From 28067c6b810448d8fdab3928fb8bcd2c2e872aa4 Mon Sep 17 00:00:00 2001
From: Chris Maffeo <cmaffeo2@illinois.edu>
Date: Tue, 18 Feb 2020 16:49:33 -0600
Subject: [PATCH] Added routines for obtaining a bounding box for a
nanostructure
---
mrdna/segmentmodel.py | 38 +++++++++++++++++++++++++++++---------
1 file changed, 29 insertions(+), 9 deletions(-)
diff --git a/mrdna/segmentmodel.py b/mrdna/segmentmodel.py
index ff074d9..74f5780 100644
--- a/mrdna/segmentmodel.py
+++ b/mrdna/segmentmodel.py
@@ -453,6 +453,16 @@ class Segment(ConnectableElement, Group):
tck, u = self.position_spline_params
return np.mean(self.contour_to_position(u), axis=0)
+ def get_bounding_box( self, num_points=3 ):
+ positions = np.zeros( num_points, 3)
+ i = 0
+ for c in linspace(0,1,num_points):
+ positions[i] = self.contour_to_position(c)
+ i += 1
+ min_ = np.array([np.min(positions[:,i]) for i in range(3)])
+ max_ = np.array([np.max(positions[:,i]) for i in range(3)])
+ return min_,max_
+
def _get_location_positions(self):
return [self.contour_to_nt_pos(l.address) for l in self.locations]
@@ -558,7 +568,7 @@ class Segment(ConnectableElement, Group):
if self.quaternion_spline_params is not None:
## TODO: performance: don't shift between quaternion and matrix representations so much
tck, u = self.quaternion_spline_params
- orientations = [self.contour_to_orientation(v) for v in u]
+ orientations = np.array([self.contour_to_orientation(v) for v in u])
for i in ids:
orientations[i,:] = rotation_matrix.dot(orientations[i])
quats = [quaternion_from_matrix(o) for o in orientations]
@@ -3215,17 +3225,27 @@ proc calcforces {} {
}
""")
- def dimensions_from_structure( self, padding_factor=1.5, isotropic=False ):
- positions = []
+ def get_bounding_box( self, num_points=3 ):
+ positions = np.zeros( (len(self.segments)*num_points, 3) )
+ i = 0
for s in self.segments:
- positions.append(s.contour_to_position(0))
- positions.append(s.contour_to_position(0.5))
- positions.append(s.contour_to_position(1))
- positions = np.array(positions)
- dx,dy,dz = [(np.max(positions[:,i])-np.min(positions[:,i])+30)*padding_factor for i in range(3)]
+ for c in linspace(0,1,num_points):
+ positions[i] = (s.contour_to_position(c))
+ i += 1
+ min_ = np.array([np.min(positions[:,i]) for i in range(3)])
+ max_ = np.array([np.max(positions[:,i]) for i in range(3)])
+ return min_,max_
+
+ def get_bounding_box_center( self, num_points=3 ):
+ min_,max_ = self.get_bounding_box(num_points)
+ return 0.5*(max_+min_)
+
+ def dimensions_from_structure( self, padding_factor=1.5, isotropic=False ):
+ min_,max_ = self.get_bounding_box()
+ dx,dy,dz = (max_-min_+30)*padding_factor
if isotropic:
dx = dy = dz = max((dx,dy,dz))
- return [dx,dy,dz]
+ return np.array([dx,dy,dz])
def add_grid_potential(self, grid_file, scale=1, per_nucleotide=True):
grid_file = Path(grid_file)
--
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