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Python Notebook Download

Clinopyroxene-only and Clinopyroxene-Liquid Thermobarometry.

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 the necessary python things

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

Now, load the data

[3]:
out=pt.import_excel('Cpx_Liq_Example.xlsx', sheet_name="Sheet1")
my_input=out['my_input']
Liqs=out['Liqs']
Cpxs=out['Cpxs']

Inspect the data to check it loaded properly

[4]:
Liqs.head()
[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 51.1 0.93 17.5 8.91 0.18 6.09 11.50 3.53 0.17 0.0 0.15 3.8 0.0 0.0 0.0 0.0 0
1 51.5 1.19 19.2 8.70 0.19 4.98 10.00 3.72 0.42 0.0 0.14 6.2 0.0 0.0 0.0 0.0 1
2 59.1 0.54 19.1 5.22 0.19 3.25 7.45 4.00 0.88 0.0 0.31 6.2 0.0 0.0 0.0 0.0 2
3 52.5 0.98 19.2 8.04 0.20 4.99 9.64 4.15 0.21 0.0 0.14 6.2 0.0 0.0 0.0 0.0 3
4 56.2 0.34 20.4 5.88 0.20 2.58 7.18 6.02 1.02 0.0 0.23 6.2 0.0 0.0 0.0 0.0 4
[5]:
Cpxs.head()
[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 51.5 0.50 3.70 5.18 0.09 15.8 22.8 0.24 0.0 0.66 0
1 50.3 0.73 4.12 5.83 0.00 15.0 22.7 0.24 0.0 0.28 1
2 47.3 1.75 7.85 6.51 0.14 13.1 22.5 0.25 0.0 0.22 2
3 51.1 0.63 4.41 5.66 0.13 15.6 22.6 0.23 0.0 0.27 3
4 51.0 0.56 4.14 7.33 0.20 14.4 22.4 0.31 0.0 0.09 4

Getting help

  • At any point, you can do help(pt.function) to get some more information

  • For example, here we get information on inputs/outputs for Cpx-Liq thermometry, including the equation options

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

calculate_cpx_liq_temp(*, equationT, cpx_comps=None, liq_comps=None, meltmatch=None, P=None, eq_tests=False, H2O_Liq=None, Fe3Fet_Liq=None, sigma=1, Kd_Err=0.03)
    Clinopyroxene-Liquid thermometry, calculates temperature in Kelvin
    (and equilibrium tests as an option)

    Parameters
    -------
    cpx_comps: pandas.DataFrame
        Clinopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

    liq_comps: pandas.DataFrame
        Liquid compositions with column headings SiO2_Liq, MgO_Liq etc.

    Or:

    meltmatch: pandas.DataFrame
        Combined dataframe of cpx-Liquid compositions
        Used for calculate_cpx_liq_press_temp_matching function.

    EquationT: str
        Choice of equation:
        Cpx-Liquid
        |  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)
        |  T_Petrelli2020_Cpx_Liq (gives voting)
        |  T_Jorgenson2022_Cpx_Liq (gives voting)
        |  T_Petrelli2020_Cpx_Liq_onnx (gives consistent result every time)


    P: float, int, pandas.Series, str  ("Solve")
        Pressure in kbar
        Only needed for P-sensitive thermometers.
        If enter P="Solve", returns a partial function
        Else, enter an integer, float, or panda series


    eq_tests: bool
        If False, just returns pressure (default) as a panda series
        If True, returns pressure, Values of Eq tests,
        Values of Eq tests (Kd, EnFs, DiHd, CaTs, CrCaTs),
        as well as user-entered cpx and liq comps and components.


    Returns
    -------
    If eq_tests=False
        pandas.Series: Temperature in Kelvin
    If eq_tests=True
        pandas.DataFrame: Temperature in Kelvin +
        Eq Tests + cpx+liq comps + components

Example 1 - Calculating Temperature

1a - Temperature for a known pressure and water content

  • Here, we calculate temperature using the H2O content given in the H2O_Liq column in the user-entered spreadsheet (the default), and P=5 kbar

  • There are a number of equations (see help above), but here we use T_Put2008_eq33 for temperature

[7]:
Temp_T33=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
                        equationT="T_Put2008_eq33", P=5)-273.15 # Default Kelvin
Temp_T33
[7]:
0     1091.562867
1     1031.605285
2      999.303442
3     1032.536881
4      979.726983
5     1076.821062
6     1096.927022
7     1097.227100
8     1113.361468
9     1084.298534
10    1091.178760
11    1071.691587
12    1089.996302
13    1028.359107
14    1028.359107
15    1028.359107
16    1093.611526
17            NaN
18    1087.546061
19    1104.700975
dtype: float64

1b - Temperature, overwriting the spreadsheet water content in the function itself

  • Here, we are reseting water to 0 wt%. You can see the temperatures are much higher

[8]:
Temp_T33_0H2O=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
                                        equationT="T_Put2008_eq33",
                                        P=5, H2O_Liq=0)-273.15
Temp_T33_0H2O
[8]:
0     1142.969951
1     1109.923744
2     1073.681511
3     1110.970579
4     1051.769286
5     1134.212162
6     1149.012230
7     1159.843300
8     1174.122141
9     1141.430504
10    1144.660690
11    1131.557607
12    1144.720092
13    1076.010017
14    1076.010017
15    1076.010017
16    1150.716488
17            NaN
18    1150.219143
19    1159.165100
dtype: float64

1c- Lets use the thermometer of Brugman and Till, 2019.

  • This returns a number of warnings, because the authors recomend a compositional calibration range, which our entered cpx compositions lie outside of.

[9]:
Temp_TBrug=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
                                    equationT="T_Brug2019", P=5)-273.15
Temp_TBrug
Youve selected a P-independent function
C:\Users\penny\AppData\Local\Temp\ipykernel_4540\1899851194.py:1: UserWarning: Some inputted CPX compositions have Cpx Mg#>0.65;.
  Temp_TBrug=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
C:\Users\penny\AppData\Local\Temp\ipykernel_4540\1899851194.py:1: UserWarning: Some inputted CPX compositions have Al2O3>7 wt%;.
  Temp_TBrug=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
C:\Users\penny\AppData\Local\Temp\ipykernel_4540\1899851194.py:1: UserWarning: Some inputted Liq compositions have  SiO2<70 wt%;
  Temp_TBrug=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
c:\users\penny\box\postdoc\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:2205: UserWarning: which is outside the recomended calibration range of Brugman and Till (2019)
  w.warn("which is outside the recomended calibration range of Brugman and Till (2019)")
[9]:
0     1797.689417
1     1632.682392
2     1202.363348
3     1558.356437
4     1028.947694
5     1828.840169
6     1889.290499
7     1772.472552
8     1946.324399
9     1873.382098
10    1881.239956
11    1834.031996
12    1853.418730
13    1868.159678
14    1868.159678
15    1868.159678
16    1836.411331
17    1857.905516
18    1928.817708
19    1973.023872
dtype: float64

1d - We can also specify eq_tests=True to get a full dataframe back with all the components, as well as a number of equilibrium test values

  • You could then extract just the temps using Temp_T33_0H2O_EqTests[‘T_K_calc’] or any other column you want the same way

[10]:
Temp_T33_0H2O_EqTests=pt.calculate_cpx_liq_temp(cpx_comps=Cpxs, liq_comps=Liqs,
                                                equationT="T_Put2008_eq33",
                                                P=5, H2O_Liq=0, eq_tests=True)
Temp_T33_0H2O_EqTests
Using Fe3FeT from input file to calculate Kd Fe-Mg
[10]:
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_DiHd_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 5 1416.119951 False 0.042816 0.094027 0.010858 0.021183 0.016957 0.089424 0.001586 ... -0.010858 0.013418 -0.016957 0.000000 0.009562 0.043320 0.002307 0.016184 0.000872 0.000872
1 5 1383.073744 False 0.036792 0.090778 0.007575 0.021866 0.023796 0.104285 0.024207 ... -0.007575 0.013021 -0.023796 0.000000 0.004122 0.056932 0.011994 0.017429 0.000099 0.000099
2 5 1346.831511 False 0.058659 0.172858 0.043395 0.013382 0.075254 0.006413 0.103915 ... 0.043395 0.016630 -0.075254 0.000000 0.003245 0.029418 0.042249 0.018331 0.000246 0.000246
3 5 1384.120579 False 0.034331 0.101144 0.012488 0.028698 0.031526 0.084045 0.024697 ... -0.012488 0.014301 -0.031526 0.000000 0.003909 0.050962 0.009764 0.019332 0.002997 0.002997
4 5 1324.919286 False 0.022156 0.097447 0.021106 0.049019 0.032121 0.071950 0.035481 ... -0.021106 0.010990 -0.032121 0.000000 0.001315 0.050713 0.014761 0.027953 0.005738 0.005738
5 5 1407.362162 False 0.011745 0.133688 0.008645 0.029286 0.012999 0.062059 0.001463 ... -0.008645 0.012999 0.012999 0.297720 0.277125 0.057369 0.026926 0.019470 0.006126 0.006126
6 5 1422.162230 False 0.011959 0.091270 0.008903 0.024363 0.014640 0.070686 0.003636 ... -0.008903 0.014640 0.014640 0.130559 0.110135 0.052890 0.032902 0.015664 0.002722 0.002722
7 5 1432.993300 False 0.052296 0.077646 0.007652 0.024590 0.015888 0.054068 0.010131 ... -0.007652 0.015888 0.015888 0.148010 0.125617 0.045423 0.029060 0.018272 0.011840 0.011840
8 5 1447.272141 False 0.046595 0.085580 0.020659 0.043637 0.007563 0.082456 0.039311 ... -0.020659 0.016662 0.007563 0.001101 0.017485 0.052715 0.016339 0.015821 0.008622 0.008622
9 5 1414.580504 False 0.018097 0.116199 0.015372 0.032515 0.013343 0.083869 0.008159 ... -0.015372 0.013343 0.013343 0.003516 0.010965 0.059753 0.014958 0.018210 0.004591 0.004591
10 5 1417.810690 True 0.015009 0.141280 0.004910 0.020402 0.014748 0.058127 0.012136 ... -0.004910 0.014748 0.014748 0.857156 0.836370 0.043707 0.041545 0.016472 0.003230 0.003230
11 5 1404.707607 True 0.015558 0.109274 0.013261 0.021390 0.015494 0.050272 0.041881 ... -0.013261 0.015494 0.015494 0.001648 0.014651 0.047981 0.032592 0.017360 0.009224 0.009224
12 5 1417.870092 False 0.044544 0.063790 0.019190 0.035825 0.014555 0.060011 0.001347 ... -0.019190 0.014555 0.014555 0.574581 0.555774 0.052013 0.036050 0.016889 0.002411 0.002411
13 5 1349.160017 True 0.014011 0.133748 0.047904 0.041739 0.012404 0.016132 0.150795 ... -0.047904 0.012404 0.012404 0.052732 0.042651 0.054363 0.026279 0.019044 0.018319 0.018319
14 5 1349.160017 True 0.014011 0.133748 0.047904 0.041739 0.012404 0.016132 0.150795 ... -0.047904 0.012404 0.012404 0.052732 0.042651 0.054363 0.026279 0.019044 0.018319 0.018319
15 5 1349.160017 True 0.014011 0.133748 0.047904 0.041739 0.012404 0.016132 0.150795 ... -0.047904 0.012404 0.012404 0.052732 0.042651 0.054363 0.026279 0.019044 0.018319 0.018319
16 5 1423.866488 True 0.002518 0.111409 0.013153 0.031786 0.015716 0.050165 0.001366 ... -0.013153 0.015716 0.015716 0.356916 0.342040 0.052646 0.035059 0.018291 0.002126 0.002126
17 5 NaN False NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN 0.603117 0.588110 NaN NaN NaN NaN NaN
18 5 1423.369143 False 0.006558 0.108254 0.012578 0.032440 0.014620 0.067687 0.009856 ... -0.012578 0.014620 0.014620 0.160617 0.141226 0.060201 0.021519 0.017590 0.003868 0.003868
19 5 1432.315100 False 0.007279 0.123765 0.011986 0.036870 0.014680 0.077193 0.026064 ... -0.011986 0.014680 0.014680 0.310037 0.296888 0.056968 0.030100 0.016562 0.002476 0.002476

20 rows × 130 columns

Example 2 - Calculating pressure for a known temperature

2a - Pressure at fixed temperature (T=1300 K), and pressures from Neave and Putirka (2017)

[11]:
P_FixedTNeave=pt.calculate_cpx_liq_press(cpx_comps=Cpxs, liq_comps=Liqs,
                                       equationP="P_Neave2017", T=1300)
P_FixedTNeave
[11]:
0      0.634602
1      1.655874
2      1.146083
3      1.028349
4      3.854147
5      0.077818
6      0.103202
7      3.902001
8      3.198764
9     -0.172497
10    -0.067569
11    -2.437195
12     0.531826
13   -14.887115
14   -14.887115
15   -14.887115
16     1.346623
17          NaN
18     0.405712
19     1.682571
dtype: float64

2b - Equation 30 from Putirka (2008), overwriting input water, return equilibrium tests

  • Here we change equation P, overwrite the H2O content in the function, and ask for equilibrium tests.

  • We are selecting equation 30 from Putirka (2008) this time, T=1300 K

[12]:
Temp_P30_0H2O=pt.calculate_cpx_liq_press(cpx_comps=Cpxs, liq_comps=Liqs,
                                         equationP="P_Put2008_eq30",
                                         T=1300, H2O_Liq=0, eq_tests=True)
Temp_P30_0H2O
Using Fe3FeT from input file to calculate Kd Fe-Mg
[12]:
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_DiHd_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 -2.090909 1300 False 0.015390 0.116829 0.002696 0.003912 0.019173 0.089851 0.350659 ... -0.002696 0.011202 -0.019173 0.000000 0.009562 0.047180 0.006167 0.016165 0.000891 0.000891
1 -2.604072 1300 True 0.016996 0.108121 0.006605 0.005373 0.025431 0.049622 0.286210 ... 0.006605 0.011385 -0.025431 0.000000 0.004122 0.060657 0.015718 0.017407 0.000077 0.000077
2 -2.145841 1300 False 0.069927 0.183575 0.074576 0.029940 0.076260 0.125978 0.249574 ... 0.074576 0.015625 -0.076260 0.000000 0.003245 0.030292 0.041375 0.018308 0.000223 0.000223
3 -2.334004 1300 False 0.014291 0.117657 0.003134 0.008701 0.033245 0.066274 0.281808 ... 0.003134 0.012582 -0.033245 0.000000 0.003909 0.054136 0.012939 0.019308 0.002973 0.002973
4 -0.177987 1300 False 0.016126 0.102892 0.007476 0.045035 0.032562 0.004383 0.113023 ... -0.007476 0.010549 -0.032562 0.000000 0.001315 0.051697 0.015744 0.027927 0.005712 0.005712
5 -3.656248 1300 False 0.037163 0.157156 0.008999 0.005334 0.011085 0.129807 0.319319 ... 0.008999 0.011085 0.011085 0.297720 0.277125 0.061850 0.022445 0.019441 0.006098 0.006098
6 -3.959117 1300 False 0.016846 0.121449 0.007682 0.004097 0.012259 0.135041 0.353721 ... 0.007682 0.012259 0.012259 0.130559 0.110135 0.057515 0.028277 0.015640 0.002698 0.002698
7 0.216684 1300 False 0.021028 0.105244 0.007877 0.010227 0.013249 0.108774 0.364703 ... -0.007877 0.013249 0.013249 0.148010 0.125617 0.049502 0.024980 0.018258 0.011854 0.011854
8 -0.678349 1300 False 0.012104 0.115746 0.019128 0.005116 0.004551 0.094621 0.357156 ... -0.019128 0.013650 0.004551 0.001101 0.017485 0.057933 0.011121 0.015807 0.008636 0.008636
9 -3.118628 1300 False 0.008977 0.139129 0.002217 0.008054 0.011220 0.104703 0.331041 ... -0.002217 0.011220 0.011220 0.003516 0.010965 0.064853 0.009859 0.018185 0.004566 0.004566
10 -3.748095 1300 False 0.042821 0.166301 0.012878 0.009837 0.012445 0.143123 0.361588 ... 0.012878 0.012445 0.012445 0.857156 0.836370 0.047356 0.037895 0.016447 0.003205 0.003205
11 -5.694960 1300 False 0.009248 0.131688 0.014536 0.006415 0.013332 0.163945 0.361704 ... 0.014536 0.013332 0.013332 0.001648 0.014651 0.051503 0.029071 0.017328 0.009192 0.009192
12 -3.317520 1300 False 0.016718 0.092009 0.002553 0.004621 0.012336 0.131747 0.336589 ... -0.002553 0.012336 0.012336 0.574581 0.555774 0.056241 0.031822 0.016866 0.002388 0.002388
13 -15.728385 1300 False 0.025832 0.144548 0.038085 0.028394 0.011485 0.292357 0.321371 ... 0.038085 0.011485 0.011485 0.052732 0.042651 0.056334 0.024309 0.018975 0.018250 0.018250
14 -15.728385 1300 False 0.025832 0.144548 0.038085 0.028394 0.011485 0.292357 0.321371 ... 0.038085 0.011485 0.011485 0.052732 0.042651 0.056334 0.024309 0.018975 0.018250 0.018250
15 -15.728385 1300 False 0.025832 0.144548 0.038085 0.028394 0.011485 0.292357 0.321371 ... 0.038085 0.011485 0.011485 0.052732 0.042651 0.056334 0.024309 0.018975 0.018250 0.018250
16 -2.753658 1300 False 0.026676 0.140028 0.000281 0.000271 0.013269 0.140658 0.350210 ... 0.000281 0.013269 0.013269 0.356916 0.342040 0.057028 0.030677 0.018268 0.002102 0.002102
17 NaN 1300 False 0.000180 0.108710 NaN 0.014766 NaN NaN 0.316925 ... NaN NaN NaN 0.603117 0.588110 0.058863 0.028817 NaN NaN NaN
18 -3.583424 1300 False 0.022523 0.136594 0.003481 0.002846 0.012271 0.132330 0.343415 ... 0.003481 0.012271 0.012271 0.160617 0.141226 0.065393 0.016327 0.017565 0.003843 0.003843
19 -1.870615 1300 False 0.023835 0.151004 0.003511 0.002818 0.012196 0.105839 0.342393 ... -0.003511 0.012196 0.012196 0.310037 0.296888 0.062186 0.024882 0.016543 0.002495 0.002495

20 rows × 130 columns

2c - As above, but setting a fixed Fe3Fet_Liq ratio

  • Can overwrite the Fe3Fet in the input spreadsheet to a different value, affects calculations of delta Kd as this uses just Fe2+ in the melt

  • Note, it is debated whether Kd Fe-Mg should be calculated with all Fe (to do that here, specify Fe3Fet_Liq=0, Putirka), or using just Fe2+ (e.g., Neave and Putirka, 2017)

  • you can compare the delta Kd Put 2008 in this option from the answers above. You can see, by adding 30% Fe3+, you have become further from equilibrium

[13]:
Temp_P30_0H2O_30Fe=pt.calculate_cpx_liq_press(cpx_comps=Cpxs,
                    liq_comps=Liqs, equationP="P_Put2008_eq30",
                    T=1300, H2O_Liq=0, Fe3Fet_Liq=0.3, eq_tests=True)
Temp_P30_0H2O_30Fe
[13]:
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_DiHd_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 -2.090909 1300 False 0.080646 0.212865 0.002696 0.003912 0.019173 0.089851 0.350659 ... -0.002696 0.011202 -0.019173 0.000000 0.009562 0.047180 0.006167 0.016165 0.000891 0.000891
1 -2.604072 1300 False 0.078351 0.203468 0.006605 0.005373 0.025431 0.049622 0.286210 ... 0.006605 0.011385 -0.025431 0.000000 0.004122 0.060657 0.015718 0.017407 0.000077 0.000077
2 -2.145841 1300 False 0.202528 0.316176 0.074576 0.029940 0.076260 0.125978 0.249574 ... 0.074576 0.015625 -0.076260 0.000000 0.003245 0.030292 0.041375 0.018308 0.000223 0.000223
3 -2.334004 1300 False 0.082216 0.214165 0.003134 0.008701 0.033245 0.066274 0.281808 ... 0.003134 0.012582 -0.033245 0.000000 0.003909 0.054136 0.012939 0.019308 0.002973 0.002973
4 -0.177987 1300 False 0.079595 0.198613 0.007476 0.045035 0.032562 0.004383 0.113023 ... -0.007476 0.010549 -0.032562 0.000000 0.001315 0.051697 0.015744 0.027927 0.005712 0.005712
5 -3.656248 1300 False 0.155721 0.275715 0.008999 0.005334 0.011085 0.129807 0.319319 ... 0.008999 0.011085 0.011085 0.297720 0.277125 0.061850 0.022445 0.019441 0.006098 0.006098
6 -3.959117 1300 False 0.126698 0.231301 0.007682 0.004097 0.012259 0.135041 0.353721 ... 0.007682 0.012259 0.012259 0.130559 0.110135 0.057515 0.028277 0.015640 0.002698 0.002698
7 0.216684 1300 False 0.072592 0.198864 0.007877 0.010227 0.013249 0.108774 0.364703 ... -0.007877 0.013249 0.013249 0.148010 0.125617 0.049502 0.024980 0.018258 0.011854 0.011854
8 -0.678349 1300 False 0.085341 0.213191 0.019128 0.005116 0.004551 0.094621 0.357156 ... -0.019128 0.013650 0.004551 0.001101 0.017485 0.057933 0.011121 0.015807 0.008636 0.008636
9 -3.118628 1300 False 0.115456 0.245608 0.002217 0.008054 0.011220 0.104703 0.331041 ... -0.002217 0.011220 0.011220 0.003516 0.010965 0.064853 0.009859 0.018185 0.004566 0.004566
10 -3.748095 1300 False 0.163805 0.287285 0.012878 0.009837 0.012445 0.143123 0.361588 ... 0.012878 0.012445 0.012445 0.857156 0.836370 0.047356 0.037895 0.016447 0.003205 0.003205
11 -5.694960 1300 False 0.115844 0.238283 0.014536 0.006415 0.013332 0.163945 0.361704 ... 0.014536 0.013332 0.013332 0.001648 0.014651 0.051503 0.029071 0.017328 0.009192 0.009192
12 -3.317520 1300 False 0.078749 0.187476 0.002553 0.004621 0.012336 0.131747 0.336589 ... -0.002553 0.012336 0.012336 0.574581 0.555774 0.056241 0.031822 0.016866 0.002388 0.002388
13 -15.728385 1300 False 0.139535 0.258251 0.038085 0.028394 0.011485 0.292357 0.321371 ... 0.038085 0.011485 0.011485 0.052732 0.042651 0.056334 0.024309 0.018975 0.018250 0.018250
14 -15.728385 1300 False 0.139535 0.258251 0.038085 0.028394 0.011485 0.292357 0.321371 ... 0.038085 0.011485 0.011485 0.052732 0.042651 0.056334 0.024309 0.018975 0.018250 0.018250
15 -15.728385 1300 False 0.139535 0.258251 0.038085 0.028394 0.011485 0.292357 0.321371 ... 0.038085 0.011485 0.011485 0.052732 0.042651 0.056334 0.024309 0.018975 0.018250 0.018250
16 -2.753658 1300 False 0.140741 0.254093 0.000281 0.000271 0.013269 0.140658 0.350210 ... 0.000281 0.013269 0.013269 0.356916 0.342040 0.057028 0.030677 0.018268 0.002102 0.002102
17 NaN 1300 False 0.102375 0.211265 NaN 0.014766 NaN NaN 0.316925 ... NaN NaN NaN 0.603117 0.588110 0.058863 0.028817 NaN NaN NaN
18 -3.583424 1300 False 0.134808 0.248879 0.003481 0.002846 0.012271 0.132330 0.343415 ... 0.003481 0.012271 0.012271 0.160617 0.141226 0.065393 0.016327 0.017565 0.003843 0.003843
19 -1.870615 1300 False 0.136682 0.263851 0.003511 0.002818 0.012196 0.105839 0.342393 ... -0.003511 0.012196 0.012196 0.310037 0.296888 0.062186 0.024882 0.016543 0.002495 0.002495

20 rows × 130 columns

Example 3 - Iterating pressure and temperature

  • In reality, unlesa you are an experimentalist, you rarely know one of pressure or temperature

  • In Keith Putirka’s spreadsheets, you can link up columns to iterate P and T towards a solution, this can be done here using the function calculate_cpx_liq_press_temp…

3a - Iterating equation 30 from Putirka (2008) for P, and equation 33 from Putirka (2008) for T

  • Without specifying anything else, you get a dataframe with columns for calculated pressure and temperature

[14]:
PT_iter_30_31=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs,
                                              equationP="P_Put2008_eq30",
                                              equationT="T_Put2008_eq33")
PT_iter_30_31
[14]:
P_kbar_calc T_K_calc Delta_P_kbar_Iter Delta_T_K_Iter
0 2.530914 1352.408784 0.0 0.0
1 1.786845 1290.151507 0.0 0.0
2 1.171520 1255.933868 0.0 0.0
3 2.143416 1292.669093 0.0 0.0
4 2.763538 1243.469600 0.0 0.0
5 0.535148 1328.353392 0.0 0.0
6 0.493360 1347.611001 0.0 0.0
7 6.377576 1377.397773 0.0 0.0
8 5.742168 1390.374437 0.0 0.0
9 1.396263 1339.752950 0.0 0.0
10 0.644651 1342.785453 0.0 0.0
11 -1.883972 1312.045437 0.0 0.0
12 1.211110 1344.398677 0.0 0.0
13 -14.685454 1217.300913 0.0 0.0
14 -14.685454 1217.300913 0.0 0.0
15 -14.685454 1217.300913 0.0 0.0
16 2.168816 1352.629530 0.0 0.0
17 NaN NaN NaN NaN
18 1.315338 1342.522331 0.0 0.0
19 3.494361 1370.176239 0.0 0.0

3b - Same as above, but with eq_tests=True

  • Get all equilibrium tests, and input compostions as a larger dataframe.

[15]:
PT_iter_30_31_EqTests=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpxs, liq_comps=Liqs, equationP="P_Put2008_eq30",
                                                      equationT="T_Put2008_eq33", eq_tests=True)
PT_iter_30_31_EqTests
Using Fe3FeT from input file to calculate Kd Fe-Mg
[15]:
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 2.530914 1352.408784 True 0.0 0.0 0.027981 0.106599 0.011304 0.009468 0.018174 ... -0.011304 0.012201 -0.018174 0.000000 0.009562 0.045314 0.004301 0.016178 0.000878 0.000878
1 1.786845 1290.151507 True 0.0 0.0 0.014591 0.110154 0.009917 0.002832 0.025619 ... -0.009917 0.011198 -0.025619 0.000000 0.004122 0.061147 0.016209 0.017420 0.000090 0.000090
2 1.171520 1255.933868 False 0.0 0.0 0.080784 0.193537 0.046615 0.050574 0.077199 ... 0.046615 0.014686 -0.077199 0.000000 0.003245 0.031201 0.040466 0.018319 0.000234 0.000234
3 2.143416 1292.669093 False 0.0 0.0 0.012502 0.119081 0.015465 0.006444 0.033388 ... -0.015465 0.012439 -0.033388 0.000000 0.003909 0.054443 0.013245 0.019323 0.002988 0.002988
4 2.763538 1243.469600 False 0.0 0.0 0.002154 0.115101 0.025085 0.033393 0.033542 ... -0.025085 0.009568 -0.033542 0.000000 0.001315 0.054153 0.018201 0.027943 0.005728 0.005728
5 0.535148 1328.353392 False 0.0 0.0 0.030307 0.151008 0.003629 0.013160 0.011592 ... -0.003629 0.011592 0.011592 0.297720 0.277125 0.060562 0.023733 0.019456 0.006112 0.006112
6 0.493360 1347.611001 True 0.0 0.0 0.005393 0.109769 0.003040 0.009391 0.013188 ... -0.003040 0.013188 0.013188 0.130559 0.110135 0.055566 0.030226 0.015652 0.002710 0.002710
7 6.377576 1377.397773 False 0.0 0.0 0.039499 0.089259 0.019500 0.013119 0.014794 ... -0.019500 0.014794 0.014794 0.148010 0.125617 0.046990 0.027492 0.018277 0.011835 0.011835
8 5.742168 1390.374437 False 0.0 0.0 0.033597 0.097318 0.031008 0.032361 0.006409 ... -0.031008 0.015508 0.006409 0.001101 0.017485 0.054544 0.014511 0.015824 0.008619 0.008619
9 1.396263 1339.752950 True 0.0 0.0 0.000606 0.131231 0.013266 0.018402 0.011957 ... -0.013266 0.011957 0.011957 0.003516 0.010965 0.062936 0.011776 0.018199 0.004581 0.004581
10 0.644651 1342.785453 False 0.0 0.0 0.032515 0.157280 0.000830 0.003555 0.013284 ... 0.000830 0.013284 0.013284 0.857156 0.836370 0.045922 0.039329 0.016460 0.003218 0.003218
11 -1.883972 1312.045437 False 0.0 0.0 0.006324 0.129135 0.000306 0.002289 0.013585 ... -0.000306 0.013585 0.013585 0.001648 0.014651 0.051055 0.029518 0.017340 0.009204 0.009204
12 1.211110 1344.398677 True 0.0 0.0 0.027407 0.081454 0.015105 0.018898 0.013175 ... -0.015105 0.013175 0.013175 0.574581 0.555774 0.054522 0.033541 0.016879 0.002401 0.002401
13 -14.685454 1217.300913 False 0.0 0.0 0.046407 0.162372 0.017760 0.005585 0.009986 ... 0.017760 0.009986 0.009986 0.052732 0.042651 0.060200 0.020443 0.018975 0.018250 0.018250
14 -14.685454 1217.300913 False 0.0 0.0 0.046407 0.162372 0.017760 0.005585 0.009986 ... 0.017760 0.009986 0.009986 0.052732 0.042651 0.060200 0.020443 0.018975 0.018250 0.018250
15 -14.685454 1217.300913 False 0.0 0.0 0.046407 0.162372 0.017760 0.005585 0.009986 ... 0.017760 0.009986 0.009986 0.052732 0.042651 0.060200 0.020443 0.018975 0.018250 0.018250
16 2.168816 1352.629530 True 0.0 0.0 0.014033 0.127948 0.012246 0.016146 0.014315 ... -0.012246 0.014315 0.014315 0.356916 0.342040 0.055025 0.032680 0.018283 0.002118 0.002118
17 NaN NaN False NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN 0.603117 0.588110 NaN NaN NaN NaN NaN
18 1.315338 1342.522331 True 0.0 0.0 0.012280 0.126901 0.010194 0.015470 0.013084 ... -0.010194 0.013084 0.013084 0.160617 0.141226 0.063446 0.018274 0.017580 0.003858 0.003858
19 3.494361 1370.176239 True 0.0 0.0 0.007055 0.136635 0.015152 0.023994 0.013518 ... -0.015152 0.013518 0.013518 0.310037 0.296888 0.059237 0.027832 0.016558 0.002479 0.002479

20 rows × 132 columns

Example 3 - Cpx-only Barometry

  • Very similar to above, just don’t need liq_comps input

3a -Pressure only, using equation 32b (at T=1300 K), and H2O=0

  • This equation requires H2O content in the liquid. If you don’t enter anything, it assumes H2O=0

  • else specify using H2O_Liq=….

  • it prints a warning telling you that by defualt, this is what the function is doing

[16]:
eq32b_noH=pt.calculate_cpx_only_press(cpx_comps=Cpxs, T=1300,
       equationP="P_Put2008_eq32b")
eq32b_noH
c:\users\penny\box\postdoc\mybarometers\thermobar_outer\src\Thermobar\clinopyroxene_thermobarometry.py:3261: UserWarning: This Cpx-only barometer is sensitive to H2O content of the liquid.  By default, this function uses H2O=0 wt%, else you can enter a value of H2O_Liq in the function
  w.warn('This Cpx-only barometer is sensitive to H2O content of the liquid. '
[16]:
0     -1.567381
1     -1.202221
2     -0.317946
3     -0.996608
4      0.467437
5     -6.911370
6     -6.286860
7    -11.586143
8     -3.498491
9    -13.658623
10   -11.540359
11   -13.391546
12   -14.272181
13   -18.202136
14   -18.202136
15   -18.202136
16   -10.918332
17          NaN
18    -5.618846
19    -8.241059
dtype: float64

3b - Pressure only, using 5 wt% water

[17]:
eq32b_5H=pt.calculate_cpx_only_press(cpx_comps=Cpxs, T=1300,
       equationP="P_Put2008_eq32b", H2O_Liq=5)
eq32b_5H
[17]:
0      0.697619
1      1.062779
2      1.947054
3      1.268392
4      2.732437
5     -4.646370
6     -4.021860
7     -9.321143
8     -1.233491
9    -11.393623
10    -9.275359
11   -11.126546
12   -12.007181
13   -15.937136
14   -15.937136
15   -15.937136
16    -8.653332
17          NaN
18    -3.353846
19    -5.976059
dtype: float64

3c - Temperature-only using eq 32d at 5 kbar

[18]:
eq32d_5kbar=pt.calculate_cpx_only_temp(cpx_comps=Cpxs, equationT="T_Put2008_eq32d",
         P=5)
eq32d_5kbar
[18]:
0     1457.849197
1     1441.107847
2     1415.899813
3     1455.722443
4     1441.512524
5     1413.510042
6     1420.741893
7     1406.196227
8     1463.728870
9     1393.605045
10    1239.894412
11    1413.752159
12    1402.402180
13    1360.089648
14    1360.089648
15    1360.089648
16    1366.264788
17    1381.658652
18    1396.089868
19    1412.298889
dtype: float64

3d - Iterating P from 32b, and T from 32d, with H2O=5

[19]:
eq32b_32d_5H=pt.calculate_cpx_only_press_temp(cpx_comps=Cpxs, equationT="T_Put2008_eq32d",
       equationP="P_Put2008_eq32b", H2O_Liq=5)
eq32b_32d_5H
[19]:
P_kbar_calc T_K_calc Delta_P_kbar_Iter Delta_T_K_Iter SiO2_Cpx TiO2_Cpx Al2O3_Cpx FeOt_Cpx MnO_Cpx MgO_Cpx CaO_Cpx Na2O_Cpx K2O_Cpx Cr2O3_Cpx Sample_ID_Cpx
0 3.889323 1448.656494 0.000000e+00 0.000000e+00 51.500000 0.500000 3.700000 5.180000 0.090000 15.800000 22.800000 0.240000 0.000000 0.660000 0
1 3.721484 1430.647515 0.000000e+00 0.000000e+00 50.300000 0.730000 4.120000 5.830000 0.000000 15.000000 22.700000 0.240000 0.000000 0.280000 1
2 3.982623 1407.721620 0.000000e+00 0.000000e+00 47.300000 1.750000 7.850000 6.510000 0.140000 13.100000 22.500000 0.250000 0.000000 0.220000 2
3 4.567865 1452.151021 2.273737e-13 1.818989e-12 51.100000 0.630000 4.410000 5.660000 0.130000 15.600000 22.600000 0.230000 0.000000 0.270000 3
4 5.941303 1449.216073 0.000000e+00 0.000000e+00 51.000000 0.560000 4.140000 7.330000 0.200000 14.400000 22.400000 0.310000 0.000000 0.090000 4
5 -4.055503 1340.840204 2.273737e-13 1.591616e-12 51.893554 0.985648 4.374039 6.014774 0.427037 15.882719 23.735224 0.640989 0.214311 1.476325 5
6 -3.177367 1354.783292 0.000000e+00 0.000000e+00 51.671503 1.222254 4.424609 5.755014 0.537483 15.849673 23.601948 0.563064 0.149984 1.460116 6
7 -9.419020 1291.083222 0.000000e+00 0.000000e+00 52.462671 0.861366 4.306456 5.521574 0.093405 15.931721 22.890467 0.839256 0.829141 1.605553 7
8 1.553018 1435.084291 0.000000e+00 0.000000e+00 52.341465 0.558521 4.332519 5.307290 0.531408 16.743678 22.820816 0.356148 0.151447 1.328233 8
9 -11.734771 1261.200708 -2.273737e-13 -1.591616e-12 51.905733 0.643097 3.880163 5.651830 0.614606 16.146415 23.347921 0.633203 0.761539 1.027509 9
10 -9.147761 1140.304548 0.000000e+00 0.000000e+00 51.705545 0.697444 4.406196 5.746137 0.443832 15.907585 23.482415 1.175728 0.571973 1.486151 10
11 -11.304233 1282.889304 0.000000e+00 0.000000e+00 52.200291 1.467759 4.077400 5.607369 0.832626 16.045590 23.182285 0.587935 0.620943 1.170469 11
12 -12.328411 1264.435549 0.000000e+00 0.000000e+00 51.725315 0.745761 4.586485 5.736715 0.550561 16.407623 23.315817 0.675033 0.814865 1.349343 12
13 -16.327464 1195.406373 0.000000e+00 0.000000e+00 51.892711 0.918335 3.879493 6.001785 0.224476 16.766705 23.174609 1.106705 0.543648 0.719778 13
14 -16.327464 1195.406373 0.000000e+00 0.000000e+00 51.892711 0.918335 3.879493 6.001785 0.224476 16.766705 23.174609 1.106705 0.543648 0.719778 14
15 -16.327464 1195.406373 0.000000e+00 0.000000e+00 51.892711 0.918335 3.879493 6.001785 0.224476 16.766705 23.174609 1.106705 0.543648 0.719778 15
16 -9.015029 1257.554261 0.000000e+00 0.000000e+00 52.006582 1.490563 4.633149 6.150716 0.383969 15.823193 23.087157 1.171789 0.580258 1.072711 16
17 NaN NaN NaN NaN 51.986059 1.119548 4.107258 5.587534 0.869447 16.661904 23.616120 1.072980 0.242730 1.081595 17
18 -2.884479 1333.597198 0.000000e+00 0.000000e+00 52.066983 1.070126 4.261572 5.845278 0.276057 16.038723 23.311110 1.126327 0.115970 1.390647 18
19 -5.607345 1327.248553 2.273737e-13 1.818989e-12 51.973899 0.809677 4.665615 5.864718 0.840250 16.467894 23.319302 0.872607 0.404349 0.946857 19

Example 4 - Plotting a Cpx-Liq Rhodes diagram to assess Fe-Mg equilibrium using fixed Kd values

  • The function calculate_cpx_rhodes_diagram_lines calculates the lines needed for the plot in a number of ways

  • There is disagrement in the literature as to whether Kd Fe-Mg should be assessed using just Fe2+ in the melt, or FeT, so we show both scenarios here.

Step 1 - Calculate Mg# for liq and cpxs

  • A number of functions in thermobar let you do it, we use this one here because it returns Mg#s for both phases

[20]:
cpx_comps_Fe3=pt.calculate_clinopyroxene_liquid_components(liq_comps=Liqs,
                cpx_comps=Cpxs, Fe3Fet_Liq=0.2)
  • Mgnos of Cpx are stored in the column accesed by cpx_comps_Fe3[‘Mgno_Cpx’]

  • Mgnos of Liq are stored in the column accesed by cpx_comps_Fe3[‘Mgno_Liq_noFe3’], for no Fe3, or cpx_comps_Fe3[‘Mgno_Liq_Fe2’] using just Fe2+ (e.g., 20% of Fe is 3+ here)

Step 2 - Calculate equilibrium lines to show on the rhodes diagram

  • You tell the function the min and max glass Mg# you want to show, e.g., the xspan of your plot

  • It returns lines for Kd=0.28+-0.08 after Putirka (2008)

[21]:
# Want to calculate Mg# to show on diagram between say 0.4 and 0.7 for the glass
eq_lines_1=pt.calculate_cpx_rhodes_diagram_lines(Min_Mgno=0.4, Max_Mgno=0.7)
eq_lines_1
[21]:
Mg#_Liq Eq_Cpx_Mg# (Kd=0.28) Eq_Cpx_Mg# (Kd=0.2) Eq_Cpx_Mg# (Kd=0.36)
0 0.400000 0.704225 0.769231 0.649351
1 0.403030 0.706845 0.771462 0.652217
2 0.406061 0.709445 0.773672 0.655065
3 0.409091 0.712025 0.775862 0.657895
4 0.412121 0.714586 0.778032 0.660707
... ... ... ... ...
95 0.687879 0.887273 0.916801 0.859588
96 0.690909 0.888681 0.917874 0.861287
97 0.693939 0.890081 0.918941 0.862979
98 0.696970 0.891473 0.920000 0.864662
99 0.700000 0.892857 0.921053 0.866337

100 rows × 4 columns

Step 3 - Combine these on a plot

  • You might need to adust the x and y limits of this plot for your own data

[22]:
fig, (ax1) = plt.subplots(1, 1, figsize = (6,5))
ax1.plot(eq_lines_1['Mg#_Liq'], eq_lines_1['Eq_Cpx_Mg# (Kd=0.2)'], ':r', label="K$_d$=0.2")
ax1.plot(eq_lines_1['Mg#_Liq'], eq_lines_1['Eq_Cpx_Mg# (Kd=0.36)'], ':r', label="K$_d$=0.36")
ax1.plot(eq_lines_1['Mg#_Liq'], eq_lines_1['Eq_Cpx_Mg# (Kd=0.28)'], '-r', label="K$_d$=0.28")

ax1.plot(cpx_comps_Fe3['Mgno_Liq_noFe3'], cpx_comps_Fe3['Mgno_Cpx'], '*k', mfc='yellow', ms=8, label="No Fe3")
ax1.plot(cpx_comps_Fe3['Mgno_Liq_Fe2'], cpx_comps_Fe3['Mgno_Cpx'], 'dk', mfc='green', ms=8, label="20% Fe3")
ax1.legend()
ax1.set_xlabel('Mg# Glass')
ax1.set_ylabel('Mg# Cpx')
# adjust x and y limits
ax1.set_xlim([0.4, 0.7])
ax1.set_ylim([0.6, 0.95])
[22]:
(0.6, 0.95)
../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liq_Thermobarometry_49_1.png

Example 5 - Rhodes diagram using equation 35 of Putirka to calculate Kd as a function of T.

  • Here, we plot the equilibrium fields on the Rhodes diagram using equation 35 of Putirka, which is T-sensitive

  • Must specify T in Kelvin. Then return column for default 0.28, as well as the results for Putirka eq 35.

[23]:
eq_lines_2=pt.calculate_cpx_rhodes_diagram_lines(Min_Mgno=0.4, Max_Mgno=0.7, T=1300)
eq_lines_2.head()
[23]:
Mg#_Liq Eq_Cpx_Mg# (Kd=0.28) Eq_Cpx_Mg# (Kd=0.2) Eq_Cpx_Mg# (Kd=0.36) Kd_Eq35_P2008 Eq_Cpx_Mg# (Kd from Eq 35 P2008) Eq_Cpx_Mg# (Eq 35 P2008)+0.08 Eq_Cpx_Mg# (Eq 35 P2008)-0.08
0 0.400000 0.704225 0.769231 0.649351 0.239475 0.735720 0.676036 0.806964
1 0.403030 0.706845 0.771462 0.652217 0.239475 0.738165 0.678791 0.808921
2 0.406061 0.709445 0.773672 0.655065 0.239475 0.740589 0.681528 0.810858
3 0.409091 0.712025 0.775862 0.657895 0.239475 0.742993 0.684246 0.812775
4 0.412121 0.714586 0.778032 0.660707 0.239475 0.745377 0.686945 0.814673
[24]:
fig, (ax1) = plt.subplots(1, 1, figsize = (6,5))
ax1.plot(eq_lines_2['Mg#_Liq'], eq_lines_2['Eq_Cpx_Mg# (Kd from Eq 35 P2008)'], '-r', label="K$_d$=Put eq 35")
ax1.plot(eq_lines_2['Mg#_Liq'], eq_lines_2['Eq_Cpx_Mg# (Eq 35 P2008)+0.08'], ':r', label="K$_d$=Put eq 35 + 0.08")
ax1.plot(eq_lines_2['Mg#_Liq'], eq_lines_2['Eq_Cpx_Mg# (Eq 35 P2008)-0.08'], ':r', label="K$_d$=Put eq 35 - 0.08")
ax1.set_xlim([0.4, 0.7])
ax1.plot(cpx_comps_Fe3['Mgno_Liq_noFe3'], cpx_comps_Fe3['Mgno_Cpx'], '*k', mfc='yellow', ms=8, label="No Fe3")
ax1.plot(cpx_comps_Fe3['Mgno_Liq_Fe2'], cpx_comps_Fe3['Mgno_Cpx'], 'dk', mfc='green', ms=8, label="20% Fe3")
ax1.legend()
ax1.set_xlabel('Mg# Glass')
ax1.set_ylabel('Mg# Cpx')

# adjust x and y limits
ax1.set_xlim([0.4, 0.7])
ax1.set_ylim([0.6, 0.95])
[24]:
(0.6, 0.95)
../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liq_Thermobarometry_52_1.png

Example 6 - You can also specify a minimum and maximum Kd value you wish to calculate Rhodes lines for (here 0.2, 0.3)

[25]:
eq_lines_3=pt.calculate_cpx_rhodes_diagram_lines(Min_Mgno=0.4, Max_Mgno=0.7, KdMin=0.2, KdMax=0.3)
eq_lines_3.head()
[25]:
Mg#_Liq Eq_Cpx_Mg# (Kd=0.28) Eq_Cpx_Mg# (Kd=0.2) Eq_Cpx_Mg# (Kd=0.36) Eq_Cpx_Mg# (KdMin=0.2) Eq_Cpx_Mg# (KdMax=0.3)
0 0.400000 0.704225 0.769231 0.649351 0.769231 0.689655
1 0.403030 0.706845 0.771462 0.652217 0.771462 0.692348
2 0.406061 0.709445 0.773672 0.655065 0.773672 0.695021
3 0.409091 0.712025 0.775862 0.657895 0.775862 0.697674
4 0.412121 0.714586 0.778032 0.660707 0.778032 0.700309
[26]:
fig, (ax1) = plt.subplots(1, 1, figsize = (6,5))
ax1.plot(eq_lines_3['Mg#_Liq'], eq_lines_3['Eq_Cpx_Mg# (KdMin=0.2)'], ':r', label="K$_d$=Put eq 35 + 0.08")
ax1.plot(eq_lines_3['Mg#_Liq'], eq_lines_3['Eq_Cpx_Mg# (KdMax=0.3)'], ':r', label="K$_d$=Put eq 35 - 0.08")
ax1.set_xlim([0.4, 0.7])
ax1.plot(cpx_comps_Fe3['Mgno_Liq_noFe3'], cpx_comps_Fe3['Mgno_Cpx'], '*k', mfc='yellow', ms=8, label="No Fe3")
ax1.plot(cpx_comps_Fe3['Mgno_Liq_Fe2'], cpx_comps_Fe3['Mgno_Cpx'], 'dk', mfc='green', ms=8, label="20% Fe3")
ax1.legend()
ax1.set_xlabel('Mg# Glass')
ax1.set_ylabel('Mg# Cpx')

# adjust x and y limits
ax1.set_xlim([0.4, 0.7])
ax1.set_ylim([0.6, 0.95])
[26]:
(0.6, 0.95)
../../_images/Examples_Cpx_Cpx_Liq_Thermobarometry_Cpx_Liq_Thermobarometry_55_1.png

Example 7 - Can get all options by specifying a temp, and a min and max Kd

  • Can then plot them however you want.

[27]:
eq_lines_4=pt.calculate_cpx_rhodes_diagram_lines(Min_Mgno=0.4, Max_Mgno=0.7, T=1300, KdMin=0.2, KdMax=0.3)
eq_lines_4.head()
[27]:
Mg#_Liq Eq_Cpx_Mg# (Kd=0.28) Eq_Cpx_Mg# (Kd=0.2) Eq_Cpx_Mg# (Kd=0.36) Kd_Eq35_P2008 Eq_Cpx_Mg# (Kd from Eq 35 P2008) Eq_Cpx_Mg# (Eq 35 P2008)+0.08 Eq_Cpx_Mg# (Eq 35 P2008)-0.08 Eq_Cpx_Mg# (KdMin=0.2) Eq_Cpx_Mg# (KdMax=0.3)
0 0.400000 0.704225 0.769231 0.649351 0.239475 0.735720 0.676036 0.806964 0.769231 0.689655
1 0.403030 0.706845 0.771462 0.652217 0.239475 0.738165 0.678791 0.808921 0.771462 0.692348
2 0.406061 0.709445 0.773672 0.655065 0.239475 0.740589 0.681528 0.810858 0.773672 0.695021
3 0.409091 0.712025 0.775862 0.657895 0.239475 0.742993 0.684246 0.812775 0.775862 0.697674
4 0.412121 0.714586 0.778032 0.660707 0.239475 0.745377 0.686945 0.814673 0.778032 0.700309
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