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Olivine-Liquid Hygrometry¶
This workbook shows how to perform calculations for Olivine-Liquid hygrometry
You can download the Excel spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Liquid_Ol_Liq_Themometry/Liquid_only_Thermometry.xlsx
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
[2]:
# Loading various python things
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import Thermobar as pt
[3]:
# Setting plotting parameters
# 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
Step 1 - load data¶
[4]:
out=pt.import_excel('Liquid_only_Thermometry.xlsx', sheet_name="Ol-Liq")
my_input=out['my_input']
myLiquids1=out['Liqs']
myOls1=out['Ols']
display(myOls1.head())
display(myLiquids1.head())
| SiO2_Ol | TiO2_Ol | Al2O3_Ol | FeOt_Ol | MnO_Ol | MgO_Ol | CaO_Ol | Na2O_Ol | K2O_Ol | Cr2O3_Ol | NiO_Ol | Sample_ID_Ol | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 40.5 | 0.02 | 0.08 | 12.40 | 0.17 | 47.4 | 0.30 | 0.0 | 0 | 0.03 | 0.0 | 0 |
| 1 | 41.3 | 0.03 | 0.11 | 9.59 | 0.14 | 50.2 | 0.31 | 0.0 | 0 | 0.00 | 0.0 | 1 |
| 2 | 39.7 | 0.05 | 0.11 | 15.60 | 0.18 | 44.5 | 0.31 | 0.0 | 0 | 0.03 | 0.0 | 2 |
| 3 | 40.5 | 0.05 | 0.10 | 13.20 | 0.18 | 46.8 | 0.29 | 0.0 | 0 | 0.02 | 0.0 | 3 |
| 4 | 40.5 | 0.00 | 0.10 | 9.41 | 0.10 | 49.3 | 0.31 | 0.0 | 0 | 0.00 | 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 | 57.023602 | 0.623106 | 16.332899 | 4.36174 | 0.103851 | 4.19180 | 6.94858 | 3.59702 | 0.896895 | 0.000000 | 0.226584 | 5.59 | 0.2 | 0.0 | 0.0 | 0.0 | 0 |
| 1 | 57.658600 | 0.654150 | 17.194799 | 3.90621 | 0.084105 | 2.86892 | 5.91538 | 3.85948 | 1.018600 | 0.000000 | 0.214935 | 6.55 | 0.2 | 0.0 | 0.0 | 0.0 | 1 |
| 2 | 60.731201 | 0.862054 | 17.144199 | 4.07781 | 0.077488 | 2.50867 | 5.22075 | 4.45556 | 1.414160 | 0.000000 | 0.319638 | 3.14 | 0.2 | 0.0 | 0.0 | 0.0 | 2 |
| 3 | 61.532799 | 0.440860 | 16.508801 | 3.32990 | 0.037520 | 1.64150 | 4.34294 | 4.40860 | 1.407000 | 0.000000 | 0.215740 | 6.20 | 0.2 | 0.0 | 0.0 | 0.0 | 3 |
| 4 | 52.969101 | 0.803412 | 17.563000 | 5.93217 | 0.149472 | 3.78351 | 7.65110 | 3.80219 | 0.551178 | 0.037368 | 0.196182 | 6.58 | 0.2 | 0.0 | 0.0 | 0.0 | 4 |
Example 1 - Simple calculation for just the H2O content of the liquid¶
outputs just the wt% H2O in the liquid, using the hygrometer of Gavrilenko et al. (2016)
[5]:
H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016")
H2O_Calc_Thermobar
[5]:
0 2.030582
1 1.364482
2 0.344844
3 -0.344697
4 2.860205
5 1.508569
6 -1.769123
7 1.909273
8 0.834843
9 2.860872
dtype: float64
Example 2 - Melt matching¶
in reality, you may not have olivine-liquid pairs that you know are in equilibrium.
Instead, you can feed in all measured olivines and liquids, and it will pair up all possible matches
For the equilibrium tests, if you want to use Toplis, you need a temperature
In this example, we use a constant temperature
[6]:
H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016", eq_tests=True,
T=1300)
H2O_Calc_Thermobar.head()
Considering N=10 Ol & N=10 Liqs, which is a total of N=100 Liq- Ol pairs, be patient if this is >>1 million!
You have not selected a pressure, so we have calculated Toplis Kd at 1kbar
[6]:
| H2O_calc | Temp used for calcs | P used for calcs | Kd Meas | Kd calc (Toplis) | ΔKd, Toplis (M-P) | ΔKd, Roeder (M-P) | ΔKd, Matzen (M-P) | SiO2_Liq | TiO2_Liq | ... | ID_Ol | Si_Ol_cat_frac | Mg_Ol_cat_frac | Fet_Ol_cat_frac | Ca_Ol_cat_frac | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.030582 | 1300 | 1 | 0.314264 | 0.326180 | -0.011916 | 0.014264 | -0.025736 | 57.023602 | 0.623106 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 1 | 1.682297 | 1300 | 1 | 0.240169 | 0.327530 | -0.087362 | -0.059831 | -0.099831 | 57.658600 | 0.654150 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 2 | 0.704945 | 1300 | 1 | 0.201173 | 0.308546 | -0.107373 | -0.098827 | -0.138827 | 60.731201 | 0.862054 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 3 | -0.777583 | 1300 | 1 | 0.161199 | 0.312636 | -0.151436 | -0.138801 | -0.178801 | 61.532799 | 0.440860 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 4 | 3.105922 | 1300 | 1 | 0.208562 | 0.334102 | -0.125540 | -0.091438 | -0.131438 | 52.969101 | 0.803412 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
5 rows × 73 columns
We can also specify an olivine-liquid thermometer¶
The hygrometer isn’t T sensitive, so once we have a H2O estimate, we can plug that into a liquid-olivine thermometer. That gives us a temp for solving equilibrium at
[7]:
H2O_Calc_Teq22=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016", equationT='T_Put2008_eq22',
eq_tests=True, P=5,
T=1300)
H2O_Calc_Teq22.head()
Considering N=10 Ol & N=10 Liqs, which is a total of N=100 Liq- Ol pairs, be patient if this is >>1 million!
[7]:
| H2O_calc | T_K_calc | P used for calcs | Kd Meas | Kd calc (Toplis) | ΔKd, Toplis (M-P) | ΔKd, Roeder (M-P) | ΔKd, Matzen (M-P) | SiO2_Liq | TiO2_Liq | ... | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | DMg_Meas | CNML | CSiO2L | NF | Den_Beat93 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.030582 | 1306.094822 | 5 | 0.314264 | 0.331693 | -0.017429 | 0.014264 | -0.025736 | 57.023602 | 0.623106 | ... | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 | 9.490384 | 0.170076 | 0.556427 | -0.760264 | 9.813745 |
| 1 | 1.682297 | 1246.358585 | 5 | 0.240169 | 0.320346 | -0.080177 | -0.059831 | -0.099831 | 57.658600 | 0.654150 | ... | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 | 13.711088 | 0.137693 | 0.568999 | -0.815019 | 10.226597 |
| 2 | 0.704945 | 1278.487099 | 5 | 0.201173 | 0.308304 | -0.107131 | -0.098827 | -0.138827 | 60.731201 | 0.862054 | ... | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 | 16.264149 | 0.121869 | 0.577797 | -0.790522 | 10.330149 |
| 3 | -0.777583 | 1196.823888 | 5 | 0.161199 | 0.295190 | -0.133991 | -0.138801 | -0.178801 | 61.532799 | 0.440860 | ... | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 | 24.065168 | 0.097450 | 0.604665 | -0.765547 | 10.732471 |
| 4 | 3.105922 | 1265.970449 | 5 | 0.208562 | 0.331112 | -0.122550 | -0.091438 | -0.131438 | 52.969101 | 0.803412 | ... | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 | 10.410636 | 0.186517 | 0.522022 | -0.840343 | 10.135819 |
5 rows × 78 columns
Example 2b- Filtering these matches¶
This is all Ol-Liq matches, you probably want to filter based on your choice of Kd filter
First, lets plot up these equilibrium tests
[8]:
fig, ((ax1, ax2, ax3)) = plt.subplots(1,3, figsize = (10,3), sharex=True) # adjust dimensions
ax1.hist(H2O_Calc_Teq22['ΔKd, Roeder (M-P)'], ec='k')
ax2.hist(H2O_Calc_Teq22['ΔKd, Toplis (M-P)'], ec='k')
ax3.hist(H2O_Calc_Teq22['ΔKd, Matzen (M-P)'], ec='k')
ax1.set_xlabel('ΔKd, Roeder')
ax2.set_xlabel('ΔKd, Toplis')
ax3.set_xlabel('ΔKd, Matzen')
[8]:
Text(0.5, 0, 'ΔKd, Matzen')
Now we can filter out based on common values¶
[9]:
H2O_Calc_Teq22_FiltRoedder=H2O_Calc_Teq22.loc[H2O_Calc_Teq22['ΔKd, Roeder (M-P)'].between(-0.03, 0.03)]
H2O_Calc_Teq22_FiltToplis=H2O_Calc_Teq22.loc[H2O_Calc_Teq22['ΔKd, Toplis (M-P)'].between(-0.03, 0.03)]
H2O_Calc_Teq22_FiltMatzen=H2O_Calc_Teq22.loc[H2O_Calc_Teq22['ΔKd, Matzen (M-P)'].between(-0.03, 0.03)]
Now we can plot up values for this¶
[11]:
plt.plot(H2O_Calc_Teq22_FiltRoedder['Al2O3_Ol'], H2O_Calc_Teq22_FiltRoedder['H2O_calc'], 'ok')
[11]:
[<matplotlib.lines.Line2D at 0x22511538eb0>]
[11]:
H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016", equationT="T_Put2008_eq22",
eq_tests=True)
H2O_Calc_Thermobar.head()
Considering N=10 Ol & N=10 Liqs, which is a total of N=100 Liq- Ol pairs, be patient if this is >>1 million!
You have not selected a pressure, so we have calculated Toplis Kd at 1kbar
H2O_Calc
0 2.030582
1 1.682297
2 0.704945
3 -0.777583
4 3.105922
...
95 1.784544
96 0.795259
97 1.909273
98 2.169834
99 2.860872
Length: 100, dtype: float64
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
Input In [11], in <cell line: 1>()
----> 1 H2O_Calc_Thermobar=pt.calculate_ol_liq_hygr_matching(liq_comps=myLiquids1, ol_comps=myOls1,
2 equationH="H_Gavr2016", equationT="T_Put2008_eq22",
3 eq_tests=True)
4 H2O_Calc_Thermobar.head()
File g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\olivine_liquid_olivine_spinel_thermometry.py:248, in calculate_ol_liq_hygr_matching(liq_comps, ol_comps, equationH, eq_tests, T, equationT, P, Fe3Fet_Liq, iterations)
244 CalcH2O=calculate_ol_liq_hygr(meltmatch=Combo_Ol_liqs,
245 equationH=equationH, T=T, P=P, eq_tests=eq_tests,
246 Fe3Fet_Liq=Fe3Fet_Liq)
247 if equationT is not None:
--> 248 CalcH2O=calculate_ol_liq_hygr(meltmatch=Combo_Ol_liqs,
249 equationH=equationH, equationT=equationT, P=P, eq_tests=eq_tests,
250 Fe3Fet_Liq=Fe3Fet_Liq)
255 return CalcH2O
File g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\olivine_liquid_olivine_spinel_thermometry.py:132, in calculate_ol_liq_hygr(liq_comps, ol_comps, equationH, eq_tests, P, T, meltmatch, equationT, Fe3Fet_Liq)
130 print('H2O_Calc')
131 print(H2O_Calc)
--> 132 T=calculate_ol_liq_temp(ol_comps=Liq_Ols, liq_comps=Liq_Ols,
133 equationT=equationT, H2O_Liq=H2O_Calc, P=P).T_K_calc
134 # If melt match do on combined dataframe
135 # if meltmatch is not None:
136 # T=calculate_ol_liq_temp(ol_comps=ol_comps, liq_comps=liq_comps,
137 # equationT=equationT, H2O_Liq=H2O_Calc, P=P).T_K_calc
139 ol_fo = (Liq_Ols['MgO_Ol'] / 40.3044) / \
140 ((Liq_Ols['MgO_Ol'] / 40.3044) + Liq_Ols['FeOt_Ol'] / 71.844)
File g:\my drive\postdoc\pymme\mybarometers\thermobar_outer\src\Thermobar\olivine_liquid_olivine_spinel_thermometry.py:530, in calculate_ol_liq_temp(equationT, liq_comps, ol_comps, meltmatch, P, NiO_Ol_Mol, H2O_Liq, Fe3Fet_Liq, eq_tests)
523 if eq_tests is False:
524 KdFeMg_Meas = (
525 ((Liq_Ols['FeOt_Ol'] / 71.844) / (Liq_Ols['MgO_Ol'] / 40.3044)) /
526 ((Liq_Ols['FeOt_Liq'] * (1 - Liq_Ols['Fe3Fet_Liq']
527 ) / 71.844) / (Liq_Ols['MgO_Liq'] / 40.3044))
528 )
529 df = pd.DataFrame(
--> 530 data={'T_K_calc': T_K, 'Kd (Fe-Mg) Meas': KdFeMg_Meas})
531 return df
533 if eq_tests is True:
UnboundLocalError: local variable 'T_K' referenced before assignment
[12]:
CalcH2O=pt.calculate_ol_liq_hygr(meltmatch=H2O_Calc_Thermobar,
equationH="H_Gavr2016", P=5, T=1300)
CalcH2O
[20]:
H2O_Calc_Thermobar.head()
[20]:
| SiO2_Liq | TiO2_Liq | Al2O3_Liq | FeOt_Liq | MnO_Liq | MgO_Liq | CaO_Liq | Na2O_Liq | K2O_Liq | Cr2O3_Liq | ... | ID_Ol | Si_Ol_cat_frac | Mg_Ol_cat_frac | Fet_Ol_cat_frac | Ca_Ol_cat_frac | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 57.023602 | 0.623106 | 16.332899 | 4.36174 | 0.103851 | 4.19180 | 6.94858 | 3.59702 | 0.896895 | 0.000000 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 1 | 57.658600 | 0.654150 | 17.194799 | 3.90621 | 0.084105 | 2.86892 | 5.91538 | 3.85948 | 1.018600 | 0.000000 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 2 | 60.731201 | 0.862054 | 17.144199 | 4.07781 | 0.077488 | 2.50867 | 5.22075 | 4.45556 | 1.414160 | 0.000000 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 3 | 61.532799 | 0.440860 | 16.508801 | 3.32990 | 0.037520 | 1.64150 | 4.34294 | 4.40860 | 1.407000 | 0.000000 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 4 | 52.969101 | 0.803412 | 17.563000 | 5.93217 | 0.149472 | 3.78351 | 7.65110 | 3.80219 | 0.551178 | 0.037368 | ... | 0.0 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
5 rows × 65 columns
[18]:
Liq_Ols=H2O_Calc_Thermobar
import inspect
func = pt.Liquid_olivine_hygr_funcs_by_name["H_Gavr2016"]
sig=inspect.signature(func)
kwargs = {name: Liq_Ols[name] for name, p in sig.parameters.items() if p.kind == inspect.Parameter.KEYWORD_ONLY}
H2O_Calc_np=func(**kwargs)
[21]:
H2O_Calc_np
[21]:
array([ 2.03058223, 1.68229723, 0.70494526, -0.77758337, 3.10592163,
2.61247201, 1.7569024 , 2.65237953, 2.97082766, 3.4382364 ,
1.76002335, 1.36448163, 0.34484373, -1.21046986, 2.86020533,
2.33649615, 1.43635461, 2.40467736, 2.70382963, 3.24578146,
1.76002335, 1.36448163, 0.34484373, -1.21046986, 2.86020533,
2.33649615, 1.43635461, 2.40467736, 2.70382963, 3.24578146,
2.30114112, 2.00011282, 1.06504679, -0.34469688, 3.35163792,
2.88844787, 2.07745018, 2.9000817 , 3.2378257 , 3.63069135,
1.76002335, 1.36448163, 0.34484373, -1.21046986, 2.86020533,
2.33649615, 1.43635461, 2.40467736, 2.70382963, 3.24578146,
0.94834669, 0.41103486, -0.73546085, -2.50912934, 2.12305644,
1.50856857, 0.47471126, 1.66157084, 1.90283551, 2.66841664,
-0.9455655 , -1.81367429, -3.25617155, -5.53933479, 0.40304236,
-0.42326244, -1.76912324, -0.07234437, 0.03384924, 1.32123206,
1.21890558, 0.72885045, -0.37535933, -2.07624285, 2.36877274,
1.78454443, 0.79525904, 1.90927301, 2.16983355, 2.86087158,
-0.13388885, -0.86022751, -2.17586697, -4.24067531, 1.14019125,
0.40466514, -0.80747988, 0.67076215, 0.83484336, 1.89859688,
1.21890558, 0.72885045, -0.37535933, -2.07624285, 2.36877274,
1.78454443, 0.79525904, 1.90927301, 2.16983355, 2.86087158])
[ ]:
ts,
Fe3Fet_Liq=Fe3Fet_Liq)" T=T, P=P, eq_tests=eq_tes
Example 2 - Calculating equilibrium tests¶
In this example, we calculate equilibrium tests, as water contents calculated from non-equilibrium pairs are likely to be nonsense.
A variety of options are included for calculating Kd bewteen olivine and liquid, P and T are needed for Toplis Kd parameterization, here we enter fixed values (in kbar and K)
[6]:
H2O_Calc_Thermobar_eqTest1=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016", eq_tests=True, P=1, T=1300)
H2O_Calc_Thermobar_eqTest1
[6]:
| H2O_calc | Temp used for calcs | P used for calcs | Kd Meas | Kd calc (Toplis) | ΔKd, Toplis | ΔKd, Roeder | ΔKd, Matzen | SiO2_Ol | TiO2_Ol | ... | Mg_Number_Liq_Fe3 | Si_Ol_cat_frac | Mg_Ol_cat_frac | Fet_Ol_cat_frac | Ca_Ol_cat_frac | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.030582 | 1300 | 1 | 0.314264 | 0.326180 | 0.011916 | 0.014264 | 0.025736 | 40.5 | 0.02 | ... | 0.681666 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 1 | 1.364482 | 1300 | 1 | 0.175383 | 0.321910 | 0.146527 | 0.124617 | 0.164617 | 41.3 | 0.03 | ... | 0.620707 | 0.331038 | 0.599846 | 0.064284 | 0.002662 | 0.001039 | 0.0 | 0.0 | 0.000950 | 0.000181 |
| 2 | 0.344844 | 1300 | 1 | 0.269582 | 0.314838 | 0.045256 | 0.030418 | 0.070418 | 39.7 | 0.05 | ... | 0.578196 | 0.331560 | 0.554039 | 0.108956 | 0.002774 | 0.001083 | 0.0 | 0.0 | 0.001273 | 0.000314 |
| 3 | -0.344697 | 1300 | 1 | 0.173799 | 0.314138 | 0.140339 | 0.126201 | 0.166201 | 40.5 | 0.05 | ... | 0.523445 | 0.332170 | 0.572216 | 0.090539 | 0.002548 | 0.000967 | 0.0 | 0.0 | 0.001250 | 0.000309 |
| 4 | 2.860205 | 1300 | 1 | 0.152172 | 0.328355 | 0.176184 | 0.147828 | 0.187828 | 40.5 | 0.00 | ... | 0.586967 | 0.330886 | 0.600452 | 0.064294 | 0.002714 | 0.000963 | 0.0 | 0.0 | 0.000692 | 0.000000 |
| 5 | 1.508569 | 1300 | 1 | 0.159554 | 0.332767 | 0.173213 | 0.140446 | 0.180446 | 40.5 | 0.02 | ... | 0.565465 | 0.331178 | 0.591229 | 0.072488 | 0.002979 | 0.000964 | 0.0 | 0.0 | 0.001039 | 0.000123 |
| 6 | -1.769123 | 1300 | 1 | 0.215915 | 0.342292 | 0.126376 | 0.084085 | 0.124085 | 40.2 | 0.04 | ... | 0.528731 | 0.334056 | 0.552506 | 0.106326 | 0.003650 | 0.002155 | 0.0 | 0.0 | 0.001056 | 0.000250 |
| 7 | 1.909273 | 1300 | 1 | 0.404699 | 0.412296 | 0.007597 | 0.104699 | 0.064699 | 39.6 | 0.04 | ... | 0.620033 | 0.333016 | 0.530297 | 0.131513 | 0.002973 | 0.000595 | 0.0 | 0.0 | 0.001353 | 0.000253 |
| 8 | 0.834843 | 1300 | 1 | 0.314115 | 0.391918 | 0.077802 | 0.014115 | 0.025885 | 39.8 | 0.05 | ... | 0.583864 | 0.334067 | 0.539307 | 0.120735 | 0.003417 | 0.000594 | 0.0 | 0.0 | 0.001564 | 0.000316 |
| 9 | 2.860872 | 1300 | 1 | 0.429135 | 0.289064 | 0.140072 | 0.129135 | 0.089135 | 39.7 | 0.03 | ... | 0.665729 | 0.330714 | 0.546417 | 0.117735 | 0.002945 | 0.000589 | 0.0 | 0.0 | 0.001411 | 0.000188 |
10 rows × 70 columns
Can filter outputs, so only get calculated H2O contents for those with delta Kd Roeder <0.1¶
[7]:
InEq=H2O_Calc_Thermobar_eqTest1.loc[H2O_Calc_Thermobar_eqTest1['ΔKd, Roeder']<0.1]
InEq
[7]:
| H2O_calc | Temp used for calcs | P used for calcs | Kd Meas | Kd calc (Toplis) | ΔKd, Toplis | ΔKd, Roeder | ΔKd, Matzen | SiO2_Ol | TiO2_Ol | ... | Mg_Number_Liq_Fe3 | Si_Ol_cat_frac | Mg_Ol_cat_frac | Fet_Ol_cat_frac | Ca_Ol_cat_frac | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.030582 | 1300 | 1 | 0.314264 | 0.326180 | 0.011916 | 0.014264 | 0.025736 | 40.5 | 0.02 | ... | 0.681666 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 2 | 0.344844 | 1300 | 1 | 0.269582 | 0.314838 | 0.045256 | 0.030418 | 0.070418 | 39.7 | 0.05 | ... | 0.578196 | 0.331560 | 0.554039 | 0.108956 | 0.002774 | 0.001083 | 0.0 | 0.0 | 0.001273 | 0.000314 |
| 6 | -1.769123 | 1300 | 1 | 0.215915 | 0.342292 | 0.126376 | 0.084085 | 0.124085 | 40.2 | 0.04 | ... | 0.528731 | 0.334056 | 0.552506 | 0.106326 | 0.003650 | 0.002155 | 0.0 | 0.0 | 0.001056 | 0.000250 |
| 8 | 0.834843 | 1300 | 1 | 0.314115 | 0.391918 | 0.077802 | 0.014115 | 0.025885 | 39.8 | 0.05 | ... | 0.583864 | 0.334067 | 0.539307 | 0.120735 | 0.003417 | 0.000594 | 0.0 | 0.0 | 0.001564 | 0.000316 |
4 rows × 70 columns
We can also use a thermometer to calculate temperatures to perform Toplis calculations at
[8]:
H2O_Calc_Thermobar_eqTest2=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016", eq_tests=True, P=1, equationT="T_Put2008_eq22")
H2O_Calc_Thermobar_eqTest2
[8]:
| H2O_calc | Temp used for calcs | P used for calcs | Kd Meas | Kd calc (Toplis) | ΔKd, Toplis | ΔKd, Roeder | ΔKd, Matzen | SiO2_Ol | TiO2_Ol | ... | Mg_Number_Liq_Fe3 | Si_Ol_cat_frac | Mg_Ol_cat_frac | Fet_Ol_cat_frac | Ca_Ol_cat_frac | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.030582 | 1339.898440 | 1 | 0.314264 | 0.334334 | 0.020071 | 0.014264 | 0.025736 | 40.5 | 0.02 | ... | 0.681666 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 1 | 1.364482 | 1291.397650 | 1 | 0.175383 | 0.320100 | 0.144717 | 0.124617 | 0.164617 | 41.3 | 0.03 | ... | 0.620707 | 0.331038 | 0.599846 | 0.064284 | 0.002662 | 0.001039 | 0.0 | 0.0 | 0.000950 | 0.000181 |
| 2 | 0.344844 | 1306.135888 | 1 | 0.269582 | 0.316037 | 0.046455 | 0.030418 | 0.070418 | 39.7 | 0.05 | ... | 0.578196 | 0.331560 | 0.554039 | 0.108956 | 0.002774 | 0.001083 | 0.0 | 0.0 | 0.001273 | 0.000314 |
| 3 | -0.344697 | 1262.432148 | 1 | 0.173799 | 0.306525 | 0.132726 | 0.126201 | 0.166201 | 40.5 | 0.05 | ... | 0.523445 | 0.332170 | 0.572216 | 0.090539 | 0.002548 | 0.000967 | 0.0 | 0.0 | 0.001250 | 0.000309 |
| 4 | 2.860205 | 1291.067606 | 1 | 0.152172 | 0.326438 | 0.174266 | 0.147828 | 0.187828 | 40.5 | 0.00 | ... | 0.586967 | 0.330886 | 0.600452 | 0.064294 | 0.002714 | 0.000963 | 0.0 | 0.0 | 0.000692 | 0.000000 |
| 5 | 1.508569 | 1298.780775 | 1 | 0.159554 | 0.332505 | 0.172951 | 0.140446 | 0.180446 | 40.5 | 0.02 | ... | 0.565465 | 0.331178 | 0.591229 | 0.072488 | 0.002979 | 0.000964 | 0.0 | 0.0 | 0.001039 | 0.000123 |
| 6 | -1.769123 | 1340.534263 | 1 | 0.215915 | 0.350786 | 0.134871 | 0.084085 | 0.124085 | 40.2 | 0.04 | ... | 0.528731 | 0.334056 | 0.552506 | 0.106326 | 0.003650 | 0.002155 | 0.0 | 0.0 | 0.001056 | 0.000250 |
| 7 | 1.909273 | 1353.150970 | 1 | 0.404699 | 0.425290 | 0.020592 | 0.104699 | 0.064699 | 39.6 | 0.04 | ... | 0.620033 | 0.333016 | 0.530297 | 0.131513 | 0.002973 | 0.000595 | 0.0 | 0.0 | 0.001353 | 0.000253 |
| 8 | 0.834843 | 1319.895010 | 1 | 0.314115 | 0.396665 | 0.082549 | 0.014115 | 0.025885 | 39.8 | 0.05 | ... | 0.583864 | 0.334067 | 0.539307 | 0.120735 | 0.003417 | 0.000594 | 0.0 | 0.0 | 0.001564 | 0.000316 |
| 9 | 2.860872 | 1355.562175 | 1 | 0.429135 | 0.298723 | 0.130413 | 0.129135 | 0.089135 | 39.7 | 0.03 | ... | 0.665729 | 0.330714 | 0.546417 | 0.117735 | 0.002945 | 0.000589 | 0.0 | 0.0 | 0.001411 | 0.000188 |
10 rows × 70 columns
We can also change the proportion of Fe3 used for the equilibrium tests
[9]:
H2O_Calc_Thermobar_eqTest3=pt.calculate_ol_liq_hygr(liq_comps=myLiquids1, ol_comps=myOls1,
equationH="H_Gavr2016", eq_tests=True,
P=1, equationT="T_Put2008_eq22", Fe3Fet_Liq=0.3)
H2O_Calc_Thermobar_eqTest3
[9]:
| H2O_calc | Temp used for calcs | P used for calcs | Kd Meas | Kd calc (Toplis) | ΔKd, Toplis | ΔKd, Roeder | ΔKd, Matzen | SiO2_Ol | TiO2_Ol | ... | Mg_Number_Liq_Fe3 | Si_Ol_cat_frac | Mg_Ol_cat_frac | Fet_Ol_cat_frac | Ca_Ol_cat_frac | Al_Ol_cat_frac | Na_Ol_cat_frac | K_Ol_cat_frac | Mn_Ol_cat_frac | Ti_Ol_cat_frac | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.030582 | 1339.898440 | 1 | 0.359158 | 0.334334 | 0.024824 | 0.059158 | 0.019158 | 40.5 | 0.02 | ... | 0.709915 | 0.331677 | 0.578691 | 0.084925 | 0.002632 | 0.000772 | 0.0 | 0.0 | 0.001179 | 0.000123 |
| 1 | 1.364482 | 1291.397650 | 1 | 0.200438 | 0.320100 | 0.119662 | 0.099562 | 0.139562 | 41.3 | 0.03 | ... | 0.651600 | 0.331038 | 0.599846 | 0.064284 | 0.002662 | 0.001039 | 0.0 | 0.0 | 0.000950 | 0.000181 |
| 2 | 0.344844 | 1306.135888 | 1 | 0.308094 | 0.316037 | 0.007943 | 0.008094 | 0.031906 | 39.7 | 0.05 | ... | 0.610379 | 0.331560 | 0.554039 | 0.108956 | 0.002774 | 0.001083 | 0.0 | 0.0 | 0.001273 | 0.000314 |
| 3 | -0.344697 | 1262.432148 | 1 | 0.198628 | 0.306525 | 0.107897 | 0.101372 | 0.141372 | 40.5 | 0.05 | ... | 0.556601 | 0.332170 | 0.572216 | 0.090539 | 0.002548 | 0.000967 | 0.0 | 0.0 | 0.001250 | 0.000309 |
| 4 | 2.860205 | 1291.067606 | 1 | 0.173911 | 0.326438 | 0.152527 | 0.126089 | 0.166089 | 40.5 | 0.00 | ... | 0.618922 | 0.330886 | 0.600452 | 0.064294 | 0.002714 | 0.000963 | 0.0 | 0.0 | 0.000692 | 0.000000 |
| 5 | 1.508569 | 1298.780775 | 1 | 0.182347 | 0.332505 | 0.150158 | 0.117653 | 0.157653 | 40.5 | 0.02 | ... | 0.597943 | 0.331178 | 0.591229 | 0.072488 | 0.002979 | 0.000964 | 0.0 | 0.0 | 0.001039 | 0.000123 |
| 6 | -1.769123 | 1340.534263 | 1 | 0.246761 | 0.350786 | 0.104026 | 0.053239 | 0.093239 | 40.2 | 0.04 | ... | 0.561827 | 0.334056 | 0.552506 | 0.106326 | 0.003650 | 0.002155 | 0.0 | 0.0 | 0.001056 | 0.000250 |
| 7 | 1.909273 | 1353.150970 | 1 | 0.462513 | 0.425290 | 0.037223 | 0.162513 | 0.122513 | 39.6 | 0.04 | ... | 0.650950 | 0.333016 | 0.530297 | 0.131513 | 0.002973 | 0.000595 | 0.0 | 0.0 | 0.001353 | 0.000253 |
| 8 | 0.834843 | 1319.895010 | 1 | 0.358989 | 0.396665 | 0.037676 | 0.058989 | 0.018989 | 39.8 | 0.05 | ... | 0.615901 | 0.334067 | 0.539307 | 0.120735 | 0.003417 | 0.000594 | 0.0 | 0.0 | 0.001564 | 0.000316 |
| 9 | 2.860872 | 1355.562175 | 1 | 0.490441 | 0.298723 | 0.191718 | 0.190441 | 0.150441 | 39.7 | 0.03 | ... | 0.694759 | 0.330714 | 0.546417 | 0.117735 | 0.002945 | 0.000589 | 0.0 | 0.0 | 0.001411 | 0.000188 |
10 rows × 70 columns