This page was generated from docs/Examples/Other_features/fo2_to_Fe3FeT.ipynb. Interactive online version: .

Converting back and forth from Fe3Fet to different redox buffer positions.

You can download the spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Other_features/Redox_Conversions.xlsx

[1]:

# Loading various python things
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import Thermobar as pt

Load in data

No suffix in file, so add here to say they are liquids

[2]:

load=pt.import_excel('Redox_Conversions.xlsx', sheet_name="Liqs", suffix='_Liq')
Liqs=load['Liqs']
Liqs.head()

[2]:

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	H2O_Liq	Sample_ID_Liq
0	51.456179	2.601690	13.529073	11.114610	0.185873	6.698477	10.974609	2.406926	0.483801	0.248535	0.508277	0
1	51.462403	2.641448	13.717522	11.175642	0.189570	6.497934	10.814571	2.451116	0.493423	0.253478	0.518386	1
2	51.491657	2.771451	13.666925	11.521905	0.200504	6.275374	10.459901	2.509225	0.520502	0.268098	0.548285	2
3	51.508276	2.872896	13.569897	11.795709	0.208908	6.138356	10.217900	2.543802	0.541088	0.279336	0.571268	3
4	51.506960	3.058952	13.350351	12.214913	0.223782	5.856971	9.977521	2.592645	0.577148	0.299224	0.611940	4

Example 1: Calculating Fe3Fet for a series of liquid compositions

Remember, Fe3Fet_Liq to buffer positions is very dependent on temperature, so first we need to choose a thermometer

[3]:

# Calculate temperature
T_HT87=pt.calculate_liq_only_temp(liq_comps=Liqs, equationT='T_Helz1987_MgO')

1a: Using a value for logfo2

[4]:

logfo2=-7.58429552677443

# Calculate oxides using Kress 1991 and 3 kbar, and
myLiquids_Fe3_Kress_norm=pt.convert_fo2_to_fe_partition(liq_comps=Liqs, T_K=T_HT87,
                            P_kbar=3, fo2=10**logfo2, model="Kress1991", renorm=True)
myLiquids_Fe3_Kress_norm.head()

[4]:

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	Fe2O3_Liq	FeO_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	Fe3Fet_Liq	ln_XFe2FeO3_XFeO
0	51.573474	2.607633	13.559981	2.813568	8.606944	0.186298	6.713780	10.999680	2.412425	0.484906	0.249103	0.227286	-1.916290
1	51.582033	2.647602	13.749479	2.869927	8.617879	0.190011	6.513072	10.839765	2.456826	0.494572	0.254069	0.230563	-1.897726
2	51.620474	2.778399	13.701184	2.993012	8.856176	0.201006	6.291105	10.486121	2.515515	0.521807	0.268770	0.233184	-1.883008
3	51.644329	2.880498	13.605809	3.084121	9.050289	0.209461	6.154600	10.244940	2.550534	0.542520	0.280075	0.234673	-1.874703
4	51.654905	3.067754	13.388765	3.261699	9.313550	0.224426	5.873824	10.006230	2.600105	0.578808	0.300085	0.239613	-1.847396

[5]:

plt.plot(myLiquids_Fe3_Kress_norm['MgO_Liq'], myLiquids_Fe3_Kress_norm['Fe3Fet_Liq'], '-r')
plt.xlabel('MgO Liq')
plt.ylabel('Fe3Fet Liq')

[5]:

Text(0, 0.5, 'Fe3Fet Liq')

../../_images/Examples_Other_features_fo2_to_Fe3FeT_8_1.png

1b: At a known buffer (here, QFM)

[6]:

# Calculate oxides using Kress 1991 and 3 kbar, and
myLiquids_Fe3_Kress_norm_QFM=pt.convert_fo2_to_fe_partition(liq_comps=Liqs, T_K=1200+273.15,
                            P_kbar=1.734, fo2="QFM", fo2_offset=0, model="Kress1991", renorm=True)
myLiquids_Fe3_Kress_norm_QFM.head()

[6]:

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	Fe2O3_Liq	FeO_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	Fe3Fet_Liq	ln_XFe2FeO3_XFeO
0	51.622224	2.610098	13.572799	1.866153	9.470432	0.186474	6.720126	11.010078	2.414705	0.485364	0.249339	0.150603	-2.422469
1	51.633633	2.650250	13.763233	1.867267	9.531780	0.190201	6.519587	10.850608	2.459283	0.495067	0.254323	0.149855	-2.428329
2	51.676499	2.781414	13.716054	1.905119	9.848144	0.201224	6.297933	10.497502	2.518245	0.522373	0.269062	0.148259	-2.440912
3	51.703454	2.883796	13.621385	1.936526	10.097010	0.209701	6.161646	10.256669	2.553453	0.543141	0.280396	0.147176	-2.449517
4	51.720280	3.071636	13.405710	1.992969	10.471292	0.224710	5.881258	10.018894	2.603396	0.579541	0.300465	0.146216	-2.457186

[7]:

plt.plot(myLiquids_Fe3_Kress_norm_QFM['MgO_Liq'], myLiquids_Fe3_Kress_norm_QFM['Fe3Fet_Liq'], '-r')
plt.xlabel('MgO Liq')
plt.ylabel('Fe3Fet Liq')

[7]:

Text(0, 0.5, 'Fe3Fet Liq')

../../_images/Examples_Other_features_fo2_to_Fe3FeT_11_1.png

1c: At buffer with offset (here NNO+1)

[8]:

# Calculate oxides using Kress 1991 and 3 kbar, and
myLiquids_Fe3_Kress_norm_NNO1=pt.convert_fo2_to_fe_partition(liq_comps=Liqs, T_K=T_HT87,
                            P_kbar=3, fo2="NNO", fo2_offset=1, model="Kress1991", renorm=True)
myLiquids_Fe3_Kress_norm_NNO1.head()

[8]:

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	Fe2O3_Liq	FeO_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	Fe3Fet_Liq	ln_XFe2FeO3_XFeO
0	51.546390	2.606264	13.552860	3.339932	8.127207	0.186200	6.710254	10.993904	2.411158	0.484651	0.248972	0.269955	-1.687440
1	51.557365	2.646336	13.742904	3.349248	8.180990	0.189920	6.509957	10.834581	2.455651	0.494335	0.253947	0.269205	-1.691251
2	51.598076	2.777193	13.695239	3.427946	8.459594	0.200919	6.288375	10.481571	2.514423	0.521581	0.268654	0.267191	-1.701514
3	51.623335	2.879327	13.600278	3.491627	8.678603	0.209376	6.152098	10.240776	2.549497	0.542300	0.279962	0.265793	-1.708666
4	51.637241	3.066705	13.384186	3.604510	9.000728	0.224349	5.871815	10.002808	2.599216	0.578610	0.299982	0.264891	-1.713293

[9]:

plt.plot(myLiquids_Fe3_Kress_norm_NNO1['MgO_Liq'], myLiquids_Fe3_Kress_norm_NNO1['Fe3Fet_Liq'], '-r')
plt.xlabel('MgO Liq')
plt.ylabel('Fe3Fet Liq')

[9]:

Text(0, 0.5, 'Fe3Fet Liq')

../../_images/Examples_Other_features_fo2_to_Fe3FeT_14_1.png

1d: With different normalization routines:

So far, we used Norm=True, this renormalizes the other oxides to account for the changing FeO and Fe2O3 amount, can also put false, doesn’t affect the Fe3FeT but affects the oxide concs. You can see this by comparing the other oxide contents

[10]:

logfo2=-7.58429552677443
print('Original')
display(Liqs.loc[[0]])

# No renormalizatoin
myLiquids_Fe3_Kress_nonorm=pt.convert_fo2_to_fe_partition(liq_comps=Liqs.loc[[0]], T_K=T_HT87.loc[[0]],
                            P_kbar=3, fo2=10**logfo2, model="Kress1991", renorm=False)
print('No Norm')
display(myLiquids_Fe3_Kress_nonorm)


# With renormalization
myLiquids_Fe3_Kress_norm=pt.convert_fo2_to_fe_partition(liq_comps=Liqs.loc[[0]], T_K=T_HT87.loc[[0]],
                            P_kbar=3, fo2=10**logfo2, model="Kress1991", renorm=True)
print('Renorm')
display(myLiquids_Fe3_Kress_norm)

Original

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	Cr2O3_Liq	P2O5_Liq	H2O_Liq	Fe3Fet_Liq	NiO_Liq	CoO_Liq	CO2_Liq	Sample_ID_Liq
0	51.456179	2.60169	13.529073	11.11461	0.185873	6.698477	10.974609	2.406926	0.483801	0.0	0.248535	0.508277	0.0	0.0	0.0	0.0	0

No Norm

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	Cr2O3_Liq	...	H2O_Liq	Fe3Fet_Liq	NiO_Liq	CoO_Liq	CO2_Liq	Sample_ID_Liq	FeO_Liq	Fe2O3_Liq	XFe3Fe2	ln_XFe2FeO3_XFeO
0	51.456179	2.60169	13.529073	11.11461	0.185873	6.698477	10.974609	2.406926	0.483801	0.0	...	0.508277	0.227286	0.0	0.0	0.0	0	8.589075	2.807726	0.147152	-1.91629

1 rows × 21 columns

Renorm

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	Fe2O3_Liq	FeO_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	Fe3Fet_Liq	ln_XFe2FeO3_XFeO
0	51.573474	2.607633	13.559981	2.813568	8.606944	0.186298	6.71378	10.99968	2.412425	0.484906	0.249103	0.227286	-1.91629

Converting from FeO and Fe2O3 proportions to fo2

This function goes the other way, it takes a liquid with FeO and Fe2O3, and a temperature and pressure, and converts to a buffer position/delta fo2 vlaue

[11]:

## The inversion isn't quite perfect, but is very close
calc_fo2=pt.convert_fe_partition_to_fo2(liq_comps=myLiquids_Fe3_Kress_norm_NNO1,
                                        T_K=T_HT87, P_kbar=3, model="Kress1991", renorm=False)

using inputted FeO and Fe2O3 contents
(49,)

[12]:

calc_fo2

[12]:

	DeltaQFM_Frost1991	DeltaNNO_Frost1991	fo2_calc	SiO2_Liq	TiO2_Liq	Al2O3_Liq	Fe2O3_Liq	FeO_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	Fe3Fet_Liq	ln_XFe2FeO3_XFeO	FeOt_Liq
0	1.587252	0.980283	7.999607e-08	51.546390	2.606264	13.552860	3.339932	8.127207	0.186200	6.710254	10.993904	2.411158	0.484651	0.248972	0.269955	-1.687440	11.132479
1	1.587349	0.980431	7.139051e-08	51.557365	2.646336	13.742904	3.349248	8.180990	0.189920	6.509957	10.834581	2.455651	0.494335	0.253947	0.269205	-1.691251	11.194643
2	1.587300	0.980439	6.284887e-08	51.598076	2.777193	13.695239	3.427946	8.459594	0.200919	6.288375	10.481571	2.514423	0.521581	0.268654	0.267191	-1.701514	11.544059
3	1.587231	0.980406	5.807824e-08	51.623335	2.879327	13.600278	3.491627	8.678603	0.209376	6.152098	10.240776	2.549497	0.542300	0.279962	0.265793	-1.708666	11.820369
4	1.586615	0.979863	4.928451e-08	51.637241	3.066705	13.384186	3.604510	9.000728	0.224349	5.871815	10.002808	2.599216	0.578610	0.299982	0.264891	-1.713293	12.244066
5	1.586086	0.979386	4.382653e-08	51.638351	3.199578	13.221434	3.682642	9.209909	0.234892	5.673066	9.879096	2.629078	0.603989	0.314080	0.264593	-1.714823	12.523550
6	1.585330	0.978706	3.698529e-08	51.634502	3.398218	12.978289	3.796698	9.516734	0.250723	5.387579	9.701648	2.668614	0.641860	0.335248	0.264151	-1.717094	12.933002
7	1.584851	0.978275	3.316046e-08	51.628396	3.530278	12.816809	3.870774	9.716984	0.261292	5.205129	9.588480	2.691642	0.666987	0.349380	0.263860	-1.718595	13.199906
8	1.584167	0.977661	2.831006e-08	51.614145	3.727939	12.575476	3.979141	10.011395	0.277175	4.942417	9.425986	2.721464	0.704516	0.370618	0.263425	-1.720831	13.591826
9	1.583734	0.977274	2.556757e-08	51.601373	3.859495	12.415173	4.049654	10.203931	0.287788	4.774137	9.322264	2.738345	0.729436	0.384809	0.263138	-1.722315	13.847810
10	1.583119	0.976725	2.205359e-08	51.577516	4.056607	12.175604	4.152968	10.487474	0.303752	4.531307	9.173226	2.759398	0.766683	0.406155	0.262708	-1.724532	14.224314
11	1.582732	0.976380	2.004672e-08	51.558600	4.187924	12.016501	4.220276	10.673160	0.314429	4.375443	9.078035	2.770713	0.791434	0.420430	0.262423	-1.726005	14.470564
12	1.582732	0.976380	2.004672e-08	51.558600	4.187924	12.016501	4.220276	10.673160	0.314429	4.375443	9.078035	2.770713	0.791434	0.420430	0.262423	-1.726005	14.470564
13	1.582544	0.976214	1.913018e-08	51.548269	4.253568	11.937139	4.253476	10.765036	0.319778	4.299228	9.031643	2.775586	0.803787	0.427583	0.262280	-1.726740	14.592314
14	1.582362	0.976051	1.826626e-08	51.537371	4.319209	11.857907	4.286379	10.856281	0.325135	4.224120	8.986032	2.779949	0.816127	0.434746	0.262138	-1.727475	14.713166
15	1.582183	0.975893	1.745137e-08	51.525916	4.384849	11.778808	4.318990	10.946905	0.330500	4.150092	8.941187	2.783811	0.828454	0.441920	0.261996	-1.728209	14.833132
16	1.582009	0.975739	1.668222e-08	51.513916	4.450493	11.699847	4.351312	11.036914	0.335874	4.077119	8.897096	2.787183	0.840768	0.449106	0.261855	-1.728942	14.952224
17	1.581827	0.975579	1.589241e-08	51.529100	4.496905	11.625147	4.382480	11.122869	0.341191	3.998756	8.848689	2.792709	0.854256	0.456956	0.261735	-1.729562	15.066224
18	1.581695	0.975511	1.375841e-08	52.612734	4.263861	11.713650	4.246476	10.710464	0.327012	3.766118	8.463650	2.870163	0.979760	0.463878	0.262946	-1.723305	14.531444
19	1.581520	0.975392	1.214620e-08	53.545497	4.063865	11.790118	4.128805	10.357587	0.314846	3.566451	8.133268	2.936798	1.087593	0.469841	0.263994	-1.717905	14.072685
20	1.581327	0.975246	1.094722e-08	54.320717	3.898105	11.853887	4.030598	10.065866	0.304765	3.400949	7.859484	2.992153	1.177063	0.474801	0.264868	-1.713409	13.692598
21	1.581123	0.975083	9.993273e-09	54.998878	3.753437	11.909834	3.944379	9.811827	0.295969	3.256496	7.620568	3.040558	1.255220	0.479143	0.265636	-1.709471	13.360979
22	1.580911	0.974907	9.198004e-09	55.614102	3.622435	11.960703	3.865888	9.582234	0.288004	3.125681	7.404243	3.084456	1.326045	0.483083	0.266334	-1.705893	13.060760
23	1.580231	0.974324	7.416449e-09	57.204632	3.284890	12.092753	3.661803	8.992664	0.267487	2.788582	6.846957	3.197883	1.508776	0.493280	0.268149	-1.696627	12.287555
24	1.579221	0.973432	5.683154e-09	59.154871	2.871874	12.255080	3.408360	8.274897	0.242386	2.376084	6.165149	3.336912	1.732547	0.505790	0.270403	-1.685169	11.341740
25	1.578437	0.972719	4.846800e-09	60.316296	2.627669	12.352587	3.256571	7.852786	0.227552	2.132128	5.762176	3.419607	1.865233	0.513254	0.271747	-1.678367	10.783049
26	1.577633	0.971978	4.206919e-09	61.346095	2.412078	12.439490	3.121347	7.481528	0.214461	1.916728	5.406509	3.492877	1.982573	0.519881	0.272940	-1.672346	10.290116
27	1.576543	0.970962	3.560534e-09	62.556232	2.160024	12.542227	2.961789	7.049199	0.199161	1.664854	4.990808	3.578904	2.120038	0.527678	0.274342	-1.665294	9.714217
28	1.575718	0.970186	3.184888e-09	63.363583	1.992753	12.611191	2.855025	6.763360	0.189011	1.497674	4.715018	3.636246	2.211458	0.532887	0.275275	-1.660613	9.332311
29	1.574891	0.969404	2.877135e-09	64.098829	1.841131	12.674334	2.757645	6.505031	0.179815	1.346111	4.465098	3.688427	2.294479	0.537637	0.276122	-1.656372	8.986360
30	1.574065	0.968618	2.621649e-09	64.771438	1.703098	12.732418	2.668493	6.270519	0.171445	1.208111	4.237641	3.736124	2.370207	0.541988	0.276893	-1.652519	8.671629
31	1.572966	0.967569	2.343280e-09	65.583633	1.537408	12.803026	2.560853	5.989896	0.161403	1.042426	3.964702	3.793663	2.461326	0.547249	0.277817	-1.647907	8.294152
32	1.572146	0.966783	2.170925e-09	66.136981	1.425242	12.851474	2.487602	5.800502	0.154609	0.930241	3.780003	3.832823	2.523170	0.550840	0.278441	-1.644799	8.038846
33	1.571302	0.965970	2.020282e-09	66.659137	1.320178	12.897561	2.418719	5.623593	0.148248	0.825132	3.607073	3.869732	2.581271	0.554235	0.279022	-1.641906	7.799956
34	1.570115	0.964823	1.845036e-09	67.320571	1.188397	12.956564	2.331963	5.402444	0.140275	0.693250	3.390294	3.916410	2.654442	0.558545	0.279746	-1.638315	7.500744
35	1.569236	0.963970	1.736422e-09	67.764932	1.100791	12.996645	2.274078	5.255923	0.134980	0.605547	3.246275	3.947716	2.703295	0.561449	0.280221	-1.635955	7.302138
36	1.568370	0.963128	1.643674e-09	68.168968	1.021921	13.033462	2.221829	5.124396	0.130216	0.526562	3.116695	3.976137	2.747456	0.564096	0.280645	-1.633855	7.123598
37	1.567206	0.961992	1.539031e-09	68.657179	0.928027	13.078620	2.159486	4.968416	0.124551	0.432482	2.962573	4.010397	2.800356	0.567305	0.281140	-1.631405	6.911521
38	1.566354	0.961159	1.473449e-09	68.983138	0.866257	13.109207	2.118394	4.866183	0.120829	0.370557	2.861274	4.033219	2.835374	0.569456	0.281459	-1.629825	6.772314
39	1.565521	0.960342	1.417047e-09	69.277768	0.811201	13.137225	2.081729	4.775369	0.117516	0.315335	2.771067	4.053804	2.866770	0.571406	0.281738	-1.628446	6.648508
40	1.564441	0.959282	1.353801e-09	69.626379	0.747242	13.170940	2.039103	4.670311	0.113672	0.251141	2.666395	4.078091	2.903531	0.573723	0.282053	-1.626889	6.505096
41	1.563655	0.958509	1.313931e-09	69.857694	0.705689	13.193732	2.011406	4.602379	0.111179	0.209403	2.598487	4.094157	2.927632	0.575268	0.282251	-1.625913	6.412242
42	1.562891	0.957756	1.279569e-09	70.065498	0.669123	13.214572	1.987043	4.542868	0.108990	0.172645	2.538813	4.108545	2.949032	0.576662	0.282419	-1.625084	6.330810
43	1.562148	0.957023	1.250000e-09	70.251670	0.637130	13.233607	1.965748	4.491067	0.107078	0.140454	2.486686	4.121392	2.967954	0.577917	0.282560	-1.624391	6.259847
44	1.561192	0.956077	1.217011e-09	70.469170	0.600944	13.256412	1.941707	4.432888	0.104923	0.103998	2.427862	4.136333	2.989668	0.579393	0.282708	-1.623657	6.180035
45	1.560499	0.955392	1.196463e-09	70.611226	0.578222	13.271741	1.926656	4.396676	0.103574	0.081069	2.391034	4.146039	3.003551	0.580365	0.282794	-1.623237	6.130281
46	1.559812	0.954711	1.179003e-09	70.737740	0.558946	13.285850	1.913944	4.366299	0.102436	0.061577	2.359909	4.154628	3.015600	0.581238	0.282857	-1.622923	6.088466
47	1.558883	0.953788	1.159793e-09	70.886236	0.538023	13.303220	1.900261	4.333956	0.101211	0.040344	2.326343	4.164613	3.029184	0.582277	0.282910	-1.622663	6.043810
48	1.557488	0.952397	1.144100e-09	71.036962	0.523079	13.323848	1.890969	4.313347	0.100377	0.024876	2.303243	4.174388	3.040906	0.583383	0.282883	-1.622798	6.014841

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