# Released under GPL v2 or later.
""" Generates a text table with mineral properties. """
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']
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:
phases = [minerals.Murakami_fe_perovskite(), minerals.ferropericlase(0.5), minerals.stishovite()]
molar_abundances = [0.7, 0.2, 0.1]
#seismic model for comparison:
seismic_model = burnman.seismic.prem() # pick from .prem() .slow() .fast() (see burnman/seismic.py)
number_of_points = 20 #set on how many depth slices the computations should be done
# we will do our computation and comparison at the following depth values:
depths = np.linspace(700, 2800, number_of_points)
#alternatively, we could use the values where prem is defined:
#depths = seismic_model.internal_depth_list()
seis_p, seis_rho, seis_vp, seis_vs, seis_vphi = seismic_model.evaluate_all_at(depths)
geotherm = burnman.geotherm.brown_shankland
temperature = [geotherm(p) for p in seis_p]