def closest_group(req_id):
'''
处理学院聚集度
----------
parameter
req_id 请求编号
return
渲染的模板
'''
global G_igraph, G_nx, nodes_vector, aca_graph, data
paramas = request.args # GET 请求参数
if req_id == 0: # 渲染 coefficient 页面
return render_template('new_coefficience.html')
elif req_id == 1: # 选择两个学院
# 参数：学院 ID
aca1_id = int(paramas['acaId1']) - 1
aca2_id = int(paramas['acaId2']) - 1
# 分别计算两个院的 Cluster Coefficient
aca1_graph_json = dp.graph_json(
nx.DiGraph(aca_graph[aca1_id].get_edgelist()), nodes_vector)
aca1_coefficient = c.cluster_coefficient_calculation(
nx.DiGraph(
aca_graph[aca1_id].
get_edgelist()).to_undirected(reciprocal=False))
aca2_graph_json = dp.graph_json(
nx.DiGraph(aca_graph[aca2_id].get_edgelist()), nodes_vector)
aca2_coefficient = c.cluster_coefficient_calculation(
nx.DiGraph(
aca_graph[aca2_id].
get_edgelist()).to_undirected(reciprocal=False))
# 根据请求返回需要的对应格式的数据
data = {'AcademyGraph1': aca1_graph_json,
'AcademyGraph2': aca2_graph_json,
'AcademyCoefficient1': aca1_coefficient,
'AcademyCoefficient2': aca2_coefficient}
return make_response(json.dumps(data))
def student_equivalence(req_id):
global G_igraph, G_nx, nodes_vector, data
paramas = request.args
if req_id == 0:
return render_template('/new_equivalence.html')
elif req_id == 1:
stu_id = int(paramas['studentId'])
graph_json = dp.graph_json(G_nx, nodes_vector) # 图信息
G = G_nx.to_undirected(reciprocal=False)
matrix = nx.to_numpy_matrix(G)
# 分别计算struct-cosine 和 regular-cosine，结果存入字典
array1 = c.select_similar(nodes_vector, matrix, stu_id, 0)
array2 = c.select_similar(nodes_vector, matrix, stu_id, 1)
top_ten_list = {'structral': array1, 'regular': array2}
data = {'Graph': graph_json,
'Rank': top_ten_list} # 封装成 dict
return make_response(json.dumps(data))
def top_students(req_id):
'''
处理名人推荐、学院搜索
----------
parameter
req_id 请求编号
return
前端需要的数据
'''
global G_igraph, G_nx, nodes_vector, aca_graph, data
paramas = request.args # GET 请求参数
if req_id == 0: # 渲染 Centrality 页面
return render_template('new_centrality.html')
elif req_id == 1: # 请求全校学生节点中心度值
ig_list = c.centrality_calculation_by_igraph(G_igraph)
nx_list = c.centrality_calculation_by_networkx(G_nx)
graph_json = dp.graph_json(G_nx, nodes_vector) # 图信息
c_list = ig_list + nx_list # Centrality 计算结果
# c_list = c.test(G_nx) # 测试：只使用 networkx 进行计算
# 对 Centrality 计算结果进行深度拷贝，用于计算 Top-N
centrality_list = copy.deepcopy(c_list)
top_n_list, rank_list = top_rank(
centrality_list,
min(10, len(G_nx.nodes())))
data = {'Centrality': c_list,
'Graph': graph_json,
'Rank': rank_list} # 封装成 dict
return make_response(json.dumps(data))
elif req_id == 2: # 请求某个院学生节点中心度值
aca_id = int(paramas['search']) - 1 # 参数：学院 ID
ig_list = c.centrality_calculation_by_igraph(aca_graph[aca_id])
nx_list = c.centrality_calculation_by_networkx(
nx.DiGraph(aca_graph[aca_id].get_edgelist()))
graph_json = dp.graph_json(
nx.DiGraph(aca_graph[aca_id].get_edgelist()), nodes_vector) # 图信息
c_list = ig_list + nx_list # Centrality 计算结果
# c_list = c.test(G_nx)
centrality_list = copy.deepcopy(c_list)
top_ten_list, rank_list = top_rank(centrality_list, 10)
print rank_list
print top_ten_list
data = {'Centrality': c_list,
'Graph': graph_json,
'Rank': rank_list} # 封装成 dict
return make_response(json.dumps(data))
elif req_id == 3: # 请求某个学生节点中心度值
stu_id = int(paramas['search']) # 参数：学生 ID
ig_list = c.centrality_calculation_by_igraph(G_igraph)
nx_list = c.centrality_calculation_by_networkx(G_nx)
graph_json = dp.graph_json(G_nx, nodes_vector) # 图信息
c_list = ig_list + nx_list # Centrality 计算结果
# c_list = c.test(G_nx)
centrality_list = copy.deepcopy(c_list)
rank_list = []
for c_dict in centrality_list: # 构造 Rank
c_dict[c_dict.keys()[0]] = []
rank_list.append(c_dict)
stu_centrality_list = []
# 取出所有计算该节点 Centrality 算法中对应的值，并以 key-value 存储
for centrality in c_list:
stu_centrality = {}
centrality_key = centrality.keys()[0]
centrality_value = centrality.values()[0][stu_id]
stu_centrality[centrality_key] = centrality_value
stu_centrality_list.append(stu_centrality)
data = {'StudentCentrality': stu_centrality_list,
'Centrality': c_list,
'Graph': graph_json,
'Rank': rank_list} # 封装成 dict
return make_response(json.dumps(data))
