def fermion_comb(particles):
return factorial(SITES) / factorial(SITES -
particles) / factorial(particles)
DIMENSION = int(fermion_comb(num_up_ferms) * fermion_comb(num_up_ferms))
print(DIMENSION)
LENGTH = int(b.np.sqrt(SITES))
#def hop(state, state_dict, next_states, build_hop):
# return b.hop_lr_ob(state, state_dict, next_states, build_hop,1,[LENGTH,LENGTH])
def hop(state, state_dict, next_states, build_hop):
return b.hop_nn_2d_ob(state, state_dict, next_states, build_hop,
[LENGTH, LENGTH])
state_dict, [hop_mat] = b.make_matrices_and_states(initial_state, DIMENSION,
hop)
import pickle
pickle.dump(state_dict, open("s16_FH2d_nn_statedict.p", "wb"))
b.save_sparse_csr("l16_FH2d_nn_hop", hop_mat)
int_mat = b.make_diag_ops(state_dict, lambda x: np.dot(x['up'], x['down']))
b.save_sparse_csr("l16_FH2d_nn_int", int_mat)
reflat = J_real.reshape(1, J_real.size)
imflat = J_imag.reshape(1, J_imag.size)
np.savetxt("flatJreal.CSV", reflat, delimiter=',')
np.savetxt("flatJimag.CSV", imflat, delimiter=',')
# datadir ='/projectnb/twambl/170207_1_s16f4/'
# J_real = np.loadtxt(datadir+"flatJreal.CSV",delimiter=',')
# J_imag = np.loadtxt(datadir+"flatJimag.CSV",delimiter=',')
# J_coup = (J_real + 1j*J_imag).reshape(SITES,SITES,SITES,SITES)
#print(J_coup)
def SYKint(state, state_dict, next_states, build_mat):
return b.SYK_model(state, state_dict, next_states, build_mat, J_coup)
funcs = [SYKint]
#funcs = [SYKint] + [b.correlation(i,j) for i in range(SITES) for j in range(SITES)]
state_dict, mats = b.make_matrices_and_states(initial_state, DIMENSION, *funcs)
import pickle
pickle.dump(state_dict, open("s" + str(SITES) + "SYK_statedict.p", "wb"))
b.save_sparse_csr("s" + str(SITES) + "_SYK_mat", mats[0])
# for i in range(SITES):
# for j in range(SITES):
# b.save_sparse_csr("s"+str(SITES)+"_corr"+str(i)+str(j),mats[SITES*i+j+1])
data_dir = "/projectnb/twambl/170525_1_s18f9_makemats/"
SITES = 18
state_dict = pickle.load(
open(data_dir + "s" + str(SITES) + "SYK_statedict.p", "rb"))
sigma = 1
rands = np.random.normal(0, sigma, (SITES, SITES))
T_coup = (rands + np.transpose(rands, (1, 0))) / np.sqrt(2)
T_flat = T_coup.reshape(1, T_coup.size)
np.savetxt("flatTcoup.CSV", T_flat, delimiter=',')
def pert_ham_wconst(state, state_dict, next_states, build_mat):
return b.quad_pert_ham(state, state_dict, next_states, build_mat, T_coup)
#funcs = [pert_ham_wconst]
#funcs = [SYKint] + [b.correlation(i,j) for i in range(SITES) for j in range(SITES)]
mat = b.make_matrices_from_states(state_dict, pert_ham_wconst)
#import pickle
#pickle.dump( state_dict, open( "s"+str(SITES)+"SYK_statedict.p", "wb" ) )
b.save_sparse_csr("s" + str(SITES) + "_pert_mat", mat)
# for i in range(SITES):
# for j in range(SITES):
# b.save_sparse_csr("s"+str(SITES)+"_corr"+str(i)+str(j),mats[SITES*i+j+1])
initial_fermion_down = initial_fermion_up[:]
initial_state = b.State({
'up': initial_fermion_up,
'down': initial_fermion_down
})
print(str(initial_state))
from math import factorial
def fermion_comb(particles):
return factorial(LENGTH) / factorial(LENGTH -
particles) / factorial(particles)
DIMENSION = int(fermion_comb(num_up_ferms) * fermion_comb(num_up_ferms))
print(DIMENSION)
state_dict, [hop_mat] = b.make_matrices_and_states(initial_state, DIMENSION,
b.hop_lr_a1_ob)
import pickle
pickle.dump(state_dict, open("s15_FH_lrt_statedict.p", "wb"))
b.save_sparse_csr("l15_FH_lrt_hop", hop_mat)
int_mat = b.make_diag_ops(state_dict, lambda x: np.dot(x['up'], x['down']))
b.save_sparse_csr("l15_FH_lrt_int", int_mat)