NetworkX-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Link:\n",
    "    \"\"\" Connection between two nodes \"\"\"\n",
    "\n",
    "    def __init__(self, x1, y1, x2, y2, weight=1):\n",
    "        self.x1 = x1\n",
    "        self.y1 = y1\n",
    "        self.x2 = x2\n",
    "        self.y2 = y2\n",
    "        self.weight = weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "\n",
    "    def __init__(self, x, y, act_func=None):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.act_func = act_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.Link object at 0x7fba2bd3b0f0>, <__main__.Link object at 0x7fba2bd3b240>, <__main__.Link object at 0x7fba2bd3b048>, <__main__.Link object at 0x7fba2bd3b0b8>, <__main__.Link object at 0x7fba2bd3b160>, <__main__.Link object at 0x7fba2bd3b198>, <__main__.Link object at 0x7fba2bd3b1d0>, <__main__.Link object at 0x7fba2bd3b128>, <__main__.Link object at 0x7fba2bd3b080>, <__main__.Link object at 0x7fba2bd3b320>, <__main__.Link object at 0x7fba2bd3b2e8>]\n"
     ]
    }
   ],
   "source": [
    "i1 = Link(-1,-1,-0.8,-0.8)\n",
    "i2 = Link(0,-1,-0.8,-0.8)\n",
    "i3 = Link(1,-1,-0.8,-0.8)\n",
    "h1 = Link(-0.8,-0.8, 0, 0)\n",
    "h2 = Link(0,-0.8,0,0)\n",
    "h3 = Link(0,0,0.5,0.5)\n",
    "h4 = Link(0,0,0.4,0.4)\n",
    "h5 = Link(0.6,0.6,0.7,0.7)\n",
    "h6 = Link(0.4,0.4,0,1)\n",
    "h7 = Link(-0.8,-0.8, 0, 1)\n",
    "o1 = Link(0.5,0.5,0,1)\n",
    "links = [i1, i2, i3, h1, h2, h3, h4, h5, h6, h7, o1]\n",
    "print(links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[G.add_edge((l.x1,l.y1), (l.x2,l.y2)) for l in links]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.drawing.nx_pylab.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_locs = [(-1,-1),(0, -1), (1, -1)]\n",
    "output_locs = [(0, 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=list(path for input in input_locs for output in output_locs for path in nx.all_simple_paths(G, source=input, target=output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges=list(set([row[col_i]+row[col_i+1] for row in r for col_i in range(len(row)-1)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 3 into shape (2,2,newaxis)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'reshape'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-100-8bb9b40c498d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    290\u001b[0m            [5, 6]])\n\u001b[1;32m    291\u001b[0m     \"\"\"\n\u001b[0;32m--> 292\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;31m# a downstream library like 'pandas'.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 3 into shape (2,2,newaxis)"
     ]
    }
   ],
   "source": [
    "np.reshape(r, (2, 2, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(-1, -1),\n",
       " (-0.8, -0.8),\n",
       " (0, -1),\n",
       " (0, 0),\n",
       " (0, 1),\n",
       " (0.4, 0.4),\n",
       " (0.5, 0.5),\n",
       " (1, -1)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([con for e in [(-1,-1), (0,-1), (1,-1)] for path in nx.all_simple_paths(G, source=e, target=(0,1)) for con in path])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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