def tabular_figs02():
fig=plt.figure()
axes=[]
for i in range(len(sigmas)):
axes.append([])
for j in range(len(nbins)):
if sigmas[i]==0: iscatter=False
else: iscatter= True
rst = main(
## main variables
sigma=sigmas[i], psi_nbin=nbins[j],
## minor
sin2psimx=0.5, iscatter=iscatter,
iplot=False,dec_inv_frq=1,
dec_interp=1)
model_rs, flow_weight, flow_dsa = rst
ax=fig.add_subplot(gs[i,j])
ax.locator_params(nbins=4)
axes[i].append(ax)
# von mises
lab1=r'$(\langle \sigma^c \rangle)^{VM}$'
lab2=r'$(\sigma^\mathrm{RS})^{VM}$'
ax.plot(flow_weight.epsilon_vm,
flow_weight.sigma_vm,'k-',label=lab1)
ax.plot(flow_dsa.epsilon_vm,
flow_dsa.sigma_vm,'kx',label=lab2)
if i==len(sigmas)-1 and j==0:
#VM Deco
axes_label.__vm__(ax=ax,ft=10)
else:
mpl_lib.rm_lab(ax,axis='x')
mpl_lib.rm_lab(ax,axis='y')
for iax in range(len(fig.axes)):
fig.axes[iax].set_ylim(0,)
fig.axes[iax].set_xlim(0,)
tune_xy_lim(fig.axes)
tune_x_lim(fig.axes,axis='y')
## annotations
for j in range(len(nbins)):
if j==0: s=r'$N^\psi$=%i'%nbins[j]
else:
s = '%i'%nbins[j]
axes[0][j].annotate(
s=s,horizontalalignment='center',
size=14,xy=(0.5,1.2),
xycoords='axes fraction')
for i in range(len(sigmas)):
if i==0:
s=r'$s^\mathrm{CSE}$=%i'%(
sigmas[i]*(10**6))
elif i==len(sigmas)-1:
s=r'%i $\mu$ strain'%(
sigmas[i]*(10**6))
else: s='%i'%(sigmas[i]*(10**6))
axes[i][-1].annotate(
s=s,
verticalalignment='center',
horizontalalignment='center',
rotation=270,
size=14,xy=(1.20,0.5),
xycoords='axes fraction')
fancy_legend(
fig.axes[0],size=11,nscat=1,ncol=1,
bbox_to_anchor=(-0.4,1))
# vm
fig.axes[len(sigmas)*len(nbins)-len(nbins)].\
set_xticks(np.arange(0.,1.01,0.5))
fig.savefig('tab2.pdf')
fig.savefig('tab2.png')
fig.clf()
def tabular_figs02():
fig = plt.figure()
axes = []
for i in xrange(len(sigmas)):
axes.append([])
for j in xrange(len(nbins)):
if sigmas[i] == 0: iscatter = False
else: iscatter = True
rst = main(
## main variables
sigma=sigmas[i],
psi_nbin=nbins[j],
## minor
sin2psimx=0.5,
iscatter=iscatter,
iplot=False,
dec_inv_frq=1,
dec_interp=1)
model_rs, flow_weight, flow_dsa = rst
ax = fig.add_subplot(gs[i, j])
ax.locator_params(nbins=4)
axes[i].append(ax)
# von mises
lab1 = r'$(\langle \sigma^c \rangle)^{VM}$'
lab2 = r'$(\sigma^\mathrm{RS})^{VM}$'
ax.plot(flow_weight.epsilon_vm,
flow_weight.sigma_vm,
'k-',
label=lab1)
ax.plot(flow_dsa.epsilon_vm, flow_dsa.sigma_vm, 'kx', label=lab2)
if i == len(sigmas) - 1 and j == 0:
#VM Deco
axes_label.__vm__(ax=ax, ft=10)
else:
mpl_lib.rm_lab(ax, axis='x')
mpl_lib.rm_lab(ax, axis='y')
for iax in xrange(len(fig.axes)):
fig.axes[iax].set_ylim(0, )
fig.axes[iax].set_xlim(0, )
tune_xy_lim(fig.axes)
tune_x_lim(fig.axes, axis='y')
## annotations
for j in xrange(len(nbins)):
if j == 0: s = r'$N^\psi$=%i' % nbins[j]
else:
s = '%i' % nbins[j]
axes[0][j].annotate(s=s,
horizontalalignment='center',
size=14,
xy=(0.5, 1.2),
xycoords='axes fraction')
for i in xrange(len(sigmas)):
if i == 0:
s = r'$s^\mathrm{CSE}$=%i' % (sigmas[i] * (10**6))
elif i == len(sigmas) - 1:
s = r'%i $\mu$ strain' % (sigmas[i] * (10**6))
else:
s = '%i' % (sigmas[i] * (10**6))
axes[i][-1].annotate(s=s,
verticalalignment='center',
horizontalalignment='center',
rotation=270,
size=14,
xy=(1.20, 0.5),
xycoords='axes fraction')
fancy_legend(fig.axes[0],
size=11,
nscat=1,
ncol=1,
bbox_to_anchor=(-0.4, 1))
# vm
fig.axes[len(sigmas)*len(nbins)-len(nbins)].\
set_xticks(np.arange(0.,1.01,0.5))
fig.savefig('tab2.pdf')
fig.savefig('tab2.png')
fig.clf()
def tabular_figs01():
"""
Ehkl vs sin2psi plot
y (sigmas)
x (nbins)
"""
fig=plt.figure()
axes=[]
for i in range(len(sigmas)):
axes.append([])
for j in range(len(nbins)):
ax=fig.add_subplot(gs[i,j])
axes[i].append(ax)
ax.locator_params(nbins=4);
if sigmas[i]==0: iscatter=False
else: iscatter= True
rst = main(
## main variables
sigma=sigmas[i], psi_nbin=nbins[j],
## minor
sin2psimx=0.5, iscatter=iscatter,
istep=2,iplot=False,dec_inv_frq=1,
dec_interp=1)
model_rs, sig11, sig22, dsa11,dsa22,\
raw_psis,raw_vfs, raw_sfs,\
full_Ei,dec_intp = rst
# raise IOError
x = sin2psi_opt(model_rs.psis, 1)
ax.plot(
x, model_rs.tdat[0]*1e6,'k.',
label=\
r'$\tilde{ \langle\varepsilon^e \rangle}^G$')
ax.plot(
x, model_rs.Ei[0]*1e6,'kx',
label=\
r'$\mathbb{F}_{ij} \sigma^\mathrm{RS}_{ij}$')
x = sin2psi_opt(raw_psis, 1)
# ax.plot(x, full_Ei[0]*1e6,'k-') ## continuous Ei
## ax.plot(raw_psis, dec_intp[0][0]*1e6,'k--')
## True Ei? = F_ij * <sigma>^c_{ij}
model_rs.psis = raw_psis.copy()
model_rs.npsi = len(model_rs.psis)
model_rs.cffs = raw_sfs.copy()
## plug the weight average stress
model_rs.sigma=[sig11, sig22, 0, 0, 0, 0]
model_rs.calc_Ei()
ax.plot(
x,model_rs.Ei[0]*1e6,'k-',
label=r'$\mathbb{F}_{ij} \langle\sigma\rangle^c_{ij}$'
)
if i==len(sigmas)-1 and j==0:
deco(ax=ax,iopt=0,hkl=None,ipsi_opt=1)
else:
mpl_lib.rm_lab(ax,axis='x')
mpl_lib.rm_lab(ax,axis='y')
for j in range(len(nbins)):
if j==0: s=r'$N^\psi$=%i'%nbins[j]
else:
s = '%i'%nbins[j]
axes[0][j].annotate(
s=s,horizontalalignment='center',
size=14,xy=(0.5,1.2),
xycoords='axes fraction')
for i in range(len(sigmas)):
if i==0:
s=r'$s^\mathrm{CSE}$=%i'%(
sigmas[i]*(10**6))
elif i==len(sigmas)-1:
s=r'%i $\mu$ strain'%(
sigmas[i]*(10**6))
else: s='%i'%(sigmas[i]*(10**6))
axes[i][-1].annotate(
s=s,
verticalalignment='center',
horizontalalignment='center',
rotation=270,
size=14,xy=(1.20,0.5),
xycoords='axes fraction')
for iax in range(len(fig.axes)):
fig.axes[iax].set_ylim(-1500,)
tune_xy_lim(fig.axes)
tune_x_lim(fig.axes,axis='y')
# plt.annotate(s=r'$\varepsilon^\mathrm{CSE}$ Counting Statistics Error',
# size=20,rotation=90,
# verticalalignment='center',
# horizontalalignment='center',
# xy=(1.01,0.5),
# xycoords='figure fraction')
# plt.annotate(s=r'Number of $\psi$',size=20,
# horizontalalignment='center',
# xy=(0.6,0.93),
# xycoords='figure fraction')
fig.axes[len(sigmas)*len(nbins)-len(nbins)].\
set_xticks(np.arange(-0.5,0.501,0.5))
fancy_legend(
fig.axes[0],size=11,nscat=1,ncol=1,
bbox_to_anchor=(-0.4,1))
fig.savefig('tab.pdf')
fig.savefig('tab.png')
fig.clf()
def tabular_figs01():
"""
Ehkl vs sin2psi plot
y (sigmas)
x (nbins)
"""
fig = plt.figure()
axes = []
for i in xrange(len(sigmas)):
axes.append([])
for j in xrange(len(nbins)):
ax = fig.add_subplot(gs[i, j])
axes[i].append(ax)
ax.locator_params(nbins=4)
if sigmas[i] == 0: iscatter = False
else: iscatter = True
rst = main(
## main variables
sigma=sigmas[i],
psi_nbin=nbins[j],
## minor
sin2psimx=0.5,
iscatter=iscatter,
istep=2,
iplot=False,
dec_inv_frq=1,
dec_interp=1)
model_rs, sig11, sig22, dsa11,dsa22,\
raw_psis,raw_vfs, raw_sfs,\
full_Ei,dec_intp = rst
# raise IOError
x = sin2psi_opt(model_rs.psis, 1)
ax.plot(
x, model_rs.tdat[0]*1e6,'k.',
label=\
r'$\tilde{ \langle\varepsilon^e \rangle}^G$')
ax.plot(
x, model_rs.Ei[0]*1e6,'kx',
label=\
r'$\mathbb{F}_{ij} \sigma^\mathrm{RS}_{ij}$')
x = sin2psi_opt(raw_psis, 1)
# ax.plot(x, full_Ei[0]*1e6,'k-') ## continuous Ei
## ax.plot(raw_psis, dec_intp[0][0]*1e6,'k--')
## True Ei? = F_ij * <sigma>^c_{ij}
model_rs.psis = raw_psis.copy()
model_rs.npsi = len(model_rs.psis)
model_rs.cffs = raw_sfs.copy()
## plug the weight average stress
model_rs.sigma = [sig11, sig22, 0, 0, 0, 0]
model_rs.calc_Ei()
ax.plot(x,
model_rs.Ei[0] * 1e6,
'k-',
label=r'$\mathbb{F}_{ij} \langle\sigma\rangle^c_{ij}$')
if i == len(sigmas) - 1 and j == 0:
deco(ax=ax, iopt=0, hkl=None, ipsi_opt=1)
else:
mpl_lib.rm_lab(ax, axis='x')
mpl_lib.rm_lab(ax, axis='y')
for j in xrange(len(nbins)):
if j == 0: s = r'$N^\psi$=%i' % nbins[j]
else:
s = '%i' % nbins[j]
axes[0][j].annotate(s=s,
horizontalalignment='center',
size=14,
xy=(0.5, 1.2),
xycoords='axes fraction')
for i in xrange(len(sigmas)):
if i == 0:
s = r'$s^\mathrm{CSE}$=%i' % (sigmas[i] * (10**6))
elif i == len(sigmas) - 1:
s = r'%i $\mu$ strain' % (sigmas[i] * (10**6))
else:
s = '%i' % (sigmas[i] * (10**6))
axes[i][-1].annotate(s=s,
verticalalignment='center',
horizontalalignment='center',
rotation=270,
size=14,
xy=(1.20, 0.5),
xycoords='axes fraction')
for iax in xrange(len(fig.axes)):
fig.axes[iax].set_ylim(-1500, )
tune_xy_lim(fig.axes)
tune_x_lim(fig.axes, axis='y')
# plt.annotate(s=r'$\varepsilon^\mathrm{CSE}$ Counting Statistics Error',
# size=20,rotation=90,
# verticalalignment='center',
# horizontalalignment='center',
# xy=(1.01,0.5),
# xycoords='figure fraction')
# plt.annotate(s=r'Number of $\psi$',size=20,
# horizontalalignment='center',
# xy=(0.6,0.93),
# xycoords='figure fraction')
fig.axes[len(sigmas)*len(nbins)-len(nbins)].\
set_xticks(np.arange(-0.5,0.501,0.5))
fancy_legend(fig.axes[0],
size=11,
nscat=1,
ncol=1,
bbox_to_anchor=(-0.4, 1))
fig.savefig('tab.pdf')
fig.savefig('tab.png')
fig.clf()
