def drawSpinWave(rpath, kpt, ibnd, Lx=5, Ly=5):
global tb, eig0_set, Tc, Tc_MF, magList_HF, magList_MF, S
# create mc main directory
print('output spin waves on band%d at kpt %.3f %.3f %.3f' % (ibnd, *kpt))
io.loadParam(updateGUI=False, rpath=rpath)
# initialize tight-binding model
tb = WannierKit.TBmodel()
__tbInit()
eig, vec = tb.solveHk(kpt=kpt, return_orb=True)
#print(vec[:,ibnd])
tb.plot2DStructure(vec[:, ibnd],
kpt=np.array(kpt),
Lx=Lx,
Ly=Ly,
S=io.S[0])
def mainLoop(rpath):
global tb, eig0_set, Tc, Tc_MF, magList_HF, magList_MF, S
# create mc main directory
print('start renormalized spin-wave theoretical calculations')
#mainDir=rpath+'swt/'
#aux.createDir(mainDir)
io.loadParam(updateGUI=False, rpath=rpath)
# initialize tight-binding model
tb = WannierKit.TBmodel()
__tbInit()
tb.plotbands()
eig0_min = np.min(eig0_set)
print('spin-wave gap at 0K is: %.6f' % eig0_min)
if (eig0_min > 0):
Sigma = 0
for eig in eig0_set:
Sigma += 1. / eig
Tc_ = (S + 1) * len(eig0_set) / 3. / Sigma
print('mean field estimation for Tc: %.3f' % Tc_)
exit()
print('start scf procedure for each temperature, to get M-T curv')
# calc M-T curv
Tc, magList_HF = __MTCurv(path='./', draw=True, algo='HF_longWave')
print('Hatree-Fock and long wave approximation give Tc:', Tc,
'(Kelvin)')
print
Tc_MF, magList_MF = __MTCurv(path='./', draw=True, algo='meanfield')
print('Mean-field approximation give Tc:', Tc_MF, '(Kelvin)')
print('Spin-wave theory Tc (Hatree-Fock long wave limit): {:.1f}\n'.
format(Tc))
print(
'Spin-wave theory Tc (Mean-field approximation): {:.1f}\n'.format(
Tc_MF))
__spinWave_T_vs_Occ('./', algo='HF_longWave')
__spinWave_T_vs_Occ('./', algo='Mean_Field')
return Tc
else:
print('find zero or negative spin-wave gap!')
print('Spin-wave theory find no Tc!\n')
return 0
def startSimulation(updateGUI=True, rpath=''):
time0 = time.time()
# clean possible existed files
with open('./out', 'w') as fout:
fout.write('#T #H\n')
with open('./spinDotSpin.txt', 'w') as fout:
fout.write('#T #H\n')
if updateGUI:
gui.submitBtn.config(state='disabled')
io.collectParam()
else:
if not io.loadParam(updateGUI=False, rpath=rpath):
'Error: win::startSimulation load file failed. stop this function.'
return
TList = np.linspace(io.T0, io.T1, io.nT)
HList = np.linspace(io.H0, io.H1, io.nH)
bondList = [
lat.Bond(
bond_data[0],
bond_data[1], # source and target
np.array([int(x) for x in bond_data[2]]), # over lat.
bond_data[3],
bond_data[4],
bond_data[5], # strength Jxx, Jyy, Jzz
bond_data[6],
bond_data[7],
bond_data[8], # strength Jxy, Jxz, Jyz
bond_data[9],
bond_data[10],
bond_data[11], # strength Jyx, Jzx, Jzy
True if io.modelType != 'Ising' else False) # On
for bond_data in io.bondList
] # ergodic
LMatrix = np.array(io.LMatrix)
pos = np.array(io.pos)
if (io.modelType == 'Ising'):
if io.algorithm != 'Metropolis' and io.algorithm != 'Wolff':
print(
'For now, only Metropolis and Wolff algorithm is supported for Ising model'
)
if updateGUI: gui.submitBtn.config(state='normal')
return
paramPack = []
for iH, H in enumerate(HList):
for iT, T in enumerate(TList):
paramPack.append([
iH * len(TList) + iT, T, bondList, LMatrix, pos, io.S,
io.DList, H, io.nsweep, io.nthermal, io.ninterval,
io.LPack[0], io.LPack[1], io.LPack[2], io.algorithm,
io.GcOrb, io.orbGroupList, io.groupInSC, io.dipoleAlpha,
io.spinFrame
])
TResult = []
HResult = []
SpinIResult = []
SpinJResult = []
susResult = []
energyResult = []
capaResult = []
u4Result = []
autoCorrResult = []
while (True): # using pump strategy to reduce the costs of RAM
if len(paramPack) == 0:
break
# pump tasks
paramPack_tmp = []
for index in range(io.ncores):
paramPack_tmp.append(paramPack.pop(0))
if len(paramPack) == 0:
break
pool = Pool(processes=io.ncores)
for result in pool.imap_unordered(startMC, paramPack_tmp):
ID, T, h, spin_i, spin_j, spin_ij, autoCorr, E, E2, U4, N = result
TResult.append(T)
HResult.append(h)
SpinIResult.append(spin_i)
SpinJResult.append(spin_j)
susResult.append((spin_ij - spin_i * spin_j) / T)
autoCorrResult.append(autoCorr)
energyResult.append(E)
capaResult.append((E2 - E * E) / T**2 * N)
u4Result.append(U4)
pool.close()
if updateGUI:
gui.updateResultViewer(
TList=TResult if io.xAxisType == 'T' else HResult,
magList=[(si + sj) / 2
for si, sj in zip(SpinIResult, SpinJResult)],
susList=capaResult)
# continuous model settings
elif (io.modelType == 'XY' or io.modelType == 'Heisenberg'):
for bond in bondList:
bond.On = True # switch on the vector type bonding
if io.algorithm != 'Metropolis' and io.algorithm != 'Wolff':
print(
'For now, only Metropolis and Wolff algorithm is supported for O(n) model'
)
if updateGUI: gui.submitBtn.config(state='normal')
return
On = 2 if io.modelType == 'XY' else 3
paramPack = []
for iH, H in enumerate(HList):
for iT, T in enumerate(TList):
paramPack.append([
iH * len(TList) + iT, T, bondList, LMatrix, pos, io.S,
io.DList, H, io.nsweep, io.nthermal, io.ninterval,
io.LPack[0], io.LPack[1], io.LPack[2], io.algorithm, On,
io.GcOrb, io.orbGroupList, io.groupInSC, io.dipoleAlpha,
io.spinFrame
])
TResult = []
HResult = []
SpinIResult = []
SpinJResult = []
susResult = []
energyResult = []
capaResult = []
u4Result = []
autoCorrResult = []
while (True): # using pump strategy to reduce the costs of RAM
if len(paramPack) == 0:
break
# pump tasks
paramPack_tmp = []
for index in range(io.ncores):
paramPack_tmp.append(paramPack.pop(0))
if len(paramPack) == 0:
break
pool = Pool(processes=io.ncores)
for result in pool.imap_unordered(startMCForOn, paramPack_tmp):
ID, T, h, spin_i, spin_j, spin_ij, autoCorr, E, E2, U4, N = result
TResult.append(T)
HResult.append(h)
SpinIResult.append(np.sqrt(sum(spin_i * spin_i)))
SpinJResult.append(np.sqrt(sum(spin_j * spin_j)))
susResult.append((spin_ij - np.dot(spin_i, spin_j)) / T)
autoCorrResult.append(autoCorr)
energyResult.append(E)
capaResult.append((E2 - E * E) / T**2 * N)
u4Result.append(U4)
pool.close()
if updateGUI:
gui.updateResultViewer(
TList=TResult if io.xAxisType == 'T' else HResult,
magList=SpinIResult,
susList=capaResult)
else:
print("for now only Ising, XY and Heisenberg model is supported")
if updateGUI: gui.submitBtn.config(state='normal')
return
# writting result file
f = open('./result.txt', 'w')
f.write(
'#Temp #<Si> #<Sj> #Susc #Energy_per_orb(K) #capacity_per_orb(K/K) #Binder_cumulate #auto-corr.\n'
)
for T, si, sj, sus, energy, capa, u4, autoCorr in zip(
TResult, SpinIResult, SpinJResult, susResult, energyResult,
capaResult, u4Result, autoCorrResult):
f.write('%.3f %.6f %.6f %.6f %.6f %.6f %.6f %.6f\n' %
(T, si, sj, sus, energy, capa, u4, autoCorr))
f.close()
if updateGUI: gui.submitBtn.config(state='normal')
print("time elapsed %.3f s" % (time.time() - time0))
return