change sorting algorithm to the mergesort

freude · Jul 1, 2020 · d180b2b · d180b2b
1 parent 25b470f
commit d180b2b
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Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/examples/si_nw_transmission.py b/examples/si_nw_transmission.py
@@ -20,7 +20,7 @@ def main():
 
     # define leads indices
     right_lead = [0, 33, 35, 17, 16, 51, 25,  9, 53, 68,  1,  8, 24]
-    left_lead = [40, 66, 58, 47, 48, 71, 72, 73, 74, 65]
+    left_lead = [40, 66, 58, 47, 48, 71, 72, 73, 74, 65, 6, 22, 7, 23, 14, 30, 15, 31]
 
     # create a Hamiltonian object storing the Hamiltonian matrices
     # hamiltonian = Hamiltonian(xyz=path, nn_distance=2.4,
@@ -78,7 +78,7 @@ 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(np.imag(grd[jj]))) / 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)

diff --git a/nanonet/tb/hamiltonian.py b/nanonet/tb/hamiltonian.py
@@ -545,7 +545,7 @@ def get_hamiltonians_block_tridiagonal(self, left=None, right=None, optimized=Tr
             hr = right
 
         if optimized:
-            subblocks = split_into_subblocks_optimized(h0, hl, hr)
+            subblocks = split_into_subblocks_optimized(h0, left=hl, right=hr)
         else:
             subblocks = split_into_subblocks(h0, hl, hr)
 

diff --git a/nanonet/tb/sorting_algorithms.py b/nanonet/tb/sorting_algorithms.py
@@ -52,7 +52,7 @@ def sort_projection(coords=None, left_lead=None, right_lead=None, **kwargs):
     vec = np.mean(coords[left_lead], axis=0) - np.mean(coords[right_lead], axis=0)
     keys = np.dot(coords, vec) / np.linalg.norm(vec)
 
-    return np.argsort(keys)
+    return np.argsort(keys, kind='mergesort')
 
 
 def sort_capacitance(coords, mat, left_lead, right_lead, **kwargs):