{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_nodes_from([1,2,3])\n",
    "G.add_edge(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n",
      "[(3, 4)]\n"
     ]
    }
   ],
   "source": [
    "print(G.nodes())\n",
    "print(G.edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-2623cf771fd5>:3: UserWarning: Matplotlib is currently using ps, which is a non-GUI backend, so cannot show the figure.\n",
      "  plt.show()\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "nx.draw(G)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 0.125)\n",
      "(2, 1, 0.125)\n",
      "(3, 4, 0.375)\n",
      "(4, 3, 0.375)\n"
     ]
    }
   ],
   "source": [
    "FG = nx.Graph()\n",
    "FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2), (3, 4, 0.375)])\n",
    "for n, nbrs in FG.adj.items():\n",
    "   for nbr, eattr in nbrs.items():\n",
    "       wt = eattr['weight']\n",
    "       if wt < 0.5: print(f\"({n}, {nbr}, {wt:.3})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 0.125)\n",
      "(3, 4, 0.375)\n"
     ]
    }
   ],
   "source": [
    "for (u, v, wt) in FG.edges.data('weight'):\n",
    "    if wt < 0.5:\n",
    "        print(f\"({u}, {v}, {wt:.3})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ItemsView(AdjacencyView({1: {2: {'weight': 0.5}}, 2: {}, 3: {1: {'weight': 0.75}}}))\n",
      "[3]\n",
      "[2]\n"
     ]
    }
   ],
   "source": [
    "DG = nx.DiGraph()\n",
    "DG.add_weighted_edges_from([(1, 2, 0.5), (3, 1, 0.75)])\n",
    "DG.out_degree(1, weight='weight')\n",
    "print(DG.adj.items())\n",
    "print(list(DG.predecessors(1)))\n",
    "print(list(DG.successors(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 0 1 0 0 1 1 0 0]\n",
      " [1 0 1 1 1 1 0 1 0 1]\n",
      " [0 1 0 0 1 1 1 1 0 1]\n",
      " [0 0 1 0 0 0 0 1 1 1]\n",
      " [1 0 0 1 1 1 0 1 1 1]\n",
      " [0 0 0 0 0 0 0 0 1 1]\n",
      " [0 0 1 0 0 0 1 1 1 1]\n",
      " [1 0 1 0 0 1 1 0 0 0]\n",
      " [1 1 1 1 1 0 1 0 0 0]\n",
      " [1 1 1 0 0 0 0 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.random.randint(0, 2, size=(10, 10))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = nx.DiGraph(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 1., 0., 0., 1., 1., 0., 0.],\n",
       "       [1., 0., 1., 1., 1., 1., 0., 1., 0., 1.],\n",
       "       [0., 1., 0., 0., 1., 1., 1., 1., 0., 1.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 1., 1., 1.],\n",
       "       [1., 0., 0., 1., 1., 1., 0., 1., 1., 1.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
       "       [0., 0., 1., 0., 0., 0., 1., 1., 1., 1.],\n",
       "       [1., 0., 1., 0., 0., 1., 1., 0., 0., 0.],\n",
       "       [1., 1., 1., 1., 1., 0., 1., 0., 0., 0.],\n",
       "       [1., 1., 1., 0., 0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.nodes()\n",
    "D.edges()\n",
    "nx.to_numpy_array(D)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5ef0042cb263260037aa2928643ae94e240dd3afaec7872ebebe4f07619ddd0c"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 ('ml')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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