# BurnMan - a lower mantle toolkit

# Copyright (C) 2012, 2013, Heister, T., Unterborn, C., Rose, I. and Cottaar, S.

# Released under GPL v2 or later.

"""

Shows the various ways to input geotherms: Built-in geotherms (geotherm1 and 2), basic linear (geotherm3),

loaded in from a data file (geotherm4) of your choice. Geotherm 1 is from Brown & Shankland (1981) and

geotherm2 from Watson & Baxter (2007).

requires:

teaches:

- geotherms

"""

import os, sys, numpy as np, matplotlib.pyplot as plt

#hack to allow scripts to be placed in subdirectories next to burnman:

if not os.path.exists('burnman') and os.path.exists('../burnman'):

sys.path.insert(1,os.path.abspath('..'))

import burnman

from burnman import minerals

if __name__ == "__main__":

# we want to evaluate several geotherms at these values

pressures = np.arange(1e9,128e9,3e9)

#load two builtin geotherms and evaluate the temperatures at all pressures

geotherm1 = burnman.geotherm.brown_shankland

temperature1 = [geotherm1(p) for p in pressures]

geotherm2 = burnman.geotherm.watson_baxter

temperature2 = [geotherm2(p) for p in pressures]

#a geotherm is actually just a function that returns a temperature given pressure in Pa

#so we can just write our own function

geotherm3 = lambda p: 1500+(2500-1500)*p/128e9

temperature3 = [geotherm3(p) for p in pressures]

#what about a geotherm defined from datapoints given in a file (our inline)?

table = [[1e9,1600],[30e9,1700],[130e9,2700]]

#this could also be loaded from a file, just uncomment this

#table = tools.read_table("data/example_geotherm.txt")

table_pressure = np.array(table)[:,0]

table_temperature = np.array(table)[:,1]

my_geotherm = lambda p: burnman.tools.lookup_and_interpolate(table_pressure, table_temperature, p)

temperature4 = [my_geotherm(p) for p in pressures]

#finally, we can also calculate a self consistent geotherm for an assemblage of minerals

#based on self compression of the composite rock. First we need to define an assemblage

phases = [minerals.mg_fe_perovskite(0.1), minerals.ferropericlase(0.4)]

for ph in phases:

ph.set_method("mgd")

molar_abundances = [0.7, 0.3]

#next, define an anchor temperature at which we are starting. Perhaps 1500 K for the upper mantle

T0 = 1500.

#then generate temperature values using the self consistent function. This takes more time than the above methods

temperature5 = burnman.geotherm.self_consistent(pressures, T0, phases, molar_abundances)

#you can also look at burnman/geotherm.py to see how the geotherms are implemented

plt.plot(pressures/1e9,temperature1,'-r',label="Brown, Shankland")

plt.plot(pressures/1e9,temperature2,'-g',label="Watson, Baxter")

plt.plot(pressures/1e9,temperature3,'-b',label="handwritten linear")

plt.plot(pressures/1e9,temperature4,'-k',label="handwritten from table")

plt.plot(pressures/1e9,temperature5,'-m',label="Self consistent with perovskite (70%) and ferropericlase(30%)")

plt.legend(loc='lower right')

plt.xlim([0, 130])

plt.xlabel('Pressure/GPa')

plt.ylabel('Temperature')

plt.show()