This page was generated from
docs/Examples/Cpx_Cpx_Liq_Thermobarometry/MachineLearning_Cpx_Liq_Thermobarometry.ipynb.
Interactive online version:
.
Machine-Learning-based Clinopyroxene-only and Clinopyroxene-Liquid Thermobarometry.
This notebook goes through the options for clinopyroxene-Liquid thermobarometry and clinopyroxene-only thermobarometry
Cpx-Liq matching is not covered in this tutorial, there is a separate folder “Cpx_Liquid_melt_matching” for that
You can download the excel spreadsheet from: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Cpx_Cpx_Liq_Thermobarometry/Cpx_Liq_Example.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
For Machine learning, you also need to pip install the .pkl files that have saved the pretrained model. This is to keep Thermobar smaller so we can still release on Pip
[2]:
#!pip install "https://github.com/PennyWieser/Thermobar_onnx/archive/refs/tags/v.0.0.4.zip"
First, load the necessary python things
[3]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import Thermobar as pt
pt.__version__
[3]:
'1.0.32'
Now, load the data
[4]:
out=pt.import_excel('Cpx_Liq_Example.xlsx', sheet_name="Sheet1")
my_input=out['my_input']
Liqs=out['Liqs']
Cpxs=out['Cpxs']
[5]:
import joblib as j
j.__version__
[5]:
'1.2.0'
Using saved models
This uses pickles, the model will change with different versions of python
But, it does give you the voting approach of Jorgenson, with the median and standard deviation of the trees
[6]:
P_T_EqTests_pkl=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
equationP="P_Jorgenson2022_Cpx_Liq",
equationT="T_Jorgenson2022_Cpx_Liq",
H2O_Liq=3, eq_tests=False)
Im normalizing using the Jorgenson method, e.g. 100 total, 2dp
Im normalizing using the Jorgenson method, e.g. 100 total, 2dp
Youve selected a P-independent function
---------------------------------------------------------------------------
UnpicklingError Traceback (most recent call last)
c:\Users\penny\Box\Postdoc\MyBarometers\Thermobar_outer\docs\Examples\Cpx_Cpx_Liq_Thermobarometry\MachineLearning_Cpx_Liq_Thermobarometry.ipynb Cell 12 line 1
----> <a href='vscode-notebook-cell:/c%3A/Users/penny/Box/Postdoc/MyBarometers/Thermobar_outer/docs/Examples/Cpx_Cpx_Liq_Thermobarometry/MachineLearning_Cpx_Liq_Thermobarometry.ipynb#X13sZmlsZQ%3D%3D?line=0'>1</a> P_T_EqTests_pkl=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
<a href='vscode-notebook-cell:/c%3A/Users/penny/Box/Postdoc/MyBarometers/Thermobar_outer/docs/Examples/Cpx_Cpx_Liq_Thermobarometry/MachineLearning_Cpx_Liq_Thermobarometry.ipynb#X13sZmlsZQ%3D%3D?line=1'>2</a> equationP="P_Jorgenson2022_Cpx_Liq",
<a href='vscode-notebook-cell:/c%3A/Users/penny/Box/Postdoc/MyBarometers/Thermobar_outer/docs/Examples/Cpx_Cpx_Liq_Thermobarometry/MachineLearning_Cpx_Liq_Thermobarometry.ipynb#X13sZmlsZQ%3D%3D?line=2'>3</a> equationT="T_Jorgenson2022_Cpx_Liq",
<a href='vscode-notebook-cell:/c%3A/Users/penny/Box/Postdoc/MyBarometers/Thermobar_outer/docs/Examples/Cpx_Cpx_Liq_Thermobarometry/MachineLearning_Cpx_Liq_Thermobarometry.ipynb#X13sZmlsZQ%3D%3D?line=3'>4</a> H2O_Liq=3, eq_tests=False)
File c:\users\penny\box\postdoc\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2385, in calculate_cpx_liq_press_temp(liq_comps, cpx_comps, meltmatch, equationP, equationT, T, P, iterations, Fe3Fet_Liq, H2O_Liq, T_K_guess, eq_tests)
2383 if equationT is not None:
2384 if ('Petrelli' in equationT or "Jorgenson" in equationT) and "onnx" not in equationT:
-> 2385 T_func_all=calculate_cpx_liq_temp(meltmatch=Combo_liq_cpxs,
2386 equationT=equationT, P="Solve")
2387 T_func = T_func_all.T_K_calc
2388 Median_T=T_func_all.Median_Trees
File c:\users\penny\box\postdoc\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2209, in calculate_cpx_liq_temp(equationT, cpx_comps, liq_comps, meltmatch, P, eq_tests, H2O_Liq, Fe3Fet_Liq, sigma, Kd_Err)
2207 # Easiest to treat Machine Learning ones differently
2208 if ('Petrelli' in equationT or "Jorgenson" in equationT) and "onnx" not in equationT:
-> 2209 df_stats=func(meltmatch=Combo_liq_cpxs)
2210 T_K=df_stats['T_K_calc']
2212 elif ('Petrelli' in equationT or "Jorgenson" in equationT) and "onnx" in equationT:
File c:\users\penny\box\postdoc\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:941, in T_Jorgenson2022_Cpx_Liq(P, cpx_comps, liq_comps, meltmatch)
938 Thermobar_dir=Path(Thermobar_onnx.__file__).parent
940 with open(Thermobar_dir/'ETR_Temp_Jorg21_Cpx_Liq_NotNorm_sklearn_1_3.pkl', 'rb') as f:
--> 941 ETR_Temp_J22_Cpx_Liq=load(f)
944 Pred_T_K=ETR_Temp_J22_Cpx_Liq.predict(x_test)
945 df_stats, df_voting=get_voting_stats_ExtraTreesRegressor(x_test, ETR_Temp_J22_Cpx_Liq)
UnpicklingError: invalid load key, 'x'.
[ ]:
P_T_EqTests_pkl=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
equationP="P_Petrelli2020_Cpx_Liq",
equationT="T_Petrelli2020_Cpx_Liq",
T=1300,
H2O_Liq=0, eq_tests=False)
P_T_EqTests_pkl
c:\users\penny\onedrive\documents\postdoc_missing\mybarometers\thermobar_outer\src\Thermobar\core.py:1561: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
cpx_calc.loc[(AlVI_minus_Na<0), 'Jd']=cpx_calc['Al_VI_cat_6ox']
Youve selected a P-independent function
Youve selected a T-independent function
Youve selected a T-independent function
P_kbar_calc | T_K_calc | Delta_P_kbar_Iter | Delta_T_K_Iter | Median_Trees_P | Std_Trees_P | IQR_Trees_P | Median_Trees_T | Std_Trees_T | IQR_Trees_T | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 2.012874 | 1436.353636 | 0 | 0 | 2.0000 | 2.014248 | 2.014248 | 1434.15 | 37.734604 | 37.734604 |
1 | 2.385743 | 1422.872182 | 0 | 0 | 2.0000 | 2.516823 | 2.516823 | 1423.15 | 58.692923 | 58.692923 |
2 | 2.031163 | 1388.809636 | 0 | 0 | 2.0000 | 1.335548 | 1.335548 | 1398.15 | 100.495001 | 100.495001 |
3 | 2.175246 | 1418.986727 | 0 | 0 | 2.0000 | 2.290651 | 2.290651 | 1423.15 | 62.539834 | 62.539834 |
4 | 2.194894 | 1358.247818 | 0 | 0 | 2.0000 | 2.305614 | 2.305614 | 1373.15 | 80.220643 | 80.220643 |
5 | 6.846097 | 1483.411818 | 0 | 0 | 5.0000 | 7.297546 | 7.297546 | 1473.15 | 90.448301 | 90.448301 |
6 | 6.049409 | 1473.710000 | 0 | 0 | 4.0500 | 6.731814 | 6.731814 | 1466.15 | 85.025018 | 85.025018 |
7 | 9.449931 | 1483.408909 | 0 | 0 | 8.0000 | 8.203280 | 8.203280 | 1473.15 | 88.209247 | 88.209247 |
8 | 4.635460 | 1462.542727 | 0 | 0 | 3.9745 | 5.282807 | 5.282807 | 1449.15 | 63.993766 | 63.993766 |
9 | 6.107720 | 1462.148182 | 0 | 0 | 4.9850 | 6.226865 | 6.226865 | 1448.65 | 68.141951 | 68.141951 |
10 | 10.808077 | 1506.979455 | 0 | 0 | 9.7000 | 8.622634 | 8.622634 | 1493.15 | 95.431122 | 95.431122 |
11 | 5.851677 | 1461.882727 | 0 | 0 | 4.0430 | 6.151823 | 6.151823 | 1453.15 | 70.670782 | 70.670782 |
12 | 7.826454 | 1482.080909 | 0 | 0 | 5.0000 | 7.960571 | 7.960571 | 1473.15 | 94.254987 | 94.254987 |
13 | 10.300083 | 1496.978000 | 0 | 0 | 8.0000 | 8.729611 | 8.729611 | 1473.15 | 92.537615 | 92.537615 |
14 | 10.300083 | 1496.978000 | 0 | 0 | 8.0000 | 8.729611 | 8.729611 | 1473.15 | 92.537615 | 92.537615 |
15 | 10.300083 | 1496.978000 | 0 | 0 | 8.0000 | 8.729611 | 8.729611 | 1473.15 | 92.537615 | 92.537615 |
16 | 10.775014 | 1500.690364 | 0 | 0 | 9.6000 | 9.103614 | 9.103614 | 1478.15 | 97.447848 | 97.447848 |
17 | 9.463969 | 1500.291818 | 0 | 0 | 8.0000 | 8.543886 | 8.543886 | 1473.15 | 99.245966 | 99.245966 |
18 | 11.731851 | 1506.468182 | 0 | 0 | 10.0000 | 9.319109 | 9.319109 | 1493.15 | 93.497859 | 93.497859 |
19 | 9.431157 | 1495.166727 | 0 | 0 | 7.2500 | 8.513357 | 8.513357 | 1483.15 | 95.327742 | 95.327742 |
Using onnx models
-Using Onnx means you will always get the same answer. But, you dont get the voting results.
[ ]:
P_T_EqTests_onnx=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
equationP="P_Petrelli2020_Cpx_Liq_onnx",
equationT="T_Petrelli2020_Cpx_Liq_onnx",
T=1300,
H2O_Liq=0, eq_tests=True)
P_T_EqTests_onnx
c:\users\penny\onedrive\documents\postdoc_missing\mybarometers\thermobar_outer\src\Thermobar\core.py:1561: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
cpx_calc.loc[(AlVI_minus_Na<0), 'Jd']=cpx_calc['Al_VI_cat_6ox']
Youve selected a P-independent function
Youve selected a T-independent function
Youve selected a T-independent function
Using Fe3FeT from input file to calculate Kd Fe-Mg
c:\users\penny\onedrive\documents\postdoc_missing\mybarometers\thermobar_outer\src\Thermobar\core.py:1561: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
cpx_calc.loc[(AlVI_minus_Na<0), 'Jd']=cpx_calc['Al_VI_cat_6ox']
P_kbar_calc | T_K_calc | Eq Tests Neave2017? | Delta_P_kbar_Iter | Delta_T_K_Iter | Delta_Kd_Put2008 | Delta_Kd_Mas2013 | Delta_EnFs_Mollo13 | Delta_EnFs_Put1999 | Delta_CaTs_Put1999 | ... | 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 | 2.012875 | 1436.355103 | False | 0 | 0 | 0.047419 | 0.090012 | 0.001449 | 0.024126 | 0.016574 | ... | 0.001449 | 0.013801 | -0.016574 | 0.000000 | 0.009562 | 0.042741 | 0.001728 | 0.016176 | 0.000880 | 0.000880 |
1 | 2.385741 | 1422.873779 | False | 0 | 0 | 0.045964 | 0.082381 | 0.006055 | 0.027422 | 0.023015 | ... | 0.006055 | 0.013802 | -0.023015 | 0.000000 | 0.004122 | 0.055375 | 0.010437 | 0.017422 | 0.000092 | 0.000092 |
2 | 2.031165 | 1388.808716 | False | 0 | 0 | 0.048797 | 0.163165 | 0.066580 | 0.001942 | 0.074375 | ... | 0.066580 | 0.017510 | -0.074375 | 0.000000 | 0.003245 | 0.028704 | 0.042963 | 0.018321 | 0.000236 | 0.000236 |
3 | 2.175245 | 1418.988159 | False | 0 | 0 | 0.042373 | 0.094228 | 0.002781 | 0.034579 | 0.030821 | ... | 0.002781 | 0.015006 | -0.030821 | 0.000000 | 0.003909 | 0.049805 | 0.008608 | 0.019322 | 0.002988 | 0.002988 |
4 | 2.194893 | 1358.247559 | False | 0 | 0 | 0.030098 | 0.090116 | 0.007655 | 0.053517 | 0.031539 | ... | -0.007655 | 0.011571 | -0.031539 | 0.000000 | 0.001315 | 0.049482 | 0.013529 | 0.027939 | 0.005724 | 0.005724 |
5 | 6.846100 | 1483.413330 | False | 0 | 0 | 0.005372 | 0.116855 | 0.004940 | 0.039434 | 0.014345 | ... | -0.004940 | 0.014345 | 0.014345 | 0.297720 | 0.277125 | 0.054752 | 0.029543 | 0.019475 | 0.006131 | 0.006131 |
6 | 6.049410 | 1473.711060 | False | 0 | 0 | 0.023545 | 0.078386 | 0.005900 | 0.031687 | 0.015636 | ... | -0.005900 | 0.015636 | 0.015636 | 0.130559 | 0.110135 | 0.051265 | 0.034527 | 0.015666 | 0.002724 | 0.002724 |
7 | 9.449942 | 1483.410156 | False | 0 | 0 | 0.063562 | 0.067061 | 0.016248 | 0.032413 | 0.016880 | ... | -0.016248 | 0.016880 | 0.016880 | 0.148010 | 0.125617 | 0.044143 | 0.030339 | 0.018284 | 0.011828 | 0.011828 |
8 | 4.635460 | 1462.543457 | False | 0 | 0 | 0.050014 | 0.082419 | 0.017233 | 0.046123 | 0.007871 | ... | -0.017233 | 0.016970 | 0.007871 | 0.001101 | 0.017485 | 0.052259 | 0.016795 | 0.015820 | 0.008623 | 0.008623 |
9 | 6.107720 | 1462.150146 | False | 0 | 0 | 0.028846 | 0.106569 | 0.012896 | 0.038910 | 0.014222 | ... | -0.012896 | 0.014222 | 0.014222 | 0.003516 | 0.010965 | 0.057974 | 0.016738 | 0.018212 | 0.004594 | 0.004594 |
10 | 10.808082 | 1506.980347 | False | 0 | 0 | 0.004871 | 0.122097 | 0.012649 | 0.033275 | 0.016476 | ... | -0.012649 | 0.016476 | 0.016476 | 0.857156 | 0.836370 | 0.041477 | 0.043774 | 0.016485 | 0.003244 | 0.003244 |
11 | 5.851676 | 1461.884766 | False | 0 | 0 | 0.028515 | 0.096888 | 0.008876 | 0.030977 | 0.016659 | ... | -0.008876 | 0.016659 | 0.016659 | 0.001648 | 0.014651 | 0.046359 | 0.034214 | 0.017362 | 0.009226 | 0.009226 |
12 | 7.826456 | 1482.082642 | False | 0 | 0 | 0.058960 | 0.048230 | 0.020680 | 0.045986 | 0.015751 | ... | -0.020680 | 0.015751 | 0.015751 | 0.574581 | 0.555774 | 0.050106 | 0.037956 | 0.016896 | 0.002418 | 0.002418 |
13 | 10.300095 | 1496.979736 | False | 0 | 0 | 0.019680 | 0.100772 | 0.045940 | 0.065435 | 0.015095 | ... | -0.045940 | 0.015095 | 0.015095 | 0.052732 | 0.042651 | 0.049537 | 0.031106 | 0.019058 | 0.018333 | 0.018333 |
14 | 10.300095 | 1496.979736 | False | 0 | 0 | 0.019680 | 0.100772 | 0.045940 | 0.065435 | 0.015095 | ... | -0.045940 | 0.015095 | 0.015095 | 0.052732 | 0.042651 | 0.049537 | 0.031106 | 0.019058 | 0.018333 | 0.018333 |
15 | 10.300095 | 1496.979736 | False | 0 | 0 | 0.019680 | 0.100772 | 0.045940 | 0.065435 | 0.015095 | ... | -0.045940 | 0.015095 | 0.015095 | 0.052732 | 0.042651 | 0.049537 | 0.031106 | 0.019058 | 0.018333 | 0.018333 |
16 | 10.775019 | 1500.692017 | False | 0 | 0 | 0.019647 | 0.093438 | 0.022763 | 0.043110 | 0.017213 | ... | -0.022763 | 0.017213 | 0.017213 | 0.356916 | 0.342040 | 0.050433 | 0.037272 | 0.018307 | 0.002141 | 0.002141 |
17 | 9.463977 | 1500.293457 | False | 0 | 0 | 0.046416 | 0.060569 | 0.024181 | 0.052928 | 0.015462 | ... | -0.024181 | 0.015462 | 0.015462 | 0.603117 | 0.588110 | 0.051726 | 0.035954 | 0.017911 | 0.017911 | 0.017911 |
18 | 11.731860 | 1506.469360 | False | 0 | 0 | 0.025058 | 0.088923 | 0.023740 | 0.043642 | 0.016187 | ... | -0.023740 | 0.016187 | 0.016187 | 0.160617 | 0.141226 | 0.057375 | 0.024345 | 0.017608 | 0.003885 | 0.003885 |
19 | 9.431162 | 1495.168457 | False | 0 | 0 | 0.021281 | 0.110671 | 0.019382 | 0.046185 | 0.015849 | ... | -0.019382 | 0.015849 | 0.015849 | 0.310037 | 0.296888 | 0.054943 | 0.032126 | 0.016572 | 0.002465 | 0.002465 |
20 rows × 132 columns
[ ]:
P_T_EqTests_pkl_Jorg=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
equationP="P_Jorgenson2022_Cpx_Liq",
equationT="T_Jorgenson2022_Cpx_Liq",
T=1300,
H2O_Liq=0, eq_tests=False)
P_T_EqTests_pkl_Jorg
Im normalizing using the Jorgenson method, e.g. 100 total, 2dp
Im normalizing using the Jorgenson method, e.g. 100 total, 2dp
Youve selected a P-independent function
c:\users\penny\onedrive\documents\postdoc_missing\mybarometers\thermobar_outer\src\Thermobar\core.py:1561: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
cpx_calc.loc[(AlVI_minus_Na<0), 'Jd']=cpx_calc['Al_VI_cat_6ox']
Youve selected a T-independent function
Youve selected a T-independent function
P_kbar_calc | T_K_calc | Delta_P_kbar_Iter | Delta_T_K_Iter | Median_Trees_P | Std_Trees_P | IQR_Trees_P | Median_Trees_T | Std_Trees_T | IQR_Trees_T | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 1.000000 | 1355.150000 | 0 | 0 | 1.00 | 0.000000 | 0.000000 | 1355.15 | 3.183231e-12 | 3.183231e-12 |
1 | 2.000000 | 1285.150000 | 0 | 0 | 2.00 | 0.000000 | 0.000000 | 1285.15 | 4.092726e-12 | 4.092726e-12 |
2 | 2.000000 | 1238.150000 | 0 | 0 | 2.00 | 0.000000 | 0.000000 | 1238.15 | 4.092726e-12 | 4.092726e-12 |
3 | 2.000000 | 1273.150000 | 0 | 0 | 2.00 | 0.000000 | 0.000000 | 1273.15 | 4.088325e-12 | 4.088325e-12 |
4 | 2.000000 | 1238.150000 | 0 | 0 | 2.00 | 0.000000 | 0.000000 | 1238.15 | 4.092726e-12 | 4.092726e-12 |
5 | 8.419458 | 1446.550498 | 0 | 0 | 8.00 | 7.241728 | 7.241728 | 1433.15 | 9.848431e+01 | 9.848431e+01 |
6 | 7.142953 | 1432.000746 | 0 | 0 | 7.00 | 6.398140 | 6.398140 | 1423.15 | 8.921307e+01 | 8.921307e+01 |
7 | 10.956871 | 1450.309204 | 0 | 0 | 10.00 | 8.394058 | 8.394058 | 1433.15 | 1.100438e+02 | 1.100438e+02 |
8 | 5.233430 | 1401.965920 | 0 | 0 | 4.01 | 5.269948 | 5.269948 | 1408.15 | 6.391195e+01 | 6.391195e+01 |
9 | 7.305813 | 1417.891294 | 0 | 0 | 7.00 | 6.391484 | 6.391484 | 1413.15 | 7.970738e+01 | 7.970738e+01 |
10 | 12.012990 | 1466.025622 | 0 | 0 | 10.00 | 8.862633 | 8.862633 | 1448.15 | 1.268004e+02 | 1.268004e+02 |
11 | 6.400005 | 1412.503234 | 0 | 0 | 5.00 | 5.518597 | 5.518597 | 1413.15 | 8.188373e+01 | 8.188373e+01 |
12 | 8.628219 | 1439.751990 | 0 | 0 | 8.00 | 7.451500 | 7.451500 | 1428.15 | 9.935043e+01 | 9.935043e+01 |
13 | 11.173898 | 1470.112687 | 0 | 0 | 10.00 | 8.591048 | 8.591048 | 1449.15 | 1.162777e+02 | 1.162777e+02 |
14 | 11.173898 | 1470.112687 | 0 | 0 | 10.00 | 8.591048 | 8.591048 | 1449.15 | 1.162777e+02 | 1.162777e+02 |
15 | 11.173898 | 1470.112687 | 0 | 0 | 10.00 | 8.591048 | 8.591048 | 1449.15 | 1.162777e+02 | 1.162777e+02 |
16 | 11.642547 | 1469.455970 | 0 | 0 | 10.00 | 8.798096 | 8.798096 | 1448.15 | 1.235193e+02 | 1.235193e+02 |
17 | 11.593090 | 1468.349005 | 0 | 0 | 10.00 | 8.791730 | 8.791730 | 1448.15 | 1.283919e+02 | 1.283919e+02 |
18 | 13.392448 | 1468.339055 | 0 | 0 | 10.00 | 8.665804 | 8.665804 | 1448.15 | 1.180593e+02 | 1.180593e+02 |
19 | 9.767323 | 1465.881343 | 0 | 0 | 9.70 | 7.868123 | 7.868123 | 1448.15 | 1.155477e+02 | 1.155477e+02 |
[ ]:
P_T_EqTests_onnx_Jorg=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
equationP="P_Jorgenson2022_Cpx_Liq_onnx",
equationT="T_Jorgenson2022_Cpx_Liq_onnx",
T=1300,
H2O_Liq=0, eq_tests=False)
P_T_EqTests_onnx_Jorg
Im normalizing using the Jorgenson method, e.g. 100 total, 2dp
Im normalizing using the Jorgenson method, e.g. 100 total, 2dp
Youve selected a P-independent function
c:\users\penny\onedrive\documents\postdoc_missing\mybarometers\thermobar_outer\src\Thermobar\core.py:1561: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
cpx_calc.loc[(AlVI_minus_Na<0), 'Jd']=cpx_calc['Al_VI_cat_6ox']
Youve selected a T-independent function
Youve selected a T-independent function
P_kbar_calc | T_K_calc | Delta_P_kbar_Iter | Delta_T_K_Iter | |
---|---|---|---|---|
0 | 1.000001 | 1355.152954 | 0 | 0 |
1 | 2.000002 | 1285.151611 | 0 | 0 |
2 | 2.000002 | 1238.816772 | 0 | 0 |
3 | 2.000002 | 1273.152954 | 0 | 0 |
4 | 2.000002 | 1238.821777 | 0 | 0 |
5 | 8.419460 | 1452.618042 | 0 | 0 |
6 | 7.142956 | 1438.607666 | 0 | 0 |
7 | 10.956876 | 1446.971313 | 0 | 0 |
8 | 5.233430 | 1408.817017 | 0 | 0 |
9 | 7.305816 | 1418.339111 | 0 | 0 |
10 | 12.012997 | 1482.637695 | 0 | 0 |
11 | 6.400006 | 1409.916504 | 0 | 0 |
12 | 8.628222 | 1452.076050 | 0 | 0 |
13 | 11.173905 | 1468.081055 | 0 | 0 |
14 | 11.173905 | 1468.081055 | 0 | 0 |
15 | 11.173905 | 1468.081055 | 0 | 0 |
16 | 11.642552 | 1479.100586 | 0 | 0 |
17 | 11.593097 | 1460.817383 | 0 | 0 |
18 | 13.392457 | 1472.319458 | 0 | 0 |
19 | 9.767327 | 1466.672852 | 0 | 0 |
[ ]: