def __init__(self, environment):
"""
:param environment:
:type environment: core.environment.Environment
"""
self.env = environment
locs = np.random.rand(self.env.segment_count, 2) * self.env.grid_height
self.segments = [segment.Segment(nd) for nd in locs]
for i, seg in enumerate(self.segments):
seg.segment_id = i
self.clusters = []
def __init__(self, environment):
"""
:param environment:
:type environment: core.environment.Environment
"""
self.env = environment
locs = np.random.rand(self.env.segment_count, 2) * self.env.grid_height
self.segments = [segment.Segment(nd) for nd in locs]
self._center = linalg.centroid(locs)
# Create the centroid cluster
self.centroid = ToCSCentroid(self.env)
self.clusters = list() # type: typing.List[ToCSCluster]
self._length_threshold = 0.5
def __init__(self, environment):
"""
:param environment:
:type environment: core.environment.Environment
"""
self.env = environment
locs = np.random.rand(self.env.segment_count, 2) * self.env.grid_height
self.segments = [segment.Segment(loc) for loc in locs]
self.grid = grid.Grid(self.segments, self.env)
self.cells = list(self.grid.cells())
segment_centroid = np.mean(locs, axis=0)
logger.debug("Centroid located at %s", segment_centroid)
self.damaged = self.grid.closest_cell(segment_centroid)
self.energy_model = LOAFEnergyModel(self, self.env)
self.virtual_clusters = list() # type: List[LoafVirtualCluster]
self.clusters = list() # type: List[LoafCluster]
# Create a virtual cell to represent the center of the damaged area
virtual_center_cell = self.damaged
self.virtual_hub = LoafVirtualHub(self.env)
self.virtual_hub.add(virtual_center_cell)
self.virtual_hub.cluster_id = self.env.mdc_count - 1
self.hub = LoafHub(self.env)
self.hub.add(virtual_center_cell)
self.hub.cluster_id = self.env.mdc_count - 1
self.em_is_large = False
self.ec_is_large = False
def test_segments_have_unique_ids():
segments = [segment.Segment([0, 0]) for _ in range(10)]
segment_ids_list = [s.segment_id for s in segments]
segments_ids_set = set(segment_ids_list)
assert len(segment_ids_list) == len(segments_ids_set)
def test_segments_have_arbitrary_cluster_ids():
seg = segment.Segment(np.array([0, 0]))
seg.cluster_id = 88
assert seg.cluster_id == 88
def test_segments_can_be_used_for_drawing_lines():
segment_1 = segment.Segment(np.array([0, -1]))
segment_2 = segment.Segment(np.array([0, 1]))
assert segment_1.location.distance(segment_2.location) == 2
def __init__(self, environment):
"""
:param environment:
:type environment: core.environment.Environment
"""
### Debugs for paper ####
S0 = np.array([9.0,13.0])
S1 = np.array([4.0,12.0])
S2 = np.array([2.0,9.0])
S3 = np.array([7.0,9.0])
S4 = np.array([12.0,9.0])
S5 = np.array([17.0,9.0])
S6 = np.array([17.0,4.0])
S7 = np.array([14.0,0.0])
S8 = np.array([11.0,4.0])
S9 = np.array([5.0,0.0])
S10 = np.array([5.0,6.0])
S11 = np.array([0.0,4.0])
# Data Volume Table
S0_DataVol = np.array([0, 34])
S1_DataVol = np.array([1,31])
S2_DataVol = np.array([2,29])
S3_DataVol = np.array([3,50])
S4_DataVol = np.array([4,17])
S5_DataVol = np.array([5,13])
S6_DataVol = np.array([6,1])
S7_DataVol = np.array([7,2])
S8_DataVol = np.array([8,7])
S9_DataVol = np.array([9,1])
S10_DataVol = np.array([10,22])
S11_DataVol = np.array([11,1])
# List of initial clusters and data volumes
self.dataVol = list()
self.dataVol.append(S0_DataVol)
self.dataVol.append(S1_DataVol)
self.dataVol.append(S2_DataVol)
self.dataVol.append(S3_DataVol)
self.dataVol.append(S4_DataVol)
self.dataVol.append(S5_DataVol)
self.dataVol.append(S6_DataVol)
self.dataVol.append(S7_DataVol)
self.dataVol.append(S8_DataVol)
self.dataVol.append(S9_DataVol)
self.dataVol.append(S10_DataVol)
self.dataVol.append(S11_DataVol)
self.env = environment
#locs = np.random.rand(self.env.segment_count, 2) * self.env.grid_height
locs = list()
locs.append(S0)
locs.append(S1)
locs.append(S2)
locs.append(S3)
locs.append(S4)
locs.append(S5)
locs.append(S6)
locs.append(S7)
locs.append(S8)
locs.append(S9)
locs.append(S10)
locs.append(S11)
locs = np.array(locs)
# For the node within
self.segments = [segment.Segment(loc) for loc in locs]
self.grid = grid.Grid(self.segments, self.env)
self.cells = list(self.grid.cells())
# segment_centroid = np.mean(locs, axis=0)
# setting to S3 for testing
segment_centroid = np.array(S3)
logger.debug("Centroid located at %s", segment_centroid)
self.damaged = self.grid.closest_cell(segment_centroid)
self.energy_model = LOAFEnergyModel(self, self.env)
self.virtual_clusters = list() # type: List[LoafVirtualCluster]
self.clusters = list() # type: List[LoafCluster]
# Create a virtual cell to represent the center of the damaged area
virtual_center_cell = self.damaged
self.virtual_hub = LoafVirtualHub(self.env)
self.virtual_hub.add(virtual_center_cell)
self.virtual_hub.cluster_id = self.env.mdc_count - 1
self.hub = LoafHub(self.env)
self.hub.add(virtual_center_cell)
self.hub.cluster_id = self.env.mdc_count - 1
self.em_is_large = False
self.ec_is_large = False