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""" | |
Compute density of states. | |
""" | |
import torch | |
import torch as tr | |
import matplotlib.pyplot as plt | |
from evaluate_chebyshev import chepyshev_evaluate_m | |
from fast_math.tensor_init import init_vrand | |
from memory_profiler import profile | |
import time | |
def test_ham(t, eps, dim): | |
ham = tr.zeros(size=(dim, dim), dtype=tr.complex128) | |
for i in range(dim): | |
ham[i][i] = eps | |
if i < dim - 1: | |
ham[i][i + 1] = t | |
if i > 0: | |
ham[i][i - 1] = t | |
return ham | |
# | |
# n = 10 ** 4 + 5000 # dimension of the problem | |
# t = 2 # t value hiper. of hamiltonian | |
# eps0 = 0.3 # t value hiper. of hamiltonian7 | |
# m = 10 # cut of if the Chebyshev polinomials | |
# | |
# | |
# cp_t_ = [] | |
# i_pow=[1,2,3,4, 5, 10, 100, 200, 300,500, 1000, 1500, 2000, 2100] | |
# for i in i_pow: | |
# n=10*i | |
# phi_ = init_vrand(n) | |
# ham = test_ham(t=t, eps=eps0, dim=n) | |
# | |
# start = time.time() | |
# cp = chepyshev_evaluate_m(phi_l=phi_, phi_r=phi_, ham=ham, m_limit=m) | |
# end = time.time() | |
# cp_t = end - start | |
# print("i_pow:",i) | |
# print("cp:", cp) | |
# print("time:", cp_t) | |
# cp_t_.append(cp_t) | |