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How to use populate_graph method in localstack

Best Python code snippet using localstack_python

poc_1.py

Source:poc_1.py

...27		x = fix_point(uniform(x_low, x_high))28		y = fix_point(uniform(y_low, y_high))29		ret_group.append((x,y))30	return ret_group31def populate_graph(nx_G, coor_list, nc, res_tup, num_c):32	for coor in coor_list:33		nx_G.add_node(nc, pos=coor, res=uniform(res_tup[0], res_tup[1]),34                        mem=num_c)35		nc += 136	return nc37def populate_graph_random(nx_G, coor_list, nc, res_tup, num_groups):38	for coor in coor_list:39		num_c = randint(1, num_groups)40		nx_G.add_node(nc, pos=coor, res=uniform(res_tup[0], res_tup[1]),41                        mem=num_c)42		nc += 143	return nc44def populate_edges(nx_G, dist_thres):45	for n_1, attr_1 in nx_G.nodes(data=True):46		G.add_edges_from([(n_1, n_2) for n_2, attr_2 in nx_G.nodes(data=True)47			              if n_1 != n_2 48			              and (distance(attr_1['pos'], attr_2['pos']) <= dist_thres)])49def build_data(nx_G):50	ret_data = []51	header = ["node", "x", "y", "res_val", "connections", "cluster_num"]52	ret_data.append(header)53	for n, attr in nx_G.nodes(data=True):54		tmp = [n, attr['pos'][0], attr['pos'][1], attr['res'], nx_G.edges(n), attr['mem']]55		ret_data.append(tmp)56	return ret_data57def write_to_csv(fname, data):58	tmp_f = open(fname, 'w')59	with tmp_f:60		writer = csv.writer(tmp_f)61		writer.writerows(data)62def save_g(nx_G, fname):63    json.dump(dict(nodes=[[n, nx_G.node[n]] for n in nx_G.nodes()],64                   edges=[[u, v, nx_G.edge[u][v]] for u,v in nx_G.edges()],65    			   attrs=[[n, attr['pos'], attr['res']] for n, attr in nx_G.nodes(data=True)]),66              open(fname, 'w'), indent=2)67def load_g(fname):68    nx_G = nx.Graph()69    d = json.load(open(fname))70    nx_G.add_nodes_from(d['nodes'])71    nx_G.add_edges_from(d['edges'])72    attr = d['attrs']73    return nx_G, attr74####################################75G = nx.Graph()76group_1 = gen_random_points(-1, 1, -1, 1, 250)77group_2 = gen_random_points(-1, 1, -1, 1, 250)78group_3 = gen_random_points(-1, 1, -1, 1, 250)79group_4 = gen_random_points(-1, 1, -1, 1, 250)80NODE_COUNT = populate_graph_random(G, group_1, NODE_COUNT, (0.0, 0.25), 4)81NODE_COUNT = populate_graph_random(G, group_2, NODE_COUNT, (0.25, 0.5), 4)82NODE_COUNT = populate_graph_random(G, group_3, NODE_COUNT, (0.5, 0.75), 4)83NODE_COUNT = populate_graph_random(G, group_4, NODE_COUNT, (0.75, 1), 4)84# group_11 = gen_normal_points(1, 1, 0.3, 0.25, 25)85# group_01 = gen_normal_points(-1, 1, 0.3, 0.25, 25)86# group_10 = gen_normal_points(1, -1, 0.3, 0.25, 25)87# group_00 = gen_normal_points(-1, -1, 0.3, 0.25, 25)88# NODE_COUNT = populate_graph(G, group_11, NODE_COUNT, (0.75, 1.0), 0)89# NODE_COUNT = populate_graph(G, group_01, NODE_COUNT, (0.5, 0.75), 1)90# NODE_COUNT = populate_graph(G, group_10, NODE_COUNT, (0.25, 0.5), 2)91# NODE_COUNT = populate_graph(G, group_00, NODE_COUNT, (0.0, 0.25), 3)92populate_edges(G, 0.10)93res_list = [attr['res'] for _,attr in G.nodes(data=True)]94nx.draw(G, cmap=plt.get_cmap('Blues'), node_color=res_list)95plt.show()96write_to_csv("poc_2.csv", build_data(G))...

graphPlay.py

Source:graphPlay.py

...11		self.visited = visited12		self.ID = ID13# Let's manually build a graph. Create 10 nodes and keep an id for each node in this graph dictionary.14graph = {ID : Node() for ID in range(10)}15def populate_graph(graph, node_ID, value, children):16	graph[node_ID].value = value17	graph[node_ID].children = children18	graph[node_ID].ID = node_ID19populate_graph(graph, 0, 5, [graph[1],graph[9]])20populate_graph(graph, 1, 9, [graph[2],graph[4]])21populate_graph(graph, 2, 7, [graph[3]])22populate_graph(graph, 3, 2, [graph[1]])23populate_graph(graph, 4, 3, [graph[5],graph[6],graph[7]])24populate_graph(graph, 5, 7, [])25populate_graph(graph, 6, 3, [])26populate_graph(graph, 7, 5, [graph[8]])27populate_graph(graph, 8, 8, [])28populate_graph(graph, 9, 1, [graph[8]])29def bfs(graph,root):30	# Let's do a breadth first search that prints when it visits each node.31	q = Queue()32	q.put(root)33	while q:34		current_node = q.get()35		current_node.visited = True36		print(f'We just visited node {current_node.ID}\n')37		for child in current_node.children:38			if not child.visited:39				q.put(child)40def dfs(graph,root):41	# Let's do a depth first search that prints when it visits each node.42	# Notice we don't visit a node twice, otherwise we would get caught in ...

scholargraph.py

Source:scholargraph.py

1import scholarly as sch2import networkx as nx3def populate_graph(paper, graph):4    '''5    :param paper: scholarly.Publication6    :param graph: networkx.classes.digraph.DiGraph7    :return: None8    '''9    # make node for initial paper in the graph10    graph.add_node(paper.bib['ID'], paper=paper)11    print paper.bib['ID']12    # get citations13    for citation in paper.get_citedby():14        # retrieve full paper information15        citation = citation.fill()16        # check to see if the paper is already on the graph17        if citation.bib['ID'] in graph.nodes():18            # if it already is just add the new edge19            graph.add_edge(paper.bib['ID'], citation.bib['ID'])20        else:21            # if not add the node, and the edge, and populate its children22            graph.add_node(citation.bib['ID'], paper=citation)23            graph.add_edge(paper.bib['ID'], citation.bib['ID'])24            populate_graph(citation, graph)25def test_pop():26    import matplotlib.pyplot as plt27    G = nx.DiGraph()28    search_query = sch.search_pubs_query('10.1109/THS.2013.6698999')29    P = search_query.next()30    P = P.fill()31    populate_graph(P, G)32    nx.draw_spectral(G)...

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