From 96049456993ad7b2f5a5429610be3e750f00a920 Mon Sep 17 00:00:00 2001
From: Joseph Weston <joseph@weston.cloud>
Date: Mon, 13 Aug 2018 17:03:09 +0200
Subject: [PATCH] mask points where the density is zero
Previously we masked points that are further than a "typical" distance
from any site. This was determined as the minimal nearest-neighbor
distance from 10 arbitrarily chosen sites. Now we mask points where
the bump function is zero (as it has finite support).
Closes #219.
---
kwant/plotter.py | 17 +++++++----------
1 file changed, 7 insertions(+), 10 deletions(-)
diff --git a/kwant/plotter.py b/kwant/plotter.py
index 12d340e2..301d6ede 100644
--- a/kwant/plotter.py
+++ b/kwant/plotter.py
@@ -1873,9 +1873,12 @@ def interpolate_density(syst, density, relwidth=None, abswidth=None, n=9,
(bbox_min, bbox_max), width, padding, field)
if mask:
+ # Field is zero when we are > 0.5*width from any site (as bump has
+ # finite support), so we mask positions a little further than this.
field = _mask(field,
- boundaries,
- np.array([s.pos for s in syst.sites]))
+ box=boundaries,
+ coords=np.array([s.pos for s in syst.sites]),
+ cutoff=0.6*width)
return field, boundaries
@@ -2183,21 +2186,15 @@ def current(syst, current, relwidth=0.05, **kwargs):
**kwargs)
-def _mask(field, box, coords):
+def _mask(field, box, coords, cutoff):
tree = spatial.cKDTree(coords)
- # Select 10 sites to compare -- comparing them all is too costly.
- points = _sample_array(coords, 10)
- min_dist = np.min(tree.query(points, 2)[0][:, 1])
-
# Build the mask initially as a 2D array
dims = tuple(slice(boxmin, boxmax, 1j * shape)
for (boxmin, boxmax), shape in zip(box, field.shape))
mask = np.mgrid[dims].reshape(len(box), -1).T
- # '0.4' (which is just below sqrt(2) - 1) makes tree.query() exact
- # in the common case of a square lattice.
- mask = tree.query(mask, eps=0.4)[0] > min_dist
+ mask = tree.query(mask, distance_upper_bound=cutoff)[0] == np.inf
return np.ma.masked_array(field, mask)
--
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