import pandas as pd
from MagmaPandas.Fe_redox.Fe3Fe2_models import Fe3Fe2_models_dict
from MagmaPandas.fO2.fO2_calculate import calculate_fO2
from MagmaPandas.Kd.Ol_melt.FeMg.Kd_models import Kd_olmelt_FeMg_models_dict
from MagmaPandas.magma_protocol import Magma
# configuration needs to be imported last to avoid circular imports
from MagmaPandas.configuration import configuration # isort: skip
[docs]
def observed_FeMg_Kd(
melt: Magma,
forsterite: pd.Series,
P_bar,
T_K: None | float | pd.Series = None,
**kwargs,
) -> pd.Series:
"""
Calulate observed Kds based on given melt and olivine compositions.
Parameters
----------
melt : MagmaFrame
melt composition in oxide wt. %
forsterite : float, pandas Series
olivine forsterite content
P_bar : float, pandas Series
pressures in bar
T_K : None, float, pandas Series
temperatures in Kelvin. If set to None, temperatures are calculated according to the melt thermometer set in the global configuration.
Returns
-------
Kds : pandas Series
"""
if isinstance(P_bar, (int, float)):
P_bar = pd.Series(P_bar, index=melt.index)
if isinstance(forsterite, (int, float)):
forsterite = pd.Series(forsterite, index=melt.index)
forsterite = forsterite[melt.index]
melt_x_moles = melt.moles()
Fe3Fe2_model = Fe3Fe2_models_dict[configuration.Fe3Fe2_model]
dfO2 = kwargs.get("dfO2", configuration.dfO2)
if T_K is None:
T_K = melt_x_moles.temperature(P_bar)
fO2 = calculate_fO2(T_K=T_K, P_bar=P_bar, dfO2=dfO2)
Fe3Fe2 = Fe3Fe2_model.calculate_Fe3Fe2(
melt_mol_fractions=melt_x_moles, T_K=T_K, fO2=fO2, P_bar=P_bar
)
melt_x_moles = melt_x_moles.normalise()
Fe2_FeTotal = 1 / (1 + Fe3Fe2)
melt_MgFe = melt_x_moles["MgO"] / (melt_x_moles["FeO"] * Fe2_FeTotal)
olivine_MgFe = forsterite / (1 - forsterite)
Kd_observed = melt_MgFe / olivine_MgFe
return Kd_observed
[docs]
def calculate_FeMg_Kd(
melt_mol_fractions: pd.Series | pd.DataFrame,
T_K: float | pd.Series,
*args,
**kwargs,
) -> pd.Series:
"""
Calculate equilibrium Kds for given melt compositions.
Kd models are set in the global configuration
Parameters
----------
melt_mol_fractions : pandas Series, pandas Dataframe
melt composition in oxide mol fractions
forsterite_initial : float, pandas Series
initial forsterite contents. Forsterite values are iteratively adjusted and initial values are not necessarily in Fe-Mg equilibrium with melts.
T_K : float, pandas Series
temperatures in Kelvin
Fe3Fe2 : float, pandas Series
melt |Fe3Fe2| ratios
Returns
-------
Kds : pandas Series
"""
Kd_model_name = kwargs.get("Kd_model", configuration.Kd_model)
Kd_model = Kd_olmelt_FeMg_models_dict[Kd_model_name]
if Kd_model_name == "blundy2020":
dfO2 = kwargs.get("dfO2", configuration.dfO2)
P_bar = kwargs["P_bar"]
kwargs["fO2"] = calculate_fO2(T_K=T_K, P_bar=P_bar, dfO2=dfO2)
return Kd_model.calculate_Kd(
melt_mol_fractions=melt_mol_fractions, T_K=T_K, *args, **kwargs
)
# if isinstance(melt_mol_fractions, pd.Series):
# Kd_func = it.calculate_Kd
# elif isinstance(melt_mol_fractions, pd.DataFrame):
# Kd_func = vec.calculate_Kd
# return Kd_func(
# melt_mol_fractions=melt_mol_fractions,
# forsterite_initial=forsterite_initial,
# T_K=T_K,
# Fe3Fe2=Fe3Fe2,
# *args,
# **kwargs,
# )