Tutorial 6: Custom Thermobarometers

In this tutorial we will show you how to apply your own thermobarometer in meltPT. Each thermobarometer is implemented as a separate python class. There are a few key features that these classes much contain to work properly. On top of these necessary features, you can include any other methods/ properties you need to get the thermobarometer working.

The key features are: - accepts a single-row pandas dataframe on instantiation - includes one or more of the methods: compute_pressure_temperature, compute_pressure, compute_temperature - these methods should return a dictionary containing calculated pressures and temperatures and their uncertainties

Let’s design a simple minumum working example. No matter what you give it, it will return a pressure of 1 +/- 0.1 GPa and a temperature of 1000 +/- 100 oC.

from meltPT import *

class ExampleThermobarometer:

    # Insantiation should accept only a dataframe
    def __init__(self, df):
        self.df = df
        self.P_err = 0.1
        self.T_err = 100.

    # To run with Suite.compute_pressure_temperature, we need a
    # compute_pressure_temperature_method.
    # It should return a dictionary including P, P_err, T, and T_err
    # (can include nans).
    def compute_pressure_temperature(self):
        return {'P': 1., 'P_err': self.P_err, 'T': 1000., 'T_err': self.T_err}

    # Same for use with Suite.compute_pressure. For now expected to be
    # temperature dependent.
    def compute_pressure(self, T):
        return {'P': 1., 'P_err': self.P_err, 'T': T, 'T_err': np.nan}

    # Same for use with Suite.compute_temperature. For now expected to be
    # pressure dependent.
    def compute_temperature(self, P):
        return {'P': P, 'P_err': np.nan, 'T': 1000., 'T_err': self.T_err}

Now let’s try it out on our sample from Plank & Forsyth (2016)

s = Suite("../Data/PF16_UT09DV04.csv", src_FeIII_totFe=0.17, src_Fo=0.9)
b = BacktrackOlivineFractionation()
s.backtrack_compositions(backtracker=b)

/home/mcnab/Melting/meltPT/meltPT/parse.py:77: UserWarning: Input csv does not contain a Fe2O3 column: we will try to fill it for you, or set it to zero.
  warnings.warn(message)
/home/mcnab/Melting/meltPT/meltPT/parse.py:77: UserWarning: Input csv does not contain a Cr2O3 column: we will try to fill it for you, or set it to zero.
  warnings.warn(message)
/home/mcnab/Melting/meltPT/meltPT/parse.py:77: UserWarning: Input csv does not contain a NiO column: we will try to fill it for you, or set it to zero.
  warnings.warn(message)
/home/mcnab/Melting/meltPT/meltPT/parse.py:77: UserWarning: Input csv does not contain a CoO column: we will try to fill it for you, or set it to zero.
  warnings.warn(message)
/home/mcnab/Melting/meltPT/meltPT/parse.py:77: UserWarning: Input csv does not contain a CO2 column: we will try to fill it for you, or set it to zero.
  warnings.warn(message)
/home/mcnab/Melting/meltPT/meltPT/parse.py:77: UserWarning: Input csv does not contain a FeO_tot column: we will try to fill it for you, or set it to zero.
  warnings.warn(message)

Call the compute_pressure_temperature method and feed it our custom thermobarometer.

s.compute_pressure_temperature(method=ExampleThermobarometer)
print(s.PT)

     P  P_err       T  T_err
0  1.0    0.1  1000.0  100.0

Now let’s try out it out as a barometer.

s.compute_pressure(method=ExampleThermobarometer, T=1300.)
print(s.PT)

     P  P_err       T  T_err
0  1.0    0.1  1300.0    NaN

… and as a thermometer.

s.compute_temperature(method=ExampleThermobarometer, P=2.)
print(s.PT)

     P  P_err       T  T_err
0  2.0    NaN  1000.0  100.0