def startSimulaton():
time0 = time.time()
gui.submitBtn.config(state='disabled')
io.collectParam()
TList = np.linspace(io.T0, io.T1, io.nT)
bondList=[lat.Bond(bond_data[0],bond_data[1],\
np.array([int(x) for x in bond_data[2]]),\
bond_data[3],bond_data[4],bond_data[5]) \
for bond_data in io.bondList]
LMatrix = np.array(io.LMatrix)
pos = np.array(io.pos)
if (io.modelType == 'Ising'):
if io.algorithm != 'Metroplis' and io.algorithm != 'Wolff':
print(
'For now, only Metroplis and Wolff algorithm is supported for Ising model'
)
gui.submitBtn.config(state='normal')
return
paramPack = []
for iT, T in enumerate(TList):
paramPack.append([
iT, T, bondList, LMatrix, pos, io.S, io.DList, io.nsweep,
io.nthermal, io.LPack[0], io.LPack[1], io.LPack[2],
io.algorithm
])
TResult = []
magResult = []
susResult = []
energyResult = []
capaResult = []
u4Result = []
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):
ID, T, mData, eData = result
TResult.append(T)
magResult.append(np.mean(mData))
susResult.append(np.std(mData))
energyResult.append(np.mean(eData))
capaResult.append(np.std(eData))
u4Result.append(np.mean(mData * mData)**2 / np.mean(mData**4))
pool.close()
gui.updateResultViewer(TList=TResult,
magList=magResult,
susList=susResult)
# 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 != 'Metroplis' and io.algorithm != 'Wolff':
print(
'For now, only Metroplis and Wolff algorithm is supported for O(n) model'
)
gui.submitBtn.config(state='normal')
return
On = 2 if io.modelType == 'XY' else 3
paramPack = []
for iT, T in enumerate(TList):
paramPack.append([
iT, T, bondList, LMatrix, pos, io.S, io.DList, io.nsweep,
io.nthermal, io.LPack[0], io.LPack[1], io.LPack[2],
io.algorithm, On
])
TResult = []
magResult = []
susResult = []
energyResult = []
capaResult = []
u4Result = []
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, mData, eData = result
TResult.append(T)
magResult.append(np.mean(mData))
susResult.append(np.std(mData))
energyResult.append(np.mean(eData))
capaResult.append(np.std(eData))
u4Result.append(np.mean(mData * mData)**2 / np.mean(mData**4))
pool.close()
gui.updateResultViewer(TList=TResult,
magList=magResult,
susList=susResult)
else:
print("for now only Ising, XY and Heisenberg model is supported")
gui.submitBtn.config(state='normal')
return
# writting result file
f = open('./result.txt', 'w')
f.write('#Temp #Spin #Susc #energy #capacity #Binder cumulante\n')
for T, mag, sus, energy, capa, u4 in zip(TResult, magResult, susResult,
energyResult, capaResult,
u4Result):
f.write('%.3f %.6f %.6f %.6f %.6f %.6f\n' %
(T, mag, sus, energy, capa, u4))
f.close()
gui.submitBtn.config(state='normal')
print("time elapsed %.3f s" % (time.time() - time0))
return
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