From 8a61b14726815159749ee4f243b3d91a0519fa93 Mon Sep 17 00:00:00 2001
From: Michael Wimmer <wimmer@lorentz.leidenuniv.nl>
Date: Wed, 29 Aug 2012 15:42:18 +0200
Subject: [PATCH] add sorting to the eigenmodes; sorted according to k-vector
---
kwant/physics/selfenergy.py | 27 +++++++++++++++++++++++++--
1 file changed, 25 insertions(+), 2 deletions(-)
diff --git a/kwant/physics/selfenergy.py b/kwant/physics/selfenergy.py
index 0211fceb..d9661466 100644
--- a/kwant/physics/selfenergy.py
+++ b/kwant/physics/selfenergy.py
@@ -624,6 +624,15 @@ def modes(h_onslice, h_hop, tol=1e6):
holding the singular value decomposition of the hopping matrix, or a
single None if `h_hop` is invertible.
+ The first `nmodes` columns in `vecs` correspond to incoming modes
+ (coming from the lead into the system), the following `nmodes`
+ columns correspond to outgoing modes (going into the lead,
+ away from the system). The remaining columns are evanescent modes,
+ decaying away from the system. The propagating modes are sorted
+ according to their k-vector, with outgoing modes having k sorted in
+ ascending order, and incoming modes having k sorted in descending
+ order.
+
Notes
-----
Only propagating modes and modes decaying away from the system
@@ -698,9 +707,23 @@ def modes(h_onslice, h_hop, tol=1e6):
lprop = np.logical_not(rprop)
nmodes = np.sum(rprop)
- vecs = np.c_[prop_vecs[n:, lprop], prop_vecs[n:, rprop],
+
+ # Sort modes according to their k-vector (1/lambda = e^{-ik}):
+ # - right-going modes: sort that k is in ascending order
+ # - left-going modes: sort that k is in descending order
+ # (note that k can be positive or negative). With this convention,
+ # the modes of a simple square lattice strip are ordered as
+ # expected (lowest subband first, etc.)
+
+ prop_ev = ev[propselect]
+ rsort = np.argsort((1j * np.log(prop_ev[rprop])).real)
+ lsort = np.argsort((-1j * np.log(prop_ev[lprop])).real)
+
+ vecs = np.c_[prop_vecs[n:, lprop][:, lsort],
+ prop_vecs[n:, rprop][:, rsort],
evan_vecs[n:]]
- vecslmbdainv = np.c_[prop_vecs[:n, lprop], prop_vecs[:n, rprop],
+ vecslmbdainv = np.c_[prop_vecs[:n, lprop][:, lsort],
+ prop_vecs[:n, rprop][:, rsort],
evan_vecs[:n]]
else:
--
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