Batch calculations

Functions that join calculations together to enable them to be run for multiple conditions. This might be to create a degassing calculations (i.e., run over multiple P) or P^v _sat for a whole spreadsheet of melt compositions. More details can be found in the Worked Examples section.

VolFe.batch_calculations.calc_Pvsat(setup, 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], first_row=0, last_row=None, p_tol=0.1, nr_step=1.0, nr_tol=1e-09)

Calculates the pressure of vapor saturation for multiple melt compositions given volatile-free melt composition, volatile content, temperature, and an fO2 estimate.

Parameters:

• setup (pandas.DataFrame) – Melt compositions to be used, requires following headers: Sample; T_C; DNNO or DFMQ or logfO2 or (Fe2O3 and FeO) or Fe3FeT or S6ST; SiO2, TiO2, Al2O3, (Fe2O3T or FeOT unless Fe2O3 and FeO given), MnO, MgO, CaO, Na2O, K2O, P2O5; H2O and/or CO2ppm and/or STppm and/or Xppm. Note: concentrations (unless otherwise stated) are in wt%

• models (_type_pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

• first_row (int, optional) – Integer of the first row in the setup file to run (note the first row under the headers is row 0). Defaults to 0.

• last_row (float, optional) – Integer of the last row in the setup file to run (note the first row under the headers is row 0). Defaults to None.

• p_tol (float, optional) – Required tolerance for convergence of Pvsat in bars. Defaults to 1.0e-1.

• nr_step (float, optional) – Step size for Newton-Raphson solver for melt speciation (this can be made smaller if there are problems with convergence.) Defaults to 1.0.

• nr_tol (float, optional) – Tolerance for the Newton-Raphson solver for melt speciation in weight fraction (this can be made larger if there are problems with convergence.) Defaults to 1.0e-9.

Returns:

Results of pressure of vapor saturation calculation

Return type:

pandas.DataFrame

Outputs

results_saturation_pressures: csv file (if output csv = yes in models)

VolFe.batch_calculations.calc_comp_error(setup, run, iterations=100, 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])

Generates a specified number of random melt compositions given errors on the composition.

Parameters:

• setup (pandas.DataFrame) – Input data of melt compositions and 1sd uncertainties. Assumed 1sd uncertainties are absolute unless sd_type is specified (A = absolute, R = relative).

• run (int) – Row number of input data

• iterations (int, optional) – Number of compositions within error to produce. Defaults to 100

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

Returns:

Results of Monte Carlo compositions

Return type:

pandas.DataFrame

Outputs

‘random_compositions.csv’ if ‘output csv’ is True

VolFe.batch_calculations.calc_comp_error_function(setup, function='calc_Pvsat', first_row=0, last_row=None, iterations=100, 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])

Calculates Pvsat or fO2-from-melt-S and uncertainity based on uncertainty on inputted melt composition and T.

Parameters:

• setup (pandas.DataFrame) – Input data of melt compositions and 1sd uncertainties. Assumed 1sd uncertainties are absolute unless sd_type is specified (A = absolute, R = relative).

• function (str, optional) – Which calculation - calc_pvsat or calc_melt_S_oxybarometer - to run. Defaults to “calc_pvsat”.

• first_row (int, optional) – First row on input file to run. Defaults to 0.

• last_row (int, optional) – Last row of input file to run. Defaults to None.

• iterations (int, optional) – Number of random melt compositions to produce using Monte Carlo approach to run calculations on. Defaults to 100.

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

VolFe.batch_calculations.calc_fugacity_coefficients(setup, first_row=0, last_row=None, 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])

Calculates values of fugacity coefficients for given conditions.

Parameters:

• setup (pandas.DataFrame) – Input data

• first_row (int, optional) – First row of input data to run calculation for. Defaults to 0.

• last_row (int, optional) – Last row of input data to run calculation for. Defaults to None.

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

Returns:

Values of fugacity coefficients

Return type:

pandas.DataFrame

Outputs:

‘results_fugacity_coefficients.csv’ is ‘output_csv’ is True

VolFe.batch_calculations.calc_gassing(setup, 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=0, nr_step=1.0, nr_tol=1e-09, dp_step='auto', psat_tol=0.1, dwtg=1e-06, i_nr_step=0.1, i_nr_tol=1e-09, nr_step_eq=1.0, suppress_warnings=True)

Calculates the pressure of vapor saturation for multiple melt compositions given volatile-free melt composition, volatile content, temperature, and an fO2 estimate.

Parameters:

• setup (pandas.DataFrame) – Melt composition to be used, requires following headers (notes in [] are not part of the headers): Sample; T_C; DNNO or DFMQ or logfO2 or (Fe2O3 and FeO) or Fe3FeT or S6ST [at initial pressure]; SiO2, TiO2, Al2O3, (Fe2O3T or FeOT unless Fe2O3 and FeO given), MnO, MgO, CaO, Na2O, K2O, P2O5 [concentrations are in wt%]; (H2O and/or CO2ppm and/or STppm and/or Xppm) [concentration of H2O in wt%]; final_P [IF regassing, pressure calculation stops at in bars]; wt_g [IF starting from given pressure and gas is present, can specifiy the gas present in wt%]; initial_CO2wtpc [IF starting from given pressure and gas is present, can specifiy initial composition using initial CO2 dissolved in the melt in wt%].

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

• run (int, optional) – Integer of the row in the setup file to run (note the first row under the headers is row 0). Defaults to 0.

• nr_step (float, optional) – Step size for Newton-Raphson solver for melt speciation (typically 1 is fine, but this can be made smaller if there are problems with convergence). Defaults to 1.0.

• nr_tol (float, optional) – Tolerance for the Newton-Raphson solver for melt speciation in weight fraction (can be increased if there are problems with convergence). Defaults to 1.0e-9.

• dp_step (float, optional) – Pressure step size for gassing calculation in bars. Defaults to “auto”.

• psat_tol (float, optional) – Required tolerance for convergence of Pvsat in bars. Defaults to 0.1.

• dwtg (float, optional) – Amount of gas to add at each step if regassing in an open-system in wt fraction total system. Defaults to 1.0e-6.

• i_nr_step (float, optional) – Step-size for newton-raphson convergence for isotopes (can be increased if there are problems with convergence). Defaults to 1.0e-1.

• i_nr_tol (float, optional) – Tolerance for newton-raphson convergence for isotopes (can be increased if there are problems with convergence). Defaults to 1.0-9.

• nr_step_eq (float, optional) – Step-size for new-raphson solver for isotopes. Defaults to 1.0.

• suppress_warnings (bool, optional) – Suppress runtime warnings. Defaults to True.

Returns:

Results of degassing calculation.

Return type:

pandas.DataFrame

Outputs

If output csv = yes in models results_gassing_chemistry: csv file

VolFe.batch_calculations.calc_isobar(setup, run=0, 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], initial_P=1000.0, final_P=10000.0, step_P=1000.0)

Calculates H2O-CO2-only isobars for given T, melt composition, initial/final P, and P step-size.

Parameters:

• setup (pandas.DataFrame) – Melt composition to be used, requires following headers (notes in [] are not part of the headers): Sample; T_C; DNNO or DFMQ or logfO2 or (Fe2O3 and FeO) or Fe3FeT or S6ST; SiO2, TiO2, Al2O3, (Fe2O3T or FeOT unless Fe2O3 and FeO given), MnO, MgO, CaO, Na2O, K2O, P2O5[concentrations are in wt%].

• run (int, optional) – Integer of the row in the setup file to run (note the first row under the headers is row 0). Defaults to 0.

• models (_type_, optional) – Model options. Defaults to mdv.default_models.

• initial_P (float, optional) – Starting pressure in bar for isobar calculation. Defaults to 1000.0.

• final_P (float, optional) – Final pressure in bar for isobar calculation. Defaults to 10000.0.

• step_P (float, optional) – Pressure step in bar for calculating isobars between starting and final pressures. Defaults to 1000.0.

Returns:

Results from isobar calculation

Return type:

pandas.DataFrame

Outputs

If output csv = yes in models, results_isobars: csv file

VolFe.batch_calculations.calc_isopleth(setup, run=0, 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], final_P=10000.0, step_XH2O=0.2)

Calculates H2O-CO2-only isopleths for given T, melt composition, up to a final P, and XH2O step-size.

Parameters:

• setup (pandas.DataFrame) – Melt composition to be used, requires following headers (notes in [] are not part of the headers): Sample; T_C; DNNO or DFMQ or logfO2 or (Fe2O3 and FeO) or Fe3FeT or S6ST; SiO2, TiO2, Al2O3, (Fe2O3T or FeOT unless Fe2O3 and FeO given), MnO, MgO, CaO, Na2O, K2O, P2O5[concentrations are in wt%].

• run (int, optional) – Integer of the row in the setup file to run (note the first row under the headers is row 0). Defaults to 0.

• models (_type_, optional) – Model options. Defaults to mdv.default_models.

• final_P (float, optional) – Final pressure in bar for isobar calculation. Defaults to 10000.0.

• step_XH2O (float, optional) – XH2O step in mole fraction for calculating isopleths. Defaults to 0.20.

Returns:

Results from isopleth calculation

Return type:

pandas.DataFrame

Outputs

If output csv = yes in models, results_isopleth: csv file

VolFe.batch_calculations.calc_isotopes_gassing(setup, R_i_d, first_row=0, last_row=None, nr_step=1.0, nr_tol=1e-09, 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])

Calculates the isotopic ratio and delta value of all species and melt and vapor during degassing.

Parameters:

• setup (pandas.DataFrame) – Output from a degassing calculation.

• R_i_d (dict) – Bulk isotope ratios as delta values.

• first_row (int, optional) – First row in setup to run. Defaults to 0.

• last_row (_type_, optional) – Last run in setup to run. Defaults to None.

• nr_step (float, optional) – Step-size for Newton-Raphson solver. Defaults to 1.0.

• nr_tol (float, optional) – Tolerance for Newton-Raphson solver. Defaults to 1.0e-9.

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

Returns:

Results of isotopic fractionation during degassing.

Return type:

pandas.DataFrame

VolFe.batch_calculations.calc_melt_S_oxybarometer(setup, first_row=0, last_row=None, 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], p_tol=0.1, nr_step=1.0, nr_tol=1e-09)

Calculates the range in oxygen fugacity based on the melt sulfur content for multiple melt compositions given volatile-free melt composition, volatile content, temperature, and either pressure or assumes Pvsat.

Parameters:

• setup (pandas.DataFrame) – Melt compositions to be used, requires following headers: Sample, T_C, SiO2, TiO2, Al2O3, (Fe2O3T or FeOT unless Fe2O3 and FeO given), MnO, MgO, CaO, Na2O, K2O, P2O5, H2O and/or CO2ppm and/or STppm and/or Xppm. Note: concentrations (unless otherwise stated) are in wt%. Optional: P_bar is pressure is given (otherwise calculation is at Pvsat). Fe3FeT if P_bar is specified.

• models (pandas.DataFrame, optional) – Model options

• first_row (int, optional) – First row in the setup file to run (note the first row under the headers is row 0). Default = 0

• last_row – (int, optional): Last row in the setup file to run (note the first row under the headers is row 0). Default = None

• p_tol (float, optional) – Required tolerance for convergence of Pvsat in bars. Default = 1.e-1

• nr_step (float, optional) – Step size for Newton-Raphson solver for melt speciation (this can be made smaller if there are problems with convergence.). Default = 1

• nr_tol (float, optional) – Tolerance for the Newton-Raphson solver for melt speciation in weight fraction (this can be made larger if there are problems with convergence). Default = 1.e-9

Returns:

Results of fO2 range from melt sulfur content calculation

Return type:

pandas.DataFrame

Outputs

fO2_range_from_S: csv file (if output csv = True in models)

VolFe.batch_calculations.calc_pure_solubility(setup, run=0, 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], initial_P=5000.0)

Calculates the solubility of pure H2O and pure CO2.

Parameters:

• setup (pandas.DataFrame) – Melt compositions to be used, requires following headers: Sample; T_C; SiO2, TiO2, Al2O3, (Fe2O3T or FeOT unless Fe2O3 and FeO given), MnO, MgO, CaO, Na2O, K2O, P2O5, H2O; Note: concentrations (unless otherwise stated) are in wt%.

• run (int, optional) – Row number of input data

• models (pandas.DataFrame) – Model options

• initial_P (float, optional) – Highest pressure in bar for solubility calculation. Default = 5000.0

Returns:

Results of pure solubility calculation.

Return type:

pandas.DataFrame

Outputs

results_pure_solubility.csv: csv file (if output csv = yes in models)

VolFe.batch_calculations.calc_sol_consts(setup, first_row=0, last_row=None, 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])

Calculate solubility functions for given melt composition and conditions.

Parameters:

• setup (pandas.DataFrame) – Input data

• first_row (int, optional) – First row of input data to run calculation for. Defaults to 0.

• last_row (int, optional) – Last row of input data to run calculation for. Defaults to None.

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

Returns:

Values of solubility functions

Return type:

pandas.DataFrame

Outputs:

‘capacities.csv’ is ‘output_csv’ is True

VolFe.batch_calculations.calc_sulfur_vcomp(setup, 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])

Calculates SCAS, SCSS, Csulfide and Csulfate for multiple melt compositions

Parameters:

• setup (pandas.DataFrame) – Input data

• models (pandas.DataFrame, optional) – Model options. Defaults to mdv.default_models.

Returns:

SCAS, SCSS, Csulfide and Csulfate for multiple melt compositions

Return type:

pandas.DataFrame

VolFe.batch_calculations.options_from_setup(run, models, setup)

# WORK IN PROGRESS # Allows model options to be read from the setup file rather than models file.

Parameters:

• run (int) – Integer of the row in the setup file to read from (note the first row under the headers is row 0).

• models (pandas.DataFrame) – Model options

• setup (pandas.DataFrame) – Melt compositions to be used, require header using the same labels as row labels from models file if you want to use that option.

Returns:

Models options with the options updated from the setup file.

Return type:

pandas.DataFrame

VolFe.batch_calculations.results_isotopes_gas_melt(comp, run)

Outputs headers and values for melt and vapor composition for isotope fractionation calculations.

Parameters:

• comp (pandas.DataFrame) – Composition of the melt and vapor by species, including weight fraction of vapor.

• run (float) – Index of interest.

Returns:

Headers. Values.

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_isotopes_model_options(models)

Outputs headers and values for model options related to isotopic fractionation.

Parameters:

models (pandas.DataFrame) – Model options.

Returns:

Headers. Values.

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_f_p_xg_y_M_C_K_d(PT, melt_wf, models)

Creates DataFrames with headers and values for fO2, fugacities, partial pressures, vapor mole fractions, fugacity coefficients, molecular masses, solubility constants, equilibrium constants, and melt density

Parameters:

• PT (dict) – Pressure (bars) as “P” and temperature (‘C) as “T”

• melt_wf (dict:) – Melt composition

• models (pandas.DataFrame) – Model options used in calculations

Returns:

Headers and values for for fO2, fugacities, partial pressures, vapor mole fractions, fugacity coefficients, molecular masses, solubility constants, equilibrium constants, and melt density

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_isotopes(PT, R_all_species_S, R_m_g_S, R_all_species_C, R_m_g_C, R_all_species_H, R_m_g_H)

Outputs headers and values for isotope ratios for all species and melt and vapor for all elements.

Parameters:

• PT (dict) – Pressure (bars) as “P” and temperature (‘C) as “T”.

• R_all_species_S (dict) – S isotope ratios of all S-bearing species.

• R_m_g_S (dict) – S isotope ratio of melt and vapor.

• R_all_species_C (dict) – C isotope ratios of all C-bearing species.

• R_m_g_C (dict) – C isotope ratio of melt and vapor.

• R_all_species_H (dict) – H isotope ratios of all H-bearing species.

• R_m_g_H (dict) – H isotope ratio of melt and vapor.

Returns:

Headers. Values.

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_isotopes_d(R_all_species_S, R_m_g_S, R_all_species_C, R_m_g_C, R_all_species_H, R_m_g_H)

Outputs headers and values for isotope ratios for all species and melt and vapor for all elements as delta-values.

Parameters:

• R_all_species_S (dict) – S isotope ratios of all S-bearing species.

• R_m_g_S (dict) – S isotope ratio of melt and vapor.

• R_all_species_C (dict) – C isotope ratios of all C-bearing species.

• R_m_g_C (dict) – C isotope ratio of melt and vapor.

• R_all_species_H (dict) – H isotope ratios of all H-bearing species.

• R_m_g_H (dict) – H isotope ratio of melt and vapor.

Returns:

Headers. Values.

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_melt_comp_etc(PT, melt_comp, conc, frac, melt_wf)

Creates DataFrames with headers and values for pressure, temperature, and melt composition (concentration and ratios)

Parameters:

• PT (dict) – Pressure (bars) as “P” and temperature (‘C) as “T”

• melt_comp (dict) – Melt composition in weight fraction (major and minor elements)

• conc (dict) – Volatile concentrations in the melt in weight fraction

• frac (dict) – Ratios of each species over volatile element

• melt_wf (dict) – Melt composition (specifically for sulfide composition)

Returns:

Headers and values for pressure, temperature, and melt composition (concentration and ratios)

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_melt_vol()

Creates DataFrame with headers for volatile concentrations in melt

Returns:

Headers for volatile concentrations in melt

Return type:

pandas.DataFrame

VolFe.batch_calculations.results_table_model_options(models)

Creates DataFrames with headers and values for options used in calculations and datetime of calculations

Parameters:

models (pandas.DataFrame) – Model options used in calculations

Returns:

Headers and values for model options used in calculations and datetime of calculation

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_open_all_gas()

Creates DataFrame of headers for cumulative gas composition

Returns:

Headers for cumulative gas composition

Return type:

pandas.DataFrame

VolFe.batch_calculations.results_table_sample_name(setup, run)

Creates DataFrames with headers and values for Sample name

Parameters:

• setup (pandas.DataFrame) – DataFrame with melt compositions to be used.

• run (int) – Row number to be read from DataFrame

Returns:

Header and value for sample name

Return type:

tuple(pandas.DataFrame,pandas.DataFrame)

VolFe.batch_calculations.results_table_sat(sulf_sat_result, PT, melt_wf, models)

Creates DataFrame of headers and values for sulfur and graphite saturation results.

Parameters:

• sulf_sat_result (dict) – Sulfur saturation results

• PT (dict) – Pressure in bars as “P” and temperature in ‘C as “T”

• melt_wf (dict) – Melt composition (SiO2, TiO2, etc. including volatiles)

• models (pandas.DataFrame) – Model options

Returns:

Headers and values for sulfur and graphite saturation results

Return type:

pandas.DataFrame