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Plag-Liq and Kspar-Liq Matching

This notebook shows how to assess all possible matches for Plag-Liq and Kspar-Liq pairs where there aren’t obvious pairwise analyses
You can download the excel spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Feldspar_Thermobarometry/Feldspar_Liquid.xlsx

[1]:

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

Example 1 - Plag and Liquid.

[2]:

## Load in the liquids
out_Liq=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name="Liq_only")
Liqs=out_Liq['Liqs']
# Load in the Plags
out_Plag=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name="Plag_only")
Plags=out_Plag['Plags']

[3]:

## Inspect them to check they read in right
display(Plags.head())
display(Liqs.head())

	SiO2_Plag	TiO2_Plag	Al2O3_Plag	FeOt_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Sample_ID_Plag
0	57.3	0.09	26.6	0.43	0.03	8.33	6.11	0.49	Plag1
1	56.5	0.12	26.9	0.47	0.05	8.95	5.66	0.47	Plag2
2	57.6	0.11	26.3	0.50	0.07	8.50	6.27	0.40	Plag3_core
3	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	Plag4
4	56.7	0.14	27.6	0.69	0.11	9.46	5.58	0.48	Plag5

	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	Sample_ID_Liq
0	49.1	3.22	14.4	14.8	0.14	3.20	6.72	3.34	1.70	1.13	Glass1
1	49.2	3.89	15.3	13.7	0.12	3.88	6.76	3.44	1.22	0.83	Glass2
2	49.6	3.79	15.8	13.0	0.14	4.26	6.59	3.65	1.04	0.63	Glass3
3	47.1	4.21	12.0	17.8	0.18	3.40	7.28	2.93	2.02	2.32	Glass4
4	48.1	3.88	13.2	16.4	0.16	4.02	6.51	3.36	1.36	1.59	Glass5

Please check your feldspars are actually plagioclases! If you apply plag-liq thermometers to kspars, you get stupid results, that mess up the iterative calculations

Lets plot to inspect and check they are actually plag, else you will have convergence issues

-This function relies heavily on the ternary plot package from Marc Harper et al. 2015 - https://github.com/marcharper/python-ternary, if you use these figures, you must cite that (Marc Harper et al. (2015). python-ternary: Ternary Plots in Python. Zenodo. 10.5281/zenodo.594435) as well as Thermobar.

You may have problems with this package if you have the separate “ternary” package installed (yes, there are python packages called ternary and python-ternary- Yay!). I (penny) got the error “module ternary has no attribute figure, so had to uninstall the ternary I had through pip (pip uninstall ternary), and re-install python-ternary through conda in the command line “conda install python-ternary”. If you have everything in pip, or conda, keep in 1 environment, don’t follow my bad example here!

[4]:

Plag_tern_points = pt.tern_points_fspar(fspar_comps=Plags)

fig, tax = pt.plot_fspar_classification(major_grid=True, ticks=False)

# Here we plot the data ontop, with plag as red triangles
tax.scatter(
    Plag_tern_points,
    edgecolor="k",
    marker="^",
    facecolor="red",
    label='Plag',
   s=90
)

[4]:

<AxesSubplot:>

../../_images/Examples_Feldspar_Thermobarometry_Fspar_Liq_Matching_7_1.png

[5]:

Plag_components=pt.calculate_cat_fractions_plagioclase(plag_comps=Plags)
RealPlags=Plags.loc[Plag_components['An_Plag']>0.05].reset_index(drop=True)

Example 1a - All possible Liq-Plag matches, An-Ab equilibrium

You can specify to use the An-Ab equilibrium test of Putirka (2008) as in this example

[6]:

MM_dict=pt.calculate_fspar_liq_temp_matching(liq_comps=Liqs, plag_comps=RealPlags,
                                             equationT="T_Put2008_eq24a", P=5,
                                                Ab_An_P2008=True, H2O_Liq=2)
Av_Matches=MM_dict['Av_PTs']
All_Matches=MM_dict['All_PTs']

Considering N=28 Fspar & N=24 Liqs, which is a total of N=672 Liq-Fspar pairs, be patient if this is >>1 million!
Applying filter to only average those that pass the An-Ab eq test of Putirka, 2008
Done!!! I found a total of N=327 Fspar-Liq matches using the specified filter. N=28 Fspar out of the N=28 Fspar that you input matched to 1 or more liquids

Example 1b - All possible Liq-Plag matches, no filter

This could be used if you want to develop your own equilibrium filters

[7]:

MM_dict=pt.calculate_fspar_liq_temp_matching(liq_comps=Liqs, plag_comps=RealPlags,
                                             equationT="T_Put2008_eq24a", P=5,
                                                Ab_An_P2008=False, H2O_Liq=2)
Av_Matches=MM_dict['Av_PTs']
All_Matches=MM_dict['All_PTs']

Considering N=28 Fspar & N=24 Liqs, which is a total of N=672 Liq-Fspar pairs, be patient if this is >>1 million!
We are returning all pairs, if you want to use the Ab-An equilibrium test of Putirka (2008), enter Ab_An_P2008=True
Done!!! I found a total of N=672 Fspar-Liq matches using the specified filter. N=28 Fspar out of the N=28 Fspar that you input matched to 1 or more liquids

Example 1c - Iterating T and H2O for all possible Plag-Liq matches

[8]:

T_H_Iter_dict=pt.calculate_fspar_liq_temp_hygr_matching(liq_comps=Liqs, plag_comps=RealPlags,
        equationT="T_Put2008_eq24a", equationH="H_Waters2015", P=5)
T_H_Calc_Av=T_H_Iter_dict.get('Av_HTs')
T_H_Calc_All=T_H_Iter_dict.get('All_HTs')
T_H_Evol=T_H_Iter_dict.get('T_H_Evolution')

Considering N=28 Fspar & N=24 Liqs, which is a total of N=672 Liq- Fspar pairs, be patient if this is >>1 million!

100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.58it/s]

Applying filter to only average those that pass the An-Ab eq test of Putirka, 2008
Done!!! I found a total of N=327 Fspar-Liq matches using the specified filter. N=28 Fspar out of the N=28 Fspar that you input matched to 1 or more liquids

[9]:

# Maybe you want to plot a histogram of H2O contents
plt.hist(T_H_Calc_Av['Mean_H2O_calc'])
plt.xlabel('H$_2$O Calc (wt%)')

[9]:

Text(0.5, 0, 'H$_2$O Calc (wt%)')

../../_images/Examples_Feldspar_Thermobarometry_Fspar_Liq_Matching_15_1.png

Example 2 - Kspar-Liquid calculations

[10]:

# Load in the Kspars
out_Kspar=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name="Kspar_only")
Kspars=out_Kspar['Kspars']

# Load in the liquids you want to match with
out_Liq2=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name="Evolved_Liq_only")
Liqs=out_Liq2['Liqs']

Check actually kspar

[11]:

Kspar_tern_points = pt.tern_points_fspar(fspar_comps=Kspars)
fig, tax = pt.plot_fspar_classification(major_grid=True, ticks=False)

tax.scatter(
    Kspar_tern_points,
    edgecolor="k",
    marker="o",
    facecolor="cyan",
   s=60, label='Kspar'
)

[11]:

<AxesSubplot:>

../../_images/Examples_Feldspar_Thermobarometry_Fspar_Liq_Matching_19_1.png

[12]:

Kspar_components=pt.calculate_cat_fractions_kspar(kspar_comps=Kspars)
RealKspars=Kspars.loc[Kspar_components['An_Kspar']<0.05].reset_index(drop=True)

2a - Lets calculate temperature for all liq-kspar matches

Currently no equilibrium tests exist for Kspar-Liquid that we are aware off. If you know of ones I can implement, please reach out!

[15]:

melt_match_2=pt.calculate_fspar_liq_temp_matching(liq_comps=Liqs, kspar_comps=RealKspars,
                                             equationT="T_Put2008_eq24b", P=5,
                                                Ab_An_P2008=True, H2O_Liq=2)
melt_match_2['Av_PTs'].head()

Considering N=15 Fspar & N=15 Liqs, which is a total of N=225 Liq-Fspar pairs, be patient if this is >>1 million!
Sorry, no equilibrium tests implemented for Kspar-Liquid. Weve returned all possible pairs, you will have to filter them yourselves
Done!!! I found a total of N=225 Fspar-Liq matches using the specified filter. N=15 Fspar out of the N=15 Fspar that you input matched to 1 or more liquids

[15]:

	ID_Fspar	Mean_Sample_ID_Kspar	Mean_T_K_calc	Mean_SiO2_Liq	Mean_TiO2_Liq	Mean_Al2O3_Liq	Mean_FeOt_Liq	Mean_MnO_Liq	Mean_MgO_Liq	Mean_CaO_Liq	Mean_Na2O_Liq	Mean_K2O_Liq	Mean_P2O5_Liq	Mean_H2O_Liq	Mean_Sample_ID_Liq	Mean_SiO2_Liq_mol_frac	Mean_MgO_Liq_mol_frac	Mean_MnO_Liq_mol_frac	Mean_FeOt_Liq_mol_frac	Mean_CaO_Liq_mol_frac	Mean_Al2O3_Liq_mol_frac	Mean_Na2O_Liq_mol_frac	Mean_K2O_Liq_mol_frac	Mean_TiO2_Liq_mol_frac	Mean_P2O5_Liq_mol_frac	Mean_Si_Liq_cat_frac	Mean_Mg_Liq_cat_frac	Mean_Mn_Liq_cat_frac	Mean_Fet_Liq_cat_frac	Mean_Ca_Liq_cat_frac	Mean_Al_Liq_cat_frac	Mean_Na_Liq_cat_frac	Mean_K_Liq_cat_frac	Mean_Ti_Liq_cat_frac	Mean_P_Liq_cat_frac	Mean_Mg_Number_Liq_NoFe3	Mean_Mg_Number_Liq_Fe3	Mean_ID_liq	Mean_Sample_ID_liq	# of Liqs Averaged	Std_Sample_ID_Kspar	Std_T_K_calc	Std_SiO2_Liq	Std_Na2O_Liq	Std_K2O_Liq	Std_Sample_ID_Liq	Std_SiO2_Liq_mol_frac	Std_MgO_Liq_mol_frac	Std_MnO_Liq_mol_frac	Std_FeOt_Liq_mol_frac	Std_CaO_Liq_mol_frac	Std_Al2O3_Liq_mol_frac	Std_Na2O_Liq_mol_frac	Std_K2O_Liq_mol_frac	Std_TiO2_Liq_mol_frac	Std_P2O5_Liq_mol_frac	Std_Si_Liq_cat_frac	Std_Mg_Liq_cat_frac	Std_Mn_Liq_cat_frac	Std_Fet_Liq_cat_frac	Std_Ca_Liq_cat_frac	Std_Al_Liq_cat_frac	Std_Na_Liq_cat_frac	Std_K_Liq_cat_frac	Std_Ti_Liq_cat_frac	Std_P_Liq_cat_frac	Std_ID_liq	Std_Sample_ID_liq	Sample_ID_Kspar
0	0	Kspar1	1138.165453	62.976667	0.45	18.56	3.17	0.27	0.23	1.64	5.71	6.223333	0.02	2.0	7.0	0.709623	0.003864	0.002577	0.029872	0.0198	0.123242	0.062378	0.044734	0.003814	0.000095	0.576767	0.00314	0.002094	0.024278	0.016092	0.200325	0.101348	0.072699	0.0031	0.000155	0.114521	0.114521	7.0	7.0	15	Kspar1	27.575211	0.703732	0.507093	0.351866	4.472136	0.007112	0.000006	0.000004	0.000045	0.00003	0.000186	0.005578	0.002604	0.000006	1.436252e-07	0.009076	0.000015	0.00001	0.000115	0.000076	0.000947	0.008549	0.003937	0.000015	7.329643e-07	4.472136	4.472136	Kspar1
1	1	Kspar2	1087.464362	62.976667	0.45	18.56	3.17	0.27	0.23	1.64	5.71	6.223333	0.02	2.0	7.0	0.709623	0.003864	0.002577	0.029872	0.0198	0.123242	0.062378	0.044734	0.003814	0.000095	0.576767	0.00314	0.002094	0.024278	0.016092	0.200325	0.101348	0.072699	0.0031	0.000155	0.114521	0.114521	7.0	7.0	15	Kspar2	25.122349	0.703732	0.507093	0.351866	4.472136	0.007112	0.000006	0.000004	0.000045	0.00003	0.000186	0.005578	0.002604	0.000006	1.436252e-07	0.009076	0.000015	0.00001	0.000115	0.000076	0.000947	0.008549	0.003937	0.000015	7.329643e-07	4.472136	4.472136	Kspar2
2	2	Kspar3	977.039695	62.976667	0.45	18.56	3.17	0.27	0.23	1.64	5.71	6.223333	0.02	2.0	7.0	0.709623	0.003864	0.002577	0.029872	0.0198	0.123242	0.062378	0.044734	0.003814	0.000095	0.576767	0.00314	0.002094	0.024278	0.016092	0.200325	0.101348	0.072699	0.0031	0.000155	0.114521	0.114521	7.0	7.0	15	Kspar3	20.190486	0.703732	0.507093	0.351866	4.472136	0.007112	0.000006	0.000004	0.000045	0.00003	0.000186	0.005578	0.002604	0.000006	1.436252e-07	0.009076	0.000015	0.00001	0.000115	0.000076	0.000947	0.008549	0.003937	0.000015	7.329643e-07	4.472136	4.472136	Kspar3
3	3	Kspar4	1023.046234	62.976667	0.45	18.56	3.17	0.27	0.23	1.64	5.71	6.223333	0.02	2.0	7.0	0.709623	0.003864	0.002577	0.029872	0.0198	0.123242	0.062378	0.044734	0.003814	0.000095	0.576767	0.00314	0.002094	0.024278	0.016092	0.200325	0.101348	0.072699	0.0031	0.000155	0.114521	0.114521	7.0	7.0	15	Kspar4	22.177217	0.703732	0.507093	0.351866	4.472136	0.007112	0.000006	0.000004	0.000045	0.00003	0.000186	0.005578	0.002604	0.000006	1.436252e-07	0.009076	0.000015	0.00001	0.000115	0.000076	0.000947	0.008549	0.003937	0.000015	7.329643e-07	4.472136	4.472136	Kspar4
4	4	Kspar5	1073.413522	62.976667	0.45	18.56	3.17	0.27	0.23	1.64	5.71	6.223333	0.02	2.0	7.0	0.709623	0.003864	0.002577	0.029872	0.0198	0.123242	0.062378	0.044734	0.003814	0.000095	0.576767	0.00314	0.002094	0.024278	0.016092	0.200325	0.101348	0.072699	0.0031	0.000155	0.114521	0.114521	7.0	7.0	15	Kspar5	24.463649	0.703732	0.507093	0.351866	4.472136	0.007112	0.000006	0.000004	0.000045	0.00003	0.000186	0.005578	0.002604	0.000006	1.436252e-07	0.009076	0.000015	0.00001	0.000115	0.000076	0.000947	0.008549	0.003937	0.000015	7.329643e-07	4.472136	4.472136	Kspar5

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