/
realdata_adiabat.py
286 lines (220 loc) · 9.63 KB
/
realdata_adiabat.py
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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 os, sys
from time import time
import pymc
import math
import cProfile
from scipy.stats import norm
import matplotlib.mlab as mlab
if not os.path.exists('burnman') and os.path.exists('../burnman'):
sys.path.insert(1,os.path.abspath('..'))
import burnman
from burnman import minerals
number_of_points = 5 #set on how many depth slices the computations should be done
# velocity constraints from seismology
seismic_model = burnman.seismic.PREM() # pick from .prem() .slow() .fast() (see code/seismic.py)
depths = np.linspace(1000e3,2500e3, number_of_points)
seis_p, seis_rho, seis_vp, seis_vs, seis_vphi = seismic_model.evaluate_all_at(depths)
seismic_model2 = burnman.seismic.PREM() # pick from .prem() .slow() .fast() (see code/seismic.py)
depths = np.linspace(1000e3,2500e3, number_of_points)
seis_p2, seis_rho2, seis_vp2, seis_vs2, seis_vphi2 = seismic_model2.evaluate_all_at(depths)
'''
print seis_vs-seis_vs2
plt.plot(depths,seis_vs,'r')
plt.plot(depths,seis_vs2,'k')
plt.plot(depths,seis_vp,'r')
plt.plot(depths,seis_vp2,'k')
#plt.plot(depths,seis_rho)
#plt.plot(depths,seis_rho2)
plt.show()
stophere
'''
seis_G= seis_vs**2.*seis_rho
seis_K= seis_vphi**2*seis_rho
'''
#velocities from known material (still named seis*)
amount_perovskite = 0.8
pv=minerals.SLB_2011.mg_fe_perovskite(.06)
pc=minerals.SLB_2011.ferropericlase(.2)
rock = burnman.Composite( [amount_perovskite, 1.0-amount_perovskite],[ pv,pc ] )
pressures = np.linspace(25e9,130e9,number_of_points)
temperature = burnman.geotherm.brown_shankland(pressures)
rock.set_method('slb3')
print "Calculations are done for:"
rock.debug_print()
seis_p=pressures
seis_rho, seis_vp, seis_vs, seis_vphi, seis_K, seis_G = \
burnman.velocities_from_rock(rock, pressures, temperature, \
burnman.averaging_schemes.VoigtReussHill())
depths= burnman.depths_for_rock(rock, pressures, temperature, \
burnman.averaging_schemes.VoigtReussHill())
'''
print "preparations done"
# Priors on unknown parameters:
fraction_pv = pymc.Uniform('fraction_pv', 0.0, 1.0)
fe_pv = pymc.Uniform('fe_pv', 0.0, 0.5)
fe_pc= pymc.Uniform('fe_pc', 0.0, 0.5)
T0 = pymc.Normal('T0',mu=1500.,tau=1./500.)
def calc_all_velocities(fraction_pv, fe_pv, fe_pc,T0):
method = 'slb3' #slb3|slb2|mgd3|mgd2
pv=minerals.other.mg_fe_perovskite(fe_pv)
pc=minerals.other.ferropericlase(fe_pc)
rock = burnman.Composite( [fraction_pv, 1.0-fraction_pv],[ pv,pc ] )
temperature = burnman.geotherm.adiabatic(seis_p,T0, rock)
mat_rho, mat_vp, mat_vs, mat_vphi, mat_K, mat_G = burnman.velocities_from_rock(rock,seis_p, temperature)
return mat_vp, mat_vs, mat_rho, mat_vphi, mat_K, mat_G
def nrmse(funca,funcb):
"""
Normalized root mean square error for one profile
:type funca: list of arrays of float
:param funca: array calculated values
:type funcb: list of arrays of float
:param funcb: array of values (observed or calculated) to compare to
:returns: RMS error
:rtype: array of floats
"""
diff=np.array(funca-funcb)
diff=diff*diff
rmse=np.sqrt(np.sum(diff)/len(diff))
nrmse=rmse/(np.max(funcb)-np.min(funcb))
return nrmse
def error(fraction_pv, fe_pv, fe_pc,T0):
if True:
if fraction_pv>0. and fraction_pv<1.0 and fe_pv>0. and fe_pv<1.0 and fe_pc>0. and fe_pc<1.0 and T0>300.:
mat_vp, mat_vs, mat_rho, mat_vphi, mat_K, mat_G = calc_all_velocities(fraction_pv, fe_pv, fe_pc,T0)
misfit = nrmse(mat_rho,seis_rho)+nrmse(mat_K,seis_K)+nrmse(mat_G,seis_G)
return misfit
else:
return 1.e30
#except:
# return 1e30
# rewrite as function of f_pv and fe_content
@pymc.deterministic
def calc_misfit(fraction_pv=fraction_pv, fe_pv=fe_pv, fe_pc=fe_pc,T0=T0):
return error(fraction_pv,fe_pv,fe_pc,T0)
#sig = 1e-2
#misfit = pymc.Normal('d',mu=theta,tau=1.0/(sig*sig),value=0.,observed=True,trace=True)
#sig = pymc.Uniform('sig', 0.0, 100.0, value=1.)
sig=1.e-2 # Some sorts of error
obs = pymc.Normal('d',mu=calc_misfit,tau=1.0/(sig*sig),value=0,observed=True,trace=True)
model = [fraction_pv, fe_pv, fe_pc, T0,obs]
things = ['fraction_pv', 'fe_pv','fe_pc','T0']
whattodo = ""
if len(sys.argv)<3:
print "options:"
print "run <dbname>"
print "continue <dbname>"
print "plot <dbname1> <dbname2> ..."
else:
whattodo = sys.argv[1]
dbname = sys.argv[2]
if whattodo=="run":
#pymc.MAP(model).fit() # Find minimum to start search from
S = pymc.MCMC(model, db='pickle', dbname=dbname)
S.sample(iter=100, burn=0, thin=1)
S.db.close()
whattodo="continue"
if whattodo=="continue":
n_runs = 100
for l in range(0,n_runs):
db = pymc.database.pickle.load(dbname)
print "*** run=%d/%d, # samples: %d" % (l, n_runs, db.trace('fraction_pv').stats()['n'] )
S = pymc.MCMC(model, db=db)
#S.sample(iter=100, burn=10, thin=1)
S.sample(iter=10, burn=0, thin=1) # Search space for 100000 acceptable steps, forget first 1000 and save every 10.
S.db.close()
if whattodo=="plot":
files=sys.argv[2:]
print "files:",files
b=0#10000 # burn number
i=1
for t in things:
print t
if t=='misfit':
continue
trace=[]
print "trace:",t
for filename in files:
db = pymc.database.pickle.load(filename)
newtrace=db.trace(t,chain=None).gettrace(burn=b,chain=None)
if (trace!=[]):
trace = np.append(trace, newtrace)
else:
trace=newtrace
print " adding ", newtrace.size, "burn = ",b
print " total size ", trace.size
print "mean = ", trace.mean()
print trace[0:100]
for bin in [10,20,50,100]:
hist,bin_edges=np.histogram(trace,bins=bin)
a=np.argmax(hist)
print "maxlike = ", bin_edges[a], bin_edges[a+1], (bin_edges[a]+bin_edges[a+1])/2.0
(mu, sigma) = norm.fit(np.array(trace))
print "mu, sigma: %e %e" % (mu, sigma)
plt.subplot(2,(len(things)+1)/2,i)
n, bins, patches = plt.hist(np.array(trace), 60, normed=1, facecolor='green', alpha=0.75)
y = mlab.normpdf( bins, mu, sigma)
l = plt.plot(bins, y, 'r--', linewidth=2)
plt.title("%s, mean: %.3e, std dev.: %.3e" % (t,mu,sigma),fontsize='small')
#pymc.Matplot.histogram(np.array(trace),t,rows=2,columns=len(things)/2,num=i)
i=i+1
plt.savefig("example_inv_big_pv.png")
plt.show()
if whattodo=="test":
db = pymc.database.pickle.load(dbname)
S = pymc.MCMC(model, db=db)
for t in things:
print db.trace(t).stats()
print "means:"
for t in things:
print t,"\t",db.trace(t).stats()['mean']
print "#samples: %d" % db.trace('fraction_pv').stats()['n']
pymc.raftery_lewis(S, q=0.025, r=0.01)
b = 1
t = 1
scores = pymc.geweke(S, intervals=20)
print scores
pymc.Matplot.trace(db.trace('deviance',chain=None).gettrace(burn=1000,thin=t,chain=None),'deviance',rows=2,columns=4,num=1)
pymc.Matplot.trace(db.trace('fraction_pv',chain=None).gettrace(thin=t,chain=None),'fraction_pv',rows=2,columns=4,num=2)
pymc.Matplot.histogram(np.array(db.trace('fraction_pv',chain=None).gettrace(burn=b,chain=None)),'fraction_pv',rows=2,columns=4,num=6)
pymc.Matplot.trace(db.trace('fe_pv',chain=None).gettrace(thin=t,chain=None),'fe_pv',rows=2,columns=4,num=3)
pymc.Matplot.histogram(np.array(db.trace('fe_pv',chain=None).gettrace(burn=b,chain=None)),'fe_pv',rows=2,columns=4,num=7)
pymc.Matplot.trace(db.trace('fe_pc',chain=None).gettrace(thin=t,chain=None),'fe_pc',rows=2,columns=4,num=4)
pymc.Matplot.histogram(np.array(db.trace('fe_pc',chain=None).gettrace(burn=b,chain=None)),'fe_pc',rows=2,columns=4,num=8)
plt.show()
if whattodo=="show":
values = [float(i) for i in sys.argv[2:]]
#mat_vp, mat_vs, mat_rho = calc_velocities(values[0], values[1], values[2])
mat_vp,mat_vs, mat_rho, mat_vphi=calc_all_velocities(values[0], values[1], values[2])
plt.subplot(2,2,1)
plt.plot(seis_p/1.e9,mat_vs/1.e3,color='r',linestyle='-',marker='^',markerfacecolor='r',markersize=4)
plt.plot(seis_p/1.e9,seis_vs/1.e3,color='k',linestyle='-',marker='v',markerfacecolor='k',markersize=4)
plt.ylim([4, 8])
plt.title("Vs (km/s)")
# plot Vphi
plt.subplot(2,2,2)
plt.plot(seis_p/1.e9,mat_vp/1.e3,color='r',linestyle='-',marker='^',markerfacecolor='r',markersize=4)
plt.plot(seis_p/1.e9,seis_vp/1.e3,color='k',linestyle='-',marker='v',markerfacecolor='k',markersize=4)
plt.ylim([10, 14])
plt.title("Vp (km/s)")
# plot density
plt.subplot(2,2,3)
plt.plot(seis_p/1.e9,mat_rho/1.e3,color='r',linestyle='-',marker='^',markerfacecolor='r',markersize=4,label='model 1')
plt.plot(seis_p/1.e9,seis_rho/1.e3,color='k',linestyle='-',marker='v',markerfacecolor='k',markersize=4,label='ref')
plt.title("density (kg/m^3)")
plt.legend(loc='upper left')
plt.ylim([4, 8])
#plt.savefig("output_figures/example_inv_big_pv_show.png")
plt.show()
if whattodo=="profile2":
#run with:
#python -m cProfile -o output.pstats example_inv_big_pv.py profile2 1
#gprof2dot.py -f pstats output.pstats | dot -Tpng -o output.png
[error(0.5,0.1,0.2) for i in range(0,1000)]
if whattodo=="profile":
#just run normally
print error(0.5,0.1,0.2)
cProfile.run("[error(0.5,0.1,0.2) for i in range(0,1000)]")