def do_molgrid(self):
self.molgrid = Grid.from_prefix("molecule", self.work)
if self.molgrid is not None:
self.molgrid.weights = self.molgrid.load("weights")
else:
# we have to generate a new grid. The grid is constructed taking
# into account the following considerations:
# 1) Grid points within the cusp region are discarded
# 2) The rest of the molecular and surrounding volume is sampled
# with spherical grids centered on the atoms. Around each atom,
# 'scale_steps' of shells are placed with lebedev grid points
# (num_lebedev). The lebedev weights are used in the fit to
# avoid preferential directions within one shell.
# 3) The radii of the shells start from scale_min*(cusp_radius+0.2)
# and go up to scale_max*(cusp_radius+0.2).
# 4) Each shell will be randomly rotated around the atom to avoid
# global preferential directions in the grid.
# 5) The default parameters for the grid should be sufficient for
# sane ESP fitting. The ESP cost function should discard points
# with a density larger than a threshold, i.e. 1e-5 a.u. A
# gradual transition between included and discarded points around
# this threshold will improve the quality of the fit.
lebedev_xyz, lebedev_weights = get_lebedev_grid(50)
self.do_noble_radii()
scale_min = 1.5
scale_max = 30.0
scale_steps = 30
scale_factor = (scale_max/scale_min)**(1.0/(scale_steps-1))
scales = scale_min*scale_factor**numpy.arange(scale_steps)
points = []
weights = []
pb = log.pb("Constructing molecular grid", scale_steps)
for scale in scales:
pb()
radii = scale*self.noble_radii
for i in xrange(self.molecule.size):
rot = Rotation.random()
for j in xrange(len(lebedev_xyz)):
my_point = radii[i]*numpy.dot(rot.r, lebedev_xyz[j]) + self.molecule.coordinates[i]
distances = numpy.sqrt(((self.molecule.coordinates - my_point)**2).sum(axis=1))
if (distances < scales[0]*self.noble_radii).any():
continue
points.append(my_point)
weights.append(lebedev_weights[j])
pb()
points = numpy.array(points)
weights = numpy.array(weights)
self.molgrid = Grid("molecule", self.work, points)
self.molgrid.weights = weights
self.molgrid.dump("weights", weights)
def fn():
FileNew = context.application.plugins.get_action("FileNew")
FileNew()
context.application.main.select_nodes([context.application.model.universe])
Frame = context.application.plugins.get_node("Frame")
frame = Frame(transformation=Translation(numpy.random.uniform(-5, 5, 3)))
context.application.model.universe.add(frame)
frame.set_transformation(Rotation.random())
def generate_points(self, center, rs):
points = numpy.zeros((self.num_lebedev * len(rs), 3), float)
start = 0
for r in rs:
end = start + self.num_lebedev
xyz = self.lebedev_xyz
if self.do_random:
rot = Rotation.random()
xyz = numpy.dot(xyz, rot.r)
points[start:end] = r * xyz + center
start = end
return points
def generate_points(self, center, rs):
points = numpy.zeros((self.num_lebedev*len(rs),3), float)
start = 0
for r in rs:
end = start + self.num_lebedev
xyz = self.lebedev_xyz
if self.do_random:
rot = Rotation.random()
xyz = numpy.dot(xyz,rot.r)
points[start:end] = r*xyz+center
start = end
return points
