224 lines
7.2 KiB
Python
224 lines
7.2 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import networkx as nx
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plt.rcParams['font.sans-serif'] = 'SimHei'
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count_prod = pd.read_csv("output_result/risk/count_prod.csv")
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print(count_prod)
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# category
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print(count_prod.describe())
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# prod_networkx
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# BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
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# BomNodes.set_index('Code', inplace=True)
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# BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
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# BomCateNet.fillna(0, inplace=True)
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bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
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bom_nodes['Code'] = bom_nodes['Code'].astype(str)
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bom_nodes.set_index('Index', inplace=True)
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bom_cate_net = pd.read_csv('input_data/input_product_data/合成结点.csv')
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g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())
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labels_dict = {}
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for code in g_bom.nodes:
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node_attr = bom_nodes.loc[code].to_dict()
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index_list = count_prod[count_prod['id_product'] == code].index.tolist()
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index = index_list[0] if len(index_list) == 1 else -1
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node_attr['count'] = count_prod['count'].get(index, 0)
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node_attr['node_size'] = (count_prod['count'].get(index, 0))/10
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node_attr['node_color'] = count_prod['count'].get(index, 0)
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labels_dict[code] = node_attr
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nx.set_node_attributes(g_bom, labels_dict)
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# print(labels_dict)
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pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
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dict_node_name = nx.get_node_attributes(g_bom, 'Name')
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node_labels = {}
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for node in nx.nodes(g_bom):
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node_labels[node] = f"{node} {str(dict_node_name[node])}"
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# node_labels[node] = f"{str(dict_node_name[node])}"
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colors = list(nx.get_node_attributes(g_bom, 'node_color').values())
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vmin = min(colors)
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vmax = max(colors)
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cmap = plt.cm.Blues
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# 创建绘图对象
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fig = plt.figure(figsize=(10, 10), dpi=300)
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ax = fig.add_subplot(111)
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# 绘制网络图（优化样式参数）
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nx.draw(g_bom, pos,
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node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()),
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labels=node_labels,
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font_size=3,
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node_color=colors,
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cmap=cmap,
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vmin=vmin,
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vmax=vmax,
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edge_color='#808080', # 中性灰
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width=0.3,
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edgecolors='#404040',
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linewidths=0.2)
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# 创建颜色条（修正实现方式）
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sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
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sm.set_array([])
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# 设置颜色条位置和样式
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cax = fig.add_axes([0.88, 0.3, 0.015, 0.4]) # 右侧垂直对齐
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cb = plt.colorbar(sm, cax=cax)
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cb.ax.tick_params(labelsize=4, width=0.5, colors='#333333')
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cb.outline.set_linewidth(0.5)
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cb.set_label('Risk Level', fontsize=5, labelpad=2)
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# 添加图元信息
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ax.set_title("Production Risk Network", fontsize=6, pad=8, color='#2F2F2F')
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plt.text(0.5, 0.02, 'Data: USTB Production System | Viz: DeepSeek-R1',
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ha='center', fontsize=3, color='#666666',
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transform=fig.transFigure)
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# 调整边界和保存
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plt.subplots_adjust(left=0.05, right=0.85, top=0.95, bottom=0.1) # 适应颜色条
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plt.savefig(r"output_result/risk/count_prod_network.png", # 规范路径格式
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dpi=600,
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bbox_inches='tight',
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pad_inches=0.05,
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transparent=False)
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plt.close()
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# dcp_prod
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count_dcp = pd.read_csv("output_result/risk/count_dcp.csv",
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dtype={
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'up_id_firm': str,
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'down_id_firm': str
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})
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count_dcp_prod = count_dcp.groupby(
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['up_id_product',
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'down_id_product'])['count'].sum()
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count_dcp_prod = count_dcp_prod.reset_index()
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count_dcp_prod.sort_values('count', inplace=True, ascending=False)
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count_dcp_prod.to_csv('output_result\\risk\\count_dcp_prod.csv',
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index=False,
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encoding='utf-8-sig')
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count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 1000]
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# print(count_dcp_prod)
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list_prod = count_dcp_prod['up_id_product'].tolist(
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) + count_dcp['down_id_product'].tolist()
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list_prod = list(set(list_prod))
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# init graph bom
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BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
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BomNodes.set_index('Index', inplace=True)
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g_bom = nx.MultiDiGraph()
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g_bom.add_nodes_from(list_prod)
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bom_labels_dict = {}
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for code in list_prod:
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dct_attr = BomNodes.loc[code].to_dict()
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bom_labels_dict[code] = dct_attr
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nx.set_node_attributes(g_bom, bom_labels_dict)
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count_max = count_dcp_prod['count'].max()
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count_min = count_dcp_prod['count'].min()
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k = 5 / (count_max - count_min)
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for _, row in count_dcp_prod.iterrows():
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# print(row)
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lst_add_edge = [(
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row['up_id_product'],
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row['down_id_product'],
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{
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'count': row['count']
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})]
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g_bom.add_edges_from(lst_add_edge)
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# dcp_networkx
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pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
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node_labels = nx.get_node_attributes(g_bom, 'Name')
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temp = {}
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for key, value in node_labels.items():
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temp[key] = str(key) + " " + value
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node_labels = temp
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node_labels ={
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38: 'SiC Substrate',
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39: 'GaN Substrate',
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40: 'Si Substrate',
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41: 'AlN Substrate',
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42: 'DUV LED Substrate',
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43: 'InP Substrate',
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44: 'Mono-Si Wafer',
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45: 'Poly-Si Wafer',
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46: 'InP Cryst./Wafer',
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47: 'SiC Cryst./Wafer',
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48: 'GaAs Wafer',
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49: 'GaN Cryst./Wafer',
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50: 'Si Epi Wafer',
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51: 'SiC Epi Wafer',
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52: 'AlN Epi',
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53: 'GaN Epi',
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54: 'InP Epi',
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55: 'LED Epi Wafer',
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90: 'Power Devices',
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91: 'Diode',
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92: 'Transistor',
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93: 'Thyristor',
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94: 'Rectifier',
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95: 'IC Fab',
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99: 'Wafer Test'
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}
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colors = nx.get_edge_attributes(g_bom, "count")
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colors = [w for (n1, n2, _), w in colors.items()]
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vmin = min(colors)
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vmax = max(colors)
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cmap = plt.cm.Blues
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pos_new = {node: (p[1], p[0]) for node, p in pos.items()} # 字典推导式优化
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fig = plt.figure(figsize=(8, 8), dpi=300)
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plt.subplots_adjust(right=0.85) # 关键调整：右侧保留15%空白
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# 使用Axes对象精准控制
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main_ax = fig.add_axes([0.1, 0.1, 0.75, 0.8]) # 主图占左75%宽，上下各留10%边距
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nx.draw(g_bom, pos_new,
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ax=main_ax,
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node_size=50,
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labels=node_labels,
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font_size=5,
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width=1.5,
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edge_color=colors,
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edge_cmap=cmap,
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edge_vmin=vmin,
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edge_vmax=vmax,
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)
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main_ax.axis('off')
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# 颜色条定位系统
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cbar_ax = fig.add_axes([0.86, 0.15, 0.015, 0.3]) # 右边缘86%位置，底部15%起，占30%高度
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sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
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sm._A = [] # 必需的空数组
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# 微调颜色条样式
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cbar = fig.colorbar(sm, cax=cbar_ax, orientation='vertical')
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cbar.ax.tick_params(labelsize=4,
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width=0.3, # 刻度线粗细
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length=1.5, # 刻度线长度
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pad=0.8) # 标签与条间距
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cbar.outline.set_linewidth(0.5) # 边框线宽
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# 输出前验证边界
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print(f"Colorbar position: {cbar_ax.get_position().bounds}") # 应输出(0.86,0.15,0.015,0.3)
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# 专业级保存参数
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plt.savefig("output_result/risk/count_dcp_prod_network.png",
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dpi=900,
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bbox_inches='tight', # 自动裁剪白边
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pad_inches=0.05, # 保留0.05英寸边距
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metadata={'CreationDate': None}) # 避免时间戳污染元数据
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plt.close()
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