Two Feldspar thermometry¶

First, we consider analyses of touching Kspar-Plag pairs
Then we consider the situation common in lavas where you have a lot of individual Plag-Kspar crystals, and you want to assess all possible combinations from a single sample or deposit
You can download the excel spreadsheet this reads from here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Feldspar_Thermobarometry/Two_Feldspar_input.xlsx

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

In [1]:

#!pip install Thermobar

Import python things and set plotting parameters¶

In [20]:

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

# 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

Example 1 - Touching Pairs pressure calculations¶

Make an excel spreadsheet where you have rows for each touching pair, with the column headings SiO2_Kspar for SiO2 in the Kspar, SiO2_Plag for SiO2 in the Plag etc. The column order doesnt matter.
FeO is always as FeOt

Loading in excel spreadsheet¶

In [3]:

out=pt.import_excel('Two_Feldspar_input.xlsx', sheet_name="Paired_Plag_Kspar")
# This extracts a dataframe of all inputs
my_input=out['my_input']
# This extracts a dataframe of kspar compositions from the dictionary "out"
Kspars=out['Kspars']
# This extracts a dataframe of plag compositions from the dictionary "out"
Plags=out['Plags']

check that your inputs have been read in properly, to do this, do "name".head() to look at the first few rows¶

In [4]:

display(Plags.head())
display(Kspars.head())

	SiO2_Plag	Al2O3_Plag	FeOt_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Sample_ID_Plag
0	56.3	31.4	0.31	0.05	14.60	2.70	0.71	0
1	60.5	24.8	0.07	0.00	5.92	7.36	1.42	1
2	57.8	26.7	0.08	0.00	8.06	6.08	1.13	2
3	46.2	33.5	0.31	0.00	17.40	1.22	0.30	3
4	50.3	31.1	0.54	0.06	14.50	2.76	0.79	4

	SiO2_Kspar	Al2O3_Kspar	FeOt_Kspar	MgO_Kspar	CaO_Kspar	Na2O_Kspar	K2O_Kspar	Sample_ID_Kspar
0	65.0	19.1	0.11	0.03	0.51	2.02	13.40	0
1	65.5	19.6	0.07	0.00	0.75	4.81	9.36	1
2	65.4	19.4	0.05	0.00	0.59	3.13	11.50	2
3	64.6	18.8	0.09	0.00	0.39	1.15	14.80	3
4	61.8	19.2	0.51	0.03	0.66	1.71	12.90	4

If at any point you want to find out more about a function, and what equations it offers, you can use the help function

In [5]:

help(pt.calculate_plag_kspar_temp)

Help on function calculate_plag_kspar_temp in module Thermobar.feldspar:

calculate_plag_kspar_temp(*, plag_comps=None, kspar_comps=None, Two_Fspar_Match=None, equationT=None, P=None, eq_tests=False)
    Two feldspar thermometer (Kspar-Plag), returns temperature in Kelvin
    
    Parameters
    ----------
    
    plag_comps: pandas.DataFrame
        Plag compositions with column headings SiO2_Plag, MgO_Plag etc.
    
    kspar_comps: pandas.DataFrame
        Kspar compositions with column headings SiO2_Kspar, MgO_Kspar etc.
    
    EquationT: str
        choose from:
    
            |   T_Put2008_eq27a (P-dependent, H2O-independent)
            |   T_Put2008_eq27b (P-dependent, H2O-independent)
            |   T_Put_Global_2Fspar (P-dependent, H2O-independent)
    
    P: float, int, pandas.Series, str
        Pressure in kbar to perform calculations at.
        Only needed for P-sensitive thermometers.
        If P="Solve", returns a partial function,
        else, enter an integer, float, or panda series.
    
    Returns
    -------
    If eq_tests is False
        pandas.Series: Temperature in Kelvin
    If eq_tests is True
        pandas.DataFrame: Temperature in Kelvin+eq Tests + input compositions

You can also do help on the equations themselves to get a bit more information,

In [6]:

help(pt.T_Put2008_eq27a)

Help on function T_Put2008_eq27a in module Thermobar.feldspar:

T_Put2008_eq27a(P, *, K_Barth, Si_Kspar_cat_frac, Ca_Kspar_cat_frac, An_Kspar, An_Plag, Ab_Plag)
    Two feldspar thermometer: Equation 27a of Putirka (2008).
    :cite:`putirka2008thermometers`
    
    SEE±23°C for calibration
    
    SEE±44°C for test data

1a - Calculating T, fixed P¶

The function calculate_plag_kspar_temp calculates T in Kelvin using inputted Kspar, Plag compositions, a user-selected equation, and a pressure estimate (5 kbar here).

In [8]:

T_27a_5kbar=pt.calculate_plag_kspar_temp(plag_comps=Plags, kspar_comps=Kspars, 
                                         equationT="T_Put2008_eq27a", P=5)-273.15
T_27a_5kbar

Out[8]:

0     944.011837
1     912.626406
2     912.835560
3     902.422454
4     908.475430
5     799.429554
6     818.616248
7     796.533005
8     728.113848
9     727.752376
10    703.051858
11    709.813651
12    730.346965
13    754.219939
14    692.524231
15    647.728144
dtype: float64

1b - Can also specify a panda series of pressures for the original spreadsheet¶

here, presure stored in a column in GPa, so need to multiply by 10 to get kbar for the function

In [10]:

T_27a_5kbar=pt.calculate_plag_kspar_temp(plag_comps=Plags, kspar_comps=Kspars, 
                                         equationT="T_Put2008_eq27a", 
                                         P=10*my_input['P_GPa'])-273.15
T_27a_5kbar

Out[10]:

0      989.600535
1      955.165833
2     1053.643565
3      943.994277
4      950.619830
5      831.883113
6      930.082357
7      988.688652
8      969.169184
9      754.549692
10     728.029781
11     735.283052
12     757.339241
13     783.040474
14     716.746820
15     668.870563
dtype: float64

1c - Can also specify Eq_Test=True, this calculates activities based on Elkins and Grove (1990), following the protocol in the supplementary spreadsheets of Putirka.¶

Note, Putirka (2008) don't give any values of equilibrium tests unlike Cpx-Opx, or Cpx-Liq etc. Instead, they say

"As a test for equilibrium, one can compare the activities between those calculated for alkali and plagioclase feldspar, using the models of Elkins and Grove (1990) (columns AX – AZ). These should nominally be zero, but tests are required to determine whether this measure of equilibrium is effective at reducing error for T calculated using experimental data."

Thus, we don't apply any filters by default, but show users how to filter based on these values below

In [12]:

T_27a_5kbar_EqTests=pt.calculate_plag_kspar_temp(plag_comps=Plags, kspar_comps=Kspars, 
                    equationT="T_Put2008_eq27a", P=10*my_input['P_GPa'], eq_tests=True)
T_27a_5kbar_EqTests.head()

Out[12]:

	T_K_calc	Delta_An	Delta_Ab	Delta_Or	a_Ab_plg	a_An_plg	a_Or_plg	a_Ab_kspar	a_An_kspar	a_Or_kspar	SiO2_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	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	An_Plag	Ab_Plag	Or_Plag	SiO2_Kspar	Al2O3_Kspar	FeOt_Kspar	MgO_Kspar	CaO_Kspar	Na2O_Kspar	K2O_Kspar	Sample_ID_Kspar	Si_Kspar_cat_prop	Mg_Kspar_cat_prop	Fet_Kspar_cat_prop	Ca_Kspar_cat_prop	Al_Kspar_cat_prop	Na_Kspar_cat_prop	K_Kspar_cat_prop	sum	Si_Kspar_cat_frac	Mg_Kspar_cat_frac	Fet_Kspar_cat_frac	Ca_Kspar_cat_frac	Al_Kspar_cat_frac	Na_Kspar_cat_frac	K_Kspar_cat_frac	An_Kspar	Ab_Kspar	Or_Kspar	K_Barth	T	P
0	1262.750535	0.045987	0.187872	0.208913	0.582154	0.919522	1.024046	0.394282	0.873535	0.815133	56.3	31.4	0.31	0.05	14.60	2.70	0.71	0	0.937017	0.001241	0.004315	0.260354	0.615922	0.087126	0.015075	1.921050	0.487763	0.000646	0.002246	0.135527	0.320617	0.045354	0.007847	0.718109	0.240312	0.041580	65.0	19.1	0.11	0.03	0.51	2.02	13.40	0	1.081813	0.000744	0.001531	0.009095	0.374653	0.065183	0.284513	1.817533	0.595210	0.000410	0.000842	0.005004	0.206133	0.035864	0.156538	0.025348	0.181675	0.792977	0.755998	1262.750535	10
1	1228.315833	0.248834	-0.042074	-0.075490	0.653809	0.839507	0.602659	0.695883	0.590672	0.678149	60.5	24.8	0.07	0.00	5.92	7.36	1.42	1	1.006919	0.000000	0.000974	0.105568	0.486461	0.237500	0.030150	1.867572	0.539159	0.000000	0.000522	0.056527	0.260478	0.127171	0.016144	0.282859	0.636357	0.080784	65.5	19.6	0.07	0.00	0.75	4.81	9.36	1	1.090135	0.000000	0.000974	0.013374	0.384461	0.155214	0.198735	1.842893	0.591535	0.000000	0.000529	0.007257	0.208618	0.084223	0.107838	0.036410	0.422555	0.541035	0.664022	1228.315833	10
2	1326.793565	0.412296	-0.015576	-0.233532	0.610283	0.861556	0.516050	0.625859	0.449261	0.749582	57.8	26.7	0.08	0.00	8.06	6.08	1.13	2	0.961982	0.000000	0.001113	0.143730	0.523730	0.196196	0.023993	1.850743	0.519781	0.000000	0.000602	0.077661	0.282983	0.106009	0.012964	0.394951	0.539121	0.065928	65.4	19.4	0.05	0.00	0.59	3.13	11.50	2	1.088471	0.000000	0.000696	0.010521	0.380538	0.101002	0.244172	1.825399	0.596292	0.000000	0.000381	0.005764	0.208468	0.055332	0.133763	0.029579	0.283957	0.686464	0.526704	1326.793565	20
3	1217.144277	-0.125097	0.344129	0.379772	0.600615	0.925594	1.265507	0.256487	1.050691	0.885735	46.2	33.5	0.31	0.00	17.40	1.22	0.30	3	0.768920	0.000000	0.004315	0.310285	0.657114	0.039368	0.006370	1.786372	0.430437	0.000000	0.002415	0.173696	0.367848	0.022038	0.003566	0.871531	0.110578	0.017891	64.6	18.8	0.09	0.00	0.39	1.15	14.80	3	1.075156	0.000000	0.001253	0.006955	0.368768	0.037109	0.314238	1.803480	0.596156	0.000000	0.000695	0.003856	0.204476	0.020577	0.174240	0.019410	0.103570	0.877020	0.936627	1217.144277	10
4	1223.769830	-0.442938	0.223217	0.445615	0.589090	0.932291	1.275140	0.365873	1.375229	0.829525	50.3	31.1	0.54	0.06	14.50	2.76	0.79	4	0.837157	0.001489	0.007516	0.258571	0.610037	0.089063	0.016774	1.820606	0.459823	0.000818	0.004128	0.142025	0.335074	0.048919	0.009213	0.709566	0.244404	0.046030	61.8	19.2	0.51	0.03	0.66	1.71	12.90	4	1.028555	0.000744	0.007098	0.011769	0.376615	0.055180	0.273897	1.753859	0.586453	0.000424	0.004047	0.006711	0.214735	0.031462	0.156168	0.034530	0.161891	0.803579	0.662392	1223.769830	10

Example 2 - Combining all possible Plag-Kspar Pairs¶

Say you don't have touching Plag-Kspar pairs, but instead a list of Kspar compositions you have measured on the probe in one sheet of an excel spreadsheet, and a set of Plag compositions in another sheet
The functions ending in "_matching" in Thermobar consider all possible combinations of Plags and Kspars

Import Kspars¶

In [13]:

out2=pt.import_excel('Two_Feldspar_input.xlsx', sheet_name="Kspar")
Kspars2=out2['Kspars']
Kspars2.head()

Out[13]:

	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	1
1	65.4	19.4	0.05	0.00	0.59	3.13	11.50	2
2	64.6	18.8	0.09	0.00	0.39	1.15	14.80	3
3	61.8	19.2	0.51	0.03	0.66	1.71	12.90	4
4	65.1	19.2	0.05	0.00	0.36	2.87	12.60	5

Import plags¶

In [14]:

out3=pt.import_excel('Two_Feldspar_input.xlsx', sheet_name="Plag")
Plags3=out3['Plags']
Plags3.head()

Out[14]:

	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

Calculate T for all matches using equation 27a at 5 kbar¶

In [15]:

T_27a_5kbar_All=pt.calculate_plag_kspar_temp_matching(plag_comps=Plags3, kspar_comps=Kspars2, 
                                                      equationT="T_Put2008_eq27a", P=5)
T_27a_5kbar_All

Considering 77 Kspar-Plag pairs, be patient if this is >>1 million!

Out[15]:

	Delta_An	Delta_Ab	Delta_Or	a_Ab_plg	a_An_plg	a_Or_plg	a_Ab_kspar	a_An_kspar	a_Or_kspar	T_K_calc	SiO2_Kspar	TiO2_Kspar	Al2O3_Kspar	FeOt_Kspar	MnO_Kspar	MgO_Kspar	CaO_Kspar	Na2O_Kspar	K2O_Kspar	Cr2O3_Kspar	Sample_ID_Kspar	Si_Kspar_cat_prop	Mg_Kspar_cat_prop	Fet_Kspar_cat_prop	Ca_Kspar_cat_prop	Al_Kspar_cat_prop	Na_Kspar_cat_prop	K_Kspar_cat_prop	Mn_Kspar_cat_prop	Ti_Kspar_cat_prop	Cr_Kspar_cat_prop	sum	Si_Kspar_cat_frac	Mg_Kspar_cat_frac	Fet_Kspar_cat_frac	Ca_Kspar_cat_frac	Al_Kspar_cat_frac	Na_Kspar_cat_frac	K_Kspar_cat_frac	Mn_Kspar_cat_frac	Ti_Kspar_cat_frac	Cr_Kspar_cat_frac	An_Kspar	Ab_Kspar	Or_Kspar	ID_Kspar	SiO2_Plag	TiO2_Plag	Al2O3_Plag	FeOt_Plag	MnO_Plag	MgO_Plag	CaO_Plag	Na2O_Plag	K2O_Plag	Cr2O3_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	Cr_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	Cr_Plag_cat_frac	An_Plag	Ab_Plag	Or_Plag	ID_Plag	K_Barth	T	P
0	0.295531	0.002892	-0.326051	0.638916	0.902959	0.322702	0.636023	0.607427	0.648754	1269.748822	65.5	0.0	19.6	0.07	0.0	0.00	0.75	4.81	9.36	0	1	1.090135	0.000000	0.000974	0.013374	0.384461	0.155214	0.198735	0.0	0.0	0.0	1.842893	0.591535	0.000000	0.000529	0.007257	0.208618	0.084223	0.107838	0.0	0.0	0.0	0.036410	0.422555	0.541035	0.0	57.3	0.09	26.6	0.43	0	0.03	8.33	6.11	0.49	0	0	0.95366	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.0	0.001127	0.0	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.0	0.000613	0.0	0.417129	0.553656	0.029215	0	0.763208	1269.748822	5
1	-0.092624	0.110226	-0.329378	0.641223	0.956165	0.410594	0.530997	1.048788	0.739972	1197.800282	65.4	0.0	19.4	0.05	0.0	0.00	0.59	3.13	11.50	0	2	1.088471	0.000000	0.000696	0.010521	0.380538	0.101002	0.244172	0.0	0.0	0.0	1.825399	0.596292	0.000000	0.000381	0.005764	0.208468	0.055332	0.133763	0.0	0.0	0.0	0.029579	0.283957	0.686464	1.0	57.3	0.09	26.6	0.43	0	0.03	8.33	6.11	0.49	0	0	0.95366	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.0	0.001127	0.0	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.0	0.000613	0.0	0.417129	0.553656	0.029215	0	0.512876	1197.800282	5
2	-2.324993	0.335739	0.000517	0.648692	1.149221	0.890109	0.312954	3.474214	0.889592	1013.354929	64.6	0.0	18.8	0.09	0.0	0.00	0.39	1.15	14.80	0	3	1.075156	0.000000	0.001253	0.006955	0.368768	0.037109	0.314238	0.0	0.0	0.0	1.803480	0.596156	0.000000	0.000695	0.003856	0.204476	0.020577	0.174240	0.0	0.0	0.0	0.019410	0.103570	0.877020	2.0	57.3	0.09	26.6	0.43	0	0.03	8.33	6.11	0.49	0	0	0.95366	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.0	0.001127	0.0	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.0	0.000613	0.0	0.417129	0.553656	0.029215	0	0.187066	1013.354929	5
3	-1.933109	0.255822	-0.187706	0.645605	1.065363	0.647145	0.389783	2.998471	0.834851	1081.998018	61.8	0.0	19.2	0.51	0.0	0.03	0.66	1.71	12.90	0	4	1.028555	0.000744	0.007098	0.011769	0.376615	0.055180	0.273897	0.0	0.0	0.0	1.753859	0.586453	0.000424	0.004047	0.006711	0.214735	0.031462	0.156168	0.0	0.0	0.0	0.034530	0.161891	0.803579	3.0	57.3	0.09	26.6	0.43	0	0.03	8.33	6.11	0.49	0	0	0.95366	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.0	0.001127	0.0	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.0	0.000613	0.0	0.417129	0.553656	0.029215	0	0.292404	1081.998018	5
4	0.064453	0.110505	-0.269422	0.643095	1.001463	0.498868	0.532590	0.937010	0.768290	1145.332986	65.1	0.0	19.2	0.05	0.0	0.00	0.36	2.87	12.60	0	5	1.083478	0.000000	0.000696	0.006420	0.376615	0.092612	0.267527	0.0	0.0	0.0	1.827347	0.592924	0.000000	0.000381	0.003513	0.206099	0.050681	0.146402	0.0	0.0	0.0	0.017513	0.252653	0.729834	4.0	57.3	0.09	26.6	0.43	0	0.03	8.33	6.11	0.49	0	0	0.95366	0.000744	0.005985	0.148545	0.521768	0.197164	0.010404	0.0	0.001127	0.0	1.839397	0.518464	0.000405	0.003254	0.080757	0.283663	0.107189	0.005656	0.0	0.000613	0.0	0.417129	0.553656	0.029215	0	0.456335	1145.332986	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
72	-0.006407	0.168807	-0.263274	0.642376	1.029167	0.542268	0.473569	1.035574	0.805542	1120.942214	64.9	0.0	19.3	0.06	0.0	0.03	0.29	2.30	13.30	0	7	1.080149	0.000744	0.000835	0.005171	0.378576	0.074219	0.282390	0.0	0.0	0.0	1.822085	0.592809	0.000409	0.000458	0.002838	0.207771	0.040733	0.154982	0.0	0.0	0.0	0.014294	0.205149	0.780557	6.0	56.1	0.21	27.8	0.56	0	0.09	9.94	5.53	0.38	0	6	0.933688	0.002233	0.007794	0.177255	0.545307	0.178448	0.008068	0.0	0.002629	0.0	1.855422	0.503221	0.001204	0.004201	0.095534	0.293899	0.096176	0.004348	0.0	0.001417	0.0	0.487271	0.49055	0.02218	6	0.418202	1120.942214	5
73	0.527046	0.050488	-0.219397	0.642228	1.028053	0.539065	0.591741	0.501007	0.758462	1122.277113	64.9	0.0	18.6	0.00	0.0	0.00	0.17	3.02	12.10	0	8	1.080149	0.000000	0.000000	0.003032	0.364845	0.097453	0.256911	0.0	0.0	0.0	1.802390	0.599287	0.000000	0.000000	0.001682	0.202423	0.054069	0.142539	0.0	0.0	0.0	0.008482	0.272674	0.718843	7.0	56.1	0.21	27.8	0.56	0	0.09	9.94	5.53	0.38	0	6	0.933688	0.002233	0.007794	0.177255	0.545307	0.178448	0.008068	0.0	0.002629	0.0	1.855422	0.503221	0.001204	0.004201	0.095534	0.293899	0.096176	0.004348	0.0	0.001417	0.0	0.487271	0.49055	0.02218	6	0.555855	1122.277113	5
74	0.643794	-0.002624	-0.263600	0.637859	0.995527	0.452140	0.640482	0.351733	0.715739	1163.393766	66.0	0.0	18.9	0.05	0.0	0.00	0.18	3.73	11.30	0	9	1.098457	0.000000	0.000696	0.003210	0.370730	0.120364	0.239925	0.0	0.0	0.0	1.833381	0.599143	0.000000	0.000380	0.001751	0.202211	0.065651	0.130865	0.0	0.0	0.0	0.008830	0.331125	0.660044	8.0	56.1	0.21	27.8	0.56	0	0.09	9.94	5.53	0.38	0	6	0.933688	0.002233	0.007794	0.177255	0.545307	0.178448	0.008068	0.0	0.002629	0.0	1.855422	0.503221	0.001204	0.004201	0.095534	0.293899	0.096176	0.004348	0.0	0.001417	0.0	0.487271	0.49055	0.02218	6	0.675008	1163.393766	5
75	0.713796	-0.043818	-0.276888	0.635087	0.975323	0.404172	0.678905	0.261527	0.681060	1191.228218	66.0	0.0	19.2	0.00	0.0	0.00	0.18	4.31	10.40	0	10	1.098457	0.000000	0.000000	0.003210	0.376615	0.139080	0.220816	0.0	0.0	0.0	1.838177	0.597579	0.000000	0.000000	0.001746	0.204885	0.075662	0.120128	0.0	0.0	0.0	0.008840	0.383028	0.608132	9.0	56.1	0.21	27.8	0.56	0	0.09	9.94	5.53	0.38	0	6	0.933688	0.002233	0.007794	0.177255	0.545307	0.178448	0.008068	0.0	0.002629	0.0	1.855422	0.503221	0.001204	0.004201	0.095534	0.293899	0.096176	0.004348	0.0	0.001417	0.0	0.487271	0.49055	0.02218	6	0.780814	1191.228218	5
76	0.406945	0.094842	-0.195144	0.644493	1.045231	0.590208	0.549651	0.638286	0.785353	1102.198221	65.5	0.0	18.9	0.11	0.0	0.04	0.17	2.58	12.50	0	11	1.090135	0.000992	0.001531	0.003032	0.370730	0.083254	0.265404	0.0	0.0	0.0	1.815078	0.600599	0.000547	0.000844	0.001670	0.204250	0.045868	0.146222	0.0	0.0	0.0	0.008620	0.236726	0.754654	10.0	56.1	0.21	27.8	0.56	0	0.09	9.94	5.53	0.38	0	6	0.933688	0.002233	0.007794	0.177255	0.545307	0.178448	0.008068	0.0	0.002629	0.0	1.855422	0.503221	0.001204	0.004201	0.095534	0.293899	0.096176	0.004348	0.0	0.001417	0.0	0.487271	0.49055	0.02218	6	0.482573	1102.198221	5

77 rows × 85 columns

Examine equilibrium tests¶

As it is currently unclear what delta values are acceptable, we leave it up to the users to investigate the experimental data, but provide an example here of how you can filter based on these values
First, plot up the delta values to get an idea of the degree of disequilibrium

In [21]:

fig, ((ax0, ax1, ax2)) = plt.subplots(1,3, figsize = (15,5)) # adjust dimensions of figure here
ax0.hist(T_27a_5kbar_All['Delta_An'])
ax1.hist(T_27a_5kbar_All['Delta_Ab'])
ax2.hist(T_27a_5kbar_All['Delta_Or'])
ax0.set_xlabel('Delta An')
ax1.set_xlabel('Delta Ab')
ax2.set_xlabel('Delta Or')
ax0.set_ylabel('# of measurements')

Out[21]:

Text(0, 0.5, '# of measurements')

Filtering outputs¶

Here we make filters, the "filt_a_..." considers all avlues of Delta An <1, and is a bool, so a series of true and false statements

In [22]:

# This says accept pairs within Delta An between +-1
filt_a_An=abs(T_27a_5kbar_All['Delta_An'])<1 

# This says accept pairs with Delta Ab between +-0.15
filt_a_Ab=abs(T_27a_5kbar_All['Delta_Ab'])<0.15 

# This says accept pairs with Delta Or between +-0.2
filt_a_Or=abs(T_27a_5kbar_All['Delta_Or'])<0.2 

Apply filters¶

Here, we apply these filters to only consider pairs meeting all 3 criteria above

In [23]:

# This filters pairs to only those meeting these three criteria defined above
Filt_pairs=T_27a_5kbar_All.loc[filt_a_An&filt_a_Ab&filt_a_Or] 
Filt_pairs

Out[23]:

	Delta_An	Delta_Ab	Delta_Or	a_Ab_plg	a_An_plg	a_Or_plg	a_Ab_kspar	a_An_kspar	a_Or_kspar	T_K_calc	SiO2_Kspar	Al2O3_Kspar	FeOt_Kspar	MgO_Kspar	CaO_Kspar	Na2O_Kspar	K2O_Kspar	Sample_ID_Kspar	Si_Kspar_cat_prop	Mg_Kspar_cat_prop	Fet_Kspar_cat_prop	Ca_Kspar_cat_prop	Al_Kspar_cat_prop	Na_Kspar_cat_prop	K_Kspar_cat_prop	sum	Si_Kspar_cat_frac	Mg_Kspar_cat_frac	Fet_Kspar_cat_frac	Ca_Kspar_cat_frac	Al_Kspar_cat_frac	Na_Kspar_cat_frac	K_Kspar_cat_frac	An_Kspar	Ab_Kspar	Or_Kspar	ID_Kspar	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	ID_Plag	K_Barth	T	P
7	0.483175	0.034127	-0.149859	0.645062	1.051235	0.611800	0.610935	0.568060	0.761658	1095.067777	64.9	18.6	0.00	0.00	0.17	3.02	12.10	8	1.080149	0.000000	0.000000	0.003032	0.364845	0.097453	0.256911	1.802390	0.599287	0.000000	0.000000	0.001682	0.202423	0.054069	0.142539	0.008482	0.272674	0.718843	7.0	57.3	0.09	26.6	0.43	0.03	8.33	6.11	0.49	0	0.95366	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	0	0.492498	1095.067777	5
8	0.615621	-0.015126	-0.197861	0.643541	1.012541	0.522500	0.658666	0.396920	0.720361	1133.531892	66.0	18.9	0.05	0.00	0.18	3.73	11.30	9	1.098457	0.000000	0.000696	0.003210	0.370730	0.120364	0.239925	1.833381	0.599143	0.000000	0.000380	0.001751	0.202211	0.065651	0.130865	0.008830	0.331125	0.660044	8.0	57.3	0.09	26.6	0.43	0.03	8.33	6.11	0.49	0	0.95366	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	0	0.59807	1133.531892	5
10	0.345956	0.076966	-0.124167	0.645848	1.071757	0.663643	0.568882	0.725801	0.787810	1076.240814	65.5	18.9	0.11	0.04	0.17	2.58	12.50	11	1.090135	0.000992	0.001531	0.003032	0.370730	0.083254	0.265404	1.815078	0.600599	0.000547	0.000844	0.001670	0.204250	0.045868	0.146222	0.008620	0.236726	0.754654	10.0	57.3	0.09	26.6	0.43	0.03	8.33	6.11	0.49	0	0.95366	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	0	0.427569	1076.240814	5
18	0.512559	0.037062	-0.132206	0.637768	1.044104	0.627760	0.600706	0.531545	0.759966	1109.293338	64.9	18.6	0.00	0.00	0.17	3.02	12.10	8	1.080149	0.000000	0.000000	0.003032	0.364845	0.097453	0.256911	1.802390	0.599287	0.000000	0.000000	0.001682	0.202423	0.054069	0.142539	0.008482	0.272674	0.718843	7.0	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	1	0.525847	1109.293338	5
19	0.635801	-0.013936	-0.186124	0.635049	1.008142	0.531789	0.648985	0.372340	0.717913	1149.132699	66.0	18.9	0.05	0.00	0.18	3.73	11.30	9	1.098457	0.000000	0.000696	0.003210	0.370730	0.120364	0.239925	1.833381	0.599143	0.000000	0.000380	0.001751	0.202211	0.065651	0.130865	0.008830	0.331125	0.660044	8.0	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	1	0.638568	1149.132699	5
21	0.385029	0.080548	-0.102697	0.639175	1.063141	0.683812	0.558627	0.678111	0.786509	1089.816751	65.5	18.9	0.11	0.04	0.17	2.58	12.50	11	1.090135	0.000992	0.001531	0.003032	0.370730	0.083254	0.265404	1.815078	0.600599	0.000547	0.000844	0.001670	0.204250	0.045868	0.146222	0.008620	0.236726	0.754654	10.0	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	1	0.456521	1089.816751	5
33	0.342311	-0.030924	-0.141247	0.599513	0.920277	0.503433	0.630437	0.577966	0.644680	1287.496420	65.5	19.6	0.07	0.00	0.75	4.81	9.36	1	1.090135	0.000000	0.000974	0.013374	0.384461	0.155214	0.198735	1.842893	0.591535	0.000000	0.000529	0.007257	0.208618	0.084223	0.107838	0.036410	0.422555	0.541035	0.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.842297	1287.496420	5
34	-0.016692	0.078517	-0.097687	0.602335	0.973861	0.640315	0.523817	0.990553	0.738002	1213.058221	65.4	19.4	0.05	0.00	0.59	3.13	11.50	2	1.088471	0.000000	0.000696	0.010521	0.380538	0.101002	0.244172	1.825399	0.596292	0.000000	0.000381	0.005764	0.208468	0.055332	0.133763	0.029579	0.283957	0.686464	1.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.566024	1213.058221	5
37	0.135572	0.079969	0.010847	0.604626	1.019448	0.777728	0.524657	0.883876	0.766882	1158.895794	65.1	19.2	0.05	0.00	0.36	2.87	12.60	5	1.083478	0.000000	0.000696	0.006420	0.376615	0.092612	0.267527	1.827347	0.592924	0.000000	0.000381	0.003513	0.206099	0.050681	0.146402	0.017513	0.252653	0.729834	4.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.503623	1158.895794	5
38	-0.006681	0.113073	0.044122	0.605423	1.035764	0.832018	0.492350	1.042445	0.787896	1141.211077	65.0	19.2	0.00	0.00	0.35	2.51	12.80	6	1.081813	0.000000	0.000000	0.006241	0.376615	0.080995	0.271774	1.817438	0.595241	0.000000	0.000000	0.003434	0.207223	0.044566	0.149537	0.017385	0.225607	0.757008	5.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.449712	1141.211077	5
39	-0.043394	0.123834	0.151849	0.607099	1.070842	0.958524	0.483265	1.114235	0.806676	1105.813911	64.9	19.3	0.06	0.03	0.29	2.30	13.30	7	1.080149	0.000744	0.000835	0.005171	0.378576	0.074219	0.282390	1.822085	0.592809	0.000409	0.000458	0.002838	0.207771	0.040733	0.154982	0.014294	0.205149	0.780557	6.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.408932	1105.813911	5
40	0.532550	0.004782	0.193227	0.607036	1.069505	0.953451	0.602254	0.536955	0.760224	1107.101559	64.9	18.6	0.00	0.00	0.17	3.02	12.10	8	1.080149	0.000000	0.000000	0.003032	0.364845	0.097453	0.256911	1.802390	0.599287	0.000000	0.000000	0.001682	0.202423	0.054069	0.142539	0.008482	0.272674	0.718843	7.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.543534	1107.101559	5
41	0.654607	-0.045279	0.096222	0.605172	1.030592	0.814507	0.650451	0.375986	0.718285	1146.727383	66.0	18.9	0.05	0.00	0.18	3.73	11.30	9	1.098457	0.000000	0.000696	0.003210	0.370730	0.120364	0.239925	1.833381	0.599143	0.000000	0.000380	0.001751	0.202211	0.065651	0.130865	0.008830	0.331125	0.660044	8.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.660046	1146.727383	5
42	0.727768	-0.084143	0.052277	0.603986	1.006519	0.736668	0.688129	0.278752	0.684391	1173.513726	66.0	19.2	0.00	0.00	0.18	4.31	10.40	10	1.098457	0.000000	0.000000	0.003210	0.376615	0.139080	0.220816	1.838177	0.597579	0.000000	0.000000	0.001746	0.204885	0.075662	0.120128	0.008840	0.383028	0.608132	9.0	57.2	0.16	27.0	0.62	0.06	9.03	5.58	0.84	3	0.951996	0.001489	0.00863	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	0.44864	0.50167	0.049691	3	0.763506	1173.513726	5
51	0.523165	0.038410	-0.114109	0.634669	1.039392	0.645113	0.596258	0.516227	0.759222	1115.672334	64.9	18.6	0.00	0.00	0.17	3.02	12.10	8	1.080149	0.000000	0.000000	0.003032	0.364845	0.097453	0.256911	1.802390	0.599287	0.000000	0.000000	0.001682	0.202423	0.054069	0.142539	0.008482	0.272674	0.718843	7.0	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	0.09079	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	4	0.54357	1115.672334	5
52	0.642779	-0.013464	-0.172384	0.631304	1.004789	0.544454	0.644769	0.362009	0.716838	1156.136597	66.0	18.9	0.05	0.00	0.18	3.73	11.30	9	1.098457	0.000000	0.000696	0.003210	0.370730	0.120364	0.239925	1.833381	0.599143	0.000000	0.000380	0.001751	0.202211	0.065651	0.130865	0.008830	0.331125	0.660044	8.0	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	0.09079	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	4	0.660091	1156.136597	5
53	0.714493	-0.053705	-0.193872	0.629168	0.983325	0.488624	0.682873	0.268832	0.682497	1183.512115	66.0	19.2	0.00	0.00	0.18	4.31	10.40	10	1.098457	0.000000	0.000000	0.003210	0.376615	0.139080	0.220816	1.838177	0.597579	0.000000	0.000000	0.001746	0.204885	0.075662	0.120128	0.008840	0.383028	0.608132	9.0	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	0.09079	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	4	0.763558	1183.512115	5
54	0.399564	0.082237	-0.081872	0.636410	1.057692	0.704065	0.554173	0.658128	0.785937	1095.900950	65.5	18.9	0.11	0.04	0.17	2.58	12.50	11	1.090135	0.000992	0.001531	0.003032	0.370730	0.083254	0.265404	1.815078	0.600599	0.000547	0.000844	0.001670	0.204250	0.045868	0.146222	0.008620	0.236726	0.754654	10.0	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	0.09079	0.291365	0.096907	0.005485	0.000943	0.469971	0.501636	0.028393	4	0.471908	1095.900950	5
62	0.511263	0.039141	-0.156412	0.639821	1.042718	0.603550	0.600680	0.531454	0.759962	1109.330429	64.9	18.6	0.00	0.00	0.17	3.02	12.10	8	1.080149	0.000000	0.000000	0.003032	0.364845	0.097453	0.256911	1.802390	0.599287	0.000000	0.000000	0.001682	0.202423	0.054069	0.142539	0.008482	0.272674	0.718843	7.0	56.0	0.21	26.6	0.56	0.1	8.99	5.7	0.45	5	0.932024	0.002481	0.007794	0.160314	0.521768	0.183934	0.009555	0.002629	1.820499	0.511961	0.001363	0.004281	0.088061	0.286607	0.101035	0.005248	0.001444	0.453118	0.519877	0.027005	5	0.524498	1109.330429	5
65	0.383681	0.082652	-0.128906	0.641253	1.061674	0.657600	0.558601	0.677993	0.786506	1089.852134	65.5	18.9	0.11	0.04	0.17	2.58	12.50	11	1.090135	0.000992	0.001531	0.003032	0.370730	0.083254	0.265404	1.815078	0.600599	0.000547	0.000844	0.001670	0.204250	0.045868	0.146222	0.008620	0.236726	0.754654	10.0	56.0	0.21	26.6	0.56	0.1	8.99	5.7	0.45	5	0.932024	0.002481	0.007794	0.160314	0.521768	0.183934	0.009555	0.002629	1.820499	0.511961	0.001363	0.004281	0.088061	0.286607	0.101035	0.005248	0.001444	0.453118	0.519877	0.027005	5	0.45535	1089.852134	5
76	0.406945	0.094842	-0.195144	0.644493	1.045231	0.590208	0.549651	0.638286	0.785353	1102.198221	65.5	18.9	0.11	0.04	0.17	2.58	12.50	11	1.090135	0.000992	0.001531	0.003032	0.370730	0.083254	0.265404	1.815078	0.600599	0.000547	0.000844	0.001670	0.204250	0.045868	0.146222	0.008620	0.236726	0.754654	10.0	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	0.49055	0.02218	6	0.482573	1102.198221	5