IIabm/AmendFirm_20230216.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "\n",
    "BomNodes = pd.read_csv('BomNodes.csv', index_col=0)\n",
    "BomNodes.set_index('Code', inplace=True)\n",
    "BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)\n",
    "BomCateNet.fillna(0, inplace=True)\n",
    "\n",
    "G = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())\n",
    "\n",
    "labels_dict = {}\n",
    "for code in G.nodes:\n",
    "    labels_dict[code] = BomNodes.loc[code].to_dict()\n",
    "nx.set_node_attributes(G, labels_dict)\n",
    "\n",
    "pos = nx.nx_agraph.graphviz_layout(G, prog=\"twopi\", args=\"\")\n",
    "temp = {}\n",
    "for key, value in nx.get_node_attributes(G, 'Name').items():\n",
    "    temp[key] = key + \" \"+ value\n",
    "node_labels = temp\n",
    "plt.figure(figsize=(12, 12), dpi=300)\n",
    "nx.draw_networkx_nodes(G, pos)\n",
    "nx.draw_networkx_edges(G, pos)\n",
    "nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_nodes = {0: sorted([node for node in G.nodes() if G.out_degree(node)==0])}\n",
    "level = 1\n",
    "while True:\n",
    "    nodes = [list(G.predecessors(node)) for node in dict_nodes[level-1]]\n",
    "    nodes = sorted(list(set([i for j in nodes for i in j])))\n",
    "    if nodes:\n",
    "        dict_nodes[level] = nodes\n",
    "        level += 1\n",
    "    else:\n",
    "        break\n",
    "\n",
    "Firm = pd.read_csv(\"Firm.csv\")\n",
    "Firm.fillna(0, inplace=True)\n",
    "Firm_copy = Firm.copy()\n",
    "\n",
    "for tier in list(dict_nodes.keys())[1:]:\n",
    "    for node in dict_nodes[tier]:\n",
    "        list_neighbors = list(G.neighbors(node))\n",
    "        firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).all(axis=1)].to_list()\n",
    "        if firm_list:\n",
    "            Firm_copy.loc[firm_list, node] = 1\n",
    "            Firm_copy.loc[firm_list, list_neighbors] = 0\n",
    "Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "firm_num_dict = {}\n",
    "for node in nx.nodes(G):\n",
    "    firm_num_dict[node]= sum(Firm_copy[node]==1)\n",
    "nx.set_node_attributes(G, firm_num_dict, name=\"Num_Firm\")\n",
    "\n",
    "pos = nx.nx_agraph.graphviz_layout(G, prog=\"twopi\", args=\"\")\n",
    "temp = {}\n",
    "for key, value in nx.get_node_attributes(G, 'Num_Firm').items():\n",
    "    # temp[key] = key + \" \"+ str(value)\n",
    "    temp[key] = str(value)\n",
    "node_labels = temp\n",
    "plt.figure(figsize=(12, 12), dpi=300)\n",
    "nx.draw_networkx_nodes(G, pos)\n",
    "nx.draw_networkx_edges(G, pos)\n",
    "nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dict_nodes.keys())[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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