Random Graph Generating (One Component).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import deque\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def BFS(start):\n",
    "    queue = deque()  \n",
    "    queue.append(start)\n",
    "    \n",
    "    visited = [False] * (nodes+1)\n",
    "    visited[start] = True\n",
    "    \n",
    "    while queue:\n",
    "        u = queue.popleft()\n",
    "        for v in G.adj[u]:\n",
    "            if not visited[v]:\n",
    "                queue.append(v)\n",
    "                visited[v] = True\n",
    "    visited[0] = True\n",
    "    for i in range(nodes+1):\n",
    "        if visited[i] == False:\n",
    "            return False\n",
    "    return True\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def CreateGraph(node, edge):\n",
    "    G = nx.Graph()\n",
    "    \n",
    "    for i in range(1, node+1):\n",
    "        G.add_node(i)\n",
    "        \n",
    "    for i in range(edge):\n",
    "        u, v = random.randint(1, node), random.randint(1, node)\n",
    "        G.add_edge(u, v)\n",
    "\n",
    "    return G\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawing Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def DrawGraph(G, color):\n",
    "    pos = nx.spring_layout(G)\n",
    "    nx.draw(G, pos, with_labels = True, node_color = color, edge_color = 'black' ,width = 1, alpha = 0.7)  #with_labels=true is to show the node number in the output graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Driving Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node: 8\n",
      "Edges: 16\n",
      "[1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    },
    {
     "data": {
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EGIAkKZwIz0XYluLxuCLRiCKRuUc0GpVdYyuxOyGnyylrwtL4q+O655P3qLahVuVl5XK6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\n3ltNWUGZLFmLZv1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdb12e1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node: 8\n",
      "Edges: 15\n",
      "[1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ewfTp0/Hss8/CYDAoXRb5IIYzEfmU8vJy3HjjjUqX4cbhcOCdd97Byy+/jEmT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdb133c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    nodes = int(input(\"Node: \"))\n",
    "    edges = int(input(\"Edges: \"))\n",
    "    G = CreateGraph(nodes, edges)\n",
    "    \n",
    "    print(G.nodes)\n",
    "    DrawGraph(G, \"green\")\n",
    "    plt.show()\n",
    "    \n",
    "    while not BFS(1):\n",
    "        nodes = int(input(\"Node: \"))\n",
    "        edges = int(input(\"Edges: \"))\n",
    "        G = CreateGraph(nodes, edges)\n",
    "\n",
    "        print(G.nodes)\n",
    "        DrawGraph(G, \"green\")\n",
    "        plt.show()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}