# Define tensors
deltaC = sqa.tensor('deltac', [i1,i2], [hsym])
deltaA = sqa.tensor('deltaa', [x1,x2], [hsym])
deltaV = sqa.tensor('deltav', [a1,a2], [hsym])
# Define the normal-ordered overlap
Cin = sqa.tensor('V', [i,j,p,a], [])
Cout = sqa.tensor('b', [k,l,q,b], [])
if (withC):
Op1 = sqa.term( 1.0, [''], [Cin, sqa.sfExOp([p, i]) , sqa.sfExOp([a, j])])
Op2 = sqa.term( 1.0, [''], [Cout, sqa.sfExOp([l, b]) , sqa.sfExOp([k, q])])
else:
Op1 = sqa.term( 1.0, [''], [sqa.sfExOp([p, i]) , sqa.sfExOp([a, j])])
Op2 = sqa.term( 1.0, [''], [sqa.sfExOp([l, b]) , sqa.sfExOp([k, q])])
result = sqa.normalOrder(sqa.multiplyTerms(Op2, Op1))
# Simplify the result
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
sqa.termChop(result)
sqa.combineTerms(result)
extendedR=[]
for t in result:
extendedR += sqa.contractCoreOps_sf(t)
for t in extendedR:
t.contractDeltaFuncs_new()
sqa.termChop(extendedR)
sqa.combineTerms(extendedR)
result = []
commutator += sqa.commutator(HD_C, E_aiEbj)
commutator += sqa.commutator(HD_A, E_aiEbj)
commutator += sqa.commutator(HD_V, E_aiEbj)
commutator += sqa.commutator(HD_CA1, E_aiEbj)
commutator += sqa.commutator(HD_CA2, E_aiEbj)
commutator += sqa.commutator(HD_CV1, E_aiEbj)
commutator += sqa.commutator(HD_CV2, E_aiEbj)
commutator += sqa.commutator(HD_AV1, E_aiEbj)
commutator += sqa.commutator(HD_AV2, E_aiEbj)
commutator += sqa.commutator(T_C, E_aiEbj)
commutator += sqa.commutator(T_A, E_aiEbj)
commutator += sqa.commutator(T_V, E_aiEbj)
result = []
for t in commutator:
result += sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2, t))
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
sqa.termChop(result)
sqa.combineTerms(result)
extendedR=[]
for t in result:
extendedR += sqa.contractCoreOps_sf(t)
for t in extendedR:
t.contractDeltaFuncs_new()
sqa.termChop(extendedR)
sqa.combineTerms(extendedR)
commutator = []
commutator += sqa.commutator(T_C, Omu)
commutator += sqa.commutator(T_A, Omu)
commutator += sqa.commutator(T_V, Omu)
commutator += sqa.commutator(HD_A, Omu)
commutator += sqa.commutator(HD_C, Omu)
commutator += sqa.commutator(HD_V, Omu)
commutator += sqa.commutator(HD_CA1, Omu)
commutator += sqa.commutator(HD_CA2, Omu)
commutator += sqa.commutator(HD_CV1, Omu)
commutator += sqa.commutator(HD_CV2, Omu)
commutator += sqa.commutator(HD_AV1, Omu)
commutator += sqa.commutator(HD_AV2, Omu)
result = []
for t in commutator:
result += sqa.normalOrder(sqa.multiplyTerms(Onu, t))
result = simplify_all(result, [deltaC, deltaA, deltaV], cumulantE4=cumulantE4)
# Perturber-dependent stuff
Class = 'AAAC'
tensors = common_tensors(result, 'caaa', pttype='MRLCC')
tensors = order_tensors(tensors)
nbrTensors = write_tensors('MRLCC_' + Class, tensors)
nbrEqs = write_code('MRLCC_' + Class, tensors, result)
nbr_bVec = bVec(Class, tensors)
# Write out GetMethodInfo
print ' static void GetMethodInfo(FMethodInfo &Out) {'
print ' Out = FMethodInfo();'
print ' Out.pName = "MRLCC_' + Class + '";'
commutator2 += sqa.commutator(HD_CA1, E_aiEbj1b)
commutator2 += sqa.commutator(HD_CA2, E_aiEbj1b)
commutator2 += sqa.commutator(HD_CV1, E_aiEbj1b)
commutator2 += sqa.commutator(HD_CV2, E_aiEbj1b)
commutator2 += sqa.commutator(HD_AV1, E_aiEbj1b)
commutator2 += sqa.commutator(HD_AV2, E_aiEbj1b)
commutator2 += sqa.commutator(T_C, E_aiEbj1b)
commutator2 += sqa.commutator(T_A, E_aiEbj1b)
commutator2 += sqa.commutator(T_V, E_aiEbj1b)
result1 = []
result2 = []
result3 = []
result4 = []
for t in commutator1:
result1 += sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2a, t))
result2 += sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2b, t))
for t in commutator2:
result3 += sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2a, t))
result4 += sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2b, t))
for t in result1+result2+result3+result4:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result1)
sqa.termChop(result1)
sqa.combineTerms(result1)
sqa.removeVirtOps_sf(result2)
sqa.termChop(result2)
sqa.combineTerms(result2)
sqa.removeVirtOps_sf(result3)
sqa.termChop(result3)
# in this case the pattern is diverse,
# and we exclude known patterns and H0
elif pattern(h_term) in pattern_of.values()\
or code(indices_of_ExOp(h_term)) in exop_h0:
continue
# Loop over Emu and Enu terms of Eq.(1)
for left_term in psi1_left:
for right_term in psi1_right:
# Continue only is total pattern is neutral
if is_non_zero([left_term, h_term, right_term]):
log_H[H.index(h_term)] = 'evaluated'
# Define the normal-ordered A_tau of Eq.(1)
Atau = sqa.multiplyTerms(
sqa.multiplyTerms(left_term, h_term), right_term)
result_for_class = sqa.normalOrder(Atau)
result_for_class = simplify_all(result_for_class,
[deltaC, deltaA, deltaV])
result += result_for_class
# Write out a message about this contribution
#if (len(result_for_class)!=0):
if print_log:
print '// <E_{:3}E_{:3} | E_{:5} | E_{:3}E_{:3}> ={:5} Class:{:6}'.format(\
code(indices_of_ExOp(left_term )[0]),\
code(indices_of_ExOp(left_term )[1]),\
code(indices_of_ExOp(h_term )),\
code(indices_of_ExOp(right_term)[0]),\
code(indices_of_ExOp(right_term)[1]),\
len(result_for_class),\
symm(indTens_H4[tau][:4]))
Vtau = sqa.term(factor[code(listtau)] * 0.5, [''],
[Wtau, sqa.sfExOp(listtau)])
# <Psi1|
bnu = sqa.tensor('b' + indTens_Psi1[nu][-1],
indTens_Psi1[nu][4:8],
symm(indTens_Psi1[nu][4:8]))
Onu = sqa.term(
1.0, [''],
[bnu, sqa.sfExOp(listnu[:2]),
sqa.sfExOp(listnu[2:4])])
#Onu = sqa.term( 1.0, [''], [bnu, sqa.sfExOp([listnu[i] for i in [0,2,3,1]])])
# Define the normal-ordered: W_nu = <O^T_nu V_tau> W_tau
term = sqa.multiplyTerms(Onu, Vtau)
#print term
result = sqa.normalOrder(term)
result = mussardCode.simplify_all(result,
[deltaC, deltaA, deltaV],
cumulantE4=cumulantE4)
# Write out a message about this contribution
if (len(result) != 0):
global_result += result
print '// <Psi1_{:5} | V_{:5} > ={:5} Class:{:6}'.format(\
indExOp_Psi1[nu][-1],\
code(listtau),\
len(result),\
indExOp_H4[tau][-1])
#print '( W'+indTens_Psi1[nu][-1],code(indTens_Psi1[nu][4:8]),code(listnu),')',\
# \\\\\\\\\\\\\\\\
# CAN MODIFY THIS
# ////////////////
if is_non_zero([leftTerm, hTerm, rightTerm]):
log_H[H.index(hTerm)] = 'evaluated'
#if is_non_zero([leftTerm,rightTerm]):
#if is_non_zero([hTerm]):
# log_H[H.index(hTerm)]='evaluated'
#if is_non_zero([leftTerm,hTerm]):
# log_H[H.index(hTerm)]='evaluated'
# \\\\\\\\\\\\\\\\
# CAN MODIFY THIS
# ////////////////
# Define the normal-ordered: A_tau = D^nu D^mu <E^T_nu E_tau E_mu>
term = sqa.multiplyTerms(
sqa.multiplyTerms(leftTerm, hTerm), rightTerm)
#term=sqa.multiplyTerms(leftTerm, rightTerm)
#term=hTerm
#term=sqa.multiplyTerms(leftTerm, hTerm)
#comm=sqa.commutator(hTerm, rightTerm)
#result=[]
#for t in comm:
# result+=sqa.normalOrder(sqa.multiplyTerms(leftTerm, t))
result = sqa.normalOrder(term)
result = simplify_all(result, [deltaC, deltaA, deltaV])
# Write out a message about this contribution
#if (len(result)!=0):
global_result += result
# \\\\\\\\\\\\\\\\
commutator = []
commutator += sqa.commutator(HD_C, E_aiEbj)
commutator += sqa.commutator(HD_A, E_aiEbj)
commutator += sqa.commutator(HD_V, E_aiEbj)
commutator += sqa.commutator(HD_CA1, E_aiEbj)
commutator += sqa.commutator(HD_CA2, E_aiEbj)
commutator += sqa.commutator(HD_CV1, E_aiEbj)
commutator += sqa.commutator(HD_CV2, E_aiEbj)
commutator += sqa.commutator(HD_AV1, E_aiEbj)
commutator += sqa.commutator(HD_AV2, E_aiEbj)
commutator += sqa.commutator(T_C, E_aiEbj)
commutator += sqa.commutator(T_A, E_aiEbj)
commutator += sqa.commutator(T_V, E_aiEbj)
result = sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2, E_aiEbj))
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
sqa.termChop(result)
sqa.combineTerms(result)
extendedR = []
for t in result:
extendedR += sqa.contractCoreOps_sf(t)
for t in extendedR:
t.contractDeltaFuncs_new()
sqa.termChop(extendedR)
sqa.combineTerms(extendedR)
#first excitation
E_aiEbj1a = sqa.term(1.0, [""], [sqa.sfExOp([a, i]), sqa.sfExOp([p, q])])
E_aiEbj1b = sqa.term(1.0, [""], [sqa.sfExOp([p, i]), sqa.sfExOp([a, q])])
E_aiEbj2a = sqa.term(1.0, [""], [sqa.sfExOp([s, r]), sqa.sfExOp([j, b])])
E_aiEbj2b = sqa.term(1.0, [""], [sqa.sfExOp([s, b]), sqa.sfExOp([j, r])])
commutator = []
commutator += sqa.commutator(HD_A, E_aiEbj1a)
commutator += sqa.commutator(T_C, E_aiEbj1a)
commutator += sqa.commutator(T_A, E_aiEbj1a)
commutator += sqa.commutator(T_V, E_aiEbj1a)
result = []
for t in commutator:
result += sqa.normalOrder(sqa.multiplyTerms(E_aiEbj2a, t))
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
sqa.termChop(result)
sqa.combineTerms(result)
extendedR = []
for t in result:
extendedR += sqa.contractCoreOps_sf(t)
for t in extendedR:
t.contractDeltaFuncs_new()
sqa.termChop(extendedR)
sqa.combineTerms(extendedR)
Cout2b = sqa.tensor('b2', [j, s, r, b], [])
if (withC):
Op1a = sqa.term(1.0, [''], [Cin1a, sqa.sfExOp([a, i]), sqa.sfExOp([p, q])])
Op1b = sqa.term(1.0, [''], [Cin1b, sqa.sfExOp([p, i]), sqa.sfExOp([a, q])])
Op2a = sqa.term(1.0, [''],
[Cout2a, sqa.sfExOp([s, r]),
sqa.sfExOp([j, b])])
Op2b = sqa.term(1.0, [''],
[Cout2b, sqa.sfExOp([s, b]),
sqa.sfExOp([j, r])])
else:
Op1a = sqa.term(1.0, [''], [sqa.sfExOp([a, i]), sqa.sfExOp([p, q])])
Op1b = sqa.term(1.0, [''], [sqa.sfExOp([p, i]), sqa.sfExOp([a, q])])
Op2a = sqa.term(1.0, [''], [sqa.sfExOp([s, r]), sqa.sfExOp([j, b])])
Op2b = sqa.term(1.0, [''], [sqa.sfExOp([s, b]), sqa.sfExOp([j, r])])
result = sqa.normalOrder(sqa.multiplyTerms(Op2a, Op1a))
result += sqa.normalOrder(sqa.multiplyTerms(Op2b, Op1a))
result += sqa.normalOrder(sqa.multiplyTerms(Op2a, Op1b))
result += sqa.normalOrder(sqa.multiplyTerms(Op2b, Op1b))
# Simplify the result
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
sqa.termChop(result)
sqa.combineTerms(result)
extendedR = []
for t in result:
extendedR += sqa.contractCoreOps_sf(t)
for t in extendedR:
t.contractDeltaFuncs_new()
T_C = sqa.term(1.0, [""], [K_C, sqa.sfExOp([i1, i2])])
T_A = sqa.term(1.0, [""], [K_A, sqa.sfExOp([x1, x2])])
T_V = sqa.term(1.0, [""], [K_V, sqa.sfExOp([a1, a2])])
Cin = sqa.tensor("p", [a, b, c, d], [Dsym_c])
Cout = sqa.tensor("Ap", [c, d, r, s], [Dsym_c])
#first excitation
E_aiEbj = sqa.term(1.0, [""], [sqa.sfExOp([a, p]), sqa.sfExOp([b, q])])
E_aiEbj2 = sqa.term(1.0, [""], [sqa.sfExOp([s, d]), sqa.sfExOp([r, c])])
result = []
result += sqa.normalOrder(
sqa.multiplyTerms(sqa.term(1.0, [""], [Cin]),
sqa.multiplyTerms(E_aiEbj2, E_aiEbj)))
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
for t in result:
print t
sqa.termChop(result)
sqa.combineTerms(result)
print result
for t in result:
print t
exit(0)
HD2b = sqa.term(1.0, [""], [V_klcd2, sqa.sfExOp([m, e, f, n])])
HD2c = sqa.term(1.0, [""], [V_klcd3, sqa.sfExOp([m, u, n, v])])
HD2d = sqa.term(1.0, [""], [V_klcd4, sqa.sfExOp([m, u, v, n])])
HD2e = sqa.term(1.0, [""], [V_klcd5, sqa.sfExOp([u, e, n, v])])
HD2f = sqa.term(1.0, [""], [V_klcd6, sqa.sfExOp([u, e, v, n])])
HD4 = sqa.term(1.0, [""], [V_kl, sqa.sfExOp([m, n])])
HD5 = sqa.term(1.0, [""], [V_cd, sqa.sfExOp([e, f])])
HD6 = sqa.term(1.0, [""], [V_uv, sqa.sfExOp([u, v])])
#first excitation
E_aiEbj = sqa.term(1.0, [""], [sqa.sfExOp([a, i]), sqa.sfExOp([b, q])])
E_aiEbj2 = sqa.term(1.0, [""], [sqa.sfExOp([r, d]), sqa.sfExOp([j, c])])
result = []
result += sqa.normalOrder((sqa.multiplyTerms(E_aiEbj2, E_aiEbj)))
for t in result:
t.contractDeltaFuncs_new()
sqa.removeVirtOps_sf(result)
sqa.termChop(result)
sqa.combineTerms(result)
extendedR = []
for t in result:
extendedR += sqa.contractCoreOps_sf(t)
for t in extendedR:
# print t
t.contractDeltaFuncs_new()
sqa.termChop(extendedR)
continue
# ... except if the class is 'other',
# in this case the pattern is diverse,
# and we include all "other" signatures
elif pattern([hTerm]) in pattern_of.values():
continue
# Loop over Emu and Enu terms of 'd_nu w_tau d_mu <E^T_nu E_tau E_mu>'
for leftTerm in [Psi1Left[i] for i in list_of_pt2_for_pt3]:
for rightTerm in [Psi1Right[i] for i in list_of_pt2_for_pt3]:
if is_non_zero([leftTerm,hTerm]):
log_H[H.index(hTerm)]='evaluated'
# Define the normal-ordered: A_tau = D^nu D^mu <E^T_nu E_tau E_mu>
term=sqa.multiplyTerms(leftTerm, hTerm)
result=sqa.normalOrder(term)
result=simplify_all(result,[deltaC, deltaA, deltaV])
# Write out a message about this contribution
#if (len(result)!=0):
global_result+=result
print '// <E_{:3}E_{:3} | E_{:5}> ={:5} Class:{:6}'.format(\
code([i.indices for i in leftTerm.tensors if isinstance(i,sqa.sfExOp)][0]),\
code([i.indices for i in leftTerm.tensors if isinstance(i,sqa.sfExOp)][1]),\
code([i.indices for i in hTerm.tensors if isinstance(i,sqa.sfExOp)]),\
len(result),\
this_class)
# CALCULATIONS ------------------------------------------------------------------------------------
print '//'
print '// The actual calculation yields:'
global_result = []
log_H = {}
if (True):
# Loop over Emu and Enu terms of 'd_nu w_tau d_mu <E^T_nu E_tau E_mu>'
for leftTerm in Psi1Left:
for rightTerm in Psi1Right:
if is_non_zero([leftTerm, rightTerm]):
# Define the normal-ordered: A_tau = D^nu D^mu <E^T_nu E_tau E_mu>
term = sqa.multiplyTerms(leftTerm, rightTerm)
result = sqa.normalOrder(term)
result = simplify_all(result, [deltaC, deltaA, deltaV])
# Write out a message about this contribution
#if (len(result)!=0):
global_result += result
print '// <E_{:3}E_{:3} | E_{:3}E_{:3}> ={:5}'.format(\
code([i.indices for i in leftTerm.tensors if isinstance(i,sqa.sfExOp)][0]),\
code([i.indices for i in leftTerm.tensors if isinstance(i,sqa.sfExOp)][1]),\
code([i.indices for i in rightTerm.tensors if isinstance(i,sqa.sfExOp)][0]),\
code([i.indices for i in rightTerm.tensors if isinstance(i,sqa.sfExOp)][1]),\
len(result))
# OUTPUT ------------------------------------------------------------------------------------------
