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Python Notebook Download

Plag - Liquid hygrometry

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

first, load python things, set plotting parameters

[2]:

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

Loading plagioclase- liquid pairs

[3]:

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)

[4]:

display(Plags.head())
display(Liqs_PL.head())
SiO2_Plag TiO2_Plag Al2O3_Plag FeOt_Plag MnO_Plag MgO_Plag CaO_Plag Na2O_Plag K2O_Plag Cr2O3_Plag Sample_ID_Plag
0 57.3 0.09 26.6 0.43 0.0 0.03 8.33 6.11 0.49 0.0 0
1 56.5 0.12 26.9 0.47 0.0 0.05 8.95 5.66 0.47 0.0 1
2 57.6 0.11 26.3 0.50 0.0 0.07 8.50 6.27 0.40 0.0 2
3 57.2 0.16 27.0 0.62 0.0 0.06 9.03 5.58 0.84 0.0 3
4 56.7 0.14 27.6 0.69 0.0 0.11 9.46 5.58 0.48 0.0 4
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 49.1 3.22 14.4 14.8 0.14 3.20 6.72 3.34 1.70 0.0 1.13 0.0 0.0 0.0 0.0 0.0 0
1 49.2 3.89 15.3 13.7 0.12 3.88 6.76 3.44 1.22 0.0 0.83 0.0 0.0 0.0 0.0 0.0 1
2 49.6 3.79 15.8 13.0 0.14 4.26 6.59 3.65 1.04 0.0 0.63 0.0 0.0 0.0 0.0 0.0 2
3 47.1 4.21 12.0 17.8 0.18 3.40 7.28 2.93 2.02 0.0 2.32 0.0 0.0 0.0 0.0 0.0 3
4 48.1 3.88 13.2 16.4 0.16 4.02 6.51 3.36 1.36 0.0 1.59 0.0 0.0 0.0 0.0 0.0 4

Example 1 - Plagioclase-Liquid Hygrometry

  • For these first examples, we enter plag comps and liquid comps, and specify a temperature (as with existing tools)

1a - Using equation H of Putirka (2005)

  • Performing calculations at 1000 Celcius, and pressure stored in the P_GPa column of the inputted spreadsheet

[5]:

H_put2005=pt.calculate_fspar_liq_hygr(liq_comps=Liqs_PL, plag_comps=Plags,
                                    equationH="H_Put2005_eqH",
                                    T=1000+273.15, P=my_input_PL['P_GPa']*10)
H_put2005.head()

[5]:
H2O_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 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 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 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 Cr2O3_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 Cr_Liq_cat_frac Mg_Number_Liq_NoFe3 Mg_Number_Liq_Fe3 P T
0 3.686570 Low T: Yes 0.056252 0.141146 0.029165 0.360876 0.694802 0.000050 0.510102 57.3 0.09 26.6 0.43 0.0 0.03 8.33 6.11 0.49 0.0 0 0.953660 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 49.1 3.22 14.4 14.8 0.14 3.20 6.72 3.34 1.70 0.0 1.13 0.0 0.0 0.0 0.0 0.0 0 0.549988 0.053436 0.001328 0.138640 0.080652 0.095052 0.036269 0.012146 0.027130 0.005358 0.0 0.478739 0.046513 0.001156 0.120680 0.070204 0.165477 0.063141 0.021146 0.023616 0.009328 0.0 0.278194 0.278194 10.0 1273.15
1 3.522063 Low T: Yes 0.083157 0.227579 0.028164 0.369968 0.746123 0.000168 0.455475 56.5 0.12 26.9 0.47 0.0 0.05 8.95 5.66 0.47 0.0 1 0.940345 0.001241 0.006542 0.159601 0.527653 0.182643 0.009979 0.0 0.001502 0.0 1.829506 0.513989 0.000678 0.003576 0.087237 0.288413 0.099832 0.005455 0.0 0.000821 0.0 0.453125 0.518543 0.028332 49.2 3.89 15.3 13.7 0.12 3.88 6.76 3.44 1.22 0.0 0.83 0.0 0.0 0.0 0.0 0.0 1 0.545500 0.064131 0.001127 0.127030 0.080306 0.099965 0.036975 0.008628 0.032442 0.003896 0.0 0.474569 0.055792 0.000980 0.110512 0.069864 0.173933 0.064334 0.015012 0.028224 0.006778 0.0 0.335475 0.335475 10.0 1273.15
2 3.659699 Low T: Yes 0.051532 0.238397 0.023142 0.366729 0.796700 0.000293 0.500004 57.6 0.11 26.3 0.50 0.0 0.07 8.50 6.27 0.40 0.0 2 0.958653 0.001737 0.006959 0.151576 0.515884 0.202327 0.008493 0.0 0.001377 0.0 1.847006 0.519031 0.000940 0.003768 0.082066 0.279308 0.109543 0.004598 0.0 0.000746 0.0 0.418261 0.558303 0.023435 49.6 3.79 15.8 13.0 0.14 4.26 6.59 3.65 1.04 0.0 0.63 0.0 0.0 0.0 0.0 0.0 2 0.547269 0.070071 0.001308 0.119955 0.077907 0.102731 0.039042 0.007319 0.031455 0.002943 0.0 0.475045 0.060823 0.001136 0.104124 0.067626 0.178348 0.067779 0.012707 0.027304 0.005108 0.0 0.368736 0.368736 10.0 1273.15
3 2.369975 Low T: Yes 0.078766 0.183502 0.049673 0.369873 0.685172 0.000018 0.461028 57.2 0.16 27.0 0.62 0.0 0.06 9.03 5.58 0.84 0.0 3 0.951996 0.001489 0.008630 0.161027 0.529614 0.180061 0.017835 0.0 0.002003 0.0 1.852655 0.513855 0.000804 0.004658 0.086917 0.285868 0.097191 0.009627 0.0 0.001081 0.0 0.448640 0.501670 0.049691 47.1 4.21 12.0 17.8 0.18 3.40 7.28 2.93 2.02 0.0 2.32 0.0 0.0 0.0 0.0 0.0 3 0.521269 0.056096 0.001687 0.164747 0.086327 0.078262 0.031436 0.014260 0.035047 0.010869 0.0 0.459338 0.049431 0.001487 0.145174 0.076070 0.137927 0.055402 0.025132 0.030883 0.019156 0.0 0.254001 0.254001 7.0 1273.15
4 2.229733 Low T: Yes 0.119752 0.278277 0.028227 0.350220 0.779913 0.000166 0.369633 56.7 0.14 27.6 0.69 0.0 0.11 9.46 5.58 0.48 0.0 4 0.943674 0.002729 0.009604 0.168695 0.541383 0.180061 0.010192 0.0 0.001753 0.0 1.858092 0.507873 0.001469 0.005169 0.090790 0.291365 0.096907 0.005485 0.0 0.000943 0.0 0.469971 0.501636 0.028393 48.1 3.88 13.2 16.4 0.16 4.02 6.51 3.36 1.36 0.0 1.59 0.0 0.0 0.0 0.0 0.0 4 0.531999 0.066283 0.001499 0.151693 0.077147 0.086033 0.036027 0.009595 0.032280 0.007444 0.0 0.467035 0.058189 0.001316 0.133169 0.067727 0.151055 0.063254 0.016846 0.028338 0.013071 0.0 0.304076 0.304076 7.0 1273.15

1b - using hygrometer of Waters 2015

[6]:

H_waters2015=pt.calculate_fspar_liq_hygr(liq_comps=Liqs_PL, plag_comps=Plags,
                                         equationH="H_Waters2015",
                                         T=1000+273.15, P=my_input_PL['P_GPa']*10)
H_waters2015.head()

[6]:
H2O_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 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 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 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 Cr2O3_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 Cr_Liq_cat_frac Mg_Number_Liq_NoFe3 Mg_Number_Liq_Fe3 P T
0 2.183611 Low T: Yes 0.056252 0.141146 0.029165 0.360876 0.694802 0.000050 0.510102 57.3 0.09 26.6 0.43 0.0 0.03 8.33 6.11 0.49 0.0 0 0.953660 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 49.1 3.22 14.4 14.8 0.14 3.20 6.72 3.34 1.70 0.0 1.13 0.0 0.0 0.0 0.0 0.0 0 0.549988 0.053436 0.001328 0.138640 0.080652 0.095052 0.036269 0.012146 0.027130 0.005358 0.0 0.478739 0.046513 0.001156 0.120680 0.070204 0.165477 0.063141 0.021146 0.023616 0.009328 0.0 0.278194 0.278194 10.0 1273.15
1 2.671574 Low T: Yes 0.083157 0.227579 0.028164 0.369968 0.746123 0.000168 0.455475 56.5 0.12 26.9 0.47 0.0 0.05 8.95 5.66 0.47 0.0 1 0.940345 0.001241 0.006542 0.159601 0.527653 0.182643 0.009979 0.0 0.001502 0.0 1.829506 0.513989 0.000678 0.003576 0.087237 0.288413 0.099832 0.005455 0.0 0.000821 0.0 0.453125 0.518543 0.028332 49.2 3.89 15.3 13.7 0.12 3.88 6.76 3.44 1.22 0.0 0.83 0.0 0.0 0.0 0.0 0.0 1 0.545500 0.064131 0.001127 0.127030 0.080306 0.099965 0.036975 0.008628 0.032442 0.003896 0.0 0.474569 0.055792 0.000980 0.110512 0.069864 0.173933 0.064334 0.015012 0.028224 0.006778 0.0 0.335475 0.335475 10.0 1273.15
2 2.742674 Low T: Yes 0.051532 0.238397 0.023142 0.366729 0.796700 0.000293 0.500004 57.6 0.11 26.3 0.50 0.0 0.07 8.50 6.27 0.40 0.0 2 0.958653 0.001737 0.006959 0.151576 0.515884 0.202327 0.008493 0.0 0.001377 0.0 1.847006 0.519031 0.000940 0.003768 0.082066 0.279308 0.109543 0.004598 0.0 0.000746 0.0 0.418261 0.558303 0.023435 49.6 3.79 15.8 13.0 0.14 4.26 6.59 3.65 1.04 0.0 0.63 0.0 0.0 0.0 0.0 0.0 2 0.547269 0.070071 0.001308 0.119955 0.077907 0.102731 0.039042 0.007319 0.031455 0.002943 0.0 0.475045 0.060823 0.001136 0.104124 0.067626 0.178348 0.067779 0.012707 0.027304 0.005108 0.0 0.368736 0.368736 10.0 1273.15
3 1.082099 Low T: Yes 0.078766 0.183502 0.049673 0.369873 0.685172 0.000018 0.461028 57.2 0.16 27.0 0.62 0.0 0.06 9.03 5.58 0.84 0.0 3 0.951996 0.001489 0.008630 0.161027 0.529614 0.180061 0.017835 0.0 0.002003 0.0 1.852655 0.513855 0.000804 0.004658 0.086917 0.285868 0.097191 0.009627 0.0 0.001081 0.0 0.448640 0.501670 0.049691 47.1 4.21 12.0 17.8 0.18 3.40 7.28 2.93 2.02 0.0 2.32 0.0 0.0 0.0 0.0 0.0 3 0.521269 0.056096 0.001687 0.164747 0.086327 0.078262 0.031436 0.014260 0.035047 0.010869 0.0 0.459338 0.049431 0.001487 0.145174 0.076070 0.137927 0.055402 0.025132 0.030883 0.019156 0.0 0.254001 0.254001 7.0 1273.15
4 1.544051 Low T: Yes 0.119752 0.278277 0.028227 0.350220 0.779913 0.000166 0.369633 56.7 0.14 27.6 0.69 0.0 0.11 9.46 5.58 0.48 0.0 4 0.943674 0.002729 0.009604 0.168695 0.541383 0.180061 0.010192 0.0 0.001753 0.0 1.858092 0.507873 0.001469 0.005169 0.090790 0.291365 0.096907 0.005485 0.0 0.000943 0.0 0.469971 0.501636 0.028393 48.1 3.88 13.2 16.4 0.16 4.02 6.51 3.36 1.36 0.0 1.59 0.0 0.0 0.0 0.0 0.0 4 0.531999 0.066283 0.001499 0.151693 0.077147 0.086033 0.036027 0.009595 0.032280 0.007444 0.0 0.467035 0.058189 0.001316 0.133169 0.067727 0.151055 0.063254 0.016846 0.028338 0.013071 0.0 0.304076 0.304076 7.0 1273.15

1c - Can also just enter XAn and XAb rather than full plag compositions

[7]:

H_waters2015_an=pt.calculate_fspar_liq_hygr(liq_comps=Liqs_PL, XAn=0.5, XAb=0.4,
                                            equationH="H_Waters2015", T=1000+273.15,
                                            P=my_input_PL['P_GPa']*10)
H_waters2015_an.head()

[7]:
H2O_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_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 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 Cr2O3_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 Cr_Liq_cat_frac Mg_Number_Liq_NoFe3 Mg_Number_Liq_Fe3 An_Plag Ab_Plag Or_Plag P T
0 2.345190 Low T: Yes 0.139124 0.294802 0.000050 0.360876 0.694802 0.000050 0.307452 49.1 3.22 14.4 14.8 0.14 3.20 6.72 3.34 1.70 0.0 1.13 0.0 0.0 0.0 0.0 0.0 0 0.549988 0.053436 0.001328 0.138640 0.080652 0.095052 0.036269 0.012146 0.027130 0.005358 0.0 0.478739 0.046513 0.001156 0.120680 0.070204 0.165477 0.063141 0.021146 0.023616 0.009328 0.0 0.278194 0.278194 0.5 0.4 0 10.0 1273.15
1 2.781646 Low T: Yes 0.130032 0.346123 0.000168 0.369968 0.746123 0.000168 0.318411 49.2 3.89 15.3 13.7 0.12 3.88 6.76 3.44 1.22 0.0 0.83 0.0 0.0 0.0 0.0 0.0 1 0.545500 0.064131 0.001127 0.127030 0.080306 0.099965 0.036975 0.008628 0.032442 0.003896 0.0 0.474569 0.055792 0.000980 0.110512 0.069864 0.173933 0.064334 0.015012 0.028224 0.006778 0.0 0.335475 0.335475 0.5 0.4 0 10.0 1273.15
2 2.906070 Low T: Yes 0.133271 0.396700 0.000293 0.366729 0.796700 0.000293 0.299668 49.6 3.79 15.8 13.0 0.14 4.26 6.59 3.65 1.04 0.0 0.63 0.0 0.0 0.0 0.0 0.0 2 0.547269 0.070071 0.001308 0.119955 0.077907 0.102731 0.039042 0.007319 0.031455 0.002943 0.0 0.475045 0.060823 0.001136 0.104124 0.067626 0.178348 0.067779 0.012707 0.027304 0.005108 0.0 0.368736 0.368736 0.5 0.4 0 10.0 1273.15
3 1.185319 Low T: Yes 0.130127 0.285172 0.000018 0.369873 0.685172 0.000018 0.329835 47.1 4.21 12.0 17.8 0.18 3.40 7.28 2.93 2.02 0.0 2.32 0.0 0.0 0.0 0.0 0.0 3 0.521269 0.056096 0.001687 0.164747 0.086327 0.078262 0.031436 0.014260 0.035047 0.010869 0.0 0.459338 0.049431 0.001487 0.145174 0.076070 0.137927 0.055402 0.025132 0.030883 0.019156 0.0 0.254001 0.254001 0.5 0.4 0 7.0 1273.15
4 1.630797 Low T: Yes 0.149780 0.379913 0.000166 0.350220 0.779913 0.000166 0.277041 48.1 3.88 13.2 16.4 0.16 4.02 6.51 3.36 1.36 0.0 1.59 0.0 0.0 0.0 0.0 0.0 4 0.531999 0.066283 0.001499 0.151693 0.077147 0.086033 0.036027 0.009595 0.032280 0.007444 0.0 0.467035 0.058189 0.001316 0.133169 0.067727 0.151055 0.063254 0.016846 0.028338 0.013071 0.0 0.304076 0.304076 0.5 0.4 0 7.0 1273.15

2d - Manually iterating temperature and water content for plagioclase-liquid thermobarometry.

  • In reality, it is unlikely we will know our temperature, and hygrometers are extremely sensitive to T.

  • Here, we have written a forloop to demonstrate this, evaluating temperatures between 1100 and 1200 K

  • This helps demonstrate how important T is, but worry not, below we have a function which does this hard work for you!

[8]:

# Consider 10 evenly spaced temperatures between 1100 and 1200
T_lin=np.linspace(1100, 1200, 10)

# Empty, ready to fill with calculated H2O contents
H_Waters=np.empty([len(T_lin)], dtype=float)

for i in range(0, len(T_lin)):
    import warnings as w
    with w.catch_warnings():
        w.simplefilter('ignore')

        H_Waters[i]=pt.calculate_fspar_liq_hygr(liq_comps=Liqs_PL.iloc[[0]], XAn=0.5, XAb=0.4,
                                            equationH="H_Waters2015", T=T_lin[i], P=5).H2O_calc

  • You can see that calculated water declines rapidly with increasing temperature.

[9]:

H_Waters

[9]:

array([5.85205619, 5.58481803, 5.3233017 , 5.06731657, 4.81667722,
       4.57120382, 4.33072252, 4.09506579, 3.86407271, 3.63758928])

[11]:

plt.plot(T_lin, H_Waters, 'ok')
plt.xlabel('Temperature (K)')
plt.ylabel('Calculated H$_2$O (wt%)')

[11]:

Text(0, 0.5, 'Calculated H$_2$O (wt%)')
../../_images/Examples_Feldspar_Thermobarometry_Plag_Liquid_Hygrometry_20_1.png

Example 2e - Using Thermobar’s built in functions for iterating T and H

  • Here we show how to iterate temperature using equation 23 from Putirka (2008), and H2O from Waters and Lange 2015.

  • This function returns a dictionary. This contains calculated Temps and H2O contents with equilibrium tests as 1 dataframe, and the change in T and H2O with # of iterations as the second dataframe.

  • The first dataframe, key “T_H_calc”, contains columns for Delta T (last 2 iterations), which tells you the change in temp between the final and penultimate calculation step. Same for Delta H. If these numbers are not small, it might be worth inpecting the second dataframe, which shows the evolution of T and H with time. This can help you select the number of iterations.

[12]:

dict_HT=pt.calculate_fspar_liq_temp_hygr(liq_comps=Liqs_PL, plag_comps=Plags,
        equationT="T_Put2008_eq23", equationH="H_Waters2015", P=5, iterations=30)


Calc_T_H=dict_HT['T_H_calc'] # Extracts calculated T and H2O
T_H_Iter=dict_HT['T_H_Evolution'] # Extracts EVolution of T and H against pressure

[14]:

# These are the results
Calc_T_H.head()

[14]:
# of iterations T_K_calc Delta T (last 2 iters) H2O_calc Delta H (last 2 iters) 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 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 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 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 Cr2O3_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 Cr_Liq_cat_frac Mg_Number_Liq_NoFe3 Mg_Number_Liq_Fe3 P T
0 30 1417.964612 0.000004 -0.229984 -8.279212e-08 High T: No 0.108250 0.161148 0.029102 0.525378 0.392509 0.000113 0.510102 57.3 0.09 26.6 0.43 0.0 0.03 8.33 6.11 0.49 0.0 0 0.953660 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 49.1 3.22 14.4 14.8 0.14 3.20 6.72 3.34 1.70 0.0 1.13 0.0 0.0 0.0 0.0 0.0 0 0.549988 0.053436 0.001328 0.138640 0.080652 0.095052 0.036269 0.012146 0.027130 0.005358 0.0 0.478739 0.046513 0.001156 0.120680 0.070204 0.165477 0.063141 0.021146 0.023616 0.009328 0.0 0.278194 0.278194 5 1417.964609
1 30 1403.727030 -0.000007 0.464469 1.752394e-07 High T: No 0.074873 0.083416 0.027979 0.527997 0.435127 0.000353 0.455475 56.5 0.12 26.9 0.47 0.0 0.05 8.95 5.66 0.47 0.0 1 0.940345 0.001241 0.006542 0.159601 0.527653 0.182643 0.009979 0.0 0.001502 0.0 1.829506 0.513989 0.000678 0.003576 0.087237 0.288413 0.099832 0.005455 0.0 0.000821 0.0 0.453125 0.518543 0.028332 49.2 3.89 15.3 13.7 0.12 3.88 6.76 3.44 1.22 0.0 0.83 0.0 0.0 0.0 0.0 0.0 1 0.545500 0.064131 0.001127 0.127030 0.080306 0.099965 0.036975 0.008628 0.032442 0.003896 0.0 0.474569 0.055792 0.000980 0.110512 0.069864 0.173933 0.064334 0.015012 0.028224 0.006778 0.0 0.335475 0.335475 5 1403.727038
2 30 1386.373565 -0.000013 0.790049 3.226131e-07 High T: No 0.092285 0.074881 0.022872 0.510546 0.483422 0.000564 0.500004 57.6 0.11 26.3 0.50 0.0 0.07 8.50 6.27 0.40 0.0 2 0.958653 0.001737 0.006959 0.151576 0.515884 0.202327 0.008493 0.0 0.001377 0.0 1.847006 0.519031 0.000940 0.003768 0.082066 0.279308 0.109543 0.004598 0.0 0.000746 0.0 0.418261 0.558303 0.023435 49.6 3.79 15.8 13.0 0.14 4.26 6.59 3.65 1.04 0.0 0.63 0.0 0.0 0.0 0.0 0.0 2 0.547269 0.070071 0.001308 0.119955 0.077907 0.102731 0.039042 0.007319 0.031455 0.002943 0.0 0.475045 0.060823 0.001136 0.104124 0.067626 0.178348 0.067779 0.012707 0.027304 0.005108 0.0 0.368736 0.368736 5 1386.373578
3 30 1481.986125 0.000027 -2.139884 -5.628982e-07 High T: No 0.086294 0.117287 0.049636 0.534934 0.384383 0.000055 0.461028 57.2 0.16 27.0 0.62 0.0 0.06 9.03 5.58 0.84 0.0 3 0.951996 0.001489 0.008630 0.161027 0.529614 0.180061 0.017835 0.0 0.002003 0.0 1.852655 0.513855 0.000804 0.004658 0.086917 0.285868 0.097191 0.009627 0.0 0.001081 0.0 0.448640 0.501670 0.049691 47.1 4.21 12.0 17.8 0.18 3.40 7.28 2.93 2.02 0.0 2.32 0.0 0.0 0.0 0.0 0.0 3 0.521269 0.056096 0.001687 0.164747 0.086327 0.078262 0.031436 0.014260 0.035047 0.010869 0.0 0.459338 0.049431 0.001487 0.145174 0.076070 0.137927 0.055402 0.025132 0.030883 0.019156 0.0 0.254001 0.254001 5 1481.986099
4 30 1442.673949 0.000017 -1.155612 -3.667844e-07 High T: Yes 0.011269 0.026814 0.027968 0.481241 0.474822 0.000425 0.369633 56.7 0.14 27.6 0.69 0.0 0.11 9.46 5.58 0.48 0.0 4 0.943674 0.002729 0.009604 0.168695 0.541383 0.180061 0.010192 0.0 0.001753 0.0 1.858092 0.507873 0.001469 0.005169 0.090790 0.291365 0.096907 0.005485 0.0 0.000943 0.0 0.469971 0.501636 0.028393 48.1 3.88 13.2 16.4 0.16 4.02 6.51 3.36 1.36 0.0 1.59 0.0 0.0 0.0 0.0 0.0 4 0.531999 0.066283 0.001499 0.151693 0.077147 0.086033 0.036027 0.009595 0.032280 0.007444 0.0 0.467035 0.058189 0.001316 0.133169 0.067727 0.151055 0.063254 0.016846 0.028338 0.013071 0.0 0.304076 0.304076 5 1442.673932 
[15]:

# This looks at the evolution of T and H to help you work out if you have selected enuogh iterations
T_H_Iter.head()

[15]:
Iteration Sample_0_H_calc Sample_1_T_calc Sample_1_H_calc Sample_2_T_calc Sample_2_H_calc Sample_3_T_calc Sample_3_H_calc Sample_4_T_calc Sample_4_H_calc Sample_5_T_calc Sample_5_H_calc Sample_6_T_calc Sample_6_H_calc Sample_7_T_calc Sample_7_H_calc Sample_8_T_calc Sample_8_H_calc Sample_9_T_calc Sample_9_H_calc Sample_10_T_calc Sample_10_H_calc Sample_11_T_calc Sample_11_H_calc Sample_12_T_calc Sample_12_H_calc Sample_13_T_calc Sample_13_H_calc Sample_14_T_calc Sample_14_H_calc Sample_15_T_calc Sample_15_H_calc Sample_16_T_calc Sample_16_H_calc Sample_17_T_calc Sample_17_H_calc Sample_18_T_calc Sample_18_H_calc Sample_19_T_calc Sample_19_H_calc Sample_20_T_calc Sample_20_H_calc Sample_21_T_calc Sample_21_H_calc Sample_22_T_calc Sample_22_H_calc Sample_23_T_calc Sample_23_H_calc Sample_24_T_calc Sample_24_H_calc Sample_25_T_calc Sample_25_H_calc Sample_26_T_calc Sample_26_H_calc Sample_27_T_calc Sample_27_H_calc Sample_0_T_calc
0 0.0 -0.087322 1416.126478 0.176491 1407.113949 0.299199 1421.667041 -0.818119 1411.109458 -0.439779 1410.227538 -0.203387 1412.721774 -0.060597 1413.356763 0.112622 1397.428945 -0.217659 1404.854023 -0.165124 1413.039645 -0.167563 1416.609140 -0.191621 1481.766938 -0.359261 1467.385079 -0.588008 1513.765265 0.069490 1488.133042 -0.244081 1478.992697 -0.242585 1467.511455 -0.350064 1462.011805 -0.384360 1519.824304 -0.051396 1509.044859 -0.155080 1484.425069 -0.224469 1472.185803 -0.302052 1460.413509 -0.386713 1321.919610 1.882819 1336.049868 1.548647 1304.244873 1.521361 1326.074637 1.602497 1411.778771
1 1.0 -0.141517 1411.393983 0.285806 1399.199728 0.484783 1444.255318 -1.326025 1422.987760 -0.712887 1415.687603 -0.329652 1414.347558 -0.098163 1410.344126 0.182399 1403.174626 -0.352941 1409.241640 -0.267443 1417.533106 -0.271133 1421.772447 -0.309977 1492.252094 -0.578185 1484.372965 -0.948083 1511.700385 0.111149 1495.262110 -0.391997 1486.022650 -0.390258 1477.558871 -0.563993 1473.010486 -0.620150 1521.357126 -0.082002 1513.651827 -0.248197 1490.953551 -0.360655 1480.881787 -0.486204 1471.437570 -0.623559 1278.980019 3.055367 1299.890378 2.509892 1270.176881 2.470795 1289.118452 2.600583 1414.122280
2 2.0 -0.175135 1408.475586 0.353584 1394.327257 0.600088 1458.573558 -1.639829 1430.447364 -0.882081 1419.094746 -0.407950 1415.356956 -0.121443 1408.482845 0.225658 1406.765776 -0.436934 1411.971899 -0.330791 1420.322302 -0.335092 1424.977026 -0.383003 1498.695156 -0.711193 1494.921953 -1.167561 1510.464419 0.136139 1499.605914 -0.481434 1490.325973 -0.479968 1483.749302 -0.694357 1479.820179 -0.764379 1522.270919 -0.100219 1516.427424 -0.304025 1494.934255 -0.443109 1486.220315 -0.598189 1478.251302 -0.768182 1253.512996 3.788743 1278.342768 3.108561 1249.747642 3.064960 1267.043902 3.224531 1415.579912
3 3.0 -0.195982 1406.671059 0.395634 1391.314069 0.671797 1467.532161 -1.833055 1435.100404 -0.986727 1421.214264 -0.456469 1415.983083 -0.135867 1407.330942 0.252487 1409.003100 -0.489046 1413.666671 -0.369990 1422.049225 -0.374566 1426.960096 -0.428028 1502.629766 -0.791857 1501.406698 -1.300949 1509.723685 0.151136 1502.241063 -0.535438 1492.949027 -0.534396 1487.540317 -0.773656 1484.009011 -0.852441 1522.815159 -0.111058 1518.094963 -0.337467 1497.351733 -0.492970 1489.480364 -0.666180 1482.434928 -0.856321 1238.065864 4.248277 1265.263802 3.481886 1237.276058 3.437330 1253.603001 3.615209 1416.485333
4 4.0 -0.208907 1405.553425 0.421732 1389.445586 0.716421 1473.091196 -1.951775 1437.990416 -1.051382 1422.530218 -0.486520 1416.371245 -0.144802 1406.617307 0.269131 1410.394166 -0.521363 1414.717064 -0.394238 1423.116751 -0.398920 1428.185019 -0.455778 1505.023456 -0.840724 1505.368483 -1.381876 1509.279416 0.160138 1503.835437 -0.568021 1494.543747 -0.567392 1489.853308 -0.821840 1486.575379 -0.906149 1523.139118 -0.117506 1519.095122 -0.357490 1498.816290 -0.523098 1491.464716 -0.707419 1484.993191 -0.909973 1228.568313 4.536554 1257.236758 3.714855 1229.578810 3.670919 1245.323619 3.860057 1417.047278

  • In this instance, we can be relatively confident that we converge on a solution, based on the small Delta T and Delta H values between 10 and 15 iterations

[17]:

fig, (ax2, ax3) = plt.subplots(1, 2, figsize=(10,5))

ax2.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_1_T_calc'], '-r', label='Sample 1')
ax2.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_2_T_calc'], '-b', label='Sample 2')
ax2.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_3_T_calc'], '-g', label='Sample 3')

ax2.set_ylabel('Calculated T (K)')
ax2.set_xlabel('# of iterations')

ax3.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_1_H_calc'], '-r', label='Sample 1')
ax3.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_2_H_calc'], '-b', label='Sample 2')
ax3.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_3_H_calc'], '-g', label='Sample 3')

ax3.set_ylabel('Calculated H$_2$O')
ax3.set_xlabel('# of iterations')

plt.tight_layout()
../../_images/Examples_Feldspar_Thermobarometry_Plag_Liquid_Hygrometry_27_0.png

or plot all samples….

[20]:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))
# Iterates over # of samples
for i in range(0, len(Calc_T_H)):
    ax1.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_{}_H_calc'.format(i)], '-b' )
    ax2.plot(T_H_Iter['Iteration'], T_H_Iter['Sample_{}_T_calc'.format(i)], '-k' )

ax1.set_xlabel('Iteration')
ax1.set_ylabel('H$_2$O Calc (wt%)')
ax2.set_ylabel('T Calc (K)')
ax2.set_xlabel('Iteration')
fig.tight_layout()
../../_images/Examples_Feldspar_Thermobarometry_Plag_Liquid_Hygrometry_29_0.png

Example 5 - How sensitive is Plag-Liq hygrometry and thermometry to pressure

[21]:

# Consider 10 pressures evenly spaced between 0 and 13 kbar
P_lin=np.linspace(0, 13, 10)

# Empty, ready to fill with calculated H2O contents
HP_Waters=np.empty([len(P_lin)], dtype=float)
 # Empty, ready to fill with calculated temperatures contents
T_Put=np.empty([len(P_lin)], dtype=float)

for i in range(0, len(P_lin)):
    import warnings as w
    with w.catch_warnings():
        w.simplefilter('ignore')
        # Calculate H2O
        HP_Waters[i]=pt.calculate_fspar_liq_hygr(liq_comps=Liqs_PL.iloc[[0]], plag_comps=Plags.iloc[[0]],
                                            equationH="H_Waters2015", T=1200, P=P_lin[i]).H2O_calc
        # Calculate temperature
        T_Put[i]=pt.calculate_fspar_liq_temp(liq_comps=Liqs_PL.iloc[[0]], plag_comps=Plags.iloc[[0]],
                                            equationT="T_Put2008_eq23", P=P_lin[i])

[24]:

plt.plot(P_lin, HP_Waters, 'or')
plt.xlabel('Pressure (kbar)')
plt.ylabel('H$_2$O Calc')

[24]:

Text(0, 0.5, 'H$_2$O Calc')
../../_images/Examples_Feldspar_Thermobarometry_Plag_Liquid_Hygrometry_32_1.png 
[26]:

plt.plot(P_lin, T_Put, 'or')
plt.xlabel('Pressure (kbar)')
plt.ylabel('H$_2$O Calc')

[26]:

Text(0, 0.5, 'H$_2$O Calc')
../../_images/Examples_Feldspar_Thermobarometry_Plag_Liquid_Hygrometry_33_1.png