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
from burnman import tools
if __name__ == "__main__":
phases = [minerals.stishovite(), \
minerals.periclase(), \
minerals.wustite(), \
minerals.ferropericlase(0), \
minerals.mg_fe_perovskite(0), \
minerals.mg_perovskite(), \
minerals.fe_perovskite(), \
minerals.Catalli_fe_perovskite("on"), \
minerals.Murakami_fe_perovskite(), \
minerals.Murakami_fe_periclase("on")]
#minerals.Speziale_fe_periclase("on"), \
#mg_fe_perovskite_pt_dependent
#ferropericlase_pt_dependent
params = ['ref_V','ref_K','K_prime','ref_mu','mu_prime','molar_mass','n','ref_Debye','ref_grueneisen','q0','eta_0s']
def create_list(mineral,paramname):
row = [ p.__class__.__name__ ]
#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")
# To compute seismic velocities and other properties, we need to supply
# burnman with a list of minerals (phaes) and their molar abundances. Minerals
# are classes found in burnman.minerals and are derived from
# burnman.minerals.material.
# Here are a few ways to define phases and molar_abundances:
#Example 1: two simple fixed minerals
if True:
phases = [minerals.Murakami_fe_perovskite(), minerals.Murakami_fe_periclase_LS()]
amount_perovskite = 0.95
molar_abundances = [amount_perovskite, 1.0-amount_perovskite]
#Example 2: specify fixed iron content
if False:
phases = [minerals.mg_fe_perovskite(0.8), minerals.ferropericlase(0.8)]
amount_perovskite = 0.95
molar_abundances = [amount_perovskite, 1.0-amount_perovskite]
#Example 3: input weight percentages
#See comments in burnman/composition.py for references to partition coefficent calculation
if False:
weight_percents = {'Mg':0.213, 'Fe': 0.08, 'Si':0.27, 'Ca':0., 'Al':0.}
phase_fractions,relative_molar_percent = burnman.calculate_phase_percents(weight_percents)
iron_content = lambda p,t: burnman.calculate_partition_coefficient(p,t,relative_molar_percent)
phases = [minerals.mg_fe_perovskite_pt_dependent(iron_content,0), \
minerals.ferropericlase_pt_dependent(iron_content,1)]
molar_abundances = [phase_fractions['pv'],phase_fractions['fp']]
#Example 4: three materials
if False: