def test_grid_distances():
N = 10
p = numpy.random.normal(0, 1, (N, 3))
c = numpy.random.normal(0, 1, (3,))
d1 = numpy.sqrt(((p - c) ** 2).sum(axis=1))
d2 = numpy.zeros(d1.shape, float)
grid_distances(c, p, d2)
error = abs(d1 - d2).max()
assert error < 1e-5
def test_grid_distances():
N = 10
p = numpy.random.normal(0, 1, (N, 3))
c = numpy.random.normal(0, 1, (3, ))
d1 = numpy.sqrt(((p - c)**2).sum(axis=1))
d2 = numpy.zeros(d1.shape, float)
grid_distances(c, p, d2)
error = abs(d1 - d2).max()
assert (error < 1e-5)
def __getitem__(self, index):
if self.save_mem:
# in case we want to save memory, the grid distances are always
# computed from scratch. For reasons of efficiency, the same result
# array is used to avoid repetetive memory allocation
grid_distances(self.centers[index], self.grid.points, self._result)
return self._result
else:
result = self._cache.get(index)
if result is None:
result = numpy.zeros(self.grid.size, float)
grid_distances(self.centers[index], self.grid.points, result)
self._cache[index] = result
return result
