Feldspar-liquid thermobarometry¶

Unlike for Cpx and Opx, because the components are calculated the same for Plag and Kspar, we have a single function, calculate_liq_fspar_temp and calculate_liq_fspar_press.
If the mineral is plagioclase, the functions should use plag_comps=dataframe, while if its Kspar, kspar_comps=dataframe
You can download the excel spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Feldspar_Thermobarometry/Feldspar_Liquid.xlsx

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

In [2]:

#!pip install Thermobar

first, load python things¶

In [3]:

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

Loading plagioclase- liquid pairs¶

In [4]:

out_PL=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name="Plag_Liquid")
# This extracts a dataframe of all inputs
my_input_PL=out_PL['my_input']
# This extracts a dataframe of plag compositions from the dictionary "out"
Plags=out_PL['Plags']
Liqs_PL=out_PL['Liqs']

Lets check these inputs look good (e.g. not just a load of zeros)¶

In [5]:

display(Plags.head())
display(Liqs_PL.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	0
1	56.5	0.12	26.9	0.47	0.05	8.95	5.66	0.47	1
2	57.6	0.11	26.3	0.50	0.07	8.50	6.27	0.40	2
3	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3
4	56.7	0.14	27.6	0.69	0.11	9.46	5.58	0.48	4

	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	0
1	49.2	3.89	15.3	13.7	0.12	3.88	6.76	3.44	1.22	0.83	1
2	49.6	3.79	15.8	13.0	0.14	4.26	6.59	3.65	1.04	0.63	2
3	47.1	4.21	12.0	17.8	0.18	3.40	7.28	2.93	2.02	2.32	3
4	48.1	3.88	13.2	16.4	0.16	4.02	6.51	3.36	1.36	1.59	4

Loading Kspar-liquid pairs¶

In [6]:

out_KL=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name="Kspar_Liquid")
my_input_KL=out_KL['my_input']
Kspars=out_KL['Kspars']
Liqs_KL=out_KL['Liqs']
# As before, we inspect the outputs
display(Kspars.head())
display(Liqs_KL.head())

	SiO2_Kspar	Al2O3_Kspar	FeOt_Kspar	MgO_Kspar	CaO_Kspar	Na2O_Kspar	K2O_Kspar	Sample_ID_Kspar
0	65.5	19.6	0.07	0.00	0.75	4.81	9.36	0
1	65.4	19.4	0.05	0.00	0.59	3.13	11.50	1
2	64.6	18.8	0.09	0.00	0.39	1.15	14.80	2
3	61.8	19.2	0.51	0.03	0.66	1.71	12.90	3
4	65.1	19.2	0.05	0.00	0.36	2.87	12.60	4

	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	61.71	0.45	18.56	3.17	0.27	0.23	1.64	6.11	7.09	0.02	2	0
1	61.71	0.45	18.56	3.17	0.27	0.23	1.64	6.11	7.09	0.02	3	1
2	62.71	0.45	18.56	3.17	0.27	0.23	1.64	6.11	6.09	0.02	5	2
3	62.71	0.45	18.56	3.17	0.27	0.23	1.64	6.11	6.09	0.02	5	3
4	62.71	0.45	18.56	3.17	0.27	0.23	1.64	6.11	6.09	0.02	5	4

Example 1 - Plagioclase-Liquid thermomometry¶

to get more information, can always do help(pt.function)

In [7]:

help(pt.calculate_fspar_liq_temp)

Help on function calculate_fspar_liq_temp in module Thermobar.feldspar:

calculate_fspar_liq_temp(*, plag_comps=None, kspar_comps=None, meltmatch_plag=None, meltmatch_kspar=None, liq_comps=None, equationT=None, P=None, H2O_Liq=None, eq_tests=False)
    Liquid-Feldspar thermometery (same function for Plag and Kspar),
    returns temperature in Kelvin.
    
    Parameters
    -------
    
    liq_comps: pandas.DataFrame
        liquid compositions with column headings SiO2_Liq, MgO_Liq etc.
    
    kspar_comps or plag_comps (pandas.DataFrame)
    
        Specify kspar_comps=... for Kspar-Liquid thermometry (with column headings SiO2_Kspar, MgO_Kspar) etc
    
        Specify plag_comps=... for Plag-Liquid thermometry (with column headings SiO2_Plag, MgO_Plag) etc
    
    EquationT: str
    
        choose from:
    
            |   T_Put2008_eq24b (Kspar-Liq, P-dependent, H2O-independent
            |   T_Put2008_eq23 (Plag-Liq, P-dependent, H2O-dependent)
            |   T_Put2008_eq24a (Plag-Liq, P-dependent, H2O-dependent)
    
    P: float, int, pandas.Series, str  ("Solve")
        Pressure in kbar to perform calculations at
        Only needed for P-sensitive thermometers.
        If enter P="Solve", returns a partial function
        Else, enter an integer, float, or panda series
    
    H2O_Liq: optional.
        If None, uses H2O_Liq column from input.
        If int, float, pandas.Series, uses this instead of H2O_Liq Column
    
    Returns
    -------
    
        Temperature in Kelvin: pandas.Series
            If eq_tests is False
    
        Temperature in Kelvin + eq Tests + input compositions: pandas.DataFrame
            If eq_tests is True

Temperature using equation 23 at 5 kbar¶

In [9]:

T_PL_eq23_5kbar=pt.calculate_fspar_liq_temp(plag_comps=Plags, liq_comps=Liqs_PL, 
                                            equationT="T_Put2008_eq23", P=5)-273.15
T_PL_eq23_5kbar.head()

Out[9]:

0    1134.869018
1    1150.684489
2    1146.913528
3    1113.581667
4    1119.243338
dtype: float64

Temperature using equation 24a at 5 kbar¶

In [10]:

T_PL_eq24a_5kbar=pt.calculate_fspar_liq_temp(plag_comps=Plags, liq_comps=Liqs_PL, 
                                            equationT="T_Put2008_eq24a", P=5)-273.15
T_PL_eq24a_5kbar.head()

Out[10]:

0    1130.080284
1    1147.341341
2    1142.641914
3    1115.989656
4    1127.162158
dtype: float64

Equilibrium tests¶

We can also calculate equilibrium tests following the methods described in the supporting information of Putirka (2008) (Fspar-Liq spreadsheet)
By default, no filtering is done, but specifiying eq_tests=True calculates the relevant equilibrium parameters, and then below we show how to filter outputs with different values of these

In [11]:

T_PL_eq23_5kbar_EqTests=pt.calculate_fspar_liq_temp(plag_comps=Plags, liq_comps=Liqs_PL, 
                        equationT="T_Put2008_eq23", P=5, eq_tests=True)
T_PL_eq23_5kbar_EqTests.head()

Out[11]:

	T_K_calc	Pass An-Ab Eq Test Put2008?	Delta_An	Delta_Ab	Delta_Or	Pred_An_EqE	Pred_Ab_EqF	Pred_Or_EqG	Obs_Kd_Ab_An	SiO2_Plag	TiO2_Plag	Al2O3_Plag	FeOt_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Sample_ID_Plag	Si_Plag_cat_prop	Mg_Plag_cat_prop	Fet_Plag_cat_prop	Ca_Plag_cat_prop	Al_Plag_cat_prop	Na_Plag_cat_prop	K_Plag_cat_prop	Ti_Plag_cat_prop	sum	Si_Plag_cat_frac	Mg_Plag_cat_frac	Fet_Plag_cat_frac	Ca_Plag_cat_frac	Al_Plag_cat_frac	Na_Plag_cat_frac	K_Plag_cat_frac	Ti_Plag_cat_frac	An_Plag	Ab_Plag	Or_Plag	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	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	P	T
0	1408.019018	High T: No	0.101016	0.152353	0.029108	0.518145	0.401303	0.000107	0.510102	57.3	0.09	26.6	0.43	0.03	8.33	6.11	0.49	0	0.953660	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.001127	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.000613	0.417129	0.553656	0.029215	49.1	3.22	14.4	14.8	0.14	3.20	6.72	3.34	1.70	1.13	0	0.549988	0.053436	0.001328	0.138640	0.080652	0.095052	0.036269	0.012146	0.027130	0.005358	0.478739	0.046513	0.001156	0.120680	0.070204	0.165477	0.063141	0.021146	0.023616	0.009328	0.278194	0.278194	5	1408.019018
1	1423.834489	High T: No	0.089866	0.102459	0.027941	0.542990	0.416085	0.000391	0.455475	56.5	0.12	26.9	0.47	0.05	8.95	5.66	0.47	1	0.940345	0.001241	0.006542	0.159601	0.527653	0.182643	0.009979	0.001502	1.829506	0.513989	0.000678	0.003576	0.087237	0.288413	0.099832	0.005455	0.000821	0.453125	0.518543	0.028332	49.2	3.89	15.3	13.7	0.12	3.88	6.76	3.44	1.22	0.83	1	0.545500	0.064131	0.001127	0.127030	0.080306	0.099965	0.036975	0.008628	0.032442	0.003896	0.474569	0.055792	0.000980	0.110512	0.069864	0.173933	0.064334	0.015012	0.028224	0.006778	0.335475	0.335475	5	1423.834489
2	1420.063528	High T: No	0.117189	0.110312	0.022764	0.535451	0.447991	0.000671	0.500004	57.6	0.11	26.3	0.50	0.07	8.50	6.27	0.40	2	0.958653	0.001737	0.006959	0.151576	0.515884	0.202327	0.008493	0.001377	1.847006	0.519031	0.000940	0.003768	0.082066	0.279308	0.109543	0.004598	0.000746	0.418261	0.558303	0.023435	49.6	3.79	15.8	13.0	0.14	4.26	6.59	3.65	1.04	0.63	2	0.547269	0.070071	0.001308	0.119955	0.077907	0.102731	0.039042	0.007319	0.031455	0.002943	0.475045	0.060823	0.001136	0.104124	0.067626	0.178348	0.067779	0.012707	0.027304	0.005108	0.368736	0.368736	5	1420.063528
3	1386.731667	High T: No	0.021553	0.029279	0.049656	0.470193	0.472391	0.000034	0.461028	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.008630	0.161027	0.529614	0.180061	0.017835	0.002003	1.852655	0.513855	0.000804	0.004658	0.086917	0.285868	0.097191	0.009627	0.001081	0.448640	0.501670	0.049691	47.1	4.21	12.0	17.8	0.18	3.40	7.28	2.93	2.02	2.32	3	0.521269	0.056096	0.001687	0.164747	0.086327	0.078262	0.031436	0.014260	0.035047	0.010869	0.459338	0.049431	0.001487	0.145174	0.076070	0.137927	0.055402	0.025132	0.030883	0.019156	0.254001	0.254001	5	1386.731667
4	1392.393338	High T: Yes	0.021115	0.029107	0.028064	0.448856	0.530743	0.000329	0.369633	56.7	0.14	27.6	0.69	0.11	9.46	5.58	0.48	4	0.943674	0.002729	0.009604	0.168695	0.541383	0.180061	0.010192	0.001753	1.858092	0.507873	0.001469	0.005169	0.090790	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	48.1	3.88	13.2	16.4	0.16	4.02	6.51	3.36	1.36	1.59	4	0.531999	0.066283	0.001499	0.151693	0.077147	0.086033	0.036027	0.009595	0.032280	0.007444	0.467035	0.058189	0.001316	0.133169	0.067727	0.151055	0.063254	0.016846	0.028338	0.013071	0.304076	0.304076	5	1392.393338

Here we filter based on whether any given pair passes the An-Ab test within the values recomended by Putirka (2008) (T>1050 is 0.28+-0.11, and T<1050 is 0.1+-0.05)¶

The code uses the calculated temperature to decide what value to compare against, and the print Yes or No, as well as the temperature category it placed it into
Then, we make a variable of true/false called Eq_Filt, where we want it to read True if any of the column 'Pass An-Ab Eq Test Put2008?' contains the word yes
By using Loc, we extract the rows of the dataframe T_PL_eq23_5kbar_EqTests where Eq_Filt is True
The new dataframe "T_PL_eq23_filt" contains only pairs passing this filter

In [13]:

Eq_filt=T_PL_eq23_5kbar_EqTests['Pass An-Ab Eq Test Put2008?'].str.contains("Yes")
T_PL_eq23_filt=T_PL_eq23_5kbar_EqTests.loc[Eq_filt]
T_PL_eq23_filt.head()

Out[13]:

	T_K_calc	Pass An-Ab Eq Test Put2008?	Delta_An	Delta_Ab	Delta_Or	Pred_An_EqE	Pred_Ab_EqF	Pred_Or_EqG	Obs_Kd_Ab_An	SiO2_Plag	TiO2_Plag	Al2O3_Plag	FeOt_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Sample_ID_Plag	Si_Plag_cat_prop	Mg_Plag_cat_prop	Fet_Plag_cat_prop	Ca_Plag_cat_prop	Al_Plag_cat_prop	Na_Plag_cat_prop	K_Plag_cat_prop	Ti_Plag_cat_prop	sum	Si_Plag_cat_frac	Mg_Plag_cat_frac	Fet_Plag_cat_frac	Ca_Plag_cat_frac	Al_Plag_cat_frac	Na_Plag_cat_frac	K_Plag_cat_frac	Ti_Plag_cat_frac	An_Plag	Ab_Plag	Or_Plag	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	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	P	T
4	1392.393338	High T: Yes	0.021115	0.029107	0.028064	0.448856	0.530743	0.000329	0.369633	56.7	0.14	27.6	0.69	0.11	9.46	5.58	0.48	4	0.943674	0.002729	0.009604	0.168695	0.541383	0.180061	0.010192	0.001753	1.858092	0.507873	0.001469	0.005169	0.090790	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	48.1	3.88	13.2	16.40	0.16	4.02	6.51	3.36	1.36	1.59	4	0.531999	0.066283	0.001499	0.151693	0.077147	0.086033	0.036027	0.009595	0.032280	0.007444	0.467035	0.058189	0.001316	0.133169	0.067727	0.151055	0.063254	0.016846	0.028338	0.013071	0.304076	0.304076	5	1392.393338
6	1410.107078	High T: Yes	0.007695	0.037224	0.021375	0.479576	0.527774	0.000804	0.331605	56.1	0.21	27.8	0.56	0.09	9.94	5.53	0.38	6	0.933688	0.002233	0.007794	0.177255	0.545307	0.178448	0.008068	0.002629	1.855422	0.503221	0.001204	0.004201	0.095534	0.293899	0.096176	0.004348	0.001417	0.487271	0.490550	0.022180	49.5	4.23	14.8	14.00	0.16	4.63	6.36	3.76	1.04	0.69	6	0.540606	0.075382	0.001480	0.127867	0.074423	0.095250	0.039809	0.007245	0.034749	0.003190	0.471941	0.065807	0.001292	0.111626	0.064970	0.166303	0.069505	0.012650	0.030336	0.005570	0.370875	0.370875	5	1410.107078
7	1418.246494	High T: Yes	0.026281	0.006622	0.020336	0.507864	0.503560	0.001144	0.366685	56.4	0.20	27.3	0.53	0.08	9.61	5.48	0.36	7	0.938681	0.001985	0.007377	0.171370	0.535499	0.176834	0.007644	0.002504	1.841894	0.509628	0.001078	0.004005	0.093040	0.290733	0.096007	0.004150	0.001359	0.481582	0.496938	0.021480	49.5	4.04	15.3	13.50	0.13	4.65	6.39	3.62	0.90	0.66	7	0.543380	0.076096	0.001209	0.123933	0.075158	0.098973	0.038523	0.006302	0.033359	0.003067	0.473796	0.066351	0.001054	0.108063	0.065533	0.172598	0.067180	0.010990	0.029087	0.005348	0.380415	0.380415	5	1418.246494
8	1388.282711	High T: Yes	0.044579	0.021938	0.029839	0.444467	0.502815	0.000237	0.368925	56.0	0.14	27.0	0.64	0.09	9.68	5.26	0.50	8	0.932024	0.002233	0.008908	0.172619	0.529614	0.169735	0.010616	0.001753	1.827502	0.509999	0.001222	0.004874	0.094456	0.289802	0.092878	0.005809	0.000959	0.489046	0.480877	0.030077	52.0	4.04	12.5	14.30	0.15	3.30	6.71	2.80	1.41	1.17	8	0.573262	0.054234	0.001401	0.131838	0.079258	0.081206	0.029924	0.009915	0.033501	0.005460	0.508885	0.048144	0.001243	0.117033	0.070358	0.144173	0.053128	0.017603	0.029739	0.009694	0.291461	0.291461	5	1388.282711
9	1397.830490	High T: Yes	0.107368	0.061476	0.020652	0.445034	0.488335	0.000087	0.197672	54.5	0.00	27.5	0.75	0.24	11.10	4.74	0.35	9	0.907059	0.005955	0.010439	0.197941	0.539422	0.152955	0.007431	0.000000	1.821202	0.498055	0.003270	0.005732	0.108687	0.296190	0.083986	0.004080	0.000000	0.552402	0.426859	0.020739	57.8	1.87	13.7	9.35	0.20	3.41	6.33	3.82	1.94	0.30	9	0.626884	0.055134	0.001837	0.084806	0.073559	0.087560	0.040164	0.013421	0.015256	0.001377	0.548684	0.048257	0.001608	0.074227	0.064383	0.153275	0.070308	0.023494	0.013353	0.002411	0.393976	0.393976	5	1397.830490

Can also add filters based on Delta An, Ab, Or values¶

Here, new dataframe "T_PL_eq23_filt2" has calculated and predicted An values within 0.05, 0.03 for Or and 0.03 for Ab

In [14]:

Eq_filt_An=T_PL_eq23_5kbar_EqTests['Delta_An']<0.05
Eq_filt_Or=T_PL_eq23_5kbar_EqTests['Delta_Or']<0.03
Eq_filt_Ab=T_PL_eq23_5kbar_EqTests['Delta_Ab']<0.03

T_PL_eq23_filt2=T_PL_eq23_5kbar_EqTests.loc[Eq_filt&Eq_filt_An&Eq_filt_Ab&Eq_filt_Or]
T_PL_eq23_filt2

Out[14]:

	T_K_calc	Pass An-Ab Eq Test Put2008?	Delta_An	Delta_Ab	Delta_Or	Pred_An_EqE	Pred_Ab_EqF	Pred_Or_EqG	Obs_Kd_Ab_An	SiO2_Plag	TiO2_Plag	Al2O3_Plag	FeOt_Plag	MnO_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Sample_ID_Plag	Si_Plag_cat_prop	Mg_Plag_cat_prop	Fet_Plag_cat_prop	Ca_Plag_cat_prop	Al_Plag_cat_prop	Na_Plag_cat_prop	K_Plag_cat_prop	Mn_Plag_cat_prop	Ti_Plag_cat_prop	sum	Si_Plag_cat_frac	Mg_Plag_cat_frac	Fet_Plag_cat_frac	Ca_Plag_cat_frac	Al_Plag_cat_frac	Na_Plag_cat_frac	K_Plag_cat_frac	Mn_Plag_cat_frac	Ti_Plag_cat_frac	An_Plag	Ab_Plag	Or_Plag	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	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	P	T
4	1392.393338	High T: Yes	0.021115	0.029107	0.028064	0.448856	0.530743	0.000329	0.369633	56.70	0.14	27.60	0.69	0.00	0.11	9.46	5.58	0.48	4	0.943674	0.002729	0.009604	0.168695	0.541383	0.180061	0.010192	0.000000	0.001753	1.858092	0.507873	0.001469	0.005169	0.090790	0.291365	0.096907	0.005485	0.000000	0.000943	0.469971	0.501636	0.028393	48.10	3.88	13.20	16.40	0.16	4.02	6.51	3.36	1.36	1.59	0.00	4	0.531999	0.066283	0.001499	0.151693	0.077147	0.086033	0.036027	0.009595	0.032280	0.007444	0.467035	0.058189	0.001316	0.133169	0.067727	0.151055	0.063254	0.016846	0.028338	0.013071	0.304076	0.304076	5	1392.393338
7	1418.246494	High T: Yes	0.026281	0.006622	0.020336	0.507864	0.503560	0.001144	0.366685	56.40	0.20	27.30	0.53	0.00	0.08	9.61	5.48	0.36	7	0.938681	0.001985	0.007377	0.171370	0.535499	0.176834	0.007644	0.000000	0.002504	1.841894	0.509628	0.001078	0.004005	0.093040	0.290733	0.096007	0.004150	0.000000	0.001359	0.481582	0.496938	0.021480	49.50	4.04	15.30	13.50	0.13	4.65	6.39	3.62	0.90	0.66	0.00	7	0.543380	0.076096	0.001209	0.123933	0.075158	0.098973	0.038523	0.006302	0.033359	0.003067	0.473796	0.066351	0.001054	0.108063	0.065533	0.172598	0.067180	0.010990	0.029087	0.005348	0.380415	0.380415	5	1418.246494
8	1388.282711	High T: Yes	0.044579	0.021938	0.029839	0.444467	0.502815	0.000237	0.368925	56.00	0.14	27.00	0.64	0.00	0.09	9.68	5.26	0.50	8	0.932024	0.002233	0.008908	0.172619	0.529614	0.169735	0.010616	0.000000	0.001753	1.827502	0.509999	0.001222	0.004874	0.094456	0.289802	0.092878	0.005809	0.000000	0.000959	0.489046	0.480877	0.030077	52.00	4.04	12.50	14.30	0.15	3.30	6.71	2.80	1.41	1.17	0.00	8	0.573262	0.054234	0.001401	0.131838	0.079258	0.081206	0.029924	0.009915	0.033501	0.005460	0.508885	0.048144	0.001243	0.117033	0.070358	0.144173	0.053128	0.017603	0.029739	0.009694	0.291461	0.291461	5	1388.282711
19	1514.271339	High T: Yes	0.042248	0.013302	0.005014	0.804288	0.219359	0.000286	0.296268	48.73	0.06	31.46	0.35	0.00	0.22	15.41	2.60	0.09	19	0.811027	0.005458	0.004872	0.274799	0.617099	0.083900	0.001911	0.000000	0.000751	1.799816	0.450617	0.003033	0.002707	0.152682	0.342868	0.046616	0.001062	0.000000	0.000417	0.762040	0.232661	0.005299	48.07	1.63	16.16	10.16	0.20	7.72	11.39	2.57	0.50	0.28	0.06	19	0.510696	0.122268	0.001800	0.090269	0.129654	0.101171	0.026469	0.003388	0.013026	0.001259	0.451030	0.107983	0.001589	0.079723	0.114506	0.178702	0.046753	0.005985	0.011504	0.002224	0.575272	0.575272	5	1514.271339
23	1431.136469	High T: Yes	0.013393	0.027520	0.018570	0.605008	0.335482	0.000026	0.290997	53.35	0.23	28.83	0.83	0.01	0.32	12.67	4.11	0.32	23	0.887919	0.007940	0.011552	0.225938	0.565510	0.132626	0.006794	0.000141	0.002879	1.841300	0.482224	0.004312	0.006274	0.122706	0.307126	0.072028	0.003690	0.000077	0.001564	0.618401	0.363002	0.018596	48.01	4.17	13.25	13.39	0.23	4.81	9.57	3.47	1.42	0.86	0.26	23	0.519558	0.077599	0.002108	0.121182	0.110965	0.084498	0.036404	0.009802	0.033944	0.003940	0.457904	0.068391	0.001858	0.106802	0.097797	0.148941	0.064168	0.017278	0.029916	0.006944	0.390366	0.390366	5	1431.136469

Example 2 - Plagioclase-Liquid barometry¶

Note, Putirka (2008) questions whether plagioclase barometers are at all accurate, so use with extreme caution

In [15]:

Press=pt.calculate_fspar_liq_press(liq_comps=Liqs_PL, plag_comps=Plags, 
                                   equationP="P_Put2008_eq25", T=1300)
Press.head()

Out[15]:

0    4.041798
1    3.060993
2    3.936693
3    2.432249
4    0.986835
dtype: float64

In [16]:

Press=pt.calculate_fspar_liq_press(liq_comps=Liqs_PL, plag_comps=Plags, 
                                   equationP="P_Put2008_eq25", T=1300, eq_tests=True)
Press.head()

Out[16]:

	Pass An-Ab Eq Test Put2008?	P_kbar_calc	Delta_An	Delta_Ab	Delta_Or	Pred_An_EqE	Pred_Ab_EqF	Pred_Or_EqG	Obs_Kd_Ab_An	SiO2_Plag	TiO2_Plag	Al2O3_Plag	FeOt_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Sample_ID_Plag	Si_Plag_cat_prop	Mg_Plag_cat_prop	Fet_Plag_cat_prop	Ca_Plag_cat_prop	Al_Plag_cat_prop	Na_Plag_cat_prop	K_Plag_cat_prop	Ti_Plag_cat_prop	sum	Si_Plag_cat_frac	Mg_Plag_cat_frac	Fet_Plag_cat_frac	Ca_Plag_cat_frac	Al_Plag_cat_frac	Na_Plag_cat_frac	K_Plag_cat_frac	Ti_Plag_cat_frac	An_Plag	Ab_Plag	Or_Plag	SiO2_Liq	TiO2_Liq	Al2O3_Liq	FeOt_Liq	MnO_Liq	MgO_Liq	CaO_Liq	Na2O_Liq	K2O_Liq	P2O5_Liq	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	P	T
0	Low T: Yes	4.041798	0.035285	0.052459	0.029156	0.452414	0.501197	0.000059	0.510102	57.3	0.09	26.6	0.43	0.03	8.33	6.11	0.49	0	0.953660	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.001127	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.000613	0.417129	0.553656	0.029215	49.1	3.22	14.4	14.8	0.14	3.20	6.72	3.34	1.70	1.13	0	0.549988	0.053436	0.001328	0.138640	0.080652	0.095052	0.036269	0.012146	0.027130	0.005358	0.478739	0.046513	0.001156	0.120680	0.070204	0.165477	0.063141	0.021146	0.023616	0.009328	0.278194	0.278194	4.041798	1300
1	Low T: Yes	3.060993	0.024460	0.002030	0.028134	0.477584	0.516514	0.000198	0.455475	56.5	0.12	26.9	0.47	0.05	8.95	5.66	0.47	1	0.940345	0.001241	0.006542	0.159601	0.527653	0.182643	0.009979	0.001502	1.829506	0.513989	0.000678	0.003576	0.087237	0.288413	0.099832	0.005455	0.000821	0.453125	0.518543	0.028332	49.2	3.89	15.3	13.7	0.12	3.88	6.76	3.44	1.22	0.83	1	0.545500	0.064131	0.001127	0.127030	0.080306	0.099965	0.036975	0.008628	0.032442	0.003896	0.474569	0.055792	0.000980	0.110512	0.069864	0.173933	0.064334	0.015012	0.028224	0.006778	0.335475	0.335475	3.060993	1300
2	Low T: Yes	3.936693	0.042934	0.013869	0.023090	0.461195	0.572172	0.000346	0.500004	57.6	0.11	26.3	0.50	0.07	8.50	6.27	0.40	2	0.958653	0.001737	0.006959	0.151576	0.515884	0.202327	0.008493	0.001377	1.847006	0.519031	0.000940	0.003768	0.082066	0.279308	0.109543	0.004598	0.000746	0.418261	0.558303	0.023435	49.6	3.79	15.8	13.0	0.14	4.26	6.59	3.65	1.04	0.63	2	0.547269	0.070071	0.001308	0.119955	0.077907	0.102731	0.039042	0.007319	0.031455	0.002943	0.475045	0.060823	0.001136	0.104124	0.067626	0.178348	0.067779	0.012707	0.027304	0.005108	0.368736	0.368736	3.936693	1300
3	Low T: Yes	2.432249	0.004628	0.023672	0.049670	0.444011	0.525342	0.000021	0.461028	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.008630	0.161027	0.529614	0.180061	0.017835	0.002003	1.852655	0.513855	0.000804	0.004658	0.086917	0.285868	0.097191	0.009627	0.001081	0.448640	0.501670	0.049691	47.1	4.21	12.0	17.8	0.18	3.40	7.28	2.93	2.02	2.32	3	0.521269	0.056096	0.001687	0.164747	0.086327	0.078262	0.031436	0.014260	0.035047	0.010869	0.459338	0.049431	0.001487	0.145174	0.076070	0.137927	0.055402	0.025132	0.030883	0.019156	0.254001	0.254001	2.432249	1300
4	Low T: Yes	0.986835	0.031026	0.061152	0.028197	0.438945	0.562788	0.000196	0.369633	56.7	0.14	27.6	0.69	0.11	9.46	5.58	0.48	4	0.943674	0.002729	0.009604	0.168695	0.541383	0.180061	0.010192	0.001753	1.858092	0.507873	0.001469	0.005169	0.090790	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	48.1	3.88	13.2	16.4	0.16	4.02	6.51	3.36	1.36	1.59	4	0.531999	0.066283	0.001499	0.151693	0.077147	0.086033	0.036027	0.009595	0.032280	0.007444	0.467035	0.058189	0.001316	0.133169	0.067727	0.151055	0.063254	0.016846	0.028338	0.013071	0.304076	0.304076	0.986835	1300

Example 4 - Kspar-Liquid thermometry¶

In [18]:

T_KL_24=pt.calculate_fspar_liq_temp(kspar_comps=Kspars, liq_comps=Liqs_KL, 
                                    equationT="T_Put2008_eq24b", P=5)-273.15
T_KL_24.head()

Out[18]:

0    931.611608
1    874.979583
2    700.236443
3    745.871566
4    795.809510
dtype: float64

In [19]:

# Currently, we haven't implemented any equilibrium tests for Kfeldspar. 
T_KL_24=pt.calculate_fspar_liq_temp(kspar_comps=Kspars, liq_comps=Liqs_KL, 
                                    equationT="T_Put2008_eq24b", P=5, eq_tests=True)-273.15
T_KL_24.head()

Sorry, no equilibrium tests implemented for Kspar-Liquid

Out[19]:

0    931.611608
1    874.979583
2    700.236443
3    745.871566
4    795.809510
dtype: float64