2023-02-16 16:11:54 +08:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAACyIAAAsQCAYAAABS037aAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAC4jAAAuIwF4pT92AAEAAElEQVR4nOzdebidZWEu7metPe+dZO+MJIQwCciMiKIgoDhrHavVWmvLqcdTT61V69j253Dac1pFW6dWW6sWR9SCKCBQFChzmEkIkDCGzHP2PK7h98faWckmCbAUAeW+rytX1v6G932/bw25rs2zHgrVarUaAAAAAAAAAAAAAIAGFJ/sBQAAAAAAAAAAAAAAv3kEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANa36iJ+zt7c2VV15Z/3nRokVpa2t7opcBAAAAAAAAAAAAAL/RxsbGsnr16vrPL3zhC9PT0/OEzf+EB5GvvPLKvOENb3iipwUAAAAAAAAAAACA32o/+clP8vrXv/4Jm6/4hM0EAAAAAAAAAAAAAPzWEEQGAAAAAAAAAAAAABrW/ERPuGjRoik//+QnP8khhxzyRC8DAAAAAAAAAAAAAH6j3XfffXnDG95Q//nhOd1ftyc8iNzW1jbl50MOOSRHHXXUE70MAAAAAAAAAAAAAPit8vCc7q9b8QmdDQAAAAAAAAAAAAD4rSCIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAADQMEFkAAAAAAAAAAAAAKBhgsgAAAAAAAAAAAAAQMMEkQEAAAAAAAAAAACAhgkiAwAAAAAAAAAAAAANE0QGAAAAAAAAAAAAABomiAwAAAAAAAAAAAAANEwQGQAAAAAAAAAAAABomCAyAAAAAAAAAAAAANAwQWQAAAAAAAAAAAAAoGGCyAAAAAAAAAAAAABAwwSRAQAAAAAAAAAAAICGCSIDAAAAAAAAAAAAAA0TRAYAAAAAAAAAAAAAGiaIDAAAAAAAAAAAAAA0TBAZAAAAAAAAAAAAAGiYIDIAAAAAAAAAAAAA0DBBZAAAAAAAAAAAAACgYYLIAAAAAAAAAAAAAEDDBJEBAAAAAAAAAAAAgIYJIgMAAAAAAAAAAAAADRNEBgAAAAAAAAAAAAAaJogMAAAAAAAAAAAAADRMEBkAAAAAAAAAAAAAaJggMgAAAAAAAAAAAAD
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"text/plain": [
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"<Figure size 3600x3600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import networkx as nx\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
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"\n",
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"BomNodes = pd.read_csv('BomNodes.csv', index_col=0)\n",
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"BomNodes.set_index('Code', inplace=True)\n",
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"BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)\n",
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"BomCateNet.fillna(0, inplace=True)\n",
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"\n",
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"G = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())\n",
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"\n",
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"labels_dict = {}\n",
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"for code in G.nodes:\n",
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" labels_dict[code] = BomNodes.loc[code].to_dict()\n",
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"nx.set_node_attributes(G, labels_dict)\n",
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"\n",
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"pos = nx.nx_agraph.graphviz_layout(G, prog=\"twopi\", args=\"\")\n",
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"temp = {}\n",
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"for key, value in nx.get_node_attributes(G, 'Name').items():\n",
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" temp[key] = key + \" \"+ value\n",
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"node_labels = temp\n",
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"plt.figure(figsize=(12, 12), dpi=300)\n",
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"nx.draw_networkx_nodes(G, pos)\n",
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"nx.draw_networkx_edges(G, pos)\n",
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"nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=6)\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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2023-02-16 16:58:02 +08:00
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"execution_count": 75,
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2023-02-16 16:11:54 +08:00
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"metadata": {},
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2023-02-16 16:58:02 +08:00
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"outputs": [],
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2023-02-16 16:11:54 +08:00
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"source": [
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"dict_nodes = {0: sorted([node for node in G.nodes() if G.out_degree(node)==0])}\n",
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"level = 1\n",
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"while True:\n",
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" nodes = [list(G.predecessors(node)) for node in dict_nodes[level-1]]\n",
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" nodes = sorted(list(set([i for j in nodes for i in j])))\n",
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" if nodes:\n",
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" dict_nodes[level] = nodes\n",
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" level += 1\n",
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" else:\n",
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" break\n",
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"\n",
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"Firm = pd.read_csv(\"Firm.csv\")\n",
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"Firm.fillna(0, inplace=True)\n",
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"Firm_copy = Firm.copy()\n",
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"\n",
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"for tier in list(dict_nodes.keys())[1:]:\n",
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" for node in dict_nodes[tier]:\n",
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" list_neighbors = list(G.neighbors(node))\n",
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" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).all(axis=1)].to_list()\n",
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" if firm_list:\n",
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" Firm_copy.loc[firm_list, node] = 1\n",
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" Firm_copy.loc[firm_list, list_neighbors] = 0\n",
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"Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig')"
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]
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},
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{
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"cell_type": "code",
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2023-02-16 16:58:02 +08:00
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"execution_count": 89,
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2023-02-16 16:11:54 +08:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2023-02-16 16:58:02 +08:00
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"image/png": "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2023-02-16 16:11:54 +08:00
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"text/plain": [
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2023-02-16 16:58:02 +08:00
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"<Figure size 3600x3600 with 1 Axes>"
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2023-02-16 16:11:54 +08:00
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]
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},
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"metadata": {},
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2023-02-16 16:58:02 +08:00
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"output_type": "display_data"
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2023-02-16 16:11:54 +08:00
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}
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],
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"source": [
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2023-02-16 16:58:02 +08:00
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"firm_num_dict = {}\n",
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"for node in nx.nodes(G):\n",
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" firm_num_dict[node]= sum(Firm_copy[node]==1)\n",
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"nx.set_node_attributes(G, firm_num_dict, name=\"Num_Firm\")\n",
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"\n",
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"pos = nx.nx_agraph.graphviz_layout(G, prog=\"twopi\", args=\"\")\n",
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"temp = {}\n",
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"for key, value in nx.get_node_attributes(G, 'Num_Firm').items():\n",
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" # temp[key] = key + \" \"+ str(value)\n",
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" temp[key] = str(value)\n",
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"node_labels = temp\n",
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"plt.figure(figsize=(12, 12), dpi=300)\n",
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"nx.draw_networkx_nodes(G, pos)\n",
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"nx.draw_networkx_edges(G, pos)\n",
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"nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=6)\n",
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"plt.show()"
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2023-02-16 16:11:54 +08:00
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 71,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[1, 2, 3, 4, 5]"
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]
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},
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"execution_count": 71,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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|
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"list(dict_nodes.keys())[1:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
|
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"kernelspec": {
|
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"display_name": "base",
|
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|
"language": "python",
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"name": "python3"
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|
},
|
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"language_info": {
|
|
|
|
"codemirror_mode": {
|
|
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|
"name": "ipython",
|
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|
"version": 3
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|
},
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|
"file_extension": ".py",
|
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|
|
"mimetype": "text/x-python",
|
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|
"name": "python",
|
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|
|
"nbconvert_exporter": "python",
|
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|
|
"pygments_lexer": "ipython3",
|
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|
|
"version": "3.8.8"
|
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|
},
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|
"orig_nbformat": 4,
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|
"vscode": {
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|
|
"interpreter": {
|
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"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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