#observable_list = ["E","R","delta","surfacetwist","stress"]
for js, Lambda in enumerate(Lambdas):
scan['\\Lambda'] = Lambda
obsfwd = ObservableData(["strain"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
#obsfwd.sort_observables()
#obsfwd.remove_duplicates()
if len(obsfwd.data) == 6 or obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ", Lambda)
Larray[js] = np.nan
continue
strains = obsfwd.data[:, 0]
stresses = np.gradient(obsfwd.E(), strains)
stresses[0] = 0.0
youngs[js] = (stresses[1] - stresses[0]) / (strains[1] -
strains[0])
breaks[js] = strains[np.argmax(
stresses[stresses == stresses])] * 100
print(Lambda, breaks[js])
ax["youngs"].plot(Larray,
youngs,
scan['\\gamma_s']=gamma
scan['k_{24}']=k24
scan['\\omega']=omega
scan['\\Lambda']=Lambda
obsfwd = ObservableData(["strain","averagetwist"],scan_dir='scanforward',
scan=scan,loadsuf=loadsuf,
savesuf=savesuf)
Req = obsfwd.R()[0]
print(Req)
strains = obsfwd.data[:,0]
stresses = np.gradient(obsfwd.E(),strains)*K22*q*q/1000 # stress in kPa
stresses[0] = 0.0
ysfwd = [stresses,obsfwd.data[:,1],obsfwd.delta()]
if obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ",Lambda)
for j,observable in enumerate(observable_list):
if observable == 'surfacetwist':
ylabel = r'$<\psi>$' + ' (' + r"$\si{\radian}$" + ')'
elif observable == "stress":
ylabel = r"$\tilde{\sigma}$" + ' (' + r"$\si{\kilo\pascal}$" + ')'
elif observable == 'delta':
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))
savesuf=savesuf)
if not obsfwd.file_exists:
continue
obsfwd.sort_observables()
obsfwd.remove_duplicates()
Lambdas = obsfwd.data[:,0]
#obsbkwd = ObservableData(["\\Lambda"],scan_dir='scanbackward',scan=scan,loadsuf=loadsuf,
# savesuf=savesuf)
#obsbkwd.sort_observables()
ysfwd = [obsfwd.E(),obsfwd.R(),obsfwd.eta(),obsfwd.delta(),obsfwd.surfacetwist()]
#ysbkwd = [obsbkwd.E(),obsbkwd.R(),obsbkwd.eta(),obsbkwd.delta(),obsbkwd.surfacetwist()]
for i,observable in enumerate(observable_list):
if observable == 'surfacetwist':
ylabel = r'$\psi(R)$'
elif observable == 'eta':
ylabel = r'$2\pi/\eta$'
ysfwd[i] = 2*np.pi/ysfwd[i]
elif observable == 'delta':
ylabel = r'$\delta/\delta_0$'
ysfwd[i] = ysfwd[i]/np.sqrt(2/3)
elif len(observable) > 1:
scan['\\gamma_s'] = gamma
scan['k_{24}'] = k24
scan['\\omega'] = omega
scan['\\Lambda'] = Lambda
obsfwd = ObservableData(["strain", "averagetwist"],
scan_dir=f'scanforward{type}',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
Req = obsfwd.R()[0]
print(Req)
strains = obsfwd.data[:, 0]
stresses = np.gradient(obsfwd.E(),
strains) * K22 * q * q / 1000 # stress in kPa
stresses[0] = 0.0
avgtwist = obsfwd.data[:, 1]
minindex = np.argmin(avgtwist)
ysfwd = [stresses, avgtwist, obsfwd.delta()]
if obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ", Lambda)
for j, observable in enumerate(observable_list):
if observable == 'surfacetwist':
ylabel = r'$<\psi>$' + ' (' + r"$\si{\radian}$" + ')'
elif observable == "stress":
ylabel = r"$\tilde{\sigma}$" + ' (' + r"$\si{\kilo\pascal}$" + ')'
elif observable == 'delta':
for js, Lambda in enumerate(Lambdas):
scan['\\Lambda'] = Lambda
print(Lambda)
obsfwd = ObservableData(["strain"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
#obsfwd.sort_observables()
#obsfwd.remove_duplicates()
strains = obsfwd.data[:, 0]
stresses = np.gradient(obsfwd.E(), strains)
stresses[0] = 0.0
#ysfwd = [obsfwd.E(),obsfwd.R(),obsfwd.delta(),obsfwd.surfacetwist(),stresses]
ysfwd = [stresses, obsfwd.delta(), obsfwd.surfacetwist()]
if obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ", Lambda)
continue
for i, observable in enumerate(observable_list):
if observable == 'surfacetwist':
ylabel = r'$\psi(R)$'
elif observable == "stress":
ylabel = r"$\sigma$"
elif observable == 'delta':
ylabel = r'$\delta/\delta_0$'
scan = {}
scan['\\Lambda'] = Lambda
scan['k_{24}'] = k24
scan['\\omega'] = omega
obsfwd = ObservableData(["\\gamma_s"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
gammas = obsfwd.data[:, 0]
ysfwd = [
obsfwd.E(),
obsfwd.R(),
obsfwd.eta(),
obsfwd.delta(),
obsfwd.surfacetwist()
]
"""
obsbkwd = ObservableData(["\\gamma_s"],scan_dir='scanbackward',scan=scan,loadsuf=loadsuf,
savesuf=savesuf)
ysbkwd = [obsbkwd.R(),obsbkwd.surfacetwist()]
g_small,g_large,g_eq = find_metastable(obsfwd.R(),obsbkwd.R(),
obsfwd.E(),obsbkwd.E())
obsfwd = ObservableData(["\\Lambda"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
obsbkwd = ObservableData(["\\Lambda"],
scan_dir='scanbackward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
obsbkwd.sort_observables()
observable_list = ['E', 'R', 'eta', 'delta', 'surfacetwist']
ysfwd = [
obsfwd.E(),
obsfwd.R(),
obsfwd.eta(),
obsfwd.delta(),
obsfwd.surfacetwist()
]
ysbkwd = [
obsbkwd.E(),
obsbkwd.R(),
obsbkwd.eta(),
obsbkwd.delta(),
obsbkwd.surfacetwist()
]
for i, observable in enumerate(observable_list):
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
Lambdas = obsfwd.data[:, 0]
if js == 1:
obsbkwd = ObservableData(["\\Lambda"],
scan_dir='scanbackward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
obsbkwd.sort_observables()
ysbkwd = [obsbkwd.E(), obsbkwd.eta(), obsbkwd.delta()]
ysfwd = [obsfwd.E(), obsfwd.eta(), obsfwd.delta()]
for i, observable in enumerate(observable_list):
if observable == 'surfacetwist':
ylabel = r'$\psi(R)$'
elif observable == 'eta':
ylabel = r'$2\pi/\eta$'
ysfwd[i] = 2 * np.pi / ysfwd[i]
if js == 1:
ysbkwd[i] = 2 * np.pi / ysbkwd[i]
elif observable == 'delta':
ylabel = r'$\delta/\delta_0$'
ysfwd[i] = ysfwd[i] / np.sqrt(2 / 3)
K33 = 30.0
scan = {}
scan['\\gamma_s'] = str(gamma)
scan['k_{24}'] = str(k24)
scan['\\Lambda'] = str(Lambda)
scan['\\omega'] = str(omega)
scan['K_{33}'] = str(K33)
observablestuff = ObservableData(scan=scan,loadsuf=loadsuf,savesuf=loadsuf,
name=f"observables_frustrated")
delta = observablestuff.delta()
eta = observablestuff.eta()
E0 = observablestuff.E()
print(delta,eta,E0)
psistuff = PsiData(scan=scan,loadsuf=loadsuf,savesuf=savesuf,name=f"psivsr_frustrated",
sfile_format="pdf")
rs = psistuff.r()
psis = psistuff.psi()
psiprimes = psistuff.psiprime()
R0s = np.copy(rs[:len(rs)-10:5])
Es = np.copy(R0s)*0
strains = np.array([0.0, 0.01, 0.02, 0.03], float)
colors = sns.color_palette("hls", len(strains))
fig = plt.figure()
fig.set_size_inches(width, height)
ax = fig.add_subplot(1, 1, 1)
obsfwd = ObservableData(["strain"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
if obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ", Lambda)
for i, u in enumerate(strains):
if i == 0:
strain = None
else:
strain = str(u)
psi = PsiData(scan=scan,
loadsuf=loadsuf,
savesuf=savesuf,
K_22 = 6 # pN
for i, observable in enumerate(observable_list):
ax[observable] = fig.add_subplot(3, 1, i + 1)
obsfwd = ObservableData(["strain", "averagetwist"],
scan_dir='scanforward',
scan=scan,
loadsuf=loadsuf,
savesuf=savesuf)
Req = obsfwd.R()[0] / q * 1000 # nm
strains = obsfwd.data[:, 0]
stresses = np.gradient(obsfwd.E(), strains) * K_22 * q * q / 1e3 # in kPa
stresses[0] = 0.0
ysfwd = [stresses, obsfwd.surfacetwist(), obsfwd.delta()]
if obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ", Lambda)
for i, observable in enumerate(observable_list):
if observable == 'surfacetwist':
ylabel = r'$\psi(R)$' + ' (' + r'$\si{\radian}$' + ')'
elif observable == "stress":
ylabel = r"$\sigma$" + ' (' + r'$\si{\kilo\pascal}$' + ')'
elif observable == 'delta':
ylabel = r'$\delta/\delta_0$'
ysfwd[i] = ysfwd[i] / np.sqrt(2 / 3)
ax[observable] = fig[observable].add_subplot(1,1,1)
for j,Lambda in enumerate(Lambdas):
scan = {}
scan['\\Lambda']=Lambda
scan['k_{24}']=k24
scan['\\omega']=omega
obsfwd = ObservableData(["\\gamma_s"],scan_dir='scanforward',scan=scan,loadsuf=loadsuf,
savesuf=savesuf)
gammas = obsfwd.data[:,0]
ysfwd = [obsfwd.E(),obsfwd.R(),obsfwd.eta(),
obsfwd.delta(),obsfwd.surfacetwist()]
"""
obsbkwd = ObservableData(["\\gamma_s"],scan_dir='scanbackward',scan=scan,loadsuf=loadsuf,
savesuf=savesuf)
ysbkwd = [obsbkwd.R(),obsbkwd.surfacetwist()]
g_small,g_large,g_eq = find_metastable(obsfwd.R(),obsbkwd.R(),
obsfwd.E(),obsbkwd.E())
print(g_small,g_large,g_eq)
"""
ax[observable] = fig.add_subplot(2,1,i+1)
for kj,Lambda in enumerate(['1.0','10.0']):
scan['\\Lambda']=Lambda
for js,omega in enumerate(omegas):
scan['\\omega']=omega
print(omega)
obsfwd = ObservableData(["strain"],scan_dir='scanforward',scan=scan,loadsuf=loadsuf,
savesuf=savesuf)
if not isinstance(obsfwd.E(),np.ndarray):
ys_arrays["stress_max"][js]=0.0
ys_arrays["strain_max"][js]=0.0
continue
if obsfwd.E()[0] > 1e299:
print("bad calculation at Lambda = ",Lambda)
continue
strains = obsfwd.data[:,0]
stresses = np.gradient(obsfwd.E(),strains)
stresses[0] = 0.0
max_index = np.argmax(stresses)