IIabm/AmendFirm_20230216.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1\n",
      "['1.1.1', '1.1.2', '1.1.3']\n",
      "[]\n",
      "1.2\n",
      "['1.2.1', '1.2.2', '1.2.3']\n",
      "[]\n",
      "1.3.1\n",
      "['1.3.1.1', '1.3.1.2', '1.3.1.3', '1.3.1.4', '1.3.1.5', '1.3.1.6', '1.3.1.7']\n",
      "[]\n",
      "1.3.2\n",
      "['1.3.2.1']\n",
      "[58, 130, 135]\n",
      "1.3.3\n",
      "['1.3.3.1', '1.3.3.2', '1.3.3.3', '1.3.3.4', '1.3.3.5', '1.3.3.6', '1.3.3.7']\n",
      "[]\n",
      "1.3.4\n",
      "['1.3.4.1', '1.3.4.2', '1.3.4.3']\n",
      "[58, 77, 80, 81, 130]\n",
      "1.3.5\n",
      "['1.3.5.1']\n",
      "[114, 130, 151]\n",
      "1.4.1\n",
      "['1.4.1.1', '1.4.1.2', '1.4.1.3', '1.4.1.4', '1.4.1.5']\n",
      "[]\n",
      "1.4.2\n",
      "['1.4.2.1', '1.4.2.2', '1.4.2.3', '1.4.2.4', '1.4.2.5', '1.4.2.6', '1.4.2.7']\n",
      "[]\n",
      "1.4.3\n",
      "['1.4.3.1', '1.4.3.2', '1.4.3.3', '1.4.3.4', '1.4.3.5', '1.4.3.6']\n",
      "[]\n",
      "1.4.4\n",
      "['1.4.4.1', '1.4.4.2', '1.4.4.3', '1.4.4.4', '1.4.4.5']\n",
      "[]\n",
      "1.4.5\n",
      "['1.4.5.1', '1.4.5.2', '1.4.5.3', '1.4.5.4', '1.4.5.5', '1.4.5.6', '1.4.5.7', '1.4.5.8', '1.4.5.9']\n",
      "[]\n",
      "2\n",
      "['2.1', '2.2', '2.3']\n",
      "[]\n",
      "2.1.1\n",
      "['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
      "[22, 38, 60, 78, 80, 85, 99, 106, 117, 131, 143, 148]\n",
      "2.1.2\n",
      "['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
      "[6, 33, 47, 49, 58, 62, 79, 81, 82, 84, 99, 144, 149, 159, 165]\n",
      "2.1.3\n",
      "['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
      "[13, 22, 26, 73, 74, 79, 97, 99, 106, 108, 126, 135, 148]\n",
      "2.1.4.1\n",
      "['2.1.4.1.1', '2.1.4.1.2', '2.1.4.1.3', '2.1.4.1.4']\n",
      "[13, 22, 31, 45, 79, 81, 84, 99, 102, 115, 117, 137, 154, 163]\n",
      "2.1.4.2\n",
      "['2.1.4.2.1', '2.1.4.2.2']\n",
      "[13, 22, 31, 45, 79, 81, 84, 99, 102, 115, 117, 137, 154, 163]\n",
      "2.3\n",
      "['2.3.1', '2.3.2', '2.3.3']\n",
      "[13, 16, 22, 23, 57, 79, 84, 95, 99, 124, 126, 155, 161, 168]\n",
      "1\n",
      "['1.1', '1.2', '1.3', '1.4', '2']\n",
      "[]\n",
      "1.3\n",
      "['1.3.1', '1.3.2', '1.3.3', '1.3.4', '1.3.5']\n",
      "[]\n",
      "1.4\n",
      "['1.4.1', '1.4.2', '1.4.3', '1.4.4', '1.4.5']\n",
      "[]\n",
      "2\n",
      "['2.1', '2.2', '2.3']\n",
      "[]\n",
      "2.1\n",
      "['2.1.1', '2.1.2', '2.1.3', '2.1.4']\n",
      "[]\n",
      "2.1.4\n",
      "['2.1.4.1', '2.1.4.2']\n",
      "[13, 22, 31, 45, 79, 81, 84, 99, 102, 115, 117, 137, 154, 163]\n",
      "1\n",
      "['1.1', '1.2', '1.3', '1.4', '2']\n",
      "[]\n",
      "2\n",
      "['2.1', '2.2', '2.3']\n",
      "[]\n",
      "2.1\n",
      "['2.1.1', '2.1.2', '2.1.3', '2.1.4']\n",
      "[99]\n",
      "1\n",
      "['1.1', '1.2', '1.3', '1.4', '2']\n",
      "[]\n",
      "2\n",
      "['2.1', '2.2', '2.3']\n",
      "[]\n",
      "1\n",
      "['1.1', '1.2', '1.3', '1.4', '2']\n",
      "[]\n"
     ]
    }
   ],
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5', '2.1.1']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(nx.dfs_postorder_nodes(G, '2.1.1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dict_nodes.keys())[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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