def _generate_graph(self):
"""
This function generates a graph representing the airline network.
:return :The graph representing the network.
"""
with open(self.routes_file, encoding="utf8") as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
g = Graph()
for line in csv_reader:
#decode srcfrom file
src_airport_cord = line[3]
dst_airport_cord = line[5]
if src_airport_cord in self.cordinates and dst_airport_cord in self.cordinates:
d = self._haversine(self.cordinates[src_airport_cord],
self.cordinates[dst_airport_cord])
#add_edge(from_node, to_node, weight, stops, airline_code)
g.add_edge(line[2], line[4], d, line[7], line[0])
#decode src from file
# print(self.cordinates.get(self.source.lower()))
# print(self.source)
if self.cordinates.get(self.source.lower(),
None) == line[3]:
self.source_code = line[2]
self.source_airline = line[0]
#decode dest from file
if self.cordinates.get(self.destination.lower(),
None) == line[5]:
self.destination_code = line[4]
return g
def algorithm_runningTime(bdlist):
nValues1 = []
tValues1 = []
nValues2 = []
tValues2 = []
for bd in bdlist:
g = Graph(100, 100, bd.d) # We 'Draw' a map in a coordinate system
mapFlight = g.read_graph() # read map
distanceMatrix = g.generateMatrix() # create distance matrix
flightPlanMatrix = g.randomFlightPlan(
distanceMatrix, bd.b) # create flight plan matrix
# intialize map
startPoint = random.randint(0, bd.d - 1)
endPoint = random.randint(0, bd.d - 1)
# decide start and
start1 = time.time() #start time
algorithm.uniform_cost_search(flightPlanMatrix, startPoint,
endPoint) #call method
end1 = time.time() #end time
start2 = time.time() #start time
algorithm.a_star_search(flightPlanMatrix, distanceMatrix, startPoint,
endPoint) #call method
end2 = time.time() #end time
quantity = bd.d * math.log10(bd.b)
nValues1.append(quantity)
tValues1.append((end1 - start1) * 1000)
nValues2.append(quantity)
tValues2.append((end2 - start2) * 1000)
return nValues1, tValues1, nValues2, tValues2
def test_has_vertex(self):
g = Graph()
assert g.has_vertex('A') is False
g.add_vertex('B')
assert g.has_vertex('B') is True
g.add_edge('C', 'D')
assert g.has_vertex('C') is True
assert g.has_vertex('D') is True
def test_get_neighbors(self):
g = Graph()
g.add_edge('A', 'B')
g.add_edge('C', 'D')
g.add_edge('E', 'F')
g.add_edge('G', 'H')
self.assertCountEqual(g.get_neighbors('A'), ['B'])
self.assertCountEqual(g.get_neighbors('B'), [])
self.assertCountEqual(g.get_neighbors('C'), ['D'])
self.assertCountEqual(g.get_neighbors('D'), [])
with self.assertRaises(KeyError):
g.get_neighbors('Z')
def test_one():
graph = Graph(a='bc', b='ade', c='af', d='b', e='bf', f='ce')
assert graph.vertices == ('a', 'b', 'c', 'd', 'e', 'f')
assert graph.edges == (('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'd'),
('b', 'e'), ('c', 'a'), ('c', 'f'), ('d', 'b'),
('e', 'b'), ('e', 'f'), ('f', 'c'), ('f', 'e'))
assert graph.depth_first('a') == {'a', 'b', 'c', 'd', 'e', 'f'}
assert tuple(sorted(graph.depth_first_paths(
'a', 'e'))) == (['a', 'b', 'e'], ['a', 'c', 'f', 'e'])
assert graph.breadth_first('a') == {'a', 'b', 'c', 'd', 'e', 'f'}
assert tuple(sorted(graph.breadth_first_paths(
'a', 'e'))) == (['a', 'b', 'e'], ['a', 'c', 'f', 'e'])
assert graph.dijkstra('a', 'e') == (2, ['a', 'b', 'e'])
assert graph.a_star('a', 'e') == (2, ['a', 'b', 'e'])
assert graph.greedy_best_first('a', 'e') == ['a', 'b', 'e']
assert graph.warshall('a', 'e') == [2, ('a', 'b', 'e')]
def test_two():
g = (
(0, 4, 0, 0, 0, 0, 0, 8, 0), # 0
(4, 0, 8, 0, 0, 0, 0, 11, 0), # 1
(0, 8, 0, 7, 0, 4, 0, 0, 2), # 2
(0, 0, 7, 0, 9, 14, 0, 0, 0), # 3
(0, 0, 0, 9, 0, 10, 0, 0, 0), # 4
(0, 0, 4, 14, 10, 0, 2, 0, 0), # 5
(0, 0, 0, 0, 0, 2, 0, 1, 6), # 6
(8, 11, 0, 0, 0, 0, 1, 0, 7), # 7
(0, 0, 2, 0, 0, 0, 6, 7, 0) # 8
)
rows = len(g)
cols = len(g[0])
assert rows == cols
graph = Graph()
for row in range(rows):
graph[row] = set(col for col in range(cols) if g[row][col] != 0)
assert len(graph.vertices) == rows
assert len(graph.edges) == sum(
len(list(col for col in range(cols) if g[row][col] != 0))
for row in range(rows))
def heuristic(a, b):
return g[a][b]
assert graph.dijkstra(0, 0, heuristic)[0] == 0
assert graph.dijkstra(0, 1, heuristic)[0] == 4
assert graph.dijkstra(0, 2, heuristic)[0] == 12
assert graph.dijkstra(0, 3, heuristic)[0] == 19
assert graph.dijkstra(0, 4, heuristic)[0] == 21
assert graph.dijkstra(0, 5, heuristic)[0] == 11
assert graph.dijkstra(0, 6, heuristic)[0] == 9
assert graph.dijkstra(0, 7, heuristic)[0] == 8
assert graph.dijkstra(0, 8, heuristic)[0] == 14
def __init__(self, graph_obj: Graph):
# some_query_img_objects = (query_video_distinct_frames.get_objects(0, 2))
# img_objects_list contains 3 elements
# nodes_matched = self.match_node_with_frames(some_query_img_objects, graph_obj)
print("atleast started")
# nodes_matched = []
# self.nodes_matched.append(graph_obj.get_node(2))
self.nodes_matched.append(graph_obj.get_node(0))
# self.find_edge_with_nodes(0)
return
def test_get_vertices(self):
g = Graph()
g.add_vertex('A')
g.add_vertex('B')
g.add_vertex('C')
g.add_vertex('D')
self.assertCountEqual(g.get_vertices(), ['A', 'B', 'C', 'D'])
def setUp(self):
self.graph = Graph()
self.graph.add_vertex(1)
self.graph.add_vertex(2)
self.graph.add_vertex(3)
self.graph.add_vertex(4)
self.graph.add_vertex(5)
self.graph.add_vertex(6)
self.graph.add_vertex(7)
self.graph.add_edge(5, 3)
self.graph.add_edge(6, 3)
self.graph.add_edge(7, 1)
self.graph.add_edge(4, 7)
self.graph.add_edge(1, 2)
self.graph.add_edge(7, 6)
self.graph.add_edge(2, 4)
self.graph.add_edge(3, 5)
self.graph.add_edge(2, 3)
self.graph.add_edge(4, 6)
def read_graph_from_file(file_name):
'''Read graph from a file'''
file = open(file_name, 'r')
file_list = file.readlines()
vertices = file_list[1].rstrip().split(',')
valid_types = 'gGdD'
graph_type = ''
directed = False
weighted = False
edges_list = []
if file_list[0].rstrip() in valid_types:
graph_type = file_list[0].upper()
else:
raise ValueError('G or D is not specified')
if graph_type == 'D':
directed = True
for item in range(2, len(file_list)):
edge = file_list[item].rstrip().replace('(', '').replace(')', '').split(',')
if len(edge) == 3:
weighted = True
edges_list.append(edge)
graph = Graph(weighted, directed)
for edge in edges_list:
if weighted:
graph.add_weighted_edge(int(edge[0]), int(edge[1]), int(edge[2]))
else:
graph.add_edge(int(edge[0]), int(edge[1]))
return graph
def test_get_edge_list(self):
g = Graph(weighted=True, directed=False)
g.add_weighted_edge('A', 'B', 5)
g.add_weighted_edge('C', 'D', 3)
g.add_weighted_edge('E', 'F', 2)
g.add_weighted_edge('G', 'H', 1)
self.assertCountEqual(g.get_edge_list(), [('A', 'B', 5), ('C', 'D', 3),
('E', 'F', 2),
('G', 'H', 1)])
def erdoes_gallai(dsequence):
""" Checks if the condition of the Erdoes-Gallai inequality
is fullfilled
"""
if sum(dsequence) % 2:
# sum of sequence is odd
return False
if Graph.is_degree_sequence(dsequence):
for k in range(1,len(dsequence) + 1):
left = sum(dsequence[:k])
right = k * (k-1) + sum([min(x,k) for x in dsequence[k:]])
if left > right:
return False
else:
# sequence is increasing
return False
return True
def build_graph(directed):
g = Graph(directed)
vertices = []
for val in ['a', 'b', 'c', 'd', 'e', 'f', 'g']:
vertex = Vertex(val)
vertices.append(vertex)
g.add_vertex(vertex)
for v in range(len(vertices)):
v_idx = randrange(0, len(vertices) - 1)
v1 = vertices[v_idx]
v_idx = randrange(0, len(vertices) - 1)
v2 = vertices[v_idx]
g.add_edge(v1, v2, randrange(1, 10))
print_graph(g)
def locate_new_edge_using_angle(initial_edge: Edge,
graph_obj: Graph,
angle_turned,
allowed_angle_error: int = 20):
located_edge = None
for new_edge in initial_edge.angles:
if angle_turned < new_edge[
1] + allowed_angle_error and angle_turned > new_edge[
1] - allowed_angle_error:
print("new edge located is " + new_edge[0] +
" as stored angle is " + str(new_edge[1]) +
" and query angle is " + str(angle_turned))
located_edge = new_edge[0]
locations = located_edge.split("_")
located_edge_obj = graph_obj.get_edge(locations[0], locations[1])
return located_edge_obj
else:
print(new_edge[0] + " is not matched as stored angle is " +
str(new_edge[1]) + " and query angle is " +
str(angle_turned))
return "no edge found"
def match_node_with_frames(some_query_img_objects: list, graph_obj: Graph):
search_list = graph_obj.Nodes
node_confidence = []
# node_confidence is list of (node.identity:int , confidence:int , total_fraction_matched:float)
for node in search_list:
for img_obj in some_query_img_objects:
node_images: vo2.DistinctFrames = node.node_images
if node_images is not None:
for data_obj in node_images.get_objects():
image_fraction_matched, min_good_matches = mt.SURF_returns(
img_obj.get_elements(), data_obj.get_elements(),
2500, 0.7)
if min_good_matches > 100 and image_fraction_matched != -1:
if image_fraction_matched > 0.05 or min_good_matches > 225:
print("Match found btw" +
str(img_obj.get_time()) +
" of query video and " +
str(data_obj.get_time()) +
" of node data")
if len(node_confidence
) > 0 and node_confidence[-1][
0] == node.identity:
entry = node_confidence[-1]
node_confidence[-1] = (
node.identity, entry[1] + 1,
entry[2] + image_fraction_matched)
# print(str(node.identity) + " matched by " + str(image_fraction_matched))
else:
node_confidence.append(
(node.identity, 1,
image_fraction_matched))
node_confidence = sorted(node_confidence,
key=lambda x: (x[1], x[2]),
reverse=True)
print(node_confidence)
final_node_list = []
for entry in node_confidence:
final_node_list.append(graph_obj.get_node(entry[0]))
return final_node_list
if state == goalTest:
return True
for neighbor in state.neighbors():
update_cost(graph, neighbor, state)
if neighbor.get_label() not in list(
set([node.get_label() for node in frontier + explored])):
neighbor.f = neighbor.cost + neighbor.goal_cost
heapq.heappush(frontier, neighbor)
elif find_by_label(frontier, neighbor) >= 0:
update_frontier(frontier, neighbor)
neighbor.f = neighbor.cost + neighbor.goal_cost
return False
if __name__ == "__main__":
graph = Graph()
graph.add_node_from([
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N",
"O"
])
graph.add_edges_from([
("A", "B", 2),
("A", "C", 1),
("A", "D", 3),
("B", "E", 5),
("B", "F", 4),
("C", "G", 6),
("C", "H", 3),
("D", "I", 2),
("D", "J", 4),
("F", "K", 2),
state = heapq.heappop(frontier)
explored.append(state)
if state == goalTest:
print(df)
return True
for neighbor in state.neighbors():
if neighbor.get_label() not in list(
set([node.get_label() for node in frontier + explored])):
heapq.heappush(frontier, neighbor)
elif find_by_label(array_of_node=frontier, node=neighbor) >= 0:
update_frontier(frontier=frontier, new_node=neighbor)
return False
if __name__ == "__main__":
graph = Graph()
graph.add_node_from([
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N",
"O"
])
graph.add_edges_from([
("A", "B"),
("A", "C"),
("A", "D"),
("B", "E"),
("B", "F"),
("C", "G"),
("C", "H"),
("D", "I"),
("D", "J"),
("F", "K"),
def test_init(self):
g = Graph()
assert isinstance(g, Graph)
def test_add_vertex(self):
g = Graph()
g.add_vertex('A')
g.add_vertex('B')
g.add_vertex('C')
g.add_vertex('D')
assert g.has_vertex('A') is True
assert g.has_vertex('B') is True
assert g.has_vertex('C') is True
assert g.has_vertex('D') is True
assert g.has_vertex('E') is False
assert g.has_vertex('F') is False
assert g.has_vertex('G') is False
assert g.has_vertex('H') is False
g.add_edge('A', 'B')
g.add_edge('C', 'D')
self.assertCountEqual(g.get_neighbors('A'), ['B'])
self.assertCountEqual(g.get_neighbors('B'), [])
self.assertCountEqual(g.get_neighbors('C'), ['D'])
self.assertCountEqual(g.get_neighbors('D'), [])
g.add_edge('A', 'F')
g.add_edge('B', 'E')
self.assertCountEqual(g.get_neighbors('A'), ['B', 'F'])
self.assertCountEqual(g.get_neighbors('B'), ['E'])
def test_size(self):
g = Graph()
assert g.size == 0
g.add_vertex('A')
g.add_vertex('B')
g.add_vertex('C')
g.add_vertex('D')
assert g.size == 4
g.add_edge('A', 'B')
g.add_edge('C', 'D')
g.add_edge('E', 'F')
g.add_edge('G', 'H')
assert g.size == 8
with self.assertRaises(KeyError):
g.add_vertex('A')
assert g.size == 8
i = i + 1
continue
a = (len(keypoints), descriptors,
vo.serialize_keypoints(keypoints), gray.shape)
img_obj = vo.ImgObj(a[0], a[1], i, a[2], a[3])
self.query_objects.add_img_obj(img_obj)
if write_to_disk:
general.save_to_memory(img_obj, 'image' + str(i) + '.pkl',
folder)
cv2.imwrite(folder + '/jpg/image' + str(i) + '.jpg', gray)
if (cv2.waitKey(1) & 0xFF == ord('q')) or break_video:
break
# Calling the localisation functions
self.handle_edges()
i = i + 1
cap.release()
cv2.destroyAllWindows()
graph1: Graph = Graph.load_graph("new_objects/graph.pkl")
realTimeMatching = RealTimeMatching(graph1)
url = "http://10.194.36.234:8080/shot.jpg"
realTimeMatching.save_query_objects(url, livestream=True, frames_skipped=0)
from tree import TriangleNode, parseArrayIntoTree, parseEdgeArrayIntoTree, cutTreeIntoPatches
from stl_reader import Reader
from graph2 import Graph, TreeNode, treeLength
from utilities import getMatrixArbitraryAxis
from dxf_writer import DXFWriter
from evolution import TreeWorld
# Assumes SolidPython is in site-packages or elsewhwere in sys.path
from solid import *
from solid.utils import *
SEGMENTS = 48
filename = "kitten-122.stl"
triangles = Reader.read("stl/" + filename)
# triangles = Reader.read("stl/icosahedron.stl")
g = Graph(triangles)
n = len(g.nodes)
print "There are a total of ", n, " nodes"
hFn = lambda e: g.defaultHeuristic(e)
msp = g.toMSPTree(hFn)
edge_rep = msp.makeEdgeRepresentation()
hFn = lambda e: -g.defaultHeuristic(e)
msp2 = g.toMSPTree(hFn)
edge_rep2 = msp2.makeEdgeRepresentation()
world = TreeWorld(g, [edge_rep, edge_rep2])
child1 = world.generateFittest()
print child1
tn = parseEdgeArrayIntoTree(g.nodes, child1)
print treeLength(msp, set()), "faces"
def main():
projects = Graph()
projects.addVertex('a')
projects.addVertex('b')
projects.addVertex('c')
projects.addVertex('d')
projects.addVertex('e')
projects.addVertex('f')
projects.addEdge('f', 'a')
projects.addEdge('a', 'd')
projects.addEdge('f', 'b')
projects.addEdge('b', 'd')
projects.addEdge('d', 'c')
projectList = []
topologicalSort(projects, projectList)
length = len(projectList)
for i in xrange(length / 2):
projectList[i], projectList[length - i -
1] = projectList[length - i -
1], projectList[i]
print projectList
class Test(unittest.TestCase):
def setUp(self):
self.graph = Graph()
self.graph.add_vertex(1)
self.graph.add_vertex(2)
self.graph.add_vertex(3)
self.graph.add_vertex(4)
self.graph.add_vertex(5)
self.graph.add_vertex(6)
self.graph.add_vertex(7)
self.graph.add_edge(5, 3)
self.graph.add_edge(6, 3)
self.graph.add_edge(7, 1)
self.graph.add_edge(4, 7)
self.graph.add_edge(1, 2)
self.graph.add_edge(7, 6)
self.graph.add_edge(2, 4)
self.graph.add_edge(3, 5)
self.graph.add_edge(2, 3)
self.graph.add_edge(4, 6)
def test_vertices(self):
vertices = {
1: {2},
2: {3, 4},
3: {5},
4: {6, 7},
5: {3},
6: {3},
7: {1, 6}
}
self.assertDictEqual(self.graph.vertices, vertices)
def test_bft(self):
bft = [
"1\n2\n3\n4\n5\n6\n7\n", "1\n2\n3\n4\n5\n7\n6\n",
"1\n2\n3\n4\n6\n7\n5\n", "1\n2\n3\n4\n6\n5\n7\n",
"1\n2\n3\n4\n7\n6\n5\n", "1\n2\n3\n4\n7\n5\n6\n",
"1\n2\n4\n3\n5\n6\n7\n", "1\n2\n4\n3\n5\n7\n6\n",
"1\n2\n4\n3\n6\n7\n5\n", "1\n2\n4\n3\n6\n5\n7\n",
"1\n2\n4\n3\n7\n6\n5\n", "1\n2\n4\n3\n7\n5\n6\n"
]
stdout_ = sys.stdout
sys.stdout = io.StringIO()
self.graph.bft(1)
output = sys.stdout.getvalue()
self.assertIn(output, bft)
sys.stdout = stdout_ # Restore stdout
def test_dft(self):
dft = [
"1\n2\n3\n5\n4\n6\n7\n", "1\n2\n3\n5\n4\n7\n6\n",
"1\n2\n4\n7\n6\n3\n5\n", "1\n2\n4\n6\n3\n5\n7\n"
]
stdout_ = sys.stdout
sys.stdout = io.StringIO()
self.graph.dft(1)
output = sys.stdout.getvalue()
self.assertIn(output, dft)
sys.stdout = stdout_ # Restore stdout
def test_dft_recursive(self):
dft = [
"1\n2\n3\n5\n4\n6\n7\n", "1\n2\n3\n5\n4\n7\n6\n",
"1\n2\n4\n7\n6\n3\n5\n", "1\n2\n4\n6\n3\n5\n7\n"
]
stdout_ = sys.stdout
sys.stdout = io.StringIO()
self.graph.dft_recursive(1)
output = sys.stdout.getvalue()
self.assertIn(output, dft)
sys.stdout = stdout_ # Restore stdout
def test_bfs(self):
bfs = [1, 2, 4, 6]
self.assertListEqual(self.graph.bfs(1, 6), bfs)
def test_dfs(self):
dfs = [[1, 2, 4, 6], [1, 2, 4, 7, 6]]
self.assertIn(self.graph.dfs(1, 6), dfs)
def test_dfs_recursive(self):
dfs = [[1, 2, 4, 6], [1, 2, 4, 7, 6]]
self.assertIn(self.graph.dfs_recursive(1, 6), dfs)
18 : [17, 19, 9],
19 : [18, 20, 11],
20 : [13, 16, 19]
} """
g = {
1: [2, 3, 4],
2: [1, 5],
3: [1, 4, 5],
4: [1, 3, 6],
5: [2, 3, 6],
6: [4, 5]
}
graph = Graph(g)
vertices = graph.vertices()
grados = []
numVertices = 0
nodosAdyacentes = []
nodosRecorridos = []
gradoAdyacentes = []
hamiltoniano = True
nodoActual = None
#creamos la lista de grados vacía pero
#con tantos elementos como vértices
#Cada entrada contendrá el grado del nodo i, siendo i la posición
#relativa en la lista
for item in vertices:
from tree import TriangleNode, parseArrayIntoTree, parseEdgeArrayIntoTree, cutTreeIntoPatches
from stl_reader import Reader
from graph2 import Graph,TreeNode,treeLength
from utilities import getMatrixArbitraryAxis
from dxf_writer import DXFWriter
from evolution import TreeWorld
# Assumes SolidPython is in site-packages or elsewhwere in sys.path
from solid import *
from solid.utils import *
SEGMENTS = 48
filename = "kitten-122.stl"
triangles = Reader.read("stl/" + filename)
# triangles = Reader.read("stl/icosahedron.stl")
g = Graph(triangles)
n = len(g.nodes)
print "There are a total of ", n, " nodes"
hFn = lambda e: g.defaultHeuristic(e)
msp = g.toMSPTree(hFn)
edge_rep = msp.makeEdgeRepresentation()
hFn = lambda e: -g.defaultHeuristic(e)
msp2 = g.toMSPTree(hFn)
edge_rep2 = msp2.makeEdgeRepresentation()
world = TreeWorld(g, [edge_rep, edge_rep2])
child1 = world.generateFittest()
print child1
tn = parseEdgeArrayIntoTree(g.nodes, child1)
print treeLength(msp,set()), "faces"
}
g2 = {
"a": ["d", "f"],
"b": ["c"],
"c": ["b", "c", "d", "e"],
"d": ["a", "c"],
"e": ["c"],
"f": ["a"]
}
g3 = {
"a": ["d", "f"],
"b": ["c", "b"],
"c": ["b", "c", "d", "e"],
"d": ["a", "c"],
"e": ["c"],
"f": ["a"]
}
graph = Graph(g)
print(graph)
print(graph.is_connected())
graph = Graph(g2)
print(graph)
print(graph.is_connected())
graph = Graph(g3)
print(graph)
print(graph.is_connected())
state = heapq.heappop(frontier)
explored.append(state)
if goalTest == state:
return True
for neighbor in state.neighbors():
update_cost(graph, current_node=neighbor, prev_node=state)
if neighbor.get_label() not in list(
set([e.get_label() for e in frontier + explored])):
heapq.heappush(frontier, neighbor)
elif find_by_label(frontier, neighbor) is not -1:
update_frontier(frontier, neighbor)
return False
if __name__ == "__main__":
graph = Graph()
graph.add_node("S")
graph.add_node_from(["S", "A", "B", "C", "D", "E", "F", "G", "H"])
graph.add_edges_from(
[
("S", "A", 3),
("S", "B", 6),
("S", "C", 2),
("A", "D", 3),
("B", "D", 4),
("B", "G", 9),
("B", "E", 2),
("C", "E", 1),
("D", "F", 5),
("E", "H", 5),
("F", "E", 6),
# chosse a vertex from graph as a starting point
start_vertex = vertices[0]
vertices_encountered.add(start_vertex)
if len(vertices_encountered) != len(vertices):
for vertex in gdi from graph2 import Graph
g = {"a": ["d"],
"b": ["c"],
"c": ["b", "c", "d", "e"],
"d": ["a", "c"],
"e": ["c"],
"f": []
}
graph = Graph(g)
print(graph)
for node in graph.vertices():
print(graph.vertex_degree(node))
print("List of isolated vertices:")
print(graph.find_isolated_vertices())
print("""A path from "a" to "e":""")
print(graph.find_path("a", "e"))
print("""All pathes from "a" to "e":""")
print(graph.find_all_paths("a", "e"))
print("The maximum degree of the graph is:")
x = [graph.vertices[vertex_id].x for vertex_id in graph.vertices]
y = [graph.vertices[vertex_id].y for vertex_id in graph.vertices]
graph_layout = dict(zip(node_indices, zip(x, y)))
graph_renderer.layout_provider = StaticLayoutProvider(graph_layout=graph_layout)
plot.renderers.append(graph_renderer)
labelSource = ColumnDataSource(data=dict(x=x, y=y, names=[graph.vertices[vertex_id].value for vertex_id in graph.vertices]))
labels = LabelSet(x='x', y='y', text='names', level='glyph',
text_align='center', text_baseline='middle', source=labelSource, render_mode='canvas')
plot.add_layout(labels)
output_file('graph.html')
show(plot)
graph = Graph() # Instantiate your graph
graph.add_vertex('0')
graph.add_vertex('1')
graph.add_vertex('2')
graph.add_vertex('3')
graph.add_edge('0', '1')
graph.add_edge('0', '3')
graph.add_edge('1', '2')
smth = BokehGraph(graph)
smth.draw()
def main():
try:
with open(sys.argv[1], 'r') as tweets, open(sys.argv[2], 'w') as output:
# make a pretty global dictionary to store time and hashtag info
hashtag_dict = OrderedDict()
result = '0.00'
# iterate over the input file, one line at a time
for line in tweets:
tweet = line.decode('utf-8')
tweet_json = json.loads(tweet) # dict object
if 'created_at' in tweet_json: # needed to address keyError
# get the time of the tweet
created_at = datetime.strptime(
tweet_json['created_at'].encode('utf-8'),
"%a %b %d %H:%M:%S +0000 %Y")
# get the time of the last tweet
if len(hashtag_dict.keys()) == 0:
time_last_tweet = created_at
else:
time_last_tweet = sorted(hashtag_dict.keys())[-1]
# ensure to only address tweets not over 60 seconds older
# than the last
time_delta = created_at - time_last_tweet
if time_delta.total_seconds() > - 60:
# if 'entities' in tweet_json:
if 'hashtags' in tweet_json['entities']:
hashtags = tweet_json['entities']['hashtags']
hashtag_list = [i['text'].encode('utf-8').strip()
for i in hashtags]
# keep only unique tags
hashtag_list = list(set(hashtag_list))
# update hashtag_dict with tweet of at least 2 tags
if len(hashtag_list) > 1:
hashtag_dict[created_at] = hashtag_list
# for those with 0 or 1 tag, the result is the
# same as in the last
else:
output.write(str(result) + '\n')
continue
else:
output.write(str(result) + '\n')
continue
# ignore tweets over 60 seconds older than the last
else:
continue
# remove the tweets more than 60 second older than the
# current tweet
sorted_key_list = sorted(hashtag_dict.keys())
for k in sorted_key_list:
time_elapsed = created_at - k
if time_elapsed.total_seconds() > 60:
del hashtag_dict[k]
else:
break
# finally, initialize the graph, and derive average degree
graph = Graph()
for t in hashtag_dict:
for tag in hashtag_dict[t]:
graph.add_vertex(tag)
length = len(hashtag_dict[t])
for start in range(length - 1):
for end in range(start+1, length):
graph.add_edge(
hashtag_dict[t][start],
hashtag_dict[t][end])
result = average_degree_graph(graph)
# ignore those lines without 'created_at'
else:
continue
# for each valid line, report the result
print result
output.write(str(result) + '\n')
else:
print 'All done!'
except IOError as err:
print 'File error:', str(err)
