def plot(self, nbin_sin2psi=2, iopt=0, ylim=None, mxnphi=None, hkl='211'):
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.cm as cm
if hasattr(self, 'vf'): nh = 2
else: nh = 1
if mxnphi == None: mxnphi = self.nphi
figs = wide_fig(nw=mxnphi, nh=nh, w0=0, w1=0, left=0.2, right=0.15)
# mx = max(self.flow.epsilon_vm)
# mn = min(self.flow.epsilon_vm)
mx = 1.
mn = 0.
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx, mn=mn)
colors = []
self.flow.nstp = len(self.flow.epsilon_vm)
for i in xrange(mxnphi):
for j in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[j]
cl = c.to_rgba(eps)
if i == 0: colors.append(cl)
y = self.ig[j, i, :]
figs.axes[i].plot(np.sign(self.psi) *
sin(self.psi * np.pi / 180.)**2,
y,
'-x',
color=cl)
if j == 0:
figs.axes[i].set_title(r'$\phi: %3.1f^\circ{}$' %
self.phi[i])
deco = axes_label.__deco__
rm_inner = mpl_lib.rm_inner
ticks_bin_u = mpl_lib.ticks_bins_ax_u
deco(figs.axes[0], iopt=2, ipsi_opt=1)
for i in xrange(len(figs.axes)):
figs.axes[i].set_xlim(-0.5, 0.5)
mpl_lib.tune_xy_lim(figs.axes)
rm_inner(figs.axes)
ticks_bin_u(figs.axes, n=4)
# color bar
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88, b.y0, 0.03, b.y1 - b.y0])
mpl_lib.add_cb(ax=axcb,
filled=False,
norm=norm,
levels=self.flow.epsilon_vm,
colors=colors,
ylab='Equivalent strain')
def plot_all(self):
"""
Plot dhkl against psi
"""
from MP.lib import mpl_lib
wide_fig = mpl_lib.wide_fig
tune_xy_lim = mpl_lib.tune_xy_lim
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
deco = axes_label.__deco__
rm_inner =mpl_lib.rm_inner
ticks_bin_u = mpl_lib.ticks_bins_ax_u
figs = wide_fig(nw=self.nphi,nh=2,w0=0,w1=0,
left=0.2,right=0.15,
useOffset=False)
mx = max(self.flow.epsilon_vm)
mn = min(self.flow.epsilon_vm)
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx = mx, mn=mn)
for i in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[i]
cl = c.to_rgba(eps)
for j in xrange(self.nphi):
X = self.psi
Y = []
for ipsi in xrange(self.npsi):
y = self.P_scan[i].protophi[j].\
ppscans[ipsi].dspc
Y.append(y)
figs.axes[j].plot(X,Y,'-x',color=cl)
_Y_=np.array(Y).copy()
_Y_ = (_Y_+_Y_[::-1])/2.
# _Y_ = _Y_[:len(_Y_)/2+1]
figs.axes[j+self.nphi].plot(X,_Y_,'-x',color=cl)
if i==0:
figs.axes[j].set_title(
r'$\phi: %3.1f^\circ{}$'%self.phi[j])
figs.axes[j+self.nphi].set_title(
r'$\phi: %3.1f^\circ{}$'%self.phi[j])
ticks_bin_u(figs.axes,n=4)
deco(figs.axes[0+self.nphi],iopt=4)
tune_xy_lim(figs.axes)
rm_inner(figs.axes)
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88,b.y0,0.03,b.y1-b.y0])
mpl_lib.add_cb(axcb,cmap=cmap,filled=True,
ylab='Equivalent Strain',
norm=norm,format='%5.3f')
def plot(self, nbin_sin2psi=2, iopt=0,ylim=None,
mxnphi=None,hkl='211'):
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.cm as cm
if hasattr(self,'vf'): nh = 2
else: nh = 1
if mxnphi==None: mxnphi = self.nphi
figs = wide_fig(nw=mxnphi,nh=nh,w0=0,w1=0,
left=0.2,right=0.15)
# mx = max(self.flow.epsilon_vm)
# mn = min(self.flow.epsilon_vm)
mx = 1.
mn = 0.
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx,mn=mn)
colors=[]
self.flow.nstp = len(self.flow.epsilon_vm)
for i in xrange(mxnphi):
for j in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[j]
cl = c.to_rgba(eps)
if i==0: colors.append(cl)
y = self.ig[j,i,:]
figs.axes[i].plot(
np.sign(self.psi)*sin(self.psi*np.pi/180.)**2,
y,'-x',color=cl)
if j==0:
figs.axes[i].set_title(
r'$\phi: %3.1f^\circ{}$'%self.phi[i])
deco = axes_label.__deco__
rm_inner =mpl_lib.rm_inner
ticks_bin_u = mpl_lib.ticks_bins_ax_u
deco(figs.axes[0],iopt=2,ipsi_opt=1)
for i in xrange(len(figs.axes)):
figs.axes[i].set_xlim(-0.5,0.5)
mpl_lib.tune_xy_lim(figs.axes)
rm_inner(figs.axes)
ticks_bin_u(figs.axes,n=4)
# color bar
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88,b.y0,0.03,b.y1-b.y0])
mpl_lib.add_cb(ax=axcb,filled=False,norm=norm,
levels=self.flow.epsilon_vm,
colors=colors,ylab='Equivalent strain')
def plot_xy_disc_e33dot(
dat,
istp=-1,
ax=None,
icol=11,
fang=90,
dist_bb=[5, 10], ## backbone
dist_rb=[1, 5]):
"""
Extract DIC data from 'disc' area
"""
from MP.lib.mpl_lib import wide_fig as wf
from MP.lib.mpl_lib import rm_all_lab as ral
import matplotlib as mpl
from MP.lib import mpl_lib
from MP import ssort
sort = ssort.shellSort
d = dat[istp, :, :]
dz = np.log10(abs(d[:, icol]))
for i in range(len(dz)):
if dz[i] == np.inf or dz[i] == -np.inf:
dz[i] = np.nan
mn = min(dz)
mx = max(dz)
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, m = mpl_lib.norm_cmap(mn=mn, mx=mx, cm_name='brg')
n = d.shape[0]
## find values in the back-bone branch shape
ids = []
xs = []
ys = []
zs = []
for i in range(n):
x, y, z = d[i, 0], d[i, 1], -d[i, icol]
if np.isnan(z): pass
else:
zs.append(d[i, icol])
ids.append(i)
xs.append(d[i, 0])
ys.append(d[i, 1])
c = m.to_rgba(np.log10(z))
ax.plot(x, y, 'o', ms=1.5, color=c, mfc=c, mec='None')
val, ind = sort(zs)
imn = ids[ind[0]]
ax.plot(d[imn, 0], d[imn, 1], 'g.') ## minimum
## draw a backbone grid
x0, y0 = d[imn, 0], d[imn, 1] # center of backbone
db = np.array(dist_bb)
dr = np.array(dist_rb)
DB = []
DR = []
for i in range(len(db)):
DB.append(-db[-i - 1])
DB.append(0)
for i in range(len(db)):
DB.append(db[i])
for i in range(len(dr)):
DR.append(-dr[-i - 1])
DR.append(0)
for i in range(len(dr)):
DR.append(dr[i])
db = DB[::]
dr = DR[::]
xy_db_dr = np.zeros((len(db), len(dr), 2))
for i in range(len(db)):
for j in range(len(dr)):
xy_db_dr[i, j, :] = db[i], dr[j]
## rotate xy by fang
for i in range(len(db)):
for j in range(len(dr)):
y, x = xy_db_dr[i, j, :]
xy_db_dr[i, j, :] = rot_xy([x, y], fang)
## translate
xy_db_dr[:, :, 0] = xy_db_dr[:, :, 0] + x0
xy_db_dr[:, :, 1] = xy_db_dr[:, :, 1] + y0
## print xy_db_dr.shape
## return xy_db_dr
if len(db) > 3:
nw = len(db) / 3
nh = 4
fig = wf(nw=nw,
nh=nh,
w0=0.0,
ws=1.0,
w1=0.0,
h0=0.0,
hs=1.0,
h1=0.0,
uw=2.3,
uh=2.3,
left=0.2,
up=0.1,
down=0.2,
iarange=True)
fig1 = wf(nw=2,
nh=2,
w0=0.1,
ws=0.7,
w1=0.2,
h0=0.1,
hs=0.7,
h1=0.2,
uw=2.3,
uh=2.3,
left=0.2,
up=0.1,
down=0.2,
iarange=True)
iax = 0
sym = [
'k^', 'k+', 'kd', 'kx', 'k<', 'k>', 'k*', 'k1', 'k2', 'k3', 'k4', 'k8',
'ks', 'kp', 'kh', 'kH', 'kD'
]
## thickness strain rate
epst_dot_av = []
epst_dot_std = []
## epsxx
exx_av = []
exx_std = []
## epsyy
eyy_av = []
eyy_std = []
for i in range(len(db)):
indices_along_ribs = []
for j in range(len(dr)):
x, y = xy_db_dr[i, j, :]
ind = find_nearest([x, y], xs, ys, err=2)
if not (np.isnan(ind)):
indices_along_ribs.append(ids[ind])
epsx = []
epsy = []
epst_dot = []
coord = []
for k in range(len(indices_along_ribs)):
indx = indices_along_ribs[k]
x = d[indx, 0]
y = d[indx, 1]
coord.append([x, y])
## ax.plot(x,y,sym[i],ms=3)
ex = dat[:, indx, 3]
ey = dat[:, indx, 4]
et = dat[:, indx, 12]
ex_d = dat[:, indx, 9]
ey_d = dat[:, indx, 10]
ez_d = ex_d + ey_d ## positive value
epst_dot.append(ez_d)
epsx.append(ex)
epsy.append(ey)
fig.axes[iax].plot(ez_d)
iax = iax + 1
x, y = np.array(coord).T[:]
ax.plot(x, y, '-', color='k', alpha=0.5)
epst_dot = np.array(epst_dot).T
epsx = np.array(epsx).T
epsy = np.array(epsy).T
D = []
D_e = []
E_ex = []
E_exe = []
E_ey = []
E_eye = []
for j in range(len(epst_dot)): ## number of ribs
D.append(np.mean(epst_dot[j]))
D_e.append(np.std(epst_dot[j]))
E_ex.append(np.mean(epsx[j]))
E_exe.append(np.std(epsx[j]))
E_ey.append(np.mean(epsy[j]))
E_eye.append(np.std(epsy[j]))
## fig1.axes[0].plot(D,label=db[i])
epst_dot_av.append(D)
epst_dot_std.append(D_e)
exx_av.append(E_ex)
exx_std.append(E_exe)
eyy_av.append(E_ey)
eyy_std.append(E_eye)
## find maximum D value's index to trim the data
ndat = (len(epst_dot_av) - 1) / 2 + 1
ref = epst_dot_av[ndat - 1] ## data at necking center spot
_ref_ = []
_ind_ = []
for i in range(len(ref)):
if not (np.isnan(ref[i])):
_ref_.append(ref[i])
_ind_.append(i)
_val_, _i_ = sort(_ref_)
ix = _ind_[_i_[-1]]
mdat = np.zeros((2, 2, ndat))
c = []
for i in range(ndat):
i0 = i
i1 = -i - 1
## thickness strain rate
d1 = epst_dot_av[i0]
d2 = epst_dot_av[i1]
e1 = epst_dot_std[i0]
e2 = epst_dot_std[i1]
d = []
e = []
## epsilon xx
ex1 = exx_av[i0]
ex2 = exx_av[i1]
exe1 = exx_std[i0]
exe2 = exx_std[i1]
ex = []
exe = []
## epsilon yy
ey1 = eyy_av[i0]
ey2 = eyy_av[i1]
eye1 = eyy_std[i0]
eye2 = eyy_std[i1]
ey = []
eye = []
for j in range(len(d1)): # along the time axis
d.append((d1[j] + d2[j]) / 2.)
e.append((e1[j] + e2[j]) / 2.)
ex.append((ex1[j] + ex2[j]) / 2.)
exe.append((exe1[j] + exe2[j]) / 2.)
ey.append((ey1[j] + ey2[j]) / 2.)
eye.append((eye1[j] + eye2[j]) / 2.)
xind = np.arange(len(d)) + 1
p = 0.7
i0 = int(float(len(xind)) * p)
## find maximum value and index?
l, = fig1.axes[0].plot(xind, d, '-', label=abs(db[-i - 1]))
fig1.axes[1].plot(xind[i0:ix + 1], d[i0:ix + 1], '+')
fig1.axes[2].errorbar(xind[i0:ix + 1],
d[i0:ix + 1],
yerr=e1[i0:ix + 1],
fmt='+')
l, = fig1.axes[3].plot(ex[i0:ix + 1], ey[i0:ix + 1])
fig1.axes[3].errorbar(ex[ix],
ey[ix],
color=l.get_color(),
xerr=exe[ix],
yerr=eye[ix])
mdat[0, 0, i] = ex[ix]
mdat[0, 1, i] = ey[ix]
mdat[1, 0, i] = exe[ix]
mdat[1, 1, i] = eye[ix]
c.append(l.get_color())
## deco
from MP.lib.mpl_lib import tune_xy_lim
tune_xy_lim(fig.axes)
fig1.axes[2].set_yscale('log')
fig1.axes[2].set_ylim(1e-3)
fig1.axes[0].legend(loc='best',ncol=2,fontsize=5).\
get_frame().set_alpha(0.5)
return fig, fig1, mdat, c
def plot_xy_disc_e33dot(
dat,istp=-1,ax=None,icol=11,fang=90,
dist_bb=[5,10], ## backbone
dist_rb=[1,5]):
"""
Extract DIC data from 'disc' area
"""
from MP.lib.mpl_lib import wide_fig as wf
from MP.lib.mpl_lib import rm_all_lab as ral
import matplotlib as mpl
from MP.lib import mpl_lib
from MP import ssort
sort=ssort.shellSort
d=dat[istp,:,:]
dz = np.log10(abs(d[:,icol]))
for i in range(len(dz)):
if dz[i]==np.inf or dz[i]==-np.inf:
dz[i] = np.nan
mn=min(dz);mx=max(dz)
norm=mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, m = mpl_lib.norm_cmap(
mn=mn,mx=mx,cm_name='brg')
n=d.shape[0]
## find values in the back-bone branch shape
ids = []; xs = []; ys = []; zs = []
for i in range(n):
x,y,z=d[i,0],d[i,1],-d[i,icol]
if np.isnan(z): pass
else:
zs.append(d[i,icol])
ids.append(i)
xs.append(d[i,0])
ys.append(d[i,1])
c = m.to_rgba(np.log10(z))
ax.plot(x,y,'o',ms=1.5,
color=c,mfc=c,mec='None')
val, ind = sort(zs)
imn = ids[ind[0]]
ax.plot(d[imn,0],d[imn,1],'g.') ## minimum
## draw a backbone grid
x0,y0=d[imn,0],d[imn,1] # center of backbone
db=np.array(dist_bb); dr=np.array(dist_rb)
DB=[]; DR=[]
for i in range(len(db)):
DB.append(-db[-i-1])
DB.append(0)
for i in range(len(db)):
DB.append(db[i])
for i in range(len(dr)):
DR.append(-dr[-i-1])
DR.append(0)
for i in range(len(dr)):
DR.append(dr[i])
db=DB[::];dr=DR[::]
xy_db_dr = np.zeros((len(db),len(dr),2))
for i in range(len(db)):
for j in range(len(dr)):
xy_db_dr[i,j,:] = db[i], dr[j]
## rotate xy by fang
for i in range(len(db)):
for j in range(len(dr)):
y,x = xy_db_dr[i,j,:]
xy_db_dr[i,j,:] = rot_xy([x,y],fang)
## translate
xy_db_dr[:,:,0] = xy_db_dr[:,:,0] + x0
xy_db_dr[:,:,1] = xy_db_dr[:,:,1] + y0
## print xy_db_dr.shape
## return xy_db_dr
if len(db)>3:
nw = len(db)/3
nh = 4
fig=wf(nw=nw,nh=nh,w0=0.0,ws=1.0,w1=0.0,
h0=0.0,hs=1.0,h1=0.0,uw=2.3,uh=2.3,
left=0.2,up=0.1,down=0.2,iarange=True)
fig1=wf(nw=2,nh=2,w0=0.1,ws=0.7,w1=0.2,
h0=0.1,hs=0.7,h1=0.2,uw=2.3,uh=2.3,
left=0.2,up=0.1,down=0.2,iarange=True)
iax=0
sym=['k^','k+','kd','kx','k<','k>','k*',
'k1','k2','k3','k4','k8','ks','kp',
'kh','kH','kD']
## thickness strain rate
epst_dot_av = []; epst_dot_std = []
## epsxx
exx_av = []; exx_std = []
## epsyy
eyy_av = []; eyy_std = []
for i in range(len(db)):
indices_along_ribs = []
for j in range(len(dr)):
x,y = xy_db_dr[i,j,:]
ind = find_nearest([x,y],xs,ys,err=2)
if not(np.isnan(ind)):
indices_along_ribs.append(ids[ind])
epsx = []; epsy = []; epst_dot = []
coord = []
for k in range(len(indices_along_ribs)):
indx = indices_along_ribs[k]
x = d[indx,0]; y = d[indx,1]
coord.append([x,y])
## ax.plot(x,y,sym[i],ms=3)
ex=dat[:,indx,3]
ey=dat[:,indx,4]
et=dat[:,indx,12]
ex_d=dat[:,indx,9]
ey_d=dat[:,indx,10]
ez_d=ex_d+ey_d ## positive value
epst_dot.append(ez_d)
epsx.append(ex)
epsy.append(ey)
fig.axes[iax].plot(ez_d)
iax = iax + 1
x,y = np.array(coord).T[:]
ax.plot(x,y,'-',color='k',alpha=0.5)
epst_dot=np.array(epst_dot).T
epsx =np.array(epsx).T
epsy =np.array(epsy).T
D = []; D_e = [];
E_ex = []; E_exe = [];
E_ey = []; E_eye = [];
for j in range(len(epst_dot)): ## number of ribs
D.append(np.mean(epst_dot[j]))
D_e.append(np.std(epst_dot[j]))
E_ex.append(np.mean(epsx[j]))
E_exe.append(np.std(epsx[j]))
E_ey.append(np.mean(epsy[j]))
E_eye.append(np.std(epsy[j]))
## fig1.axes[0].plot(D,label=db[i])
epst_dot_av.append(D)
epst_dot_std.append(D_e)
exx_av.append(E_ex)
exx_std.append(E_exe)
eyy_av.append(E_ey)
eyy_std.append(E_eye)
## find maximum D value's index to trim the data
ndat = (len(epst_dot_av)-1)/2+1
ref=epst_dot_av[ndat-1] ## data at necking center spot
_ref_=[]; _ind_ =[]
for i in range(len(ref)):
if not(np.isnan(ref[i])):
_ref_.append(ref[i]); _ind_.append(i)
_val_, _i_ = sort(_ref_)
ix = _ind_[_i_[-1]]
mdat = np.zeros((2,2,ndat))
c = []
for i in range(ndat):
i0 = i
i1 = -i -1
## thickness strain rate
d1=epst_dot_av[i0]; d2=epst_dot_av[i1]
e1=epst_dot_std[i0]; e2=epst_dot_std[i1]
d =[]; e=[]
## epsilon xx
ex1 = exx_av[i0]; ex2 = exx_av[i1]
exe1 = exx_std[i0]; exe2 = exx_std[i1]
ex=[]; exe=[]
## epsilon yy
ey1 = eyy_av[i0]; ey2 = eyy_av[i1]
eye1 = eyy_std[i0]; eye2 = eyy_std[i1]
ey=[]; eye=[]
for j in range(len(d1)): # along the time axis
d.append((d1[j]+d2[j])/2.)
e.append((e1[j]+e2[j])/2.)
ex.append((ex1[j]+ex2[j])/2.)
exe.append((exe1[j]+exe2[j])/2.)
ey.append((ey1[j]+ey2[j])/2.)
eye.append((eye1[j]+eye2[j])/2.)
xind=np.arange(len(d))+1
p = 0.7
i0 = int(float(len(xind))*p)
## find maximum value and index?
l, =fig1.axes[0].plot(xind,d,'-',label=abs(db[-i-1]))
fig1.axes[1].plot(xind[i0:ix+1],d[i0:ix+1],'+')
fig1.axes[2].errorbar(xind[i0:ix+1],d[i0:ix+1],
yerr=e1[i0:ix+1],fmt='+')
l, =fig1.axes[3].plot(ex[i0:ix+1],ey[i0:ix+1])
fig1.axes[3].errorbar(
ex[ix],ey[ix],color=l.get_color(),
xerr=exe[ix],yerr=eye[ix])
mdat[0,0,i] = ex[ix]
mdat[0,1,i] = ey[ix]
mdat[1,0,i] = exe[ix]
mdat[1,1,i] = eye[ix]
c.append(l.get_color())
## deco
from MP.lib.mpl_lib import tune_xy_lim
tune_xy_lim(fig.axes)
fig1.axes[2].set_yscale('log')
fig1.axes[2].set_ylim(1e-3)
fig1.axes[0].legend(loc='best',ncol=2,fontsize=5).\
get_frame().set_alpha(0.5)
return fig,fig1,mdat,c
def plot(self,nbin_sin2psi=2,iopt=0,ylim=None,
mxnphi=None,hkl='211'):
"""
Arguments
=========
nbin_sin2psi = 2
iopt = iopt
ylim = None
mxnphi = None
hkl = '211'
"""
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
if hasattr(self, 'vf'):
nh = 3
else: nh = 2
if type(mxnphi)==type(None): mxnphi = self.nphi
figs = wide_fig(nw=mxnphi,nh=nh,w0=0,w1=0,
left=0.2,right=0.15)
mx = max(self.flow.epsilon_vm)
mn = min(self.flow.epsilon_vm)
#mx = 1.
#mn = 0.
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx,mn=mn)
colors=[]
self.flow.nstp = len(self.flow.epsilon_vm)
for i in xrange(mxnphi):
for j in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[j]
cl = c.to_rgba(eps)
if i==0: colors.append(cl)
y = self.sf[j,i,:,0].copy() * 1e12
l, = figs.axes[i].plot(
np.sign(self.psi)*sin(self.psi*np.pi/180.)**2,
y,'-x',color=cl)
y = self.sf[j,i,:,1].copy() * 1e12
figs.axes[i+mxnphi].plot(
np.sign(self.psi)*sin(self.psi*np.pi/180.)**2,
y,'-+',color=cl)
if j==0:
figs.axes[i].set_title(
r'$\phi: %3.1f^\circ{}$'%self.phi[i])
if nh==3:
for i in xrange(mxnphi):
for j in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[j]
cl = c.to_rgba(eps)
y = self.vf[j,i,:]
figs.axes[i+mxnphi*2].plot(
np.sign(self.psi)*sin(self.psi*np.pi/180.)**2,
y,'-',color=cl)
deco = axes_label.__deco__
rm_inner =mpl_lib.rm_inner
ticks_bin_u = mpl_lib.ticks_bins_ax_u
rm_inner(figs.axes[:mxnphi*2])
if nh==3: rm_inner(figs.axes[mxnphi*2:mxnphi*3])
deco(figs.axes[0],iopt=1,ipsi_opt=1,hkl=hkl)
if nh==3: deco(figs.axes[mxnphi*2],iopt=7,ipsi_opt=1,hkl=hkl)
mpl_lib.tune_xy_lim(figs.axes[:mxnphi*2])
if ylim!=None:
for i in xrange(len(figs.axes[:mxnphi*2])):
figs.axes[i].set_ylim(ylim)
if nh==3: mpl_lib.tune_xy_lim(figs.axes[mxnphi*2:mxnphi*3])
ticks_bin_u(figs.axes[:mxnphi*2],n=4)
if nh==3: ticks_bin_u(figs.axes[mxnphi*2:mxnphi*3],n=4)
# color bar
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88,b.y0,0.03,b.y1-b.y0])
mpl_lib.add_cb(ax=axcb,filled=False,norm=norm,
levels=self.flow.epsilon_vm,
format='%5.3f',
colors=colors,ylab='Equivalent strain')
if nh==1 or nh==2: return
""" SF(phi,psi) vs plastic strain """
## binning sin2psi
nbin = nbin_sin2psi
indx = self.__binning__(nbin=nbin,mx=0.5)
for i in xrange(len(indx)):
print '%i bin'%(i+1)
for j in xrange(len(indx[i])):
print '%4i'%indx[i][j],
print '%4.2f'%(self.sin2psi[indx[i][j]]),
print
if mxnphi!=None: mxnphi=self.nphi
figs_p = wide_fig(nw=mxnphi,nh=nbin,
w0=0,w1=0,left=0.2,right=0.15)
eps = self.flow.epsilon_vm
for i in xrange(mxnphi):
for j in xrange(nbin):
ax = figs_p.axes[i+mxnphi*j]
if j==nbin-1: ax.set_title(
r'$\phi: %3.1f^\circ{}$'%self.phi[i])
idx = indx[j]
for k in xrange(len(idx)):
y = self.sf[:,i,[idx[k]],0][::]
ax.plot(eps,y,'x')
for i in xrange(nbin):
axes=[]
for j in xrange(mxnphi*2):
axes.append(figs_p.axes[j+mxnphi*i])
#mpl_lib.tune_xy_lim(axes)
if i==0 and j==0: rm_inner(axes[1:])
else: rm_inner(axes)
deco(figs_p.axes[0],iopt=6,hkl=hkl)
mpl_lib.tune_xy_lim(figs_p.axes)
#print 'no'
ticks_bin_u(figs_p.axes,n=3)
if ylim!=None:
for i in xrange(len(figs_p.axes)):
figs_p.axes[i].set_ylim(ylim)
if iopt==1:
for i in xrange(len(figs.axes)):
figs.axes[i].set_xlim(0.0,0.5)
for i in xrange(3):
figs.axes[i].set_ylim(-2,2)
figs.axes[3+i].set_ylim(0.,0.1)
plt.draw();plt.show()
def main(
path="../dat/BB/",
fref="Bsteel_fref_DIC.txt",
fn_sf="YJ_BB_10times.sff",
fexp=None,
iso_SF=False,
ishow=False,
ind_plot=False,
psi_offset=0.0,
psi_sym=False,
fc=None,
fn_str=None,
ifix_d0=False,
d0_ref=1.17025,
iwgt=False,
):
"""
Arguments
=========
path
fref
fn_sf
fexp
iso_SF
ishow
ind_plot
psi_offset
psi_sym
fc
fn_str
ifix_d0
d0_ref=1.17025
iwgt=False
"""
from MP.mat import mech
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
eqv = axes_label.__eqv__
tune_xy_lim = mpl_lib.tune_xy_lim
deco = axes_label.__deco__
rm_inner = mpl_lib.rm_inner
rm_lab = mpl_lib.rm_lab
ticks_bin_u = mpl_lib.ticks_bins_ax_u
plt.ioff()
if type(fexp) == type(None):
fexp = ("/Users/yj/repo/evpsc-dev/" "exp_dat/Bsteel/bulge/EXP_BULGE_JINKIM.txt",)
RS_graphs = PdfPages("RS_Graphs.pdf")
mystress = StressAnalysis(path=path, fref=fref, fn_sf=fn_sf, isym=psi_sym, fc_sf=fc, fn_str=fn_str)
if psi_offset != 0:
mystress.put_psi_offset(psi_offset)
if psi_sym:
print "psi symmetry has been applied."
mystress.apply_sym()
mystress.nstp
mystress.EXP.plot_all()
RS_graphs.savefig(plt.gcf())
# isotropic stress factor?
if iso_SF:
mystress.SF.get_iso_sf(E=204e9, nu=0.3)
# calc stress
eps_vm = mystress.EXP.flow.epsilon_vm
dknot = []
s11 = []
s22 = []
# s12 = []
Eis = []
eps = []
igs = []
d_ehkl = np.zeros((mystress.EXP.nphi))
for istp in xrange(mystress.nstp):
stress, d0 = mystress.find_sigma(ivo=[0, 1], istp=istp, iplot=False, iwgt=iwgt, ifix_d0=ifix_d0, d0=d0_ref)
dknot.append(d0)
s11.append(stress[0])
s22.append(stress[1])
Eis.append(mystress.Ei) # fitted Ei
ehkl = mystress.EXP.ehkl[istp] # experimental eps_hkl
ig = mystress.IG.ig[istp] # experimental eps_ig
eps.append(ehkl.copy())
igs.append(ig.copy())
if istp == 0:
for iphi in xrange(mystress.EXP.nphi):
d_ehkl[iphi] = np.array(ehkl[iphi][::]).std()
print "-----------------------------------"
print "Standard deviation in d_ehkl at istp=0\n"
print "phi:",
for iphi in xrange(mystress.EXP.nphi):
print "%7.0f " % mystress.EXP.phis[iphi],
print "avg"
print "std:",
for iphi in xrange(mystress.EXP.nphi):
print "%7.1e " % d_ehkl[iphi],
print "%7.1e " % d_ehkl.mean()
# macro flow object
mystress.flow = mech.FlowCurve()
mystress.flow.get_stress(s11, 0, 0)
mystress.flow.get_stress(s22, 1, 1)
mystress.flow.set_zero_sigma_ij(i=2, j=2)
# mystress.flow.get_stress(s12,0,1)
# mystress.flow.get_stress(s12,1,0)
mystress.flow.set_zero_sigma_ij(i=0, j=1)
mystress.flow.set_zero_sigma_ij(i=1, j=0)
mystress.flow.set_zero_sigma_ij(i=2, j=1)
mystress.flow.set_zero_sigma_ij(i=1, j=2)
mystress.flow.set_zero_sigma_ij(i=0, j=2)
mystress.flow.set_zero_sigma_ij(i=2, j=0)
mystress.flow.get_vm_stress()
# ------------------------------------------------------------#
# plots that illustrate the fitted curves...
Eis = np.array(Eis)
eps = np.array(eps)
igs = np.array(igs)
figs = wide_fig(nw=mystress.EXP.nphi, w0=0, w1=0, left=0.2, right=0.15)
from MP.lib import mpl_lib
# mx = eps_vm[mystress.EXP.flow.nstp-1],
# mn = eps_vm[0])
mx = 1.0
mn = 0.0
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx, mn=mn) # c.to_rbga()
for iphi in xrange(mystress.EXP.nphi):
x = mystress.EXP.psi
ax = figs.axes[iphi]
ax.set_title(r"$\phi: %3.1f^\circ{}$" % mystress.EXP.phi[iphi])
for istp in xrange(mystress.EXP.flow.nstp):
y_fit = Eis[istp, iphi]
y1 = eps[istp, iphi] # e^{hkl}
y0 = igs[istp, iphi] # e^{ig}
y_exp = y1 - y0
ax.plot(x, y_fit, "--", color=c.to_rgba(eps_vm[istp]))
ax.plot(x, y_exp, "x", color=c.to_rgba(eps_vm[istp]))
deco(ax=figs.axes[0], iopt=5)
rm_inner(figs.axes)
ticks_bin_u(figs.axes)
tune_xy_lim(figs.axes)
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88, b.y0, 0.03, b.y1 - b.y0])
mpl_lib.add_cb(axcb, cmap=cmap, filled=True, ylab="Equivalent Strain", norm=norm)
RS_graphs.savefig(plt.gcf())
# ------------------------------------------------------------#
# Save fitted curves into individual plots to pdf files
if ind_plot:
from matplotlib.backends.backend_pdf import PdfPages
pdf_pages = PdfPages("Fit_results.pdf")
x = mystress.EXP.psi
for istp in xrange(mystress.EXP.flow.nstp):
figx = wide_fig(nw=mystress.EXP.nphi + 1, w0=0, w1=0, left=0.2, right=0.15)
for iphi in xrange(mystress.EXP.nphi):
x = mystress.EXP.psi
ax = figx.axes[iphi]
ax.set_title(r"$\phi: %3.1f^\circ{}$" % mystress.EXP.phi[iphi])
y_fit = Eis[istp, iphi]
y1 = eps[istp, iphi] # e^{hkl}
y0 = igs[istp, iphi] # e^{ig}
y_exp = y1 - y0
ax.plot(x, y_fit, "r-", label="Fitting")
ax.plot(x, y_exp, "bx", label=r"$\varepsilon^{hkl}" "-\varepsilon^{IG}$")
ax = figx.axes[-1]
eqv(ax, ft=8, zero_xy=False)
ax = ax.twinx()
eqv(ax, ft=8, zero_xy=False)
ax.plot(eps_vm, mystress.flow.sigma_vm, "b-")
ax.plot(eps_vm[istp], mystress.flow.sigma_vm[istp], "ro")
# for i in xrange(len(figx.axes)-2):
# figx.axes[i].set_ylim(-0.0010,0.0006)
fancy_legend(figx.axes[0])
rm_inner(figx.axes[:4])
ticks_bin_u(figx.axes[:4])
# tune_xy_lim(figx.axes)
ax.set_xlim(0.0, 1)
rm_lab(ax=figx.axes[-2], axis="y")
deco(figx.axes[0], iopt=5)
pdf_pages.savefig(figx)
plt.close(figx)
pdf_pages.close()
plt.ion()
# ------------------------------------------------------------#
## flow stress curve plotting
figs = wide_fig(nw=2, w1=0.2)
figs.axes[0].plot(eps_vm, mystress.flow.sigma_vm, "x")
figs.axes[0].plot(
mystress.SF_orig.flow.epsilon_vm, np.zeros((mystress.SF_orig.flow.nstp)), "o", ms=8, mfc="None", mec="r"
)
figs.axes[1].plot(eps_vm, dknot, "o")
eqv(figs.axes[0], ft=8, zero_xy=True)
eqv(figs.axes[1], ft=8, zero_xy=False)
figs.axes[1].set_ylabel(r"$\mathrm{d}_o$", dict(fontsize=12))
RS_graphs.savefig(plt.gcf())
# ------------------------------------------------------------#
## flow stress in plane stress space
figs = wide_fig(nw=2)
figs.axes[0].plot(mystress.flow.sigma[0, 0], mystress.flow.sigma[1, 1], "-x")
figs.axes[1].plot(mystress.EXP.flow.epsilon[0, 0], mystress.EXP.flow.epsilon[1, 1], "-x")
axes_label.__plane__(ax=figs.axes[0], ft=10, iopt=0)
axes_label.__plane__(ax=figs.axes[1], ft=10, iopt=1)
RS_graphs.savefig(plt.gcf())
RS_graphs.close()
if not (ishow):
plt.close("all")
plt.ion()
return mystress
def plot(self, nbin_sin2psi=2, iopt=0, ylim=None, mxnphi=None, hkl='211'):
"""
Arguments
=========
nbin_sin2psi = 2
iopt = iopt
ylim = None
mxnphi = None
hkl = '211'
"""
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
if hasattr(self, 'vf'):
nh = 3
else:
nh = 2
if type(mxnphi) == type(None): mxnphi = self.nphi
figs = wide_fig(nw=mxnphi, nh=nh, w0=0, w1=0, left=0.2, right=0.15)
mx = max(self.flow.epsilon_vm)
mn = min(self.flow.epsilon_vm)
#mx = 1.
#mn = 0.
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx, mn=mn)
colors = []
self.flow.nstp = len(self.flow.epsilon_vm)
for i in xrange(mxnphi):
for j in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[j]
cl = c.to_rgba(eps)
if i == 0: colors.append(cl)
y = self.sf[j, i, :, 0].copy() * 1e12
l, = figs.axes[i].plot(np.sign(self.psi) *
sin(self.psi * np.pi / 180.)**2,
y,
'-x',
color=cl)
y = self.sf[j, i, :, 1].copy() * 1e12
figs.axes[i + mxnphi].plot(np.sign(self.psi) *
sin(self.psi * np.pi / 180.)**2,
y,
'-+',
color=cl)
if j == 0:
figs.axes[i].set_title(r'$\phi: %3.1f^\circ{}$' %
self.phi[i])
if nh == 3:
for i in xrange(mxnphi):
for j in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[j]
cl = c.to_rgba(eps)
y = self.vf[j, i, :]
figs.axes[i + mxnphi * 2].plot(
np.sign(self.psi) * sin(self.psi * np.pi / 180.)**2,
y,
'-',
color=cl)
deco = axes_label.__deco__
rm_inner = mpl_lib.rm_inner
ticks_bin_u = mpl_lib.ticks_bins_ax_u
rm_inner(figs.axes[:mxnphi * 2])
if nh == 3: rm_inner(figs.axes[mxnphi * 2:mxnphi * 3])
deco(figs.axes[0], iopt=1, ipsi_opt=1, hkl=hkl)
if nh == 3: deco(figs.axes[mxnphi * 2], iopt=7, ipsi_opt=1, hkl=hkl)
mpl_lib.tune_xy_lim(figs.axes[:mxnphi * 2])
if ylim != None:
for i in xrange(len(figs.axes[:mxnphi * 2])):
figs.axes[i].set_ylim(ylim)
if nh == 3: mpl_lib.tune_xy_lim(figs.axes[mxnphi * 2:mxnphi * 3])
ticks_bin_u(figs.axes[:mxnphi * 2], n=4)
if nh == 3: ticks_bin_u(figs.axes[mxnphi * 2:mxnphi * 3], n=4)
# color bar
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88, b.y0, 0.03, b.y1 - b.y0])
mpl_lib.add_cb(ax=axcb,
filled=False,
norm=norm,
levels=self.flow.epsilon_vm,
format='%5.3f',
colors=colors,
ylab='Equivalent strain')
if nh == 1 or nh == 2: return
""" SF(phi,psi) vs plastic strain """
## binning sin2psi
nbin = nbin_sin2psi
indx = self.__binning__(nbin=nbin, mx=0.5)
for i in xrange(len(indx)):
print '%i bin' % (i + 1)
for j in xrange(len(indx[i])):
print '%4i' % indx[i][j],
print '%4.2f' % (self.sin2psi[indx[i][j]]),
print
if mxnphi != None: mxnphi = self.nphi
figs_p = wide_fig(nw=mxnphi, nh=nbin, w0=0, w1=0, left=0.2, right=0.15)
eps = self.flow.epsilon_vm
for i in xrange(mxnphi):
for j in xrange(nbin):
ax = figs_p.axes[i + mxnphi * j]
if j == nbin - 1:
ax.set_title(r'$\phi: %3.1f^\circ{}$' % self.phi[i])
idx = indx[j]
for k in xrange(len(idx)):
y = self.sf[:, i, [idx[k]], 0][::]
ax.plot(eps, y, 'x')
for i in xrange(nbin):
axes = []
for j in xrange(mxnphi * 2):
axes.append(figs_p.axes[j + mxnphi * i])
#mpl_lib.tune_xy_lim(axes)
if i == 0 and j == 0: rm_inner(axes[1:])
else: rm_inner(axes)
deco(figs_p.axes[0], iopt=6, hkl=hkl)
mpl_lib.tune_xy_lim(figs_p.axes)
#print 'no'
ticks_bin_u(figs_p.axes, n=3)
if ylim != None:
for i in xrange(len(figs_p.axes)):
figs_p.axes[i].set_ylim(ylim)
if iopt == 1:
for i in xrange(len(figs.axes)):
figs.axes[i].set_xlim(0.0, 0.5)
for i in xrange(3):
figs.axes[i].set_ylim(-2, 2)
figs.axes[3 + i].set_ylim(0., 0.1)
plt.draw()
plt.show()
def main(path='../dat/BB/',
fref='Bsteel_fref_DIC.txt',
fn_sf='YJ_BB_10times.sff',
fexp=None,
iso_SF=False,
ishow=False,
ind_plot=False,
psi_offset=0.0,
psi_sym=False,
fc=None,
fn_str=None,
ifix_d0=False,
d0_ref=1.17025,
iwgt=False):
"""
Arguments
=========
path
fref
fn_sf
fexp
iso_SF
ishow
ind_plot
psi_offset
psi_sym
fc
fn_str
ifix_d0
d0_ref=1.17025
iwgt=False
"""
from MP.mat import mech
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
eqv = axes_label.__eqv__
tune_xy_lim = mpl_lib.tune_xy_lim
deco = axes_label.__deco__
rm_inner = mpl_lib.rm_inner
rm_lab = mpl_lib.rm_lab
ticks_bin_u = mpl_lib.ticks_bins_ax_u
plt.ioff()
if type(fexp) == type(None):
fexp='/Users/yj/repo/evpsc-dev/'\
'exp_dat/Bsteel/bulge/EXP_BULGE_JINKIM.txt',
RS_graphs = PdfPages('RS_Graphs.pdf')
mystress = StressAnalysis(path=path,
fref=fref,
fn_sf=fn_sf,
isym=psi_sym,
fc_sf=fc,
fn_str=fn_str)
if psi_offset != 0: mystress.put_psi_offset(psi_offset)
if psi_sym:
print 'psi symmetry has been applied.'
mystress.apply_sym()
mystress.nstp
mystress.EXP.plot_all()
RS_graphs.savefig(plt.gcf())
# isotropic stress factor?
if iso_SF: mystress.SF.get_iso_sf(E=204e9, nu=0.3)
# calc stress
eps_vm = mystress.EXP.flow.epsilon_vm
dknot = []
s11 = []
s22 = []
# s12 = []
Eis = []
eps = []
igs = []
d_ehkl = np.zeros((mystress.EXP.nphi))
for istp in xrange(mystress.nstp):
stress, d0 = mystress.find_sigma(ivo=[0, 1],
istp=istp,
iplot=False,
iwgt=iwgt,
ifix_d0=ifix_d0,
d0=d0_ref)
dknot.append(d0)
s11.append(stress[0])
s22.append(stress[1])
Eis.append(mystress.Ei) # fitted Ei
ehkl = mystress.EXP.ehkl[istp] # experimental eps_hkl
ig = mystress.IG.ig[istp] # experimental eps_ig
eps.append(ehkl.copy())
igs.append(ig.copy())
if istp == 0:
for iphi in xrange(mystress.EXP.nphi):
d_ehkl[iphi] = np.array(ehkl[iphi][::]).std()
print '-----------------------------------'
print 'Standard deviation in d_ehkl at istp=0\n'
print 'phi:',
for iphi in xrange(mystress.EXP.nphi):
print '%7.0f ' % mystress.EXP.phis[iphi],
print 'avg'
print 'std:',
for iphi in xrange(mystress.EXP.nphi):
print '%7.1e ' % d_ehkl[iphi],
print '%7.1e ' % d_ehkl.mean()
# macro flow object
mystress.flow = mech.FlowCurve()
mystress.flow.get_stress(s11, 0, 0)
mystress.flow.get_stress(s22, 1, 1)
mystress.flow.set_zero_sigma_ij(i=2, j=2)
# mystress.flow.get_stress(s12,0,1)
# mystress.flow.get_stress(s12,1,0)
mystress.flow.set_zero_sigma_ij(i=0, j=1)
mystress.flow.set_zero_sigma_ij(i=1, j=0)
mystress.flow.set_zero_sigma_ij(i=2, j=1)
mystress.flow.set_zero_sigma_ij(i=1, j=2)
mystress.flow.set_zero_sigma_ij(i=0, j=2)
mystress.flow.set_zero_sigma_ij(i=2, j=0)
mystress.flow.get_vm_stress()
#------------------------------------------------------------#
# plots that illustrate the fitted curves...
Eis = np.array(Eis)
eps = np.array(eps)
igs = np.array(igs)
figs = wide_fig(nw=mystress.EXP.nphi, w0=0, w1=0, left=0.2, right=0.15)
from MP.lib import mpl_lib
# mx = eps_vm[mystress.EXP.flow.nstp-1],
# mn = eps_vm[0])
mx = 1.0
mn = 0.0
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx, mn=mn) # c.to_rbga()
for iphi in xrange(mystress.EXP.nphi):
x = mystress.EXP.psi
ax = figs.axes[iphi]
ax.set_title(r'$\phi: %3.1f^\circ{}$' % mystress.EXP.phi[iphi])
for istp in xrange(mystress.EXP.flow.nstp):
y_fit = Eis[istp, iphi]
y1 = eps[istp, iphi] # e^{hkl}
y0 = igs[istp, iphi] # e^{ig}
y_exp = y1 - y0
ax.plot(x, y_fit, '--', color=c.to_rgba(eps_vm[istp]))
ax.plot(x, y_exp, 'x', color=c.to_rgba(eps_vm[istp]))
deco(ax=figs.axes[0], iopt=5)
rm_inner(figs.axes)
ticks_bin_u(figs.axes)
tune_xy_lim(figs.axes)
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88, b.y0, 0.03, b.y1 - b.y0])
mpl_lib.add_cb(axcb,
cmap=cmap,
filled=True,
ylab='Equivalent Strain',
norm=norm)
RS_graphs.savefig(plt.gcf())
#------------------------------------------------------------#
# Save fitted curves into individual plots to pdf files
if ind_plot:
from matplotlib.backends.backend_pdf import PdfPages
pdf_pages = PdfPages('Fit_results.pdf')
x = mystress.EXP.psi
for istp in xrange(mystress.EXP.flow.nstp):
figx = wide_fig(nw=mystress.EXP.nphi + 1,
w0=0,
w1=0,
left=0.2,
right=0.15)
for iphi in xrange(mystress.EXP.nphi):
x = mystress.EXP.psi
ax = figx.axes[iphi]
ax.set_title(
r'$\phi: %3.1f^\circ{}$'%\
mystress.EXP.phi[iphi])
y_fit = Eis[istp, iphi]
y1 = eps[istp, iphi] # e^{hkl}
y0 = igs[istp, iphi] # e^{ig}
y_exp = y1 - y0
ax.plot(x, y_fit, 'r-', label='Fitting')
ax.plot(x,y_exp,'bx',
label=r'$\varepsilon^{hkl}'\
'-\varepsilon^{IG}$')
ax = figx.axes[-1]
eqv(ax, ft=8, zero_xy=False)
ax = ax.twinx()
eqv(ax, ft=8, zero_xy=False)
ax.plot(eps_vm, mystress.flow.sigma_vm, 'b-')
ax.plot(eps_vm[istp], mystress.flow.sigma_vm[istp], 'ro')
# for i in xrange(len(figx.axes)-2):
# figx.axes[i].set_ylim(-0.0010,0.0006)
fancy_legend(figx.axes[0])
rm_inner(figx.axes[:4])
ticks_bin_u(figx.axes[:4])
#tune_xy_lim(figx.axes)
ax.set_xlim(0., 1)
rm_lab(ax=figx.axes[-2], axis='y')
deco(figx.axes[0], iopt=5)
pdf_pages.savefig(figx)
plt.close(figx)
pdf_pages.close()
plt.ion()
#------------------------------------------------------------#
## flow stress curve plotting
figs = wide_fig(nw=2, w1=0.2)
figs.axes[0].plot(eps_vm, mystress.flow.sigma_vm, 'x')
figs.axes[0].plot(mystress.SF_orig.flow.epsilon_vm,
np.zeros((mystress.SF_orig.flow.nstp)),
'o',
ms=8,
mfc='None',
mec='r')
figs.axes[1].plot(eps_vm, dknot, 'o')
eqv(figs.axes[0], ft=8, zero_xy=True)
eqv(figs.axes[1], ft=8, zero_xy=False)
figs.axes[1].set_ylabel(r'$\mathrm{d}_o$', dict(fontsize=12))
RS_graphs.savefig(plt.gcf())
#------------------------------------------------------------#
## flow stress in plane stress space
figs = wide_fig(nw=2)
figs.axes[0].plot(mystress.flow.sigma[0, 0], mystress.flow.sigma[1, 1],
'-x')
figs.axes[1].plot(mystress.EXP.flow.epsilon[0, 0],
mystress.EXP.flow.epsilon[1, 1], '-x')
axes_label.__plane__(ax=figs.axes[0], ft=10, iopt=0)
axes_label.__plane__(ax=figs.axes[1], ft=10, iopt=1)
RS_graphs.savefig(plt.gcf())
RS_graphs.close()
if not (ishow): plt.close('all')
plt.ion()
return mystress
def main(path='/Users/yj/repo/rs_pack/dat/11Jul12',
fref='Bsteel_BB_00.txt',fn_sf='YJ_BB_10times.sff',
fexp=None,iso_SF=False,ishow=False,ind_plot=False,
psi_offset=0.0,psi_sym=False,fc=None,fn_str=None,
iwgt=False):
"""
Arguments
=========
path
fref
fn_sf
fexp
iso_SF
ishow
ind_plot
psi_offset
psi_sym
fc
fn_str
"""
from MP.mat import mech
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
eqv = axes_label.__eqv__
tune_xy_lim = mpl_lib.tune_xy_lim
deco = axes_label.__deco__
rm_inner =mpl_lib.rm_inner
rm_lab = mpl_lib.rm_lab
ticks_bin_u = mpl_lib.ticks_bins_ax_u
plt.ioff()
if type(fexp)==type(None):
fexp='/Users/yj/repo/evpsc-dev/'\
'exp_dat/Bsteel/bulge/EXP_BULGE_JINKIM.txt',
RS_graphs = PdfPages('RS_Graphs.pdf')
mystress = StressAnalysis(path=path,fref=fref,
fn_sf=fn_sf,isym=psi_sym,
fc_sf=None,fn_str=fn_str)
if psi_offset!=0: mystress.put_psi_offset(psi_offset)
if psi_sym:
print 'psi symmetry has been applied.'
mystress.apply_sym()
mystress.nstp
mystress.EXP.plot_all()
RS_graphs.savefig(plt.gcf())
# isotropic stress factor?
if iso_SF: mystress.SF.get_iso_sf(E=204e9,nu=0.3)
# calc stress
eps_vm = mystress.EXP.flow.epsilon_vm
dknot = []
s11 = []; s22 = []
Eis = []; eps = []; igs = []
d_ehkl = np.zeros((mystress.EXP.nphi))
for istp in range(mystress.nstp):
stress, d0 = mystress.find_sigma(
ivo=[0,1],istp=istp,iplot=False,iwgt=iwgt)
dknot.append(d0)
s11.append(stress[0])
s22.append(stress[1])
Eis.append(mystress.Ei) # fitted Ei
ehkl = mystress.EXP.ehkl[istp] # experimental eps_hkl
ig = mystress.IG.ig[istp] # experimental eps_ig
eps.append(ehkl.copy())
igs.append(ig.copy())
if istp==0:
for iphi in range(mystress.EXP.nphi):
d_ehkl[iphi] = np.array(ehkl[iphi][::]).std()
print '-----------------------------------'
print 'Stard deviation in d_ehkl at istp=0\n'
print 'phi:',
for iphi in range(mystress.EXP.nphi):
print '%7.0f '%mystress.EXP.phis[iphi],
print 'avg'
print 'std:',
for iphi in range(mystress.EXP.nphi):
print '%7.1e '%d_ehkl[iphi],
print '%7.1e '%d_ehkl.mean()
# macro flow object
mystress.flow = mech.FlowCurve()
mystress.flow.get_stress(s11,0,0)
mystress.flow.get_stress(s22,1,1)
mystress.flow.set_zero_sigma_ij(i=2,j=2)
mystress.flow.set_zero_shear_stress()
mystress.flow.get_vm_stress()
#------------------------------------------------------------#
# plots that illustrate the fitted curves...
Eis = np.array(Eis)
eps = np.array(eps)
igs = np.array(igs)
figs = wide_fig(nw=mystress.EXP.nphi,w0=0,w1=0,left=0.2,
right=0.15)
from MP.lib import mpl_lib
# mx = eps_vm[mystress.EXP.flow.nstp-1],
# mn = eps_vm[0])
mx = 1.0; mn = 0.0
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(
mx=mx,mn=mn) # c.to_rbga()
for iphi in range(mystress.EXP.nphi):
x = mystress.EXP.psi
ax = figs.axes[iphi]
ax.set_title(
r'$\phi: %3.1f^\circ{}$'%mystress.EXP.phi[iphi])
for istp in range(mystress.EXP.flow.nstp):
y_fit = Eis[istp,iphi]
y1=eps[istp,iphi] # e^{hkl}
y0=igs[istp,iphi] # e^{ig}
y_exp = y1-y0
ax.plot(x,y_fit,'--',color=c.to_rgba(eps_vm[istp]))
ax.plot(x,y_exp,'x',color=c.to_rgba(eps_vm[istp]))
deco(ax=figs.axes[0],iopt=5)
rm_inner(figs.axes)
ticks_bin_u(figs.axes)
tune_xy_lim(figs.axes)
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88,b.y0,0.03,b.y1-b.y0])
mpl_lib.add_cb(axcb,cmap=cmap,filled=True,
ylab='Equivalent Strain',norm=norm)
RS_graphs.savefig(plt.gcf())
#------------------------------------------------------------#
# Save fitted curves into individual plots to pdf files
if ind_plot:
from matplotlib.backends.backend_pdf import PdfPages
pdf_pages = PdfPages('Fit_results.pdf')
x = mystress.EXP.psi
for istp in range(mystress.EXP.flow.nstp):
figx = wide_fig(nw=mystress.EXP.nphi+1,
w0=0,w1=0,left=0.2,
right=0.15)
for iphi in range(mystress.EXP.nphi):
x = mystress.EXP.psi
ax = figx.axes[iphi]
ax.set_title(
r'$\phi: %3.1f^\circ{}$'%\
mystress.EXP.phi[iphi])
y_fit = Eis[istp,iphi]
y1=eps[istp,iphi] # e^{hkl}
y0=igs[istp,iphi] # e^{ig}
y_exp = y1-y0
ax.plot(x,y_fit,'r-',label='Fitting')
ax.plot(x,y_exp,'bx',
label=r'$\varepsilon^{hkl}'\
'-\varepsilon^{IG}$')
ax = figx.axes[-1]
eqv(ax,ft=8,zero_xy=False)
ax = ax.twinx()
eqv(ax,ft=8,zero_xy=False)
ax.plot(eps_vm,mystress.flow.sigma_vm,'b-')
ax.plot(eps_vm[istp],
mystress.flow.sigma_vm[istp],'ro')
for i in range(len(figx.axes)-2):
figx.axes[i].set_ylim(-0.0010,0.0006)
fancy_legend(figx.axes[0])
rm_inner(figx.axes[:4])
ticks_bin_u(figx.axes[:4])
#tune_xy_lim(figx.axes)
ax.set_xlim(0.,1)
rm_lab(ax=figx.axes[-2],axis='y')
deco(figx.axes[0],iopt=5)
pdf_pages.savefig(figx)
plt.close(figx)
pdf_pages.close()
plt.ion()
#------------------------------------------------------------#
## flow stress curve plotting
figs = wide_fig(nw=2,w1=0.2)
figs.axes[0].plot(eps_vm, mystress.flow.sigma_vm,'x')
figs.axes[0].plot(mystress.SF_orig.flow.epsilon_vm,
np.zeros((mystress.SF_orig.flow.nstp)),
'o',ms=8,mfc='None',mec='r')
figs.axes[1].plot(eps_vm, dknot,'o')
eqv(figs.axes[0],ft=8,zero_xy=True)
eqv(figs.axes[1],ft=8,zero_xy=False)
figs.axes[1].set_ylabel(r'$\mathrm{d}_o$',dict(fontsize=12))
RS_graphs.savefig(plt.gcf())
#------------------------------------------------------------#
## flow stress in plane stress space
figs = wide_fig(nw=2)
figs.axes[0].plot(
mystress.flow.sigma[0,0],
mystress.flow.sigma[1,1],'-x')
figs.axes[1].plot(
mystress.EXP.flow.epsilon[0,0],
mystress.EXP.flow.epsilon[1,1],'-x')
axes_label.__plane__(ax=figs.axes[0],ft=10,iopt=0)
axes_label.__plane__(ax=figs.axes[1],ft=10,iopt=1)
RS_graphs.savefig(plt.gcf())
RS_graphs.close()
if not(ishow): plt.close('all')
plt.ion()
return mystress
def plot_all(self):
"""
Plot dhkl against psi
"""
from MP.lib import mpl_lib
wide_fig = mpl_lib.wide_fig
tune_xy_lim = mpl_lib.tune_xy_lim
from MP.lib import axes_label
from MP.lib import mpl_lib
import matplotlib as mpl
deco = axes_label.__deco__
rm_inner = mpl_lib.rm_inner
ticks_bin_u = mpl_lib.ticks_bins_ax_u
figs = wide_fig(nw=self.nphi,
nh=2,
w0=0,
w1=0,
left=0.2,
right=0.15,
useOffset=False)
mx = max(self.flow.epsilon_vm)
mn = min(self.flow.epsilon_vm)
norm = mpl.colors.Normalize(vmin=mn, vmax=mx)
cmap, c = mpl_lib.norm_cmap(mx=mx, mn=mn)
for i in xrange(self.flow.nstp):
eps = self.flow.epsilon_vm[i]
cl = c.to_rgba(eps)
for j in xrange(self.nphi):
X = self.psi
Y = []
for ipsi in xrange(self.npsi):
y = self.P_scan[i].protophi[j].\
ppscans[ipsi].dspc
Y.append(y)
figs.axes[j].plot(X, Y, '-x', color=cl)
_Y_ = np.array(Y).copy()
_Y_ = (_Y_ + _Y_[::-1]) / 2.
# _Y_ = _Y_[:len(_Y_)/2+1]
figs.axes[j + self.nphi].plot(X, _Y_, '-x', color=cl)
if i == 0:
figs.axes[j].set_title(r'$\phi: %3.1f^\circ{}$' %
self.phi[j])
figs.axes[j + self.nphi].set_title(
r'$\phi: %3.1f^\circ{}$' % self.phi[j])
ticks_bin_u(figs.axes, n=4)
deco(figs.axes[0 + self.nphi], iopt=4)
tune_xy_lim(figs.axes)
rm_inner(figs.axes)
b = figs.axes[-1].get_position()
axcb = figs.add_axes([0.88, b.y0, 0.03, b.y1 - b.y0])
mpl_lib.add_cb(axcb,
cmap=cmap,
filled=True,
ylab='Equivalent Strain',
norm=norm,
format='%5.3f')
