def ex_consistency(
ifig=50,nxphi=3,exp_ref=[],exp_lab=[],mod_ext=None,
mod_ref='STR_STR.OUT',sin2psimx=None,iscatter=False,
sigma=5e-5,psimx=None,psi_nbin=1,ig_sub=True,istep=None,
hkl=None,iplot=True,iwind=False,wdeg=2,ipsi_opt=1,
fn_sff=None,pmargin=None,path='',sf_ext=None,ig_ext=None,
vf_ext=None,iwgt=False,verbose=False,ilog=False,
dec_inv_frq=1,dec_interp=1,theta_b=None,ird=1.):
"""
Consistency check between 'weighted average' stress and
the stress obtained following the stress analysis method
(SF, IG strain)
----------------------------------------
## Data visualization options
ifig = 50
nxphi : display only first nxphi of results
along phi axis
hkl : hkl of plane, for the sake of labels.
path : place holder for strain path
----------------------------------------
## Data process parameters
#1. Main options
sf_ext : Overwrite stress factor
ig_ext : Overwrite IG strain
vf_ext : Overwrite grain volume fraction (nstp,nphi,npsi)
iscatter (False) : introducing a random scattering of 'ehkl'
#2. tilting angle restriction/treatments
sin2psimx : limiting the maximum sin2psi values, with which
stress-fitting is carried out
psimx : limiting the maximum psi value
psi_nbin : If other than 1, limit the number of data
#3. IG strain-specific
ig_sub : flag whether or not subtract the IG strain
#4. DEC related
dec_inv_frq : Inverse Frequency of DEC measures along EVM (nstp)
dec_interp: Interpolation method for the incomplete DEC
#5. Misc.
exp_ref : experimental reference
mod_ref : model's weighted average flow curves are given
----------------------------------------
## misc. options
iplot (True) : flag whether or not MPL plot is performed
ipsi_opt 0: sin2psi
1: sign(psi) * sin2psi
2: psi
istep : If other than None, analysis is carried out
only for the given istep
----------------------------------------
## debugging options
verbose : False
ilog : False
----------------------------------------
## Diffraction condition parameters
theta_b : None (Bragg's angle)
ird : Intensity expected for random distribution
"""
if ilog:
fn = 'ex_consistency.log'
f = open(fn,'w')
write_args(
f=f,ihead=True,ifig=ifig,nxphi=nxphi,exp_ref=exp_ref,
exp_lab=exp_lab,mod_ext=mod_ext,mod_ref=mod_ref,
sin2psimx=sin2psimx,iscatter=iscatter,sigma=sigma,
psimx=psimx,psi_nbin=psi_nbin,ig_sub=ig_sub,
istep=istep,hkl=hkl,iplot=iplot,iwind=iwind,
wdeg=wdeg,ipsi_opt=ipsi_opt,fn_sff=fn_sff,
pmargin=pmargin,path=path,sf_ext=sf_ext,
ig_ext=ig_ext,vf_ext=vf_ext,iwgt=iwgt,
verbose=verbose,ilog=ilog)
f.close()
print 'log has been saved to ',fn
from rs import ResidualStress,u_epshkl,\
u_epshkl_geom_inten,filter_psi,filter_psi3,\
psi_reso,psi_reso2,psi_reso3,psi_reso4
from mst_ex import use_intp_sfig, return_vf
from MP.mat import mech # mech is a module
FlowCurve = mech.FlowCurve
if iplot:
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from MP.lib import mpl_lib,axes_label
plt.ioff()
wide_fig = mpl_lib.wide_fig
fancy_legend = mpl_lib.fancy_legend
## Collection of figures at various plastic strains
fe = PdfPages('all_ehkl_fits_%s_%s.pdf'%(hkl,path))
fs = PdfPages('all_stress_factors_%s_%s.pdf'%(hkl,path))
f_er = PdfPages('all_Ei-ehkl-e0_%s_%s.pdf'%(hkl,path))
fig1 = wide_fig(ifig,nw=2,nh=1,left=0.2,uw=3.5,
w0=0,w1=0.3,right=0,iarange=True)
## ax1: Equivalent Stress/Strain
## ax2: Stress path in the plane stress space (RD/TD)
ax1 = fig1.axes[0]; ax2 = fig1.axes[1]
pass
#------------------------------------------------------------#
## i_ip = 1: ioption for the model data
model_rs = ResidualStress(
mod_ext=mod_ext,fnmod_ig='igstrain_fbulk_ph1.out',
fnmod_sf='igstrain_fbulk_ph1.out',i_ip=1)
## Process the sf/eps0/ehkl/wgt and so forth
## according to the parameters given
ivf_ext=True; isf_ext=True; iig_ext=True
if type(sf_ext)==type(None):isf_ext=False
if type(ig_ext)==type(None):iig_ext=False
if type(vf_ext)==type(None):ivf_ext=False
"""
## final data should be saved to
'model_sfs'
'model_igs'
'model_ehkl'
And eventually to 'model_tdats' inside the loop
"""
## SF/IG/ehkl
model_ehkls = np.copy(model_rs.dat_model.ehkl)
if isf_ext and iig_ext:
model_sfs = sf_ext.copy()
model_igs = ig_ext.copy()
elif isf_ext!=iig_ext:
raise IOError, 'isf_ext should equal to iig_ext'
## if isf_ext False and isf_ext False
else:
model_sfs = model_rs.dat_model.sf.copy()
model_igs = model_rs.dat_model.ig.copy()
## VF
if not(ivf_ext): model_vfs, model_ngr = return_vf()
else: model_vfs = vf_ext.copy()
## whether or not vf would be used as weights in
## the least-sq estimator
if not(iwgt): wgt = None # overwrite wgt
# Apply process
# 0. Use of interpolated SF?
# 1. Limit the range of sin2psi (or psi)
# 2. Finite number of tiltings
# 3. Assign tdat
# 4. Perturb ehkl (common)
# 5. Filter based on vf?
## Unstaged but the 'raw' arrays
raw_psis = model_rs.dat_model.psi.copy()
raw_vfs = model_vfs.copy()
raw_ehkl = np.copy(model_rs.dat_model.ehkl)
raw_sfs = model_sfs.copy()
## staged arrays for stress estimation
model_rs.psis = model_rs.dat_model.psi.copy()
model_rs.phis = model_rs.dat_model.phi.copy()
model_rs.npsi = len(model_rs.psis)
model_rs.nphi = len(model_rs.phis)
sin2psis_init = np.sin(model_rs.psis)**2
# 0. Use interpolated SF
_sf_, _ig_ = use_intp_sfig(
dec_inv_frq,iopt=dec_interp,
iplot=False,iwgt=False)
## swapping axes to comply with what is used
## in dat_model.sf
_sf_ = _sf_.sf.swapaxes(1,3).swapaxes(2,3)
_sf_ = _sf_ *1e6
# 0.5 Mask DEC where volume fraction
## model_ngr or model_vfs
for i in range(len(model_vfs)):
for j in range(len(model_vfs[i])):
for k in range(len(model_vfs[i][j])):
if model_ngr[i,j,k]==0:
_sf_[i,:,j,k]=np.nan
# _ig_[i,j,k]=np.nan
raw_sfs[i,:,j,k]=np.nan
model_sfs[i,:,j,k]=np.nan
raw_ehkl[i,j,k] = np.nan
for m in range(6):
if _sf_[i,m,j,k]==0 or raw_sfs[i,m,j,k]==0:
# print 'encountered zero sf'
_sf_[i,m,j,k] = np.nan
raw_sfs[i,m,j,k] = np.nan
model_sfs[i,m,j,k] = np.nan
raw_ehkl[i,j,k] = np.nan
# 1. Limit the range of sin2psi (or psi)
if type(sin2psimx)!=type(None) or \
type(psimx)!=type(None):
if type(sin2psimx)!=type(None):
bounds=[0., sin2psimx]
elif type(psimx)!=type(None):
bounds=[0.,np.sin(psimx*np.pi/180.)**2]
raw_sfs,model_sfs,model_igs,model_vfs,\
model_ehkls,raw_psis,_sf_,raw_vfs\
= filter_psi3(
sin2psis_init,bounds,
raw_sfs,model_sfs,model_igs,model_vfs,
model_ehkls,raw_psis,_sf_,raw_vfs)
## reduce the psis in model_rs
model_rs.psis, = filter_psi3(
sin2psis_init,bounds,
model_rs.psis.copy())
model_rs.npsi = len(model_rs.psis)
DEC_interp = _sf_.copy()
# 2. Finite number of tiltings
if psi_nbin!=1:
model_sfs, model_igs, model_vfs, \
model_ehkls, _sf_ \
= psi_reso4(
model_rs.psis, psi_nbin,
model_sfs,model_igs,
model_vfs,model_ehkls,_sf_)
model_rs.psis, = psi_reso4(
model_rs.psis.copy(),psi_nbin,
model_rs.psis.copy())
model_rs.npsi = len(model_rs.psis)
# 3. Assign tdat
if ig_sub: model_tdats = model_ehkls - model_igs
else: model_tdats = model_ehkls.copy()
# 4. Perturb ehkl (common)
if iscatter:
nstp, nphi, npsi = model_ehkls.shape
tdats = np.zeros((nstp,nphi,npsi))
for istp in range(nstp):
for iphi in range(nphi):
# ## Previous perturbation rule
# tdats[istp,iphi,:] = u_epshkl(
# model_tdats[istp,iphi],
# sigma=sigma)
## New perturbation rule
if type(theta_b).__name__== 'NoneType':
raise IOError, 'theta_b should be given'
for ipsi in range(npsi):
## multitudes of random
mrd = model_vfs[istp,iphi,ipsi]/ird
tdats[istp,iphi,ipsi] = u_epshkl_geom_inten(
e = model_tdats[istp,iphi,ipsi],
sigma = sigma,
psi = model_rs.psis[ipsi],
## need to calculate the below...
theta_b = theta_b,
mrd = mrd)
else: tdats=model_tdats.copy()
## end of data process
#------------------------------------------------------------#
if mod_ext==None: mod_ref='STR_STR.OUT'
else: mod_ref='%s.%s'%(mod_ref.split('.')[0],
mod_ext)
flow_weight = FlowCurve(name='Model weighted')
flow_weight.get_model(fn=mod_ref)
## calc Von Mises stress/strain
flow_weight.get_eqv()
if len(flow_weight.epsilon_vm)<5: lc='k.'
else: lc='k-'
if iplot:
ax1.plot(
flow_weight.epsilon_vm,
flow_weight.sigma_vm,
lc,label=r'$\langle \sigma^c \rangle$',
alpha=1.0)
axes_label.__eqv__(ax1,ft=10)
## plot all stress factors at individual
## deformation levels
stress = []
if verbose or True: print '%8s%8s%8s%8s%8s%8s'%(
'S11','S22','S33','S23','S13','S12')
################################################
## *Serial* Loop over the deformation steps
ref_psis = model_rs.psis.copy()
nstp = model_rs.dat_model.nstp
# nstp = 3 ## debugging
for istp in range(nstp):
"""
Dimensions of data arrays for:
==============================
sf (nstp, k, nphi, npsi)
ig (nstp, nphi, npsi)
ehkl (nstp, nphi, npsi)
strain (nstp, 6)
vf (nstp, nphi, npsi)
"""
if iplot==False and istep!=None:
nstp=1
istp = istep
print 'processing: %2.2i/%2.2i'%(istp,nstp)
#model_rs.sf = model_sfs[istp].copy()
## filter signals where any data is nan
ref = _sf_[istp].copy()
inds = []
## masking can be improved by being
## specific on phi axis -> require fix in RS.rs
# for iphi in range(len(ref[0])):
for ipsi in range(len(ref_psis)):
if not(np.isnan(ref[0:2,:,ipsi]).any()):
inds.append(ipsi)
model_rs.sf = _sf_[istp][:,:,inds]
model_rs.psis = ref_psis[inds]
model_rs.npsi = len(model_rs.psis)
model_rs.eps0 = model_igs[istp][:,inds]
model_rs.ehkl = model_ehkls[istp][:,inds]
model_rs.tdat = tdats[istp][:,inds]
#-----------------------------------#
## find the sigma ...
s11 = model_rs.dat_model.\
flow.sigma[0,0][istp]
s22 = model_rs.dat_model.\
flow.sigma[1,1][istp]
## find the stress by fitting
## the elastic strains
dsa_sigma = model_rs.find_sigma(
ivo=[0,1],
init_guess=[0,0,0,0,0,0],#[s11,s22,0,0,0,0],
weight = wgt) # None
if verbose or True:
for i in range(6): print '%+7.1f'%(dsa_sigma[i]),
print ''
stress.append(dsa_sigma)
full_Ei = np.zeros((model_rs.nphi,len(raw_psis)))
for iphi in range(model_rs.nphi):
for ipsi in range(len(raw_psis)):
for k in range(2):
full_Ei[iphi,ipsi] \
= full_Ei[iphi,ipsi]+\
raw_sfs[istp,k,iphi,ipsi]*dsa_sigma[k]
if istep!=None and iplot==False:
return model_rs, s11,s22, dsa_sigma[0],dsa_sigma[1],\
raw_psis.copy(),\
raw_vfs[istp].copy(),raw_sfs[istp].copy(),\
full_Ei.copy(),\
DEC_interp[istp].copy()
#-----------------------------------#
if istp==0: ileg=True
else: ileg=True #False
if (istep!=None and istp==istep) or\
(istep==None and istp==nstp-1)\
and iplot:
fig2,fig3,fig4=__model_fit_plot__(
model_rs,ifig=ifig+istp*2+10,
istp=istp, nxphi=nxphi,stress_wgt=None,
ivo=None,hkl=hkl,ileg=ileg,iwind=iwind,
wdeg=wdeg)
elif iplot:
plt.ioff()
f1,f2,f3=__model_fit_plot__(
model_rs,ifig=ifig+istp*2+10,
## stress_wgt: the mechanical stress.
## will be used as a reference line
istp=istp,nxphi=nxphi,stress_wgt=[s11,s22,0,0,0,0],
wgt=raw_vfs[istp].copy(),wgt_psi=raw_psis,
full_Ei = full_Ei,
full_DEC = raw_sfs[istp].copy(),
DEC_interp = DEC_interp[istp].copy(),
ivo=[0,1],hkl=hkl,ileg=ileg,iwind=False,
ipsi_opt=ipsi_opt)
fs.savefig(f2);fe.savefig(f1);f_er.savefig(f3)
f1.clf();plt.draw();f2.clf();plt.draw();f3.clf();plt.draw()
plt.close(f1);plt.close(f2);plt.close(f3);plt.ion()
# end of the serial loop over deformation steps
############################################################
if iplot: fe.close(); fs.close(); f_er.close()
stress = np.array(stress).T # diffraction stress
flow_dsa = FlowCurve(name='Diffraction Stress')
flow_dsa.get_6stress(stress)
flow_dsa.get_33strain(model_rs.dat_model.flow.epsilon)
flow_dsa.get_eqv()
## Various plots
if iplot:
ax1.plot(flow_dsa.epsilon_vm,flow_dsa.sigma_vm,'k+',
label=r'$\hat{\sigma}^{RS}$')
for i in range(len(exp_ref)):
f = exp_ref[i]; lab = exp_lab[i]
edat = np.loadtxt(f).T
ax1.plot(edat[0],edat[1],'-',lw=2,label=lab)
## ax1.set_ylim(0.,800)
fancy_legend(ax1,size=10,nscat=2)
sigma_wgt = flow_weight.sigma
if iplot:
ax2.plot(sigma_wgt[0,0],sigma_wgt[1,1],'k-')
ax2.plot(flow_dsa.sigma[0,0],flow_dsa.sigma[1,1],'k+')
## connector
npoints = len(sigma_wgt[0,0])
wgtx = sigma_wgt[0,0]; wgty = sigma_wgt[1,1]
dsax = flow_dsa.sigma[0,0]; dsay = flow_dsa.sigma[1,1]
for i in range(npoints):
ax2.plot([wgtx[i],dsax[i]],[wgty[i],dsay[i]],'k-',alpha=0.2)
ax2.set_ylim(-100,700); ax2.set_xlim(-100,700)
ax2.set_aspect('equal')
ax2.set_xlabel(r'$\bar{\Sigma}_{11}$',dict(fontsize=15))
ax2.set_ylabel(r'$\bar{\Sigma}_{22}$',dict(fontsize=15))
ax2.locator_params(nbins=3)
ax2.set_xticks(np.linspace(300,700,3),dict(fontsize=4))
ax2.set_yticks(np.linspace(300,700,3),dict(fontsize=4))
ax2.grid('on'); plt.show()
## save figures
fig1.savefig('flow_%s_%s.pdf'%(hkl,path))
## fig2.savefig('ehkl_%s_fit_%s.pdf'%(hkl,path))
## fig3.savefig('sf_%s_%s.pdf'%(hkl,path))
## fig4.savefig('ehkl_fit_err_%s_%s.pdf'%(hkl,path))
# close figures
plt.close(fig1); plt.close(fig2); plt.close(fig3); plt.close(fig4)
return model_rs, flow_weight, flow_dsa
def ex_consistency(
ifig=50,nxphi=3,exp_ref=[],exp_lab=[],mod_ext=None,
mod_ref='STR_STR.OUT',sin2psimx=None,iscatter=False,
sigma=5e-5,psimx=None,psi_nbin=1,ig_sub=True,istep=None,
hkl=None,iplot=True,iwind=False,wdeg=2,ipsi_opt=1,
fn_sff=None,pmargin=None,path='',sf_ext=None,ig_ext=None,
vf_ext=None,iwgt=False,verbose=False,ilog=True,
dec_inv_frq=1,dec_interp=1,theta_b=None,ird=1.,nfrq=None):
"""
Consistency check between 'weighted average' stress and
the stress obtained following the stress analysis method
(SF, IG strain)
----------------------------------------
## Data visualization options
ifig = 50
nxphi : display only first nxphi of results
along phi axis
hkl : hkl of plane, for the sake of labels.
path : place holder for strain path
----------------------------------------
## Data process parameters
#1. Main options
sf_ext : Overwrite stress factor
ig_ext : Overwrite IG strain
vf_ext : Overwrite grain volume fraction (nstp,nphi,npsi)
iscatter (False) : introducing a random scattering of 'ehkl'
sigma : Level of standard deviation in the Gaussian distribution
used to 'perturb' the lattice strain.
#2. tilting angle restriction/treatments
sin2psimx : limiting the maximum sin2psi values, with which
stress-fitting is carried out
psimx : limiting the maximum psi value
psi_nbin : If other than 1, limit the number of data
#3. IG strain-specific
ig_sub : flag whether or not subtract the IG strain
#4. DEC related
dec_inv_frq : Inverse Frequency of DEC measures along EVM (nstp)
dec_interp: Interpolation method for the incomplete DEC
#5. Misc.
exp_ref : experimental reference
mod_ref : model's weighted average flow curves are given
----------------------------------------
## misc. options
iplot (True) : flag whether or not MPL plot is performed
ipsi_opt 0: sin2psi
1: sign(psi) * sin2psi
2: psi
istep : If other than None, analysis is carried out
only for the given istep
----------------------------------------
## debugging options
verbose : False
ilog : False
----------------------------------------
## Diffraction condition parameters
theta_b : None (Bragg's angle) (given in radian)
ird : Intensity expected for random distribution
nfrq None
"""
import time
from MP import progress_bar
uet = progress_bar.update_elapsed_time
t0 = time.time()
if ilog:
fn = 'ex_consistency.log'
f = open(fn,'w')
write_args(
f=f,ihead=True,ifig=ifig,nxphi=nxphi,exp_ref=exp_ref,
exp_lab=exp_lab,mod_ext=mod_ext,mod_ref=mod_ref,
sin2psimx=sin2psimx,iscatter=iscatter,sigma=sigma,
psimx=psimx,psi_nbin=psi_nbin,ig_sub=ig_sub,
istep=istep,hkl=hkl,iplot=iplot,iwind=iwind,
wdeg=wdeg,ipsi_opt=ipsi_opt,fn_sff=fn_sff,
pmargin=pmargin,path=path,sf_ext=sf_ext,
ig_ext=ig_ext,vf_ext=vf_ext,iwgt=iwgt,
verbose=verbose,ilog=ilog,nfrq=nfrq,
ird=ird,theta_b=theta_b)
f.close()
print 'log has been saved to ',fn
from rs import ResidualStress,u_epshkl,\
u_epshkl_geom_inten,filter_psi,filter_psi3,\
psi_reso,psi_reso2,psi_reso3,psi_reso4
from mst_ex import use_intp_sfig, return_vf
from MP.mat import mech # mech is a module
FlowCurve = mech.FlowCurve
if iplot:
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from MP.lib import mpl_lib,axes_label
plt.ioff()
wide_fig = mpl_lib.wide_fig
fancy_legend = mpl_lib.fancy_legend
## Collection of figures at various plastic strains
fe = PdfPages('all_ehkl_fits_%s_%s.pdf'%(hkl,path))
fs = PdfPages('all_stress_factors_%s_%s.pdf'%(hkl,path))
f_er = PdfPages('all_Ei-ehkl-e0_%s_%s.pdf'%(hkl,path))
fig1 = wide_fig(ifig,nw=2,nh=1,left=0.2,uw=3.5,
w0=0,w1=0.3,right=0,iarange=True)
fig_vm = plt.figure(figsize=(3.5,3.5))
ax_vm = fig_vm.add_subplot(111)
## ax1: Equivalent Stress/Strain
## ax2: Stress path in the plane stress space (RD/TD)
ax1 = fig1.axes[0]; ax2 = fig1.axes[1]
pass
#------------------------------------------------------------#
## i_ip = 1: ioption for the model data
model_rs = ResidualStress(
mod_ext=mod_ext,fnmod_ig='igstrain_fbulk_ph1.out',
fnmod_sf='igstrain_fbulk_ph1.out',i_ip=1)
## Process the sf/eps0/ehkl/wgt and so forth
## according to the parameters given
ivf_ext=True; isf_ext=True; iig_ext=True
if type(sf_ext)==type(None):isf_ext=False
if type(ig_ext)==type(None):iig_ext=False
if type(vf_ext)==type(None):ivf_ext=False
"""
## final data should be saved to
'model_sfs'
'model_igs'
'model_ehkl'
And eventually to 'model_tdats' inside the loop
"""
## SF/IG/ehkl
model_ehkls = np.copy(model_rs.dat_model.ehkl)
if isf_ext and iig_ext:
model_sfs = sf_ext.copy()
model_igs = ig_ext.copy()
elif isf_ext!=iig_ext:
raise IOError, 'isf_ext should equal to iig_ext'
## if isf_ext False and isf_ext False
else:
model_sfs = model_rs.dat_model.sf.copy()
model_igs = model_rs.dat_model.ig.copy()
## VF
if not(ivf_ext): model_vfs, model_ngr = return_vf()
else: model_vfs = vf_ext.copy()
## whether or not vf would be used as weights in
## the least-sq estimator
if not(iwgt): wgt = None # overwrite wgt
# Apply process
# 0. Use of interpolated SF?
# 1. Limit the range of sin2psi (or psi)
# 2. Finite number of tiltings
# 3. Assign tdat
# 4. Perturb ehkl (common)
# 5. Filter based on vf?
## Unstaged but the 'raw' arrays
raw_psis = model_rs.dat_model.psi.copy()
raw_vfs = model_vfs.copy()
raw_ehkl = np.copy(model_rs.dat_model.ehkl)
raw_sfs = model_sfs.copy()
## staged arrays for stress estimation
model_rs.psis = model_rs.dat_model.psi.copy()
model_rs.phis = model_rs.dat_model.phi.copy()
model_rs.npsi = len(model_rs.psis)
model_rs.nphi = len(model_rs.phis)
sin2psis_init = np.sin(model_rs.psis)**2
# 0. Use interpolated SF
_sf_, _ig_ = use_intp_sfig(
dec_inv_frq,iopt=dec_interp,
iplot=False,iwgt=False)
## swapping axes to comply with what is used
## in dat_model.sf
_sf_ = _sf_.sf.swapaxes(1,3).swapaxes(2,3)
_sf_ = _sf_ *1e6
# 0.5 Mask DEC where volume fraction
## model_ngr or model_vfs
for i in xrange(len(model_vfs)):
for j in xrange(len(model_vfs[i])):
for k in xrange(len(model_vfs[i][j])):
if model_ngr[i,j,k]==0:
_sf_[i,:,j,k]=np.nan
# _ig_[i,j,k]=np.nan
raw_sfs[i,:,j,k]=np.nan
model_sfs[i,:,j,k]=np.nan
raw_ehkl[i,j,k] = np.nan
for m in xrange(6):
if _sf_[i,m,j,k]==0 or raw_sfs[i,m,j,k]==0:
# print 'encountered zero sf'
_sf_[i,m,j,k] = np.nan
raw_sfs[i,m,j,k] = np.nan
model_sfs[i,m,j,k] = np.nan
raw_ehkl[i,j,k] = np.nan
# 1. Limit the range of sin2psi (or psi)
if type(sin2psimx)!=type(None) or \
type(psimx)!=type(None):
if type(sin2psimx)!=type(None):
bounds=[0., sin2psimx]
elif type(psimx)!=type(None):
bounds=[0.,np.sin(psimx*np.pi/180.)**2]
raw_sfs,model_sfs,model_igs,model_vfs,\
model_ehkls,raw_psis,_sf_,raw_vfs\
= filter_psi3(
sin2psis_init,bounds,
raw_sfs,model_sfs,model_igs,model_vfs,
model_ehkls,raw_psis,_sf_,raw_vfs)
## reduce the psis in model_rs
model_rs.psis, = filter_psi3(
sin2psis_init,bounds,
model_rs.psis.copy())
model_rs.npsi = len(model_rs.psis)
DEC_interp = _sf_.copy()
# 2. Finite number of tiltings
if psi_nbin!=1:
model_sfs, model_igs, model_vfs, \
model_ehkls, _sf_ \
= psi_reso4(
model_rs.psis, psi_nbin,
model_sfs,model_igs,
model_vfs,model_ehkls,_sf_)
model_rs.psis, = psi_reso4(
model_rs.psis.copy(),psi_nbin,
model_rs.psis.copy())
model_rs.npsi = len(model_rs.psis)
# 3. Assign tdat
if ig_sub: model_tdats = model_ehkls - model_igs
else: model_tdats = model_ehkls.copy()
# 4. Perturb ehkl (common)
if iscatter:
nstp, nphi, npsi = model_ehkls.shape
tdats = np.zeros((nstp,nphi,npsi))
for istp in xrange(nstp):
for iphi in xrange(nphi):
# ## Previous perturbation rule
# tdats[istp,iphi,:] = u_epshkl(
# model_tdats[istp,iphi],
# sigma=sigma)
## New perturbation rule
if type(theta_b).__name__== 'NoneType':
raise IOError, 'theta_b should be given'
for ipsi in xrange(npsi):
## multitudes of random
mrd = model_vfs[istp,iphi,ipsi]/ird
tdats[istp,iphi,ipsi] = u_epshkl_geom_inten(
e = model_tdats[istp,iphi,ipsi],
sigma = sigma,
psi = model_rs.psis[ipsi],
## need to calculate the below...
theta_b = theta_b,
mrd = mrd)
else: tdats=model_tdats.copy()
## end of data process
#------------------------------------------------------------#
if mod_ext==None: mod_ref='STR_STR.OUT'
else: mod_ref='%s.%s'%(mod_ref.split('.')[0],
mod_ext)
flow_weight = FlowCurve(name='Model weighted')
flow_weight.get_model(fn=mod_ref)
## calc Von Mises stress/strain
flow_weight.get_eqv()
if len(flow_weight.epsilon_vm)<5: lc='k.'
else: lc='k-'
if iplot:
for a in [ax1,ax_vm]:
a.plot(
flow_weight.epsilon_vm,
flow_weight.sigma_vm,
lc,label=r'$\langle \sigma^c \rangle$',
alpha=1.0)
axes_label.__eqv__(a,ft=10)
## plot all stress factors at individual
## deformation levels
stress = []
if verbose or True: print '%8s%8s%8s%8s%8s%8s'%(
'S11','S22','S33','S23','S13','S12')
################################################
## *Serial* Loop over the deformation steps
ref_psis = model_rs.psis.copy()
nstp = model_rs.dat_model.nstp
# nstp = 3 ## debugging
for istp in xrange(nstp):
"""
Dimensions of data arrays for:
==============================
sf (nstp, k, nphi, npsi)
ig (nstp, nphi, npsi)
ehkl (nstp, nphi, npsi)
strain (nstp, 6)
vf (nstp, nphi, npsi)
"""
if type(nfrq).__name__=='int':
if istp % nfrq !=0:
continue
if iplot==False and type(istep)!=type(None):
nstp = 1
istp = istep
print 'processing: %2.2i/%2.2i'%(istp,nstp),
#model_rs.sf = model_sfs[istp].copy()
## filter signals where any data is nan
ref = _sf_[istp].copy()
inds = []
## masking can be improved by being
## specific on phi axis -> require fix in RS.rs
# for iphi in xrange(len(ref[0])):
for ipsi in xrange(len(ref_psis)):
if not(np.isnan(ref[0:2,:,ipsi]).any()):
inds.append(ipsi)
model_rs.sf = _sf_[istp][:,:,inds]
model_rs.psis = ref_psis[inds]
model_rs.npsi = len(model_rs.psis)
model_rs.eps0 = model_igs[istp][:,inds]
model_rs.ehkl = model_ehkls[istp][:,inds]
model_rs.tdat = tdats[istp][:,inds]
#-----------------------------------#
## find the sigma ...
s11 = model_rs.dat_model.\
flow.sigma[0,0][istp]
s22 = model_rs.dat_model.\
flow.sigma[1,1][istp]
## find the stress by fitting
## the elastic strains
dsa_sigma = model_rs.find_sigma(
ivo=[0,1],
init_guess=[0,0,0,0,0,0],#[s11,s22,0,0,0,0],
weight = wgt) # None
if verbose or True:
for i in xrange(6): print '%+7.1f'%(dsa_sigma[i]),
print ''
stress.append(dsa_sigma)
full_Ei = np.zeros((model_rs.nphi,len(raw_psis)))
for iphi in xrange(model_rs.nphi):
for ipsi in xrange(len(raw_psis)):
for k in xrange(2):
full_Ei[iphi,ipsi] \
= full_Ei[iphi,ipsi]+\
raw_sfs[istp,k,iphi,ipsi]*dsa_sigma[k]
if type(istep)!=type(None) and iplot==False:
return model_rs, s11,s22, dsa_sigma[0],dsa_sigma[1],\
raw_psis.copy(),\
raw_vfs[istp].copy(),raw_sfs[istp].copy(),\
full_Ei.copy(),\
DEC_interp[istp].copy()
#-----------------------------------#
if istp==0: ileg=True
else: ileg=True #False
if (istep!=None and istp==istep) or\
(istep==None and istp==nstp-1)\
and iplot:
fig2,fig3,fig4=__model_fit_plot__(
model_rs,ifig=ifig+istp*2+10,
istp=istp, nxphi=nxphi,stress_wgt=None,
ivo=None,hkl=hkl,ileg=ileg,iwind=iwind,
wdeg=wdeg)
elif iplot:
plt.ioff()
f1,f2,f3=__model_fit_plot__(
model_rs,ifig=ifig+istp*2+10,
## stress_wgt: the mechanical stress.
## will be used as a reference line
istp=istp,nxphi=nxphi,stress_wgt=[s11,s22,0,0,0,0],
wgt=raw_vfs[istp].copy(),wgt_psi=raw_psis,
full_Ei = full_Ei,
full_DEC = raw_sfs[istp].copy(),
DEC_interp = DEC_interp[istp].copy(),
ivo=[0,1],hkl=hkl,ileg=ileg,iwind=False,
ipsi_opt=ipsi_opt)
fs.savefig(f2);fe.savefig(f1);f_er.savefig(f3)
f1.clf();plt.draw();f2.clf();plt.draw();f3.clf();plt.draw()
plt.close(f1);plt.close(f2);plt.close(f3);plt.ion()
# end of the serial loop over deformation steps
############################################################
if iplot: fe.close(); fs.close(); f_er.close()
stress = np.array(stress).T # diffraction stress
flow_dsa = FlowCurve(name='Diffraction Stress')
flow_dsa.get_6stress(stress)
if type(nfrq).__name__=='NoneType':
flow_dsa.get_33strain(model_rs.dat_model.flow.epsilon)
elif type(nfrq).__name__=='int':
flow_dsa.get_33strain(model_rs.dat_model.flow.epsilon[:,:,::nfrq])
flow_dsa.get_eqv()
sigma_wgt = flow_weight.sigma
## Various plots
if iplot:
for a in [ax1, ax_vm]:
a.plot(flow_dsa.epsilon_vm,flow_dsa.sigma_vm,'k+',
label=r'$\hat{\sigma}^\mathrm{RS}$')
for i in xrange(len(exp_ref)):
f = exp_ref[i]; lab = exp_lab[i]
edat = np.loadtxt(f).T
ax1.plot(edat[0],edat[1],'-',lw=2,label=lab)
## ax1.set_ylim(0.,800)
for a in [ax1,ax_vm]:
fancy_legend(a,size=10,nscat=1)
ax2.plot(sigma_wgt[0,0],sigma_wgt[1,1],'k-')
ax2.plot(flow_dsa.sigma[0,0],flow_dsa.sigma[1,1],'k+')
## connector
npoints = len(sigma_wgt[0,0])
wgtx = sigma_wgt[0,0]; wgty = sigma_wgt[1,1]
dsax = flow_dsa.sigma[0,0]; dsay = flow_dsa.sigma[1,1]
# for i in xrange(npoints):
# ax2.plot([wgtx[i],dsax[i]],[wgty[i],dsay[i]],'k-',alpha=0.2)
ax2.set_ylim(-100,700); ax2.set_xlim(-100,700)
ax2.set_aspect('equal')
ax2.set_xlabel(r'$\bar{\Sigma}_{11}$ [MPa]',dict(fontsize=15))
ax2.set_ylabel(r'$\bar{\Sigma}_{22}$ [MPa]',dict(fontsize=15))
ax2.locator_params(nbins=3)
ax2.set_xticks(np.linspace(300,700,3),dict(fontsize=4))
ax2.set_yticks(np.linspace(300,700,3),dict(fontsize=4))
ax2.grid('on'); plt.show()
## save figures
fig1.savefig('flow_%s_%s.pdf'%(hkl,path))
fig_vm.savefig('all_flow_vm.pdf', bbox_inches='tight')
fig_vm.savefig('all_flow_vm.ps', bbox_inches='tight')
## fig2.savefig('ehkl_%s_fit_%s.pdf'%(hkl,path))
## fig3.savefig('sf_%s_%s.pdf'%(hkl,path))
## fig4.savefig('ehkl_fit_err_%s_%s.pdf'%(hkl,path))
# close figures
try:
figs=[fig1,fig2,fig3,fig4]
for f in figs:
try: plt.close(f)
except: pass
except: pass
uet(time.time()-t0)
print
return model_rs, flow_weight, flow_dsa
def DEC_intp(ss=[1,2,4],intps=[0,3,4],inds=[79,90,120]):
"""
DEC interpolation comparison
intps=0: NN (piecewise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
inds=[70,40]
"""
from MP.mat import mech # mech is a module
FlowCurve = mech.FlowCurve
from mst_ex import use_intp_sfig, return_vf
from rs import ResidualStress
model_rs = ResidualStress(
mod_ext=None,fnmod_ig='igstrain_fbulk_ph1.out',
fnmod_sf='igstrain_fbulk_ph1.out',i_ip=1)
psi = model_rs.dat_model.psi
sin2psi = sin2psi_opt(psi,1)
DEC_raw = model_rs.dat_model.sf.copy()
fc = FlowCurve(name='Weighted Average')
fc.get_model(fn='STR_STR.OUT')
fc.get_eqv()
evm = fc.epsilon_vm.copy()
fig = plt.figure()
gs=gridspec.GridSpec(
len(ss),len(intps),
wspace=0,hspace=0,left=0.25,right=0.8,top=0.8)
axes=[]
ms=['o','x','+','d','>','t']
for i in range(len(ss)):
axes.append([])
for j in range(len(intps)):
## interpolated DECs
print i,j
print ss[i],intps[j]
# if i==1 and j==0:
# raise IOError
_sf_, _ig_ = use_intp_sfig(
ss[i],iopt=intps[j],
iplot=False,iwgt=False)
dum = _sf_.sf.swapaxes(-1,-2).swapaxes(-2,-3)
DEC_intp = dum.copy()
ax = fig.add_subplot(gs[i,j])
ax.locator_params(nbins=4)
axes[i].append(ax)
for k in range(len(inds)):
ind = inds[k]
val_sin2psi = sin2psi[ind]
val_psi = psi[ind] * 180./np.pi
lab1, lab2 = None, None
if k==0:
lab1=r'Actual $\mathbb{F}_{ij}$'
lab2=r'Interpolated $\mathbb{F}^{\ I}_{ij}$'
y_raw = DEC_raw[:,0,0,ind]*1e6
ax.plot(evm, y_raw,'k-',
label=lab1)
y_intp = DEC_intp[:,0,0,ind]*1e12
ax.plot(evm,y_intp,'k--',
label=lab2)
ax.fill_between(evm,y_raw,y_intp,
facecolor='gray',alpha=0.5)
ax.plot(
evm[::ss[i]],
DEC_raw[:,0,0,ind][::ss[i]]*1e6,
ms[k],mfc='None',mec='black',
label=r'$\psi=%2.0f^\circ{}$'%val_psi)
if i==len(ss)-1 and j==0:
pass
else:
mpl_lib.rm_lab(ax,axis='x')
mpl_lib.rm_lab(ax,axis='y')
deco(ax=axes[-1][0],ft=10,iopt=6,ij=[1,1])
axes[-1][0].grid('off')
tune_xy_lim(fig.axes)
tune_x_lim(fig.axes,axis='y')
## annotations
for j in range(len(intps)):
if j==0: s = 'Piecewise'
if j==1: s = 'Quadratic'
if j==2: s = 'Linear fit'
if j==3: s = 'Power law fit'
axes[0][j].annotate(
s=s, horizontalalignment='center',
size=10,xy=(0.5,1.2),
xycoords='axes fraction')
for i in range(len(ss)):
s = r'$f^{\ \mathbb{F}}=^1/_{%i}$'%ss[i]
axes[i][-1].annotate(
s=s,
verticalalignment='center',
horizontalalignment='center',
rotation=270,
size=10,xy=(1.20,0.5),
xycoords='axes fraction')
fancy_legend(axes[0][0],size=10,nscat=1,ncol=1,
bbox_to_anchor=(-0.1,1))
fig.savefig('dec_intp.pdf')
fig.savefig('dec_intp.png')
return model_rs
def DEC_intp(ss=[1, 2, 4], intps=[0, 3, 4], inds=[79, 90, 120]):
"""
DEC interpolation comparison
intps=0: NN (piecewise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
inds=[70,40]
"""
from MP.mat import mech # mech is a module
FlowCurve = mech.FlowCurve
from mst_ex import use_intp_sfig, return_vf
from rs import ResidualStress
model_rs = ResidualStress(mod_ext=None,
fnmod_ig='igstrain_fbulk_ph1.out',
fnmod_sf='igstrain_fbulk_ph1.out',
i_ip=1)
psi = model_rs.dat_model.psi
sin2psi = sin2psi_opt(psi, 1)
DEC_raw = model_rs.dat_model.sf.copy()
fc = FlowCurve(name='Weighted Average')
fc.get_model(fn='STR_STR.OUT')
fc.get_eqv()
evm = fc.epsilon_vm.copy()
fig = plt.figure()
gs = gridspec.GridSpec(len(ss),
len(intps),
wspace=0,
hspace=0,
left=0.25,
right=0.8,
top=0.8)
axes = []
ms = ['o', 'x', '+', 'd', '>', 't']
for i in xrange(len(ss)):
axes.append([])
for j in xrange(len(intps)):
## interpolated DECs
print i, j
print ss[i], intps[j]
# if i==1 and j==0:
# raise IOError
_sf_, _ig_ = use_intp_sfig(ss[i],
iopt=intps[j],
iplot=False,
iwgt=False)
dum = _sf_.sf.swapaxes(-1, -2).swapaxes(-2, -3)
DEC_intp = dum.copy()
ax = fig.add_subplot(gs[i, j])
ax.locator_params(nbins=4)
axes[i].append(ax)
for k in xrange(len(inds)):
ind = inds[k]
val_sin2psi = sin2psi[ind]
val_psi = psi[ind] * 180. / np.pi
lab1, lab2 = None, None
if k == 0:
lab1 = r'Actual $\mathbb{F}_{ij}$'
lab2 = r'Interpolated $\mathbb{F}^{\ I}_{ij}$'
y_raw = DEC_raw[:, 0, 0, ind] * 1e6
ax.plot(evm, y_raw, 'k-', label=lab1)
y_intp = DEC_intp[:, 0, 0, ind] * 1e12
ax.plot(evm, y_intp, 'k--', label=lab2)
ax.fill_between(evm,
y_raw,
y_intp,
facecolor='gray',
alpha=0.5)
ax.plot(evm[::ss[i]],
DEC_raw[:, 0, 0, ind][::ss[i]] * 1e6,
ms[k],
mfc='None',
mec='black',
label=r'$\psi=%2.0f^\circ{}$' % val_psi)
if i == len(ss) - 1 and j == 0:
pass
else:
mpl_lib.rm_lab(ax, axis='x')
mpl_lib.rm_lab(ax, axis='y')
deco(ax=axes[-1][0], ft=10, iopt=6, ij=[1, 1])
axes[-1][0].grid('off')
tune_xy_lim(fig.axes)
tune_x_lim(fig.axes, axis='y')
## annotations
for j in xrange(len(intps)):
if j == 0: s = 'Piecewise'
if j == 1: s = 'Quadratic'
if j == 2: s = 'Linear fit'
if j == 3: s = 'Power law fit'
axes[0][j].annotate(s=s,
horizontalalignment='center',
size=10,
xy=(0.5, 1.2),
xycoords='axes fraction')
for i in xrange(len(ss)):
s = r'$f^{\ \mathbb{F}}=^1/_{%i}$' % ss[i]
axes[i][-1].annotate(s=s,
verticalalignment='center',
horizontalalignment='center',
rotation=270,
size=10,
xy=(1.20, 0.5),
xycoords='axes fraction')
fancy_legend(axes[0][0],
size=10,
nscat=1,
ncol=1,
bbox_to_anchor=(-0.1, 1))
fig.savefig('dec_intp.pdf')
fig.savefig('dec_intp.png')
return model_rs
