def setUp(self):
#np.random.seed(6)
self.mpts = 100000 #Adding a zero here changes the cython results dramatically
self.npts = 10000
self.high = 50
self.M = np.random.randint(0, self.high,
(self.mpts, 3)).astype(np.int16)
self.P = np.random.randint(0, self.high,
(self.npts, 3)).astype(np.int16)
self.testpose = (-2., 42., 80., 0.2, 0.1, 2.6)
self.testposeinv = poseutil.inverse(self.testpose)
self.Pxformed = poseutil.check_and_transform_points(
self.P, self.testpose)[0]
self.ol = occupiedlist.OccupiedList(1)
self.ol.addpoints(self.M)
self.Mkdt = spatial.cKDTree(self.M)
self.maparray = np.zeros((self.high, self.high, self.high),
dtype=np.bool)
#self.maparray = ndsparse.ndsparse((self.high, self.high, self.high))
self.maparray[self.M[:, 0], self.M[:, 1], self.M[:, 2]] = True
# set up packed arrays map
ids = occupiedlist._pack(self.M)
self.mapvoxels_int16 = dict.fromkeys(ids, 1)
self.mapset = set(ids)
#self.mapvoxels_int16 = collections.defaultdict(int)
#for ID in ids:
# self.mapvoxels_int16[ID.tostring()] += 1
# bloom map
self.bloom = occupiedlist.BloomFilter(self.mpts)
self.bloom.add_voxel_ids(occupiedlist._pack(self.M))
# dictionary of dictionaries
#D = dict.fromkeys(self.mpts[:,0], dict())
self.Pint = self.P.astype(int)
D = dict()
# first initialise
for a, b, c in self.M:
D.setdefault(a, dict())
D[a].setdefault(b, dict())
D[a][b][c] = 0
for a, b, c in self.M:
D[a][b][c] += 1
self.nestedDict = D
def setUp(self):
#np.random.seed(6)
self.mpts = 100000 #Adding a zero here changes the cython results dramatically
self.npts = 10000
self.high = 50
self.M = np.random.randint(0, self.high, (self.mpts, 3)).astype(np.int16)
self.P = np.random.randint(0, self.high, (self.npts, 3)).astype(np.int16)
self.testpose = (-2., 42., 80., 0.2, 0.1, 2.6)
self.testposeinv = poseutil.inverse(self.testpose)
self.Pxformed = poseutil.check_and_transform_points(self.P, self.testpose)[0]
self.ol = occupiedlist.OccupiedList(1)
self.ol.addpoints(self.M)
self.Mkdt = spatial.cKDTree(self.M)
self.maparray = np.zeros((self.high, self.high, self.high), dtype=np.bool)
#self.maparray = ndsparse.ndsparse((self.high, self.high, self.high))
self.maparray[self.M[:, 0], self.M[:, 1], self.M[:, 2]] = True
# set up packed arrays map
ids = occupiedlist._pack(self.M)
self.mapvoxels_int16 = dict.fromkeys(ids, 1)
self.mapset = set(ids)
#self.mapvoxels_int16 = collections.defaultdict(int)
#for ID in ids:
# self.mapvoxels_int16[ID.tostring()] += 1
# bloom map
self.bloom = occupiedlist.BloomFilter(self.mpts)
self.bloom.add_voxel_ids(occupiedlist._pack(self.M))
# dictionary of dictionaries
#D = dict.fromkeys(self.mpts[:,0], dict())
self.Pint = self.P.astype(int)
D = dict()
# first initialise
for a, b, c in self.M:
D.setdefault(a, dict())
D[a].setdefault(b, dict())
D[a][b][c] = 0
for a, b, c in self.M:
D[a][b][c] += 1
self.nestedDict = D
def packed_overlap_with_bloom(self):
#PV = occupiedlist.pointstovoxels(self.P, 1)
#ids = occupiedlist._pack(PV)
ids = occupiedlist._pack(self.P)
#import pydb; pydb.set_trace()
overlaps = self.bloom.contains(ids)
#return len(overlap)
return np.sum(overlaps)
def packed_overlap_with_bloom(self):
#PV = occupiedlist.pointstovoxels(self.P, 1)
#ids = occupiedlist._pack(PV)
ids = occupiedlist._pack(self.P)
#import pydb; pydb.set_trace()
overlaps = self.bloom.contains(ids)
#return len(overlap)
return np.sum(overlaps)
def packed_set(self):
Pset = occupiedlist._pack(self.P)
overlap = self.mapset.intersection(Pset)
return len(overlap)
def packed_set(self):
Pset = occupiedlist._pack(self.P)
overlap = self.mapset.intersection(Pset)
return len(overlap)
