Core functions (e.g., cation fractions)

Thermobar.core.amp_components_ferric_ferrous(sites_df, norm_cations)[source]

amp_components_ferric_ferrous calculates the Fe3+ and Fe2+ apfu values of amphibole and adjusts the generic stoichiometry such that charge balance is maintained. This is based off the “f parameters” listed in Holland and Blundy (1994). Following Mutch et al. (2016) spreadsheet.

Parameters:
  • sites_df (pandas DataFrame) – output from the get_amp_sites function. you do not need to modify this at all

  • norm_cations (pandas DataFrame) – This is the dataframe output from calculate_23oxygens_amphibole. You should not have to modify this dataframe at all.

Returns:

norm_cations_hb

amphibole apfu values for each cation, however two things are different:
  1. FeOt is replaced by individual Fe2O3 and FeO columns

  2. all apfu values from the generic mineral recalculation have been

    adjusted by the “f” parameter from Holland and Blundy (1994) to maintain charge balance and stoichiometry

Return type:

pandas DataFrame

Thermobar.core.amp_components_ferric_ferrous_mutch(sites_df, norm_cations)[source]

amp_components_ferric_ferrous calculates the Fe3+ and Fe2+ apfu values of amphibole and adjusts the generic stoichiometry such that charge balance is maintained. This is based off the “f parameters” listed in Holland and Blundy (1994), using the averaging in the spreadsheet supplied by Euan Mutch

Parameters:
  • sites_df (pandas DataFrame) – output from the get_amp_sites function. you do not need to modify this at all

  • norm_cations (pandas DataFrame) – This is the dataframe output from calculate_23oxygens_amphibole. You should not have to modify this dataframe at all.

Returns:

norm_cations_hb

amphibole apfu values for each cation, however two things are different:
  1. FeOt is replaced by individual Fe2O3 and FeO columns

  2. all apfu values from the generic mineral recalculation have been

    adjusted by the “f” parameter from Holland and Blundy (1994) to maintain charge balance and stoichiometry

Return type:

pandas DataFrame

Thermobar.core.calculate_13cations_amphibole_ridolfi(amp_comps)[source]
Import amphibole compositions using amp_comps=My_amphiboles, returns

components calculated on basis of 13 cations following Ridolfi supporting information

Parameters
amp_comps: pandas.DataFrame

amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

cation fractions for amphiboles with column headings of the form SiO2_Amp_13_cat…

Thermobar.core.calculate_23oxygens_amphibole(amp_comps)[source]

Import amphibole compositions using amp_comps=My_Amps, returns cations on the basis of 23 oxygens.

Parameters
amp_comps: pandas.DataFrame

amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

cations on the basis of 23 oxygens, with column headings of the form Si_Amp_cat_23ox. For simplicity, and consistency of column labelling to aid calculations, oxide names are preserved, so outputs are Na_Amp_cat_23ox rather than Na_Amp_cat_23ox.

Thermobar.core.calculate_6oxygens_clinopyroxene(cpx_comps)[source]

Import clinopyroxene compositions using cpx_comps=My_Cpxs, returns cations on the basis of 6 oxygens.

Parameters
cpx_comps: pandas.DataFrame

clinopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

pandas DataFrame

cations on the basis of 6 oxygens, with column headings of the form …Cpx_cat_6ox.

Thermobar.core.calculate_6oxygens_orthopyroxene(opx_comps)[source]

Import orthopyroxene compositions using opx_comps=My_Opxs, returns cations on the basis of 6 oxygens.

Parameters
opx_comps: pandas.DataFrame

Orthopyroxene compositions with column headings SiO2_Opx, MgO_Opx etc.

pandas DataFrame

cations on the basis of 6 oxygens, with column headings of the form …Opx_cat_6ox.

Thermobar.core.calculate_anhydrous_cat_fractions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous cation fractions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous cation fractions for the liquid with column headings of the form _Liq_cat_frac, as well as the initial dataframe of liquid compositions.

Thermobar.core.calculate_anhydrous_cat_proportions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous cation proportions (e.g., mole proportions * no of cations)

Parameters
liq_comps: pandas.DataFrame

Panda DataFrame of liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous cation proportions for the liquid with column headings of the form S_Liq_cat_prop

Thermobar.core.calculate_anhydrous_mol_fractions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole fractions

Parameters
liq_comps: pandas.DataFrame

Panda DataFrame of liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole fractions for the liquid with column headings of the form SiO2_Liq_mol_frac

Thermobar.core.calculate_anhydrous_mol_fractions_liquid_Ni(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole fractions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole fractions for the liquid with column headings of the form SiO2_Liq_mol_frac

Thermobar.core.calculate_anhydrous_mol_proportions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole proportions

Parameters
liq_comps: pandas.DataFrame

Panda DataFrame of liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole proportions for the liquid with column headings of the form SiO2_Liq_mol_prop

Thermobar.core.calculate_anhydrous_mol_proportions_liquid_Ni(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole proportions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole proportions for the liquid with column headings of the form SiO2_Liq_mol_prop

Thermobar.core.calculate_cat_fractions_kspar(*, kspar_comps=None)[source]

Import AlkaliFspar compositions using kspar_comps=My_kspars, returns cation fractions

Parameters
kspar_comps: pandas.DataFrame

AlkaliFspar compositions with column headings SiO2_Kspar, MgO_Kspar etc.

pandas DataFrame

cation fractions for AlkaliFspar with column headings of the form SiO2_Kspar_cat_frac. For simplicity, and consistency of column heading types, oxide names are preserved, so outputs are Na2O_Kspar_cat_frac rather than Na_Kspar_cat_frac.

Thermobar.core.calculate_cat_fractions_olivine(ol_comps)[source]

Import Olivine compositions using ol_comps=My_Olivines, returns cation proportions

Parameters
ol_comps: pandas.DataFrame

olivine compositions with column headings SiO2_Ol, MgO_Ol etc.

pandas DataFrame

cation fractions for olivine with column headings of the form SiO2_Ol_cat_frac. For simplicity, and consistency of column heading types, oxide names are preserved, so outputs are Na2O_Ol_cat_frac rather than Na_Ol_cat_frac.

Thermobar.core.calculate_cat_fractions_plagioclase(plag_comps)[source]

Import plagioclase compositions using plag_comps=My_plagioclases, returns cation fractions

Parameters
plag_comps: pandas.DataFrame

plagioclase compositions with column headings SiO2_Plag, MgO_Plag etc.

pandas DataFrame

cation fractions for plagioclase with column headings of the form …Plag_cat_frac.

Thermobar.core.calculate_cat_proportions_amphibole(*, amp_comps=None, oxide_headers=False)[source]

Import amphibole compositions using amp_comps=My_amphiboles, returns cation proportions

Parameters
amp_comps: pandas.DataFrame

amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

cation proportions for amphibole with column headings of the form …Amp_cat_prop

Thermobar.core.calculate_cat_proportions_amphibole_ridolfi(*, amp_comps=None)[source]

Import amphibole compositions using amp_comps=My_amphiboles, returns cation proportions

Parameters
amp_comps: pandas.DataFrame

amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

cation proportions for amphibole with column headings of the form SiO2_Amp_cat_prop For simplicity, and consistency of column heading types, oxide names are preserved, so outputs are Na2O_Amp_cat_prop rather than Na_Amp_cat_prop.

Thermobar.core.calculate_cat_proportions_kspar(*, kspar_comps=None, oxide_headers=False)[source]

Import kspar compositions using kspar_comps=My_kspars, returns cation proportions

Parameters
kspar_comps: pandas.DataFrame

kspar compositions with column headings SiO2_Kspar, MgO_Kspar etc.

oxide_headers: bool

default=False, returns as Ti_Kspar_cat_prop. =True returns Ti_Kspar_cat_prop. This is used for rapid matrix division for pre-processing of data for cation fractions etc

pandas DataFrame

cation proportions for kspar with column headings of the form SiO2_Kspar_cat_prop For simplicity, and consistency of column heading types, oxide names are preserved, so outputs are Na2O_Kspar_cat_prop rather than Na_Kspar_cat_prop.

Thermobar.core.calculate_cat_proportions_olivine(ol_comps)[source]

Import Olivine compositions using ol_comps=My_Olivines, returns cation proportions

Parameters
ol_comps: pandas.DataFrame

olivine compositions with column headings SiO2_Ol, MgO_Ol etc.

pandas DataFrame

cation proportions for olivine with column headings of the form SiO2_Ol_cat_prop For simplicity, and consistency of column heading types, oxide names are preserved, so outputs are Na2O_Ol_cat_prop rather than Na_Ol_cat_prop.

Thermobar.core.calculate_cat_proportions_plagioclase(*, plag_comps=None, oxide_headers=False)[source]

Import plagioclase compositions using plag_comps=My_plagioclases, returns cation proportions

Parameters
plag_comps: pandas.DataFrame

plagioclase compositions with column headings SiO2_Plag, MgO_Plag etc.

oxide_headers: bool

default=False, returns as Ti_Plag_cat_prop. =True returns Ti_Plag_cat_prop. This is used for rapid matrix division for pre-processing of data for cation fractions etc

pandas DataFrame

cation proportions for plagioclase with column headings of the form …Plag_cat_prop

Thermobar.core.calculate_clinopyroxene_components(cpx_comps)[source]

Import clinopyroxene compositions using cpx_comps=My_Cpxs, returns clinopyroxene components along with entered Cpx compositions

Parameters
cpx_comps: pandas.DataFrame

clinopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

pandas DataFrame

Clinopyroxene components (column headings: Cation_Sum, CrCaTs, a_cpx_En, Mgno_Cpx, Jd, CaTs, CaTi, DiHd_1996, DiHd_2003, En_Fs), cations on bases of 6 oxygens (column headings of form Cr2O3_Cpx_cat_6ox), as well as inputted Cpx compositions (column headings of form MgO_Cpx)

Thermobar.core.calculate_clinopyroxene_liquid_components(*, cpx_comps=None, liq_comps=None, meltmatch=None, Fe3Fet_Liq=None)[source]
Import clinopyroxene compositions using cpx_comps=My_Cpxs and liquid compositions using liq_comps=My_Liquids,

returns clinopyroxene and liquid components.

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, MgO_Liq etc.

AND
cpx_comps: pandas.DataFrame

clinopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

OR meltmatch: pandas.DataFrame

Panda DataFrame of merged clinopyroxene and liquid compositions used for melt matching

Fe3Fet_Liq: opt, int, float, pandas.Series

overwrites Fe3FeT ratio inliquid input

pandas DataFrame

Merged dataframe of inputted liquids, liquid mole fractions, liquid cation fractions, inputted cpx compositions, cpx cations on 6 oxygen basis, cpx components and cpx-liquid components.

Thermobar.core.calculate_cpx_liq_eq_tests(*, meltmatch=None, liq_comps=None, cpx_comps=None, Fe3Fet_Liq=None, P=None, T=None, sigma=1, Kd_Err=0.03)[source]

calculates Kd Fe-Mg, EnFs, DiHd, CaTs for cpx-liquid pairs

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.

meltmatch: pandas.DataFrame

Combined Cpx-Liquid compositions. Used for “melt match” functionality.

P_kbar: int, float, pandas.Series

Pressure in kbar at which to evaluate equilibrium tests

T: int, float, pandas.Series

Temprature in K at which to evaluate equilibrium tests

Fe3Fet_Liq: int, float, pandas.Series (optional)

Fe3FeT ratio used to assess Kd Fe-Mg equilibrium between cpx and melt. If None, uses Fe3Fet_Liq from liq_comps. If specified, overwrites the Fe3Fet_Liq column in the liquid input.

sigma: int or float

determines sigma level at which to consider the DiHd, EnFs and CaTs tests laid out by Neave et al. (2019)

Kd_Err: int or float

deviation from the Kd value of Putirka (2008) for the equilibrium tests of Neave et al. (2019)

Returns
DataFrame

Equilibrium tests, cation fractions, and inputted liquid and cpx compositions.

Thermobar.core.calculate_cpx_opx_eq_tests(cpx_comps, opx_comps)[source]

Import Cpx and Opx compositions, assesses degree of Fe-Mg disequilibrium. :param cpx_comps: Cpx compositions with column headings SiO2_Cpx, MgO_Cpx etc. :type cpx_comps: pandas.DataFrame :param opx_comps: Cpx compositions with column headings SiO2_Opx, MgO_Opx etc. :type opx_comps: pandas.DataFrame

Returns:

Return all opx and cpx components, as well as values for Kd-Fe Opx-Cpx.

Return type:

pandas DataFrame

Thermobar.core.calculate_hydrous_cat_fractions_liquid(liq_comps, oxide_headers=False)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous cation fractions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

oxide_headers=False
oxide_headers: bool

default=False, returns as Ti_Liq_cat_prop. =True returns Ti_Liq_cat_prop. This is used for rapid matrix division for pre-processing of data for cation fractions etc

pandas DataFrame

anhydrous cation fractions for the liquid with column headings of the form ..Liq_cat_frac

Thermobar.core.calculate_hydrous_cat_proportions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous cation proportions (e.g., mole proportions * no of cations)

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

hydrous cation proportions for the liquid with column headings of the form …Liq_cat_prop_hyd

Thermobar.core.calculate_hydrous_mol_fractions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole fractions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole fractions for the liquid with column headings of the form SiO2_Liq_mol_frac

Thermobar.core.calculate_hydrous_mol_fractions_liquid_redox(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole fractions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole fractions for the liquid with column headings of the form SiO2_Liq_mol_frac

Thermobar.core.calculate_hydrous_mol_proportions_liquid(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole proportions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole proportions for the liquid with column headings of the ..Liq_mol_prop

Thermobar.core.calculate_hydrous_mol_proportions_liquid_redox(liq_comps)[source]

Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole proportions

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

pandas DataFrame

anhydrous mole proportions for the liquid with column headings of the ..Liq_mol_prop

Thermobar.core.calculate_liq_mgno(liq_comps, Fe3Fet_Liq=None)[source]

calculates Liquid Mg#

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, TiO2_Liq etc.

Fe3FeT: opt, float, pandas.Series, int

Overwrites Fe3Fet_Liq column if specified

pandas Series

Mg# of liquid

Thermobar.core.calculate_mol_fractions_amphibole(amp_comps)[source]

Import amphibole compositions using amp_comps=My_amphiboles, returns mole fractions

Parameters
amp_comps: pandas.DataFrame

amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

mole fractions for amphiboles with column headings of the form SiO2_Amp_mol_frac

Thermobar.core.calculate_mol_fractions_kspar(kspar_comps)[source]

Import AlkaliFspar compositions using kspar_comps=My_kspars, returns mole fractions

Parameters
kspar_comps: pandas.DataFrame

AlkaliFspar compositions with column headings SiO2_Kspar, MgO_Kspar etc.

pandas DataFrame

mole fractions for AlkaliFspars with column headings of the form …Kspar_mol_frac

Thermobar.core.calculate_mol_fractions_olivine(ol_comps)[source]

Import Olivine compositions using ol_comps=My_Olivines, returns mole fractions

Parameters
ol_comps: pandas.DataFrame

olivine compositions with column headings SiO2_Ol, MgO_Ol etc.

pandas DataFrame

mole fractions for olivines with column headings of the form SiO2_Ol_mol_frac

Thermobar.core.calculate_mol_fractions_olivine_ni(ol_comps)[source]

Import Olivine compositions using ol_comps=My_Olivines, returns mole fractions

Parameters
ol_comps: pandas.DataFrame

olivine compositions with column headings SiO2_Ol, MgO_Ol etc.

pandas DataFrame

mole fractions for olivines with column headings of the form SiO2_Ol_mol_frac

Thermobar.core.calculate_mol_fractions_plagioclase(*, plag_comps=None)[source]

Import plagioclase compositions using plag_comps=My_plagioclases, returns mole fractions

Parameters
plag_comps: pandas.DataFrame

plagioclase compositions with column headings SiO2_Plag, MgO_Plag etc.

pandas DataFrame

mole fractions for plagioclases with column headings of the form SiO2_Plag_mol_frac

Thermobar.core.calculate_mol_proportions_amphibole(amp_comps)[source]

Import amphibole compositions using amp_comps=My_amphiboles, returns mole proportions

Parameters
amp_comps: pandas.DataFrame

Panda DataFrame of amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

mole proportions for amphiboles with column headings of the form SiO2_Amp_mol_prop

Thermobar.core.calculate_mol_proportions_amphibole_ridolfi(amp_comps)[source]

Import amphibole compositions using amp_comps=My_amphiboles, returns mole proportions

Parameters
amp_comps: pandas.DataFrame

Panda DataFrame of amphibole compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

mole proportions for amphiboles with column headings of the form SiO2_Amp_mol_prop

Thermobar.core.calculate_mol_proportions_clinopyroxene(cpx_comps)[source]

Import clinopyroxene compositions using cpx_comps=My_Cpxs, returns mole proportions

Parameters
cpx_comps: pandas.DataFrame

clinopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

pandas DataFrame

mole proportions for clinopyroxene with column headings of the form SiO2_Cpx_mol_prop

Thermobar.core.calculate_mol_proportions_kspar(*, kspar_comps=None)[source]

Import AlkaliFspar compositions using kspar_comps=My_kspars, returns mole proportions

Parameters
kspar_comps: pandas.DataFrame

Panda DataFrame of AlkaliFspar compositions with column headings SiO2_Kspar, MgO_Kspar etc.

pandas DataFrame

mole proportions for AlkaliFspars with column headings of the form SiO2_Kspar_mol_prop

Thermobar.core.calculate_mol_proportions_olivine(ol_comps)[source]

Import Olivine compositions using ol_comps=My_Olivines, returns mole proportions

Parameters
ol_comps: pandas.DataFrame

Panda DataFrame of olivine compositions with column headings SiO2_Ol, MgO_Ol etc.

pandas DataFrame

mole proportions for olivines with column headings of the form SiO2_Ol_mol_prop

Thermobar.core.calculate_mol_proportions_olivine_ni(ol_comps)[source]

Import Olivine compositions using ol_comps=My_Olivines, returns mole proportions

Parameters
ol_comps: pandas.DataFrame

olivine compositions with column headings SiO2_Ol, MgO_Ol etc.

pandas DataFrame

mole proportions for olivines with column headings of the form SiO2_Ol_mol_prop

Thermobar.core.calculate_mol_proportions_orthopyroxene(opx_comps)[source]

Import orthopyroxene compositions using opx_comps=My_Opxs, returns mole proportions

Parameters
opx_comps: pandas.DataFrame

orthopyroxene compositions with column headings SiO2_Opx, MgO_Opx etc.

pandas DataFrame

mole proportions for orthopyroxene with column headings of the form SiO2_Opx_mol_prop

Thermobar.core.calculate_mol_proportions_plagioclase(*, plag_comps=None)[source]

Import plagioclase compositions using plag_comps=My_plagioclases, returns mole proportions

Parameters
plag_comps: pandas.DataFrame

Panda DataFrame of plagioclase compositions with column headings SiO2_Plag, MgO_Plag etc.

pandas DataFrame

mole proportions for plagioclases with column headings of the form SiO2_Plag_mol_prop

Thermobar.core.calculate_orthopyroxene_components(opx_comps)[source]

Import orthopyroxene compositions using opx_comps=My_Opxs, returns orthopyroxene components along with entered Cpx compositions

Parameters
opx_comps: pandas.DataFrame

Panda DataFrame of orthopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

pandas DataFrame

-orthopyroxene compositions (MgO_Opx etc.) -cations on bases of 6 oxygens (column headings of form Cr2O3_Opx_cat_6ox, as well as Cation_Sum_Opx) -orthopyroxene components (NaAlSi2O6, FmTiAlSiO6, CrAl2SiO6, FmAl2SiO6, CaFmSi2O6, Fm2Si2O6, En_Opx, Di_Opx, Mgno_Opx)

Thermobar.core.calculate_orthopyroxene_liquid_components(*, opx_comps=None, liq_comps=None, meltmatch=None, Fe3Fet_Liq=None)[source]
Import orthopyroxene compositions using opx_comps=My_Opxs and liquid compositions using liq_comps=My_Liquids,

returns orthopyroxene and liquid components.

Parameters
liq_comps: pandas.DataFrame

liquid compositions with column headings SiO2_Liq, MgO_Liq etc.

AND
opx_comps: pandas.DataFrame

orthopyroxene compositions with column headings SiO2_Opx, MgO_Opx etc.

OR meltmatch: pandas.DataFrame

merged orthopyroxene and liquid compositions used for melt matching

pandas DataFrame

Merged dataframe of inputted liquids, liquid mole fractions, liquid cation fractions, inputted opx compositions, opx cations on 6 oxygen basis, opx components and opx-liquid components.

Thermobar.core.calculate_oxygens_amphibole(amp_comps)[source]

Import amphiboles compositions using amp_comps=My_Amps, returns number of oxygens (e.g., mol proportions * number of O in formula unit)

Parameters
amp_comps: pandas.DataFrame

amphiboles compositions with column headings SiO2_Amp, MgO_Amp etc.

pandas DataFrame

number of oxygens with column headings of the form SiO2_Amp_ox

Thermobar.core.calculate_oxygens_clinopyroxene(cpx_comps)[source]

Import clinopyroxene compositions using cpx_comps=My_Cpxs, returns number of oxygens (e.g., mol proportions * number of O in formula unit)

Parameters
cpx_comps: pandas.DataFrame

clinopyroxene compositions with column headings SiO2_Cpx, MgO_Cpx etc.

pandas DataFrame

number of oxygens with column headings of the form SiO2_Cpx_ox

Thermobar.core.calculate_oxygens_orthopyroxene(opx_comps)[source]

Import orthopyroxene compositions using opx_comps=My_Opxs, returns number of oxygens (e.g., mol proportions * number of O in formula unit)

Parameters
opx_comps: pandas.DataFrame

Panda DataFrame of orthopyroxene compositions with column headings SiO2_Opx, MgO_Opx etc.

pandas DataFrame

number of oxygens with column headings of the form SiO2_Opx_ox

Thermobar.core.calculate_plag_liq_eq_tests(*, plag_comps=None, liq_comps=None, meltmatch=None, XAb=None, XAn=None, XOr=0, P, T)[source]

Import Plag and Liq compositions, assesses An, Ab and Or equilibrium using the equations of Putirka (2005) :param liq_comps: Cpx compositions with column headings SiO2_Liq, MgO_Liq etc. :type liq_comps: pandas.DataFrame :param One of: :param 1) plag_comps: Plag compositions with column headings SiO2_Plag, MgO_Plag etc. :type 1) plag_comps: pandas.DataFrame (optional)

  1. XAn, XAb, XOr, float, int, pandas.Series

    If plag_comps is None, enter XAn and XAb for plagioclases instead. XOr is set to zero by default, but can be overwritten for equilibrium tests

3) meltmatch (pandas dataframe), dont need Liq compositions. Used for plag-liq melt matching code.

P: int, float, pandas.Series

Pressure in kbar

T: int, float, pandas.Series

Temperature in Kelvin

Returns:

Return all plag components, liq compnoents, and and cpx components, as well as values for Observed Kd (Ab-An), which Putirka (2008) suggests is the best equilibrium test. Also calculate Delta_An, which is the absolute value of measured An - predicted An from Putirka (2008).

Return type:

pandas DataFrame

Thermobar.core.classify_phases(filename=None, sheet_name=None, df=None, return_end_members=False, str_to_drop=None)[source]

Function in progress

Thermobar.core.convert_fe_partition_to_fo2(*, liq_comps, T_K, P_kbar, model='Kress1991', Fe3Fet_Liq=None, renorm=False)[source]

Calculates delta fo2 relative to QFM and NNO buffer for liq compositions with FeO and Fe2O3

Parameters
liq_comps: pandas.DataFrame

Liquid compositions with column headings SiO2_Liq, MgO_Liq, FeO_Liq and Fe2O3_Liq etc. Or, have FeOt_Liq and Fe3Fet_Liq terms. In which case, you can overwrite with

T_K: int, flt, pandas.Series

Temperature in Kelvin (buffer positions are very T-sensitive)

P_kbar: int, flt, pandas.Series

Pressure in Kbar (Buffer positions are slightly sensitive to pressure)

model: str

“Kress1991” - Uses Kress and Carmichael 1991 to calculate XFe2Fe3 from fo2 “Put2016_eq6b” - Uses Putirka (2016) expression to calculate XFe2Fe3 from fo2

renorm: bool

Following excel code of K. Iacovino. If True, renormalizes other oxide concentrations to account for change in total following partitioning of Fe into FeO and Fe2O3.

liquid compositions with calculated Fe3FeT_Liq, FeO_Liq, Fe2O3_Liq, and XFe3Fe2.

Thermobar.core.convert_fo2_to_buffer(fo2=None, T_K=None, P_kbar=None)[source]

Converts fo2 in bars to deltaNNO and delta QFM using Frost 1991 based on user-entered T in Kelvin and P in kbar

Thermobar.core.convert_fo2_to_fe_partition(*, liq_comps, T_K, P_kbar, model='Kress1991', fo2, renorm=False, fo2_offset=0)[source]

Calculates Fe3Fet_Liq, FeO and Fe2O3 based on user-specified buffer or fo2 value (in bars)

Parameters
liq_comps: pandas.DataFrame

Liquid compositions with column headings SiO2_Liq, MgO_Liq etc.

T_K: int, flt, pandas.Series

Temperature in Kelvin (buffer positions are very T-sensitive)

P_kbar: int, flt, pandas.Series

Pressure in Kbar (Buffer positions are slightly sensitive to pressure)

fo2: str (“QFM”, “NNO”) or int, flt, pandas.Series

Either a value of fo2 (enter 10*logfo2), or buffer position as a string So far, includes QFM or NNO fo2 in bars.

fo2_offset: int, flt, pandas.Series

log units offset from buffer, e.g., could specify fo2=QFM, fo2_offset=1 to perform calculations at QFM+1

model: str

“Kress1991” - Uses Kress and Carmichael 1991 to calculate XFe2Fe3 from fo2 “Put2016_eq6b” - Uses Putirka (2016) expression to calculate XFe2Fe3 from fo2

renorm: bool

Following excel code of K. Iacovino. If True, renormalizes other oxide concentrations to account for change in total following partitioning of Fe into FeO and Fe2O3.

liquid compositions with calculated Fe3Fet_Liq, FeO_Liq, Fe2O3_Liq, and XFe3Fe2.

Thermobar.core.convert_oxide_percent_to_element_weight_percent(df, suffix=None, without_oxygen=False, anhydrous=True)[source]

Converts oxide wt% to elemental wt% including oxygen by default

Parameters
df: pandas.DataFrame

Data frame of oxide compositions. Can have suffixes like “_Amp” in which case you need to specify suffix=”_Amp”

without_oxygen: str

default False, element wt% doesnt sum to 100.] if true, all elements sum to 100 w/o oxygen.

returns: pandas.DataFrame wt% of elements

Thermobar.core.get_amp_sites2(amp_apfu_df)[source]

get_amp_sites takes generalized atom per formula unit calculations from calculate_23oxygens_amphibole and puts them in the proper cation sites according to Leake et al., 1997.

Parameters:

amp_apfu_df (pandas DataFrame) – This is the dataframe output from calculate_23oxygens_amphibole. You should not have to modify this dataframe at all.

Returns:

sites_df – a samples by cation sites dimension dataframe where each column corresponds to a cation site in amphibole. The suffix at the end corresponds to which site the cation is in:

T = tetrahedral sites (8 total) C = octahedral sites (5 total) B = M4 sites (2 total) A = A site (0 - 1 total)

Return type:

pandas DataFrame

Thermobar.core.get_amp_sites_ferric_ferrous(amp_apfu_df)[source]

get_amp_sites_ferric_ferrous is very similar to get_amp_sites, however it now incorporates the newly calculated Fe2O3 and FeO apfu values such that all Fe2O3 gets incorporated into the octahedral sites before any FeO. For more information see Leake et al., 1997 Appendix A.

Parameters:

amp_apfu_df (pandas DataFrame) – amphibole apfu values for each cation, now reflecting Fe2O3 and FeO values. This is the output from amp_components_ferric_ferrous and does not need to be modified at all.

Returns:

  • sites_df (pandas DataFrame)

  • a samples by cation sites dimension dataframe where each column corresponds – to a cation site in amphibole. The suffix at the end corresponds to which site the cation is in:

    T = tetrahedral sites (8 total) C = octahedral sites (5 total) B = M4 sites (2 total) A = A site (0 - 1 total)

    See Leake et al., 1997 for a discussion on cation site prioritization

Thermobar.core.get_amp_sites_ferric_ferrous_mutch(amp_apfu_df)[source]

get_amp_sites_ferric_ferrous is very similar to get_amp_sites, however it now incorporates the newly calculated Fe2O3 and FeO apfu values such that all Fe2O3 gets incorporated into the octahedral sites before any FeO. For more information see Leake et al., 1997 Appendix A.

Parameters:

amp_apfu_df (pandas DataFrame) – amphibole apfu values for each cation, now reflecting Fe2O3 and FeO values. This is the output from amp_components_ferric_ferrous and does not need to be modified at all.

Returns:

  • sites_df (pandas DataFrame)

  • a samples by cation sites dimension dataframe where each column corresponds – to a cation site in amphibole. The suffix at the end corresponds to which site the cation is in:

    T = tetrahedral sites (8 total) C = octahedral sites (5 total) B = M4 sites (2 total) A = A site (0 - 1 total)

    See Leake et al., 1997 for a discussion on cation site prioritization

Thermobar.core.get_amp_sites_from_input(amp_comps)[source]

get amp_sites from amp_comps input from import_excel() function.

Thermobar.core.get_amp_sites_from_input_not_amp(dfin, col_drop)[source]

get amp_sites from amp_comps input from import_excel() function.

Thermobar.core.get_amp_sites_leake(amp_apfu_df)[source]

get_amp_sites takes generalized atom per formula unit calculations from calculate_23oxygens_amphibole and puts them in the proper cation sites according to Leake et al., 1997.

Parameters:

amp_apfu_df (pandas DataFrame) – This is the dataframe output from calculate_23oxygens_amphibole. You should not have to modify this dataframe at all.

Returns:

sites_df – a samples by cation sites dimension dataframe where each column corresponds to a cation site in amphibole. The suffix at the end corresponds to which site the cation is in:

T = tetrahedral sites (8 total) C = octahedral sites (5 total) B = M4 sites (2 total) A = A site (0 - 1 total)

Return type:

pandas DataFrame

Thermobar.core.get_amp_sites_mutch(amp_apfu_df)[source]

get_amp_sites takes generalized atom per formula unit calculations from calculate_23oxygens_amphibole and puts them in the proper cation sites according to the spreasheet of Mutch et al. Gives negative numbers for Na. .

Parameters:

amp_apfu_df (pandas DataFrame) – This is the dataframe output from calculate_23oxygens_amphibole. You should not have to modify this dataframe at all.

Returns:

sites_df – a samples by cation sites dimension dataframe where each column corresponds to a cation site in amphibole. The suffix at the end corresponds to which site the cation is in:

T = tetrahedral sites (8 total) C = octahedral sites (5 total) B = M4 sites (2 total) A = A site (0 - 1 total)

Return type:

pandas DataFrame

Thermobar.core.normalize_liquid_jorgenson(liq_comps)[source]

Normalizes for Jorgenson Thermometers, rounds to 2 dp