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guanjihuan
2024-01-25 16:05:04 +08:00
parent 2603da57d1
commit 275a12024d
5 changed files with 7 additions and 54 deletions
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13
2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py
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@@ -4,9 +4,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
"""
import numpy as np
import matplotlib.pyplot as plt
from math import *
def matrix_00(width, length):
h00 = np.zeros((width*length, width*length))
@@ -20,11 +17,9 @@ def matrix_00(width, length):
h00[(x+1)*width+y, x*width+y] = 1
return h00
def matrix_01(width, length):
h01 = np.identity(width*length)
return h01
def main():
height = 2 # z
@@ -40,8 +35,7 @@ def main():
for j0 in range(width):
print(' y=', j0+1, ':')
for k0 in range(length):
print(' x=', k0+1, ' ', -np.imag(G_ii_n_array[i0, k0*width+j0, k0*width+j0])/pi) # 态密度
print(' x=', k0+1, ' ', -np.imag(G_ii_n_array[i0, k0*width+j0, k0*width+j0])/np.pi) # 态密度
def G_ii_n_with_Dyson_equation(width, length, height, E, eta, h00, h01):
dim = length*width
@@ -76,27 +70,22 @@ def G_ii_n_with_Dyson_equation(width, length, height, E, eta, h00, h01):
G_ii_n_array[i, :, :] = G_ii_n_minus
return G_ii_n_array
def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
dim = H00.shape[0]
G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
return G_nn_n
def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
return G_in_n
def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
return G_ni_n
def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
return G_ii_n
if __name__ == '__main__':
main()

13
2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py
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@@ -4,9 +4,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
"""
import numpy as np
import matplotlib.pyplot as plt
from math import *
def matrix_00(width, length):
h00 = np.zeros((width*length, width*length))
@@ -20,12 +17,10 @@ def matrix_00(width, length):
h00[(x+1)*width+y, x*width+y] = 1
return h00
def matrix_01(width, length):
h01 = np.identity(width*length)
return h01
def main():
height = 2 # z
width = 3 # y
@@ -36,7 +31,6 @@ def main():
h01 = matrix_01(width, length)
G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h01)
def G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h01):
dim = length*width
G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(dim)-h00)
@@ -71,29 +65,24 @@ def G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h0
for j0 in range(width):
print(' y=', j0+1, ':')
for k0 in range(length):
print(' x=', k0+1, ' ', -np.imag(G_ii_n_minus[k0*width+j0, k0*width+j0])/pi) # 态密度
print(' x=', k0+1, ' ', -np.imag(G_ii_n_minus[k0*width+j0, k0*width+j0])/np.pi) # 态密度
def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
dim = H00.shape[0]
G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
return G_nn_n
def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
return G_in_n
def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
return G_ni_n
def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
return G_ii_n
if __name__ == '__main__':
main()

11
2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py
View File

@@ -4,9 +4,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
"""
import numpy as np
import matplotlib.pyplot as plt
from math import *
def hamiltonian(width, length, height):
h = np.zeros((width*length*height, width*length*height))
@@ -27,7 +24,6 @@ def hamiltonian(width, length, height):
h[k0*width*length+(i0+1)*width+j0, k0*width*length+i0*width+j0] = 1
return h
def main():
height = 2 # z
width = 3 # y
@@ -40,10 +36,7 @@ def main():
for j0 in range(width):
print(' y=', j0+1, ':')
for i0 in range(length):
print(' x=', i0+1, ' ', -np.imag(green[k0*width*length+i0*width+j0, k0*width*length+i0*width+j0])/pi) # 态密度
print(' x=', i0+1, ' ', -np.imag(green[k0*width*length+i0*width+j0, k0*width*length+i0*width+j0])/np.pi) # 态密度
if __name__ == "__main__":
main()
main()