def main(ngrains=100, sigma=5.0, iopt=1, ifig=1):
"""
arguments
=========
ngrains = 100
sigma = 5.
iopt = 1 (1: gauss (recommended); 2: expov; 3: logno; 4: norma)
ifig = 1
"""
import upf
import matplotlib.pyplot as plt
h = mmm()
gr = []
for i in range(ngrains):
dth = random.uniform(-180.0, 180.0)
g = gen_gamma_gr(dth, sigma, iopt=iopt)
for j in range(len(h)):
temp = np.dot(g, h[j].T)
phi1, phi, phi2 = euler(a=temp, echo=False)
gr.append([phi1, phi, phi2, 1.0 / ngrains])
fn = "gam_fib_ngr%s_sigma%s.cmb" % (str(len(gr)).zfill(5), str(sigma).zfill(3))
f = open(fn, "w")
f.writelines("Artificial gamma fibered polycrystal aggregate\n")
f.writelines("var_gam_fiber.py python script\n")
f.writelines("distribution: %s")
if iopt == 1:
f.writelines(" gauss")
if iopt == 2:
f.writelines(" expov")
if iopt == 3:
f.writelines(" logno")
if iopt == 4:
f.writelines(" norma")
f.writelines("\n")
f.writelines("B %i\n" % ngrains)
for i in range(len(gr)):
f.writelines("%7.3f %7.3f %7.3f %13.4e\n" % (gr[i][0], gr[i][1], gr[i][2], 1.0 / len(gr)))
upf.cubgr(gr=gr, ifig=ifig)
plt.figure(ifig).savefig("%s.pdf" % (fn.split(".cmb")[0]))
return np.array(gr)
def rolling(fibers=['gamma','alpha','eta','epsilon','sigma'],
wgts=[0.7,0.1,0.1,0.1],ifig=3,sigma=15,
ngr=100):
"""
"""
fn='rolling_texture_%s.cmb'%str(ngr).zfill(5)
f = open(fn,'w')
f.writelines('Artifical rolling texture for bcc\n')
f.writelines('Combinations of:')
for i in xrange(len(fibers)):
f.writelines('%3i) %s; '%(i+1,fibers[i]))
f.writelines('\n')
f.writelines('weigts of comp :')
for i in xrange(len(fibers)):
f.writelines('%3.1f; '%wgts[i])
f.writelines('\n')
import upf
import matplotlib.pyplot as plt
wgts = np.array(wgts)
wgts = wgts/wgts.sum()
total_pop = []
for i in xrange(len(fibers)):
gr = main(ngrains=ngr/len(fibers),sigma=sigma,
iopt=1,iexit=True,fiber=fibers[i])
grt = gr.T
grt[-1] = grt[-1] * wgts[i]
gr = grt.T
for j in xrange(len(gr)):
total_pop.append(gr[j])
total_pop = np.array(total_pop)
f.writelines('B %i\n'%len(total_pop))
for i in xrange(len(total_pop)):
f.writelines('%+7.3f %+7.3f %+7.3f %+13.4e\n'%(
total_pop[i][0], total_pop[i][1], total_pop[i][2], 1./len(total_pop)))
upf.cubgr(gr=total_pop,ifig=ifig)
return total_pop
def main(ngrains=100,sigma=5.,iopt=1,ifig=1):
"""
arguments
=========
ngrains = 100
sigma = 5.
iopt = 1 (1: gauss (recommended); 2: expov; 3: logno; 4: norma)
ifig = 1
"""
import upf
import matplotlib.pyplot as plt
h = mmm()
gr = []
for i in range(ngrains):
dth = random.uniform(-180., 180.)
g = gen_gamma_gr(dth, sigma, iopt=iopt)
for j in range(len(h)):
temp = np.dot(g,h[j].T)
phi1,phi,phi2 = euler(a=temp, echo=False)
gr.append([phi1,phi,phi2,1./ngrains])
fn = 'gam_fib_ngr%s_sigma%s.cmb'%(str(len(gr)).zfill(5),str(sigma).zfill(3))
f = open(fn,'w')
f.writelines('Artificial gamma fibered polycrystal aggregate\n')
f.writelines('var_gam_fiber.py python script\n')
f.writelines('distribution: %s')
if iopt==1: f.writelines(' gauss')
if iopt==2: f.writelines(' expov')
if iopt==3: f.writelines(' logno')
if iopt==4: f.writelines(' norma')
f.writelines('\n')
f.writelines('B %i\n'%ngrains)
for i in range(len(gr)):
f.writelines('%7.3f %7.3f %7.3f %13.4e\n'%(
gr[i][0], gr[i][1], gr[i][2], 1./len(gr)))
upf.cubgr(gr=gr,ifig=ifig)
plt.figure(ifig).savefig('%s.pdf'%(fn.split('.cmb')[0]))
return np.array(gr)
def odf2dg(odfn=None, mmm=False, rot=0, fnout=None, ng=None):
"""
write into weighted discrete grain files
Arguments
=========
odfn = None
mmm = False
rot = 0
"""
import os
import matplotlib.pyplot as plt
import numpy as np
os.sys.path.append('/Users/yj/Dropbox/devel/EVPSC_f2py/pyscripts')
os.sys.path.append('/Users/yj/Dropbox/devel/EVPSC_f2py/pyscripts/pf')
import cmb, upf
if type(ng) != type(None):
ng = [20000]
if mmm: ng = np.array(ng) / 4
for igr in range(len(ng)):
cmb.main(odf=odfn, ngrain=ng[igr], outputfile='dum.tex', iplot=False)
# sample symmetry recovery when mmm has been applied (reduced 90x90x90)
if mmm: grains = sample_mmm(fn='dum.tex')
else: grains = np.loadtxt('dum.tex', skiprows=4)
os.remove('dum.tex')
grains = inplanerot(rot=rot, grains=grains)
fout = open('%s_%i.cmb' % (fnout, len(grains)), 'w')
fout.writelines('dummy\ndummy\ndummy\n B %i\n ' % len(grains))
for i in range(len(grains)):
fout.writelines(
'%.5f %.5f %.5f %.7e\n' %
(grains[i][0], grains[i][1], grains[i][2], grains[i][3]))
upf.cubgr(gr=grains, ifig=igr, poles=[[1, 1, 0], [2, 0, 0], [2, 1, 1]])
plt.gcf().savefig('pf_%i.pdf' % ng[igr])
