def vec_sparse_2_dense(key_list, vec_dist, dim): """Convertes a sparse vector to a dense one.""" import key_ops dense_vec = [0] * dim for key in vec_dist: index = key_ops.key_2_index(key_list, key) dense_vec[index] = vec_dist[key] return dense_vec
def dead_prob(h, key): """Gets the probability rate of death.""" import key_ops index = key_ops.key_2_index(h.key_list, key) vec_excited_list = h.matrix[index] try: diag_entry = vec_excited_list[key] except KeyError: diag_entry = 0 prob_rate = diag_entry - h.ref_energy return prob_rate
def disp_mat(self):
"""Displays the Hamiltonian as a dense matrix."""
import key_ops
for vec in self.matrix:
array = [0] * self.para_list[4]
for key in vec:
index = key_ops.key_2_index(self.key_list, key)
array[index] = vec[key]
for i in array:
print '{0:6.3f}'.format(i),
print ''
def excited_key_and_prob(h, key_gnd): """From a ground state, gets a possible excited state, and the probability rate.""" import key_ops import random index = key_ops.key_2_index(h.key_list, key_gnd) vec_excited_list = h.matrix[index] choice_num = len(vec_excited_list) - 1 if choice_num > 0: # Connected to others. key_exc = key_gnd while key_exc == key_gnd: key_exc = random.sample(vec_excited_list, 1)[0] entry = vec_excited_list[key_exc] prob_rate = choice_num * entry return key_exc, prob_rate else: return None, 0
def __init__(self, file_name):
"""Initializes a $Hamiltonian$ object."""
import key_ops
import read_h
import vec_ops
self.para_list = []
self.eig_vecs = []
self.eig_vals = []
self.key_list = []
# Loads data from a file.
read_h.read_outof_file(file_name, self.para_list, self.eig_vecs, self.eig_vals)
# Makes an ordered list of keys.
key_ops.create_keys(self.key_list, self.para_list)
# Computes H=sum(Ei*|vi><vi|)
self.matrix = [{}] * self.para_list[4]
for i in range(self.para_list[4]):
vec = self.eig_vecs[i]
for key in vec:
index = key_ops.key_2_index(self.key_list, key)
column = vec_ops.vec_scalar_prod(vec, vec[key]*self.eig_vals[i] , False)
self.matrix[index] = vec_ops.vec_add(self.matrix[index], column, False)
# Reference determinant and reference energy.
# Selects the lowest-energy determinant.
self.ref_index = 0
self.ref_key = self.key_list[0]
self.ref_energy = self.matrix[0][self.ref_key]
for i in range(1, self.para_list[4]):
try:
temp_ref_energy = self.matrix[i][self.key_list[i]]
except KeyError:
temp_ref_energy = 0
if temp_ref_energy < self.ref_energy:
self.ref_index = i
self.ref_key = self.key_list[i]
self.ref_energy = temp_ref_energy