def initialise(self, dim): if not self.init: self.dim = dim prop = kdtree.Property() prop.dimension = dim self.index = kdtree.Index(interleaved=False, properties=prop) self.init = True
def __init__(self,
density_parameter,
precision_parameter,
space_bounds,
global_bounds,
points=None,
old_distance=None):
self.epsilon = density_parameter
self.delta = precision_parameter
self.space_bounds = space_bounds
p = index.Property()
dimension = len(space_bounds) / 2
self.dimension = dimension
p.dimension = dimension
self.tree = index.Index(properties=p)
self.id_counter = 0
self.tree.interleaved = False
self.bad_boxes = list([search_box(space_bounds.flatten())])
self.global_bounds = global_bounds
self.max_length_limit = _maxLengthOfBox(
self._createRadiusBox([0 * i for i in xrange(0, dimension)],
self.epsilon)) / 2.5e3
self.current_max_length = _maxLengthOfBox(space_bounds)
self.old_box = list([])
self.skip_list = list()
self.skip_radius_multiplier = pow(2.5, dimension)
if points is not None:
self.bad_boxes = [
search_box(self._createRadiusBox(point, old_distance))
for point in points
]
for point in points:
self.addPoint(point, False, True)
def __init__(self, dimension):
p = index.Property()
p.dimension = dimension
p.storage = index.RT_Memory
self.__idxkd = index.Index(str(p.dimension) + "d_index", properties=p)
self.__npoints = 0
self.__dimension = dimension
def setupIndex():
idxprop = index.Property()
idxprop.dimension = DIMENSIONS
idxprop.dat_extension = 'data'
idxprop.idx_extension = 'index'
idx = index.Index('3d_index', properties=idxprop)
return idx
def __init__(self, path):
total_files = int(path)
total_path = 'data/' + path + '/'
p = index.Property()
p.dimension = 128 # D
p.buffering_capacity = int(math.log(total_files, 10)**2) + 3 # M
p.dat_extension = 'data'
p.idx_extension = 'index'
idx = index.Index('face_recognition_index_' + path, properties=p)
self.total_files = total_files
self.path = path
self.total_path = total_path
for filename in os.listdir(total_path):
picture_1 = face_recognition.load_image_file(total_path + filename)
face_encoding_1 = face_recognition.face_encodings(picture_1)
if face_encoding_1:
values = face_encoding_1[0].tolist()
values = (generate_point(values))
diccionario.setdefault(len(diccionario), filename)
idx.insert(len(diccionario) - 1, values)
file = open("diccionario_" + path + ".json", "w")
json.dump(diccionario, file)
file.close()
def __init__(self, dimension_lengths, O=None):
"""
Initialize Search Space
:param dimension_lengths: range of each dimension
:param O: list of obstacles
"""
# sanity check
if len(dimension_lengths) < 2:
raise Exception("Must have at least 2 dimensions")
self.dimensions = len(dimension_lengths) # number of dimensions
# sanity checks
if any(len(i) != 2 for i in dimension_lengths):
raise Exception("Dimensions can only have a start and end")
if any(i[0] >= i[1] for i in dimension_lengths):
raise Exception("Dimension start must be less than dimension end")
self.dimension_lengths = dimension_lengths # length of each dimension
p = index.Property()
p.dimension = self.dimensions
if O is None:
self.obs = index.Index(interleaved=True, properties=p)
else:
# r-tree representation of obstacles
# sanity check
if any(len(o) / 2 != len(dimension_lengths) for o in O):
raise Exception("Obstacle has incorrect dimension definition")
if any(o[i] >= o[int(i + len(o) / 2)] for o in O
for i in range(int(len(o) / 2))):
raise Exception(
"Obstacle start must be less than obstacle end")
self.obs = index.Index(obstacle_generator(O),
interleaved=True,
properties=p)
def create(morphology, N, compartments=None):
from rtree import index
from rtree.index import Rtree
p = index.Property(dimension=3)
tree = index.Index(properties=p)
if compartments is None:
compartments = morphology.compartments
N = min(len(compartments), N)
for compartment in compartments:
tree.insert(int(compartment.id),
tuple([*compartment.midpoint, *compartment.midpoint]))
hit_detector = HitDetector.for_rtree(tree)
bounds, voxels, length, error = voxelize(
N, morphology.get_bounding_box(compartments=compartments),
hit_detector)
voxel_map = morphology.create_compartment_map(tree,
m_grid(bounds, length),
voxels, length)
if error == 0:
return VoxelCloud(bounds, voxels, length, voxel_map)
else:
raise NotImplementedError(
"Voxelization error: could not find the right amount of voxels. Try N {}"
.format(
# Suggest the closest we got to N for a next attempt
("+" if error > 0 else "") + str(error)))
def __init__(self, segmentFactory, useIndex=False):
# We will need the segmentFactory to generate segments on demand
self.segmentFactory = segmentFactory
# Prepare network
self._subnets = []
self._useIndex = useIndex
if (self._useIndex):
# These structures need to be updated for each
# segment we add to network.
# Keep index of segments so we don't need to marshal them
# in/out of rtree.
self._segments = {}
self._idsBySegment = {}
# Lookup table for subnets by segment coordinates.
# (need to update subnet lookups as they merge)
self._subnetLookupBySegment = {}
# rtree spatial index for intersection test speedup
# setup properties first
p = index.Property()
p.index_capacity = 10
p.leaf_capacity = 10
p.near_minimum_overlap_factor = 3
self._spatialIndex = index.Index(properties=p)
def __init__(self, armName, moveGroup, preemptionCheck):
# wait for linear path planner
rospy.wait_for_service('/apc/plan_linear_path')
rospy.wait_for_service('/apc/invert_trajectory')
rospy.wait_for_service('/apc/compute_ik_solution')
rospy.wait_for_service('/apc/plan_cartesian_linear_path')
self._linearPathPlanner = rospy.ServiceProxy('/apc/plan_linear_path', PlanLinearPath)
self._trajectoryInverter = rospy.ServiceProxy('/apc/invert_trajectory', InvertTrajectory)
self._inverseKinematicsSolver = rospy.ServiceProxy('/apc/compute_ik_solution', ComputeIKSolution)
self._cartesianLinearPathPlanner = rospy.ServiceProxy('/apc/plan_cartesian_linear_path',
PlanCartesianLinearPath)
self._armName = armName
self._moveGroup = moveGroup
self._roadmap = roadmap.Roadmap()
self._preemptionCheck = preemptionCheck
# We are using an RTree to save a mapping between poses and configurations in the roadmap
prop = index.Property()
prop.dimension = 6
self._poseIKCache = index.Index(properties=prop)
jointNames = ['s0', 's1', 'e0', 'e1', 'w0', 'w1', 'w2']
if armName == 'left_arm':
self._armKinematics = baxter_pykdl.baxter_kinematics('left')
self._limb = baxter_interface.Limb('left')
self._jointNames = map(lambda x: 'left_' + x, jointNames)
else:
self._armKinematics = baxter_pykdl.baxter_kinematics('right')
self._limb = baxter_interface.Limb('right')
self._jointNames = map(lambda x: 'right_' + x, jointNames)
def make_rtree(self):
with self.dataset() as nc:
sg = load_grid(nc)
def rtree_generator_function():
c = 0
centers = np.dstack((sg.center_lon, sg.center_lat))
for i, axis in enumerate(centers):
for j, (x, y) in enumerate(axis):
c += 1
yield (c, (x, y, x, y), (i, j))
logger.info(
"Building Faces (centers) Rtree Topology Cache for {0}".format(
self.name))
_, temp_file = tempfile.mkstemp(suffix='.face')
start = time.time()
p = index.Property()
p.filename = str(temp_file)
p.overwrite = True
p.storage = index.RT_Disk
p.dimension = 2
idx = index.Index(p.filename,
rtree_generator_function(),
properties=p,
overwrite=True,
interleaved=True)
idx.close()
logger.info(
"Built Faces (centers) Rtree Topology Cache in {0} seconds.".
format(time.time() - start))
shutil.move('{}.dat'.format(temp_file), self.face_tree_data_file)
shutil.move('{}.idx'.format(temp_file), self.face_tree_index_file)
def __init__(self, nodes, closed: bool, config: dict, container=None):
"""
:param nodes: initial set of nodes (list of 2/3D coordinates)
:param closed: whether nodes form a close line
:param config: morphogenesis parameters
:param container: optional list of nodes that constrain the growth (via influence on repulsion forces)
"""
self.nodes = nodes
self.config = config
# Init rtree index
self.index_props = index.Property()
self.index_props.dimension = config['DIMENSIONS']
self.index = None
self.container = container
self.VISIBILITY_RADIUS = config['VISIBILITY_RADIUS']
self.REPULSION_FAC = config['REPULSION_FAC']
self.ATTRACTION_FAC = config['ATTRACTION_FAC']
self.SPLIT_DIST_THRESHOLD = config['SPLIT_DIST_THRESHOLD']
self.SIMPLIFICATION_DIST_THRESHOLD = config[
'SIMPLIFICATION_DIST_THRESHOLD']
self.SPLIT_CROWD_THRESHOLD = config['SPLIT_CROWD_THRESHOLD']
self.RAND_OPTIMIZATION_FAC = config['RAND_OPTIMIZATION_FAC']
self.ATTRACTION = config['ATTRACTION']
self.SIMPLIFICATION = config['SIMPLIFICATION']
self.SUBDIVISION_METHOD = config['SUBDIVISION_METHOD']
self.CLOSED = closed
def build_geodb(cls, path_streetjson, filename, overwrite=True):
""" Build up a R-Tree based geodb of street segments.
Args:
path_streetjson - (str) Path to street db json file
filename - (str) filename used to store R-tree index
overwrite - (bool) whether to overwrite existing file
Note: rtree does not support read-only mode.
"""
idx_p = index.Property()
idx_p.overwrite = overwrite
idx = index.Index(filename, properties=idx_p)
with open(path_streetjson, "r") as fd_r:
for line in fd_r.readlines():
street_data = json.loads(line)
points = street_data["points"]
# Note: X(long)-Y(lat) coordinates
db_idx = 0
for seg_id, (start_pt, end_pt) in enumerate(zip(points, islice(points, 1, None))):
bbox = ( # left, bottom, right, top
min(start_pt[0], end_pt[0]), min(start_pt[1], end_pt[1]),
max(start_pt[0], end_pt[0]), max(start_pt[1], end_pt[1])
)
headings = cls.get_angels(start_pt, end_pt, False)
db_data = {"st_name": street_data["name"],
"segment_id": seg_id,
"segment_points": (start_pt, end_pt),
"headings": headings}
idx.insert(db_idx, bbox, obj=db_data)
db_idx += 1
return idx
def __init__(self):
self.s = rospy.Service('gp_query_server', Query, self.query_server)
self.best_node_srv = rospy.Service('best_node_server', BestNode,
self.best_node_srv_callback)
self.reevaluate_client = rospy.ServiceProxy('reevaluate', Reevaluate)
self.visualize_mean = rospy.get_param('~visualize/mean', False)
self.visualize_sigma = rospy.get_param('~visualize/sigma', False)
self.visualize_pts = rospy.get_param('~visualize/pts', False)
self.resolution = rospy.get_param('~visualize/resolution', 1)
self.gain_sub = rospy.Subscriber('gain_node', Node, self.gain_callback)
self.pose_sub = rospy.Subscriber('pose', Pose, self.pose_callback)
self.marker_pub = rospy.Publisher('pig_markers',
MarkerArray,
queue_size=10)
self.mean_pub = rospy.Publisher('mean_markers',
MarkerArray,
queue_size=10)
self.sigma_pub = rospy.Publisher('sigma_markers',
MarkerArray,
queue_size=10)
if self.visualize_pts:
rospy.Timer(rospy.Duration(1), self.rviz_callback)
if self.visualize_mean or self.visualize_sigma:
rospy.Timer(rospy.Duration(5), self.evaluate)
# Get environment boundaries
try:
self.min = rospy.get_param('boundary/min')
self.max = rospy.get_param('boundary/max')
except KeyError:
rospy.logwarn("Boundary parameters not specified")
rospy.logwarn("Defaulting to (-100, -100, 0), (100, 100, 3)...")
self.min = [-100, -100, 0]
self.max = [100, 100, 3]
try:
self.range = rospy.get_param('aep/gain/r_max') * 2
except KeyError:
rospy.logwarn("Range max parameter not specified")
rospy.logwarn("Defaulting to 8 m...")
self.bbx = (self.min[0], self.min[1], self.min[2], self.max[0],
self.max[1], self.max[2])
self.x = None
self.y = None
self.z = None
self.hyperparam = gp.HyperParam(l=1, sigma_f=1, sigma_n=0.1)
# Create r-tree
p = index.Property()
p.dimension = 3
self.idx = index.Index(properties=p)
self.id = 0
rospy.Timer(rospy.Duration(5), self.reevaluate_timer_callback)
def test_leaves(self):
os.chdir(os.path.expanduser("~/Development") + "/SafeDRL")
p = index.Property(dimension=4)
r = index.Index('save/rtree', properties=p, interleaved=False)
# nearest = list(r.nearest((0.5, 1.0, 0, 0.25, 0.5, 0.75, 0.5, 0.75), objects='raw'))
leaves = r.leaves()
print(leaves)
def init_search(Q, path, frontEnd):
if frontEnd == False:
total_path = 'data/' + path + '/'
else:
total_path = 'data/imageInput/'
total_files = int(path)
with open("diccionario_" + path + ".json") as json_file:
dict = json.load(json_file)
picture_1 = face_recognition.load_image_file(total_path + Q)
face_encoding_1 = face_recognition.face_encodings(picture_1)
if face_encoding_1:
values = face_encoding_1[0].tolist()
values = (generate_point(values))
p = index.Property()
p.dimension = 128 # D
p.buffering_capacity = int(math.log(total_files, 10)**2) + 3 # M
p.dat_extension = 'data'
p.idx_extension = 'index'
p.overwrite = False
idx = index.Index('face_recognition_index_' + path, properties=p)
return idx, values, dict
else:
return None, None, None
def construct_rtree_index(dataset):
p = index.Property()
rtree_idx = index.Index(properties=p)
count = 0
for coordinate in dataset:
rtree_idx.insert(count, (*coordinate, *coordinate))
count += 1
return rtree_idx
def compute_node_index(self):
prop = index.Property()
prop.dimension = 2
self._node_index = index.Index(properties=prop)
for vidx in range(len(self.graph.vs)):
position = self.get_screen_coordinates(vidx)
position += position
self._node_index.insert(vidx, position)
def __init__(self, c_space_sampler):
super(ExtendedFreeSpaceModel, self).__init__(c_space_sampler)
self._scaling_factors = c_space_sampler.get_scaling_factors()
prop = index.Property()
prop.dimension = c_space_sampler.get_space_dimension()
self._approximate_index = index.Index(properties=prop)
self._approximate_configs = []
self._temporal_mini_cache = []
def test_4d(self):
"""Test we make and query a 4D index"""
p = index.Property()
p.dimension = 4
idx = index.Index(properties = p, interleaved = False)
idx.insert(1, (0, 0, 60, 60, 22, 22.0, 128, 142))
hits = idx.intersection((-1, 1, 58, 62, 22, 24, 120, 150))
self.assertEqual(list(hits), [1])
def index_2d():
p = index.Property()
p.dimension = 2
p.dat_extension = 'data'
p.idx_extension = 'index'
st = './data/index{}'.format(time.time())
idx3d = index.Index(st, properties=p)
return idx3d
def __init__(self, dim=2):
#TODO: Review this
p = index.Property()
p.dimension = dim
self.V = index.Index(interleaved=True,
properties=p) # vertices in an rtree
self.G = nx.DiGraph()
def storeNew(filename, model, info=None):
"""
This creates a pickle file containing the list of earthquake sources
representing an earthquake source model.
:parameter filename:
The name of the file where the to store the model
:parameter model:
A list of OpenQuake hazardlib source instances
"""
# Preparing output filenames
dname = os.path.dirname(filename)
slist = re.split('\.', os.path.basename(filename))
# SIDx
p = index.Property()
p.dimension = 3
sidx = index.Rtree(os.path.join(dname, slist[0]), properties=p)
#
cpnt = 0
l_other = []
l_points = []
for src in model:
if isinstance(src,
(AreaSource, SimpleFaultSource, ComplexFaultSource,
CharacteristicFaultSource, NonParametricSeismicSource)):
l_other.append(src)
else:
if len(src.hypocenter_distribution.data) == 1:
srcs = [src]
else:
srcs = _split_point_source(src)
x, y = getcoo(srcs[0].location.longitude,
srcs[0].location.latitude)
for src in srcs:
l_points.append(src)
# calculate distances
z = src.hypocenter_distribution.data[0][1]
sidx.insert(cpnt, (x, y, z, x, y, z))
cpnt += 1
# All the other sources
fou = open(filename, 'wb')
pickle.dump(l_other, fou)
fou.close()
# Load info
if info is None:
info = _get_model_info(model)
# Points
fou = open(os.path.join(dname, slist[0]) + '_points.pkl', 'wb')
pickle.dump(l_points, fou)
fou.close()
# Info
fou = open(os.path.join(dname, slist[0]) + '_info.pkl', 'wb')
pickle.dump(info, fou)
fou.close()
def test_simple2(self):
os.chdir(os.path.expanduser("~/Development") + "/SafeDRL")
p = index.Property(dimension=4)
r = index.Index('save/rtree', properties=p, interleaved=False)
print(
list(
r.intersection((0.5, 1.0, 0, 0.25, 0.5, 0.75, 0.5, 0.75),
objects='raw')))
print(list(r.intersection((0.5, 1.0, 0, 0.25, 0.5, 0.75, 0.6, 0.75))))
def __init__(self, dimension: int):
"""
:param dimension: A positive integer that represents :math:`d`,
the dimensionality of the C-space.
"""
p = index.Property()
p.dimension = dimension
self.V = index.Index(interleaved=True, properties=p)
self.E = {} # edges in form E[child] = parent
def __init__(self):
p = index.Property()
p.dimension = 128 #D
p.buffering_capacity = 10 #M
p.dat_extension = 'data'
p.idx_extension = 'index'
self.idx = index.Index('128d', properties=p)
self.extract_features()
self.insert_all()
def make_index():
p = index.Property()
p.dimension = 3
p.dat_extension = 'data'
p.idx_extension = 'index'
p.storage = index.RT_Memory
p.overwrite = True
idx3 = index.Index(properties=p)
return idx3
def __init__(self, saved_path='', bounds=[], override=True):
p = index.Property()
p.overwrite = override
self.r_idx = index.Index(saved_path, properties=p)
for bound in bounds:
ID, left, bottom, right, top = bound
# print( ID, left, bottom, right, top)
self.r_idx.insert(ID, (left, bottom, right, top))
def test_non_stream_input():
p = index.Property()
idx = index.Index(properties=p)
for i, coords in enumerate(boxes15):
idx.add(i, coords)
assert 0 in idx.intersection((0, 0, 60, 60))
hits = list(idx.intersection((0, 0, 60, 60)))
assert hits == [0, 4, 16, 27, 35, 40, 47, 50, 76, 80]
def __init__(self):
self.index_to_device = self.sql_data_devices(True)[0]
self.device_to_index = self.sql_data_devices(False)[0]
self.device_to_site = self.sql_data_devices(False)[1]
self.site_to_devices = self.sql_data_devices(True)[1]
self.p = index.Property()
self.p.dimension = 3
self.idx = index.Index(properties=self.p)
self.load_data()
def test_stream_input(self):
p = index.Property()
sindex = index.Index(self.boxes15_stream(), properties=p)
bounds = (0, 0, 60, 60)
hits = sindex.intersection(bounds)
self.assertEqual(sorted(hits), [0, 4, 16, 27, 35, 40, 47, 50, 76, 80])
objects = list(sindex.intersection((0, 0, 60, 60), objects=True))
self.assertEqual(len(objects), 10)
self.assertEqual(objects[0].object, 42)
