label=rf"$\Lambda={Lambda}$")
else:
ax[observable].plot(strains * 100,
ysfwd[i][:],
'.-',
color=colors[js],
label=rf"$\Lambda={Lambda}$")
ax[observable].set_ylabel(ylabel, fontsize=10)
ax[observable].set_xlabel(r"$\epsilon\times100\%$", fontsize=10)
for observable in observable_list:
ax[observable].set_xscale('log')
if observable == "R":
ax[observable].legend(frameon=False)
if observable == "stress":
ax[observable].set_yscale('log')
if observable == "delta":
ax[observable].legend(frameon=False)
if observable == "surfacetwist":
ax[observable].set_ylim(top=0.4)
strainpoints = np.array([0.01, 1.4, 2.8, 5.0], float)
tilt = np.array([16, 14, 12, 11], float) * np.pi / 180
ax[observable].plot(strainpoints, tilt, 'k^')
fig.subplots_adjust(left=0.2, right=0.8, bottom=0.1, top=0.95, hspace=0.05)
fig.savefig(
obsfwd.observable_sname("multiLambda-observable-vsstrain",
plot_format="pdf"))
plt.show()
scan['mpt'] = str(mpt)
print(i)
# read in file name info
obs = ObservableData(scan=scan,
loadsuf=loadsuf,
savesuf=loadsuf,
name=f"Evsh-{mpt}")
hs = obs.data[:, 0]
dEdR_cs = obs.data[:, 1]
err_trunc = obs.data[:, 2]
err_round = obs.data[:, 3]
ax1.plot(hs, dEdR_cs, '-', label=rf'$m={mpt}$', color=colors[i])
ax2.plot(hs, err_trunc, '-', label=rf'$m={mpt}$', color=colors[i])
ax2.plot(hs, err_round, '-', color=colors[i])
ax1.set_ylabel(r'$\frac{\partial E}{\partial R_c}$')
ax1.set_xscale('log')
ax1.legend(frameon=False)
ax2.set_xscale('log')
ax2.set_yscale('log')
ax2.set_ylabel('derivative error (' + r'$\times \num{1e4}$' + ')')
fig.subplots_adjust(left=0.3)
fig.savefig(obs.observable_sname('Evsh'))
cmap='seismic',
vmin=vmin,
vmax=vmax)
axins[type].set_xticks([])
axins[type].set_yticks([])
axins[type].spines['polar'].set_linewidth(0.2)
axins[type].spines['polar'].set_linestyle((0, (2, 8)))
#axins[type].annotate(rf'$R={R:1.3f}$',xy=(5*np.pi/4,R-0.01*R),
# xytext=(5*np.pi/4,R+R-0.01*R),fontsize=20,
# color=colors[i])
cax_ticks = [-0.3, -0.2, -0.1, 0, 0.1]
cax = inset_axes(ax,
width="5%",
height="99%",
loc='lower left',
bbox_to_anchor=(1, 0, 1, 1),
bbox_transform=ax.transAxes,
borderpad=0.12)
cbar = colorbar(im, ax=ax, cax=cax, format='%.1f', ticks=cax_ticks)
cax.set_title("strain (\%)", fontsize=8)
fig.subplots_adjust(left=0.2, right=0.8, bottom=0.2, top=0.8)
#plt.show()
fig.savefig(obsfwd.observable_sname(f"phase-diagram-with-axial-strain"))
ax.plot(gamma_coexists,k24_coexists,'-',color='k',
lw=4)
ax.plot(0.04,0.5,'*',color='w',markersize=8)
ax.plot(gamma_coexists[0],k24_coexists[0],'o',color='k',
markersize=8)
ax.text(0.01,0.8,'linear\ntwist',color='k',fontsize=12)
ax.text(0.065,0.8,'constant\ntwist',color='k',fontsize=12)
ax.text(0.025,0.15,r'$(\gamma^c,k_{24}^c)$',fontsize=12)
ax.set_xlabel(rf"$\gamma$",fontsize=10)
ax.set_ylabel(r"$k_{24}$",fontsize=10)
ax.legend(frameon=False,fontsize=10)
fig.subplots_adjust(left=0.2,bottom=0.2)
fig.savefig(obsfwd.observable_sname(f"phase-diagram"))
scan_dir='scanbackward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
print(k24)
# find all spots where two distinct phases exist (two different radii values)
bool_1 = np.isclose(obsfwd.R(), obsbkwd.R(), rtol=1e-3)
find_j = np.where(bool_1 == False)[0]
if find_j.size > 0: # if two distinct phases exist, then:
# smallest gamma value that they both exist at
jsmall = find_j[0]
# largest gamma value that they both exist at
jlarge = find_j[-1]
# find the point where the fwd E becomes larger than the bkwd E
j = (np.argmin(
np.abs(obsfwd.E()[jsmall:jlarge + 1] -
obsbkwd.E()[jsmall:jlarge + 1])) + len(obsfwd.E()[:jsmall]))
pstr = f"{k24:.2e} {gammas[j]:.2e} {jsmall} {obsfwd.R()[j]:.2e} {obsbkwd.R()[j]:.2e}"
obsfwd.data[j:, :] = obsbkwd.data[j:, :]
np.savetxt(obsfwd.observable_sname("observables", plot_format="txt"),
obsfwd.data,
fmt='\t'.join(["%13.6e"] + ["%15.8e"] * 5))
label=rf"$\gamma={gamma}$")
#ax[observable].plot(Lambdas[:i_fwd+1],ysfwd[i][:i_fwd+1],'.',color=colors[i])
#ax[observable].plot(Lambdas[i_fwd:i_fwd+2],ysfwd[i][i_fwd:i_fwd+2],'-',
# color=colors[i])
#ax[observable].plot(ms_bkwd,ysbkwd[i][1:],'-',color=colors[i])
#ax[observable].plot(Lambdas[i_fwd+2:],ysfwd[i][i_fwd+2:],'.',color=colors[i])
ax[observable].set_xlabel(r"$\Lambda$",fontsize = 10)
ax[observable].set_ylabel(ylabel,fontsize = 10)
#ax[observable].tick_params("both",labelsize=18)
for observable in observable_list:
if observable == "R":
ax[observable].set_yscale('log')
ax[observable].legend(frameon=False)
ax[observable].set_xscale('log')
fig[observable].subplots_adjust(left=0.2,right=0.98,bottom=0.15,top=0.95)
fig[observable].savefig(obsfwd.observable_sname("k24"+observable,plot_format="pdf"))
plt.show()
k24 = pair[1]
scan = {}
scan['\\gamma_s'] = gamma
scan['k_{24}'] = k24
scan['\\omega'] = str(omega)
obsfwd = ObservableData(["\\Lambda"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
Lambdas = obsfwd.data[:, 0]
x, y = obsfwd.surfacetwist()[1:], 2 * np.pi / obsfwd.eta()[1:]
ax.plot(x, y, '.', color=colors[js])
ax.plot(x[-1], y[-1], 'rs')
dums = np.linspace(0.05, 0.25, num=150, endpoint=True)
ax.plot(dums, np.cos(dums), '--')
ax.set_xlabel(r"$\psi(R)$", fontsize=10)
ax.set_ylabel(r"$2\pi/\eta$", fontsize=10)
fig.subplots_adjust(left=0.1, right=0.9, bottom=0.15, top=0.95)
fig.savefig(obsfwd.observable_sname("twistvsdband", plot_format="pdf"))
plt.show()
jlarge = find_j[-1]
# find the point where the fwd E becomes larger than the bkwd E
j = (np.argmin(np.abs(obsfwd.E()[jsmall:jlarge+1]-obsbkwd.E()[jsmall:jlarge+1]))
+len(obsfwd.E()[:jsmall]))
pstr = f"{k24:.2e} {gammas[j]:.2e} {jsmall} {obsfwd.R()[j]:.2e} {obsbkwd.R()[j]:.2e}"
print(pstr)
data2d[i,j] = 1
gammacoexist.append(obsfwd.data[j,0])
k24coexist.append(k24)
obsfwd.data[j:bwdlen,:] = obsbkwd.data[j:bwdlen,:]
np.savetxt(obsfwd.observable_sname("observables",plot_format="txt"),obsfwd.data,
fmt='\t'.join(["%15.8e"]*6))
scoexist = np.array([gammacoexist,k24coexist],float)
np.savetxt(obsfwd.observable_sname("coexistence",plot_format='txt'),scoexist.T,
fmt="%8.4e")
width = 3.37
height = width
fig = plt.figure()
fig.set_size_inches(width,height)
ax = fig.add_subplot(1,1,1)
axins.plot(ts,Rfrust,'>',color=colors[1],label=rf"${frustlab}$",markersize=2)
axins.plot(ts,Rlin,'s',color=colors[0],label=rf"${linearlab}$",markersize=2)
axins.set_xticks([0,1,2,3])
axins.set_ylim(0.02,8)
#axins.set_xscale('log')
axins.set_yscale('log')
axins.tick_params(axis='x',labelsize=6,pad=0.3)
axins.tick_params(axis='y',labelsize=6,pad=0)
axins.set_xlabel(r"$t$",labelpad=-0.4)
axins.set_ylabel(r"$R$",labelpad=0.0)
axins.legend(frameon=False,loc='upper left',
fontsize=6,ncol=1,bbox_to_anchor=(-0.05,1.05),
borderpad=0.3,columnspacing=0.7,handletextpad=-0.1)
ax.legend(frameon=False,loc='lower right',fontsize=10)
fig.savefig(obsfwd.observable_sname(f"{observable}_SCALING"))
jlarge = find_j[-1]
# find the point where the fwd E becomes larger than the bkwd E
j = (np.argmin(
np.abs(obsfwd.E()[jsmall:jlarge + 1] -
obsbkwd.E()[jsmall:jlarge + 1])) + len(obsfwd.E()[:jsmall]))
pstr = f"{k24:.2e} {gammas[j]:.2e} {jsmall} {obsfwd.R()[j]:.2e} {obsbkwd.R()[j]:.2e}"
print(pstr)
data2d[i, j] = 1
gammacoexist.append(obsfwd.data[j, 0])
k24coexist.append(k24)
scoexist = np.array([gammacoexist, k24coexist], float)
np.savetxt(obsfwd.observable_sname("coexistence", plot_format='txt'),
scoexist.T,
fmt="%8.4e")
width = 3.37
height = width
fig = plt.figure()
fig.set_size_inches(width, height)
ax = fig.add_subplot(1, 1, 1)
ax.contourf(gammas, k24s, data2d, colors=["w", "g"])
ax.set_xlabel(r"$\gamma$", fontsize=10)
ax.set_ylabel(r"$k_{24}$", fontsize=10)
ax.text(0.1, 0.1, rf"$\Lambda={Lambda}$")
fig.subplots_adjust(left=0.2, bottom=0.2)
# fc='white'),
# arrowprops=dict(arrowstyle='-[, widthB=9.5, lengthB=1.0', lw=2.0))
#if j == 0:
# grid[observable].axarr[i][j]['main'].set_ylabel(ylabel)
# grid[observable].axarr[i][j]['slice_const_y'].set_ylabel(plabel)
# grid[observable].axarr[i][j]['main'].annotate(rf'$k_{{24}}={k24}$',
# xy=(-0.49,0.7),
# xytext=(-0.5,0.7),
# xycoords='axes fraction',
# ha='center',
# va='center',
# bbox=dict(boxstyle='square',
# fc='white'),
# arrowprops=dict(arrowstyle='-[, widthB=9.5, lengthB=1.0', lw=2.0))
#else:
# plt.setp(grid[observable].axarr[i][j]['slice_const_y'].get_yticklabels(),
# visible=False)
coexist = np.loadtxt(obs.observable_sname("coexistence", plot_format="txt"))
for observable in observable_list:
ax[observable].plot(coexist[:, 0], coexist[:, 1], 'k.')
ax[observable].set_xlim(right=0.3)
fig[observable].subplots_adjust(left=0.2, right=0.95, bottom=0.15)
fig[observable].savefig(obs.observable_sname(observable))
plt.show()
color=colors[0])#,
#arrowprops=dict(facecolor='black', shrink=0.001))
ax[observable].annotate(frustlab,xy=(gammas[g_eq],ysbkwd[i][g_eq]),
xytext=(gammas[g_eq]-0.007,ysbkwd[i][g_eq]*1.3),
color=colors[1])#,
#arrowprops=dict(facecolor='black', shrink=0.001))
"""
ax[observable].set_ylabel(rf"${ylabel}$",fontsize=10)
ax[observable].set_xlabel(r"$\gamma$",fontsize = 10)
ax[observable].legend(frameon=False,fontsize=10)
ax[observable].set_xlim(0,0.3)
for observable in observable_list:
fig[observable].subplots_adjust(left=0.2,bottom=0.2)
fig[observable].savefig(obsfwd.observable_sname("vary_Lambda"+observable,
plot_format="pdf"))
plt.show()
float)
bw_coexist = np.empty([len(k24s), obsbwd.data.shape[-1] + 1],
float)
if len(obsfwd.data.shape) > 1:
dum1 = obsfwd.data[-1, :]
else:
dum1 = obsfwd.data
if len(obsbwd.data.shape) > 1:
dum2 = obsbwd.data[-1, :]
else:
dum2 = obsbwd.data
fw_coexist[i] = np.concatenate(([k24], dum1))
bw_coexist[i] = np.concatenate(([k24], dum2))
np.savetxt(obsfwd.observable_sname("fw_coexist", plot_format="txt"),
fw_coexist,
fmt='%15.8e')
np.savetxt(obsbwd.observable_sname("bw_coexist", plot_format="txt"),
bw_coexist,
fmt='%15.8e')
ylabel = fr'$\{observable}$'
else:
ylabel = fr'${observable}$'
ax[observable].plot(Lambdas[:i_fwd + 1],
ysfwd[i][:i_fwd + 1],
'.',
color=colors[i])
ax[observable].plot(Lambdas[i_fwd:i_fwd + 2],
ysfwd[i][i_fwd:i_fwd + 2],
'-',
color=colors[i])
ax[observable].plot(ms_bkwd, ysbkwd[i][1:], '-', color=colors[i])
ax[observable].plot(Lambdas[i_fwd + 2:],
ysfwd[i][i_fwd + 2:],
'.',
color=colors[i])
ax[observable].set_xlabel(r"$\Lambda$", fontsize=24)
ax[observable].set_ylabel(ylabel, fontsize=24)
ax[observable].set_xscale('log')
ax[observable].tick_params("both", labelsize=18)
for observable in observable_list:
fig[observable].subplots_adjust(left=0.15, right=0.95, bottom=0.1)
fig[observable].savefig(
obsfwd.observable_sname(observable, plot_format="png"))
plt.show()
if observable == 'R':
ax[observable].set_yscale('log')
"""
ax[observable].annotate(linearlab,xy=(gammas[g_eq],ysfwd[i][g_eq]),
xytext=(gammas[g_eq]+0.007,ysfwd[i][g_eq]*0.5),
color=colors[0])#,
#arrowprops=dict(facecolor='black', shrink=0.001))
ax[observable].annotate(frustlab,xy=(gammas[g_eq],ysbkwd[i][g_eq]),
xytext=(gammas[g_eq]-0.007,ysbkwd[i][g_eq]*1.3),
color=colors[1])#,
#arrowprops=dict(facecolor='black', shrink=0.001))
"""
ax[observable].set_ylabel(rf"${ylabel}$", fontsize=10)
ax[observable].set_xlabel(r"$\gamma$", fontsize=10)
ax[observable].legend(frameon=False, fontsize=10)
ax[observable].set_xlim(0, 0.3)
for observable in observable_list:
fig[observable].subplots_adjust(left=0.2, bottom=0.2)
fig[observable].savefig(
obsfwd.observable_sname("vary_omega" + observable,
plot_format="pdf"))
plt.show()
ax[observable].plot(Lambdas[1:],
ysfwd[i][1:],
'.-',
color=colors[js],
label=rf"$k_{{24}}={k24}$")
#ax[observable].plot(Lambdas[:i_fwd+1],ysfwd[i][:i_fwd+1],'.',color=colors[i])
#ax[observable].plot(Lambdas[i_fwd:i_fwd+2],ysfwd[i][i_fwd:i_fwd+2],'-',
# color=colors[i])
#ax[observable].plot(ms_bkwd,ysbkwd[i][1:],'-',color=colors[i])
#ax[observable].plot(Lambdas[i_fwd+2:],ysfwd[i][i_fwd+2:],'.',color=colors[i])
ax[observable].set_xlabel(r"$\Lambda$", fontsize=10)
ax[observable].set_ylabel(ylabel, fontsize=10)
#ax[observable].tick_params("both",labelsize=18)
for observable in observable_list:
if observable == "R":
ax[observable].set_yscale('log')
ax[observable].legend(frameon=False)
ax[observable].set_xscale('log')
fig[observable].subplots_adjust(left=0.2,
right=0.98,
bottom=0.15,
top=0.95)
fig[observable].savefig(
obsfwd.observable_sname("gamma" + observable, plot_format="pdf"))
plt.show()
color=colors[js],
lw=3)
ax["psi(r)"].plot(1.0,
psistuff.psi()[-1],
marker=mtypes[ms + 1],
color=colors[js],
markeredgewidth=0.5,
markeredgecolor="w")
for observable in observable_list:
if observable == "R":
ax[observable].set_yscale('log')
ax[observable].set_zorder(1)
ax[observable].legend(frameon=True,
loc="upper left",
bbox_to_anchor=(0.0, 0.0))
ax[observable].tick_params(labelbottom=False)
ax[observable].set_xscale('log')
# now plot psi(r)
ax["psi(r)"].set_xlabel(r"$r/R$", fontsize=10)
ax["psi(r)"].set_ylabel(r"$\psi(r)$", fontsize=10)
fig.subplots_adjust(left=0.1, right=0.9, bottom=0.15, top=0.95)
fig.savefig(obsfwd.observable_sname("radial-twopair", plot_format="pdf"))
plt.show()
loadsuf=loadsuf,
savesuf=savesuf,
name=f"psivsr_{type}",
loadfilepath=loadfilepath,
datfile=datfile,
sfile_format="pdf")
rs = psistuff.r() / q
psis = psistuff.psi()
axins.plot(rs, psis, markertypes[i], label=f'{type} twist', lw=2)
print("Radius = ", rs[-1], " nm.")
print("surface twist = ", psis[-1], " rad.")
axins.set_xlabel(r'$\tilde{r}$' + ' (' + r'$\si{\nano\meter}$' + ')',
fontsize=8,
labelpad=0.0)
axins.set_ylabel(r'$\psi(\tilde{r})$' + ' (' + r'$\si{\radian}$' + ')',
fontsize=8)
axins.set_xlim(0, 120)
axins.set_ylim(0, 0.1)
axins.set_xticks([0, 50, 100])
axins.set_yticks([0, 0.05, 0.1])
axins.legend(frameon=False, fontsize=8)
plt.show()
fig.savefig(obsfwd.observable_sname(f"phase-diagram-with-psivsr"))
loadsuf = ["K_{33}", "k_{24}", "\\Lambda", "\\omega"]
k24s = np.linspace(0.2, 1, num=41, endpoint=True)
Lambda = '600.0'
omega = '20.0'
scan = {}
scan['\\Lambda'] = Lambda
scan['\\omega'] = omega
# load in 2d grid of data in data2d for each observable at the
# specified gamma,k24 pair.
for i, k24 in enumerate(k24s):
scan['k_{24}'] = str(k24)
obsbwd = ObservableData(["\\gamma_s"],
scan_dir='scanbackward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf,
datfile='data/inputbwd.dat')
obsbwd.data = obsbwd.data[np.argsort(obsbwd.data[:, 0])]
np.savetxt(obsbwd.observable_sname("observables_scanbackward",
plot_format="txt"),
obsbwd.data,
fmt='\t'.join(["%15.8e"] * 6))
j = 0
while (fw[j,0] != bw[j,0] and j < len(fw[:,0])):
j += 1
out_idx = np.flatnonzero(fw[j:n,0]==bw[:n-j,0])
fw = fw[out_idx+j]
bw = bw[out_idx]
jsmall,jlarge,jeq = find_metastable(fw[:,3],bw[:,3],fw[:,2],bw[:,2])
fw_line = []
bw_line = []
for k in range(7):
fw_line.append(fw[jeq,k])
bw_line.append(bw[jeq,k])
fw_coexist[i,:] = fw_line
bw_coexist[i,:] = bw_line
np.savetxt(obsfwd.observable_sname('fwd_coexist',plot_format='txt'),fw_coexist,fmt='%13.6e')
np.savetxt(obsbkwd.observable_sname('bkwd_coexist',plot_format='txt'),bw_coexist,fmt='%13.6e')
arrowprops=dict(arrowstyle='-[, widthB=9.5, lengthB=1.0',
lw=2.0))
if j == 0:
grid[observable].axarr[i][j]['main'].set_ylabel(ylabel)
grid[observable].axarr[i][j]['slice_const_y'].set_ylabel(
plabel)
grid[observable].axarr[i][j]['main'].annotate(
rf'$k_{{24}}={k24}$',
xy=(-0.49, 0.7),
xytext=(-0.5, 0.7),
xycoords='axes fraction',
ha='center',
va='center',
bbox=dict(boxstyle='square', fc='white'),
arrowprops=dict(arrowstyle='-[, widthB=9.5, lengthB=1.0',
lw=2.0))
else:
plt.setp(grid[observable].axarr[i][j]
['slice_const_y'].get_yticklabels(),
visible=False)
for observable in observable_list:
fig[observable].subplots_adjust(left=0.2, right=0.95)
fig[observable].savefig(obs.observable_sname("LO_" + observable))
plt.show()
else:
linearlab = rf"${ylabel}_L^*$"
frustlab = rf"${ylabel}_F^*$"
ax.plot(ts,
obsfwd.data[:, i + 1],
'^',
color=colors[0],
label=linearlab,
markersize=3)
ax.plot(ts,
obsbwd.data[:, i + 1],
's',
color=colors[1],
label=frustlab,
markersize=3)
if observable == "R":
ax.set_yscale('log')
#ax.plot(xs,func_l(xs),'-')
#ax.plot(xs,func_f(xs),'-')
ax.set_ylabel(rf"${ylabel}$", fontsize=10)
ax.set_xlabel(r"$t$", fontsize=10)
ax.legend(frameon=False, fontsize=10)
fig.subplots_adjust(left=0.2, bottom=0.2)
fig.savefig(obsfwd.observable_sname(f"{observable}_GAP"))
tilt = expdata[:,2]*np.pi/180.0
stresspoints = expdata[:,3]
for observable in observable_list:
if observable == "stress":
ax[observable].plot(strainpoints,stresspoints,'k^',label="expt.")
ax[observable].set_ylim(0,stresses[-1]+10)
ax[observable].legend(frameon=False)
if observable == "surfacetwist":
ax[observable].plot(strainpoints,tilt,'k^',label="expt.")
slope = np.gradient(tilt,strainpoints)[2]
ax[observable].set_xlabel(r"$\epsilon\times100\%$",fontsize = 10)
ax[observable].set_xlim(left=0)
print(f"for experiment, slope is {slope}")
fig.subplots_adjust(left=0.2,right=0.8,bottom=0.1,top=0.95,hspace=0.05)
fig.savefig(obsfwd.observable_sname("psiavg_etc-vsstrain",plot_format="pdf"))
plt.show()
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
observables_list = ["E", "R", "eta", "delta", "psi(R)"]
k24s = obsfwd.data[:, 0]
delR = (obsbwd.data[:, 3] - obsfwd.data[:, 3])
ys = np.log(delR)
popt, pcov = curve_fit(func, k24s, ys)
fig = plt.figure()
fig.set_size_inches(width, height)
ax = fig.add_subplot(1, 1, 1)
ax.set_ylabel(rf"$\Delta R$", fontsize=10)
ax.set_xlabel(r"$k_{24}$", fontsize=10)
ax.plot(k24s, delR, '.')
ax.plot(k24s, np.exp(func(k24s, popt[0], popt[1])), '-')
ax.plot(k24s, np.exp(func(k24s, 0.3, -0.5)), '-')
ax.legend(frameon=False, fontsize=10)
fig.subplots_adjust(left=0.2, bottom=0.2)
fig.savefig(obsfwd.observable_sname(f"delR-fit"))
breaks[js] = strains[np.argmax(
stresses[stresses == stresses])] * 100
print(Lambda, breaks[js])
ax["youngs"].plot(Larray,
youngs,
'o-',
color=colors[ks],
label=rf"$(\gamma,k_{{24}})=({gamma},{k24})$")
ax["youngs"].set_ylabel(r"$Y$")
ax["youngs"].set_xscale('log')
ax["youngs"].set_yscale('log')
ax["breaks"].plot(Larray,
breaks,
'o-',
color=colors[ks],
label=rf"$(\gamma,k_{{24}})=({gamma},{k24})$")
ax["breaks"].set_ylabel(r"$\epsilon_{\mathrm{max}}\times100\%$")
ax["breaks"].set_xlabel(r"$\Lambda$")
ax["breaks"].set_xscale('log')
ax["breaks"].set_ylim(top=5)
ax["breaks"].legend(loc="lower left",
bbox_to_anchor=(0.2, 0.7),
frameon=False)
fig.subplots_adjust(left=0.2, right=0.8, bottom=0.1, top=0.95, hspace=0.05)
fig.savefig(obsfwd.observable_sname("youngs-vs-Lambda", plot_format="pdf"))
plt.show()
color=colors[js],
lw=1)
ax[observable].plot(Lambdas[ms_upper:i_Lambda],
ysbkwd[i][ms_upper:i_Lambda],
'-',
color=colors[js],
lw=1)
ax[observable].plot(Lambdas[i_Lambda:],
ysbkwd[i][i_Lambda:],
'-',
color=colors[js],
lw=3)
ax[observable].set_ylabel(ylabel, fontsize=10)
ax[observable].set_xlabel(r"$\Lambda$", fontsize=10)
for observable in observable_list:
if observable == "E":
ax[observable].legend(frameon=False,
loc="best",
bbox_to_anchor=(0.2, -0.07, 0.5, 0.5))
ax[observable].set_xscale('log')
fig.subplots_adjust(left=0.2, right=0.8, bottom=0.1, top=0.95, hspace=0.05)
fig.savefig(obsfwd.observable_sname("E-eta-delta-twopair", plot_format="pdf"))
plt.show()
# xycoords='axes fraction',
# ha='center',
# va='center',
# bbox=dict(boxstyle='square',
# fc='white'),
# arrowprops=dict(arrowstyle='-[, widthB=9.5, lengthB=1.0', lw=2.0))
#if j == 0:
# grid[observable].axarr[i][j]['main'].set_ylabel(ylabel)
# grid[observable].axarr[i][j]['slice_const_y'].set_ylabel(plabel)
# grid[observable].axarr[i][j]['main'].annotate(rf'$k_{{24}}={k24}$',
# xy=(-0.49,0.7),
# xytext=(-0.5,0.7),
# xycoords='axes fraction',
# ha='center',
# va='center',
# bbox=dict(boxstyle='square',
# fc='white'),
# arrowprops=dict(arrowstyle='-[, widthB=9.5, lengthB=1.0', lw=2.0))
#else:
# plt.setp(grid[observable].axarr[i][j]['slice_const_y'].get_yticklabels(),
# visible=False)
for observable in observable_list:
fig[observable].subplots_adjust(left=0.2, right=0.95)
fig[observable].savefig(obs.observable_sname(observable))
plt.show()
if observable == "stress_max":
ylabel = r"$\sigma_{\mathrm{max}}$"
elif observable == "strain_max":
ylabel = r"$\epsilon(\sigma_{\mathrm{max}})$"
ax[observable].plot(omegaarray,ys_arrays[observable],'.-',color=colors[kj],
label=rf"$\Lambda={Lambda}$")
ax[observable].set_ylabel(ylabel,fontsize = 10)
ax[observable].set_xlabel(r"$\omega$",fontsize = 10)
for observable in observable_list:
ax[observable].set_xscale('log')
if observable == "strain_max":
ax[observable].legend(frameon=False)
fig.subplots_adjust(left=0.2,right=0.8,bottom=0.1,top=0.95,hspace=0.05)
fig.savefig(obsfwd.observable_sname("omega_dependent_failure",plot_format="pdf"))
plt.show()