def predictSpectrum(E, LMax, E_0):
print(E)
result = []
for L in range(LMax):
partitions = usefulTools.generatePartitions(L)
result += [[L, E_0 + stateEnergyFromDispersion(p, E)] for p in partitions]
return result
def calcPredictedSpectrum(LMax, g, h_22, h_33, E_0):
predSpectrum = []
for L in range(LMax):
partitions = generatePartitions(L)
predSpectrum += [[L, E_0 + stateEnergy(p, g, h_22, h_33)]
for p in partitions]
return predSpectrum
def findDispersion(spectrum, LMax):
E = {0 : 0}
E_0 = spectrum[0][1]
energyIndex = 1
for L in range(1,LMax):
E[L] = spectrum[energyIndex][1] - E_0
energyIndex += len(usefulTools.generatePartitions(L))
return E
def calcSpetrumCoulomb(LMax, E_0, h_22, h_33, f, N, m):
result = []
for L in range(LMax):
partitions = generatePartitions(L)
result += [[
L,
E_0 + f * stateEnergyCoulomb(p, N, m) + stateEnergy(p, h_22, h_33)
] for p in partitions]
return result
def convertToShur(self, N):
partitions = generatePartitions(sum(self.partition))
resultPoly = genralSymPolySchur([
schurPoly(
p,
self.coeficient *
characterTables.characterTab(p, self.partition))
for p in partitions if len(p) <= N
])
return resultPoly
def calcSpetrum(LMax, E_0, h_22, h_33, maxOrder, N, m):
result = []
for L in range(LMax):
partitions = generatePartitions(L)
levelMatrix = numpy.array([[
H(h_22, h_33, state1, state2, maxOrder, N, m)
for state2 in partitions
] for state1 in partitions])
print(levelMatrix)
energies = numpy.linalg.eigvals(levelMatrix)
result += [[L, E_0 + E] for E in energies]
return result
def generateStates(L, N):
"""
Generate a slater basis of states for angular momentum level L above ground.
"""
partitions = [item for item in generatePartitions(L) if len(item) <= N]
print(partitions)
states = []
for x in partitions:
tempState = [i for i in range(N)]
y = len(x)
for i in range(y):
tempState[N - 1 - i] = tempState[N - 1 - i] + x[y - 1 - i]
states.append(tempState)
#X = slatToSymBasisTrans.basisConversion(states, partitions, N)
X = 0
return states, X
def calcSpetrumZeroOrder(LMax, E_0, h_22, h_33):
result = []
for L in range(LMax):
partitions = generatePartitions(L)
result += [[L, E_0 + stateEnergy(p, h_22, h_33)] for p in partitions]
return result