PRIMES 2023 - Code for VDAT.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "Code for PRIMES 2023 Project - Visualizing Distributed Traces in Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
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    "id": "GbOM0RInXXu6"
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   "outputs": [],
   "source": [
    "# Imports\n",
    "import json\n",
    "import time\n",
    "import random\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
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     "timestamp": 1703095418714,
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    "outputId": "af4fc022-fa8f-4fc8-c6e9-1c4637cada20"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# Connect this notebook to Google Drive where trace files should be uploaded\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
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     "timestamp": 1703095418714,
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      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "J4os4K88YHGt"
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   "outputs": [],
   "source": [
    "# Global variables for user editing\n",
    "\n",
    "\"\"\"\n",
    "Choose file that contains traces.\n",
    "\n",
    "Ex options:\n",
    "trace3.json for set of 24 traces with services like A1, B1, C1, ...\n",
    "trace4.json for set of 2 traces with meaningful service names\n",
    "ts-admin-user-service.json for train ticket sample traces\n",
    "\"\"\"\n",
    "file_name = \"trace4.json\"\n",
    "\n",
    "definition_type = \"services\"\n",
    "# definition_type = \"topology\"\n",
    "\n",
    "threshold = 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 784,
     "status": "ok",
     "timestamp": 1703095419495,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "1jxK6k4lRIhZ",
    "outputId": "102bca55-bfb5-4919-ebc3-5e6a4808f210"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Groups: 2\n"
     ]
    }
   ],
   "source": [
    "# Standard globabl variables\n",
    "\n",
    "file_path = \"drive/MyDrive/Primes2023/TraceData/\"\n",
    "with open(file_path + file_name) as f:\n",
    "    data = json.load(f)\n",
    "    raw_trace_data = data[\"data\"]\n",
    "\n",
    "# Number of Groups\n",
    "n = len(raw_trace_data)\n",
    "print(\"Number of Groups: \" + str(n))\n",
    "\n",
    "traces = {}\n",
    "trace_ids = {}\n",
    "spanid2servicename = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1703095419495,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "nVeCgZfIayW5"
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   "outputs": [],
   "source": [
    "# Functions to retrieve and analyze traces\n",
    "\n",
    "def getTrace(index):\n",
    "    trace_id = raw_trace_data[index][\"traceID\"]\n",
    "    services = []\n",
    "    edges = []\n",
    "    processes = raw_trace_data[index][\"processes\"]\n",
    "    for process in processes:\n",
    "        services.append(processes[process][\"serviceName\"])\n",
    "    spans = raw_trace_data[index][\"spans\"]\n",
    "    for span in spans:\n",
    "        span_name = span[\"spanID\"]\n",
    "        process_id = span[\"processID\"]\n",
    "        service_name = processes[process_id][\"serviceName\"]\n",
    "        spanid2servicename[span_name] = service_name\n",
    "        if \"references\" in span and len(span[\"references\"]) != 0:\n",
    "            if span[\"references\"][0][\"refType\"] == \"CHILD_OF\":\n",
    "                edges.append([span[\"references\"][0][\"spanID\"], span_name])\n",
    "            elif span[\"references\"][0][\"refType\"] == \"PARENT_OF\":\n",
    "                edges.append([span_name, span[\"references\"][0][\"spanID\"]])\n",
    "\n",
    "    unique_services = list(set(services))\n",
    "    trace_ids[index] = trace_id\n",
    "\n",
    "    return (unique_services, edges)\n",
    "\n",
    "def trace_definition(trace_id):\n",
    "    services, edges = traces[trace_id]\n",
    "    if definition_type == \"services\":\n",
    "        return services\n",
    "    elif definition_type == \"topology\":\n",
    "        return edges\n",
    "\n",
    "def intersection(trace_a, trace_b):\n",
    "    common = [element for element in trace_a if element in trace_b]\n",
    "    return common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1703095419495,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
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     "user_tz": 300
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    "id": "t-LBhwuPa5NJ"
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   "outputs": [],
   "source": [
    "# Connected Components\n",
    "# Source: https://www.geeksforgeeks.org/connected-components-in-an-undirected-graph/\n",
    "class ConnectedComponents:\n",
    "  # function to merge two components\n",
    "  def merge(self, parent, x):\n",
    "    if parent[x] == x:\n",
    "      return x\n",
    "    return self.merge(parent, parent[x])\n",
    "\n",
    "  # function to find connected components\n",
    "  def connectedComponents(self, n, edges):\n",
    "    # list to store parents of each node\n",
    "    parent = [i for i in range(n)]\n",
    "\n",
    "    # loop to set parent of each node\n",
    "    for x in edges:\n",
    "      parent[self.merge(parent, x[0])] = self.merge(parent, x[1])\n",
    "\n",
    "    # count to store number of connected components\n",
    "    ans = 0\n",
    "    # loop to count number of connected components\n",
    "    for i in range(n):\n",
    "      if parent[i] == i:\n",
    "        ans += 1\n",
    "\n",
    "    # loop to merge all components\n",
    "    for i in range(n):\n",
    "      parent[i] = self.merge(parent, parent[i])\n",
    "\n",
    "    # map to store parent and its connected components\n",
    "    m = collections.defaultdict(list)\n",
    "    for i in range(n):\n",
    "      m[parent[i]].append(i)\n",
    "\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1703095419496,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "MhmMgIDga9gc"
   },
   "outputs": [],
   "source": [
    "# Function to identify groups\n",
    "def getGroups(threshold):\n",
    "\n",
    "    edges = []\n",
    "\n",
    "    degrees = {}\n",
    "    for i in range(0, n):\n",
    "        degrees[i] = 0\n",
    "\n",
    "    for trace_a in range(0, n - 1):\n",
    "        for trace_b in range(trace_a + 1, n):\n",
    "\n",
    "            topology_a = trace_definition(trace_ids[trace_a])\n",
    "            topology_b = trace_definition(trace_ids[trace_b])\n",
    "\n",
    "            intersection_size = len(intersection(topology_a, topology_b))\n",
    "\n",
    "            union_size = len(topology_a) + len(topology_b) - intersection_size\n",
    "\n",
    "            jaccardian_similarity = intersection_size/union_size\n",
    "\n",
    "            if jaccardian_similarity >= threshold:\n",
    "                edges.append([trace_a, trace_b])\n",
    "                edges.append([trace_b, trace_a])\n",
    "                if trace_a in degrees:\n",
    "                    degrees[trace_a] += 1\n",
    "                if trace_b in degrees:\n",
    "                    degrees[trace_b] += 1\n",
    "\n",
    "    cc = ConnectedComponents().connectedComponents(n, edges)\n",
    "\n",
    "    return (cc, degrees, len(cc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qDUn1j1ZbIn2"
   },
   "source": [
    "# Grouping Similar Traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1703095419496,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "FJrJNkNlbKXk",
    "outputId": "1403f78e-7635-4c74-a903-b99333ef2565"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2739abv293bn23456782], [268927abnc124]]\n",
      "Representative Trace for component 1: 2739abv293bn23456782\n",
      "Representative Trace for component 2: 268927abnc124\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "for i in range(0, n):\n",
    "    traces[trace_ids[i]] = getTrace(i)\n",
    "\n",
    "cc, degrees, num_groups = getGroups(threshold)\n",
    "\n",
    "# print(cc)\n",
    "\n",
    "group_num = 0\n",
    "\n",
    "components = \"\"\n",
    "\n",
    "for it in cc.items():\n",
    "    l = it[1]\n",
    "    if group_num == 0:\n",
    "        components += (\"[[\" + \", \".join([trace_ids[x] for x in l]) + \"], \")\n",
    "    elif group_num == num_groups - 1:\n",
    "        components += (\"[\" + \", \".join([trace_ids[x] for x in l]) + \"]]\")\n",
    "    else:\n",
    "        components += (\"[\" + \", \".join([trace_ids[x] for x in l]) + \"], \")\n",
    "    group_num += 1\n",
    "\n",
    "print(components)\n",
    "\n",
    "group_num = 0\n",
    "\n",
    "for it in cc.items():\n",
    "    max_degree = 0\n",
    "    max_degree_trace = it[0]\n",
    "    l = it[1]\n",
    "    degrees_cc = {}\n",
    "    for x in l:\n",
    "        y = int(x)\n",
    "        degrees_cc[y + 1] = degrees[y]\n",
    "        if degrees[y] > max_degree:\n",
    "            max_degree_trace = y\n",
    "        elif degrees[y] == max_degree:\n",
    "            options = [max_degree_trace, y]\n",
    "            max_degree_trace = random.choice(options)\n",
    "            # max_degree_trace = y\n",
    "        max_degree = degrees[max_degree_trace]\n",
    "\n",
    "    print(\"Representative Trace for component \" + str(group_num + 1) + \": \" + trace_ids[max_degree_trace])\n",
    "\n",
    "    group_num += 1\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "# print(\"Time Taken: \" + str(end - start) + \" seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1703095419496,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "BMaKfQoY9tzp",
    "outputId": "5e020c89-f085-4360-c30e-9760eba5c48e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a2312512415': 'Front End',\n",
       " 'b69273402735': 'Feed',\n",
       " 'c12563543523434': 'Friends',\n",
       " 'd68392049538467': 'Post',\n",
       " 'e098765367859948736': 'Friends Database',\n",
       " 'f5678948736278457': 'Front End',\n",
       " 'g4577652345352': 'Friends',\n",
       " 'h09876567283525': 'Friends Database'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spanid2servicename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1703095419496,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "LhKM69PI8o1W",
    "outputId": "722b2ecc-7b56-402f-cd29-698bbdb8e033"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['Feed', 'Friends', 'Post', 'Front End', 'Friends Database'],\n",
       "  [['a2312512415', 'b69273402735'],\n",
       "   ['b69273402735', 'c12563543523434'],\n",
       "   ['b69273402735', 'd68392049538467'],\n",
       "   ['c12563543523434', 'e098765367859948736']]),\n",
       " (['Friends Database', 'Friends', 'Front End'],\n",
       "  [['f5678948736278457', 'g4577652345352'],\n",
       "   ['g4577652345352', 'h09876567283525']])]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of traces without the trace ids\n",
    "traces_without_ids = list(traces.values())\n",
    "traces_without_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kDN8FWukdYgo"
   },
   "source": [
    "# Visualizing Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 58587,
     "status": "ok",
     "timestamp": 1703095478081,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
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    },
    "id": "F822N4H2ZfL-",
    "outputId": "e5c6b53e-aa70-4436-e616-2a1293bb564a"
   },
   "outputs": [
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      "  python3-soupsieve python3-sympy python3-tz python3-ufolib2 python3-unicodedata2\n",
      "  python3-webencodings unicode-data\n",
      "Suggested packages:\n",
      "  libxsimd-doc python-attr-doc python-cycler-doc python3-genshi python-lxml-doc dvipng\n",
      "  fonts-staypuft ghostscript inkscape ipython3 librsvg2-common python-matplotlib-doc\n",
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      "  libgv-python python-matplotlib python-cairo python-gi-cairo python-gi\n",
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      "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 gir1.2-atk-1.0 amd64 2.36.0-3build1 [21.0 kB]\n",
      "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 gir1.2-freedesktop amd64 1.72.0-1 [22.3 kB]\n",
      "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 gir1.2-gdkpixbuf-2.0 amd64 2.42.8+dfsg-1ubuntu0.2 [9,482 B]\n",
      "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 gir1.2-harfbuzz-0.0 amd64 2.7.4-1ubuntu3.1 [31.6 kB]\n",
      "Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpangoxft-1.0-0 amd64 1.50.6+ds-2ubuntu1 [31.0 kB]\n",
      "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 gir1.2-pango-1.0 amd64 1.50.6+ds-2ubuntu1 [44.0 kB]\n",
      "Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 gir1.2-gtk-3.0 amd64 3.24.33-1ubuntu2 [240 kB]\n",
      "Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsigc++-2.0-0v5 amd64 2.10.4-2ubuntu3 [12.1 kB]\n",
      "Get:12 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairomm-1.0-1v5 amd64 1.12.2-4build3 [45.1 kB]\n",
      "Get:13 http://archive.ubuntu.com/ubuntu jammy/main amd64 libimagequant0 amd64 2.17.0-1 [34.6 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu jammy/universe amd64 liblbfgsb0 amd64 3.0+dfsg.3-10 [29.9 kB]\n",
      "Get:15 http://archive.ubuntu.com/ubuntu jammy/main amd64 libraqm0 amd64 0.7.0-4ubuntu1 [11.7 kB]\n",
      "Get:16 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxsimd-dev amd64 7.6.0-2 [108 kB]\n",
      "Get:17 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python-matplotlib-data all 3.5.1-2build1 [2,942 kB]\n",
      "Get:18 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-appdirs all 1.4.4-2 [11.4 kB]\n",
      "Get:19 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-attr all 21.2.0-1 [44.0 kB]\n",
      "Get:20 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-gast all 0.5.2-2 [9,394 B]\n",
      "Get:21 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-beniget all 0.4.1-2 [9,904 B]\n",
      "Get:22 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-brotli amd64 1.0.9-2build6 [319 kB]\n",
      "Get:23 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-soupsieve all 2.3.1-1 [33.0 kB]\n",
      "Get:24 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-bs4 all 4.10.0-2 [79.1 kB]\n",
      "Get:25 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-cairo amd64 1.20.1-3build1 [73.7 kB]\n",
      "Get:26 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-chardet all 4.0.0-1 [98.0 kB]\n",
      "Get:27 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-cycler all 0.11.0-1 [8,156 B]\n",
      "Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-dateutil all 2.8.1-6 [78.4 kB]\n",
      "Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-decorator all 4.4.2-0ubuntu1 [10.3 kB]\n",
      "Get:30 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-numpy amd64 1:1.21.5-1ubuntu22.04.1 [3,467 kB]\n",
      "Get:31 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-ply all 3.11-5 [47.5 kB]\n",
      "Get:32 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-pythran amd64 0.10.0+ds2-1 [423 kB]\n",
      "Get:33 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-scipy amd64 1.8.0-1exp2ubuntu1 [14.7 MB]\n",
      "Get:34 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-ufolib2 all 0.13.1+dfsg1-1 [32.2 kB]\n",
      "Get:35 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-mpmath all 1.2.1-2 [419 kB]\n",
      "Get:36 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-sympy all 1.9-1 [4,312 kB]\n",
      "Get:37 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-tz all 2022.1-1ubuntu0.22.04.1 [30.7 kB]\n",
      "Get:38 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-fs all 2.4.12-1 [84.9 kB]\n",
      "Get:39 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-lxml amd64 4.8.0-1build1 [1,150 kB]\n",
      "Get:40 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-lz4 amd64 3.1.3+dfsg-1build3 [33.3 kB]\n",
      "Get:41 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-unicodedata2 amd64 14.0.0+ds-8 [376 kB]\n",
      "Get:42 http://archive.ubuntu.com/ubuntu jammy/universe amd64 unicode-data all 14.0.0-1.1 [8,206 kB]\n",
      "Get:43 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-fonttools amd64 4.29.1-2build1 [810 kB]\n",
      "Get:44 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-webencodings all 0.5.1-4 [11.8 kB]\n",
      "Get:45 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-html5lib all 1.1-3 [87.0 kB]\n",
      "Get:46 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-kiwisolver amd64 1.3.2-1build1 [48.0 kB]\n",
      "Get:47 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-pil amd64 9.0.1-1ubuntu0.1 [419 kB]\n",
      "Get:48 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-pil.imagetk amd64 9.0.1-1ubuntu0.1 [9,608 B]\n",
      "Get:49 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-packaging all 21.3-1 [30.7 kB]\n",
      "Get:50 http://archive.ubuntu.com/ubuntu jammy/universe amd64 python3-matplotlib amd64 3.5.1-2build1 [5,937 kB]\n",
      "Get:51 http://archive.ubuntu.com/ubuntu jammy/main amd64 python3-olefile all 0.46-3 [33.8 kB]\n",
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      "Extracting templates from packages: 100%\n",
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      "(Reading database ... 121658 files and directories currently installed.)\n",
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      "Selecting previously unselected package gir1.2-atk-1.0:amd64.\n",
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      "Setting up python3-attr (21.2.0-1) ...\n",
      "Setting up gir1.2-freedesktop:amd64 (1.72.0-1) ...\n",
      "Setting up python3-cairo:amd64 (1.20.1-3build1) ...\n",
      "Setting up libpangoxft-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
      "Setting up gir1.2-gdkpixbuf-2.0:amd64 (2.42.8+dfsg-1ubuntu0.2) ...\n",
      "Setting up python3-lz4 (3.1.3+dfsg-1build3) ...\n",
      "Setting up gir1.2-atk-1.0:amd64 (2.36.0-3build1) ...\n",
      "Setting up python3-unicodedata2 (14.0.0+ds-8) ...\n",
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      "Setting up python3-olefile (0.46-3) ...\n",
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      "Setting up python3-gast (0.5.2-2) ...\n",
      "Setting up gir1.2-harfbuzz-0.0:amd64 (2.7.4-1ubuntu3.1) ...\n",
      "Setting up python3-webencodings (0.5.1-4) ...\n",
      "Setting up python3-tz (2022.1-1ubuntu0.22.04.1) ...\n",
      "Setting up unicode-data (14.0.0-1.1) ...\n",
      "Setting up python3-beniget (0.4.1-2) ...\n",
      "Setting up libxsimd-dev:amd64 (7.6.0-2) ...\n",
      "Setting up python3-decorator (4.4.2-0ubuntu1) ...\n",
      "Setting up gir1.2-pango-1.0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
      "Setting up python3-packaging (21.3-1) ...\n",
      "Setting up python3-chardet (4.0.0-1) ...\n",
      "Setting up libsigc++-2.0-0v5:amd64 (2.10.4-2ubuntu3) ...\n",
      "Setting up python3-brotli (1.0.9-2build6) ...\n",
      "Setting up libraqm0:amd64 (0.7.0-4ubuntu1) ...\n",
      "Setting up python3-cycler (0.11.0-1) ...\n",
      "Setting up libimagequant0:amd64 (2.17.0-1) ...\n",
      "Setting up python3-kiwisolver (1.3.2-1build1) ...\n",
      "Setting up fonts-dejavu-core (2.37-2build1) ...\n",
      "Setting up python3-html5lib (1.1-3) ...\n",
      "Setting up python3-numpy (1:1.21.5-1ubuntu22.04.1) ...\n",
      "Setting up python3-pythran (0.10.0+ds2-1) ...\n",
      "Setting up python3-lxml:amd64 (4.8.0-1build1) ...\n",
      "Setting up python3-dateutil (2.8.1-6) ...\n",
      "Setting up python3-mpmath (1.2.1-2) ...\n",
      "Setting up python-matplotlib-data (3.5.1-2build1) ...\n",
      "Setting up python3-appdirs (1.4.4-2) ...\n",
      "Setting up python3-soupsieve (2.3.1-1) ...\n",
      "Setting up liblbfgsb0:amd64 (3.0+dfsg.3-10) ...\n",
      "Setting up python3-sympy (1.9-1) ...\n",
      "Setting up python3-scipy (1.8.0-1exp2ubuntu1) ...\n",
      "Setting up gir1.2-gtk-3.0:amd64 (3.24.33-1ubuntu2) ...\n",
      "Setting up python3-bs4 (4.10.0-2) ...\n",
      "Setting up python3-fs (2.4.12-1) ...\n",
      "Setting up libcairomm-1.0-1v5:amd64 (1.12.2-4build3) ...\n",
      "Setting up python3-graph-tool (2.58) ...\n",
      "Setting up python3-pil:amd64 (9.0.1-1ubuntu0.1) ...\n",
      "Setting up python3-pil.imagetk:amd64 (9.0.1-1ubuntu0.1) ...\n",
      "Setting up python3-ufolib2 (0.13.1+dfsg1-1) ...\n",
      "Setting up python3-fonttools (4.29.1-2build1) ...\n",
      "Setting up python3-matplotlib (3.5.1-2build1) ...\n",
      "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
      "\n",
      "Processing triggers for man-db (2.10.2-1) ...\n",
      "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n"
     ]
    }
   ],
   "source": [
    "#Using graph tool in collab\n",
    "#Source: https://colab.research.google.com/github/count0/colab-gt/blob/master/colab-gt.ipynb#scrollTo=6km1lWMF2kAm\n",
    "\n",
    "!echo \"deb http://downloads.skewed.de/apt jammy main\" >> /etc/apt/sources.list\n",
    "!apt-key adv --keyserver keyserver.ubuntu.com --recv-key 612DEFB798507F25\n",
    "!apt-get update\n",
    "!apt-get install python3-graph-tool python3-matplotlib python3-cairo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 48900,
     "status": "ok",
     "timestamp": 1703095526971,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "bacZ1cSkZhIq",
    "outputId": "9146e51a-1b52-4b02-a41d-1f1f1e973b42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree... Done\n",
      "Reading state information... Done\n",
      "The following packages will be REMOVED:\n",
      "  python3-cairo*\n",
      "0 upgraded, 0 newly installed, 1 to remove and 27 not upgraded.\n",
      "After this operation, 310 kB disk space will be freed.\n",
      "(Reading database ... 130030 files and directories currently installed.)\n",
      "Removing python3-cairo:amd64 (1.20.1-3build1) ...\n",
      "Reading package lists... Done\n",
      "Building dependency tree... Done\n",
      "Reading state information... Done\n",
      "pkg-config is already the newest version (0.29.2-1ubuntu3).\n",
      "python3-dev is already the newest version (3.10.6-1~22.04).\n",
      "python3-dev set to manually installed.\n",
      "The following additional packages will be installed:\n",
      "  libblkid-dev libcairo-script-interpreter2 libffi-dev libglib2.0-dev libglib2.0-dev-bin libice-dev\n",
      "  liblzo2-2 libmount-dev libpixman-1-dev libselinux1-dev libsepol-dev libsm-dev libxcb-render0-dev\n",
      "  libxcb-shm0-dev\n",
      "Suggested packages:\n",
      "  libcairo2-doc libgirepository1.0-dev libglib2.0-doc libgdk-pixbuf2.0-bin | libgdk-pixbuf2.0-dev\n",
      "  libxml2-utils libice-doc libsm-doc\n",
      "The following NEW packages will be installed:\n",
      "  libblkid-dev libcairo-script-interpreter2 libcairo2-dev libffi-dev libglib2.0-dev\n",
      "  libglib2.0-dev-bin libice-dev liblzo2-2 libmount-dev libpixman-1-dev libselinux1-dev libsepol-dev\n",
      "  libsm-dev libxcb-render0-dev libxcb-shm0-dev\n",
      "0 upgraded, 15 newly installed, 0 to remove and 27 not upgraded.\n",
      "Need to get 3,839 kB of archives.\n",
      "After this operation, 19.8 MB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 liblzo2-2 amd64 2.10-2build3 [53.7 kB]\n",
      "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairo-script-interpreter2 amd64 1.16.0-5ubuntu2 [62.0 kB]\n",
      "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libice-dev amd64 2:1.0.10-1build2 [51.4 kB]\n",
      "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsm-dev amd64 2:1.2.3-1build2 [18.1 kB]\n",
      "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpixman-1-dev amd64 0.40.0-1ubuntu0.22.04.1 [280 kB]\n",
      "Get:6 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-render0-dev amd64 1.14-3ubuntu3 [19.6 kB]\n",
      "Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-shm0-dev amd64 1.14-3ubuntu3 [6,848 B]\n",
      "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libffi-dev amd64 3.4.2-4 [63.7 kB]\n",
      "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglib2.0-dev-bin amd64 2.72.4-0ubuntu2.2 [117 kB]\n",
      "Get:10 http://archive.ubuntu.com/ubuntu jammy/main amd64 libblkid-dev amd64 2.37.2-4ubuntu3 [185 kB]\n",
      "Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsepol-dev amd64 3.3-1build1 [378 kB]\n",
      "Get:12 http://archive.ubuntu.com/ubuntu jammy/main amd64 libselinux1-dev amd64 3.3-1build2 [158 kB]\n",
      "Get:13 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmount-dev amd64 2.37.2-4ubuntu3 [14.5 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglib2.0-dev amd64 2.72.4-0ubuntu2.2 [1,739 kB]\n",
      "Get:15 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairo2-dev amd64 1.16.0-5ubuntu2 [692 kB]\n",
      "Fetched 3,839 kB in 2s (2,317 kB/s)\n",
      "Selecting previously unselected package liblzo2-2:amd64.\n",
      "(Reading database ... 130021 files and directories currently installed.)\n",
      "Preparing to unpack .../00-liblzo2-2_2.10-2build3_amd64.deb ...\n",
      "Unpacking liblzo2-2:amd64 (2.10-2build3) ...\n",
      "Selecting previously unselected package libcairo-script-interpreter2:amd64.\n",
      "Preparing to unpack .../01-libcairo-script-interpreter2_1.16.0-5ubuntu2_amd64.deb ...\n",
      "Unpacking libcairo-script-interpreter2:amd64 (1.16.0-5ubuntu2) ...\n",
      "Selecting previously unselected package libice-dev:amd64.\n",
      "Preparing to unpack .../02-libice-dev_2%3a1.0.10-1build2_amd64.deb ...\n",
      "Unpacking libice-dev:amd64 (2:1.0.10-1build2) ...\n",
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      "Preparing to unpack .../03-libsm-dev_2%3a1.2.3-1build2_amd64.deb ...\n",
      "Unpacking libsm-dev:amd64 (2:1.2.3-1build2) ...\n",
      "Selecting previously unselected package libpixman-1-dev:amd64.\n",
      "Preparing to unpack .../04-libpixman-1-dev_0.40.0-1ubuntu0.22.04.1_amd64.deb ...\n",
      "Unpacking libpixman-1-dev:amd64 (0.40.0-1ubuntu0.22.04.1) ...\n",
      "Selecting previously unselected package libxcb-render0-dev:amd64.\n",
      "Preparing to unpack .../05-libxcb-render0-dev_1.14-3ubuntu3_amd64.deb ...\n",
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      "Preparing to unpack .../06-libxcb-shm0-dev_1.14-3ubuntu3_amd64.deb ...\n",
      "Unpacking libxcb-shm0-dev:amd64 (1.14-3ubuntu3) ...\n",
      "Selecting previously unselected package libffi-dev:amd64.\n",
      "Preparing to unpack .../07-libffi-dev_3.4.2-4_amd64.deb ...\n",
      "Unpacking libffi-dev:amd64 (3.4.2-4) ...\n",
      "Selecting previously unselected package libglib2.0-dev-bin.\n",
      "Preparing to unpack .../08-libglib2.0-dev-bin_2.72.4-0ubuntu2.2_amd64.deb ...\n",
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      "Selecting previously unselected package libblkid-dev:amd64.\n",
      "Preparing to unpack .../09-libblkid-dev_2.37.2-4ubuntu3_amd64.deb ...\n",
      "Unpacking libblkid-dev:amd64 (2.37.2-4ubuntu3) ...\n",
      "Selecting previously unselected package libsepol-dev:amd64.\n",
      "Preparing to unpack .../10-libsepol-dev_3.3-1build1_amd64.deb ...\n",
      "Unpacking libsepol-dev:amd64 (3.3-1build1) ...\n",
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      "Preparing to unpack .../11-libselinux1-dev_3.3-1build2_amd64.deb ...\n",
      "Unpacking libselinux1-dev:amd64 (3.3-1build2) ...\n",
      "Selecting previously unselected package libmount-dev:amd64.\n",
      "Preparing to unpack .../12-libmount-dev_2.37.2-4ubuntu3_amd64.deb ...\n",
      "Unpacking libmount-dev:amd64 (2.37.2-4ubuntu3) ...\n",
      "Selecting previously unselected package libglib2.0-dev:amd64.\n",
      "Preparing to unpack .../13-libglib2.0-dev_2.72.4-0ubuntu2.2_amd64.deb ...\n",
      "Unpacking libglib2.0-dev:amd64 (2.72.4-0ubuntu2.2) ...\n",
      "Selecting previously unselected package libcairo2-dev:amd64.\n",
      "Preparing to unpack .../14-libcairo2-dev_1.16.0-5ubuntu2_amd64.deb ...\n",
      "Unpacking libcairo2-dev:amd64 (1.16.0-5ubuntu2) ...\n",
      "Setting up libglib2.0-dev-bin (2.72.4-0ubuntu2.2) ...\n",
      "Setting up libblkid-dev:amd64 (2.37.2-4ubuntu3) ...\n",
      "Setting up libpixman-1-dev:amd64 (0.40.0-1ubuntu0.22.04.1) ...\n",
      "Setting up libice-dev:amd64 (2:1.0.10-1build2) ...\n",
      "Setting up libsm-dev:amd64 (2:1.2.3-1build2) ...\n",
      "Setting up liblzo2-2:amd64 (2.10-2build3) ...\n",
      "Setting up libffi-dev:amd64 (3.4.2-4) ...\n",
      "Setting up libxcb-shm0-dev:amd64 (1.14-3ubuntu3) ...\n",
      "Setting up libsepol-dev:amd64 (3.3-1build1) ...\n",
      "Setting up libxcb-render0-dev:amd64 (1.14-3ubuntu3) ...\n",
      "Setting up libcairo-script-interpreter2:amd64 (1.16.0-5ubuntu2) ...\n",
      "Setting up libselinux1-dev:amd64 (3.3-1build2) ...\n",
      "Setting up libmount-dev:amd64 (2.37.2-4ubuntu3) ...\n",
      "Setting up libglib2.0-dev:amd64 (2.72.4-0ubuntu2.2) ...\n",
      "Processing triggers for libglib2.0-0:amd64 (2.72.4-0ubuntu2.2) ...\n",
      "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
      "\n",
      "Processing triggers for man-db (2.10.2-1) ...\n",
      "Setting up libcairo2-dev:amd64 (1.16.0-5ubuntu2) ...\n",
      "Collecting pycairo\n",
      "  Downloading pycairo-1.25.1.tar.gz (347 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m347.1/347.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "Building wheels for collected packages: pycairo\n",
      "  Building wheel for pycairo (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pycairo: filename=pycairo-1.25.1-cp310-cp310-linux_x86_64.whl size=320919 sha256=81070b30d0e26ac334534b6446ee67686da397f50becd0799b4385234cf9600b\n",
      "  Stored in directory: /root/.cache/pip/wheels/d6/d8/c4/9bb1adbc349a349ed4718627f0afffeae26d9982060568cd30\n",
      "Successfully built pycairo\n",
      "Installing collected packages: pycairo\n",
      "Successfully installed pycairo-1.25.1\n",
      "Collecting zstandard\n",
      "  Downloading zstandard-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m38.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: zstandard\n",
      "Successfully installed zstandard-0.22.0\n"
     ]
    }
   ],
   "source": [
    "# Colab uses a Python install that deviates from the system's! Bad collab! We need some workarounds.\n",
    "!apt purge python3-cairo\n",
    "!apt install libcairo2-dev pkg-config python3-dev\n",
    "!pip install --force-reinstall pycairo\n",
    "!pip install zstandard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16346,
     "status": "ok",
     "timestamp": 1703095543306,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "4O2boww4Zl5s",
    "outputId": "48c9cd60-cd0e-47a1-9024-98593c31ddc3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib-venn in /usr/local/lib/python3.10/dist-packages (0.11.9)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (3.7.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.23.5)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from matplotlib-venn) (1.11.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (4.46.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (23.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->matplotlib-venn) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->matplotlib-venn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "%pip install matplotlib-venn\n",
    "import graph_tool.all as gt\n",
    "import matplotlib_venn as venn\n",
    "from matplotlib_venn import venn2\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zJ6lVgi8CLHx"
   },
   "source": [
    "# Converting output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1703095543306,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "s-KzRTjpBCqT"
   },
   "outputs": [],
   "source": [
    "#let len(set) = s\n",
    "def rep_to_vis(R_set):\n",
    "    #adding s+3 lists\n",
    "    V_list = []\n",
    "    for i in range(3):\n",
    "        V_list.append([])\n",
    "\n",
    "    #adding node names to a set bc it's faster\n",
    "    Nodes = set()\n",
    "    #all service names\n",
    "    for index in range(len(R_set)):\n",
    "        for index2 in range(len(R_set[index][0])):\n",
    "            Nodes.update((R_set[index][0]))\n",
    "    #converting set back to list\n",
    "    node_list = list(Nodes.copy())\n",
    "\n",
    "    V_list[0] = node_list\n",
    "\n",
    "\n",
    "    #creating counts of each node starting with 0\n",
    "    call_list = []\n",
    "    for index in range(len(node_list)):\n",
    "        call_list.append(0)\n",
    "    #adding total calls of each node\n",
    "    for index in range(len(R_set)):\n",
    "        for node in node_list:\n",
    "            if node in R_set[index][0]:\n",
    "                call_list[node_list.index(node)] += 1\n",
    "\n",
    "\n",
    "    V_list[1] = call_list\n",
    "\n",
    "\n",
    "    #adding total # of traces\n",
    "    V_list[2].append(len(R_set))\n",
    "\n",
    "\n",
    "    #adding empty cells to each node\n",
    "    for i in range(len(node_list)):\n",
    "        V_list.append([])\n",
    "        for j in range(len(node_list)):\n",
    "            V_list[i+3].append(0)\n",
    "    #adding calls to each node\n",
    "    for index in range(len(R_set)):\n",
    "        for index2 in range(len(R_set[index][1])):\n",
    "            for node in node_list:\n",
    "              # change this part\n",
    "                if spanid2servicename[R_set[index][1][index2][0]] == node:\n",
    "                    V_list[3+node_list.index(node)][node_list.index(spanid2servicename[R_set[index][1][index2][1]])] += 1\n",
    "\n",
    "\n",
    "\n",
    "    return(V_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1703095543306,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "eI1OiKB_A0n5",
    "outputId": "18c2b3c4-a83d-420c-9301-04d6f2b2ebd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(['Feed', 'Friends', 'Post', 'Front End', 'Friends Database'], [['a2312512415', 'b69273402735'], ['b69273402735', 'c12563543523434'], ['b69273402735', 'd68392049538467'], ['c12563543523434', 'e098765367859948736']]), (['Friends Database', 'Friends', 'Front End'], [['f5678948736278457', 'g4577652345352'], ['g4577652345352', 'h09876567283525']])]\n",
      "[['Feed', 'Friends Database', 'Friends', 'Post', 'Front End'], [1, 2, 2, 1, 2], [2], [0, 0, 1, 1, 0], [0, 0, 0, 0, 0], [0, 2, 0, 0, 0], [0, 0, 0, 0, 0], [1, 0, 1, 0, 0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'a1': 'Front End',\n",
       " 'b1': 'Friends',\n",
       " 'c1': 'Friends Database',\n",
       " 'd1': 'Post',\n",
       " 'e1': 'Friends Database'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(traces_without_ids)\n",
    "\n",
    "print(rep_to_vis(traces_without_ids))\n",
    "\n",
    "{'a1': 'Front End',\n",
    " 'b1': 'Friends',\n",
    " 'c1': 'Friends Database',\n",
    " 'd1': 'Post',\n",
    " 'e1': 'Friends Database'}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1cNzejfyetXI"
   },
   "source": [
    "Single Group View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1703095543306,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "YjQ6fBhMbw2t"
   },
   "outputs": [],
   "source": [
    "#single group view\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def single_group_view(group1):\n",
    "    g = gt.Graph()\n",
    "    #adding number of vertices from number of nodes\n",
    "    vlist = g.add_vertex(len(group1[0]))\n",
    "\n",
    "\n",
    "    vertex_t = g.new_vp('string') #vertex text\n",
    "\n",
    "    vertex_s = g.new_vp(\"double\") #vertex size\n",
    "\n",
    "    vertex_c = g.new_vp(\"string\") #vertex_color\n",
    "\n",
    "    vertex_t_s = g.new_vp(\"double\") #vertex text size\n",
    "\n",
    "\n",
    "    totalNodeNum = 0\n",
    "    for index in range(len(group1[0])): #getting average size of a node\n",
    "        totalNodeNum += group1[1][index]\n",
    "    avg_size = 120*len(group1[0])/totalNodeNum #50 is temp scalar\n",
    "\n",
    "\n",
    "    for index in range(len(group1[0])):\n",
    "        vertex_t[index] = group1[0][index] #vertex text\n",
    "        vertex_s[index] = avg_size*group1[1][index] #vertex size\n",
    "        vertex_t_s[index] = vertex_s[index]/len(vertex_t[index]) #vertex text size\n",
    "        if group1[1][index] == group1[2][0]: #vertex_color\n",
    "            vertex_c[index] = '#fdda0d'\n",
    "        else:\n",
    "            vertex_c[index] = '#d3d3d3'\n",
    "#single group view of A\n",
    "    gt.graph_draw(g, vertex_text = vertex_t, vertex_size = vertex_s, vertex_fill_color = vertex_c, vertex_font_size=vertex_t_s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1703095543571,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "MgwOdAtdCx12",
    "outputId": "b00b552c-7ca7-4c88-f428-3cabfa9feabb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "single_group_view(rep_to_vis(traces_without_ids))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WIDpD5vrexar"
   },
   "source": [
    "Node view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1703095543571,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "r80KQdwpciVs"
   },
   "outputs": [],
   "source": [
    "#edges view\n",
    "def node_view(A, group1):\n",
    "\n",
    "\n",
    "    g = gt.Graph()\n",
    "    #adding number of vertices from number of nodes\n",
    "    vlist = g.add_vertex(len(group1[0]))\n",
    "\n",
    "\n",
    "    vertex_t = g.new_vp('string') #vertex text\n",
    "\n",
    "    vertex_s = g.new_vp(\"double\") #vertex size\n",
    "\n",
    "    vertex_c = g.new_vp(\"string\") #vertex_color\n",
    "\n",
    "    edge_s = g.new_ep(\"double\") #edge size\n",
    "\n",
    "    vertex_t_s = g.new_vp(\"double\") #vertex text size\n",
    "\n",
    "\n",
    "\n",
    "    totalNodeNum = 0\n",
    "    for index in range(len(group1[0])): #getting average size of a node: 500 is a temporary max\n",
    "        totalNodeNum += group1[1][index]\n",
    "    avg_size = 80*len(group1[0])/totalNodeNum\n",
    "\n",
    "\n",
    "    A_index = 0;\n",
    "    for index in range(len(group1[0])):\n",
    "        vertex_t[index] = group1[0][index] #vertex text\n",
    "        vertex_s[index] = avg_size*group1[1][index] #vertex size\n",
    "        vertex_t_s[index] = vertex_s[index]/len(vertex_t[index]) #vertex text size\n",
    "        if group1[1][index] == group1[2][0]: #vertex_color\n",
    "            vertex_c[index] = '#fdda0d'\n",
    "        else:\n",
    "            vertex_c[index] = '#d3d3d3'\n",
    "        if group1[0][index] == A:\n",
    "            vertex_c[index] = '#3cb043'\n",
    "            A_index = index\n",
    "\n",
    "\n",
    "    edge_total= 0 #finding total number of edges\n",
    "    for i in group1[3+A_index]:\n",
    "        edge_total = edge_total + i\n",
    "\n",
    "\n",
    "    edge_num = 0\n",
    "    for index in range(len(group1[3])): #making the edges\n",
    "        if group1[3+A_index][index] != 0:\n",
    "            e = g.add_edge(A_index,index)\n",
    "            edge_s[e] = 4*len(group1[0])*group1[3+A_index][index]/edge_total #setting edge sizes - 40 is a temp variable\n",
    "            edge_num += 1\n",
    "\n",
    "\n",
    "    gt.graph_draw(g, vertex_text = vertex_t, vertex_size = vertex_s, vertex_fill_color = vertex_c, edge_pen_width = edge_s, vertex_font_size = vertex_t_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "executionInfo": {
     "elapsed": 677,
     "status": "ok",
     "timestamp": 1703095742832,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "-wNkSruHcpbr",
    "outputId": "97ab5310-e976-455f-8b63-79afdc5a49c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 553
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#node view\n",
    "node_view('Front End',rep_to_vis(traces_without_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MjCynCXje0re"
   },
   "source": [
    "Venn diagram view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1703095543938,
     "user": {
      "displayName": "Henry Han",
      "userId": "03327113879667805021"
     },
     "user_tz": 300
    },
    "id": "WmZrQc3ecu-a"
   },
   "outputs": [],
   "source": [
    "#venn diagram view\n",
    "def venn_view(A,B):\n",
    "    set1 = set(A[0])\n",
    "    set2 = set(B[0])\n",
    "\n",
    "    plt.title(\"Sample Venn Diagram\")\n",
    "    venn2([set1, set2], ['Group 1', 'Group 2'])"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}