added option of non-optimized block detection

examples/si_nw_transmission.py

@@ -55,7 +55,7 @@ def main():
         logging.info("{} Energy: {} eV".format(j, E))
 
         # compute self-energies describing boundary conditions at the leads contacts
-        R, L = negf.surface_greens_function(E, hl, h0, hr, iterate=True, damp=damp)
+        R, L = negf.surface_greens_function(E, hl, h0, hr, iterate=False, damp=damp)
 
         s01, s02 = h01[0].shape
         s11, s12 = h01[-1].shape
@@ -77,7 +77,8 @@ def main():
 
         # compute DOS
         for jj in range(num_periods):
-            dos[j] = dos[j] + np.real(np.trace(1j * (grd[jj] - grd[jj].conj().T))) / num_periods
+            # dos[j] = dos[j] + np.real(np.trace(1j * (grd[jj] - grd[jj].conj().T))) / num_periods
+            dos[j] = dos[j] + np.real(np.trace(np.imag(grd[jj]))) / num_periods
 
         # gamma_l = 1j * (L[:s01, :s02] - L[:s01, :s02].conj().T)
         # gamma_r = 1j * (R[-s11:, -s12:] - R[-s11:, -s12:].conj().T)

nanonet/negf/greens_functions.py

@@ -10,6 +10,7 @@
 from scipy.sparse import csr_matrix
 
 
+@profile
 def surface_greens_function_poles(h_list):
     """
     Computes eigenvalues and eigenvectors for the complex band structure problem.
@@ -50,7 +51,6 @@ def surface_greens_function_poles(h_list):
     eigenvals = np.zeros(alpha.shape, dtype=np.complex)
 
     for j, item in enumerate(zip(alpha, betha)):
-
         if np.abs(item[1]) != 0.0:
             eigenvals[j] = item[0] / item[1]
         else:
@@ -107,7 +107,7 @@ def iterate_gf(E, h_0, h_l, h_r, gf, num_iter):
 
     return gf
 
-
+@profile
 def surface_greens_function(E, h_l, h_0, h_r, iterate=True, damp=0.0001j):
     """
     Computes surface self-energies using the eigenvalue decomposition.

nanonet/negf/recursive_greens_functions.py

@@ -1,10 +1,12 @@
 import copy
 import numpy as np
+import scipy.linalg as linalg
 
 
 def mat_left_div(mat_a, mat_b):
 
-    ans, resid, rank, s = np.linalg.lstsq(mat_a, mat_b, rcond=-1)
+    # ans, resid, rank, s = linalg.lstsq(mat_a, mat_b, lapack_driver='gelsy')
+    ans, resid, rank, s = np.linalg.lstsq(mat_a, mat_b, rcond=None)
 
     return ans
 

nanonet/tb/hamiltonian.py

@@ -14,7 +14,7 @@
 from nanonet.tb.diatomic_matrix_element import me
 from nanonet.tb.orbitals import Orbitals
 from nanonet.tb.aux_functions import dict2xyz
-from nanonet.tb.block_tridiagonalization import split_into_subblocks_optimized, cut_in_blocks
+from nanonet.tb.block_tridiagonalization import split_into_subblocks_optimized, cut_in_blocks, split_into_subblocks
 import nanonet.verbosity as verbosity
 
 
@@ -535,7 +535,7 @@ def get_site_coordinates(self):
 
         return np.array(self._coords)
 
-    def get_hamiltonians_block_tridiagonal(self, left=None, right=None):
+    def get_hamiltonians_block_tridiagonal(self, left=None, right=None, optimized=True):
 
         if left is None and right is None:
             hl, h0, hr = self.get_hamiltonians()
@@ -544,7 +544,11 @@ def get_hamiltonians_block_tridiagonal(self, left=None, right=None):
             h0 = self.h_matrix
             hr = right
 
-        subblocks = split_into_subblocks_optimized(h0, hl, hr)
+        if optimized:
+            subblocks = split_into_subblocks_optimized(h0, hl, hr)
+        else:
+            subblocks = split_into_subblocks(h0, hl, hr)
+
         h01, hl1, hr1 = cut_in_blocks(h0, subblocks)
 
         return hl1, h01, hr1, subblocks