This page was generated from docs/Examples/Liquid_Ol_Liq_Themometry/Olivine_MatrixGlass_Mg_Fe_Eq_MultipleSamples.ipynb. Interactive online version: .

Python Notebook Download

Multisample - Fe-Mg equilibrium between olivines and glass.

• This notebook shows how to assess Fe-Mg equilibrium between olivines and coerupted matrix glasses.

• In this example, we have multiple samples in the spreadsheet, so first subsample out the rows we want based on sample name. You will have to change the sample names to reflect whatever column you use for this. Else, see single sample if you only have 1 sample in your spreadsheet

• You can download the spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Liquid_Ol_Liq_Themometry/2018_Olivines_Glasses.xlsx

Install Thermobar if you haven’t already (remove # and press run)

# !pip install Thermobar

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

Loading a dataset

• This dataset is from Wieser et al. (2021) - https://doi.org/10.1029/2020GC009364

• It shows olivines and glasses from Kilauea from a single sample LL4

Load in the matrix glasses

# We dont have to add _Liq as its all liquids,
# just specify the _Liq in the import
MG_input=pt.import_excel('2018_Olivines_Glasses.xlsx',
                         sheet_name='Matrix_Glasses', suffix="_Liq")
MG_all=MG_input['my_input'] ## All columns
MG_Liqs=MG_input['Liqs'] ## Just Liquid columns
MG_Liqs.head()

	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	50.6243	2.8773	13.0360	11.8288	0.1830	5.8471	10.2471	2.5120	0.5234	0.0	0.2689	0.0	0.0	0.0	0.0	0.0	0
1	50.0975	2.8840	12.8885	11.2897	0.1769	5.8521	9.9658	2.4681	0.5305	0.0	0.2652	0.0	0.0	0.0	0.0	0.0	1
2	50.1517	2.5827	13.0277	11.2420	0.2141	6.6763	10.6820	2.2884	0.4462	0.0	0.2068	0.0	0.0	0.0	0.0	0.0	2
3	50.2523	2.5686	13.1034	10.9155	0.1948	6.4934	10.7276	2.3660	0.4822	0.0	0.2329	0.0	0.0	0.0	0.0	0.0	3
4	51.1145	2.6104	13.2432	11.1522	0.1738	6.5764	10.6298	2.4155	0.4704	0.0	0.2393	0.0	0.0	0.0	0.0	0.0	4

Load in the olivine data

Ols_in=pt.import_excel('2018_Olivines_Glasses.xlsx',
                         sheet_name='MIs_Ols')
Ols_Ol=Ols_in['Ols']
Ols_all=Ols_in['my_input']

Find each sample for matrix glasses Glasses

MGs_LL4_index=MG_all['SpecificID']=="LL4"
MGs_LL7_index=MG_all['SpecificID']=="LL7"
MGs_LL8_index=MG_all['SpecificID']=="LL8"

Find each sample for olivines and their melt inclusions

Ols_LL4_index=Ols_all['SpecificID']=="LL4"
Ols_LL7_index=Ols_all['SpecificID']=="LL7"
Ols_LL8_index=Ols_all['SpecificID']=="LL8"

Workflow 1: Assess if the olivines are in equilibrium with the co-erupted matrix glass

1. Calculate Mg# for liquids

Liq_Mgno_calc=pt.calculate_liq_mgno(liq_comps=MG_Liqs, Fe3Fet_Liq=0.15)

Liq_Mgno_calc.head()

# Calculate mean value to plot for each sample
Liq_Mgno_calc_mean_LL4=np.mean(Liq_Mgno_calc.loc[MGs_LL4_index])
Liq_Mgno_calc_mean_LL7=np.mean(Liq_Mgno_calc.loc[MGs_LL7_index])
Liq_Mgno_calc_mean_LL8=np.mean(Liq_Mgno_calc.loc[MGs_LL8_index])

2. Calculate Olivine Fo contents

Ol_Fo_Calc=pt.calculate_ol_fo(ol_comps=Ols_Ol)

3. Calculate fields you want to plot on a Rhodes diagram

Rhodes=pt.calculate_ol_rhodes_diagram_lines(Min_Mgno=0.5, Max_Mgno=0.7)
Rhodes.head()

	Mg#_Liq	Eq_Ol_Fo_Roeder (Kd=0.3)	Eq_Ol_Fo_Roeder (Kd=0.27)	Eq_Ol_Fo_Roeder (Kd=0.33)	Eq_Ol_Fo_Matzen (Kd=0.34)	Eq_Ol_Fo_Matzen (Kd=0.328)	Eq_Ol_Fo_Matzen (Kd=0.352)
0	0.500000	0.769231	0.787402	0.751880	0.746269	0.753012	0.739645
1	0.502020	0.770662	0.788751	0.753384	0.747796	0.754512	0.741198
2	0.504040	0.772087	0.790095	0.754883	0.749317	0.756006	0.742745
3	0.506061	0.773506	0.791432	0.756375	0.750832	0.757493	0.744286
4	0.508081	0.774919	0.792763	0.757861	0.752341	0.758975	0.745822

4. Plot the Kd model you want, along with the olivine and glass Mg

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5))

# Plotting for Roeder and Emslie
ax1.set_title('Roeder and Emslie, 1970')
# Plotting equilibrium lines
ax1.plot(Rhodes['Mg#_Liq'], Rhodes['Eq_Ol_Fo_Roeder (Kd=0.27)'], ':k')
ax1.plot(Rhodes['Mg#_Liq'], Rhodes['Eq_Ol_Fo_Roeder (Kd=0.33)'], ':k')
ax1.plot(Rhodes['Mg#_Liq'], Rhodes['Eq_Ol_Fo_Roeder (Kd=0.3)'], '-k')

# Plotting data

ax1.plot(Ol_Fo_Calc.loc[Ols_LL4_index]*0+Liq_Mgno_calc_mean_LL4,
         Ol_Fo_Calc.loc[Ols_LL4_index], 'ok', mfc='blue')

ax1.plot(Ol_Fo_Calc.loc[Ols_LL7_index]*0+Liq_Mgno_calc_mean_LL7,
         Ol_Fo_Calc.loc[Ols_LL7_index], 'ok', mfc='orange')

ax1.plot(Ol_Fo_Calc.loc[Ols_LL8_index]*0+Liq_Mgno_calc_mean_LL8,
         Ol_Fo_Calc.loc[Ols_LL8_index], 'ok', mfc='red')

ax2.set_title('Matzen (2011)')
# Plotting equilibrium lines
ax2.plot(Rhodes['Mg#_Liq'], Rhodes['Eq_Ol_Fo_Matzen (Kd=0.328)'], ':k')
ax2.plot(Rhodes['Mg#_Liq'], Rhodes['Eq_Ol_Fo_Matzen (Kd=0.352)'], ':k')
ax2.plot(Rhodes['Mg#_Liq'], Rhodes['Eq_Ol_Fo_Matzen (Kd=0.34)'], '-k')
# Plotting data
ax2.plot(Ol_Fo_Calc.loc[Ols_LL4_index]*0+Liq_Mgno_calc_mean_LL4,
         Ol_Fo_Calc.loc[Ols_LL4_index], 'ok', mfc='blue')

ax2.plot(Ol_Fo_Calc.loc[Ols_LL7_index]*0+Liq_Mgno_calc_mean_LL7,
         Ol_Fo_Calc.loc[Ols_LL7_index], 'ok', mfc='orange')

ax2.plot(Ol_Fo_Calc.loc[Ols_LL8_index]*0+Liq_Mgno_calc_mean_LL8,
         Ol_Fo_Calc.loc[Ols_LL8_index], 'ok', mfc='red')
ax1.set_ylabel('Ol Fo content')
ax2.set_ylabel('Ol Fo content')
ax1.set_xlabel('Glass Mg#')
ax2.set_xlabel('Glass Mg#')

# You may need to adjust these limits for your data
ax1.set_ylim([0.74, 0.9])
ax2.set_ylim([0.74, 0.9])
ax1.set_xlim([0.5, 0.6])
ax2.set_xlim([0.5, 0.6])

(0.5, 0.6)

• We can see that a lot of the olivines are way to primitive to be in equilibrium with the co-erupted liquid, regardless of what Kd model we use. This is the first sign that the olivines have undergone extensive post-entrapment cooling and crystallization.