def eps_sin2psi(difile=None, istep=0, phi=0, iph=1, ax=None,iopt=0):
"""
Arguments
=========
difile : diffraction input file name
istep : i-th step (starts from 0)
phi : phi angle (0, 45, 90, 135, 180 ... )
iph : i-th phase (starts from 1)
ax : None
iopt : 0->int_eps_ph%i.out; 1-> int_els_ph%i.out
"""
from sff_converter import condition
difl, nphi, phis, nbeta, neps, eps = condition(difile)
dat = intepsphiout(istep=istep,phi=phi,iph=iph,isort=True,iopt=iopt)
psi, eps, sig, ms11, ms22, ms33, ms23, ms13, ms12 = dat[0:9]
#me11, me22, me33, me23, me13, me12 = dat[9:15]
sin2psi = np.sin(psi*np.pi/180.)**2
print 'psi', psi
print 'sin2psi', sin2psi
print 'eps', eps
if ax==None: ax = plt.gca()
# if iopt==1: label=r'$\sigma_{11}=$%7.3f'%ms11
# elif iopt==1: label=r'$\sigma_{11}=$%7.3f'%ms11
label=None
ax.plot(sin2psi,eps*10**3,'o-',label=label)
ax.set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=28))
ax.set_ylabel(r'$\varepsilon^{hkl}\times10^{3}$', dict(fontsize=28))
if label!=None:
ax.legend(loc='lower right',fancybox=True).get_frame().set_alpha(0.5)
plt.tight_layout()
plt.show()
def eps_sin2psi(difile=None, istep=0, phi=0, iph=1, ax=None,iopt=0):
"""
Arguments
=========
difile : diffraction input file name
istep : i-th step (starts from 0)
phi : phi angle (0, 45, 90, 135, 180 ... )
iph : i-th phase (starts from 1)
ax : None
iopt : 0->int_eps_ph%i.out; 1-> int_els_ph%i.out
"""
from sff_converter import condition
difl, nphi, phis, nbeta, neps, eps = condition(difile)
dat = intepsphiout(istep=istep,phi=phi,iph=iph,isort=True,iopt=iopt)
psi, eps, sig, ms11, ms22, ms33, ms23, ms13, ms12 = dat[0:9]
#me11, me22, me33, me23, me13, me12 = dat[9:15]
sin2psi = np.sin(psi*np.pi/180.)**2
print 'psi', psi
print 'sin2psi', sin2psi
print 'eps', eps
if ax==None: ax = plt.gca()
# if iopt==1: label=r'$\sigma_{11}=$%7.3f'%ms11
# elif iopt==1: label=r'$\sigma_{11}=$%7.3f'%ms11
label=None
ax.plot(sin2psi,eps*10**3,'o-',label=label)
ax.set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=28))
ax.set_ylabel(r'$\varepsilon^{hkl}\times10^{3}$', dict(fontsize=28))
if label!=None:
ax.legend(loc='lower right',fancybox=True).get_frame().set_alpha(0.5)
plt.tight_layout()
plt.show()
def ex_igb_bix_t(fn="igstrain_bix_ph1.out", fnout="igstrain_loads_avg.out", flow=None):
"""
Default input fn is 'igstrain_bix_ph1.out':
this output file contains diffraction data measured
prior to the 'interruption' by unloading. Note that
this unloading is to elastically and uniaixally
deform the polycrystal in order to obtain 'stress factor'
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
difl, nphi, phis, nbeta, dum, eps = condition(fn=None)
if flow != None:
eps = flow.epsilon_vm[::]
else:
eps = eps * 2
# rst[2,nst,nphi,npsi], sf2[2,nst,nphi,npsi]
rst, sf2 = ex_igb_bix(fn=fn, ifig=123, flow=flow)
f = open(fnout, "w")
f.writelines(
"%3s %8s %13s %14s %14s %14s %14s %14s %14s\n"
% ("phi", "psi", "eps0", "F11", "F22", "F33", "F23", "F13", "F12")
)
for ist in xrange(len(rst[0])):
f.writelines("-- eps^{eff}: %5.3f\n" % eps[ist])
for iphi in xrange(nphi):
phi = phis[iphi]
psi = rst[0, ist, iphi, :]
ep = rst[1, ist, iphi, :]
for i in xrange(len(psi)):
ps = psi[i]
e = ep[i]
f11 = sf2[0, ist, iphi, i]
f22 = sf2[1, ist, iphi, i]
f.writelines("%3.0f %+7.2f %+12.7e" % (phi, ps, e))
f.writelines(" %+12.7e %+12.7e" % (f11, f22))
for j in xrange(4):
f.writelines(" %+12.7e" % 0.0)
f.writelines("\n")
f.close()
plt.close("all")
return rst
def ex_igb_bix_t(fn='igstrain_bix_ph1.out',
fnout='igstrain_loads_avg.out',
flow=None):
"""
Default input fn is 'igstrain_bix_ph1.out':
this output file contains diffraction data measured
prior to the 'interruption' by unloading. Note that
this unloading is to elastically and uniaixally
deform the polycrystal in order to obtain 'stress factor'
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
difl, nphi, phis, nbeta, dum, eps = condition(fn=None)
if flow!=None: eps = flow.epsilon_vm[::]
else: eps = eps * 2
#rst[2,nst,nphi,npsi], sf2[2,nst,nphi,npsi]
rst, sf2 = ex_igb_bix(fn=fn,ifig=123,flow=flow)
f = open(fnout,'w')
f.writelines('%3s %8s %13s %14s %14s %14s %14s %14s %14s\n'
%('phi','psi','eps0','F11','F22',
'F33','F23','F13','F12'))
for ist in xrange(len(rst[0])):
f.writelines('-- eps^{eff}: %5.3f\n'%eps[ist])
for iphi in xrange(nphi):
phi = phis[iphi]
psi = rst[0,ist,iphi,:]
ep = rst[1,ist,iphi,:]
for i in xrange(len(psi)):
ps = psi[i]
e = ep[i]
f11 = sf2[0,ist,iphi,i]
f22 = sf2[1,ist,iphi,i]
f.writelines('%3.0f %+7.2f %+12.7e'%(phi,ps,e))
f.writelines(' %+12.7e %+12.7e'%(f11,f22))
for j in xrange(4): f.writelines(' %+12.7e'%0.)
f.writelines('\n')
f.close()
plt.close('all')
return rst
def ex_igb_t(fn='igstrain_fbulk_ph1.out',
fnout='igstrain_unloads_avg.out',
mxnst=None,flow=None):
"""
Arguments
=========
fn ='igstrain_fbulk_ph1.out'
fnout='igstrain_unloads_avg.out'
mxnst=None
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
difl, nphi, phis, nbeta, dum1, dum2 = condition(fn=None)
if flow==None: raise IOError, 'Flow should be given'
elif flow!=None:
flow.get_eqv()
eps = flow.epsilon_vm[::]
def ex_igb_t(fn='igstrain_fbulk_ph1.out',
fnout='igstrain_unloads_avg.out',
mxnst=None,flow=None):
"""
Arguments
=========
fn ='igstrain_fbulk_ph1.out'
fnout='igstrain_unloads_avg.out'
mxnst=None
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
difl, nphi, phis, nbeta, dum1, dum2 = condition(fn=None)
if flow==None: raise IOError, 'Flow should be given'
elif flow!=None:
flow.get_eqv()
eps = flow.epsilon_vm[::]
def ex_igb_bix(fn='igstrain_bix_ph1.out',ifig=1,iphi=0,
mxnst=None,flow=None):
"""
"""
import matplotlib.pyplot as plt
from sff_converter import condition
from MP.ssort import sh as sort
difl, nphi, phis, nbeta, neps, eps = condition(fn=None)
if flow!=None: eps = flow.epsilon_vm[::]
else: eps = eps * 2. ## Under condition balanced biaxial
fig01 = plt.figure(ifig,figsize=(15.5,8.5))
ax01=fig01.add_subplot(231)
ax02=fig01.add_subplot(232)
ax03=fig01.add_subplot(233)
ax04=fig01.add_subplot(234)
ax05=fig01.add_subplot(235)
axes01 = [ax01,ax02,ax03,ax04,ax05]
fig02 = plt.figure(ifig+1,figsize=(15.5,8.5))
ax01=fig02.add_subplot(231)
ax02=fig02.add_subplot(232)
ax03=fig02.add_subplot(233)
ax04=fig02.add_subplot(234)
ax05=fig02.add_subplot(235)
axes02 = [ax01,ax02,ax03,ax04,ax05]
tdat = reader2(fn,iopt=2,isort=False)
nst = len(tdat[0])
npsi=len(tdat[0,0,0])
rst = np.zeros((2,nst,nphi,npsi))
sf2 = np.zeros((2,nst,nphi,npsi)) # f11 and f22
markers=['o','x','+','^','d','*','o','x','+','^','d','*']
colors =['r','g','b','k','m','y','r','g','b','k','m','y']
axe_lab = [r'(a) $\varepsilon-F_{ij}\bar{\Sigma}_{ij}$',
r'(b) $\bar{E}^{el}(\phi,\psi)$',
r'(c) $\varepsilon^{hkl}$ and $F_{ij}\bar{\Sigma}_{ij}$',
r'(d) $F_{ij}$',
r'(e) $\bar{\Sigma}_{ij}$','(f)','(g)']
if mxnst!=None: nst=mxnst
for i in range(nst):
ehkl = tdat[0,i,iphi,:] #e(hkl)
e = tdat[1,i,iphi,:] #macro
ehkle = tdat[2,i,iphi,:] #e-macro
ige = tdat[3,i,iphi,:] #e(hkl) - F^hkl_ij * Sij (ij=1,1 and 2,2)
f11 = tdat[4,i,iphi,:] #F^hkl_11
f22 = tdat[5,i,iphi,:] #F^hkl_22
s11 = tdat[6,i,iphi,:] #S11
s22 = tdat[7,i,iphi,:] #S22
psi = tdat[8,i,iphi,:] #psi
sin2psi = np.sin(psi*np.pi/180.)**2
x = np.sin(psi*np.pi/180.)**2
y0 = ehkl-e; y1=e; y2=f11*s11+f22*s22; y3=f11; y4=f22;
y5 = s11; y6= s22; y7=ehkl
newx, Y0,Y1,Y2,Y3,Y4,Y5,Y6,Y7 = sort(
x,y0,y1,y2,y3,y4,y5,y6,y7)
## ax01
l, = axes01[0].plot(newx,Y0*1e6,markers[i])
dum = ehkl - (f11*s11 + f22*s22)
x = np.sin(psi*np.pi/180.)**2
newx, newy = sort(x, dum)
axes01[0].plot(newx,newy*1e6,'-',color=l.get_color())
## ax02
axes01[1].plot(newx,Y1*1e6,markers[i],
label=r'$\bar{E}^{\mathrm{eff}}$=%4.2f'%eps[i])
## ax03
l, = axes01[2].plot(newx,Y7*1e6,markers[i])
axes01[2].plot(newx,Y2*1e6,'-',color=l.get_color())
## ax04
l, = axes01[3].plot(newx,Y3*1e6,markers[i])
axes01[3].plot(newx,Y4*1e6,'-',color=l.get_color())
## ax05
l, = axes01[4].plot(newx,Y5,markers[i])
axes01[4].plot(newx,Y6,'-',color=l.get_color())
if i==0:
axes01[0].text(0.25,0.1,
r'lines: $\varepsilon-(F_{11}\bar{\Sigma}_{11}$'+\
r'$+F_{22}\bar{\Sigma}_{22})$',
transform=axes01[0].transAxes)
axes01[0].text(0.25,0.2,r'symbols: $\varepsilon-\bar{E}^{el} $',
transform = axes01[0].transAxes)
axes01[2].text(0.6,0.3,r'symbols: $\varepsilon^{hkl}$',
transform=axes01[2].transAxes)
axes01[2].text(0.6,0.2,r'lines: $F_{ij}\bar{\Sigma_{ij}}$',
transform=axes01[2].transAxes)
axes01[3].text(0.5,0.5,r'symbols: $F_{11}$',transform=
axes01[3].transAxes)
axes01[3].text(0.5,0.4,r'lines: $F_{22}$',transform=
axes01[3].transAxes)
axes01[4].text(0.5,0.5,r'symbols: $\bar{\Sigma}_{11}$',
transform=axes01[4].transAxes)
axes01[4].text(0.5,0.4,r'lines: $\bar{\Sigma}_{22}$',
transform=axes01[4].transAxes)
axes01[0].set_ylabel(r'IG strain $[\mu\varepsilon]$',
dict(fontsize=15))
axes01[1].set_ylabel(r'Macro strain $[\mu\varepsilon]$',
dict(fontsize=15))
axes01[2].set_ylabel(r'$\varepsilon^{hkl}(\phi,\psi)$'+\
r' and $F_{ij}\bar{\Sigma}_{ij}$'+\
r' $[\mu\varepsilon]$',
dict(fontsize=15))
axes01[3].set_ylabel(r'$F_{11}$ and $F_{22}$'+\
r' $ [TPa^{-1}]$',
dict(fontsize=15))
axes01[4].set_ylabel(r'$\bar{\Sigma}_{11}$'+\
r' and $\bar{\Sigma}_{22}$ [MPa]',
dict(fontsize=15))
for j in range(nphi):
ehkl = tdat[0,i,j,:] #e(hkl)
e = tdat[1,i,j,:] #macro
ehkle = tdat[2,i,j,:] #e-macro
ige = tdat[3,i,j,:] #e(hkl) - F^hkl_ij * Sij (ij=1,1 and 2,2)
f11 = tdat[4,i,j,:] #F^hkl_11
f22 = tdat[5,i,j,:] #F^hkl_22
s11 = tdat[6,i,j,:] #S11
s22 = tdat[7,i,j,:] #S22
psi = tdat[8,i,j,:] #psi
dum = ehkl - (f11*s11 + f22*s22)
rst[0,i,j,:] = psi[:]
rst[1,i,j,:] = dum[:]
sf2[0,i,j,:]=f11[:]
sf2[1,i,j,:]=f22[:]
sin2psi = np.sin(psi*np.pi/180.)**2
x = sin2psi
y = ige*1e6
newx, newy = sort(x,y)
axes02[j].plot(newx,newy,'-')
if i==0:
axes02[j].set_title(r'$\phi=%3.0f^\circ$'%phis[j])
# axes01[0].legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
axes01[1].legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
for i in range(len(axes01)):
#axes01[i].set_title(axe_lab[i])
axes01[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes01[i].grid('on')
for i in range(len(axes02)):
axes02[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes02[i].set_ylabel(r'IG strain $[\mu\varepsilon]$', dict(fontsize=20))
axes02[i].grid('on')
fig01.tight_layout()
fig02.tight_layout()
fig01.savefig('bix_analysis.pdf')
fig02.savefig('bix_eps0_at_phis.pdf')
return rst, sf2 #rst[2,nst,nphi,npsi], sf2[2,nst,nphi,npsi]
def ex_igb(fn='igstrain_fbulk_ph1.out',ifig=2,iphi=0,isf=0,
mxnst=None,flow=None):
"""
Read ig strain file and analyze... (igstrain_fbulk_ph1.out)
igstrain_fbulk_ph1.out contains diffraction for each and every
reloading from 'unloaded' polycrystal.
Arguments
=========
fn = 'igstrain_fbulk_ph1.out'
ifig = 2
iphi=0
isf=0
mxnst=None
"""
import matplotlib.pyplot as plt
from sff_converter import condition
from MP.ssort import sh as sort
from MP.mat import voigt
from MP.lib.mpl_lib import wide_fig as wf
difl, nphi, phis, nbeta, dum1, dum2 = condition(fn=None)
if flow==None: raise IOError, 'Flow stress is missing'
eps = flow.epsilon_vm[::]
neps = flow.nstp
nw = 4
remainder = np.mod(neps,nw)
if remainder==0:
nh = neps/nw
elif remainder>0:
nh = neps/nw + 1
fig =wf(nw=2, nh=2,ifig=ifig,iarange=True); axes =fig.axes
ax01, ax02, ax03, ax04 = axes
fig01=wf(nw=nw,nh=nh,ifig=ifig+1,iarange=True,nfig=neps);axes01=fig01.axes
fig02=wf(nw=nw,nh=nh,ifig=ifig+2,iarange=True,nfig=neps);axes02=fig02.axes
fig03=wf(nw=nw,nh=nh,ifig=ifig+3,iarange=True,nfig=neps);axes03=fig03.axes
fig04=wf(nw=nw,nh=nh,ifig=ifig+4,iarange=True,nfig=neps);axes04=fig04.axes
tdat, usf = reader2(fn,iopt=1)
print tdat.shape
nsf = len(usf); nst = len(tdat[0]); npsi = len(tdat[0,0,0,0,:])
if nst!=neps: raise IOError, 'Inconsistency between neps and nst'
markers = ['o','x','+','^','d','*','o','x','+','^','d','*''o','x','+','^','d','*']
colors = ['r','g','b','k','m','y','r','g','b','k','m','y','r','g','b','k','m','y']
from string import ascii_lowercase as alphabet
axe_lab=[]
for ab in alphabet: axe_lab.append('(%s)'%ab)
yl = 0; yh = 0
avg = np.zeros((2,nst,npsi)) #
sft = np.zeros((6,nst,npsi)) # f^{hkl}
rsqt = np.zeros((6,nst,npsi)) # f^{hkl}
if mxnst!=None: nst=mxnst
for i in range(nst):
ehkl = tdat[0,i,isf,iphi,:] # e(hkl,phi,psi)
e = tdat[1,i,isf,iphi,:] # macro
ehkle = tdat[2,i,isf,iphi,:] # e - macro
fhkl = tdat[3,i,isf,iphi,:] # Fhkl
fbulk = tdat[4,i,isf,iphi,:] # Fbulk
ige = tdat[5,i,isf,iphi,:] # e - F_ij *Sij
sij = tdat[6,i,isf,iphi,:] # Sij
psi = tdat[8,i,isf,iphi,:]
rsq = tdat[9,i,isf,iphi,:]
sin2psi = np.sin(psi*np.pi/180.)**2
axes01[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
axes02[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
axes03[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
axes04[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
igys = []; igos = []; igts = []
for j in range(nsf): ## Number of elastic loading for stress factor calc.
ehkl_ = tdat[0,i,j,iphi,:] # e(hkl,phi,psi)
e_ = tdat[1,i,j,iphi,:] # macro
fhkl_ = tdat[3,i,j,iphi,:] # Fhkl
fbulk_= tdat[4,i,j,iphi,:] # Fbulk
sij_ = tdat[6,i,j,iphi,:] # Sij
rsq_ = tdat[6,i,j,iphi,:] # R^2
sft[j,i,:] = fhkl_[::] #
rsqt[j,i,:] = rsq_[::]
i1,i2 = usf[j]
igo = ehkl_ - e_ ## old way
igt = []
m = 0
for k in range(len(fbulk_)):
if fbulk_[k]==0:
m=m+1
igt.append(0.)
else: igt.append(
ehkl_[k] - \
fhkl_[k] / fbulk_[k] * e_[k])
if m!=0: print m, 'case of fbulk'+\
'(phi,psi)=0 happened'
igt = np.array(igt)
igy = ehkl_ - fhkl_ * sij_ ## new way (YJ)
if j==0:
igy_avg = igy/nsf
igo_avg = igo/nsf
igt_avg = igt/nsf
else:
igy_avg = igy_avg + igy/nsf
igo_avg = igo_avg + igo/nsf
igt_avg = igt_avg + igt/nsf
igys.append(igy);igos.append(igo);igts.append(igt)
# The old method
if j==4: m = markers[j]
else: m= '--'
y = igo
x = np.sin(psi[:]*np.pi/180.)**2
newx,newy = sort(x,y) # shell sort
axes01[i].plot(
newx,newy*1e6,m,color=colors[j],
label=r'$\Sigma_{%1i%1i}$'%(i1,i2))
if i==0:axes01[i].legend(loc='best',
fancybox=True).get_frame().set_alpha(0.5)
if j==0:axes01[i].grid('on')
yl0, yh0 = axes01[i].get_ylim()
if yl0<yl: yl = yl0
if yh0>yh: yh = yh0
# GHT
y = igt
x = np.sin(psi[:]*np.pi/180.)**2
newx,newy = sort(x,y) # shell sort
axes02[i].plot(
newx,newy*1e6,m,color=colors[j],
label=r'$\Sigma_{%1i%1i}$'%(i1,i2))
if i==0:axes02[i].legend(loc='best',
fancybox=True).get_frame().set_alpha(0.5)
if j==0:axes02[i].grid('on')
yl0, yh0 = axes02[i].get_ylim()
if yl0<yl: yl = yl0
if yh0>yh: yh = yh0
# YJ
y = igy
x = np.sin(psi[:]*np.pi/180.)**2
newx,newy = sort(x,y) # shell sort
axes03[i].plot(
newx,newy*1e6,m,color=colors[j],
label=r'$\Sigma_{%1i%1i}$'%(i1,i2))
if i==0:axes03[i].legend(loc='best',
fancybox=True).get_frame().set_alpha(0.5)
if j==0:axes03[i].grid('on')
yl0, yh0 = axes03[i].get_ylim()
if yl0<yl: yl = yl0
if yh0>yh: yh = yh0
pass # over nsf
# errors for 6 cases of SF loading
igos = np.array(igos).swapaxes(0,1)
igys = np.array(igys).swapaxes(0,1)
igts = np.array(igts).swapaxes(0,1)
igoe=[]; igye=[]; igte=[]
for m in range(len(igo_avg)):
igoe.append(igos[m].std())
igte.append(igts[m].std())
igye.append(igys[m].std())
igoe,igte,igye = np.array(igoe),np.array(igte),np.array(igye)
##
# average old
y = igo_avg
ye= igoe
x = np.sin(psi[:]*np.pi/180.)**2
newx, newy, newye = sort(x,y,ye)
axes04[i].errorbar(
x=newx,y=newy*1e6,yerr=newye*1e6,
ls='-',color='r',label='old')
# average YU
y = igy_avg
avg[0,i,:] = psi[:]; avg[1,i,:] = y[:]
ye = igye
x = np.sin(psi[:]*np.pi/180.)**2
newx, newy, newye = sort(x,y,ye)
axes04[i].errorbar(
x=newx,y=newy*1e6,yerr=newye*1e6,
ls='-',ms=3,color='b',label='YJ')
axes04[i].grid('on')
axes04[0].legend(loc='best',fancybox=True)\
.get_frame().set_alpha(0.5)
i1,i2 = voigt.ijv[:,isf]
sin2psi = np.sin(psi*np.pi/180.)**2
ax01.plot(sin2psi,ige*1e6,markers[i]#,color='k',
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
ax02.plot(sin2psi,ehkle*1e6,markers[i]#,color='k'
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
l, = ax04.plot(sin2psi,fhkl*1e6,markers[i]#,color='k'
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
if i==0: ax04.plot(sin2psi,fbulk*1e6,'-',alpha=0.2,color='k')
# ax03.plot(sin2psi,e*1e6,markers[i],
# label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
ax03.plot(sin2psi,(ehkl-fhkl/fbulk*e)*1e6,markers[i]
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
sin2psi = np.sin(psi*np.pi/180.)**2
## Deco the axes
for i in range(nst):
axes01[i].set_ylim(yl,yh)
axes02[i].set_ylim(yl,yh)
axes03[i].set_ylim(yl,yh)
#axes04[i].set_ylim(yl,yh)
axes01[i].set_xlim(0.,); axes02[i].set_xlim(0.,)
axes03[i].set_xlim(0.,); axes04[i].set_xlim(0.,)
for i in range(len(axes)):
axes[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes01[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes02[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes03[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes04[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
for i in range(len(axes)): axes[i].grid('on')
for i in range(len(axes)): axes[i].set_xlim(0.,0.5)
ax01.set_ylabel(
r'$\varepsilon^{hkl} - F^{hkl}_{ij}\bar{\Sigma}_{ij}$'+\
r' $[\mu\varepsilon]$',dict(fontsize=15))
ax02.set_ylabel(
r'$\varepsilon^{hkl} - E$ $[\mu\varepsilon]$',dict(fontsize=15))
ax03.set_ylabel(r'$\varepsilon^{hkl} - $'+
r'$F_{ij}^{hkl}/F^{\mathrm{bulk}}_{ij}$'+
r'$ \bar{E} $ $[\mu \varepsilon]$',dict(fontsize=15))
ax04.set_ylabel(r'$F_{ij}^{hkl}, F^{\mathrm{bulk}}_{ij}$ $[TPa^{-1}]$',
dict(fontsize=15))
ax01.set_title(r'(a) $\varepsilon(hkl,%3.0f^\circ,\psi)$'\
r'$ - F_{%1i%1i}(hkl,%3.0f^\circ,\psi)$'\
r'$\bar{\Sigma}_{%1i%1i}$'%(phis[iphi],i1,i2,phis[iphi],i1,i2),
dict(fontsize=13))
ax02.set_title(
r'(b) $\varepsilon(hkl,%3.0f^\circ,\psi)$'%(phis[iphi])+\
r'$ - \bar{E}(%3.0f^\circ,\psi)$'%\
(phis[iphi]),dict(fontsize=13))
ax03.set_title(
r'(c) $\varepsilon(hkl,%3.0f^\circ,\psi)$'%(phis[iphi])+
r'$-F_{%1i%1i}(hkl,%3.0f^\circ,\psi)/$'%(i1,i2,phis[iphi])+
r'$F_{%1i%1i}^{\mathrm{bulk}}(%3.0f^\circ,\psi)$'%(
i1,i2,phis[iphi])+
r'$\bar{E}(%3.0f^\circ,\psi)$'%(phis[iphi]),dict(fontsize=13))
ax04.set_title(r'(d) $F^{hkl}_{%1i%1i}$ and '%(i1,i2)+\
r'$F^{\mathrm{bulk}}_{%1i%1i}$'%
(i1,i2), dict(fontsize=13))
ax04.legend(loc='lower right',fancybox=True).get_frame().set_alpha(0.5)
# fig.tight_layout(); fig01.tight_layout(); fig02.tight_layout()
# fig03.tight_layout(); fig04.tight_layout()
fig.savefig('ig_bulk.pdf');fig01.savefig('ig_bulk_Old.pdf')
fig02.savefig('ig_bulk_GHT.pdf'); fig03.savefig('ig_bulk_YJ.pdf')
fig04.savefig('ig_bulk_avg.pdf')
## save the averaged IG strain
return avg, sft # avg[2,nst,npsi], sft[6,nst,npsi]
def ex_igb_cf(fnu='igstrain_fbulk_ph1.out',
fnl='igstrain_bix_ph1.out'):
"""
compare the ig strain at load and unloads.
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
from MP.ssort import sh as sort
difl, nphi, phis, nbeta, neps, eps = condition(fn=None)
plt.ioff()
avgu,sfs,rsqs=ex_igb_t(fn=fnu,fnout='igstrain_unloads_avg.out') # at unlaods
avgl=ex_igb_bix_t(fn=fnl,fnout='igstrain_loads_avg.out') # at loads
print avgu.shape#[2,nst,nphi,nspi]
print avgl.shape#[2,nst,iphi,npsi]
plt.close('all'); plt.ion()
fig = plt.figure(2,figsize=(15.5,8.5))
fig02 = plt.figure(203); ax_dum=fig02.add_subplot(111)
at = ['(a)','(b)','(c)','(d)','(e)','(f)']
markers=['x','o','.','d','*']
ls =['--x','--o','--.','--d','--*']
axes=[]
for i in range(nphi):
axes.append(fig.add_subplot(2,3,i+1))
for ist in range(len(avgu[0])):
for iphi in range(len(avgu[0][ist])):
ax = axes[iphi]
phi = phis[iphi]
psi = avgu[0,ist,iphi,:]
eu = avgu[1,ist,iphi,:]
el = avgl[1,ist,iphi,:]
sin2psi = np.sin(psi*np.pi/180.)**2
x = sin2psi
y0 = eu*1e6
y1 = el*1e6
newx,Y0,Y1 = sort(x,y0,y1)
if iphi==0:
l, = ax.plot(newx,Y0,'x',
label=r'$\bar{E}^{\mathrm{eff}}=%4.2f$'%(eps[ist]*2.0))
ax_dum.plot(newx,Y0,markers[ist],color='k',
label=r'$\bar{E}^{\mathrm{eff}}=%4.2f$'%(eps[ist]*2.0))
ax_dum.plot(newx,Y1,ls[ist],color='k')
elif iphi!=0:
l, = ax.plot(newx,Y0,'x')
ax.plot(newx,Y1,'-',color=l.get_color())
ax_dum.set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
ax_dum.set_ylabel(r'IG strains $[\mu\varepsilon]$', dict(fontsize=20))
ax_dum.legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
ax_dum.text(0.1, 0.85, 'Lines: at loads',transform=ax_dum.transAxes)
ax_dum.text(0.1, 0.92, 'Symbols: at unloads',transform=ax_dum.transAxes)
ax_dum.grid('on')
fig02.tight_layout()
fig02.savefig('ige_cf_phi1.pdf')
for i in range(len(axes)):
ax = axes[i]
ph = phis[i]
#ax.set_title(r'%s $\phi=%3.0f^\circ$'%(at[i],ph))
ax.set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
ax.set_ylabel(r'IG strains $[\mu\varepsilon]$', dict(fontsize=20))
if i==0:
ax.legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
ax.text(0.1, 0.85, 'Lines: at loads',transform=ax.transAxes)
ax.text(0.1, 0.92, 'Symbols: at unloads',transform=ax.transAxes)
ax.grid('on')
fig.tight_layout()
fig.savefig('ige_cf.pdf')
def intepsphiout(istep=0, phi=0, iphi=0, iph=1, isort=False, iopt=0):
"""
Arguments
=========
istep = 0 (starts from 0)
phi = -90 or iphi as index for iopt 4 and 5.
iph = 1 (phase starts from 1)
isort = False
iopt =
0: ('int_eps_ph%i.out') - at unloads,
1: ('int_els_ph%i.out') - at loads
2: ('igstrain_load_ph%i.out') - at
3: ('igstrain_unload_ph%i.out') - at
4: ('igstrain_unloads_avg.out') at unloads
5: ('igstrain_loads_avg.out') at loads
"""
from ssort import shellSort as ssort
from ssort import sh
from ssort import ind_swap
eps, sig, psi = [], [], []
if iopt==0: filename='int_eps_ph%i.out'%iph
elif iopt==1: filename='int_els_ph%i.out'%iph
elif iopt==2: filename='igstrain_load_ph%i.out'%iph
elif iopt==3: filename='igstrain_unload_ph%i.out'%iph
elif iopt==4: filename='igstrain_unloads_avg.out'
elif iopt==5: filename='igstrain_loads_avg.out'
fc(filename)
if iopt<4:
dl = open(filename, 'r').readlines()
npb = int(dl[1].split()[0])
dat = dl[2:]
dat = dat[(istep)*npb: (istep)*npb + npb]
if len(dat)==0: raise IOError, 'empty dat is returned ..'
for i in range(len(dat)):
temp = map(float, dat[i].split())
step, p, beta, psi1, psi2, eps1, eps2,\
sig1, sig2, n1, n2, v1, v2 = temp[:13]
ms11, ms22, ms33, ms23, ms13, ms12 = temp[13:19]
me11, me22, me33, me23, me13, me12 = temp[19:25]
if p==phi:
eps.append(eps1); eps.append(eps2)
sig.append(sig1); sig.append(sig2)
psi.append(psi1) # detector 1
psi.append(psi2) # detector 2
if isort:
temp1, temp2 = [], []
sortedarray, ind = ssort(psi)#, len(psi))
for k in range(len(sortedarray)):
temp1.append(eps[ind[k]-1])
temp2.append(sig[ind[k]-1])
eps = temp1
sigma = temp2
psi = sortedarray
return np.array(psi), np.array(eps), np.array(sig),\
ms11, ms22, ms33, ms23, ms13, ms12
if iopt==4 or iopt==5:
from sff_converter import condition
from pepshkl import reader3 as reader
difl, nphi, phis, nbeta, neps, eps = condition()
tdat, psi = reader(filename,isort) #[ist,iphi,ipsi]
sin2psi = np.sin(psi*np.pi/180.)**2
npsi = len(psi)
eps = tdat[istep, iphi]
return psi, eps
raise IOError, 'Unexpected ioption give in intepsphiout'
def intepsphiout(istep=0, phi=0, iphi=0, iph=1, isort=False, iopt=0):
"""
Arguments
=========
istep = 0 (starts from 0)
phi = -90 or iphi as index for iopt 4 and 5.
iph = 1 (phase starts from 1)
isort = False
iopt =
0: ('int_eps_ph%i.out') - at unloads,
1: ('int_els_ph%i.out') - at loads
2: ('igstrain_load_ph%i.out') - at
3: ('igstrain_unload_ph%i.out') - at
4: ('igstrain_unloads_avg.out') at unloads
5: ('igstrain_loads_avg.out') at loads
"""
from MP import ssort
sh=ssort.sh
ind_swap=ssort.ind_swap
ssort=ssort.shellSort
#from ssort import shellSort as ssort
#from ssort import sh
#from ssort import ind_swap
eps, sig, psi = [], [], []
if iopt==0: filename='int_eps_ph%i.out'%iph
elif iopt==1: filename='int_els_ph%i.out'%iph
elif iopt==2: filename='igstrain_load_ph%i.out'%iph
elif iopt==3: filename='igstrain_unload_ph%i.out'%iph
elif iopt==4: filename='igstrain_unloads_avg.out'
elif iopt==5: filename='igstrain_loads_avg.out'
fc(filename)
if iopt<4:
dl = open(filename, 'r').readlines()
npb = int(dl[1].split()[0])
dat = dl[2:]
dat = dat[(istep)*npb: (istep)*npb + npb]
if len(dat)==0: raise IOError, 'empty dat is returned ..'
for i in xrange(len(dat)):
temp = map(float, dat[i].split())
step, p, beta, psi1, psi2, eps1, eps2,\
sig1, sig2, n1, n2, v1, v2 = temp[:13]
ms11, ms22, ms33, ms23, ms13, ms12 = temp[13:19]
me11, me22, me33, me23, me13, me12 = temp[19:25]
if p==phi:
eps.append(eps1); eps.append(eps2)
sig.append(sig1); sig.append(sig2)
psi.append(psi1) # detector 1
psi.append(psi2) # detector 2
if isort:
temp1, temp2 = [], []
sortedarray, ind = ssort(psi)#, len(psi))
for k in xrange(len(sortedarray)):
temp1.append(eps[ind[k]-1])
temp2.append(sig[ind[k]-1])
eps = temp1
sigma = temp2
psi = sortedarray
return np.array(psi), np.array(eps), np.array(sig),\
ms11, ms22, ms33, ms23, ms13, ms12
if iopt==4 or iopt==5:
from sff_converter import condition
from pepshkl import reader3 as reader
difl, nphi, phis, nbeta, neps, eps = condition()
tdat, psi = reader(filename,isort) #[ist,iphi,ipsi]
sin2psi = np.sin(psi*np.pi/180.)**2
npsi = len(psi)
eps = tdat[istep, iphi]
return psi, eps
raise IOError, 'Unexpected ioption give in intepsphiout'
def ex_igb_bix(fn='igstrain_bix_ph1.out',ifig=1,iphi=0,
mxnst=None,flow=None):
"""
"""
import matplotlib.pyplot as plt
from sff_converter import condition
from MP.ssort import sh as sort
difl, nphi, phis, nbeta, neps, eps = condition(fn=None)
if flow!=None: eps = flow.epsilon_vm[::]
else: eps = eps * 2. ## Under condition balanced biaxial
fig01 = plt.figure(ifig,figsize=(15.5,8.5))
ax01=fig01.add_subplot(231)
ax02=fig01.add_subplot(232)
ax03=fig01.add_subplot(233)
ax04=fig01.add_subplot(234)
ax05=fig01.add_subplot(235)
axes01 = [ax01,ax02,ax03,ax04,ax05]
fig02 = plt.figure(ifig+1,figsize=(15.5,8.5))
ax01=fig02.add_subplot(231)
ax02=fig02.add_subplot(232)
ax03=fig02.add_subplot(233)
ax04=fig02.add_subplot(234)
ax05=fig02.add_subplot(235)
axes02 = [ax01,ax02,ax03,ax04,ax05]
tdat = reader2(fn,iopt=2,isort=False)
nst = len(tdat[0])
npsi=len(tdat[0,0,0])
rst = np.zeros((2,nst,nphi,npsi))
sf2 = np.zeros((2,nst,nphi,npsi)) # f11 and f22
markers=['o','x','+','^','d','*','o','x','+','^','d','*']
colors =['r','g','b','k','m','y','r','g','b','k','m','y']
axe_lab = [r'(a) $\varepsilon-F_{ij}\bar{\Sigma}_{ij}$',
r'(b) $\bar{E}^{el}(\phi,\psi)$',
r'(c) $\varepsilon^{hkl}$ and $F_{ij}\bar{\Sigma}_{ij}$',
r'(d) $F_{ij}$',
r'(e) $\bar{\Sigma}_{ij}$','(f)','(g)']
if mxnst!=None: nst=mxnst
for i in xrange(nst):
ehkl = tdat[0,i,iphi,:] #e(hkl)
e = tdat[1,i,iphi,:] #macro
ehkle = tdat[2,i,iphi,:] #e-macro
ige = tdat[3,i,iphi,:] #e(hkl) - F^hkl_ij * Sij (ij=1,1 and 2,2)
f11 = tdat[4,i,iphi,:] #F^hkl_11
f22 = tdat[5,i,iphi,:] #F^hkl_22
s11 = tdat[6,i,iphi,:] #S11
s22 = tdat[7,i,iphi,:] #S22
psi = tdat[8,i,iphi,:] #psi
sin2psi = np.sin(psi*np.pi/180.)**2
x = np.sin(psi*np.pi/180.)**2
y0 = ehkl-e; y1=e; y2=f11*s11+f22*s22; y3=f11; y4=f22;
y5 = s11; y6= s22; y7=ehkl
newx, Y0,Y1,Y2,Y3,Y4,Y5,Y6,Y7 = sort(
x,y0,y1,y2,y3,y4,y5,y6,y7)
## ax01
l, = axes01[0].plot(newx,Y0*1e6,markers[i])
dum = ehkl - (f11*s11 + f22*s22)
x = np.sin(psi*np.pi/180.)**2
newx, newy = sort(x, dum)
axes01[0].plot(newx,newy*1e6,'-',color=l.get_color())
## ax02
axes01[1].plot(newx,Y1*1e6,markers[i],
label=r'$\bar{E}^{\mathrm{eff}}$=%4.2f'%eps[i])
## ax03
l, = axes01[2].plot(newx,Y7*1e6,markers[i])
axes01[2].plot(newx,Y2*1e6,'-',color=l.get_color())
## ax04
l, = axes01[3].plot(newx,Y3*1e6,markers[i])
axes01[3].plot(newx,Y4*1e6,'-',color=l.get_color())
## ax05
l, = axes01[4].plot(newx,Y5,markers[i])
axes01[4].plot(newx,Y6,'-',color=l.get_color())
if i==0:
axes01[0].text(0.25,0.1,
r'lines: $\varepsilon-(F_{11}\bar{\Sigma}_{11}$'+\
r'$+F_{22}\bar{\Sigma}_{22})$',
transform=axes01[0].transAxes)
axes01[0].text(0.25,0.2,r'symbols: $\varepsilon-\bar{E}^{el} $',
transform = axes01[0].transAxes)
axes01[2].text(0.6,0.3,r'symbols: $\varepsilon^{hkl}$',
transform=axes01[2].transAxes)
axes01[2].text(0.6,0.2,r'lines: $F_{ij}\bar{\Sigma_{ij}}$',
transform=axes01[2].transAxes)
axes01[3].text(0.5,0.5,r'symbols: $F_{11}$',transform=
axes01[3].transAxes)
axes01[3].text(0.5,0.4,r'lines: $F_{22}$',transform=
axes01[3].transAxes)
axes01[4].text(0.5,0.5,r'symbols: $\bar{\Sigma}_{11}$',
transform=axes01[4].transAxes)
axes01[4].text(0.5,0.4,r'lines: $\bar{\Sigma}_{22}$',
transform=axes01[4].transAxes)
axes01[0].set_ylabel(r'IG strain $[\mu\varepsilon]$',
dict(fontsize=15))
axes01[1].set_ylabel(r'Macro strain $[\mu\varepsilon]$',
dict(fontsize=15))
axes01[2].set_ylabel(r'$\varepsilon^{hkl}(\phi,\psi)$'+\
r' and $F_{ij}\bar{\Sigma}_{ij}$'+\
r' $[\mu\varepsilon]$',
dict(fontsize=15))
axes01[3].set_ylabel(r'$F_{11}$ and $F_{22}$'+\
r' $ [TPa^{-1}]$',
dict(fontsize=15))
axes01[4].set_ylabel(r'$\bar{\Sigma}_{11}$'+\
r' and $\bar{\Sigma}_{22}$ [MPa]',
dict(fontsize=15))
for j in xrange(nphi):
ehkl = tdat[0,i,j,:] #e(hkl)
e = tdat[1,i,j,:] #macro
ehkle = tdat[2,i,j,:] #e-macro
ige = tdat[3,i,j,:] #e(hkl) - F^hkl_ij * Sij (ij=1,1 and 2,2)
f11 = tdat[4,i,j,:] #F^hkl_11
f22 = tdat[5,i,j,:] #F^hkl_22
s11 = tdat[6,i,j,:] #S11
s22 = tdat[7,i,j,:] #S22
psi = tdat[8,i,j,:] #psi
dum = ehkl - (f11*s11 + f22*s22)
rst[0,i,j,:] = psi[:]
rst[1,i,j,:] = dum[:]
sf2[0,i,j,:]=f11[:]
sf2[1,i,j,:]=f22[:]
sin2psi = np.sin(psi*np.pi/180.)**2
x = sin2psi
y = ige*1e6
newx, newy = sort(x,y)
axes02[j].plot(newx,newy,'-')
if i==0:
axes02[j].set_title(r'$\phi=%3.0f^\circ$'%phis[j])
# axes01[0].legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
axes01[1].legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
for i in xrange(len(axes01)):
#axes01[i].set_title(axe_lab[i])
axes01[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes01[i].grid('on')
for i in xrange(len(axes02)):
axes02[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes02[i].set_ylabel(r'IG strain $[\mu\varepsilon]$', dict(fontsize=20))
axes02[i].grid('on')
fig01.tight_layout()
fig02.tight_layout()
fig01.savefig('bix_analysis.pdf')
fig02.savefig('bix_eps0_at_phis.pdf')
return rst, sf2 #rst[2,nst,nphi,npsi], sf2[2,nst,nphi,npsi]
def ex_igb(fn='igstrain_fbulk_ph1.out',ifig=2,iphi=0,isf=0,
mxnst=None,flow=None):
"""
Read ig strain file and analyze... (igstrain_fbulk_ph1.out)
igstrain_fbulk_ph1.out contains diffraction for each and every
reloading from 'unloaded' polycrystal.
Arguments
=========
fn = 'igstrain_fbulk_ph1.out'
ifig = 2
iphi=0
isf=0
mxnst=None
"""
import matplotlib.pyplot as plt
from sff_converter import condition
from MP.ssort import sh as sort
from MP.mat import voigt
from MP.lib.mpl_lib import wide_fig as wf
difl, nphi, phis, nbeta, dum1, dum2 = condition(fn=None)
if flow==None: raise IOError, 'Flow stress is missing'
eps = flow.epsilon_vm[::]
neps = flow.nstp
nw = 4
remainder = np.mod(neps,nw)
if remainder==0:
nh = neps/nw
elif remainder>0:
nh = neps/nw + 1
fig =wf(nw=2, nh=2,ifig=ifig,iarange=True); axes =fig.axes
ax01, ax02, ax03, ax04 = axes
fig01=wf(nw=nw,nh=nh,ifig=ifig+1,iarange=True,nfig=neps);axes01=fig01.axes
fig02=wf(nw=nw,nh=nh,ifig=ifig+2,iarange=True,nfig=neps);axes02=fig02.axes
fig03=wf(nw=nw,nh=nh,ifig=ifig+3,iarange=True,nfig=neps);axes03=fig03.axes
fig04=wf(nw=nw,nh=nh,ifig=ifig+4,iarange=True,nfig=neps);axes04=fig04.axes
tdat, usf = reader2(fn,iopt=1)
print tdat.shape
nsf = len(usf); nst = len(tdat[0]); npsi = len(tdat[0,0,0,0,:])
if nst!=neps: raise IOError, 'Inconsistency between neps and nst'
markers = ['o','x','+','^','d','*','o','x','+','^','d','*''o','x','+','^','d','*']
colors = ['r','g','b','k','m','y','r','g','b','k','m','y','r','g','b','k','m','y']
from string import ascii_lowercase as alphabet
axe_lab=[]
for ab in alphabet: axe_lab.append('(%s)'%ab)
yl = 0; yh = 0
avg = np.zeros((2,nst,npsi)) #
sft = np.zeros((6,nst,npsi)) # f^{hkl}
rsqt = np.zeros((6,nst,npsi)) # f^{hkl}
if mxnst!=None: nst=mxnst
for i in xrange(nst):
ehkl = tdat[0,i,isf,iphi,:] # e(hkl,phi,psi)
e = tdat[1,i,isf,iphi,:] # macro
ehkle = tdat[2,i,isf,iphi,:] # e - macro
fhkl = tdat[3,i,isf,iphi,:] # Fhkl
fbulk = tdat[4,i,isf,iphi,:] # Fbulk
ige = tdat[5,i,isf,iphi,:] # e - F_ij *Sij
sij = tdat[6,i,isf,iphi,:] # Sij
psi = tdat[8,i,isf,iphi,:]
rsq = tdat[9,i,isf,iphi,:]
sin2psi = np.sin(psi*np.pi/180.)**2
axes01[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
axes02[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
axes03[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
axes04[i].set_title(r'%s $\bar{E}^{\mathrm{eff}} = %4.2f$'%(
axe_lab[i],eps[i]))
igys = []; igos = []; igts = []
for j in xrange(nsf): ## Number of elastic loading for stress factor calc.
ehkl_ = tdat[0,i,j,iphi,:] # e(hkl,phi,psi)
e_ = tdat[1,i,j,iphi,:] # macro
fhkl_ = tdat[3,i,j,iphi,:] # Fhkl
fbulk_= tdat[4,i,j,iphi,:] # Fbulk
sij_ = tdat[6,i,j,iphi,:] # Sij
rsq_ = tdat[6,i,j,iphi,:] # R^2
sft[j,i,:] = fhkl_[::] #
rsqt[j,i,:] = rsq_[::]
i1,i2 = usf[j]
igo = ehkl_ - e_ ## old way
igt = []
m = 0
for k in xrange(len(fbulk_)):
if fbulk_[k]==0:
m=m+1
igt.append(0.)
else: igt.append(
ehkl_[k] - \
fhkl_[k] / fbulk_[k] * e_[k])
if m!=0: print m, 'case of fbulk'+\
'(phi,psi)=0 happened'
igt = np.array(igt)
igy = ehkl_ - fhkl_ * sij_ ## new way (YJ)
if j==0:
igy_avg = igy/nsf
igo_avg = igo/nsf
igt_avg = igt/nsf
else:
igy_avg = igy_avg + igy/nsf
igo_avg = igo_avg + igo/nsf
igt_avg = igt_avg + igt/nsf
igys.append(igy);igos.append(igo);igts.append(igt)
# The old method
if j==4: m = markers[j]
else: m= '--'
y = igo
x = np.sin(psi[:]*np.pi/180.)**2
newx,newy = sort(x,y) # shell sort
axes01[i].plot(
newx,newy*1e6,m,color=colors[j],
label=r'$\Sigma_{%1i%1i}$'%(i1,i2))
if i==0:axes01[i].legend(loc='best',
fancybox=True).get_frame().set_alpha(0.5)
if j==0:axes01[i].grid('on')
yl0, yh0 = axes01[i].get_ylim()
if yl0<yl: yl = yl0
if yh0>yh: yh = yh0
# GHT
y = igt
x = np.sin(psi[:]*np.pi/180.)**2
newx,newy = sort(x,y) # shell sort
axes02[i].plot(
newx,newy*1e6,m,color=colors[j],
label=r'$\Sigma_{%1i%1i}$'%(i1,i2))
if i==0:axes02[i].legend(loc='best',
fancybox=True).get_frame().set_alpha(0.5)
if j==0:axes02[i].grid('on')
yl0, yh0 = axes02[i].get_ylim()
if yl0<yl: yl = yl0
if yh0>yh: yh = yh0
# YJ
y = igy
x = np.sin(psi[:]*np.pi/180.)**2
newx,newy = sort(x,y) # shell sort
axes03[i].plot(
newx,newy*1e6,m,color=colors[j],
label=r'$\Sigma_{%1i%1i}$'%(i1,i2))
if i==0:axes03[i].legend(loc='best',
fancybox=True).get_frame().set_alpha(0.5)
if j==0:axes03[i].grid('on')
yl0, yh0 = axes03[i].get_ylim()
if yl0<yl: yl = yl0
if yh0>yh: yh = yh0
pass # over nsf
# errors for 6 cases of SF loading
igos = np.array(igos).swapaxes(0,1)
igys = np.array(igys).swapaxes(0,1)
igts = np.array(igts).swapaxes(0,1)
igoe=[]; igye=[]; igte=[]
for m in xrange(len(igo_avg)):
igoe.append(igos[m].std())
igte.append(igts[m].std())
igye.append(igys[m].std())
igoe,igte,igye = np.array(igoe),np.array(igte),np.array(igye)
##
# average old
y = igo_avg
ye= igoe
x = np.sin(psi[:]*np.pi/180.)**2
newx, newy, newye = sort(x,y,ye)
axes04[i].errorbar(
x=newx,y=newy*1e6,yerr=newye*1e6,
ls='-',color='r',label='old')
# average YU
y = igy_avg
avg[0,i,:] = psi[:]; avg[1,i,:] = y[:]
ye = igye
x = np.sin(psi[:]*np.pi/180.)**2
newx, newy, newye = sort(x,y,ye)
axes04[i].errorbar(
x=newx,y=newy*1e6,yerr=newye*1e6,
ls='-',ms=3,color='b',label='YJ')
axes04[i].grid('on')
axes04[0].legend(loc='best',fancybox=True)\
.get_frame().set_alpha(0.5)
i1,i2 = voigt.ijv[:,isf]
sin2psi = np.sin(psi*np.pi/180.)**2
ax01.plot(sin2psi,ige*1e6,markers[i]#,color='k',
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
ax02.plot(sin2psi,ehkle*1e6,markers[i]#,color='k'
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
l, = ax04.plot(sin2psi,fhkl*1e6,markers[i]#,color='k'
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
if i==0: ax04.plot(sin2psi,fbulk*1e6,'-',alpha=0.2,color='k')
# ax03.plot(sin2psi,e*1e6,markers[i],
# label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
ax03.plot(sin2psi,(ehkl-fhkl/fbulk*e)*1e6,markers[i]
,label=r'$\bar{E}^{\mathrm{eff}} = %4.2f$'%eps[i])
sin2psi = np.sin(psi*np.pi/180.)**2
## Deco the axes
for i in xrange(nst):
axes01[i].set_ylim(yl,yh)
axes02[i].set_ylim(yl,yh)
axes03[i].set_ylim(yl,yh)
#axes04[i].set_ylim(yl,yh)
axes01[i].set_xlim(0.,); axes02[i].set_xlim(0.,)
axes03[i].set_xlim(0.,); axes04[i].set_xlim(0.,)
for i in xrange(len(axes)):
axes[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes01[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes02[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes03[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
axes04[i].set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
for i in xrange(len(axes)): axes[i].grid('on')
for i in xrange(len(axes)): axes[i].set_xlim(0.,0.5)
ax01.set_ylabel(
r'$\varepsilon^{hkl} - F^{hkl}_{ij}\bar{\Sigma}_{ij}$'+\
r' $[\mu\varepsilon]$',dict(fontsize=15))
ax02.set_ylabel(
r'$\varepsilon^{hkl} - E$ $[\mu\varepsilon]$',dict(fontsize=15))
ax03.set_ylabel(r'$\varepsilon^{hkl} - $'+
r'$F_{ij}^{hkl}/F^{\mathrm{bulk}}_{ij}$'+
r'$ \bar{E} $ $[\mu \varepsilon]$',dict(fontsize=15))
ax04.set_ylabel(r'$F_{ij}^{hkl}, F^{\mathrm{bulk}}_{ij}$ $[TPa^{-1}]$',
dict(fontsize=15))
ax01.set_title(r'(a) $\varepsilon(hkl,%3.0f^\circ,\psi)$'\
r'$ - F_{%1i%1i}(hkl,%3.0f^\circ,\psi)$'\
r'$\bar{\Sigma}_{%1i%1i}$'%(phis[iphi],i1,i2,phis[iphi],i1,i2),
dict(fontsize=13))
ax02.set_title(
r'(b) $\varepsilon(hkl,%3.0f^\circ,\psi)$'%(phis[iphi])+\
r'$ - \bar{E}(%3.0f^\circ,\psi)$'%\
(phis[iphi]),dict(fontsize=13))
ax03.set_title(
r'(c) $\varepsilon(hkl,%3.0f^\circ,\psi)$'%(phis[iphi])+
r'$-F_{%1i%1i}(hkl,%3.0f^\circ,\psi)/$'%(i1,i2,phis[iphi])+
r'$F_{%1i%1i}^{\mathrm{bulk}}(%3.0f^\circ,\psi)$'%(
i1,i2,phis[iphi])+
r'$\bar{E}(%3.0f^\circ,\psi)$'%(phis[iphi]),dict(fontsize=13))
ax04.set_title(r'(d) $F^{hkl}_{%1i%1i}$ and '%(i1,i2)+\
r'$F^{\mathrm{bulk}}_{%1i%1i}$'%
(i1,i2), dict(fontsize=13))
ax04.legend(loc='lower right',fancybox=True).get_frame().set_alpha(0.5)
# fig.tight_layout(); fig01.tight_layout(); fig02.tight_layout()
# fig03.tight_layout(); fig04.tight_layout()
fig.savefig('ig_bulk.pdf');fig01.savefig('ig_bulk_Old.pdf')
fig02.savefig('ig_bulk_GHT.pdf'); fig03.savefig('ig_bulk_YJ.pdf')
fig04.savefig('ig_bulk_avg.pdf')
## save the averaged IG strain
return avg, sft # avg[2,nst,npsi], sft[6,nst,npsi]
def ex_igb_cf(fnu='igstrain_fbulk_ph1.out',
fnl='igstrain_bix_ph1.out'):
"""
compare the ig strain at load and unloads.
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
from MP.ssort import sh as sort
difl, nphi, phis, nbeta, neps, eps = condition(fn=None)
plt.ioff()
avgu,sfs,rsqs=ex_igb_t(fn=fnu,fnout='igstrain_unloads_avg.out') # at unlaods
avgl=ex_igb_bix_t(fn=fnl,fnout='igstrain_loads_avg.out') # at loads
print avgu.shape#[2,nst,nphi,nspi]
print avgl.shape#[2,nst,iphi,npsi]
plt.close('all'); plt.ion()
fig = plt.figure(2,figsize=(15.5,8.5))
fig02 = plt.figure(203); ax_dum=fig02.add_subplot(111)
at = ['(a)','(b)','(c)','(d)','(e)','(f)']
markers=['x','o','.','d','*']
ls =['--x','--o','--.','--d','--*']
axes=[]
for i in xrange(nphi):
axes.append(fig.add_subplot(2,3,i+1))
for ist in xrange(len(avgu[0])):
for iphi in xrange(len(avgu[0][ist])):
ax = axes[iphi]
phi = phis[iphi]
psi = avgu[0,ist,iphi,:]
eu = avgu[1,ist,iphi,:]
el = avgl[1,ist,iphi,:]
sin2psi = np.sin(psi*np.pi/180.)**2
x = sin2psi
y0 = eu*1e6
y1 = el*1e6
newx,Y0,Y1 = sort(x,y0,y1)
if iphi==0:
l, = ax.plot(newx,Y0,'x',
label=r'$\bar{E}^{\mathrm{eff}}=%4.2f$'%(eps[ist]*2.0))
ax_dum.plot(newx,Y0,markers[ist],color='k',
label=r'$\bar{E}^{\mathrm{eff}}=%4.2f$'%(eps[ist]*2.0))
ax_dum.plot(newx,Y1,ls[ist],color='k')
elif iphi!=0:
l, = ax.plot(newx,Y0,'x')
ax.plot(newx,Y1,'-',color=l.get_color())
ax_dum.set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
ax_dum.set_ylabel(r'IG strains $[\mu\varepsilon]$', dict(fontsize=20))
ax_dum.legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
ax_dum.text(0.1, 0.85, 'Lines: at loads',transform=ax_dum.transAxes)
ax_dum.text(0.1, 0.92, 'Symbols: at unloads',transform=ax_dum.transAxes)
ax_dum.grid('on')
fig02.tight_layout()
fig02.savefig('ige_cf_phi1.pdf')
for i in xrange(len(axes)):
ax = axes[i]
ph = phis[i]
#ax.set_title(r'%s $\phi=%3.0f^\circ$'%(at[i],ph))
ax.set_xlabel(r'$\sin^2{\psi}$', dict(fontsize=20))
ax.set_ylabel(r'IG strains $[\mu\varepsilon]$', dict(fontsize=20))
if i==0:
ax.legend(loc='best',fancybox=True).get_frame().set_alpha(0.5)
ax.text(0.1, 0.85, 'Lines: at loads',transform=ax.transAxes)
ax.text(0.1, 0.92, 'Symbols: at unloads',transform=ax.transAxes)
ax.grid('on')
fig.tight_layout()
fig.savefig('ige_cf.pdf')
def ex_igb_t(fn="igstrain_fbulk_ph1.out", fnout="igstrain_unloads_avg.out", mxnst=None, flow=None):
"""
Arguments
=========
fn ='igstrain_fbulk_ph1.out'
fnout='igstrain_unloads_avg.out'
mxnst=None
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
difl, nphi, phis, nbeta, dum1, dum2 = condition(fn=None)
if flow == None:
raise IOError, "Flow should be given"
elif flow != None:
flow.get_eqv()
eps = flow.epsilon_vm[::]
avg = []
sfs = []
rsqs = []
for i in xrange(nphi):
a, sf, rsq = ex_igb(
fn=fn, ifig=i * 7 + 1, iphi=i, isf=0, mxnst=mxnst, flow=flow
) # a[2,nst,npsi], sf[6,nst,npsi]
avg.append(a) # average ieps0
sfs.append(sf)
rsqs.append(rsq)
avg = np.array(avg) # avg[nphi,2,nst,npsi]
avg = avg.swapaxes(0, 1) # avg[2,nphi,nst, npsi]
avg = avg.swapaxes(1, 2) # avg[2,nst, nphi,npsi]
sfs = np.array(sfs) # sfs[nphi,6,nst,npsi]
sfs = sfs.swapaxes(1, 3) # [nphi,npsi,nst, 6]
sfs = sfs.swapaxes(0, 2) # [nst, npsi,nphi,6]
sfs = sfs.swapaxes(1, 2) # [nst, nphi,npsi,6]
rsqs = np.array(rsqs) # rsqs[nphi,6,nst,npsi]
rsqs = rsqs.swapaxes(1, 3) # [nphi,npsi,nst, 6]
rsqs = rsqs.swapaxes(0, 2) # [nst, npsi,nphi,6]
rsqs = rsqs.swapaxes(1, 2) # [nst, nphi,npsi,6]
f = open(fnout, "w")
f.writelines(
"%3s %8s %13s %14s %14s %14s %14s %14s %14s\n"
% ("phi", "psi", "eps0", "F11", "F22", "F33", "F23", "F13", "F12")
)
if mxnst == None:
nst = len(sfs)
else:
nst = mxnst
for ist in xrange(nst):
f.writelines("-- eps^{eff}: %5.3f\n" % eps[ist])
for iphi in xrange(len(avg[0][ist])):
phi = phis[iphi]
psi = avg[0, ist, iphi, :]
ep = avg[1, ist, iphi, :]
for i in xrange(len(psi)):
ps = psi[i]
e = ep[i]
fij = sfs[ist, iphi, i, :]
f.writelines("%3.0f %+7.2f %+12.7e" % (phi, ps, e))
for j in xrange(len(fij)):
f.writelines(" %+12.7e" % fij[j])
f.writelines("\n")
f.close()
return avg, sfs, rsqs # avg[2,nst,nphi,npsi], sfs[
def ex_igb_cf(fnu="igstrain_fbulk_ph1.out", fnl="igstrain_bix_ph1.out"):
"""
compare the ig strain at load and unloads.
"""
import matplotlib.pyplot as plt
from sff_converter import condition
import numpy as np
from MP.ssort import sh as sort
difl, nphi, phis, nbeta, neps, eps = condition(fn=None)
plt.ioff()
avgu, sfs, rsqs = ex_igb_t(fn=fnu, fnout="igstrain_unloads_avg.out") # at unlaods
avgl = ex_igb_bix_t(fn=fnl, fnout="igstrain_loads_avg.out") # at loads
print avgu.shape # [2,nst,nphi,nspi]
print avgl.shape # [2,nst,iphi,npsi]
plt.close("all")
plt.ion()
fig = plt.figure(2, figsize=(15.5, 8.5))
fig02 = plt.figure(203)
ax_dum = fig02.add_subplot(111)
at = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
markers = ["x", "o", ".", "d", "*"]
ls = ["--x", "--o", "--.", "--d", "--*"]
axes = []
for i in xrange(nphi):
axes.append(fig.add_subplot(2, 3, i + 1))
for ist in xrange(len(avgu[0])):
for iphi in xrange(len(avgu[0][ist])):
ax = axes[iphi]
phi = phis[iphi]
psi = avgu[0, ist, iphi, :]
eu = avgu[1, ist, iphi, :]
el = avgl[1, ist, iphi, :]
sin2psi = np.sin(psi * np.pi / 180.0) ** 2
x = sin2psi
y0 = eu * 1e6
y1 = el * 1e6
newx, Y0, Y1 = sort(x, y0, y1)
if iphi == 0:
l, = ax.plot(newx, Y0, "x", label=r"$\bar{E}^{\mathrm{eff}}=%4.2f$" % (eps[ist] * 2.0))
ax_dum.plot(
newx, Y0, markers[ist], color="k", label=r"$\bar{E}^{\mathrm{eff}}=%4.2f$" % (eps[ist] * 2.0)
)
ax_dum.plot(newx, Y1, ls[ist], color="k")
elif iphi != 0:
l, = ax.plot(newx, Y0, "x")
ax.plot(newx, Y1, "-", color=l.get_color())
ax_dum.set_xlabel(r"$\sin^2{\psi}$", dict(fontsize=20))
ax_dum.set_ylabel(r"IG strains $[\mu\varepsilon]$", dict(fontsize=20))
ax_dum.legend(loc="best", fancybox=True).get_frame().set_alpha(0.5)
ax_dum.text(0.1, 0.85, "Lines: at loads", transform=ax_dum.transAxes)
ax_dum.text(0.1, 0.92, "Symbols: at unloads", transform=ax_dum.transAxes)
ax_dum.grid("on")
fig02.tight_layout()
fig02.savefig("ige_cf_phi1.pdf")
for i in xrange(len(axes)):
ax = axes[i]
ph = phis[i]
# ax.set_title(r'%s $\phi=%3.0f^\circ$'%(at[i],ph))
ax.set_xlabel(r"$\sin^2{\psi}$", dict(fontsize=20))
ax.set_ylabel(r"IG strains $[\mu\varepsilon]$", dict(fontsize=20))
if i == 0:
ax.legend(loc="best", fancybox=True).get_frame().set_alpha(0.5)
ax.text(0.1, 0.85, "Lines: at loads", transform=ax.transAxes)
ax.text(0.1, 0.92, "Symbols: at unloads", transform=ax.transAxes)
ax.grid("on")
fig.tight_layout()
fig.savefig("ige_cf.pdf")
