guan-0.0.26

API_reference.py

@@ -82,6 +82,10 @@ right_lead_surface, left_lead_surface = guan.surface_green_function_of_lead(ferm
 right_self_energy, left_self_energy = guan.self_energy_of_lead(fermi_energy, h00, h01)
 conductance = guan.calculate_conductance(fermi_energy, h00, h01, length=100)
 conductance_array = guan.calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100)
+conductance = guan.calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_density=1.0, length=100)
+conductance_array = guan.calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_density=1.0, length=100)
+conductance_array = guan.calculate_conductance_with_disorder_density_array(fermi_energy, h00, h01, disorder_density_array,disorder_intensity=2.0, length=100)
+conductance_array = guan.calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, length_array, disorder_intensity=2.0, disorder_density=1.0)
 
 # scattering matrix    #  Source code: https://py.guanjihuan.com/calculate_scattering_matrix
 if_active = guan.if_active_channel(k_of_channel)

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.24
+version = 0.0.26
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan/calculate_conductance.py

@@ -61,8 +61,8 @@ def calculate_conductance(fermi_energy, h00, h01, length=100):
         else:
             green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
             green_0n_n = green_function_in_n(green_0n_n, h01, green_nn_n)
-    right_self_energy = (right_self_energy - right_self_energy.transpose().conj())*(0+1j)
+    right_self_energy = (right_self_energy - right_self_energy.transpose().conj())*1j
-    left_self_energy = (left_self_energy - left_self_energy.transpose().conj())*(0+1j)
+    left_self_energy = (left_self_energy - left_self_energy.transpose().conj())*1j
     conductance = np.trace(np.dot(np.dot(np.dot(left_self_energy, green_0n_n), right_self_energy), green_0n_n.transpose().conj()))
     return conductance
 
@@ -74,3 +74,52 @@ def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01,
         conductance_array[i0] = np.real(calculate_conductance(fermi_energy_0, h00, h01, length))
         i0 += 1
     return conductance_array
+
+def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_density=1.0, length=100):
+    right_self_energy, left_self_energy = self_energy_of_lead(fermi_energy, h00, h01)
+    dim = np.array(h00).shape[0]
+    for ix in range(length):
+        disorder = np.zeros((dim, dim))
+        for dim0 in range(dim):
+            if np.random.uniform(0, 1)<=disorder_density:
+                disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity)
+        if ix == 0:
+            green_nn_n = green_function(fermi_energy, h00+disorder, broadening=0, self_energy=left_self_energy)
+            green_0n_n = copy.deepcopy(green_nn_n)
+        elif ix != length-1:
+            green_nn_n = green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0)
+            green_0n_n = green_function_in_n(green_0n_n, h01, green_nn_n)
+        else:
+            green_nn_n = green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
+            green_0n_n = green_function_in_n(green_0n_n, h01, green_nn_n)
+    right_self_energy = (right_self_energy - right_self_energy.transpose().conj())*1j
+    left_self_energy = (left_self_energy - left_self_energy.transpose().conj())*1j
+    conductance = np.trace(np.dot(np.dot(np.dot(left_self_energy, green_0n_n), right_self_energy), green_0n_n.transpose().conj()))
+    return conductance
+
+def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_density=1.0, length=100):
+    dim = np.array(disorder_intensity_array).shape[0]
+    conductance_array = np.zeros(dim)
+    i0 = 0
+    for disorder_intensity_0 in disorder_intensity_array:
+        conductance_array[i0] = np.real(calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=disorder_intensity_0, disorder_density=disorder_density, length=length))
+        i0 += 1
+    return conductance_array
+
+def calculate_conductance_with_disorder_density_array(fermi_energy, h00, h01, disorder_density_array,disorder_intensity=2.0, length=100):
+    dim = np.array(disorder_density_array).shape[0]
+    conductance_array = np.zeros(dim)
+    i0 = 0
+    for disorder_density_0 in disorder_density_array:
+        conductance_array[i0] = np.real(calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=disorder_intensity, disorder_density=disorder_density_0, length=length))
+        i0 += 1
+    return conductance_array
+
+def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, length_array, disorder_intensity=2.0, disorder_density=1.0):
+    dim = np.array(length_array).shape[0]
+    conductance_array = np.zeros(dim)
+    i0 = 0
+    for length_0 in length_array:
+        conductance_array[i0] = np.real(calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=disorder_intensity, disorder_density=disorder_density, length=length_0))
+        i0 += 1
+    return conductance_array