def reader3(fn="igstrain_unloads_avg.out", isort=False):
"""
Reader for files in the 'igstrain_unloads_avg.out' template.
Arguments
=========
fn = 'igstrain_unloads_avg.out'
isort = False
"""
from MP.ssort import sh as sort
# from sff_converter import condition
# difl, nphi, phis, nbeta, neps, eps = condition(difile)
ds = open(fn, "r").read()
blocks = ds.split("--")[1::]
nstp = len(blocks)
print "nstp:", nstp
# file structure
phis = []
psis = []
lines = blocks[0].split("\n")[1:-1]
for il in xrange(len(lines)):
phi, psi, eps, f11, f22, f33, f23, f13, f12 = map(float, lines[il].split())
if not (phi in phis):
phis.append(phi)
if len(phis) == 1:
psis.append(psi)
dat = np.zeros((nstp, len(phis), len(psis)))
fij = np.zeros((nstp, len(phis), len(psis), 6))
for ist in xrange(nstp):
ablock = blocks[ist]
lines = ablock.split("\n")[1:-1]
for iphi in xrange(len(phis)):
x = []
y = []
z = []
for ipsi in xrange(len(psis)):
l = len(psis) * iphi + ipsi
ph, ps, ep, f1, f2, f3, f4, f5, f6 = map(float, lines[l].split())
x.append(ps)
y.append(ep)
z.append([f1, f2, f3, f4, f5, f6])
if not (isort):
dat[ist, iphi, ipsi] = ep
fij[ist, iphi, ipsi, :] = z[0][:]
if isort:
sx, sy, sz = sort(x, y, z)
for ix in xrange(len(sx)):
dat[ist, iphi, ix] = sy[ix]
fij[ist, iphi, ix, :] = sz[ix][:]
if isort:
psis = sort(psis)
psis = np.array(psis)
return dat, psis, fij
def reader3(fn='igstrain_unloads_avg.out',isort=False):
"""
Reader for files in the 'igstrain_unloads_avg.out' template.
Arguments
=========
fn = 'igstrain_unloads_avg.out'
isort = False
"""
from MP.ssort import sh as sort
# from sff_converter import condition
# difl, nphi, phis, nbeta, neps, eps = condition(difile)
ds = open(fn,'r').read()
blocks = ds.split('--')[1::]
nstp = len(blocks)
print 'nstp:', nstp
# file structure
phis = []; psis = []
lines = blocks[0].split('\n')[1:-1]
for il in xrange(len(lines)):
phi,psi,eps,f11,f22,f33,f23,f13,f12 = \
map(float,lines[il].split())
if not(phi in phis): phis.append(phi)
if len(phis)==1: psis.append(psi)
dat = np.zeros((nstp,len(phis),len(psis)))
fij = np.zeros((nstp,len(phis),len(psis),6))
for ist in xrange(nstp):
ablock = blocks[ist]
lines = ablock.split('\n')[1:-1]
for iphi in xrange(len(phis)):
x=[]; y=[]; z=[]
for ipsi in xrange(len(psis)):
l = len(psis) * iphi + ipsi
ph,ps,ep,f1,f2,f3,f4,f5,f6 = \
map(float, lines[l].split())
x.append(ps); y.append(ep); z.append([f1,f2,f3,f4,f5,f6])
if not(isort):
dat[ist,iphi,ipsi] = ep
fij[ist,iphi,ipsi,:] = z[0][:]
if isort:
sx, sy, sz = sort(x,y,z)
for ix in xrange(len(sx)):
dat[ist,iphi,ix] = sy[ix]
fij[ist,iphi,ix,:] = sz[ix][:]
if isort: psis = sort(psis)
psis=np.array(psis)
return dat, psis, fij
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 ex02(fn='int_eps_ph1.out',istep=0,iphi=0,ax01=None,
ax01x=None,ax02=None,
label=None,ls=['-x','--o'],color='r'):
"""
Arguments
=========
fn = 'int_eps_ph1.out'
istep = 0
iphi = 0
ax01 = None
ax01x = None
ax02 = None
label = None
ls = ['-x','--o']
"""
import MP.ssort as sort
sort = sort.shellSort
psi1,psi2,eps1,eps2,sig1,sig2,\
vol1,vol2,s11,s22,e11,e22,ngr1,ngr2=\
reader(fn,istep,iphi,skiprows=2,iopt=3)
sin2psi1 = np.sin(psi1*np.pi/180.)**2
sin2psi2 = np.sin(psi2*np.pi/180.)**2
if ax01==None or ax01x==None:
fig01 = plt.figure(1)
ax01 = fig01.add_subplot(211)
ax02 = fig01.add_subplot(212)
ax01x = ax01.twinx()
if label==None:
sin2psi1 = np.sin(psi1*np.pi/180.)**2
sin2psi2 = np.sin(psi2*np.pi/180.)**2
l0, = ax01.plot(sin2psi1,eps1,'r-x')
ax01.plot(sin2psi2,eps2,'b-x')
l1, = ax01x.plot(sin2psi1,sig1,'r--o')
ax01x.plot(sin2psi2,sig2,'b--o')
# l0, = ax01.plot(psi1,eps1,'r-x')
# ax01.plot(psi2,eps2,'b-x')
# l1, = ax01x.plot(psi1,sig1,'r--o')
# ax01x.plot(psi2,sig2,'b--o')
else:
x = sin2psi1
x,ind = sort(x)
eps1 = ss(ind, eps1)
ax01.plot(sin2psi2,eps2,'b'+ls[0],label=label)
ngr1 = ss(ind, ngr1)
l1, = ax01x.plot(
x,ngr1,color+ls[0],label=label)
#ax01x.plot(sin2psi2,sig2,'b'+ls[0],label=r'')
vol1 = ss(ind, vol1)
l2, = ax02.plot(
x,vol1,color+ls[0],label=label)
#ax02.plot(sin2psi2,vol2,'b'+ls[0],label=r'')
# l0, = ax01.plot(psi1,eps1,'r'+ls[0],label=label)
# ax01.plot(psi2,eps2,'b'+ls[0],label=r'')
# l1, = ax01x.plot(psi1,sig1,'r'+ls[1],label=label)
# ax01x.plot(psi2,sig2,'b'+ls[0],label=r'')
# l2, = ax02.plot(psi1,vol1,'r'+ls[0],label=r'')
# ax02.plot(psi2,vol2,'b'+ls[0],label=r'')
if ax01==None or ax01x==None:
ax01.legend([l0,l1],[r'$\varepsilon^2$',r'$\sigma^2$'])\
.get_frame().set_alpha(0.5)
ax01.set_xlabel(r'$\sin^2{\psi}$',dict(fontsize=28))
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 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 ex02(fn='int_eps_ph1.out',istep=0,iphi=0,ax01=None,
ax01x=None,ax02=None,
label=None,ls=['-x','--o'],color='r'):
"""
Arguments
=========
fn = 'int_eps_ph1.out'
istep = 0
iphi = 0
ax01 = None
ax01x = None
ax02 = None
label = None
ls = ['-x','--o']
"""
import MP.ssort as sort
sort = sort.shellSort
psi1,psi2,eps1,eps2,sig1,sig2,\
vol1,vol2,s11,s22,e11,e22,ngr1,ngr2=\
reader(fn,istep,iphi,skiprows=2,iopt=3)
sin2psi1 = np.sin(psi1*np.pi/180.)**2
sin2psi2 = np.sin(psi2*np.pi/180.)**2
if ax01==None or ax01x==None:
fig01 = plt.figure(1)
ax01 = fig01.add_subplot(211)
ax02 = fig01.add_subplot(212)
ax01x = ax01.twinx()
if label==None:
sin2psi1 = np.sin(psi1*np.pi/180.)**2
sin2psi2 = np.sin(psi2*np.pi/180.)**2
l0, = ax01.plot(sin2psi1,eps1,'r-x')
ax01.plot(sin2psi2,eps2,'b-x')
l1, = ax01x.plot(sin2psi1,sig1,'r--o')
ax01x.plot(sin2psi2,sig2,'b--o')
# l0, = ax01.plot(psi1,eps1,'r-x')
# ax01.plot(psi2,eps2,'b-x')
# l1, = ax01x.plot(psi1,sig1,'r--o')
# ax01x.plot(psi2,sig2,'b--o')
else:
x = sin2psi1
x,ind = sort(x)
eps1 = ss(ind, eps1)
ax01.plot(sin2psi2,eps2,'b'+ls[0],label=label)
ngr1 = ss(ind, ngr1)
l1, = ax01x.plot(
x,ngr1,color+ls[0],label=label)
#ax01x.plot(sin2psi2,sig2,'b'+ls[0],label=r'')
vol1 = ss(ind, vol1)
l2, = ax02.plot(
x,vol1,color+ls[0],label=label)
#ax02.plot(sin2psi2,vol2,'b'+ls[0],label=r'')
# l0, = ax01.plot(psi1,eps1,'r'+ls[0],label=label)
# ax01.plot(psi2,eps2,'b'+ls[0],label=r'')
# l1, = ax01x.plot(psi1,sig1,'r'+ls[1],label=label)
# ax01x.plot(psi2,sig2,'b'+ls[0],label=r'')
# l2, = ax02.plot(psi1,vol1,'r'+ls[0],label=r'')
# ax02.plot(psi2,vol2,'b'+ls[0],label=r'')
if ax01==None or ax01x==None:
ax01.legend([l0,l1],[r'$\varepsilon^2$',r'$\sigma^2$'])\
.get_frame().set_alpha(0.5)
ax01.set_xlabel(r'$\sin^2{\psi}$',dict(fontsize=28))
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_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")
