_cumulate CaO_cumulate Na2O_cumulate K2O_cumulate P2O5_cumulate Cr2O3_cumulate Ni_cumulate Cu_cumulate Temperature Temperature_Olv Olv_Fo_magma Olv_Kd Olv_Fo_cumulate Pressure(kbar) Lg(fO2) dNNO density Ln(viscosity) Melt_%_magma Olv_%_magma Olv_Peritectic Fluid_%_magma Olv_%_cumulate Sample Unnamed:58 T_K P_kbar Fraction_melt Sample_ID_Liq
0 49.9010 0.9981 14.9715 8.980926 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0 0.098489 697.8 99.8 49.9010 0.9981 14.9715 0.9839 8.0964 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0998 697.8 99.8 0.0998 697.8 99.8 40.2986 0 0 0 11.7372 0 46.9121 0 0 0 0 0 8257.6 10.0 1253.281 1253.281 87.69 0.308 87.69 1 -7.72 -0.7 2.683 6.25 99.9900 0 N 0 0.0100 PetrologDefault 08:21:15 1526.431 1 0.999900 0
1 49.9978 1.0081 15.1220 8.951296 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.0 0.097851 624.6 100.7 49.9978 1.0081 15.1220 0.9743 8.0754 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.1008 624.6 100.7 0.1008 624.6 100.7 40.2698 0 0 0 11.9273 0 46.7855 0 0 0 0 0 7984.5 10.0 1243.430 1243.430 87.28 0.309 87.49 1 -7.83 -0.7 2.682 6.38 98.9950 0 N 0 1.0050 PetrologDefault 08:21:15 1516.580 1 0.989950 1
2 50.0982 1.0185 15.2770 8.916645 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.0 0.097284 554.8 101.6 50.0982 1.0185 15.2770 0.9649 8.0492 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.1018 554.8 101.6 0.1018 554.8 101.6 40.2390 0 0 0 12.1280 0 46.6507 0 0 0 0 0 7708.8 10.1 1233.064 1233.064 86.83 0.310 87.27 1 -7.94 -0.7 2.681 6.52 97.9904 0 N 0 2.0096 PetrologDefault 08:21:15 1506.214 1 0.979904 2
3 50.2003 1.0289 15.4337 8.877334 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.0 0.096823 489.9 102.6 50.2003 1.0289 15.4337 0.9561 8.0178 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.1029 489.9 102.6 0.1029 489.9 102.6 40.2069 0 0 0 12.3359 0 46.5096 0 0 0 0 0 7436.5 10.1 1222.361 1222.361 86.36 0.311 87.05 1 -8.05 -0.7 2.680 6.67 96.9959 0 N 0 3.0041 PetrologDefault 08:21:15 1495.511 1 0.969959 3
4 50.3062 1.0397 15.5950 8.832002 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.0 0.096445 428.7 103.5 50.3062 1.0397 15.5950 0.9475 7.9802 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.1040 428.7 103.5 0.1040 428.7 103.5 40.1725 0 0 0 12.5558 0 46.3589 0 0 0 0 0 7162.8 10.2 1211.080 1211.080 85.84 0.312 86.81 1 -8.18 -0.7 2.679 6.83 95.9923 0 N 0 4.0077 PetrologDefault 08:21:15 1484.230 1 0.959923 4

Calculate Smythe SCSS with a fixed sulfide composition

[36]:

Smythe_FixedSulf=ss.calculate_S2017_SCSS(df=df_out,
T_K=df_out['T_K'], P_kbar=df_out['P_kbar'],
Fe3Fet_Liq=df_out['Fe3Fet_Liq'],
Fe_FeNiCu_Sulf=0.65)
Smythe_FixedSulf.head()

Using inputted Fe_FeNiCu_Sulf ratio for calculations.
Index(['SiO2_Liq', 'TiO2_Liq', 'Al2O3_Liq', 'FeOt_Liq', 'MnO_Liq', 'MgO_Liq',
       'CaO_Liq', 'Na2O_Liq', 'K2O_Liq', 'P2O5_Liq', 'H2O_Liq', 'Fe3Fet_Liq',
       'Ni_Liq_ppm', 'Cu_Liq_ppm', 'SiO2_magma', 'TiO2_magma', 'Al2O3_magma',
       'Fe2O3_magma', 'FeO_magma', 'MnO_magma', 'MgO_magma', 'CaO_magma',
       'Na2O_magma', 'K2O_magma', 'P2O5_magma', 'Cr2O3_magma', 'Ni_magma',
       'Cu_magma', 'Cr2O3_Liq', 'Ni_Liq', 'Cu_Liq', 'SiO2_cumulate',
       'TiO2_cumulate', 'Al2O3_cumulate', 'Fe2O3_cumulate', 'FeO_cumulate',
       'MnO_cumulate', 'MgO_cumulate', 'CaO_cumulate', 'Na2O_cumulate',
       'K2O_cumulate', 'P2O5_cumulate', 'Cr2O3_cumulate', 'Ni_cumulate',
       'Cu_cumulate', 'Temperature', 'Temperature_Olv', 'Olv_Fo_magma',
       'Olv_Kd', 'Olv_Fo_cumulate', 'Pressure(kbar)', 'Lg(fO2)', 'dNNO',
       'density', 'Ln(viscosity)', 'Melt_%_magma', 'Olv_%_magma',
       'Olv_Peritectic', 'Fluid_%_magma', 'Olv_%_cumulate', 'Sample',
       'Unnamed:58', 'T_K', 'P_kbar', 'Fraction_melt', 'Sample_ID_Liq',
       'Fe_FeNiCu_Sulf_calc'],
      dtype='object')
no non ideal SCSS as no Cu/CuFeNiCu

[36]:
SCSS2_ppm_ideal_Smythe2017 SCSS2_ppm_ideal_Smythe2017_1sigma T_Input_K P_Input_kbar Fe_FeNiCu_Sulf Fe3Fet_Liq_input Si_wt_atom Ti_wt_atom Al_wt_atom Mg_wt_atom Mn_wt_atom Fe2_wt_atom Fe3_wt_atom Ca_wt_atom Na_wt_atom K_wt_atom P_wt_atom H_wt_atom Si_XA_ideal Ti_XA_ideal Al_XA_ideal Mg_XA_ideal Fe2_XA_ideal Ca_XA_ideal Na_XA_ideal K_XA_ideal H_XA_ideal Si*Fe_ideal Si_XA_non_ideal Ti_XA_non_ideal Al_XA_non_ideal Mg_XA_non_ideal Fe2_XA_non_ideal Ca_XA_non_ideal Na_XA_non_ideal K_XA_non_ideal H_XA_non_ideal Si*Fe_non_ideal log_SCSS_ideal Fe_FeNiCu_Sulf_calc SiO2_Liq TiO2_Liq Al2O3_Liq FeOt_Liq MnO_Liq MgO_Liq CaO_Liq Na2O_Liq K2O_Liq P2O5_Liq H2O_Liq Fe3Fet_Liq Ni_Liq_ppm Cu_Liq_ppm SiO2_magma TiO2_magma Al2O3_magma Fe2O3_magma FeO_magma MnO_magma MgO_magma CaO_magma Na2O_magma K2O_magma P2O5_magma Cr2O3_magma Ni_magma Cu_magma Cr2O3_Liq Ni_Liq Cu_Liq SiO2_cumulate TiO2_cumulate Al2O3_cumulate Fe2O3_cumulate FeO_cumulate MnO_cumulate MgO_cumulate CaO_cumulate Na2O_cumulate K2O_cumulate P2O5_cumulate Cr2O3_cumulate Ni_cumulate Cu_cumulate Temperature Temperature_Olv Olv_Fo_magma Olv_Kd Olv_Fo_cumulate Pressure(kbar) Lg(fO2) dNNO density Ln(viscosity) Melt_%_magma Olv_%_magma Olv_Peritectic Fluid_%_magma Olv_%_cumulate Sample Unnamed:58 T_K P_kbar Fraction_melt Sample_ID_Liq Fe_FeNiCu_Sulf_calc
0 1163.632126 317.869126 1526.431 1 0.65 0.098489 0.458760 0.006900 0.162217 0.136728 0.000777 0.062247 0.006799 0.117975 0.044478 0.002341 0.000777 0.0 -12643.917846 -77.425761 -2992.954217 -1910.042747 -2133.469827 -923.846254 -589.186980 -67.922629 -0.0 3328.767457 -12843.656196 -73.937634 -3082.117373 -1984.261077 -2172.132780 -1041.911444 -609.912056 -66.914620 -0.0 3364.402871 7.059302 0.65 49.9010 0.9981 14.9715 8.980926 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0 0.098489 697.8 99.8 49.9010 0.9981 14.9715 0.9839 8.0964 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0998 697.8 99.8 0.0998 697.8 99.8 40.2986 0 0 0 11.7372 0 46.9121 0 0 0 0 0 8257.6 10.0 1253.281 1253.281 87.69 0.308 87.69 1 -7.72 -0.7 2.683 6.25 99.9900 0 N 0 0.0100 PetrologDefault 08:21:15 1526.431 1 0.999900 0 0.65
1 1132.187652 309.279446 1516.580 1 0.65 0.097851 0.460128 0.006977 0.164018 0.131795 0.000786 0.062150 0.006740 0.119285 0.044970 0.002367 0.000785 0.0 -12681.596545 -78.282676 -3026.179002 -1841.131776 -2130.145227 -934.101871 -595.707729 -68.674435 -0.0 3333.484439 -12881.930112 -74.755944 -3116.331958 -1912.672440 -2168.747932 -1053.477703 -616.662177 -67.655269 -0.0 3369.170350 7.031907 0.65 49.9978 1.0081 15.1220 8.951296 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.0 0.097851 624.6 100.7 49.9978 1.0081 15.1220 0.9743 8.0754 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.1008 624.6 100.7 0.1008 624.6 100.7 40.2698 0 0 0 11.9273 0 46.7855 0 0 0 0 0 7984.5 10.0 1243.430 1243.430 87.28 0.309 87.49 1 -7.83 -0.7 2.682 6.38 98.9950 0 N 0 1.0050 PetrologDefault 08:21:15 1516.580 1 0.989950 1 0.65
2 1099.523987 300.356720 1506.214 1 0.65 0.097284 0.461540 0.007056 0.165875 0.126736 0.000794 0.062014 0.006682 0.120635 0.045480 0.002394 0.000794 0.0 -12720.511949 -79.173986 -3060.433090 -1770.461624 -2125.481450 -944.675207 -602.466551 -69.463239 -0.0 3336.392958 -12921.460271 -75.607099 -3151.606511 -1839.256265 -2163.999637 -1065.402284 -623.658745 -68.432367 -0.0 3372.110005 7.002633 0.65 50.0982 1.0185 15.2770 8.916645 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.0 0.097284 554.8 101.6 50.0982 1.0185 15.2770 0.9649 8.0492 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.1018 554.8 101.6 0.1018 554.8 101.6 40.2390 0 0 0 12.1280 0 46.6507 0 0 0 0 0 7708.8 10.1 1233.064 1233.064 86.83 0.310 87.27 1 -7.94 -0.7 2.681 6.52 97.9904 0 N 0 2.0096 PetrologDefault 08:21:15 1506.214 1 0.979904 2 0.65
3 1066.279038 291.275205 1495.511 1 0.65 0.096823 0.462968 0.007136 0.167753 0.121655 0.000804 0.061837 0.006628 0.122000 0.045995 0.002421 0.000803 0.0 -12759.875643 -80.066770 -3095.084610 -1699.479235 -2119.422207 -955.366577 -609.283904 -70.253349 -0.0 3337.176747 -12961.445800 -76.459662 -3187.290335 -1765.515721 -2157.830588 -1077.459984 -630.715903 -69.210751 -0.0 3372.902185 6.971930 0.65 50.2003 1.0289 15.4337 8.877334 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.0 0.096823 489.9 102.6 50.2003 1.0289 15.4337 0.9561 8.0178 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.1029 489.9 102.6 0.1029 489.9 102.6 40.2069 0 0 0 12.3359 0 46.5096 0 0 0 0 0 7436.5 10.1 1222.361 1222.361 86.36 0.311 87.05 1 -8.05 -0.7 2.680 6.67 96.9959 0 N 0 3.0041 PetrologDefault 08:21:15 1495.511 1 0.969959 3 0.65
4 1031.735153 281.838860 1484.230 1 0.65 0.096445 0.464443 0.007218 0.169688 0.116454 0.000813 0.061613 0.006575 0.123408 0.046526 0.002449 0.000813 0.0 -12800.521565 -80.994067 -3130.789539 -1626.819177 -2111.747851 -966.392392 -616.316520 -71.046416 -0.0 3335.684853 -13002.733814 -77.345183 -3224.058949 -1690.032319 -2150.017156 -1089.894870 -637.995897 -69.992049 -0.0 3371.394320 6.938997 0.65 50.3062 1.0397 15.5950 8.832002 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.0 0.096445 428.7 103.5 50.3062 1.0397 15.5950 0.9475 7.9802 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.1040 428.7 103.5 0.1040 428.7 103.5 40.1725 0 0 0 12.5558 0 46.3589 0 0 0 0 0 7162.8 10.2 1211.080 1211.080 85.84 0.312 86.81 1 -8.18 -0.7 2.679 6.83 95.9923 0 N 0 4.0077 PetrologDefault 08:21:15 1484.230 1 0.959923 4 0.65

Calculate SCAS using ZT2022

[37]:

ZT22_SCAS=ss.calculate_ZT2022_SCAS(df=df_out, T_K=df_out['T_K'])
ZT22_SCAS.head()

[37]:
SCAS6_ppm SiO2_Liq TiO2_Liq Al2O3_Liq FeOt_Liq MnO_Liq MgO_Liq CaO_Liq Na2O_Liq K2O_Liq P2O5_Liq H2O_Liq Fe3Fet_Liq Ni_Liq_ppm Cu_Liq_ppm SiO2_magma TiO2_magma Al2O3_magma Fe2O3_magma FeO_magma MnO_magma MgO_magma CaO_magma Na2O_magma K2O_magma P2O5_magma Cr2O3_magma Ni_magma Cu_magma Cr2O3_Liq Ni_Liq Cu_Liq SiO2_cumulate TiO2_cumulate Al2O3_cumulate Fe2O3_cumulate FeO_cumulate MnO_cumulate MgO_cumulate CaO_cumulate Na2O_cumulate K2O_cumulate P2O5_cumulate Cr2O3_cumulate Ni_cumulate Cu_cumulate Temperature Temperature_Olv Olv_Fo_magma Olv_Kd Olv_Fo_cumulate Pressure(kbar) Lg(fO2) dNNO density Ln(viscosity) Melt_%_magma Olv_%_magma Olv_Peritectic Fluid_%_magma Olv_%_cumulate Sample Unnamed:58 T_K P_kbar Fraction_melt Sample_ID_Liq
0 8800.801280 49.9010 0.9981 14.9715 8.980926 0 9.9763 11.9772 2.4953 0.1996 0.0998 0.0 0.098489 697.8 99.8 49.9010 0.9981 14.9715 0.9839 8.0964 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0998 697.8 99.8 0.0998 697.8 99.8 40.2986 0 0 0 11.7372 0 46.9121 0 0 0 0 0 8257.6 10.0 1253.281 1253.281 87.69 0.308 87.69 1 -7.72 -0.7 2.683 6.25 99.9900 0 N 0 0.0100 PetrologDefault 08:21:15 1526.431 1 0.999900 0
1 8329.162079 49.9978 1.0081 15.1220 8.951296 0 9.6064 12.0976 2.5203 0.2016 0.1008 0.0 0.097851 624.6 100.7 49.9978 1.0081 15.1220 0.9743 8.0754 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.1008 624.6 100.7 0.1008 624.6 100.7 40.2698 0 0 0 11.9273 0 46.7855 0 0 0 0 0 7984.5 10.0 1243.430 1243.430 87.28 0.309 87.49 1 -7.83 -0.7 2.682 6.38 98.9950 0 N 0 1.0050 PetrologDefault 08:21:15 1516.580 1 0.989950 1
2 7866.305310 50.0982 1.0185 15.2770 8.916645 0 9.2279 12.2216 2.5462 0.2037 0.1018 0.0 0.097284 554.8 101.6 50.0982 1.0185 15.2770 0.9649 8.0492 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.1018 554.8 101.6 0.1018 554.8 101.6 40.2390 0 0 0 12.1280 0 46.6507 0 0 0 0 0 7708.8 10.1 1233.064 1233.064 86.83 0.310 87.27 1 -7.94 -0.7 2.681 6.52 97.9904 0 N 0 2.0096 PetrologDefault 08:21:15 1506.214 1 0.979904 2
3 7420.997354 50.2003 1.0289 15.4337 8.877334 0 8.8486 12.3469 2.5723 0.2058 0.1029 0.0 0.096823 489.9 102.6 50.2003 1.0289 15.4337 0.9561 8.0178 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.1029 489.9 102.6 0.1029 489.9 102.6 40.2069 0 0 0 12.3359 0 46.5096 0 0 0 0 0 7436.5 10.1 1222.361 1222.361 86.36 0.311 87.05 1 -8.05 -0.7 2.680 6.67 96.9959 0 N 0 3.0041 PetrologDefault 08:21:15 1495.511 1 0.969959 3
4 6983.909186 50.3062 1.0397 15.5950 8.832002 0 8.4612 12.4760 2.5992 0.2079 0.1040 0.0 0.096445 428.7 103.5 50.3062 1.0397 15.5950 0.9475 7.9802 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.1040 428.7 103.5 0.1040 428.7 103.5 40.1725 0 0 0 12.5558 0 46.3589 0 0 0 0 0 7162.8 10.2 1211.080 1211.080 85.84 0.312 86.81 1 -8.18 -0.7 2.679 6.83 95.9923 0 N 0 4.0077 PetrologDefault 08:21:15 1484.230 1 0.959923 4

Now combine to calculate the total S, using S6+/ST ratio from Nash

[38]:

df_ST_Nash=ss.calculate_S_Total_SCSS_SCAS(Fe3Fet_Liq=df_out['Fe3Fet_Liq'],
    SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
    T_K=df_out['T_K'],
    SCAS=ZT22_SCAS['SCAS6_ppm'], model='Nash')
df_ST_Nash.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[38]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1181.411865 1163.632126 17.779739 None 0.015050 1163.632126 8800.801280 1181.411865 5.847876e+05 17.779739 5.759868e+05
1 1146.119468 1132.187652 13.931816 None 0.012156 1132.187652 8329.162079 1146.119468 6.852097e+05 13.931816 6.768805e+05
2 1110.380551 1099.523987 10.856564 None 0.009777 1099.523987 7866.305310 1110.380551 8.045448e+05 10.856564 7.966785e+05
3 1074.748164 1066.279038 8.469126 None 0.007880 1066.279038 7420.997354 1074.748164 9.417386e+05 8.469126 9.343176e+05
4 1038.301658 1031.735153 6.566504 None 0.006324 1031.735153 6983.909186 1038.301658 1.104302e+06 6.566504 1.097318e+06

Using Jugo et al. (2010)

First we need to calculate delta QFM relative to Frost, which is what Jugo has to use.

[39]:

# You will need to pip install thermobar
#!pip install Thermobar
import Thermobar as pt
pt.__version__

[39]:

'1.0.17'

[40]:

Buffer_calc=pt.convert_fo2_to_buffer(fo2=10**df_out['Lg(fO2)'],
                                     T_K=df_out['T_K'],  P_kbar=df_out['P_kbar'])
Buffer_calc.head()

[40]:
DeltaNNO_Frost1991 DeltaQFM_Frost1991 QFM_equation_Choice T_K P_kbar fo2 DeltaQFM_ONeill1987 Cut off T (K)
0 -0.777890 -0.085829 High T 1526.431 1 1.905461e-08 0.110830 871.15
1 -0.781999 -0.089502 High T 1516.580 1 1.479108e-08 0.107427 871.15
2 -0.779077 -0.086116 High T 1506.214 1 1.148154e-08 0.111103 871.15
3 -0.770841 -0.077393 High T 1495.511 1 8.912509e-09 0.120128 871.15
4 -0.774373 -0.080406 High T 1484.230 1 6.606934e-09 0.117438 871.15 
[41]:

# Then calculate buffer using Thermobar
Buffer_calc=pt.convert_fo2_to_buffer(fo2=10**df_out['Lg(fO2)'],
                                     T_K=df_out['T_K'],  P_kbar=df_out['P_kbar'])
Buffer_calc.head()

[41]:
DeltaNNO_Frost1991 DeltaQFM_Frost1991 QFM_equation_Choice T_K P_kbar fo2 DeltaQFM_ONeill1987 Cut off T (K)
0 -0.777890 -0.085829 High T 1526.431 1 1.905461e-08 0.110830 871.15
1 -0.781999 -0.089502 High T 1516.580 1 1.479108e-08 0.107427 871.15
2 -0.779077 -0.086116 High T 1506.214 1 1.148154e-08 0.111103 871.15
3 -0.770841 -0.077393 High T 1495.511 1 8.912509e-09 0.120128 871.15
4 -0.774373 -0.080406 High T 1484.230 1 6.606934e-09 0.117438 871.15 
[42]:

df_ST_Jugo=ss.calculate_S_Total_SCSS_SCAS(deltaQFM=Buffer_calc['DeltaQFM_Frost1991'],
    SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
    T_K=df_out['T_K'],
    SCAS=ZT22_SCAS['SCAS6_ppm'], model='Jugo')
df_ST_Jugo.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[42]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1169.857388 1163.632126 6.225262 -0.085829 0.005321 1163.632126 8800.801280 1169.857388 1.653855e+06 6.225262 1.645055e+06
1 1138.143086 1132.187652 5.955434 -0.089502 0.005233 1132.187652 8329.162079 1138.143086 1.591786e+06 5.955434 1.583457e+06
2 1105.398524 1099.523987 5.874536 -0.086116 0.005314 1099.523987 7866.305310 1105.398524 1.480185e+06 5.874536 1.472319e+06
3 1072.209449 1066.279038 5.930411 -0.077393 0.005531 1066.279038 7420.997354 1072.209449 1.341705e+06 5.930411 1.334284e+06
4 1037.394378 1031.735153 5.659225 -0.080406 0.005455 1031.735153 6983.909186 1037.394378 1.280223e+06 5.659225 1.273239e+06

Or can instead just enter a S6/ST ratio, if say you have done Xanes

  • This could be any float, integer or panda series, we use 20% S6+ here

[43]:

df_ST_FixedS6=ss.calculate_S_Total_SCSS_SCAS(
    SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
    SCAS=ZT22_SCAS['SCAS6_ppm'], S6St_Liq=0.2)
df_ST_FixedS6.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[43]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1454.540158 1163.632126 290.908032 None 0.2 1163.632126 8800.801280 1454.540158 44004.006400 290.908032 35203.205120
1 1415.234565 1132.187652 283.046913 None 0.2 1132.187652 8329.162079 1415.234565 41645.810394 283.046913 33316.648316
2 1374.404984 1099.523987 274.880997 None 0.2 1099.523987 7866.305310 1374.404984 39331.526550 274.880997 31465.221240
3 1332.848797 1066.279038 266.569759 None 0.2 1066.279038 7420.997354 1332.848797 37104.986770 266.569759 29683.989416
4 1289.668942 1031.735153 257.933788 None 0.2 1031.735153 6983.909186 1289.668942 34919.545930 257.933788 27935.636744

Can also use the transition of Kleinsasser et al. 2022

  • Specifically for dacitic melt compositions, only requires delta QFM

[44]:

df_ST_Klein=ss.calculate_S_Total_SCSS_SCAS(
    SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
    T_K=df_out['T_K'], deltaQFM=Buffer_calc['DeltaQFM_Frost1991'],
    SCAS=ZT22_SCAS['SCAS6_ppm'], model='Kleinsasser')
df_ST_Klein.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[44]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1164.330612 NaN NaN -0.085829 NaN 1163.632126 8800.801280 1164.330612 45637.730330 0.698486 36836.929050
1 1132.855863 NaN NaN -0.089502 NaN 1132.187652 8329.162079 1132.855863 43449.059868 0.668211 35119.897789
2 1100.183121 NaN NaN -0.086116 NaN 1099.523987 7866.305310 1100.183121 40810.649273 0.659134 32944.343963
3 1066.944441 NaN NaN -0.077393 NaN 1066.279038 7420.997354 1066.944441 37962.897991 0.665403 30541.900637
4 1032.370129 NaN NaN -0.080406 NaN 1031.735153 6983.909186 1032.370129 35900.632169 0.634975 28916.722983

Can also use ONeill and Mavrogenes to calculate the sulfate capacity. See the workbook ‘CS6_S6ST_Correction’ for more detail on this method

[45]:

calcS_OM2022_GivenFe3=ss.calculate_OM2022_S6St(df=df_out, T_K=df_out['T_K'],
                    Fe3Fet_Liq=df_out['Fe3Fet_Liq'])

calcS_OM2022_GivenFe3.head()

[45]:
S6St_Liq LnCS2_calc LnCS6_calc LnKSO2S2 LnS6S2 deltaQFM_calc SiO2_Liq TiO2_Liq Al2O3_Liq FeOt_Liq MnO_Liq MgO_Liq CaO_Liq Na2O_Liq K2O_Liq P2O5_Liq H2O_Liq Fe3Fet_Liq Ni_Liq_ppm Cu_Liq_ppm SiO2_magma TiO2_magma Al2O3_magma Fe2O3_magma FeO_magma MnO_magma MgO_magma CaO_magma Na2O_magma K2O_magma P2O5_magma Cr2O3_magma Ni_magma Cu_magma Cr2O3_Liq Ni_Liq Cu_Liq SiO2_cumulate TiO2_cumulate Al2O3_cumulate Fe2O3_cumulate FeO_cumulate MnO_cumulate MgO_cumulate CaO_cumulate Na2O_cumulate K2O_cumulate P2O5_cumulate Cr2O3_cumulate Ni_cumulate Cu_cumulate Temperature Temperature_Olv Olv_Fo_magma Olv_Kd Olv_Fo_cumulate Pressure(kbar) Lg(fO2) dNNO density Ln(viscosity) Melt_%_magma Olv_%_magma Olv_Peritectic Fluid_%_magma Olv_%_cumulate Sample Unnamed:58 T_K P_kbar Fraction_melt Sample_ID_Liq SiO2_Liq_mol_frac MgO_Liq_mol_frac MnO_Liq_mol_frac FeOt_Liq_mol_frac CaO_Liq_mol_frac Al2O3_Liq_mol_frac Na2O_Liq_mol_frac K2O_Liq_mol_frac TiO2_Liq_mol_frac P2O5_Liq_mol_frac Si_Liq_cat_frac Mg_Liq_cat_frac Mn_Liq_cat_frac Fet_Liq_cat_frac Ca_Liq_cat_frac Al_Liq_cat_frac Na_Liq_cat_frac K_Liq_cat_frac Ti_Liq_cat_frac P_Liq_cat_frac Mg_Number_Liq_NoFe3 Mg_Number_Liq_Fe3 logfo2_calc Fe2_Liq_cat_frac
0 0.010815 -2.202857 12.717791 -16.307958 -4.515928 0.070616 49.9010 0.9981 14.9715 8.980926 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0 0.098489 697.8 99.8 49.9010 0.9981 14.9715 0.9839 8.0964 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0998 697.8 99.8 0.0998 697.8 99.8 40.2986 0 0 0 11.7372 0 46.9121 0 0 0 0 0 8257.6 10.0 1253.281 1253.281 87.69 0.308 87.69 1 -7.72 -0.7 2.683 6.25 99.9900 0 N 0 0.0100 PetrologDefault 08:21:15 1526.431 1 0.999900 0 0.512524 0.152750 0.000868 0.077140 0.131805 0.090614 0.024845 0.001308 0.007711 0.000434 0.458757 0.136726 0.000777 0.069048 0.117978 0.162217 0.044478 0.002341 0.006902 0.000777 0.664440 0.687150 -7.761827 0.062247
1 0.009048 -2.291531 12.841584 -16.541783 -4.696094 0.041201 49.9978 1.0081 15.1220 8.951296 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.0 0.097851 624.6 100.7 49.9978 1.0081 15.1220 0.9743 8.0754 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.1008 624.6 100.7 0.1008 624.6 100.7 40.2698 0 0 0 11.9273 0 46.7855 0 0 0 0 0 7984.5 10.0 1243.430 1243.430 87.28 0.309 87.49 1 -7.83 -0.7 2.682 6.38 98.9950 0 N 0 1.0050 PetrologDefault 08:21:15 1516.580 1 0.989950 1 0.514720 0.147431 0.000879 0.077066 0.133442 0.091739 0.025153 0.001324 0.007806 0.000439 0.460124 0.131793 0.000786 0.068892 0.119288 0.164017 0.044970 0.002367 0.006978 0.000785 0.656710 0.679537 -7.897861 0.062150
2 0.007578 -2.386549 12.973861 -16.791165 -4.874920 0.012558 50.0982 1.0185 15.2770 8.916645 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.0 0.097284 554.8 101.6 50.0982 1.0185 15.2770 0.9649 8.0492 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.1018 554.8 101.6 0.1018 554.8 101.6 40.2390 0 0 0 12.1280 0 46.6507 0 0 0 0 0 7708.8 10.1 1233.064 1233.064 86.83 0.310 87.27 1 -7.94 -0.7 2.681 6.52 97.9904 0 N 0 2.0096 PetrologDefault 08:21:15 1506.214 1 0.979904 2 0.516994 0.141963 0.000890 0.076952 0.135134 0.092903 0.025473 0.001341 0.007906 0.000445 0.461536 0.126734 0.000794 0.068697 0.120638 0.165874 0.045480 0.002394 0.007058 0.000794 0.648476 0.671438 -8.040201 0.062014
3 0.006390 -2.486488 13.112564 -17.052315 -5.046569 -0.014195 50.2003 1.0289 15.4337 8.877334 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.0 0.096823 489.9 102.6 50.2003 1.0289 15.4337 0.9561 8.0178 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.1029 489.9 102.6 0.1029 489.9 102.6 40.2069 0 0 0 12.3359 0 46.5096 0 0 0 0 0 7436.5 10.1 1222.361 1222.361 86.36 0.311 87.05 1 -8.05 -0.7 2.680 6.67 96.9959 0 N 0 3.0041 PetrologDefault 08:21:15 1495.511 1 0.969959 3 0.519301 0.136457 0.000902 0.076798 0.136850 0.094083 0.025796 0.001358 0.008006 0.000451 0.462964 0.121653 0.000804 0.068467 0.122003 0.167752 0.045995 0.002421 0.007137 0.000803 0.639869 0.662987 -8.186003 0.061838
4 0.005400 -2.593890 13.261190 -17.331682 -5.215929 -0.039894 50.3062 1.0397 15.5950 8.832002 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.0 0.096445 428.7 103.5 50.3062 1.0397 15.5950 0.9475 7.9802 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.1040 428.7 103.5 0.1040 428.7 103.5 40.1725 0 0 0 12.5558 0 46.3589 0 0 0 0 0 7162.8 10.2 1211.080 1211.080 85.84 0.312 86.81 1 -8.18 -0.7 2.679 6.83 95.9923 0 N 0 4.0077 PetrologDefault 08:21:15 1484.230 1 0.959923 4 0.521687 0.130806 0.000913 0.076596 0.138624 0.095302 0.026130 0.001375 0.008110 0.000457 0.464439 0.116452 0.000813 0.068190 0.123411 0.169687 0.046526 0.002449 0.007220 0.000813 0.630682 0.653976 -8.339038 0.061614

Can do using logfo2

  • This is more robust, because the Oneill definition for how Fe3Fet relates to logfo2 isnt the same as the one being used by Petrolog

[46]:

calcS_OM2022_Givenlogfo2=ss.calculate_OM2022_S6St(df=df_out, T_K=df_out['T_K'],
                    logfo2=df_out['Lg(fO2)'])

calcS_OM2022_Givenlogfo2.head()

[46]:
S6St_Liq LnCS2_calc LnCS6_calc LnKSO2S2 LnS6S2 deltaQFM_calc SiO2_Liq TiO2_Liq Al2O3_Liq FeOt_Liq MnO_Liq MgO_Liq CaO_Liq Na2O_Liq K2O_Liq P2O5_Liq H2O_Liq Fe3Fet_Liq Ni_Liq_ppm Cu_Liq_ppm SiO2_magma TiO2_magma Al2O3_magma Fe2O3_magma FeO_magma MnO_magma MgO_magma CaO_magma Na2O_magma K2O_magma P2O5_magma Cr2O3_magma Ni_magma Cu_magma Cr2O3_Liq Ni_Liq Cu_Liq SiO2_cumulate TiO2_cumulate Al2O3_cumulate Fe2O3_cumulate FeO_cumulate MnO_cumulate MgO_cumulate CaO_cumulate Na2O_cumulate K2O_cumulate P2O5_cumulate Cr2O3_cumulate Ni_cumulate Cu_cumulate Temperature Temperature_Olv Olv_Fo_magma Olv_Kd Olv_Fo_cumulate Pressure(kbar) Lg(fO2) dNNO density Ln(viscosity) Melt_%_magma Olv_%_magma Olv_Peritectic Fluid_%_magma Olv_%_cumulate Sample Unnamed:58 T_K P_kbar Fraction_melt Sample_ID_Liq SiO2_Liq_mol_frac MgO_Liq_mol_frac MnO_Liq_mol_frac FeOt_Liq_mol_frac CaO_Liq_mol_frac Al2O3_Liq_mol_frac Na2O_Liq_mol_frac K2O_Liq_mol_frac TiO2_Liq_mol_frac P2O5_Liq_mol_frac Si_Liq_cat_frac Mg_Liq_cat_frac Mn_Liq_cat_frac Fet_Liq_cat_frac Ca_Liq_cat_frac Al_Liq_cat_frac Na_Liq_cat_frac K_Liq_cat_frac Ti_Liq_cat_frac P_Liq_cat_frac Mg_Number_Liq_NoFe3 Mg_Number_Liq_Fe3 Fe2Fet_Liq_calc Fe2_Liq_cat_frac
0 0.013062 -2.202857 12.716180 -16.307958 -4.324920 0.112443 49.9010 0.9981 14.9715 8.980926 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0 0.098489 697.8 99.8 49.9010 0.9981 14.9715 0.9839 8.0964 0.0998 9.9763 11.9772 2.4953 0.1996 0.0998 0.0998 697.8 99.8 0.0998 697.8 99.8 40.2986 0 0 0 11.7372 0 46.9121 0 0 0 0 0 8257.6 10.0 1253.281 1253.281 87.69 0.308 87.69 1 -7.72 -0.7 2.683 6.25 99.9900 0 N 0 0.0100 PetrologDefault 08:21:15 1526.431 1 0.999900 0 0.512524 0.152750 0.000868 0.077140 0.131805 0.090614 0.024845 0.001308 0.007711 0.000434 0.458757 0.136726 0.000777 0.069048 0.117978 0.162217 0.044478 0.002341 0.006902 0.000777 0.664440 0.687150 0.899352 0.062098
1 0.012295 -2.291531 12.838958 -16.541783 -4.386210 0.109061 49.9978 1.0081 15.1220 8.951296 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.0 0.097851 624.6 100.7 49.9978 1.0081 15.1220 0.9743 8.0754 0.1008 9.6064 12.0976 2.5203 0.2016 0.1008 0.1008 624.6 100.7 0.1008 624.6 100.7 40.2698 0 0 0 11.9273 0 46.7855 0 0 0 0 0 7984.5 10.0 1243.430 1243.430 87.28 0.309 87.49 1 -7.83 -0.7 2.682 6.38 98.9950 0 N 0 1.0050 PetrologDefault 08:21:15 1516.580 1 0.989950 1 0.514720 0.147431 0.000879 0.077066 0.133442 0.091739 0.025153 0.001324 0.007806 0.000439 0.460124 0.131793 0.000786 0.068892 0.119288 0.164017 0.044970 0.002367 0.006978 0.000785 0.656710 0.679537 0.898646 0.061909
2 0.011922 -2.386549 12.969959 -16.791165 -4.417377 0.112759 50.0982 1.0185 15.2770 8.916645 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.0 0.097284 554.8 101.6 50.0982 1.0185 15.2770 0.9649 8.0492 0.1018 9.2279 12.2216 2.5462 0.2037 0.1018 0.1018 554.8 101.6 0.1018 554.8 101.6 40.2390 0 0 0 12.1280 0 46.6507 0 0 0 0 0 7708.8 10.1 1233.064 1233.064 86.83 0.310 87.27 1 -7.94 -0.7 2.681 6.52 97.9904 0 N 0 2.0096 PetrologDefault 08:21:15 1506.214 1 0.979904 2 0.516994 0.141963 0.000890 0.076952 0.135134 0.092903 0.025473 0.001341 0.007906 0.000445 0.461536 0.126734 0.000794 0.068697 0.120638 0.165874 0.045480 0.002394 0.007058 0.000794 0.648476 0.671438 0.897532 0.061658
3 0.011826 -2.486488 13.107226 -17.052315 -4.425590 0.121808 50.2003 1.0289 15.4337 8.877334 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.0 0.096823 489.9 102.6 50.2003 1.0289 15.4337 0.9561 8.0178 0.1029 8.8486 12.3469 2.5723 0.2058 0.1029 0.1029 489.9 102.6 0.1029 489.9 102.6 40.2069 0 0 0 12.3359 0 46.5096 0 0 0 0 0 7436.5 10.1 1222.361 1222.361 86.36 0.311 87.05 1 -8.05 -0.7 2.680 6.67 96.9959 0 N 0 3.0041 PetrologDefault 08:21:15 1495.511 1 0.969959 3 0.519301 0.136457 0.000902 0.076798 0.136850 0.094083 0.025796 0.001358 0.008006 0.000451 0.462964 0.121653 0.000804 0.068467 0.122003 0.167752 0.045995 0.002421 0.007137 0.000803 0.639869 0.662987 0.896111 0.061354
4 0.011098 -2.593890 13.254914 -17.331682 -4.489806 0.119144 50.3062 1.0397 15.5950 8.832002 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.0 0.096445 428.7 103.5 50.3062 1.0397 15.5950 0.9475 7.9802 0.1040 8.4612 12.4760 2.5992 0.2079 0.1040 0.1040 428.7 103.5 0.1040 428.7 103.5 40.1725 0 0 0 12.5558 0 46.3589 0 0 0 0 0 7162.8 10.2 1211.080 1211.080 85.84 0.312 86.81 1 -8.18 -0.7 2.679 6.83 95.9923 0 N 0 4.0077 PetrologDefault 08:21:15 1484.230 1 0.959923 4 0.521687 0.130806 0.000913 0.076596 0.138624 0.095302 0.026130 0.001375 0.008110 0.000457 0.464439 0.116452 0.000813 0.068190 0.123411 0.169687 0.046526 0.002449 0.007220 0.000813 0.630682 0.653976 0.895277 0.061049 
[47]:

df_ST_OM2022=ss.calculate_S_Total_SCSS_SCAS(
    SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
    T_K=df_out['T_K'],
SCAS=ZT22_SCAS['SCAS6_ppm'], S6St_Liq=calcS_OM2022_Givenlogfo2['S6St_Liq'])
df_ST_OM2022.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[47]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1179.032345 1163.632126 15.400218 None 0.013062 1163.632126 8800.801280 1179.032345 673784.556326 15.400218 664983.755046
1 1146.280914 1132.187652 14.093262 None 0.012295 1132.187652 8329.162079 1146.280914 677455.639809 14.093262 669126.477730
2 1112.790664 1099.523987 13.266677 None 0.011922 1099.523987 7866.305310 1112.790664 659814.890490 13.266677 651948.585180
3 1079.039354 1066.279038 12.760316 None 0.011826 1066.279038 7420.997354 1079.039354 627535.251420 12.760316 620114.254066
4 1043.314130 1031.735153 11.578977 None 0.011098 1031.735153 6983.909186 1043.314130 629279.369624 11.578977 622295.460438

You can also do this in one step, but it requires you to input your dataframe of liquids

[48]:

df_ST_OM2022_1step=ss.calculate_S_Total_SCSS_SCAS(
SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
T_K=df_out['T_K'],
SCAS=ZT22_SCAS['SCAS6_ppm'],
model='OM2022', logfo2=df_out['Lg(fO2)'],
df=df_out)
df_ST_OM2022_1step.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[48]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1179.032345 1163.632126 15.400218 None 0.013062 1163.632126 8800.801280 1179.032345 673784.556326 15.400218 664983.755046
1 1146.280914 1132.187652 14.093262 None 0.012295 1132.187652 8329.162079 1146.280914 677455.639809 14.093262 669126.477730
2 1112.790664 1099.523987 13.266677 None 0.011922 1099.523987 7866.305310 1112.790664 659814.890490 13.266677 651948.585180
3 1079.039354 1066.279038 12.760316 None 0.011826 1066.279038 7420.997354 1079.039354 627535.251420 12.760316 620114.254066
4 1043.314130 1031.735153 11.578977 None 0.011098 1031.735153 6983.909186 1043.314130 629279.369624 11.578977 622295.460438 
[49]:

df_ST_OM2022_1step=ss.calculate_S_Total_SCSS_SCAS(
SCSS=Smythe_FixedSulf['SCSS2_ppm_ideal_Smythe2017'],
T_K=df_out['T_K'],
SCAS=ZT22_SCAS['SCAS6_ppm'],
model='OM2022',logfo2=df_out['Lg(fO2)'],
df=df_out)
df_ST_OM2022_1step.head()

C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)
C:\Users\penny\anaconda3\lib\site-packages\pandas\core\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  new_ix = Index(new_ix)

[49]:
Total_S_ppm S2_Tot_ppm S6_Tot_ppm deltaQFM S6St_Liq SCSS_2_ppm SCAS_6_ppm SCSS_Tot SCAS_Tot S6 in SCSS_Tot S2 in SCAS_Tot
0 1179.032345 1163.632126 15.400218 None 0.013062 1163.632126 8800.801280 1179.032345 673784.556326 15.400218 664983.755046
1 1146.280914 1132.187652 14.093262 None 0.012295 1132.187652 8329.162079 1146.280914 677455.639809 14.093262 669126.477730
2 1112.790664 1099.523987 13.266677 None 0.011922 1099.523987 7866.305310 1112.790664 659814.890490 13.266677 651948.585180
3 1079.039354 1066.279038 12.760316 None 0.011826 1066.279038 7420.997354 1079.039354 627535.251420 12.760316 620114.254066
4 1043.314130 1031.735153 11.578977 None 0.011098 1031.735153 6983.909186 1043.314130 629279.369624 11.578977 622295.460438

Lets plot up these results

[50]:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))
# Using fixed S6
ax1.plot(df_out['MgO_Liq'],
    100*df_ST_FixedS6['S6St_Liq'],
         '-b', label='Smythe_SCSS, ZT_SCAS, 20% S6')
# using Jugo
ax1.plot(df_out['MgO_Liq'],
    100*df_ST_Jugo['S6St_Liq'],
    '-m', label='Smythe_SCSS, ZT_SCAS, Jugo S6')
# Using Nash
ax1.plot(df_out['MgO_Liq'],
    100*df_ST_Nash['S6St_Liq'],
    '-c', label='Smythe_SCSS, ZT_SCAS, Nash S6')
# Using ONeill and Mavrogenes (2022)
ax1.plot(df_out['MgO_Liq'],
    100*df_ST_OM2022['S6St_Liq'],
    '-', color='grey', label='Smythe_SCSS, ZT_SCAS, OM2022 S6')


# Using Kleinsasser transition
ax2.plot(df_out['MgO_Liq'], df_ST_Klein['Total_S_ppm'],
         '-r', label='Smythe_SCSS, ZT_SCAS, Klein S6')
# Using fixed S6
ax2.plot(df_out['MgO_Liq'],
    df_ST_FixedS6['Total_S_ppm'],
         '-b', label='Smythe_SCSS, ZT_SCAS, 20% S6')
# using Jugo
ax2.plot(df_out['MgO_Liq'],
    df_ST_Jugo['Total_S_ppm'],
    '-m', label='Smythe_SCSS, ZT_SCAS, Jugo S6')
# Using Nash
ax2.plot(df_out['MgO_Liq'],
    df_ST_Nash['Total_S_ppm'],
    '-c', label='Smythe_SCSS, ZT_SCAS, Nash S6')
# Using ONeill Mavrogenes (2022)
ax2.plot(df_out['MgO_Liq'],
    df_ST_OM2022['Total_S_ppm'],
    '-', color='grey', label='Smythe_SCSS, ZT_SCAS, OM2022 S6')

ax1.set_ylabel('S$^{6+}$/S$_T$ (%)')
ax1.set_xlabel('MgO (wt%)')
ax2.set_ylabel('S total (ppm)')
ax2.set_xlabel('MgO (wt%)')
ax2.legend(fontsize=7)

[50]:

<matplotlib.legend.Legend at 0x14f0c0d04c0>
../../_images/Examples_S6_S2_Corrections_S6_S2_Corrections_Nash_Jugo_Kleinsasser_34_1.png 
[ ]:



[ ]: