164 lines
4.9 KiB
Python
164 lines
4.9 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("analysis\\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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# pie
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count_prod_trim = count_prod[count_prod['count'] > 50]
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plt.pie(count_prod_trim['count'], labels=count_prod_trim['Name'])
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plt.savefig("analysis\\count_prod_pie")
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plt.close()
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# prod_networkx
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BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
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BomNodes.set_index('Code', inplace=True)
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BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
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BomCateNet.fillna(0, inplace=True)
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G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph())
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labels_dict = {}
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for code in G.nodes:
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node_attr = BomNodes.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)
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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, labels_dict)
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# print(labels_dict)
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pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
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dict_node_name = nx.get_node_attributes(G, 'Name')
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node_labels = {}
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for node in nx.nodes(G):
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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, '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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fig = plt.figure(figsize=(10, 10), dpi=300)
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nx.draw(G,
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pos,
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node_size=list(nx.get_node_attributes(G, 'node_size').values()),
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labels=node_labels,
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font_size=6,
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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='grey')
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sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
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sm._A = []
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position = fig.add_axes([0.01, 0.05, 0.01, 0.3])
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cb = plt.colorbar(sm, fraction=0.01, cax=position)
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cb.ax.tick_params(labelsize=8)
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cb.outline.set_visible(False)
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plt.savefig("analysis\\count_prod_network")
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plt.close()
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# dcp_prod
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count_dcp = pd.read_csv("analysis\\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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'up_name_product',
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'down_id_product',
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'down_name_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('analysis\\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'] > 50]
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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('BomNodes.csv', index_col=0)
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BomNodes.set_index('Code', 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="dot", args="")
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node_labels = nx.get_node_attributes(g_bom, 'Name')
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# rename node 1
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node_labels['1'] = '解决方案'
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temp = {}
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for key, value in node_labels.items():
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temp[key] = key + " " + value
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node_labels = temp
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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 = {}
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for node, p in pos.items():
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pos_new[node] = (p[1], p[0])
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fig = plt.figure(figsize=(6, 10), dpi=300)
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# plt.subplots_adjust(right=0.7)
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nx.draw(g_bom,
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pos_new,
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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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plt.axis('off')
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axis = plt.gca()
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axis.set_xlim([1.2*x for x in axis.get_xlim()])
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axis.set_ylim([1.2*y for y in axis.get_ylim()])
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sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
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sm._A = []
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position = fig.add_axes([0.75, 0.1, 0.01, 0.2])
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cb = plt.colorbar(sm, fraction=0.01, cax=position)
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cb.ax.tick_params(labelsize=8)
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cb.outline.set_visible(False)
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plt.savefig("analysis\\count_dcp_prod_network")
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plt.close()
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