开发网站后端时，如何制作适用于小广告的模板？

网站开发后端做什么,小广告制作模板,价格网站,网站建设方式一、说明 根据定义#xff0c;图是节点#xff08;顶点#xff09;以及已识别的节点对#xff08;称为边、链接等#xff09;的集合。在 NetworkX 中#xff0c;节点可以是任何可哈希对象#xff0c;例如文本字符串、图像、XML 对象、另一个图形、自定义节点对象等。 如…一、说明 根据定义图是节点顶点以及已识别的节点对称为边、链接等的集合。在 NetworkX 中节点可以是任何可哈希对象例如文本字符串、图像、XML 对象、另一个图形、自定义节点对象等。 如果不知道networkx基础的制图请先看看下文 【python视图1】networkx操作Graph图 【python视图2】基于networkx的10个绘图技巧  二、神奇制图 2.1 绘制彩虹图 生成一个完整的图形其中包含 13 个节点呈圆形布局边按节点距离着色。节点距离由沿圆上任意两个节点之间的弧线遍历的最小节点数给出。 这样的图是 Ringel 猜想的主题它指出任何具有 2n 1 个节点的完整图都可以被任何具有 n 1 个节点的树平铺即树的副本可以放置在完整图上使得图中的每条边完整的图被恰好覆盖一次。边缘着色有助于确定如何放置树副本。 效果图  代码 import matplotlib.pyplot as plt import networkx as nx# A rainbow color mapping using matplotlibs tableau colors node_dist_to_color {1: tab:red,2: tab:orange,3: tab:olive,4: tab:green,5: tab:blue,6: tab:purple, }# Create a complete graph with an odd number of nodes nnodes 13 G nx.complete_graph(nnodes)# A graph with (2n 1) nodes requires n colors for the edges n (nnodes - 1) // 2 ndist_iter list(range(1, n 1))# Take advantage of circular symmetry in determining node distances ndist_iter ndist_iter[::-1]def cycle(nlist, n):return nlist[-n:] nlist[:-n]# Rotate nodes around the circle and assign colors for each edge based on # node distance nodes list(G.nodes()) for i, nd in enumerate(ndist_iter):for u, v in zip(nodes, cycle(nodes, i 1)):G[u][v][color] node_dist_to_color[nd]pos nx.circular_layout(G) # Create a figure with 1:1 aspect ratio to preserve the circle. fig, ax plt.subplots(figsize(8, 8)) node_opts {node_size: 500, node_color: w, edgecolors: k, linewidths: 2.0} nx.draw_networkx_nodes(G, pos, **node_opts) nx.draw_networkx_labels(G, pos, font_size14) # Extract color from edge data edge_colors [edgedata[color] for _, _, edgedata in G.edges(dataTrue)] nx.draw_networkx_edges(G, pos, width2.0, edge_coloredge_colors)ax.set_axis_off() fig.tight_layout() plt.show() 2.2  随机地理图 代码  import matplotlib.pyplot as plt import networkx as nx# Use seed when creating the graph for reproducibility G nx.random_geometric_graph(200, 0.125, seed896803) # position is stored as node attribute data for random_geometric_graph pos nx.get_node_attributes(G, pos)# find node near center (0.5,0.5) dmin 1 ncenter 0 for n in pos:x, y pos[n]d (x - 0.5) ** 2 (y - 0.5) ** 2if d dmin:ncenter ndmin d# color by path length from node near center p dict(nx.single_source_shortest_path_length(G, ncenter))plt.figure(figsize(8, 8)) nx.draw_networkx_edges(G, pos, alpha0.4) nx.draw_networkx_nodes(G,pos,nodelistlist(p.keys()),node_size80,node_colorlist(p.values()),cmapplt.cm.Reds_r, )plt.xlim(-0.05, 1.05) plt.ylim(-0.05, 1.05) plt.axis(off) plt.show() 2.3  旅行商问题 这是旅行商问题的绘图解决方案示例 该函数用于生成解决方案 christofides其中给定一组节点它计算旅行者必须遵循的节点路线以最小化总成本。 代码示例  import matplotlib.pyplot as plt import networkx as nx import networkx.algorithms.approximation as nx_app import mathG nx.random_geometric_graph(20, radius0.4, seed3) pos nx.get_node_attributes(G, pos)# Depot should be at (0,0) pos[0] (0.5, 0.5)H G.copy()# Calculating the distances between the nodes as edges weight. for i in range(len(pos)):for j in range(i 1, len(pos)):dist math.hypot(pos[i][0] - pos[j][0], pos[i][1] - pos[j][1])dist distG.add_edge(i, j, weightdist)cycle nx_app.christofides(G, weightweight) edge_list list(nx.utils.pairwise(cycle))# Draw closest edges on each node only nx.draw_networkx_edges(H, pos, edge_colorblue, width0.5)# Draw the route nx.draw_networkx(G,pos,with_labelsTrue,edgelistedge_list,edge_colorred,node_size200,width3, )print(The route of the traveller is:, cycle) plt.show() 2.4  权重的灵活绘制 图形示例  代码示例  import matplotlib.pyplot as plt import networkx as nxG nx.Graph()G.add_edge(a, b, weight0.6) G.add_edge(a, c, weight0.2) G.add_edge(c, d, weight0.1) G.add_edge(c, e, weight0.7) G.add_edge(c, f, weight0.9) G.add_edge(a, d, weight0.3)elarge [(u, v) for (u, v, d) in G.edges(dataTrue) if d[weight] 0.5] esmall [(u, v) for (u, v, d) in G.edges(dataTrue) if d[weight] 0.5]pos nx.spring_layout(G, seed7) # positions for all nodes - seed for reproducibility# nodes nx.draw_networkx_nodes(G, pos, node_size700)# edges nx.draw_networkx_edges(G, pos, edgelistelarge, width6) nx.draw_networkx_edges(G, pos, edgelistesmall, width6, alpha0.5, edge_colorb, styledashed )# node labels nx.draw_networkx_labels(G, pos, font_size20, font_familysans-serif) # edge weight labels edge_labels nx.get_edge_attributes(G, weight) nx.draw_networkx_edge_labels(G, pos, edge_labels)ax plt.gca() ax.margins(0.08) plt.axis(off) plt.tight_layout() plt.show() 2.5 barabasi_albert模型1 import networkx as nx # 导入networkx包 import matplotlib.pyplot as pltG nx.random_graphs.barabasi_albert_graph(100, 2) # 生成一个BA无标度网络G nx.draw(G) # 绘制网络G plt.savefig(ba.png) # 输出方式1: 将图像存为一个png格式的图片文件 plt.show() # 输出方式2: 在窗口中显示这幅图像 2.6 barabasi_albert模型2 import networkx as nx # 导入networkx包 import matplotlib.pyplot as pltGnx.Graph() for u, v in nx.barabasi_albert_graph(10,2,seed1).edges():G.add_edge(u,v,weightrandom.uniform(0,0.4))posnx.spring_layout(G,iterations20) edgewidth[] for (u,v,d) in G.edges(dataTrue):nodeTmp list( G.get_edge_data(u,v).values())edgewidth.append(round(nodeTmp[0]*20,2)) nx.draw_networkx_edges(G,pos,widthedgewidth) nx.draw_networkx_nodes(G,pos) plt.show() 图例显示 #!-*- coding:utf8-*-import networkx as nx import matplotlib.pyplot as plt import randomGnx.Graph() for u, v in nx.barabasi_albert_graph(10,2,seed1).edges():G.add_edge(u,v,weightrandom.uniform(0,0.4)) posnx.spring_layout(G,iterations20)#以下语句绘制以带宽为线的宽度的图 nx.draw_networkx_edges(G,pos,width[float(d[weight]*10) for (u,v,d) in G.edges(dataTrue)]) nx.draw_networkx_nodes(G,pos) plt.show() 2.7 igraph操作 结果图 代码示例  import matplotlib.pyplot as plt import networkx as nx import igraph as ig G nx.dense_gnm_random_graph(30, 40, seed42)# largest connected component components nx.connected_components(G) largest_component max(components, keylen) H G.subgraph(largest_component)# convert to igraph h ig.Graph.from_networkx(H)# Plot the same network with NetworkX and igraph fig, (ax0, ax1) plt.subplots(nrows1, ncols2, figsize(12, 6))# NetworkX draw ax0.set_title(Plot with NetworkX draw) nx.draw_kamada_kawai(H, node_size50, axax0)# igraph draw ax1.set_title(Plot with igraph plot) layout h.layout_kamada_kawai() ig.plot(h, layoutlayout, targetax1) plt.axis(off) plt.show() Graph | NetworkX 入门教程 - 知乎 (zhihu.com)