This page was generated from docs/Examples/1a. pvsat 1MI_df.ipynb. Interactive online version: Binder badge.

Python Notebook Download

1a. Calculate Pvsat for a single melt composition from a dataframe using default options

Python set-up

You need to install VolFe once on your machine, if you haven’t yet. Then we need to import a few Python packages (including VolFe).

[1]:

# Install VolFe on your machine.
# pip install VolFe # Remove the first # in this line if you have not installed VolFe on your machine before.

# import python packages
import pandas as pd
import VolFe as vf

Define the inputs

At a minimum to run a Pvsat calculations, we need a dataframe of the melt composition, an estimate of oxygen fugacity and Fe in the melt [note 1], and temperature:

  • Sample is just the name for this analysis.

  • Temperature is in °C.

  • Volatile-free melt composition is in wt% oxides [note 2]. All these oxides must be present in the dataframe, so set them to 0. if you have no data for them (although see [note 1] around FeOT).

  • H2O is all hydrogen in the melt reported as H2O in wt% [note 3].

  • CO2ppm is all carbon in the melt reported as CO2 in ppm [note 3].

  • STppm is all sulfur in the melt reported as S in ppm [note 3].

  • Xppm is all “X” in the melt reported as “X” in ppm [note 3]. “X” is an unreactive melt species whose identity can be changed - this is explored in Example 1d.

  • Fe3+FeT is the ratio of Fe3+ to FeT in the melt.

[note 1] In this example we specify oxygen fugacity using Fe3+/FeT and Fe in the melt as FeOT as is quite common for melt inclusion and matrix glass analyses - other options are possible, shown later in the notebook.

[note 2] It does not matter what the non-volatile oxides sum too - they are renormalised to 100 wt% minus the total of the volatiles (i.e., H2O + CO2 + ST + X).

[note 3] The volatile concentrations are absolute for the melt = i.e., the non-volatile melt composition is normalised to 100 wt% minus the volatiles. The volatiles are not added to the oxides and then renormalised.

The following composition is analysis TN273-01D-01-01 from Brounce et al. (2014) with the updated Fe3+/FeT from Cottrell et al. (2021), with a temperature chosen as 1200 °C.

[2]:

# Define the melt composition, fO2 estimate, and T as a dictionary.
my_analysis = {'Sample':'TN273-01D-01-01',
           'T_C': 1200., # Temperature in 'C
           'SiO2': 56.98, # wt%
           'TiO2': 1.66, # wt%
           'Al2O3': 15.52, # wt%
           'FeOT': 9.47, # wt%
           'MnO': 0.24, # wt%
           'MgO': 2.96, # wt%
           'CaO': 6.49, # wt%
           'Na2O': 4.06, # wt%
           'K2O': 0.38, # wt%
           'P2O5': 0.22, # wt%
           'H2O': 1.88, # wt%
           'CO2ppm': 13., # ppm
           'STppm': 362.83, # ppm
           'Fe3FeT': 0.155} # mole or weight fraction (they're the same)

# Turn the dictionary into a pandas dataframe, setting the index to 0.
my_analysis = pd.DataFrame(my_analysis, index=[0])

# Show the DataFrame.
print(my_analysis)

            Sample     T_C   SiO2  TiO2  Al2O3  FeOT   MnO   MgO   CaO  Na2O  \
0  TN273-01D-01-01  1200.0  56.98  1.66  15.52  9.47  0.24  2.96  6.49  4.06

    K2O  P2O5   H2O  CO2ppm   STppm  Fe3FeT
0  0.38  0.22  1.88    13.0  362.83   0.155

For this example we will use the default options in VolFe, which can be found below:

[3]:

# print default options in VolFe
print(vf.default_models)

                            option
type
COH_species     yes_H2_CO_CH4_melt
H2S_m                         True
species X                       Ar
Hspeciation                   none
fO2                       Kress91A
...                            ...
error                          0.1
print status                 False
output csv                    True
setup                        False
high precision               False

[78 rows x 1 columns]

Run the calculation

Define fO2 using Fe3+/FeT

And below runs the calculation - it outputs a dataframe of the full results.

[4]:

# runs the calculation
vf.calc_Pvsat(my_analysis)

[4]:
sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt Date
0 TN273-01D-01-01 1200.0 328.128601 57.03956 1.661735 15.536223 9.479899 0.240251 2.963094 6.496784 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 Ohmoto97 Moussallam19 Holloway92 COS DensityX 2025-03-07 16:37:48.271042

1 rows × 173 columns

Define fO2 using ΔFMQ

Alternatively, we can define the oxygen fugacity using ΔFMQ and total iron from Fe2O3,T …

[ ]:

# Define the melt composition, fO2 estimate, and T as a dictionary.
my_analysis = {'Sample':'TN273-01D-01-01',
           'T_C': 1200.,
           'SiO2': 56.98,
           'TiO2': 1.66,
           'Al2O3': 15.52,
           'Fe2O3T': 9.47, # <-- Fe2O3T instead of FeOT
           'MnO': 0.24,
           'MgO': 2.96,
           'CaO': 6.49,
           'Na2O': 4.06,
           'K2O': 0.38,
           'P2O5': 0.22,
           'H2O': 1.88,
           'CO2ppm': 13.,
           'STppm': 362.83,
           'DFMQ': 0.} # <-- DFMQ instead of Fe3+/FeT

# Turn the dictionary into a pandas dataframe, setting the index to 0.
my_analysis = pd.DataFrame(my_analysis, index=[0])

# runs the calculation
vf.calc_Pvsat(my_analysis)
sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt Date
0 TN273-01D-01-01 1200.0 322.471098 57.597308 1.677984 15.68814 8.61352 0.2426 2.992068 6.560311 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 Ohmoto97 Moussallam19 Holloway92 COS DensityX 2025-02-18 13:16:18.593337

1 rows × 173 columns

Define fO2 using FeO and Fe2O3

… or FeO and Fe2O3 …

[ ]:

# Define the melt composition, fO2 estimate, and T as a dictionary.
my_analysis = {'Sample':'TN273-01D-01-01',
           'T_C': 1200.,
           'SiO2': 56.98,
           'TiO2': 1.66,
           'Al2O3': 15.52,
           'FeO': 8.03, # <-- FeO and Fe2O3 to contraint total Fe and fO2
           'Fe2O3': 2.57, # <-- ^
           'MnO': 0.24,
           'MgO': 2.96,
           'CaO': 6.49,
           'Na2O': 4.06,
           'K2O': 0.38,
           'P2O5': 0.22,
           'H2O': 1.88,
           'CO2ppm': 13.,
           'STppm': 362.83}

# Turn the dictionary into a pandas dataframe, setting the index to 0.
my_analysis = pd.DataFrame(my_analysis, index=[0])

# runs the calculation
vf.calc_Pvsat(my_analysis)
sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt Date
0 TN273-01D-01-01 1200.0 401.474722 52.288592 1.523325 14.242172 16.859825 0.22024 2.716291 5.95565 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 Ohmoto97 Moussallam19 Holloway92 COS DensityX 2025-02-18 13:16:19.183230

1 rows × 173 columns

Define fO2 using ΔNNO

… ΔNNO and FeOT …

[ ]:

# Define the melt composition, fO2 estimate, and T as a dictionary.
my_analysis = {'Sample':'TN273-01D-01-01',
           'T_C': 1200.,
           'SiO2': 56.98,
           'TiO2': 1.66,
           'Al2O3': 15.52,
           'FeOT': 9.47, # <--
           'MnO': 0.24,
           'MgO': 2.96,
           'CaO': 6.49,
           'Na2O': 4.06,
           'K2O': 0.38,
           'P2O5': 0.22,
           'H2O': 1.88,
           'CO2ppm': 13.,
           'STppm': 362.83,
           'DNNO': 1.} # <--

# Turn the dictionary into a pandas dataframe, setting the index to 0.
my_analysis = pd.DataFrame(my_analysis, index=[0])

# runs the calculation
vf.calc_Pvsat(my_analysis)
sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt Date
0 TN273-01D-01-01 1200.0 437.868827 57.03956 1.661735 15.536223 9.479899 0.240251 2.963094 6.496784 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 Ohmoto97 Moussallam19 Holloway92 COS DensityX 2025-02-18 13:16:19.868705

1 rows × 173 columns

Define fO2 using S6+/ST

… or S6+/ST and FeOT …

[ ]:

# Define the melt composition, fO2 estimate, and T as a dictionary.
my_analysis = {'Sample':'TN273-01D-01-01',
           'T_C': 1200.,
           'SiO2': 56.98,
           'TiO2': 1.66,
           'Al2O3': 15.52,
           'FeOT': 9.47, # <--
           'MnO': 0.24,
           'MgO': 2.96,
           'CaO': 6.49,
           'Na2O': 4.06,
           'K2O': 0.38,
           'P2O5': 0.22,
           'H2O': 1.88,
           'CO2ppm': 13.,
           'STppm': 362.83,
           'S6ST': 0.23} # <--

# Turn the dictionary into a pandas dataframe, setting the index to 0.
my_analysis = pd.DataFrame(my_analysis, index=[0])

# runs the calculation
vf.calc_Pvsat(my_analysis)
sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt Date
0 TN273-01D-01-01 1200.0 390.067172 57.03956 1.661735 15.536223 9.479899 0.240251 2.963094 6.496784 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 Ohmoto97 Moussallam19 Holloway92 COS DensityX 2025-02-18 13:16:21.882443

1 rows × 173 columns

Potential error: defining fO2 in too many ways

If we try to specify too many variables to constrain total Fe and fO2, we’ll get a warning from VolFe telling you it is only using one of the options to calculate things.

[ ]:

# Define the melt composition, fO2 estimate, and T as a dictionary.
my_analysis = {'Sample':'TN273-01D-01-01',
           'T_C': 1200.,
           'SiO2': 56.98,
           'TiO2': 1.66,
           'Al2O3': 15.52,
           'FeOT': 9.47, # <--
           'MnO': 0.24,
           'MgO': 2.96,
           'CaO': 6.49,
           'Na2O': 4.06,
           'K2O': 0.38,
           'P2O5': 0.22,
           'H2O': 1.88,
           'CO2ppm': 13.,
           'STppm': 362.83,
           'DNNO': 1., # <--
           'Fe3FeT':0.155} # <--

# Turn the dictionary into a pandas dataframe, setting the index to 0.
my_analysis = pd.DataFrame(my_analysis, index=[0])

# runs the calculation
vf.calc_Pvsat(my_analysis)

C:\Users\ehughes\projects\VolFe\src\VolFe\melt_gas.py:1782: UserWarning: you entered more than one way to infer iron speciation, note that this calcualtion is only considering the entered Fe3+/FeT
  w.warn('you entered more than one way to infer iron speciation, note that this calcualtion is only considering the entered Fe3+/FeT')
sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt Date
0 TN273-01D-01-01 1200.0 327.89713 57.03956 1.661735 15.536223 9.479899 0.240251 2.963094 6.496784 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 Ohmoto97 Moussallam19 Holloway92 COS DensityX 2025-02-18 13:16:22.574874

1 rows × 173 columns