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Cpx-Liquid Melt Matching (Simple Intro)

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

This imports all the python things you need

[2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import Thermobar as pt

This sets plotting parameters

[3]:
# 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


Loading in Data

  • Gleeson et al (2020) had cpxs in the sheet “cpxs” and liquids in the “melts” tab

  • If you are loading your own data, your column headings need to be “Sample_ID”, “SiO2_Liq”, “MgO_Cpx” etc. e.g., oxide, then underscore, capital letter for phase. However, the order of columns doesnt matter

[4]:
# Loading Liquids
out=pt.import_excel('Gleeson2020JPET_Input_Pyroxene_Melts.xlsx', sheet_name="Melts")
my_input_Liqs=out['my_input']
myLiquids1=out['Liqs']

# Loading Cpxs from a different sheet
out2=pt.import_excel('Gleeson2020JPET_Input_Pyroxene_Melts.xlsx', sheet_name="Cpxs")
my_input_Cpxs=out2['my_input']
myCPXs1=out2['Cpxs']


# This loads in published barometry from Gleeson et al.
# You can delete this from your system, or swap it for something else useful.
Published=pd.read_excel('Gleeson2020JPET_Input_Pyroxene_Melts.xlsx', sheet_name="NP_Out")

[5]:
# You can check inputs have read in right using .head(). Check for columns of zeros where you expected data
display(myLiquids1.head())
display(myCPXs1.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 47.1519 1.7168 15.5321 9.7208 0.1888 5.939515 12.3617 3.7556 1.1877 0.0 0.2766 0.0 0.0 0.0 0.0 0.0 17MMSG12_1
1 46.7277 1.7708 15.4931 9.5435 0.2096 6.098350 12.3699 3.7058 1.2644 0.0 0.1887 0.0 0.0 0.0 0.0 0.0 17MMSG12_2
2 47.5265 1.8483 15.7152 9.6930 0.1678 6.184563 12.3625 3.5107 1.2066 0.0 0.2055 0.0 0.0 0.0 0.0 0.0 17MMSG12_3
3 47.2916 1.7307 15.5250 9.3999 0.1588 6.322718 12.3696 3.9281 1.2285 0.0 0.2406 0.0 0.0 0.0 0.0 0.0 17MMSG12_4
4 47.2260 1.8009 15.6438 9.0440 0.2213 6.069612 12.4081 3.8352 1.1339 0.0 0.1894 0.0 0.0 0.0 0.0 0.0 17MMSG12_5
SiO2_Cpx TiO2_Cpx Al2O3_Cpx FeOt_Cpx MnO_Cpx MgO_Cpx CaO_Cpx Na2O_Cpx K2O_Cpx Cr2O3_Cpx Sample_ID_Cpx
0 51.0169 0.5104 4.2921 3.9184 0.1135 15.9773 21.5003 0.3828 0.0 0.9642 17MMSG16_1
1 51.0208 0.5145 4.2768 3.7697 0.0917 15.9326 21.6712 0.3820 0.0 1.0514 17MMSG16_2
2 51.2990 0.4869 4.4177 3.7014 0.0983 15.9157 21.7450 0.3787 0.0 1.1905 17MMSG16_3
3 49.7147 0.7249 6.2489 3.9880 0.1128 15.0415 21.6397 0.4081 0.0 1.3550 17MMSG16_4
4 49.9807 0.7351 6.1948 4.0468 0.0951 14.9777 21.5061 0.3969 0.0 1.4862 17MMSG16_5
  • You can use help to get the documentation for this function to see the options/required inputs.

[6]:
help(pt.calculate_cpx_liq_press_temp_matching)
Help on function calculate_cpx_liq_press_temp_matching in module Thermobar.clinopyroxene_thermobarometry:

calculate_cpx_liq_press_temp_matching(*, liq_comps, cpx_comps, equationT=None, equationP=None, P=None, T=None, PMax=30, Fe3Fet_Liq=None, Kd_Match='Putirka', Kd_Err=0.03, DiHd_Err=0.06, EnFs_Err=0.05, CaTs_Err=0.03, Cpx_Quality=False, H2O_Liq=None, return_all_pairs=False, iterations=30)
    Evaluates all possible Opx-Liq pairs from  N Liquids, M Cpx compositions
    returns P (kbar) and T (K) for those in equilibrium.


    Parameters
    -----------

    liq_comps: pandas.DataFrame
        Panda DataFrame of liquid compositions with column headings SiO2_Liq etc.

    cpx_comps: pandas.DataFrame
        Panda DataFrame of cpx compositions with column headings SiO2_Cpx etc.

    equationT: str
        Specify equation for cpx thermometry (options):

        |  T_Put1996_eqT1  (P-indep, H2O-indep)
        |  T_Mas2013_eqTalk1  (P-indep, H2O-indep, alk adaption of T1)
        |  T_Brug2019  (P-indep, H2O-indep)
        |  T_Put1996_eqT2 (P-dep, H2O-indep)
        |  T_Mas2013_eqTalk2  (P-dep, H2O-indep, alk adaption of T2)
        |  T_Put1999  (P-dep, H2O-indep)
        |  T_Put2003  (P-dep, H2O-indep)
        |  T_Put1999  (P-dep, H2O-indep)
        |  T_Put2008_eq33  (P-dep, H2O-dep)
        |  T_Mas2013_eqalk33  (P-dep, H2O-dep, alk adaption of eq33)
        |  T_Mas2013_Palk2012 (P-indep, H2O_dep)


    equationP: str
        specify equation for cpx barometry (options):

        |  P_Put1996_eqP1 (T-dep, H2O-indep)
        |  P_Mas2013_eqPalk1 (T-dep, H2O-indep, alk adaption of P1)
        |  P_Put1996_eqP2 (T-dep, H2O-indep)
        |  P_Mas2013_eqPalk2 (T-dep, H2O-indep, alk adaption of P2)
        |  P_Put2003 ((T-dep, H2O-indep)
        |  P_Neave2017 (T-dep, H2O-indep)
        |  P_Put2008_eq30 (T-dep, H2O-dep)
        |  P_Put2008_eq31 (T-dep, H2O-dep)
        |  P_Put2008_eq32c (T-dep, H2O-dep)
        |  P_Mas2013_eqalk32c (T-dep, H2O-dep, alk adaption of 32c)

    Or

    T: int, float
        Can also run calculations at a fixed temperature
    P: int, float
        Can also run calculations at a fixed pressure
    Optional:



    Kd_Match: int, str, optional
        allows users to override the default of calculating Kd Fe-Mg based
        on temperature using eq 35 of putirka
        Set at fixed value (e.g., Kd_Match=0.27)
        OR
        specify Kd_Match=Masotta to use the Kd model fo Masotta et al. (2013),
        which is also a function of Na and K, for trachytic and phonolitic magmas.


    Kd_Err: int or float, Default=0.03
        Allows users to specify the permitted error on Kd Fe-Mg (default=0.03 from Neave et al. 2019)

    DiHd_Err: int or float, optional. Default=0.06
        Allows users to specify the permitted error on DiHd (default=0.06 from Neave et al. 2019)
        Compares measured and calculated values from Mollo et al. (2013)

    EnFs_Err: int or float, optional. Default=0.05
        Allows users to specify the permitted error on EnFs (default=0.05 from Neave et al. 2019)
        Compares measured and calculated values from Mollo et al. (2013)

    CaTs_Err: int or float, optional. Default=0.03
        Allows users to specify the permitted error on CaTs (default=0.03 from Neave et al. 2019)
        Compares measured and calculated values from Putirka (1999).

    Fe3Fet_Liq: float, int, pandas.Series, optional
        Fe3Fet ratio used to assess Kd Fe-Mg equilibrium between cpx and melt.
        If users don't specify, uses Fe3Fet_Liq from liq_comps.
        If specified, overwrites the Fe3Fet_Liq column in the liquid input.

    H2O_Liq: float, int, pandas.Series, optional
        If users don't specify, uses H2O_Liq from liq_comps, if specified overwrites this.

    Cpx Quality: bool, optional
        Default False. If True, filters out clinopyroxenes with cation sums outside of
        4.02-3.99 (after Neave et al. 2017)

    PMax: int or float,  optional

       Default value of 30 kbar. Uses to apply a preliminary KdFe-Mg filter
       based on the T equation specified by the user.
       Uses - 20 kbar as a lower filter.
       Users can set a lower pressure to save computation time (E.g., if
       reasonably sure crystals are forming above 10 kbar)

    Returns: dict

        Av_PTs: Average P and T for each cpx.
        E.g., if cpx1 matches Liq1, Liq4, Liq6, Liq10, averages outputs for all 4 of those liquids.
        Returns mean and 1 sigma of these averaged parameters for each Cpx.

        All_PTs: Returns output parameters for all matches (e.g, cpx1-Liq1, cpx1-Liq4) without any averaging.

Example 1 - Default melt matching function options

  • Initially, we use the default, where all equilibrium criteria are considered.

  • We specify the H2O content of the liquid is 0.5 wt%

  • This returns a dictionary, containing 2 pandas dataframes. The first df labelled All_PTs gives the Pressures and temps for every single cpx-liq match, while the dataframe labelled Av_PTs averages all the parameters for each cpx. So if Cpx1 matches Liq1, Liq9, Liq 13, those three pressures and temperatures would be averaged.

[7]:

# Here we use Neave 2017 for P, and equation 33 from Putirka 2008 for T at H2O=0.5 wt% MM1=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1, equationP="P_Neave2017", equationT="T_Put2008_eq33", H2O_Liq=0.5, iterations=30) # These lines extract pandas dataframes from the dictionary MM1 # All matches MM1_All=MM1['All_PTs'] # Averaged match per Cpx (following Neave...) MM1_Av=MM1['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
3892 Matches remaining after initial Kd filter. Now moving onto iterative calculations
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=29 Cpx-Liq matches using the specified filter. N=3 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids
[8]:
MM1_All['Sample_ID_Liq']
[8]:
2935     17MMSG12_56
2939     17MMSG12_61
2940     17MMSG12_62
2946     17MMSG12_69
2948     17MMSG12_71
2949     17MMSG12_73
2950     17MMSG12_74
2953     17MMSG12_79
2957     17MMSG12_84
2966     17MMSG12_95
2967     17MMSG12_96
2968     17MMSG12_97
2973    17MMSG12_102
2974    17MMSG12_103
2979    17MMSG12_110
2981    17MMSG12_112
2987    17MMSG12_119
2989    17MMSG12_121
2991    17MMSG12_123
3000    17MMSG12_137
3001    17MMSG12_139
3003    17MMSG12_141
3012    17MMSG12_154
3030     17MMSG12_61
3037     17MMSG12_69
3061     17MMSG12_95
3099    17MMSG12_139
3130     17MMSG12_61
3137     17MMSG12_69
Name: Sample_ID_Liq, dtype: object
  • Here, we inspect the average per Cpx, and see that only 3 Cpx analyses were found to have a match to any inputted liquid

  • One Cpx matches 28 liquids, another Cpx matches 4 liquids, and the final one matches 2 liquids.

  • You can tell from the Sample_ID_Cpx column which Cpxs these were (#64, 65, 66)

[14]:
display(MM1_Av)
Sample_ID_Cpx # of Liqs Averaged Mean_T_K_calc Std_T_K_calc Mean_P_kbar_calc Std_P_kbar_calc ID_CPX Mean_Delta_T_K_Iter Mean_Delta_P_kbar_Iter Mean_Delta_Kd_Put2008 ... Std_Delta_EnFs_I_M_Mollo13 Std_CaTs_Pred_Put1999 Std_Delta_CaTs_I_M_Put1999 Std_CrCaTs_Pred_Put1999 Std_Delta_CrCaTs_I_M_Put1999 Std_CaTi_Pred_Put1999 Std_Delta_CaTi_I_M_Put1999 Std_Jd_Pred_Put1999 Std_Delta_Jd_Put1999 Std_Delta_Jd_I_M_Put1999
0 17MMSG16_64 23 1486.069185 21.568873 5.874095 0.577561 63 0.0 0.0 0.013864 ... 0.012518 0.000795 0.000795 0.0 0.0 0.003607 0.003469 0.001904 0.001904 0.001904
1 17MMSG16_65 4 1527.514642 7.965623 7.004365 0.163063 64 0.0 0.0 0.021093 ... 0.005158 0.000236 0.000236 0.0 0.0 0.002485 0.002485 0.000572 0.000572 0.000572
2 17MMSG16_66 2 1524.073696 6.637660 6.419271 0.146893 65 0.0 0.0 0.025916 ... 0.004697 0.000310 0.000310 0.0 0.0 0.000816 0.000816 0.000894 0.000894 0.000894

3 rows × 271 columns

We can also run calculations at a fixed temperature (1450 K)

[9]:
MM1_fixedT=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1,
                                        equationP="P_Neave2017", T=1450,
                                         H2O_Liq=0.5)

# These lines extract pandas dataframes from the dictionary MM1
MM1_All_fixedT=MM1_fixedT['All_PTs']
MM1_Av_fixedT=MM1_fixedT['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=42 Cpx-Liq matches using the specified filter. N=3 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids
g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2878: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  combo_liq_cpx_fur_filt['Delta_T_K_Iter']=Delta_T_K_Iter
g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2879: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  combo_liq_cpx_fur_filt['Delta_P_kbar_Iter']=Delta_P_kbar_Iter
g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2888: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  combo_liq_cpx_fur_filt.drop(["Sample_ID_Liq"], axis=1, inplace=True)
g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2890: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  combo_liq_cpx_fur_filt.rename(columns={'T_K_calc': 'T_K_input'}, inplace=True)
[11]:
MM1_All_fixedT.head()
[11]:
Sample_ID_Cpx P_kbar_calc T_K_input Eq Tests Neave2017? Delta_Kd_Put2008 Delta_Kd_Mas2013 Delta_EnFs_Mollo13 Delta_EnFs_Put1999 Delta_CaTs_Put1999 Delta_DiHd_Mollo13 ... Delta_EnFs_I_M_Mollo13 CaTs_Pred_Put1999 Delta_CaTs_I_M_Put1999 CrCaTs_Pred_Put1999 Delta_CrCaTs_I_M_Put1999 CaTi_Pred_Put1999 Delta_CaTi_I_M_Put1999 Jd_Pred_Put1999 Delta_Jd_Put1999 Delta_Jd_I_M_Put1999
10317 17MMSG16_64 6.898724 1450 True 0.025861 0.079702 0.013569 0.004727 0.023827 0.046822 ... 0.013569 0.014860 -0.023827 0.0 0.018583 0.034450 0.007844 0.008802 0.017738 0.017738
10324 17MMSG16_64 5.621206 1450 True 0.009732 0.109236 0.043536 0.016078 0.025944 0.016473 ... 0.043536 0.012743 -0.025944 0.0 0.018583 0.028295 0.013999 0.010903 0.015637 0.015637
10328 17MMSG16_64 6.562306 1450 True 0.001378 0.070648 0.007186 0.006384 0.024758 0.015500 ... 0.007186 0.013928 -0.024758 0.0 0.018583 0.030990 0.011304 0.008573 0.017967 0.017967
10329 17MMSG16_64 5.461770 1450 True 0.008311 0.095186 0.000852 0.002116 0.024891 0.000008 ... 0.000852 0.013796 -0.024891 0.0 0.018583 0.034307 0.007987 0.011389 0.015151 0.015151
10330 17MMSG16_64 5.573106 1450 True 0.017770 0.109354 0.038498 0.017576 0.025572 0.011095 ... 0.038498 0.013115 -0.025572 0.0 0.018583 0.025677 0.016617 0.010372 0.016167 0.016167

5 rows × 131 columns

Or at fixed pressure (5 kbar here)

[12]:
MM1_fixedP=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1,
                                        P=5, equationT="T_Put2008_eq33",
                                         H2O_Liq=0.5)

# These lines extract pandas dataframes from the dictionary MM1
MM1_All_fixedP=MM1_fixedP['All_PTs']
MM1_Av_fixedP=MM1_fixedP['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=34 Cpx-Liq matches using the specified filter. N=3 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids

Example 1 b

  • We see that we only get 3 Cpx that matched. We might want to look at the distribution of equilibrium tests to work out what parameters to use

  • We can do this using return_all_pairs=True, then we can plot up the equilibrium test results

[13]:

# Here we use Neave 2017 for P, and equation 33 from Putirka 2008 for T at H2O=0.5 wt% MM_allpairs=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1, equationP="P_Neave2017", equationT="T_Put2008_eq33", H2O_Liq=0.5, return_all_pairs=True) # These lines extract pandas dataframes from the dictionary MM1 # All matches MM_allpairs_All=MM_allpairs['All_PTs'] # Averaged match per Cpx (following Neave...) MM_allpairs_Av=MM_allpairs['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
No equilibrium filters applied
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=12714 Cpx-Liq matches using the specified filter. N=78 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids
[19]:
## You can see column names like this
MM_allpairs_Av.columns[0:100]
[19]:
Index(['Sample_ID_Cpx', '# of Liqs Averaged', 'Mean_T_K_calc', 'Std_T_K_calc',
       'Mean_P_kbar_calc', 'Std_P_kbar_calc', 'ID_CPX',
       'Mean_Delta_Kd_Put2008', 'Mean_Delta_Kd_Mas2013',
       'Mean_Delta_EnFs_Mollo13', 'Mean_Delta_EnFs_Put1999',
       'Mean_Delta_CaTs_Put1999', 'Mean_Delta_DiHd_Mollo13',
       'Mean_Delta_DiHd_Put1999', 'Mean_Delta_CrCaTs_Put1999',
       'Mean_Delta_CaTi_Put1999', '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_Cr2O3_Liq',
       'Mean_P2O5_Liq', 'Mean_H2O_Liq', 'Mean_Fe3Fet_Liq', 'Mean_NiO_Liq',
       'Mean_CoO_Liq', 'Mean_CO2_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_Cr2O3_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_Cr_Liq_cat_frac',
       'Mean_Mg_Number_Liq_NoFe3', 'Mean_Mg_Number_Liq_Fe3', 'Mean_ID_Liq',
       'Mean_SiO2_Cpx', 'Mean_TiO2_Cpx', 'Mean_Al2O3_Cpx', 'Mean_FeOt_Cpx',
       'Mean_MnO_Cpx', 'Mean_MgO_Cpx', 'Mean_CaO_Cpx', 'Mean_Na2O_Cpx',
       'Mean_K2O_Cpx', 'Mean_Cr2O3_Cpx', 'Mean_Si_Cpx_cat_6ox',
       'Mean_Mg_Cpx_cat_6ox', 'Mean_Fet_Cpx_cat_6ox', 'Mean_Ca_Cpx_cat_6ox',
       'Mean_Al_Cpx_cat_6ox', 'Mean_Na_Cpx_cat_6ox', 'Mean_K_Cpx_cat_6ox',
       'Mean_Mn_Cpx_cat_6ox', 'Mean_Ti_Cpx_cat_6ox', 'Mean_Cr_Cpx_cat_6ox',
       'Mean_oxy_renorm_factor', 'Mean_Al_IV_cat_6ox', 'Mean_Al_VI_cat_6ox',
       'Mean_En_Simple_MgFeCa_Cpx', 'Mean_Fs_Simple_MgFeCa_Cpx',
       'Mean_Wo_Simple_MgFeCa_Cpx', 'Mean_Cation_Sum_Cpx', 'Mean_Ca_CaMgFe',
       'Mean_Lindley_Fe3_Cpx', 'Mean_Lindley_Fe2_Cpx',
       'Mean_Lindley_Fe3_Cpx_prop', 'Mean_CrCaTs', 'Mean_a_cpx_En',
       'Mean_Mgno_Cpx', 'Mean_Jd', 'Mean_Jd_from 0=Na, 1=Al', 'Mean_CaTs',
       'Mean_CaTi', 'Mean_DiHd_1996', 'Mean_EnFs', 'Mean_DiHd_2003',
       'Mean_Di_Cpx', 'Mean_FeIII_Wang21'],
      dtype='object')
[22]:
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize = (12,10))
ax1.hist(MM_allpairs_All['Delta_Kd_Put2008'], ec='k')
ax1.set_xlabel('Delta KD Putirka (2008)')

ax2.hist(MM_allpairs_All['Delta_EnFs_Mollo13'], ec='k')
ax2.set_xlabel('Delta_EnFs_Mollo13')

ax3.hist(MM_allpairs_All['Delta_DiHd_Mollo13'], ec='k')
ax3.set_xlabel('Delta_DiHd_Mollo13')

ax4.hist(MM_allpairs_All['Delta_CaTs_Put1999'], ec='k')
ax4.set_xlabel('Delta_CaTs_Put1999')

[22]:
Text(0.5, 0, 'Delta_CaTs_Put1999')
../../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liquid_melt_matching_Cpx_MeltMatch1_Gleeson2020_25_1.png

We can adapt the filters to get more matches

  • Here, folowing Gleeson et al. 2020, we specify that we wish to consider pairs which pass the DiHd, CaTs and EnFs equilibrium tests with sigma = 2.

  • To make the function most customizable, instead of just entering a sigma, you can enter an _Err for each equilibrium test to define what filters you want. E.g. here we consider matches within +/- 0.03 for Kd, but +-0.06 for CaTs, DiHd, and +-0.1 for EnFs. These are the “pass” values from Neave et al. (2019) for CaTs, DiHd and EnFs multiplied by 2

[23]:
MM1_2s=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1,
                                        equationP="P_Neave2017", equationT="T_Put2008_eq33",
                                        Kd_Err=0.03, EnFs_Err=0.1, DiHd_Err=0.12,
                                                CaTs_Err=0.06,
                                         H2O_Liq=0.5)
# These lines extract pandas dataframes from the dictionary MM1
MM1_2s_All=MM1_2s['All_PTs']
MM1_2s_Av=MM1_2s['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
3892 Matches remaining after initial Kd filter. Now moving onto iterative calculations
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=1008 Cpx-Liq matches using the specified filter. N=54 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids

We can look and see we get a lot more matches.

[24]:
MM1_2s_Av
[24]:
Sample_ID_Cpx # of Liqs Averaged Mean_T_K_calc Std_T_K_calc Mean_P_kbar_calc Std_P_kbar_calc ID_CPX Mean_Delta_Kd_Put2008 Mean_Delta_Kd_Mas2013 Mean_Delta_EnFs_Mollo13 ... Std_Delta_EnFs_I_M_Mollo13 Std_CaTs_Pred_Put1999 Std_Delta_CaTs_I_M_Put1999 Std_CrCaTs_Pred_Put1999 Std_Delta_CrCaTs_I_M_Put1999 Std_CaTi_Pred_Put1999 Std_Delta_CaTi_I_M_Put1999 Std_Jd_Pred_Put1999 Std_Delta_Jd_Put1999 Std_Delta_Jd_I_M_Put1999
0 17MMSG16_1 41 1485.889871 24.563876 6.181435 0.702366 0 0.017003 0.109085 0.034090 ... 0.015727 0.001063 0.001063 0.0 0.0 0.003347 0.002276 0.001916 0.001916 0.001916
1 17MMSG16_2 42 1481.111278 22.899738 6.043041 0.675803 1 0.014476 0.108837 0.040860 ... 0.016491 0.001058 0.001058 0.0 0.0 0.003308 0.002076 0.001858 0.001858 0.001858
2 17MMSG16_3 48 1478.881332 20.342782 5.946874 0.672108 2 0.014329 0.107455 0.048397 ... 0.016401 0.001067 0.001067 0.0 0.0 0.003941 0.002634 0.001926 0.001926 0.001926
3 17MMSG16_4 12 1508.719399 13.984144 7.269439 0.698513 3 0.016959 0.113074 0.041563 ... 0.010652 0.001745 0.001745 0.0 0.0 0.002842 0.002842 0.001712 0.001712 0.001712
4 17MMSG16_5 2 1504.328416 12.762583 7.987680 0.609534 4 0.015726 0.112612 0.035519 ... 0.017390 0.002786 0.002786 0.0 0.0 0.004185 0.004185 0.001962 0.001962 0.001962
5 17MMSG16_6 11 1468.442918 27.311896 6.360127 0.481471 5 0.017424 0.112557 0.060445 ... 0.023139 0.001463 0.001463 0.0 0.0 0.009994 0.005090 0.002061 0.002061 0.002061
6 17MMSG16_7 1 1488.553479 NaN 7.293616 NaN 6 0.017285 0.106398 0.026222 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 17MMSG16_8 3 1497.688206 24.208102 7.131719 0.458597 7 0.022747 0.106427 0.032062 ... 0.013816 0.000806 0.000806 0.0 0.0 0.017912 0.001510 0.004366 0.004366 0.004366
8 17MMSG16_9 14 1478.123890 30.729723 7.116592 0.838216 8 0.013937 0.111672 0.057300 ... 0.019389 0.002407 0.002407 0.0 0.0 0.003769 0.003769 0.002502 0.002502 0.002502
9 17MMSG16_15 41 1488.886000 26.181968 6.703865 0.716215 14 0.018076 0.108948 0.045087 ... 0.016411 0.001136 0.001136 0.0 0.0 0.003261 0.002201 0.001912 0.001912 0.001912
10 17MMSG16_16 42 1487.353992 23.120144 6.116487 0.680899 15 0.014177 0.107697 0.033822 ... 0.017744 0.001130 0.001130 0.0 0.0 0.003194 0.002012 0.001858 0.001858 0.001858
11 17MMSG16_17 38 1488.119090 26.755005 6.347794 0.710585 16 0.018082 0.110828 0.045613 ... 0.017549 0.001190 0.001190 0.0 0.0 0.003193 0.002312 0.001866 0.001866 0.001866
12 17MMSG16_18 24 1486.626170 30.720472 6.596753 0.797696 17 0.017821 0.107680 0.047007 ... 0.019286 0.001817 0.001817 0.0 0.0 0.003515 0.002275 0.002127 0.002127 0.002127
13 17MMSG16_19 31 1488.144727 29.126235 6.596542 0.690676 18 0.017452 0.107400 0.035286 ... 0.017125 0.001087 0.001087 0.0 0.0 0.003583 0.002398 0.001913 0.001913 0.001913
14 17MMSG16_23 2 1516.099263 8.783466 11.129027 0.922784 22 0.015429 0.024621 0.007368 ... 0.000384 0.000435 0.000435 0.0 0.0 0.003083 0.003083 0.001316 0.001316 0.001316
15 17MMSG16_25 17 1487.199233 30.379454 7.797178 0.804796 24 0.014354 0.110045 0.040049 ... 0.017164 0.002152 0.002152 0.0 0.0 0.003870 0.003714 0.002319 0.002319 0.002319
16 17MMSG16_26 2 1517.033574 13.157843 9.192633 0.638309 25 0.016485 0.122640 0.024599 ... 0.016140 0.002642 0.002642 0.0 0.0 0.004291 0.000696 0.001962 0.001962 0.001962
17 17MMSG16_27 13 1468.680275 32.487084 6.201820 0.807941 26 0.016732 0.110118 0.055827 ... 0.019313 0.002292 0.002292 0.0 0.0 0.003975 0.003975 0.002578 0.002578 0.002578
18 17MMSG16_28 1 1481.335296 NaN 6.628711 NaN 27 0.012402 0.111280 0.024218 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
19 17MMSG16_31 4 1484.167312 22.260242 8.608298 1.158593 30 0.018221 0.077493 0.008053 ... 0.006173 0.001667 0.001667 0.0 0.0 0.011392 0.010853 0.003890 0.003890 0.003890
20 17MMSG16_32 3 1493.347508 15.766560 9.022041 1.021551 31 0.020157 0.056568 0.009968 ... 0.005500 0.001333 0.001333 0.0 0.0 0.005092 0.005092 0.002447 0.002447 0.002447
21 17MMSG16_33 7 1466.794930 26.303530 7.771733 1.082330 32 0.022224 0.076776 0.011582 ... 0.019652 0.003312 0.003312 0.0 0.0 0.012156 0.012156 0.005247 0.005247 0.005247
22 17MMSG16_41 2 1484.939273 12.232729 6.729527 0.575435 40 0.015522 0.112878 0.028812 ... 0.015094 0.002203 0.002203 0.0 0.0 0.004677 0.003916 0.001961 0.001961 0.001961
23 17MMSG16_42 15 1470.419150 30.552638 6.507811 0.799564 41 0.013192 0.109001 0.034984 ... 0.016686 0.002044 0.002044 0.0 0.0 0.004277 0.004010 0.002403 0.002403 0.002403
24 17MMSG16_43 3 1487.005177 22.572010 7.104371 0.872211 42 0.020541 0.105120 0.013477 ... 0.012230 0.002087 0.002087 0.0 0.0 0.017480 0.015351 0.005004 0.005004 0.005004
25 17MMSG16_44 14 1466.049303 6.756044 5.164047 0.138327 43 0.020988 0.121122 0.060390 ... 0.002272 0.000392 0.000392 0.0 0.0 0.005397 0.005397 0.000948 0.000948 0.000948
26 17MMSG16_46 1 1450.992064 NaN 6.131601 NaN 45 0.022115 0.126785 0.019921 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
27 17MMSG16_47 6 1478.389492 30.427140 7.919723 1.160778 46 0.018992 0.094339 0.012751 ... 0.019967 0.003332 0.003332 0.0 0.0 0.009684 0.009684 0.003889 0.003889 0.003889
28 17MMSG16_48 6 1461.641359 25.435532 6.906666 1.075573 47 0.017849 0.087092 0.013369 ... 0.018997 0.002753 0.002753 0.0 0.0 0.009734 0.009734 0.003593 0.003593 0.003593
29 17MMSG16_49 4 1459.812687 21.434169 7.278039 0.511793 48 0.019831 0.102896 0.014924 ... 0.021996 0.002708 0.002708 0.0 0.0 0.012413 0.007813 0.002384 0.002384 0.002384
30 17MMSG16_50 5 1465.445525 19.200524 7.625287 0.449292 49 0.017306 0.096452 0.013228 ... 0.019041 0.002445 0.002445 0.0 0.0 0.010740 0.008390 0.002292 0.002292 0.002292
31 17MMSG16_51 5 1469.018640 28.869263 7.788495 1.207809 50 0.019463 0.089174 0.014356 ... 0.021085 0.003127 0.003127 0.0 0.0 0.010777 0.010777 0.004006 0.004006 0.004006
32 17MMSG16_52 6 1470.735693 25.889162 7.709866 1.098808 51 0.018743 0.084041 0.013599 ... 0.019372 0.002875 0.002875 0.0 0.0 0.009529 0.009415 0.003594 0.003594 0.003594
33 17MMSG16_53 4 1461.603342 21.507755 7.497221 0.515600 52 0.019794 0.101870 0.016555 ... 0.022205 0.002752 0.002752 0.0 0.0 0.012317 0.008822 0.002384 0.002384 0.002384
34 17MMSG16_54 6 1468.339060 25.775449 7.560598 1.093696 53 0.018288 0.085621 0.014316 ... 0.019271 0.002847 0.002847 0.0 0.0 0.009574 0.005766 0.003594 0.003594 0.003594
35 17MMSG16_55 6 1475.419684 26.119400 8.068702 1.109918 54 0.017422 0.088739 0.013183 ... 0.019618 0.002941 0.002941 0.0 0.0 0.009423 0.008640 0.003594 0.003594 0.003594
36 17MMSG16_56 6 1467.958418 26.131909 7.400169 1.104021 55 0.020948 0.080256 0.012871 ... 0.020907 0.002898 0.002898 0.0 0.0 0.009950 0.008418 0.003620 0.003620 0.003620
37 17MMSG16_57 5 1477.542978 19.061539 8.235879 1.005935 56 0.018641 0.071813 0.010880 ... 0.006270 0.001389 0.001389 0.0 0.0 0.010346 0.009841 0.003371 0.003371 0.003371
38 17MMSG16_58 5 1477.558853 19.608754 8.134983 1.054776 57 0.017729 0.078405 0.011768 ... 0.006724 0.001446 0.001446 0.0 0.0 0.010083 0.009600 0.003371 0.003371 0.003371
39 17MMSG16_60 1 1510.282641 NaN 9.941902 NaN 59 0.024036 0.027654 0.005248 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
40 17MMSG16_63 14 1500.519635 15.260532 6.416086 0.696749 62 0.015229 0.102342 0.048159 ... 0.010220 0.000660 0.000660 0.0 0.0 0.003081 0.003081 0.001665 0.001665 0.001665
41 17MMSG16_64 51 1477.815669 22.546369 5.778123 0.650738 63 0.014993 0.101857 0.045509 ... 0.017546 0.001118 0.001118 0.0 0.0 0.003737 0.003667 0.001768 0.001768 0.001768
42 17MMSG16_65 59 1484.092363 20.177984 6.336113 0.609397 64 0.014676 0.101845 0.051491 ... 0.017277 0.001121 0.001121 0.0 0.0 0.003646 0.003220 0.001727 0.001727 0.001727
43 17MMSG16_66 59 1480.284084 20.014002 5.790472 0.601774 65 0.014602 0.102233 0.043788 ... 0.017151 0.001095 0.001095 0.0 0.0 0.003690 0.002957 0.001727 0.001727 0.001727
44 17MMSG16_67 53 1481.980241 20.231170 5.970434 0.636838 66 0.015777 0.101115 0.044789 ... 0.016580 0.001104 0.001104 0.0 0.0 0.003672 0.003516 0.001691 0.001691 0.001691
45 17MMSG16_68 47 1481.370873 21.662019 6.333638 0.672169 67 0.017513 0.110048 0.046434 ... 0.014251 0.000988 0.000988 0.0 0.0 0.003448 0.003122 0.001912 0.001912 0.001912
46 17MMSG16_69 17 1473.621605 33.602175 6.452342 0.842672 68 0.014151 0.105720 0.052688 ... 0.018308 0.002037 0.002037 0.0 0.0 0.004104 0.004104 0.002563 0.002563 0.002563
47 17MMSG16_70 38 1482.932831 26.888222 6.325916 0.729869 69 0.017481 0.109445 0.038525 ... 0.015194 0.001061 0.001061 0.0 0.0 0.003433 0.002620 0.001956 0.001956 0.001956
48 17MMSG16_71 36 1480.126635 28.341597 6.112875 0.697415 70 0.018407 0.110369 0.044493 ... 0.015750 0.001049 0.001049 0.0 0.0 0.003518 0.002461 0.001872 0.001872 0.001872
49 17MMSG16_72 49 1477.049409 23.385781 5.736961 0.716733 71 0.014679 0.106935 0.045030 ... 0.018130 0.001135 0.001135 0.0 0.0 0.004040 0.002541 0.002031 0.002031 0.002031
50 17MMSG16_73 21 1465.895725 32.044502 5.619340 0.782025 72 0.014084 0.107360 0.040519 ... 0.016108 0.001692 0.001692 0.0 0.0 0.003906 0.002626 0.002380 0.002380 0.002380
51 17MMSG16_74 27 1471.586727 29.272904 5.505243 0.697361 73 0.016905 0.106057 0.038048 ... 0.016442 0.001438 0.001438 0.0 0.0 0.003920 0.002876 0.001977 0.001977 0.001977
52 17MMSG16_75 36 1478.943758 28.315156 6.233307 0.699542 74 0.018136 0.109754 0.041010 ... 0.014589 0.000991 0.000991 0.0 0.0 0.003641 0.002419 0.001872 0.001872 0.001872
53 17MMSG16_78 2 1447.236817 2.337629 5.104677 0.108849 77 0.017112 0.121629 0.041069 ... 0.001189 0.000188 0.000188 0.0 0.0 0.010952 0.010952 0.000930 0.000930 0.000930

54 rows × 267 columns

Example 2 - Fixed Kd value, rather than function of Temp

  • Say you want to used a fixed value for Kd Fe-Mg, rather than the default, which uses equation 35 of Putirka which is T dependent

  • Here, specifying Kd Fe-Mg = 0.27, and you want to consider +-0.08 as an equilibrium match

  • Also specifying here that Fe3Fet_Liq=0.15

  • Also changing pressure equation to equation 30

  • Using 2 sigma values from Neave et al. (2019) for DiHd, CaTs, EnFs

[25]:
MM2=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1,
                                        equationP="P_Put2008_eq30", equationT="T_Put2008_eq33",
                                        Kd_Match=0.27, Kd_Err=0.08, Fe3Fet_Liq=0.15, CaTs_Err=0.06, DiHd_Err=0.12, EnFs_Err=0.06)
MM2_All=MM2['All_PTs']
MM2_Av=MM2['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
the code is evaluating Kd matches using Kd=0.27
4172 Matches remaining after initial Kd filter. Now moving onto iterative calculations
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=2554 Cpx-Liq matches using the specified filter. N=57 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids

Example 3 - Alkaline Systems

  • You might want to use the updated Kd model from Masotta calibrated for trachyte and phonolitic magmas.

  • Here, you can change Kd_Match to “Masotta”

  • Might want to use P from P_Mas2013_Palk2012 and T_Mas2013_Talk2012

[26]:
MM3=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1,
                                    equationP="P_Mas2013_Palk2012", equationT="T_Mas2013_Talk2012",
                                    Kd_Match="Masotta", Kd_Err=0.08, Fe3Fet_Liq=0.15, CaTs_Err=0.06, DiHd_Err=0.12, EnFs_Err=0.06)
MM3_All=MM3['All_PTs']
MM3_Av=MM3['Av_PTs']
Caution, you have selected to use the Kd-Fe-Mg model of Masotta et al. (2013)which is only valid for trachyte and phonolitic magmas.  use PutKd=True to use the Kd model of Putirka (2008)
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
Youve selected a P-independent function
Youve selected a P-independent function
2352 Matches remaining after initial Kd filter. Now moving onto iterative calculations
Youve selected a P-independent function
Youve selected a T-independent function
Youve selected a T-independent function
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=1580 Cpx-Liq matches using the specified filter. N=50 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids
[27]:
MM3_All.head()
[27]:
Sample_ID_Cpx P_kbar_calc T_K_calc Eq Tests Neave2017? Delta_Kd_Put2008 Delta_Kd_Mas2013 Delta_EnFs_Mollo13 Delta_EnFs_Put1999 Delta_CaTs_Put1999 Delta_DiHd_Mollo13 ... Delta_EnFs_I_M_Mollo13 CaTs_Pred_Put1999 Delta_CaTs_I_M_Put1999 CrCaTs_Pred_Put1999 Delta_CrCaTs_I_M_Put1999 CaTi_Pred_Put1999 Delta_CaTi_I_M_Put1999 Jd_Pred_Put1999 Delta_Jd_Put1999 Delta_Jd_I_M_Put1999
0 17MMSG16_1 4.484954 1353.763834 False 0.076093 0.027189 0.028005 0.005255 0.033390 0.023960 ... -0.028005 0.014322 -0.033390 0.0 0.014105 0.065141 0.032986 0.017433 0.010033 0.010033
1 17MMSG16_1 4.536992 1359.812658 False 0.069446 0.030948 0.027431 0.007617 0.033259 0.018175 ... -0.027431 0.014452 -0.033259 0.0 0.014105 0.066164 0.034008 0.017281 0.010185 0.010185
2 17MMSG16_1 4.664336 1362.026843 False 0.070245 0.029827 0.024555 0.004171 0.033089 0.012565 ... -0.024555 0.014623 -0.033089 0.0 0.014105 0.063032 0.030877 0.016213 0.011253 0.011253
3 17MMSG16_1 4.521748 1361.019784 False 0.060027 0.043562 0.025938 0.007389 0.033548 0.021885 ... -0.025938 0.014163 -0.033548 0.0 0.014105 0.065681 0.033525 0.018096 0.009371 0.009371
4 17MMSG16_1 4.523901 1356.789776 False 0.059466 0.046108 0.028110 0.008869 0.033104 0.018335 ... -0.028110 0.014608 -0.033104 0.0 0.014105 0.069209 0.037054 0.017837 0.009630 0.009630

5 rows × 133 columns

Example 4 - Say you only want a Kd filter, using the equation of Putirka

  • Kd_Match=”Putirka” is actually a default option, so you don’t have to type it

  • Easiest way to do this without having a lot of effort in the function is just to set very high “pass” values for the other filters.

[28]:
MM4=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1,
                                        equationP="P_Neave2017", equationT="T_Put2008_eq33",
                                        Kd_Match="Putirka", Kd_Err=0.03, Fe3Fet_Liq=0.15,
                                        CaTs_Err=10, DiHd_Err=10, EnFs_Err=10)
MM4_All=MM4['All_PTs']
MM4_Av=MM4['Av_PTs']
Considering N=78 Cpx & N=163 Liqs, which is a total of N=12714 Liq-Cpx pairs, be patient if this is >>1 million!
3043 Matches remaining after initial Kd filter. Now moving onto iterative calculations
Finished calculating Ps and Ts, now just averaging the results. Almost there!
Done!!! I found a total of N=1265 Cpx-Liq matches using the specified filter. N=69 Cpx out of the N=78 Cpx that you input matched to 1 or more liquids

Example 5 - Plotting Equilibrium filters

  • Here, we compare results from the default filter (stored in MM1_All) and those using 2* preferred values (stored in MM1_2s_All)

[29]:
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 10))
# This plots distribution of Kd in matches with probability on the y axis
ax1.hist(MM1_2s_All['Delta_Kd_Put2008'], weights=np.ones_like(MM1_2s_All['Delta_Kd_Put2008']) / len(MM1_2s_All['Delta_Kd_Put2008']),
         label='pass at 2 sigma')
ax1.hist(MM1_All['Delta_Kd_Put2008'], weights=np.ones_like(MM1_All['Delta_Kd_Put2008']) / len(MM1_All['Delta_Kd_Put2008']) ,
         label='pass at 1 sigma', alpha=0.5)
ax1.set_xlabel('ΔKd Putirka 2008')
ax1.set_ylabel('Probability density')
ax1.legend()

# This plots distribution of EnFs  with probability on the y axis
ax2.hist(MM1_2s_All['Delta_EnFs_Mollo13'], weights=np.ones_like(MM1_2s_All['Delta_EnFs_Mollo13']) / len(MM1_2s_All['Delta_EnFs_Mollo13']) )#, label='All filters (2sigma)', color='salmon')
ax2.hist(MM1_All['Delta_EnFs_Mollo13'], weights=np.ones_like(MM1_All['Delta_EnFs_Mollo13']) / len(MM1_All['Delta_EnFs_Mollo13']), alpha=0.5)
ax2.set_xlabel('ΔEnFs')

# This plots distribution of DiHd with probability on the y axis
ax3.hist(MM1_2s_All['Delta_DiHd_Mollo13'], weights=np.ones_like(MM1_2s_All['Delta_DiHd_Mollo13']) / len(MM1_2s_All['Delta_DiHd_Mollo13']) )#, label='All filters (2sigma)', color='salmon')
ax3.hist(MM1_All['Delta_DiHd_Mollo13'], weights=np.ones_like(MM1_All['Delta_DiHd_Mollo13']) / len(MM1_All['Delta_DiHd_Mollo13']) , alpha=0.5)
ax3.set_xlabel('ΔDiHd')
ax3.set_ylabel('Probability density')

# This plots distribution of CaTs values with probability on the y axis
ax4.hist(MM1_2s_All['Delta_CaTs_Put1999'], weights=np.ones_like(MM1_2s_All['Delta_CaTs_Put1999']) / len(MM1_2s_All['Delta_CaTs_Put1999']) )#, label='All filters (2sigma)', color='salmon')
ax4.hist(MM1_All['Delta_CaTs_Put1999'], weights=np.ones_like(MM1_All['Delta_CaTs_Put1999']) / len(MM1_All['Delta_CaTs_Put1999']), alpha=0.5)
ax4.set_xlabel('ΔCaTs')
[29]:
Text(0.5, 0, 'ΔCaTs')
../../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liquid_melt_matching_Cpx_MeltMatch1_Gleeson2020_38_1.png

Example 6 - Plotting Pressures and Temperatures

  • Here comparing first match, with sigma=1 for EnFs, DiHd, CaTs and the second with sigma=2

  • We show average for cpx with a diamond and error bar, as well as all matches as very faint dots

[30]:
fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(12, 5))

# Subplot1
# This plots every Cpx-Liq match, with an opacity of 0.5
ax1.plot(MM1_All['T_K_calc']-273.15,  MM1_All['P_kbar_calc'], '.c', alpha=0.5)

# This plots a diamond and error bar for each Cpx averaged with as many liquids as it matches too
ax1.errorbar(MM1_Av['Mean_T_K_calc']-273.15,  MM1_Av['Mean_P_kbar_calc'],
             xerr=MM1_Av['Std_T_K_calc'].fillna(0), yerr=MM1_Av['Std_P_kbar_calc'].fillna(0),
             fmt='d', ecolor='k', elinewidth=0.8, mfc='cyan', ms=8, mec='k')
ax1.set_xlabel('Temperature (°C)')
ax1.set_ylabel('Pressure (kbar)')
ax1.set_title('1 sigma')

# Subplot2 - As above, butmatches with less stringent equilibrium filters
ax2.plot(MM1_2s_All['T_K_calc']-273.15,  MM1_2s_All['P_kbar_calc'], '.r', alpha=0.3)

ax2.errorbar(MM1_2s_Av['Mean_T_K_calc']-273.15,  MM1_2s_Av['Mean_P_kbar_calc'],
             xerr=MM1_2s_Av['Std_T_K_calc'].fillna(0), yerr=MM1_2s_Av['Std_P_kbar_calc'].fillna(0),
             fmt='d', ecolor='k', elinewidth=0.8, mfc='red', ms=8, mec='k')
ax2.set_xlabel('Temperature (°C)')
ax2.set_ylabel('Pressure (kbar)')
ax2.set_title('2 sigma')
ax1.invert_yaxis()
ax2.invert_yaxis()

../../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liquid_melt_matching_Cpx_MeltMatch1_Gleeson2020_40_0.png

Here we compare our matches to the published data for Gleeson et al. (2020)

  • The differences result from the fact that the R algorithm of D. Neave throws out matches before T and P are fully converged, and Kd is being calculated using Putirka (1996) in that code, rather than the user-selected thermometer

[31]:
fig, (ax1) = plt.subplots(1, figsize=(7, 5))

# Subplot1
ax1.errorbar(MM1_2s_Av['Mean_T_K_calc']-273.15,  MM1_2s_Av['Mean_P_kbar_calc'],
             xerr=MM1_2s_Av['Std_T_K_calc'].fillna(0), yerr=MM1_2s_Av['Std_P_kbar_calc'].fillna(0),
             fmt='d', ecolor='k', elinewidth=0.8, mfc='cyan', ms=8, mec='k', label='Thermobar')
ax1.plot(Published['Temperature'], Published['Pressure'], '*k', markerfacecolor='yellow', markersize=15, label='published')
ax1.legend()
ax1.invert_yaxis()
ax1.set_xlabel('Temperature (°C)')
ax1.set_ylabel('Pressure (kbar)')
[31]:
Text(0, 0.5, 'Pressure (kbar)')
../../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liquid_melt_matching_Cpx_MeltMatch1_Gleeson2020_42_1.png