"""
Sub-module with melt-only thermometers
"""
import inspect
import sys
import elementMass as e
import numpy as np
import pandas as pd
from MagmaPandas.core.magma_protocol import Magma
from MagmaPandas.parse_io import check_components
from MagmaPandas.thermometers.validate_temperatures import _check_temperature
from MagmaPandas.tools.calibration_range import _check_calibration_range
from MagmaPandas.tools.modify_compositions import (
_anhydrous_composition,
_get_elements,
_remove_elements,
cation_moles_per_oxygen,
)
calibration_range = {
"putirka2008_14": [
["SiO2", 31, 73.64],
[["Na2O", "K2O"], 0, 14.3],
["H2O", 0, 18.6],
],
"putirka2008_15": [
["SiO2", 31, 73.64],
[["Na2O", "K2O"], 0, 14.3],
["H2O", 0, 18.6],
],
}
errors = pd.Series(
{
"putirka2008_13": 71,
"putirka2008_14": 58,
"putirka2008_15": 46,
"putirka2008_16": 26,
"putirka2008_22": 32, # from Wieser et al. (2025) Treatise of Geochemistry
"sun2020": 49,
"shea2022": 13, # on calibration dataset, not validated
"sugawara2000_3": 33, # on calibration dataset
"sugawara2000_6a": 30, # on calibration dataset
}
)
components = {
"putirka2008_13": ["MgO"],
"putirka2008_14": ["MgO", "FeO", "Na2O", "K2O", "H2O"],
"putirka2008_15": ["MgO", "FeO", "Na2O", "K2O", "H2O"],
"putirka2008_16": ["SiO2", "Al2O3", "MgO"],
"putirka2008_22": ["SiO2", "FeO", "MnO", "MgO", "CaO", "CoO", "NiO", "H2O"],
"sun2020": ["MgO", "CaO", "K2O", "TiO2", "FeO", "CO2", "H2O"],
"shea2022": ["MgO"],
"sugawara2000_3": ["MgO"],
"sugawara2000_6a": ["MgO", "FeO", "CaO", "SiO2"],
}
_Beattie_constants = pd.DataFrame(
{
"A": [1.0, 0.259, 0.299, 0.786, 3.346],
"B": [0.0, -4.9e-2, 2.7e-2, -0.385, -3.665],
},
index=["Mg", "Mn", "Fe", "Co", "Ni"],
)
[docs]
def putirka2008_13(
melt: Magma, offset: float = 0.0, *args, **kwargs
) -> float | pd.Series:
"""
melt-only thermometer
Equation 13 from :cite:t:`Putirka2008a` calculates liquidus temperatures based on melt compositions.
Requires saturation in olivine.
SEE = 71 degrees
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
composition = check_components(
composition=melt, components=components["putirka2008_13"]
)
T_K = 26.3 * composition["MgO"] + 994.4 + 273.15
_check_temperature(T_K)
T_K = T_K + errors["putirka2008_13"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def putirka2008_14(
melt: Magma, warn=True, offset: float = 0.0, *args, **kwargs
) -> float | pd.Series:
"""
melt-only thermometer
Equation 14 from :cite:t:`Putirka2008a` calculates liquidus temperatures based on melt compositions.
Requires saturation in olivine.
SEE = 58 degrees
Applicable between:
- 0 - 14.4 GPa
- 729 - 2000 degrees C
- 31 - 73.64 wt. % SiO\ :sub:`2`
- 0 - 14.3 wt. % Na\ :sub:`2`\ O + K\ :sub:`2`\ O
- 0 - 18.6 wt. % H\ :sub:`2`\ O
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
elements = _get_elements(melt)
composition = check_components(
composition=melt, components=components["putirka2008_14"]
)
if warn:
_check_calibration_range(
melt=composition, calibration_range=calibration_range["putirka2008_14"]
)
# oxides_needed = set(["MgO", "FeO", "Na2O", "K2O"])
# composition = parse_data(melt, oxides_needed)
H2O = composition["H2O"].copy()
anhydrous_composition = _anhydrous_composition(composition, normalise=False)
# Calculate molar oxide fractions
mol_fractions = anhydrous_composition.moles()
# Melt Mg#
Mg_no = mol_fractions["MgO"] / (
mol_fractions["MgO"] + mol_fractions["FeO"]
) # Putirka doesn't specify whether this should be Fe2+ or Fe(total)
if "Fe2O3" in elements:
FeO_w, Fe2O3_w = e.compound_weights(["FeO", "Fe2O3"])
composition["FeO"] = composition["FeO"] + (
2 * composition["Fe2O3"] * (FeO_w / Fe2O3_w)
)
T_K = (
754
+ 190.6 * Mg_no
+ 25.52 * composition["MgO"]
+ 9.585 * composition["FeO"]
+ 14.87 * (composition["Na2O"] + composition["K2O"])
- 9.176 * H2O
) + 273.15
_check_temperature(T_K)
T_K = T_K + errors["putirka2008_14"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def putirka2008_15(
melt: Magma,
P_bar: float | pd.Series,
offset: float = 0.0,
warn=True,
**kwargs,
) -> float | pd.Series:
"""
melt-only thermometer
Equation 15 from :cite:t:`Putirka2008a` calculates liquidus temperatures based on melt compositions.
Requires saturation in olivine.
SEE = 46 degrees
Applicable between:
- 0 - 14.4 GPa
- 729 - 2000 degrees C
- 31 - 73.64 wt. % SiO\ :sub:`2`
- 0 - 14.3 wt. % Na\ :sub:`2`\ O + K\ :sub:`2`\ O
- 0 - 18.6 wt. % H\ :sub:`2`\ O
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
P_bar : float, pandas Series
pressures in bar.
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
elements = _get_elements(melt)
composition = check_components(
composition=melt, components=components["putirka2008_15"]
)
if warn:
_check_calibration_range(
melt=composition, calibration_range=calibration_range["putirka2008_15"]
)
# oxides_needed = set(["MgO", "FeO", "Na2O", "K2O"])
# composition = parse_data(melt, oxides_needed)
H2O = composition["H2O"].copy()
anhydrous_composition = _anhydrous_composition(composition, normalise=False)
P_GPa = P_bar / 1e4
# Calculate molar oxide fractions
mol_fractions = anhydrous_composition.moles()
# Melt Mg#
Mg_no = mol_fractions["MgO"] / (
mol_fractions["MgO"] + mol_fractions["FeO"]
) # Putirka doesn't specify whether this should be Fe2+ or Fe(total)
if "Fe2O3" in elements:
FeO_w, Fe2O3_w = e.compound_weights(["FeO", "Fe2O3"])
composition["FeO"] = composition["FeO"] + (
2 * composition["Fe2O3"] * (FeO_w / Fe2O3_w)
)
T_K = (
815.3
+ 265.5 * Mg_no
+ 15.37 * composition["MgO"]
+ 8.61 * composition["FeO"]
+ 6.646 * (composition["Na2O"] + composition["K2O"])
+ 39.16 * P_GPa
- 12.83 * H2O
) + 273.15
_check_temperature(T_K)
T_K = T_K + errors["putirka2008_15"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def putirka2008_16(
melt: Magma, P_bar: float | pd.Series, offset: float = 0.0, **kwargs
) -> float | pd.Series:
"""
melt-only thermometer
Equation 16 from :cite:t:`Putirka2008a` calculates liquiqdus temperatures based on melt compositions.
Requires equilibrium with olivine + plagioclase + clinopyroxene and saturation with additional phases drastically increases the standard error of estimate.
SEE = 26 degrees (saturation in olivine + plagioclase + clinopyroxene)
= 60 degrees (saturation with additional phases)
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
P_bar : float, pandas Series
pressures in bar
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
elements = _get_elements(melt)
composition = check_components(
composition=melt, components=components["putirka2008_16"]
)
# oxides_needed = set(["SiO2", "Al2O3", "MgO"])
# composition = parse_data(melt, oxides_needed)
# import MagmaPandas as mp
if isinstance(P_bar, pd.Series):
if not melt.index.equals(P_bar.index):
raise RuntimeError("Melt and P_bar indices don't match")
if "H2O" in elements:
composition = _anhydrous_composition(composition, normalise=False)
# Convert pressure from bars to GPa
P_GPa = P_bar / 1e4
# Calculate molar oxide fractions
mol_fractions = composition.moles()
part_1 = (
-583
+ 3141 * mol_fractions["SiO2"]
+ 15779 * mol_fractions["Al2O3"]
+ 1338.6 * mol_fractions["MgO"]
)
part_2 = -31440 * mol_fractions["SiO2"] * mol_fractions["Al2O3"] + 77.67 * P_GPa
T_K = part_1 + part_2 + 273.15
_check_temperature(T_K)
T_K = T_K + errors["putirka2008_16"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def putirka2008_22(
melt: Magma,
P_bar: float | pd.Series,
offset: float = 0.0,
**kwargs,
) -> float | pd.Series:
"""
melt-only thermometer
Equation 22 from :cite:t:`Putirka2008a`, combined with equation 12 from Beattie (1993) calculates liquidus temperatures based on melt compositions. #TODO add reference link
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
P_bar : float, pandas Series
pressures in bar.
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
axis = [0, 1][isinstance(melt, pd.DataFrame)]
composition = check_components(
composition=melt, components=components["putirka2008_22"]
)
H2O = composition["H2O"].copy()
composition = _anhydrous_composition(composition)
# if warn:
# _check_calibration_range(
# melt=composition, calibration_range=calibration_range["putirka2008_22"]
# )
P_GPa = P_bar / 1e4
cations = composition.cations()
lnD_Mg = np.log(
(
0.666
- cations[_Beattie_constants.index]
.mul(_Beattie_constants.loc[:, "B"], axis=axis)
.sum(axis=axis)
)
/ cations[_Beattie_constants.index]
.mul(_Beattie_constants.loc[:, "A"], axis=axis)
.sum(axis=axis)
) # equation 12 from Beattie (1993)
C_NM = (cations[["Fe", "Mn", "Mg", "Ca", "Co", "Ni"]]).sum(axis=axis)
NF = 7 / 2 * np.log(1 - cations["Al"]) + 7 * np.log(
1 - cations["Ti"]
) # Table 1 from Putirka (2008)
T_K = (15294.6 + 1318.8 * P_GPa + 2.4834 * P_GPa**2) / (
8.048
+ 2.8352 * lnD_Mg
+ 2.097 * np.log(1.5 * C_NM)
+ 2.575 * np.log(3 * cations["Si"])
- 1.41 * NF
+ 0.222 * H2O
+ 0.5 * P_GPa
) + 273.15 # equation 22 from Putirka (2008)
_check_temperature(T_K)
T_K = T_K + errors["putirka2008_22"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def sun2020(melt, P_bar: float | pd.Series, offset: float = 0.0, **kwargs):
"""
Equation 6 from:
:cite:t:`Sun2020a`
Calibrated at:
~ 2 - 10 GPa
~ 950 - 1600 degrees C
SEE: 49 degrees C
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
P_bar :
pressures in bar
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
P_GPa = P_bar / 1e4
composition = check_components(composition=melt, components=components["sun2020"])
moles = composition.moles()
composition_volatile_free = _remove_elements(
composition=moles, drop=["H2O", "CO2", "F", "S", "Cl"]
)
moles_unit_oxygen = cation_moles_per_oxygen(moles=composition_volatile_free)
Omega = (
2.59
+ 3.5 * (moles_unit_oxygen["Ca1O"] - 2 * moles_unit_oxygen["K2O"])
+ 4.85 * moles_unit_oxygen["Ti1/2O"]
+ 1.4
* (
moles_unit_oxygen["Mg1O"]
/ (moles_unit_oxygen["Mg1O"] + moles_unit_oxygen["Fe1O"])
)
+ 0.5 * moles_unit_oxygen["Mg1O"] * np.sqrt(composition["CO2"])
+ 5.7e-2 * composition["H2O"]
)
T_K = 1e4 / (
Omega - 0.34 * np.sqrt(P_GPa) - 1.26 * np.log(moles_unit_oxygen["Mg1O"])
)
_check_temperature(T_K)
T_K = T_K + errors["sun2020"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def shea2022(melt, offset: float = 0.0, **kwargs):
"""
Equation 1 from :cite:t:`Shea2022`
Calibrated at:
1 bar
1060 - 1500 degrees C
SEE: 13 degrees C (on calibration dataset, not validated)
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
composition = check_components(composition=melt, components=components["shea2022"])
T_K = 21.2 * composition["MgO"] + 1017 + 273.15
_check_temperature(T_K)
T_K = T_K + errors["shea2022"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def sugawara2000_3(melt, P_bar: float | pd.Series, offset: float = 0.0, **kwargs):
"""
Equation 3 from :cite:t:`Sugawara2000` with olivine-liquid parameters and corrected for H2O according to equation 7a.
Calibrated at:
<= 3.5 GPa
1266 - 1873 C
SEE: 33 degrees C
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
P_bar :
pressures in bar
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
composition = check_components(
composition=melt, components=components["sugawara2000_3"]
)
anhydrous_composition = _anhydrous_composition(composition=composition)
moles_percent = anhydrous_composition.moles() * 100
A = 1293
B = 14.60
C = 5.5e-3
T_K = A + B * moles_percent["MgO"] + C * P_bar
if "H2O" in composition.elements:
T_K = T_K - 5.403 * composition.moles()["H2O"] * 100
_check_temperature(T_K)
T_K = T_K + errors["sugawara2000_3"] * offset
return pd.Series(T_K, name="T_K").squeeze()
[docs]
def sugawara2000_6a(melt, P_bar: float | pd.Series, offset: float = 0.0, **kwargs):
"""
Equation 6a corrected for H2O according to equation 7a from :cite:t:`Sugawara2000`
Calibrated at:
<= 3.5 GPa
1266 - 1873 C
SEE: 30 degrees C
Parameters
----------
melt : Magma
melt compositions in oxide wt. %
P_bar :
pressures in bar
offset : float
offset value in standard deviations. Temperatures are calculated as ``temnperature + offset * thermometer error (SEE)``.
Returns
-------
temperatures : float, pandas Series
liquidus temperatures in Kelvin.
"""
composition = check_components(
composition=melt, components=components["sugawara2000_6a"]
)
anhydrous_composition = _anhydrous_composition(composition=composition)
anhydrous_moles_percent = anhydrous_composition.moles() * 100
T_K = (
1466
- 1.44 * anhydrous_moles_percent["SiO2"]
- 0.5 * anhydrous_moles_percent["FeO"]
+ 12.32 * anhydrous_moles_percent["MgO"]
- 3.899 * anhydrous_moles_percent["CaO"]
+ 4.3e-3 * P_bar
)
if "H2O" in composition.elements:
T_K = T_K - 5.403 * composition.moles()["H2O"] * 100
_check_temperature(T_K)
T_K = T_K + errors["sugawara2000_6a"] * offset
return pd.Series(T_K, name="T_K").squeeze()
_module = sys.modules[__name__]
_funcs = inspect.getmembers(_module, inspect.isfunction)
# collect all melt thermometers in a dictionary
melt_thermometers_dict = {
f[0]: f[1] for f in _funcs if f[1].__module__ == _module.__name__
}