isa-create-sample-assay-plan-lc-dad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ISA Create - Sample Assay Plan as a Graph: LC-Diode-Array-Assay Spectrometry\n",
    "Here we are showing how from a JSON-like dictionary describing an NMR experiment you can create a full SampleAssayPlan as a graph and visualize how this looks like\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from isatools.model import *\n",
    "from isatools.create.models import *\n",
    "import networkx as nx\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import matplotlib.pyplot as plt\n",
    "import pydot\n",
    "import math\n",
    "from graphviz import Digraph\n",
    "import pygraphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from: https://stackoverflow.com/questions/29586520/can-one-get-hierarchical-graphs-from-networkx-with-python-3/29597209\n",
    "def hierarchy_pos(G, root=None, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5):\n",
    "\n",
    "    '''\n",
    "    From Joel's answer at https://stackoverflow.com/a/29597209/2966723.  \n",
    "    Licensed under Creative Commons Attribution-Share Alike \n",
    "\n",
    "    If the graph is a tree this will return the positions to plot this in a \n",
    "    hierarchical layout.\n",
    "\n",
    "    G: the graph (must be a tree)\n",
    "\n",
    "    root: the root node of current branch \n",
    "    - if the tree is directed and this is not given, \n",
    "      the root will be found and used\n",
    "    - if the tree is directed and this is given, then \n",
    "      the positions will be just for the descendants of this node.\n",
    "    - if the tree is undirected and not given, \n",
    "      then a random choice will be used.\n",
    "\n",
    "    width: horizontal space allocated for this branch - avoids overlap with other branches\n",
    "\n",
    "    vert_gap: gap between levels of hierarchy\n",
    "\n",
    "    vert_loc: vertical location of root\n",
    "\n",
    "    xcenter: horizontal location of root\n",
    "    '''\n",
    "# NOTE: This was commented out for testing with ISA-API output (a DiGraph)   \n",
    "#     if not nx.is_tree(G):\n",
    "#         raise TypeError('cannot use hierarchy_pos on a graph that is not a tree')\n",
    "\n",
    "    if root is None:\n",
    "        if isinstance(G, nx.DiGraph):\n",
    "            root = next(iter(nx.topological_sort(G)))  #allows back compatibility with nx version 1.11\n",
    "        else:\n",
    "            root = random.choice(list(G.nodes))\n",
    "\n",
    "    def _hierarchy_pos(G, root, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5, pos = None, parent = None):\n",
    "        '''\n",
    "        see hierarchy_pos docstring for most arguments\n",
    "\n",
    "        pos: a dict saying where all nodes go if they have been assigned\n",
    "        parent: parent of this branch. - only affects it if non-directed\n",
    "\n",
    "        '''\n",
    "\n",
    "        if pos is None:\n",
    "            pos = {root:(xcenter,vert_loc)}\n",
    "        else:\n",
    "            pos[root] = (xcenter, vert_loc)\n",
    "        children = list(G.neighbors(root))\n",
    "        if not isinstance(G, nx.DiGraph) and parent is not None:\n",
    "            children.remove(parent)  \n",
    "        if len(children)!=0:\n",
    "            dx = width/len(children) \n",
    "            nextx = xcenter - width/2 - dx/2\n",
    "            for child in children:\n",
    "                nextx += dx\n",
    "                pos = _hierarchy_pos(G,child, width = dx, vert_gap = vert_gap, \n",
    "                                    vert_loc = vert_loc-vert_gap, xcenter=nextx,\n",
    "                                    pos=pos, parent = root)\n",
    "        return pos\n",
    "\n",
    "\n",
    "    return _hierarchy_pos(G, root, width, vert_gap, vert_loc, xcenter)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the structure of our sampling and assay plan, using a Python dictionary. From it we create a full `isatools.create.models.SampleAssayPlan` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pv_combination: ()\n",
      "count: 0, prev_node: sample_000\n",
      "count: 0, prev_node: extraction_000_000\n",
      "count: 0, prev_node: extraction_000_000\n",
      "pv_combination: ('Shimadzu DAD 400',)\n",
      "count: 0, prev_node: extract_000_000\n",
      "count: 1, prev_node: extract_001_000\n",
      "count: 0, prev_node: lcdad_spectroscopy_000_000\n",
      "count: 1, prev_node: lcdad_spectroscopy_000_001\n"
     ]
    }
   ],
   "source": [
    "lcdad_assay_dict = OrderedDict([\n",
    "            ('sample', [\n",
    "                {\n",
    "                    'node_type': SAMPLE,\n",
    "                    'characteristics_category': 'organism part',\n",
    "                    'characteristics_value': 'leaf',\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': None,\n",
    "                    'is_input_to_next_protocols': True\n",
    "                }\n",
    "            ]),\n",
    "            ('extraction', {}),\n",
    "            ('extract', [\n",
    "                {\n",
    "                    'node_type': SAMPLE,\n",
    "                    'characteristics_category': 'extract type',\n",
    "                    'characteristics_value': 'supernatant',\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': None,\n",
    "                    'is_input_to_next_protocols': True\n",
    "                },\n",
    "                {\n",
    "                    'node_type': SAMPLE,\n",
    "                    'characteristics_category': 'extract type',\n",
    "                    'characteristics_value': 'pellet',\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': None,\n",
    "                    'is_input_to_next_protocols': True\n",
    "                }\n",
    "            ]),\n",
    "            ('lcdad_spectroscopy', {\n",
    "                'instrument': ['Shimadzu DAD 400'],\n",
    "            }),\n",
    "            ('raw_spectral_data_file', [\n",
    "                {\n",
    "                    'node_type': DATA_FILE,\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': 2,\n",
    "                    'is_input_to_next_protocols': False\n",
    "                }\n",
    "            ])\n",
    "        ])\n",
    "lcdad_assay_plan = SampleAndAssayPlan.from_sample_and_assay_plan_dict(lcdad_assay_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `lcdad_assay_plan` object is a graph. Let's which are its `nodes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx_graph = lcdad_assay_plan.as_networkx_graph()\n",
    "# set(nx_graph.nodes)\n",
    "nx_graph.number_of_nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we print the `links` or `edges` of the graph"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# set(nx_graph.edges)\n",
    "nx_graph.number_of_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx_graph.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We output is as a `networkx` graph and we visualize it using `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nx.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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.draw(nx_graph,pos=nx.spring_layout(G),node_color=range(G.number_of_nodes()),cmap=plt.cm.Blues, with_labels=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SG1 = G.subgraph(['sample_000','extraction_000_000','extract_000_000','extract_001_000','labeling_000_000','labeling_000_003','labeled_extract_000_000','labeled_extract_000_003'])\n",
    "# print(list(SG.edges))\n",
    "pos1 = hierarchy_pos(SG1,'sample_000')    \n",
    "nx.draw(SG1, pos=pos1, with_labels=True,node_color = 'b')\n",
    "plt.savefig('hierarchy1.png')\n",
    "\n",
    "\n",
    "# SG2 = G.subgraph(['sample_001','extraction_000_001','extract_000_001','extract_001_001','labeling_000_001','labeling_000_004','labeled_extract_000_001','labeled_extract_000_004'])\n",
    "# # print(list(SG.edges))\n",
    "# pos2 = hierarchy_pos(SG2,'sample_001')    \n",
    "# nx.draw(SG2, pos=pos2, with_labels=True,node_color = 'pink')\n",
    "# plt.savefig('hierarchy2.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Digraph.gv.pdf'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating a graphviz compatible output\n",
    "dot = Digraph()\n",
    "for node in nx_graph.nodes:\n",
    "    dot.node(node)\n",
    "dot.edges(nx_graph.edges)\n",
    "filename=dot.filename\n",
    "# print(dot.source)\n",
    "dot.graph_attr['rankdir'] = 'LR' # to layout from left to right (horizontal), rather than top to bottom (vertical)\n",
    "dot.render(filename, view=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nx.draw_networkx_edges(nx_graph,pos=nx.spring_layout(nx_graph))\n",
    "# fig = go.Figure(data=[nx_graph.nodes,nx_graph.edges])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nx.draw(nx_graph, with_labels=False, font_weight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zwyCQpMbWhCcOd65+a7uOtCcyeo32zoRefnuXPQMh6wzD0LJly7R06VJ98MEHTo8DFyL4AiaetGzpORlG17lCt/u0sS3j1zAtae8hb2y46MJz+3AyBB8AX+K5fTgRgi9gomHDlnfAliVFQu6/Q8ag0sw7hyFDGtKv0IZpkEuRSEQrVqzQnXfeqYMHDzo9DlyE4AuYvEhIsUjm/7eXFkY8cWuor10wQsX5kYxeIz8W0dXjh9s0EXKpqqpKN9xwg+6++26nR4GLEHwBYxiGRvQvUCbLWtiQRpR5YwO6avxwRTMM+mFlhRpfMcCmiZBrdPtwPIIvgIaW5mf0+eGQoQFFUZumya5IOKRvf+Vs5UXT+1EvzIvoO9O/aPNUyCW6fTgewRdAkZCh0YOL0tr6QoY09rQ+njjM2e2fp1VqSGmhwr38hmORkEYPK9GNk0dmaTLkCt0+HI17dQYYN6k+sbxoWOUDivQaN6n2DZ7bh25sfAF25sAiHd71J3W0tSp8kmUoHOo6rzc40urJ0JOk4QOK9dsl12lCxUAVxMInvCI1FgkpPxrWZecO1YaHriX0fIRuH7oRfAHW2Nio+bfdpP4dezRmSLGKYmEZ6gq5sCEZkvrmR1Q5tI9++/xynXvWCC1evFjxuDfu03m8QSUFWv/A1XrjX6/VLZdWqCAWVn4srKL8iPKjYRXnRzTnK2P0+6Vf1aq7/0HF+d44j4nU0e2DxKHOQJs3b54sy9KPf/zjz/8snjSVSFqSIUVDhiLhrvdGr776qq688kqFw2ENGzZMP/vZzzR16lSnRrdFZyKpQ0c6dLgtoT4FUZX1yfv8+4V/8dw+EHwB9dZbb+n666/Xe++9p9LS0lN+fENDgyZNmqSWlpbP/2zt2rW68sorszkmkBU8ty/YeHsbQPF4XHPmzNHSpUtTCj1JGjJkiOLxuAzDUCgU0ve+9z1deumlWZ4UyA66fcFG8AXQ0qVLNWzYMN14440pf073IaGqqiqNGjVKY8aMUSzmnaewA0ej2xdsHOoMmG3btqmqqkqbN2/WGWec0avP3blzp4YPH65NmzZpxowZeu+999SvX78sTQpkl2VZmj59uiZNmqTa2lqnx0EOEXwBYlmWrr76al1yySUZ37tw/vz5SiQSWr58uU3TAblHty+YCL4AWblypR544AFt2bJF0Whml+o3NTVp7NixWrlypS666CKbJgRyr66uTmvWrNG6des8dUcipI9zfAHR2NioO+64QytWrMg49CSppKREjz76KOdI4Hl0+4KHjS8geursZcqyLE2bNk2TJ0/Wvffea9vrArlGty9YCL4A6G1nrze2b9+uCRMmaNOmTTrzzDNtfW0gl+j2BQfB53PxeFzjx49XbW2tvv71r2flayxZskSvvvqq1q5dyzkSeFZzc7MqKytVX1+vKVOmOD0OsohzfD6XTmevtxYsWKC9e/fq2WefzdrXALKNbl9wsPH5WCadvd7auHEj3T54Ht2+YCD4fKq7s3fxxRfrnnvuycnXpNsHP6Db538En0/Z2dlLFd0++AXdPn/jHJ8PdXf2li9fnrPQk+j2wT/o9vkbG58PzZs3T6Zp6ic/+UnOvzbdPvgF3T7/Ivh8pruz19DQ4NhFJnT74Bd0+/yJ4POR7s5eTU2NbrrpJkdnWbJkidavX69XXnmFcyTwLLp9/sQ5Ph9ZunSpTjvttKwV1XtjwYIF2rdvH90+eBrdPn9i4/OJ7s7e7373O40cOdLpcSTR7YM/0O3zH4LPB5zo7KWKbh/8gG6fvxB8PuBEZy9VdPvgF3T7/INzfB7nVGcvVXT74Bd0+/yDjc/jnOzspYpuH/yCbp8/EHwe5obOXqro9sEv6PZ5H8HnUW7q7KWK5/bBD+j2eR/n+DzKTZ29VPHcPvgB3T7vY+PzIDd29lJFtw9+QLfP2wg+j3FzZy9VR3f7LMtSa6epjoSppGkpZEiRcEjFeWGFQxwOhXvR7fMugs9j3NzZS1VTU5MmffkCPfPCL1U6cJhMSzr6h7A77orywiotjCg/yhF5uBPdPm8i+DyksbFRlZWVWrVqlS688EKnx0lbR9zUtk8PK5k0lZeff9KPNST1K4qoX2GEXyxwnUQioaqqKt15552aOXOm0+MgRQSfh3ihs3cqHQlTuw91qDc/dYakksKwBhTHsjYXkC66fd5D8HmElzp7J2KalrYfaJeZxk+cIWlw35iK88O2zwVkim6ft3DyxAPi8bjmzJmjRx55xLOhJ0nN7YlebXpHsyQdaInbOg9gl8WLF2vt2rXasGGD06MgBQSfB3R39rxSVO+JZVlqbE0qk8MLiaSl9rhp20yAXej2eQuHOl3Oy529o7XFk9pzqDOj4JOk4rywhpRwrg/uQ7fPOwg+F/NDZ69bU1tC+w/HMw6+aNjQF8pOfiUo4BS6fd7AoU4XW7VqlXbv3q2FCxc6PUrGTMvKOPS6Xwdwq/LyctXU1Gju3Llip3Avgs+l3P6cvd4KyZAdLbwQXT64HM/tcz8OdbqUHzp7R2vpSGpvc2faV3V2K4iGNKxfnj1DAVlCt8/dCD4X8kNn73iWZWnb/vQ6fN0MQzr08Yd6d+vv1dLSon379mn37t268MILdfvtt9s3LGADun3uFXF6AByru7NXV1fnm9CTJMMwVFIQUWNrIu1zfSFJ3/kf87XhjTcUCoVkmqbC4bAGDRpk56iALRYvXqzKykpt2LCB5/a5DOf4XMaLz9lLVUlB+u+zDEmlhRGtX7dO55133uf37TRNU0VFRWpsbLRpSsAedPvci+BzkW3btukHP/iBHnvsMV/ekDkSNjSkJNbri1wMSQWxkEoLI8rLy9Prr7+u8vJyGYahyspKvfvuuzrjjDM0a9Ysbdy4kavp4BozZsxQRUWFlixZ4vQoOArn+FzCT529UzncltCnKXb6ukNvSEnsmCs6d+/eraqqKj3xxBO65ppr9Omnn+qpp57SihUrVFRUpNmzZ2vmzJkqLS3N2vcBpIJun/sQfC7hh+fs9UZ73NTBlrjaOs0eA9CQFA4Z6lcYVt+Cnh9JlEwmFQ4fe9Nq0zT1+uuva8WKFVq3bp1mzJih2bNna9KkSb7couENPLfPXQg+F/DLc/bSkUhaam5LqC1uKmlZCslQJGyopCCs/Ggoo18SbIFwC57b5y4Enwv4rbPnNmyBcAO6fe5B8DnMj509N2MLhJPo9rkDweegeDyu8ePHq6amxtOPHPIitkA4obm5WZWVlaqvr6fb5yDqDA7yc2fP7UKhkC6//HI999xz+stf/qIxY8bolltu0Xnnnacf/ehH9AKRFXT73IGNzyF+ec6en7AFIhd4bp/zCD4HBKmz51WcC0Q20e1zFsHngKB19ryMLRDZQrfPOQRfjgW5s+d1bIGwE90+5xB8OUZnz/vYAmEXun3OIPhyiM6e/7AFIlN0+3KP4MsROnv+xhaIdNHtyz16fDlCZ8/f6AUiXXT7co+NLwfo7AUTWyBSRbcvtwi+LKOzB4lzgTg1un25Q/BlGZ09HI0tECdDty83CL4sorOHk2ELxPHo9uUGwZdFdPaQCrZAHI1uX/YRfFlCZw/p6GkLvOWWW1RSUuL0aMghun3ZRfBlQTwe17hx41RbW0tnD2npaQucM2eOJk6cyBYYAHT7soseXxbU1dVp2LBhdPaQtp56gTNnzvy8F9jU1OT0iMgiun3ZxcZns48++khVVVXavHkznT3Yii0wWOj2ZQ/BZyPLsnTVVVfpkksuobOHrOJcYDDQ7csOgs9Gzz33nBYvXkxnDznDFuh/dPvsR/DZpLGxUWPHjtXzzz9PZw+OYAv0J7p99iP4bDJ37lxZlkVnD45jC/Qfun32Ivgy8Nhjj6murk533XWXvv/979PZg+uwBfoH3T77UGfIQENDgz788EN9+9vf1qhRo5weB/g7gwYN0l133aX3339fdXV1euONNzRixAjNmjVLmzZtEu97vWPx4sVau3atNmzY4PQonkfwZWD79u2Suq7m/M1vfqPLLrvM2YGAE6AX6H10++xD8GWgO/gKCgo0YcIEPfPMM84OBKSALdC7ZsyYoYqKCi1ZssTpUTyNc3wn0B5Pas0f9+rffrtTexrb1Zk0lR8J6QsDCvVPk0fo0rMHqqRPsUzT1JNPPqlvfOMbXDgAz+JcoHfQ7cscwXeceMLUo+v/qpW/+1iGIbV2Jv/uYwpjYYVDhs4O79FDt1+hgQMHOjApYD+uCPUGun2ZIfiO0tqR0OyntujPew+rPW6e8uMLoiFNPmuAltz4RUXCHDWGv7AFuhfdvswQfJ9JJE3NfmqLtu5qUmfi1KHXLT8a0hWVg/Xg9ZW884IvsQW6E92+9BF8n3lu0y4tWft+Spve8QqiIdXd/CVNOWtAFiYD3IMt0F3o9qWH4FNXHeHKR97Unsb2tF+j6ox++uk3J9g4FeBebIHuwHP70sOJKUlbdjTqUGs8o9f4464mfXyozaaJAHejF+gOdPvSQ/BJenHLHrX3cPVmb5iWpVfe3WfTRIB30At0Ft2+3iP4JH3S2K5M/2nGk5Y+aWTjQ3CxBTrDMAwtW7ZMS5cu1QcffOD0OJ5A8EmKJ3t/QUtPOtK4MAbwI7bA3CovL1dNTc3nT4nByRF8kkoK7HlobP/imC2vA/gFW2DuVFdXa//+/aqvr3d6FNcj+CRNHlWmglhmfxWFsbDGj+CRRMCJsAVmVyQS0YoVK3TnnXfq4MGDTo/jagSfpGnnDVWm/+byo2FdVFFmz0CAj7EFZk9VVZVuuOEG3X333U6P4moEn6SivIiuPneIwmnWj/IiId16YblCIfpLQG+wBdqP5/adGsH3mdkXj1R+LNzrzzMMqTg/ohsmnp6FqYBgYAu0D92+U+POLUfZsqNRc57aorZ4ap2+kCEV5kX0zJyJOmNgUZanA4KFu8Okz7IsTZ8+XZMmTVJtba3T47gOwXecdz9u0pyn/q/iSbPHRxJ1K4yFVVIQ1ZOzxqu8rDCHEwLBwz1Ce4/n9p0YwdeD9nhSr7y7T//7je3a09imcMiQZXUVReNJU2cNKdY3p56hS0YP4HFEQA6xBfYOz+3rGcF3Cn/dd0S7D7aqtTOporyIRg4sYsMDXIAt8NR4bl/PCD4AnsYWeHI8t+/vEXwAfIMtsGc8t+9YBB8A32ELPBbP7TsWwQfA19gCu6xevVqLFi3S1q1bFYsF+77CBB+AQAj6Fki37/8j+AAETlC3QLp9XQg+AIEVxC2Qbh/BBwCSgrMF0u0j+ADgGEHYAoPe7SP4AOAE/LwFBrnbR/ABwCn4cQsMcreP4AOAXvDTFhjUbh/BBwBp8MMWGNRuH8EHABny8hYYxG4fwQcANvHqFlhXV6eXX35Z69evd/WcdiH4ACALvLQFdnf7Fi5cqFtuucXpcbKO4AOALPLKFrh582Zde+21amhoUFlZmdPjZBXBBwA54vYtsLq6Wq2trXriiSecHiWrCD4AyDG3boHNzc0aO3as6uvrNXXqVMfmyDaCDwAc5LYtcPXq1aqtrdXWrVuVl5fnyAzZRvABgAu4ZQvs7vZNnDhRixYtytnXzSWCDwBcxuktsLvb99Zbb2nUqFE5+Zq5RPABgEs5uQX6udtH8AGAB+R6C/Rzt4/gAwAPyeUW6NduH8EHAB6Viy3Qj90+gg8APC6bW6Afu30EHwD4SDa2QL91+zwTfJYlmUdNahiS8dl/AwCOZecW2FO371BrXDsOtam1M6lo2FD/wphGlhV44gpQVwdfd9glTOlEQ0YMKRwiAAHgROzYAru7fU+v3aC3D0X1zp7DioYNWepaQkxLKs4L67pzBuuSijIVxsJZ+34y5drgsyypM3niwDteNNQVgACAnmWyBR7uSOhffv6mDlt5MqInPtyZF+n6RbzwkjM0brg7br59PFcGn2VJHcnef14k1PUfAMDJ9WYLPNye0F0v/VmHWjuVMFN7/VjY0LzJX9Dkkf1tnjxzrgu+3m56x4uFpRCHPQEgJafaApOmpXte+rN2NbalHHrdYmFD931llEYPKs7O8GlyXfAlTSney7/coxmS8iK2jQMAgdHTFnjO5TP0+OZP1d7b1PvM6EFFevCa0TZPmhnXBV9HIv1trxtbHwBjUp9AAAAC60lEQVSk7+gt8MBZV6t05BfTfq1o2FDdV8/W0L75Nk6YGVcFn/nZYc5MhQ0p6t4LigDAdX7xi1+ooaFB1dXV6tOnjyRpb3OHFrzY0OtDnEcLG9I/jh6o2y8YbtOkmXPVpSCmTRFs1+sAQFCsWbNG9913n0477TQ98MADOnz4sP7y6RFFQpnFRNKS/rin2aYp7eGqjS9hKqN3Ft32/+1vmv/t2zN/IQAIiD/84Q/asWOHJCkUCskwDNU+/qLeD52ueIbbRGlBRE/cdK4dY9rCl5eBFBYWatasWU6PAQCe8dhjj2nHjh0Kh8MKh8OaMmWKRp81Sn/9sC3j1w677KILVwWfXX81RUVFmj59uk2vBgD+t27dOr322mu69dZbdf/992v48OHatKNR4W3bu45XZqAkP2rPkDZx1aHOdIvrx+MuLgDQO5988olM09SwYcM+/7OOhKlZz/xRHRmcg8qPhHTbxGH6h9ED7RjTFq6KB8Owp4bgsq0aAFxv6NChx4Se1HX7sUsq+iucwe9Uy5Lr7t7iquCTMr/lWNjghtUAYJdrxg5K+xxdJGTo4or+yndZv8x1wRcylPa7C0PcqxMA7HRaSb7+25eGfn7z6VSFDKmsKKqZE4ad+oNzzJUxEQn1PvwMdd2xhW0PAOw149zBunLMAMVSvHgiEjJUVhTT/7zqLBW58PFErrq45XiJpJRIYbqQ0XVBC6EHANnz+l8P6Om3P1Z73Ozx3p2xz57PN7G8VLMvGK4il9442dXBJ538YbRho2s7JPAAIDdMy9If9xzWf7yzTx8daFVHwlQkZKhvfkRXjBmgy0cNUJ98dwZeN9cHHwAAdnLlOT4AALKF4AMABArBBwAIFIIPABAoBB8AIFAIPgBAoBB8AIBAIfgAAIFC8AEAAoXgAwAECsEHAAgUgg8AECgEHwAgUAg+AECgEHwAgEAh+AAAgULwAQACheADAAQKwQcACBSCDwAQKAQfACBQCD4AQKD8PyrU1e4ujjoPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.drawing.draw_planar(nx_graph,node_color=range(G.number_of_nodes()),cmap=plt.cm.Blues, style='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.nx_agraph.to_agraph(nx_graph).layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.nx_agraph.to_agraph(nx_graph).write(\"isa-test.dot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.nx_agraph.read_dot(\"isa-test.dot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# G = nx.bipartite.gnmk_random_graph(3, 5, 10, seed=123)\n",
    "# top = nx.bipartite.sets(G)[3]\n",
    "# pos = nx.bipartite_layout(G, top)\n",
    "# pos = nx.planar_layout(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos=nx.drawing.layout.planar_layout(G, scale=2, center=None, dim=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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.draw(nx_graph,pos=nx.drawing.layout.planar_layout(G, scale=1, center=None, dim=2),node_color=range(G.number_of_nodes()),cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NG = nx.karate_club_graph()\n",
    "res = [0,1,2,3,4,5, 'parrot'] #I've added 'parrot', a node that's not in G\n",
    "                              #just to demonstrate that G.subgraph is okay\n",
    "                              #with nodes not in G.\n",
    "k = NG.subgraph(res)          \n",
    "# pos = nx.spring_layout(NG)  #setting the positions with respect to G, not k.\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "nx.draw_networkx(k, pos=pos)\n",
    "\n",
    "othersubgraph = NG.subgraph(range(6,NG.order()))\n",
    "nx.draw_networkx(othersubgraph, pos=pos, node_color = 'pink')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}