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Olivine-Liquid Hygrometry

This workbook shows how to perform calculations for Olivine-Liquid hygrometry
You can download the Excel spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Liquid_Ol_Liq_Themometry/Liquid_only_Thermometry.xlsx

You need to install Thermobar once on your machine, if you haven’t done this yet, uncomment the line below (remove the #)

[1]:

#!pip install Thermobar

[2]:

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

[3]:

# Setting plotting parameters
# This sets some plotting things
plt.rcParams["font.family"] = 'arial'
plt.rcParams["font.size"] =12
plt.rcParams["mathtext.default"] = "regular"
plt.rcParams["mathtext.fontset"] = "dejavusans"
plt.rcParams['patch.linewidth'] = 1
plt.rcParams['axes.linewidth'] = 1
plt.rcParams["xtick.direction"] = "in"
plt.rcParams["ytick.direction"] = "in"
plt.rcParams["ytick.direction"] = "in"
plt.rcParams["xtick.major.size"] = 6 # Sets length of ticks
plt.rcParams["ytick.major.size"] = 4 # Sets length of ticks
plt.rcParams["ytick.labelsize"] = 12 # Sets size of numbers on tick marks
plt.rcParams["xtick.labelsize"] = 12 # Sets size of numbers on tick marks
plt.rcParams["axes.titlesize"] = 14 # Overall title
plt.rcParams["axes.labelsize"] = 14 # Axes labels

Step 1 - load data

[4]:

out=pt.import_excel('Liquid_only_Thermometry.xlsx', sheet_name="Ol-Liq")
my_input=out['my_input']
myLiquids1=out['Liqs']
myOls1=out['Ols']
display(myOls1.head())
display(myLiquids1.head())

	SiO2_Ol	TiO2_Ol	Al2O3_Ol	FeOt_Ol	MnO_Ol	MgO_Ol	CaO_Ol	Cr2O3_Ol	Sample_ID_Ol
0	40.5	0.02	0.08	12.40	0.17	47.4	0.30	0.03	0
1	41.3	0.03	0.11	9.59	0.14	50.2	0.31	0.00	1
2	39.7	0.05	0.11	15.60	0.18	44.5	0.31	0.03	2
3	40.5	0.05	0.10	13.20	0.18	46.8	0.29	0.02	3
4	40.5	0.00	0.10	9.41	0.10	49.3	0.31	0.00	4

	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	Sample_ID_Liq
0	57.023602	0.623106	16.332899	4.36174	0.103851	4.19180	6.94858	3.59702	0.896895	0.000000	0.226584	5.59	0.2	0
1	57.658600	0.654150	17.194799	3.90621	0.084105	2.86892	5.91538	3.85948	1.018600	0.000000	0.214935	6.55	0.2	1
2	60.731201	0.862054	17.144199	4.07781	0.077488	2.50867	5.22075	4.45556	1.414160	0.000000	0.319638	3.14	0.2	2
3	61.532799	0.440860	16.508801	3.32990	0.037520	1.64150	4.34294	4.40860	1.407000	0.000000	0.215740	6.20	0.2	3
4	52.969101	0.803412	17.563000	5.93217	0.149472	3.78351	7.65110	3.80219	0.551178	0.037368	0.196182	6.58	0.2	4

Example 1 - Simple calculation for just the H2O content of the liquid

outputs just the wt% H2O in the liquid, using the hygrometer of Gavrilenko et al. (2016)

[5]:

H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
                                               equationH="H_Gavr2016")
H2O_Calc_Thermobar

[5]:

0    2.030582
1    1.364482
2    0.344844
3   -0.344697
4    2.860205
5    1.508569
6   -1.769123
7    1.909273
8    0.834843
9    2.860872
dtype: float64

Example 2 - Melt matching

in reality, you may not have olivine-liquid pairs that you know are in equilibrium.
Instead, you can feed in all measured olivines and liquids, and it will pair up all possible matches
For the equilibrium tests, if you want to use Toplis, you need a temperature
In this example, we use a constant temperature

[6]:

H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
                                               equationH="H_Gavr2016", eq_tests=True,
                                                    T=1300)
H2O_Calc_Thermobar.head()

Considering N=10 Ol & N=10 Liqs, which is a total of N=100 Liq- Ol pairs, be patient if this is >>1 million!
You have not selected a pressure, so we have calculated Toplis Kd at 1kbar

[6]:

	H2O_calc	Temp used for calcs	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Liq	TiO2_Liq	...	Si_Ol_cat_frac	Mg_Ol_cat_frac	Fet_Ol_cat_frac	Ca_Ol_cat_frac	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac
0	2.030582	1300	1	0.314264	0.326180	-0.011916	0.014264	-0.025736	57.023602	0.623106	...	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123
1	1.682297	1300	1	0.240169	0.327530	-0.087362	-0.059831	-0.099831	57.658600	0.654150	...	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123
2	0.704945	1300	1	0.201173	0.308546	-0.107373	-0.098827	-0.138827	60.731201	0.862054	...	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123
3	-0.777583	1300	1	0.161199	0.312636	-0.151436	-0.138801	-0.178801	61.532799	0.440860	...	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123
4	3.105922	1300	1	0.208562	0.334102	-0.125540	-0.091438	-0.131438	52.969101	0.803412	...	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123

5 rows × 73 columns

We can also specify an olivine-liquid thermometer

The hygrometer isn’t T sensitive, so once we have a H2O estimate, we can plug that into a liquid-olivine thermometer. That gives us a temp for solving equilibrium at

[7]:

H2O_Calc_Teq22=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
                                               equationH="H_Gavr2016",  equationT='T_Put2008_eq22',
                                                 eq_tests=True, P=5,
                                                    T=1300)
H2O_Calc_Teq22.head()

Considering N=10 Ol & N=10 Liqs, which is a total of N=100 Liq- Ol pairs, be patient if this is >>1 million!

[7]:

	H2O_calc	T_K_calc	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Liq	TiO2_Liq	...	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac	DMg_Meas	CNML	CSiO2L	NF	Den_Beat93
0	2.030582	1306.094822	5	0.314264	0.331693	-0.017429	0.014264	-0.025736	57.023602	0.623106	...	0.000772	0.001179	0.000123	9.490384	0.170076	0.556427	-0.760264	9.813745
1	1.682297	1246.358585	5	0.240169	0.320346	-0.080177	-0.059831	-0.099831	57.658600	0.654150	...	0.000772	0.001179	0.000123	13.711088	0.137693	0.568999	-0.815019	10.226597
2	0.704945	1278.487099	5	0.201173	0.308304	-0.107131	-0.098827	-0.138827	60.731201	0.862054	...	0.000772	0.001179	0.000123	16.264149	0.121869	0.577797	-0.790522	10.330149
3	-0.777583	1196.823888	5	0.161199	0.295190	-0.133991	-0.138801	-0.178801	61.532799	0.440860	...	0.000772	0.001179	0.000123	24.065168	0.097450	0.604665	-0.765547	10.732471
4	3.105922	1265.970449	5	0.208562	0.331112	-0.122550	-0.091438	-0.131438	52.969101	0.803412	...	0.000772	0.001179	0.000123	10.410636	0.186517	0.522022	-0.840343	10.135819

5 rows × 78 columns

Example 2b- Filtering these matches

This is all Ol-Liq matches, you probably want to filter based on your choice of Kd filter
First, lets plot up these equilibrium tests

[9]:

fig, ((ax1, ax2, ax3)) = plt.subplots(1,3, figsize = (10,3), sharex=True) # adjust dimensions
ax1.hist(H2O_Calc_Teq22['ΔKd, Roeder (M-P)'], ec='k')
ax2.hist(H2O_Calc_Teq22['ΔKd, Toplis (M-P)'], ec='k')
ax3.hist(H2O_Calc_Teq22['ΔKd, Matzen (M-P)'], ec='k')

ax1.set_xlabel('ΔKd, Roeder (M-P)')
ax2.set_xlabel('ΔKd, Toplis (M-P)')
ax3.set_xlabel('ΔKd, Matzen (M-P)')

[9]:

Text(0.5, 0, 'ΔKd, Matzen (M-P)')

../../_images/Examples_Liquid_Ol_Liq_Themometry_Olivine_Liquid_hygrometry_14_1.png

Now we can filter out based on common values

[10]:

H2O_Calc_Teq22_FiltRoedder=H2O_Calc_Teq22.loc[H2O_Calc_Teq22['ΔKd, Roeder (M-P)'].between(-0.03, 0.03)]
H2O_Calc_Teq22_FiltToplis=H2O_Calc_Teq22.loc[H2O_Calc_Teq22['ΔKd, Toplis (M-P)'].between(-0.03, 0.03)]
H2O_Calc_Teq22_FiltMatzen=H2O_Calc_Teq22.loc[H2O_Calc_Teq22['ΔKd, Matzen (M-P)'].between(-0.03, 0.03)]

Now we can plot up values for this

[11]:

plt.plot(H2O_Calc_Teq22_FiltRoedder['Al2O3_Ol'], H2O_Calc_Teq22_FiltRoedder['H2O_calc'], 'ok')

[11]:

[<matplotlib.lines.Line2D at 0x1c0317ddd60>]

../../_images/Examples_Liquid_Ol_Liq_Themometry_Olivine_Liquid_hygrometry_18_1.png

[12]:

H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
                                               equationH="H_Gavr2016", equationT="T_Put2008_eq22",
                                                     eq_tests=True)
H2O_Calc_Thermobar.head()

Considering N=10 Ol & N=10 Liqs, which is a total of N=100 Liq- Ol pairs, be patient if this is >>1 million!
You have not selected a pressure, so we have calculated Toplis Kd at 1kbar

[12]:

	H2O_calc	T_K_calc	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Liq	TiO2_Liq	...	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac	DMg_Meas	CNML	CSiO2L	NF	Den_Beat93
0	2.030582	1285.005221	1	0.314264	0.323039	-0.008775	0.014264	-0.025736	57.023602	0.623106	...	0.000772	0.001179	0.000123	9.490384	0.170076	0.556427	-0.760264	9.813745
1	1.682297	1225.765914	1	0.240169	0.311488	-0.071319	-0.059831	-0.099831	57.658600	0.654150	...	0.000772	0.001179	0.000123	13.711088	0.137693	0.568999	-0.815019	10.226597
2	0.704945	1257.615992	1	0.201173	0.300042	-0.098869	-0.098827	-0.138827	60.731201	0.862054	...	0.000772	0.001179	0.000123	16.264149	0.121869	0.577797	-0.790522	10.330149
3	-0.777583	1176.711359	1	0.161199	0.286619	-0.125420	-0.138801	-0.178801	61.532799	0.440860	...	0.000772	0.001179	0.000123	24.065168	0.097450	0.604665	-0.765547	10.732471
4	3.105922	1245.204726	1	0.208562	0.322130	-0.113568	-0.091438	-0.131438	52.969101	0.803412	...	0.000772	0.001179	0.000123	10.410636	0.186517	0.522022	-0.840343	10.135819

5 rows × 78 columns

[13]:

CalcH2O=pt.calculate_ol_liq_hygr(meltmatch=H2O_Calc_Thermobar,
   equationH="H_Gavr2016", P=5, T=1300)
CalcH2O

Column already exists in dataframe. Have ovewritten

[14]:

H2O_Calc_Thermobar.head()

[14]:

	H2O_calc	T_K_calc	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Liq	TiO2_Liq	...	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac	DMg_Meas	CNML	CSiO2L	NF	Den_Beat93
0	2.030582	1285.005221	1	0.314264	0.323039	-0.008775	0.014264	-0.025736	57.023602	0.623106	...	0.000772	0.001179	0.000123	9.490384	0.170076	0.556427	-0.760264	9.813745
1	1.682297	1225.765914	1	0.240169	0.311488	-0.071319	-0.059831	-0.099831	57.658600	0.654150	...	0.000772	0.001179	0.000123	13.711088	0.137693	0.568999	-0.815019	10.226597
2	0.704945	1257.615992	1	0.201173	0.300042	-0.098869	-0.098827	-0.138827	60.731201	0.862054	...	0.000772	0.001179	0.000123	16.264149	0.121869	0.577797	-0.790522	10.330149
3	-0.777583	1176.711359	1	0.161199	0.286619	-0.125420	-0.138801	-0.178801	61.532799	0.440860	...	0.000772	0.001179	0.000123	24.065168	0.097450	0.604665	-0.765547	10.732471
4	3.105922	1245.204726	1	0.208562	0.322130	-0.113568	-0.091438	-0.131438	52.969101	0.803412	...	0.000772	0.001179	0.000123	10.410636	0.186517	0.522022	-0.840343	10.135819

5 rows × 78 columns

[15]:

Liq_Ols=H2O_Calc_Thermobar
import inspect
func = pt.Liquid_olivine_hygr_funcs_by_name["H_Gavr2016"]
sig=inspect.signature(func)
kwargs = {name: Liq_Ols[name] for name, p in sig.parameters.items() if p.kind == inspect.Parameter.KEYWORD_ONLY}
H2O_Calc_np=func(**kwargs)

[16]:

H2O_Calc_np

[16]:

array([ 2.03058223,  1.68229723,  0.70494526, -0.77758337,  3.10592163,
        2.61247201,  1.7569024 ,  2.65237953,  2.97082766,  3.4382364 ,
        1.76002335,  1.36448163,  0.34484373, -1.21046986,  2.86020533,
        2.33649615,  1.43635461,  2.40467736,  2.70382963,  3.24578146,
        1.76002335,  1.36448163,  0.34484373, -1.21046986,  2.86020533,
        2.33649615,  1.43635461,  2.40467736,  2.70382963,  3.24578146,
        2.30114112,  2.00011282,  1.06504679, -0.34469688,  3.35163792,
        2.88844787,  2.07745018,  2.9000817 ,  3.2378257 ,  3.63069135,
        1.76002335,  1.36448163,  0.34484373, -1.21046986,  2.86020533,
        2.33649615,  1.43635461,  2.40467736,  2.70382963,  3.24578146,
        0.94834669,  0.41103486, -0.73546085, -2.50912934,  2.12305644,
        1.50856857,  0.47471126,  1.66157084,  1.90283551,  2.66841664,
       -0.9455655 , -1.81367429, -3.25617155, -5.53933479,  0.40304236,
       -0.42326244, -1.76912324, -0.07234437,  0.03384924,  1.32123206,
        1.21890558,  0.72885045, -0.37535933, -2.07624285,  2.36877274,
        1.78454443,  0.79525904,  1.90927301,  2.16983355,  2.86087158,
       -0.13388885, -0.86022751, -2.17586697, -4.24067531,  1.14019125,
        0.40466514, -0.80747988,  0.67076215,  0.83484336,  1.89859688,
        1.21890558,  0.72885045, -0.37535933, -2.07624285,  2.36877274,
        1.78454443,  0.79525904,  1.90927301,  2.16983355,  2.86087158])

Example 2 - Calculating equilibrium tests

In this example, we calculate equilibrium tests, as water contents calculated from non-equilibrium pairs are likely to be nonsense.
A variety of options are included for calculating Kd bewteen olivine and liquid, P and T are needed for Toplis Kd parameterization, here we enter fixed values (in kbar and K)

[17]:

H2O_Calc_Thermobar_eqTest1=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
                                               equationH="H_Gavr2016", eq_tests=True, P=1, T=1300)
H2O_Calc_Thermobar_eqTest1

[17]:

	H2O_calc	Temp used for calcs	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Ol	TiO2_Ol	...	Mg_Number_Liq_Fe3	Si_Ol_cat_frac	Mg_Ol_cat_frac	Fet_Ol_cat_frac	Ca_Ol_cat_frac	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac
0	2.030582	1300	1	0.314264	0.326180	-0.011916	0.014264	-0.025736	40.5	0.02	...	0.681666	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123
1	1.364482	1300	1	0.175383	0.321910	-0.146527	-0.124617	-0.164617	41.3	0.03	...	0.620707	0.331038	0.599846	0.064284	0.002662	0.001039	0.000950	0.000181
2	0.344844	1300	1	0.269582	0.314838	-0.045256	-0.030418	-0.070418	39.7	0.05	...	0.578196	0.331560	0.554039	0.108956	0.002774	0.001083	0.001273	0.000314
3	-0.344697	1300	1	0.173799	0.314138	-0.140339	-0.126201	-0.166201	40.5	0.05	...	0.523445	0.332170	0.572216	0.090539	0.002548	0.000967	0.001250	0.000309
4	2.860205	1300	1	0.152172	0.328355	-0.176184	-0.147828	-0.187828	40.5	0.00	...	0.586967	0.330886	0.600452	0.064294	0.002714	0.000963	0.000692	0.000000
5	1.508569	1300	1	0.159554	0.332767	-0.173213	-0.140446	-0.180446	40.5	0.02	...	0.565465	0.331178	0.591229	0.072488	0.002979	0.000964	0.001039	0.000123
6	-1.769123	1300	1	0.215915	0.342292	-0.126376	-0.084085	-0.124085	40.2	0.04	...	0.528731	0.334056	0.552506	0.106326	0.003650	0.002155	0.001056	0.000250
7	1.909273	1300	1	0.404699	0.412296	-0.007597	0.104699	0.064699	39.6	0.04	...	0.620033	0.333016	0.530297	0.131513	0.002973	0.000595	0.001353	0.000253
8	0.834843	1300	1	0.314115	0.391918	-0.077802	0.014115	-0.025885	39.8	0.05	...	0.583864	0.334067	0.539307	0.120735	0.003417	0.000594	0.001564	0.000316
9	2.860872	1300	1	0.429135	0.289064	0.140072	0.129135	0.089135	39.7	0.03	...	0.665729	0.330714	0.546417	0.117735	0.002945	0.000589	0.001411	0.000188

10 rows × 70 columns

Can filter outputs, so only get calculated H2O contents for those with delta Kd Roeder <0.1

[18]:

InEq=H2O_Calc_Thermobar_eqTest1.loc[H2O_Calc_Thermobar_eqTest1['ΔKd, Roeder (M-P)']<0.1]
InEq

[18]:

	H2O_calc	Temp used for calcs	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Ol	TiO2_Ol	...	Mg_Number_Liq_Fe3	Si_Ol_cat_frac	Mg_Ol_cat_frac	Fet_Ol_cat_frac	Ca_Ol_cat_frac	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac
0	2.030582	1300	1	0.314264	0.326180	-0.011916	0.014264	-0.025736	40.5	0.02	...	0.681666	0.331677	0.578691	0.084925	0.002632	0.000772	0.001179	0.000123
1	1.364482	1300	1	0.175383	0.321910	-0.146527	-0.124617	-0.164617	41.3	0.03	...	0.620707	0.331038	0.599846	0.064284	0.002662	0.001039	0.000950	0.000181
2	0.344844	1300	1	0.269582	0.314838	-0.045256	-0.030418	-0.070418	39.7	0.05	...	0.578196	0.331560	0.554039	0.108956	0.002774	0.001083	0.001273	0.000314
3	-0.344697	1300	1	0.173799	0.314138	-0.140339	-0.126201	-0.166201	40.5	0.05	...	0.523445	0.332170	0.572216	0.090539	0.002548	0.000967	0.001250	0.000309
4	2.860205	1300	1	0.152172	0.328355	-0.176184	-0.147828	-0.187828	40.5	0.00	...	0.586967	0.330886	0.600452	0.064294	0.002714	0.000963	0.000692	0.000000
5	1.508569	1300	1	0.159554	0.332767	-0.173213	-0.140446	-0.180446	40.5	0.02	...	0.565465	0.331178	0.591229	0.072488	0.002979	0.000964	0.001039	0.000123
6	-1.769123	1300	1	0.215915	0.342292	-0.126376	-0.084085	-0.124085	40.2	0.04	...	0.528731	0.334056	0.552506	0.106326	0.003650	0.002155	0.001056	0.000250
8	0.834843	1300	1	0.314115	0.391918	-0.077802	0.014115	-0.025885	39.8	0.05	...	0.583864	0.334067	0.539307	0.120735	0.003417	0.000594	0.001564	0.000316

8 rows × 70 columns

We can also use a thermometer to calculate temperatures to perform Toplis calculations at

[19]:

H2O_Calc_Thermobar_eqTest2=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
                            equationH="H_Gavr2016", eq_tests=True, P=1, equationT="T_Put2008_eq22")
H2O_Calc_Thermobar_eqTest2

[19]:

	H2O_calc	T_K_calc	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Ol	TiO2_Ol	...	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac	DMg_Meas	CNML	CSiO2L	NF	Den_Beat93
0	2.030582	1285.005221	1	0.314264	0.323039	-0.008775	0.014264	-0.025736	40.5	0.02	...	0.000772	0.001179	0.000123	9.490384	0.170076	0.556427	-0.760264	9.813745
1	1.364482	1219.815185	1	0.175383	0.304486	-0.129103	-0.124617	-0.164617	41.3	0.03	...	0.001039	0.000950	0.000181	14.212311	0.137693	0.568999	-0.815019	10.298404
2	0.344844	1265.431802	1	0.269582	0.307956	-0.038374	-0.030418	-0.070418	39.7	0.05	...	0.001083	0.001273	0.000314	15.571306	0.121869	0.577797	-0.790522	10.243083
3	-0.344697	1178.402781	1	0.173799	0.288518	-0.114719	-0.126201	-0.166201	40.5	0.05	...	0.000967	0.001250	0.000309	23.795917	0.097450	0.604665	-0.765547	10.709968
4	2.860205	1238.836216	1	0.152172	0.314913	-0.162742	-0.147828	-0.187828	40.5	0.00	...	0.000963	0.000692	0.000000	10.802119	0.186517	0.522022	-0.840343	10.209647
5	1.508569	1224.693789	1	0.159554	0.316022	-0.156468	-0.140446	-0.180446	40.5	0.02	...	0.000964	0.001039	0.000123	12.298898	0.167758	0.532846	-0.831744	10.289637
6	-1.769123	1221.171616	1	0.215915	0.324840	-0.108924	-0.084085	-0.124085	40.2	0.04	...	0.002155	0.001056	0.000250	14.115866	0.145510	0.546226	-0.818818	10.317328
7	1.909273	1179.473286	1	0.404699	0.380321	0.024377	0.104699	0.064699	39.6	0.04	...	0.000595	0.001353	0.000253	7.372863	0.220036	0.554167	-0.789435	9.844920
8	0.834843	1142.994309	1	0.314115	0.351192	-0.037077	0.014115	-0.025885	39.8	0.05	...	0.000594	0.001564	0.000316	10.512978	0.180240	0.551142	-0.873211	10.228352
9	2.860872	1318.154242	1	0.429135	0.292274	0.136862	0.129135	0.089135	39.7	0.03	...	0.000589	0.001411	0.000188	7.071114	0.228369	0.498136	-0.840454	9.673521

10 rows × 75 columns

We can also change the proportion of Fe3 used for the equilibrium tests

[20]:

H2O_Calc_Thermobar_eqTest3=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
                                               equationH="H_Gavr2016", eq_tests=True,
                                                P=1, equationT="T_Put2008_eq22", Fe3Fet_Liq=0.3)
H2O_Calc_Thermobar_eqTest3

[20]:

	H2O_calc	T_K_calc	P used for calcs	Kd Meas	Kd calc (Toplis)	ΔKd, Toplis (M-P)	ΔKd, Roeder (M-P)	ΔKd, Matzen (M-P)	SiO2_Ol	TiO2_Ol	...	Al_Ol_cat_frac	Mn_Ol_cat_frac	Ti_Ol_cat_frac	DMg_Meas	CNML	CSiO2L	NF	Den_Beat93
0	2.030582	1285.005221	1	0.359158	0.323039	0.036120	0.059158	0.019158	40.5	0.02	...	0.000772	0.001179	0.000123	9.490384	0.170076	0.556427	-0.760264	9.813745
1	1.364482	1219.815185	1	0.200438	0.304486	-0.104048	-0.099562	-0.139562	41.3	0.03	...	0.001039	0.000950	0.000181	14.212311	0.137693	0.568999	-0.815019	10.298404
2	0.344844	1265.431802	1	0.308094	0.307956	0.000138	0.008094	-0.031906	39.7	0.05	...	0.001083	0.001273	0.000314	15.571306	0.121869	0.577797	-0.790522	10.243083
3	-0.344697	1178.402781	1	0.198628	0.288518	-0.089891	-0.101372	-0.141372	40.5	0.05	...	0.000967	0.001250	0.000309	23.795917	0.097450	0.604665	-0.765547	10.709968
4	2.860205	1238.836216	1	0.173911	0.314913	-0.141003	-0.126089	-0.166089	40.5	0.00	...	0.000963	0.000692	0.000000	10.802119	0.186517	0.522022	-0.840343	10.209647
5	1.508569	1224.693789	1	0.182347	0.316022	-0.133675	-0.117653	-0.157653	40.5	0.02	...	0.000964	0.001039	0.000123	12.298898	0.167758	0.532846	-0.831744	10.289637
6	-1.769123	1221.171616	1	0.246761	0.324840	-0.078079	-0.053239	-0.093239	40.2	0.04	...	0.002155	0.001056	0.000250	14.115866	0.145510	0.546226	-0.818818	10.317328
7	1.909273	1179.473286	1	0.462513	0.380321	0.082191	0.162513	0.122513	39.6	0.04	...	0.000595	0.001353	0.000253	7.372863	0.220036	0.554167	-0.789435	9.844920
8	0.834843	1142.994309	1	0.358989	0.351192	0.007797	0.058989	0.018989	39.8	0.05	...	0.000594	0.001564	0.000316	10.512978	0.180240	0.551142	-0.873211	10.228352
9	2.860872	1318.154242	1	0.490441	0.292274	0.198167	0.190441	0.150441	39.7	0.03	...	0.000589	0.001411	0.000188	7.071114	0.228369	0.498136	-0.840454	9.673521

10 rows × 75 columns

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