Networkx_Entrepreneur-Uruguay.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "import pylab as plt\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_edge(63,64, color='black', weight=4)\n",
    "G.add_edge(64,65, color='grey', weight=1)\n",
    "G.add_edge(65,15, color='green', weight=1)\n",
    "G.add_edge(65,16, color='green', weight=1)\n",
    "G.add_edge(65,17, color='green', weight=1)\n",
    "G.add_edge(65,18, color='green', weight=1)\n",
    "G.add_edge(65,19, color='green', weight=1)\n",
    "G.add_edge(63,66, color='grey', weight=1)\n",
    "G.add_edge(66, 2, color='yellow', weight=1)\n",
    "G.add_edge(66, 4, color='yellow', weight=1)\n",
    "G.add_edge(66, 13, color='yellow', weight=1)\n",
    "G.add_edge(66, 15, color='yellow', weight=1)\n",
    "G.add_edge(66, 18, color='yellow', weight=1)\n",
    "G.add_edge(63, 67, color='grey', weight=1)\n",
    "G.add_edge(67, 1, color='orange', weight=1)\n",
    "G.add_edge(67, 6, color='orange', weight=1)\n",
    "G.add_edge(67, 7, color='orange', weight=1)\n",
    "G.add_edge(67, 8, color='orange', weight=1)\n",
    "G.add_edge(67, 9, color='orange', weight=1)\n",
    "G.add_edge(63, 68, color='grey', weight=1)\n",
    "G.add_edge(68, 2, color='red', weight=1)\n",
    "G.add_edge(68, 10, color='red', weight=1)\n",
    "G.add_edge(68, 15, color='red', weight=1)\n",
    "G.add_edge(63, 69, color='grey', weight=1)\n",
    "G.add_edge(69, 2, color='purple', weight=1)\n",
    "G.add_edge(69, 14, color='purple', weight=1)\n",
    "G.add_edge(63,70, color='grey', weight=1)\n",
    "G.add_edge(70, 2, color='blue', weight=1)\n",
    "G.add_edge(70, 3, color='blue', weight=1)\n",
    "G.add_edge(70, 5, color='blue', weight=1)\n",
    "G.add_edge(70, 11, color='blue', weight=1)\n",
    "G.add_edge(70, 12, color='blue', weight=1)\n",
    "G.add_edge(70, 17, color='blue', weight=1)\n",
    "G.add_edge(64, 65, color='black', weight=1)\n",
    "G.add_edge(65, 22, color='green', weight=1)\n",
    "G.add_edge(65, 23, color='green', weight=1)\n",
    "G.add_edge(65, 27, color='green', weight=1)\n",
    "G.add_edge(65, 33, color='green', weight=1)\n",
    "G.add_edge(65, 35, color='green', weight=1)\n",
    "G.add_edge(65, 37, color='green', weight=1)\n",
    "G.add_edge(65, 42, color='green', weight=1)\n",
    "G.add_edge(65, 44, color='green', weight=1)\n",
    "G.add_edge(65, 46, color='green', weight=1)\n",
    "G.add_edge(65, 49, color='green', weight=1)\n",
    "G.add_edge(65, 50, color='green', weight=1)\n",
    "G.add_edge(65, 52, color='green', weight=1)\n",
    "G.add_edge(65, 57, color='green', weight=1)\n",
    "G.add_edge(65, 58, color='green', weight=1)\n",
    "G.add_edge(65, 59, color='green', weight=1)\n",
    "G.add_edge(65, 62, color='green', weight=1)\n",
    "G.add_edge(64, 66, color='black', weight=1)\n",
    "G.add_edge(66, 25, color='yellow', weight=1)\n",
    "G.add_edge(66, 27, color='yellow', weight=1)\n",
    "G.add_edge(66, 32, color='yellow', weight=1)\n",
    "G.add_edge(66, 37, color='yellow', weight=1)\n",
    "G.add_edge(66, 43, color='yellow', weight=1)\n",
    "G.add_edge(66, 57, color='yellow', weight=1)\n",
    "G.add_edge(66, 58, color='yellow', weight=1)\n",
    "G.add_edge(66, 59, color='yellow', weight=1)\n",
    "G.add_edge(66, 61, color='yellow', weight=1)\n",
    "G.add_edge(64, 67, color='black', weight=1)\n",
    "G.add_edge(67, 26, color='orange', weight=1)\n",
    "G.add_edge(67, 29, color='orange', weight=1)\n",
    "G.add_edge(67, 31, color='orange', weight=1)\n",
    "G.add_edge(67, 36, color='orange', weight=1)\n",
    "G.add_edge(67, 39, color='orange', weight=1)\n",
    "G.add_edge(67, 40, color='orange', weight=1)\n",
    "G.add_edge(67, 45, color='orange', weight=1)\n",
    "G.add_edge(67, 53, color='orange', weight=1)\n",
    "G.add_edge(67, 54, color='orange', weight=1)\n",
    "G.add_edge(64, 68, color='black', weight=1)\n",
    "G.add_edge(68, 20, color='red', weight=1)\n",
    "G.add_edge(68, 24, color='red', weight=1)\n",
    "G.add_edge(68, 28, color='red', weight=1)\n",
    "G.add_edge(68, 30, color='red', weight=1)\n",
    "G.add_edge(68, 44, color='red', weight=1)\n",
    "G.add_edge(68, 55, color='red', weight=1)\n",
    "G.add_edge(68, 56, color='red', weight=1)\n",
    "G.add_edge(68, 60, color='red', weight=1)\n",
    "G.add_edge(64, 69, weight=1)\n",
    "G.add_edge(69, 25, color='purple', weight=1)\n",
    "G.add_edge(69, 30, color='purple', weight=1)\n",
    "G.add_edge(69, 33, color='purple', weight=1)\n",
    "G.add_edge(69, 34, weight=1)\n",
    "G.add_edge(69, 45, color='purple', weight=1)\n",
    "G.add_edge(69, 56, color='purple', weight=1)\n",
    "G.add_edge(69, 60, color='purple', weight=1)\n",
    "G.add_edge(64, 70, color='black', weight=1)\n",
    "G.add_edge(70, 21, color='blue', weight=1)\n",
    "G.add_edge(70, 37, color='blue', weight=1)\n",
    "G.add_edge(70, 38, color='blue', weight=1)\n",
    "G.add_edge(70, 41, color='blue', weight=1)\n",
    "G.add_edge(70, 43, color='blue', weight=1)\n",
    "G.add_edge(70, 45,color='blue',  weight=1)\n",
    "G.add_edge(70, 47, color='blue', weight=1)\n",
    "G.add_edge(70, 48, color='blue', weight=1)\n",
    "G.add_edge(70, 55, color='blue', weight=1)\n",
    "\n",
    "#H = nx.DiGraph(G)\n",
    "import matplotlib.pyplot as plt\n",
    "#Net=nx.complete_graph(G)\n",
    "#edges = G.edges()\n",
    "#colors = [G[u][v]['color'] for u,v in edges]\n",
    "#weights = [G[u][v]['weight'] for u,v in edges]\n",
    "#pos=nx.spring_layout(G)\n",
    "#nx.draw_networkx_nodes(G, pos, nodelist=['1. Acercamiento al mundo emprendedor',], node_color='r', node_size=25, alpha=0.8)\n",
    "\n",
    "pos = nx.spring_layout(G)\n",
    "\n",
    "nx.draw_networkx_nodes(G, pos, \n",
    "                       nodelist=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19],\n",
    "                       node_color='silver',\n",
    "                       node_size=500)\n",
    "nx.draw_networkx_nodes(G, pos,\n",
    "                       nodelist=[20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,52,53,54,55,56,57,58,59,60,61,62],\n",
    "                       node_color='dimgrey',\n",
    "                       node_size=500)  \n",
    "\t\t\t\t\t   \n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                      edgelist=[(65, 15), (65, 16), (65, 17), (65, 18), (65, 19), (65, 22), (65, 23), (65, 27), (65, 33), (65, 35), (65, 37), (65, 42), (65, 44), (65, 46), (65, 49), (65, 50), (65, 52), (65, 57), (65, 58), (65, 59), (65, 62)],\n",
    "                       width=1, edge_color='green')\n",
    "\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                      edgelist=[(66, 2), (66, 4), (66, 13), (66, 15), (66, 18), (66, 25), (66, 27), (66, 32), (66, 37), (66, 43), (66, 57), (66, 58), (66, 59), (66, 61)],\n",
    "                       width=1, edge_color='yellow')\n",
    "\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                      edgelist=[(67, 1), (67, 6), (67, 7), (67, 8), (67, 9), (67, 26), (67, 29), (67, 31), (67, 36), (67, 39), (67, 40), (67, 45), (67, 53), (67, 54)],\n",
    "                       width=1, edge_color='orange')\n",
    "\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                      edgelist=[(68, 2), (68, 10), (68, 15), (68, 20), (68, 24), (68, 28), (68, 30), (68, 44), (68, 55), (68, 56), (68, 60)],\n",
    "                       width=1, edge_color='red')\n",
    "\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                      edgelist=[(69, 2),(69, 14),(69, 25),(69, 30),(69, 33),(69, 34),(69, 45),(69, 56),(69, 60)],\n",
    "                       width=1, edge_color='purple')\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                      edgelist=[(70, 2),(70, 3),(70, 5), (70, 11), (70, 12), (70, 17), (70, 21),(70, 37), (70, 38), (70, 41),(70, 43),(70, 45), (70, 47),(70, 48),(70, 55)],\n",
    "                       width=1, edge_color='blue')\n",
    "#grey_edges\n",
    "\n",
    "#black_edges\n",
    "\n",
    "# labels = {}\n",
    "# labels[1] = str('ADPCEyM')\n",
    "# labels[2] ={'AJE'}\n",
    "# labels[3] ={'AC'}\n",
    "# labels[4] ={'ANII'}\n",
    "# labels[5] ={'CC'}\n",
    "# labels[6] ={'CDU'}\n",
    "# labels[7] ={'CEGETEC'}\n",
    "# labels[8] ={'C-Emprendedor'}\n",
    "# labels[9] ={'Deloitte-IEEM'}\n",
    "# labels[10] ={'CIDTI'}\n",
    "# labels[11] ={'PIE'}\t\n",
    "# labels[12] ={'CNCS'}\n",
    "# labels[13] ={'Da Vinci Labs'}\n",
    "# labels[14] ={'CUTI'}\n",
    "# labels[15] ={'DESEM'}\n",
    "# labels[16] ={'DINAPYME'}\n",
    "# labels[17] ={'Emprenur'}\n",
    "# labels[18] ={'Empresas en el día'}\n",
    "# labels[19] ={'Empretec'}\n",
    "# labels[20] ={'ENDEAVOR'}\n",
    "# labels[21] ={'Exporta Fácil'}\n",
    "# labels[22] ={'FONDO IDEAS'}\n",
    "# labels[23] ={'Fundación da Vinci'}\n",
    "# labels[24] ={'Fundación Ricaldoni'}\n",
    "# labels[25] ={'FUNDASOL'}\n",
    "# labels[26] ={'InCapital'}\n",
    "# labels[27] ={'BIOSPINN'}\n",
    "# labels[28] ={'GEPIAN'}\n",
    "# labels[29] ={'Idear'}\n",
    "# labels[30] ={'KHEM'}\n",
    "# labels[31] ={'Sinergia'}\n",
    "# labels[32] ={'INEFOP'}\n",
    "# labels[33] ={'Ingenio'}\n",
    "# labels[34] ={'IPRU'}\n",
    "# labels[35] ={'JCI'}\n",
    "# labels[36] ={'Emprecrea'}\n",
    "# labels[37] ={'Más Emprendedoras'}\n",
    "# labels[38] ={'NXTP LABS'}\n",
    "# labels[39] ={'OMEU'}\n",
    "# labels[40] ={'PIADE'}\n",
    "# labels[41] ={'Piso 40'}\n",
    "# labels[42] ={'RAFE'}\n",
    "# labels[43] ={'Red Inversores Ángeles - IEEM'}\n",
    "# labels[44] ={'Red Espacio Emprendedor'}\t\n",
    "# labels[45] ={'Red Promo'}\n",
    "# labels[46] ={'REPUBLICA MICROFINANZAS'}\n",
    "# labels[47] ={'SAINDESUR'}\n",
    "# labels[48] ={'SIGA'}\n",
    "# labels[49] ={'Sinergia CoWork'}\n",
    "# labels[50] ={'Socialab'}\n",
    "# labels[51] ={'Thales LAB'}\n",
    "# labels[52] ={'TOKAI VENTURES'}\n",
    "# labels[53] ={'UEU'}\n",
    "# labels[54] ={'Unidad Mypes IM'}\n",
    "# labels[55] ={'Nexo'}\n",
    "# labels[56] ={'Centro de Emprendedores UM'}\n",
    "# labels[57] ={'UTU'}\n",
    "# labels[58] ={'CIE'}\n",
    "# labels[59] ={'Uruguay XXI'}\n",
    "# labels[60] ={'WAYRA'}\n",
    "# labels[61] ={'Xeniors'}\n",
    "# labels[62] ={'YABT'}\n",
    "\n",
    "#nx.draw_networkx_labels(G, pos, font_size=16)\n",
    "nx.draw_networkx_labels(G,pos,font_size=7,font_family='sans-serif')\n",
    "\n",
    "#nx.draw_networkx(G,with_labels = True, node_size = 25) #edges=edges, edge_color=colors, width=weights)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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