{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# StarCraft II Replay Analysis\n",
    "\n",
    "This code pattern will guide you through StarCraft II replay analysis.\n",
    "\n",
    "> Note: Instructions assume this notebook is running in IBM Watson Studio.\n",
    "\n",
    "## Learning goals\n",
    "\n",
    "This code pattern covers the following learning goals:\n",
    "\n",
    "1. Getting started with Jupyter notebooks in Watson Studio\n",
    "3. Using IBM Cloud Object Storage to access a replay file\n",
    "3. Using sc2reader to load a replay into a Python object\n",
    "4. Examining some of the basic replay information in the result\n",
    "5. Parsing the contest details into a usable object\n",
    "6. Visualizing the contest with graphics\n",
    "7. Storing the processed replay in Cloudant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup prerequisites\n",
    "### Install the _sc2reader_, _cloudant_ and _bokeh_ Python packages from PyPI\n",
    "\n",
    "* **_sc2reader_** is an open source library for processing StarCraft 2 replay files.\n",
    "* **_cloudant_** is the Python client for using the Cloudant NoSQL DB.\n",
    "* **_bokeh_** is a Python interactive visualization library.\n",
    "* **_seaborn_** is a Python data visualization library based on matplotlib.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting cloudant==2.10.2\n",
      "  Downloading https://files.pythonhosted.org/packages/3d/01/c2e2829675fcd3f3af93a8a6a39cb6a5606c6cfa76973dacd3fac0a8b8ea/cloudant-2.10.2.tar.gz (57kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 7.3MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement not upgraded as not directly required: requests<3.0.0,>=2.7.0 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from cloudant==2.10.2)\n",
      "Requirement not upgraded as not directly required: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests<3.0.0,>=2.7.0->cloudant==2.10.2)\n",
      "Requirement not upgraded as not directly required: idna<2.7,>=2.5 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests<3.0.0,>=2.7.0->cloudant==2.10.2)\n",
      "Requirement not upgraded as not directly required: urllib3<1.23,>=1.21.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests<3.0.0,>=2.7.0->cloudant==2.10.2)\n",
      "Requirement not upgraded as not directly required: certifi>=2017.4.17 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests<3.0.0,>=2.7.0->cloudant==2.10.2)\n",
      "Building wheels for collected packages: cloudant\n",
      "  Running setup.py bdist_wheel for cloudant ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/dsxuser/.cache/pip/wheels/dd/0d/0b/6e0ca65193cde5e824aa56cacb4a96c087138464691979edba\n",
      "Successfully built cloudant\n",
      "Installing collected packages: cloudant\n",
      "Successfully installed cloudant-2.10.2\n",
      "Requirement not upgraded as not directly required: bokeh==0.12.10 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages\n",
      "Requirement not upgraded as not directly required: six>=1.5.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from bokeh==0.12.10)\n",
      "Requirement not upgraded as not directly required: PyYAML>=3.10 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from bokeh==0.12.10)\n",
      "Requirement not upgraded as not directly required: python-dateutil>=2.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from bokeh==0.12.10)\n",
      "Requirement not upgraded as not directly required: Jinja2>=2.7 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from bokeh==0.12.10)\n",
      "Requirement not upgraded as not directly required: numpy>=1.7.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from bokeh==0.12.10)\n",
      "Requirement not upgraded as not directly required: tornado>=4.3 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from bokeh==0.12.10)\n",
      "Requirement not upgraded as not directly required: MarkupSafe>=0.23 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from Jinja2>=2.7->bokeh==0.12.10)\n",
      "Collecting seaborn==0.9.0\n",
      "  Downloading https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc/seaborn-0.9.0-py3-none-any.whl (208kB)\n",
      "\u001b[K    100% |████████████████████████████████| 215kB 4.3MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement not upgraded as not directly required: pandas>=0.15.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: scipy>=0.14.0 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: numpy>=1.9.3 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: matplotlib>=1.4.3 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: python-dateutil>=2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from pandas>=0.15.2->seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: pytz>=2011k in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from pandas>=0.15.2->seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: six>=1.10 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from matplotlib>=1.4.3->seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: cycler>=0.10 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from matplotlib>=1.4.3->seaborn==0.9.0)\n",
      "Requirement not upgraded as not directly required: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from matplotlib>=1.4.3->seaborn==0.9.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.9.0\n",
      "Collecting sc2reader==1.3.1\n",
      "  Downloading https://files.pythonhosted.org/packages/f3/1d/805429dd1a044623df643369d523ba12b0c27062bb4da7a34f7f451bd703/sc2reader-1.3.1-py3-none-any.whl (281kB)\n",
      "\u001b[K    100% |████████████████████████████████| 286kB 3.3MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting mpyq>=0.2.4 (from sc2reader==1.3.1)\n",
      "  Downloading https://files.pythonhosted.org/packages/ff/10/76041d97aa01e4d0f93481942b4faf5652123acdd90fbff4e40bb8d9024c/mpyq-0.2.5.tar.gz\n",
      "Building wheels for collected packages: mpyq\n",
      "  Running setup.py bdist_wheel for mpyq ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/dsxuser/.cache/pip/wheels/59/f3/2e/c798fde3edd6431c5c9e66ebebe77d1af338487b77d058cc37\n",
      "Successfully built mpyq\n",
      "Installing collected packages: mpyq, sc2reader\n",
      "Successfully installed mpyq-0.2.5 sc2reader-1.3.1\n"
     ]
    }
   ],
   "source": [
    "# We install the prerequisites using the `!pip install` syntax here.\n",
    "# In some cases, running pip install from a notebook may require a one-time kernel restart. Check the output for messages.\n",
    "!pip install --user cloudant==2.10.2\n",
    "!pip install --user bokeh==0.12.10\n",
    "!pip install --user seaborn==0.9.0\n",
    "!pip install --user sc2reader==1.3.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "credentials_1 = {\n",
    "  \"apikey\": \"redacted\",\n",
    "  \"username\": \"redacted\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import types\n",
    "import pandas as pd\n",
    "from ibm_botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share your notebook.\n",
    "\n",
    "# ... credential code redacted ...\n",
    "\n",
    "# Your data file was loaded into a botocore.response.StreamingBody object.\n",
    "# Please read the documentation of ibm_boto3 and pandas to learn more about your possibilities to load the data.\n",
    "# ibm_boto3 documentation: https://ibm.github.io/ibm-cos-sdk-python/\n",
    "# pandas documentation: http://pandas.pydata.org/\n",
    "\n",
    "streaming_body_1 = streaming_body_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the replay\n",
    "\n",
    "Load in the replay with sc2reader. We'll use bytes that the inserted code read from our\n",
    "IBM Cloud Object Storage container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replay successfully loaded.\n"
     ]
    }
   ],
   "source": [
    "import sc2reader\n",
    "from sc2reader.engine.plugins import APMTracker, ContextLoader, SelectionTracker\n",
    "from sc2reader.events import PlayerStatsEvent, UnitBornEvent, UnitDiedEvent, UnitDoneEvent, UnitTypeChangeEvent, UpgradeCompleteEvent\n",
    "\n",
    "# Some extra code here helps catch setup errors.\n",
    "try:\n",
    "    replay_file = streaming_body_1\n",
    "except NameError:\n",
    "    print('\\n'\n",
    "          'SETUP ERROR: Please follow the directions to add a .SC2Replay file and use\\n'\n",
    "          '             \"Insert to code\" to set the streaming_body_1 variable to the resulting bytes.\\n'\n",
    "          '             You may need to rename the data_* variable.')\n",
    "    raise\n",
    "\n",
    "replay = sc2reader.load_replay(\n",
    "    replay_file,\n",
    "    engine=sc2reader.engine.GameEngine(plugins=[ContextLoader(), APMTracker(), SelectionTracker()]))\n",
    "\n",
    "print(\"Replay successfully loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print basic replay information\n",
    "\n",
    "With the replay added, we can now inspect the object in the notebook. We can easily get information\n",
    "like date, map name, participants, winner/loser, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2016-07-29 14:20:32\n",
      "Map Name: King Sejong Station LE\n",
      "Win: Player 1 - Neeb (Protoss)\n",
      "Loss: Player 2 - ShoWTimE (Protoss)\n"
     ]
    }
   ],
   "source": [
    "print(\"Date: %s\" % replay.date)\n",
    "print(\"Map Name: \" + replay.map_name)\n",
    "for player in replay.players:\n",
    "    print(\"%s: %s\" % (player.result, player))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish some unit and building groups\n",
    "\n",
    "VESPENE_UNITS = [\"Assimilator\", \"Extractor\", \"Refinery\"]\n",
    "\n",
    "SUPPLY_UNITS = [\"Overlord\", \"Overseer\", \"Pylon\", \"SupplyDepot\"]\n",
    "\n",
    "WORKER_UNITS = [\"Drone\", \"Probe\", \"SCV\", \"MULE\"]\n",
    "\n",
    "BASE_UNITS = [\"CommandCenter\", \"Nexus\", \"Hatchery\", \"Lair\", \"Hive\", \"PlanetaryFortress\", \"OrbitalCommand\"]\n",
    "\n",
    "GROUND_UNITS = [\"Barracks\", \"Factory\", \"GhostAcademy\", \"Armory\", \"RoboticsBay\", \"RoboticsFacility\", \"TemplarArchive\",\n",
    "                \"DarkShrine\", \"WarpGate\", \"SpawningPool\", \"RoachWarren\", \"HydraliskDen\", \"BanelingNest\", \"UltraliskCavern\",\n",
    "                \"LurkerDen\", \"InfestationPit\"]\n",
    "\n",
    "AIR_UNITS = [\"Starport\", \"FusionCore\", \"RoboticsFacility\", \"Stargate\", \"FleetBeacon\", \"Spire\", \"GreaterSpire\"]\n",
    "\n",
    "TECH_UNITS = [\"EngineeringBay\", \"Armory\", \"GhostAcademy\", \"TechLab\", \"FusionCore\", \"Forge\", \"CyberneticsCore\",\n",
    "              \"TwilightCouncil\", \"RoboticsFacility\", \"RoboticsBay\", \"FleetBeacon\", \"TemplarArchive\", \"DarkShrine\",\n",
    "              \"SpawningPool\", \"RoachWarren\", \"HydraliskDen\", \"BanelingNest\", \"UltraliskCavern\", \"LurkerDen\", \"Spire\",\n",
    "              \"GreaterSpire\", \"EvolutionChamber\", \"InfestationPit\"]\n",
    "\n",
    "ARMY_UNITS = [\"Marine\", \"Colossus\", \"InfestorTerran\", \"Baneling\", \"Mothership\", \"MothershipCore\", \"Changeling\", \"SiegeTank\", \"Viking\", \"Reaper\",\n",
    "              \"Ghost\", \"Marauder\", \"Thor\", \"Hellion\", \"Hellbat\", \"Cyclone\", \"Liberator\", \"Medivac\", \"Banshee\", \"Raven\", \"Battlecruiser\", \"Nuke\", \"Zealot\",\n",
    "              \"Stalker\", \"HighTemplar\", \"Disruptor\", \"DarkTemplar\", \"Sentry\", \"Phoenix\", \"Carrier\", \"Oracle\", \"VoidRay\", \"Tempest\", \"WarpPrism\", \"Observer\",\n",
    "              \"Immortal\", \"Adept\", \"Zergling\", \"Overlord\", \"Hydralisk\", \"Mutalisk\", \"Ultralisk\", \"Roach\", \"Infestor\", \"Corruptor\",\n",
    "              \"BroodLord\", \"Queen\", \"Overseer\", \"Archon\", \"Broodling\", \"InfestedTerran\", \"Ravager\", \"Viper\", \"SwarmHost\"]\n",
    "\n",
    "ARMY_AIR = [\"Mothership\", \"MothershipCore\", \"Viking\", \"Liberator\", \"Medivac\", \"Banshee\", \"Raven\", \"Battlecruiser\",\n",
    "            \"Viper\", \"Mutalisk\", \"Phoenix\", \"Oracle\", \"Carrier\", \"VoidRay\", \"Tempest\", \"Observer\", \"WarpPrism\", \"BroodLord\",\n",
    "            \"Corruptor\", \"Observer\", \"Overseer\"]\n",
    "\n",
    "ARMY_GROUND = [k for k in ARMY_UNITS if k not in ARMY_AIR]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish our event parsers\n",
    "\n",
    "def handle_count(caller, event, key, add_value, start_val=0):\n",
    "    if len(caller.players[event.unit.owner.pid][key]) == 0:\n",
    "        caller.players[event.unit.owner.pid][key].append((0, 0))\n",
    "    # Get the last value\n",
    "    last_val = caller.players[event.unit.owner.pid][key][-1][1]\n",
    "    caller.players[event.unit.owner.pid][key].append((event.frame, last_val + add_value))\n",
    "\n",
    "\n",
    "def handle_expansion_events(caller, event):\n",
    "    if type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in BASE_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"expansion_event\"].append((event.frame, \"+\", unit))\n",
    "            handle_count(caller, event, \"expansion_buildings\", 1, start_val=1)\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in BASE_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"expansion_event\"].append((event.frame, \"-\", unit))\n",
    "            handle_count(caller, event, \"expansion_buildings\", -1, start_val=1)\n",
    "    elif type(event) is UnitTypeChangeEvent:\n",
    "        if event.unit.name in BASE_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"expansion_event\"].append((event.frame, \"*\", event.unit.name))\n",
    "\n",
    "\n",
    "def handle_worker_events(caller, event):\n",
    "    if type(event) is PlayerStatsEvent:\n",
    "        caller.players[event.pid][\"workers_active\"].append((event.frame, event.workers_active_count))\n",
    "    elif type(event) is UnitBornEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in WORKER_UNITS:\n",
    "            caller.players[event.control_pid][\"worker_event\"].append((event.frame, \"+\", unit))\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in WORKER_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"worker_event\"].append((event.frame, \"-\", unit))\n",
    "\n",
    "\n",
    "def handle_supply_events(caller, event):\n",
    "    if type(event) is PlayerStatsEvent:\n",
    "        caller.players[event.pid][\"supply_available\"].append((event.frame, int(event.food_made)))\n",
    "        caller.players[event.pid][\"supply_consumed\"].append((event.frame, int(event.food_used)))\n",
    "        utilization = 0 if event.food_made == 0 else event.food_used / event.food_made\n",
    "        caller.players[event.pid][\"supply_utilization\"].append((event.frame, utilization))\n",
    "        worker_ratio = 0 if event.food_used == 0 else event.workers_active_count / event.food_used\n",
    "        caller.players[event.pid][\"worker_supply_ratio\"].append((event.frame, worker_ratio))\n",
    "    elif type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in SUPPLY_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"supply_event\"].append((event.frame, \"+\", unit))\n",
    "    elif type(event) is UnitBornEvent:\n",
    "        # Specifically for Overlord\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit == \"Overlord\":\n",
    "            caller.players[event.control_pid][\"supply_event\"].append((event.frame, \"+\", unit))\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        # Buildings/ Overlord/Overseer\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in SUPPLY_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"supply_event\"].append((event.frame, \"-\", unit))\n",
    "    elif type(event) is UnitTypeChangeEvent:\n",
    "        if event.unit_type_name == \"Overseer\":\n",
    "            caller.players[event.unit.owner.pid][\"supply_event\"].append((event.frame, \"*\", event.unit_type_name))\n",
    "\n",
    "\n",
    "def handle_vespene_events(caller, event):\n",
    "    if type(event) is PlayerStatsEvent:\n",
    "        caller.players[event.pid][\"vespene_available\"].append((event.frame, event.vespene_current))\n",
    "        caller.players[event.pid][\"vespene_collection_rate\"].append((event.frame, event.vespene_collection_rate))\n",
    "        vesp_per_worker = 0 if event.workers_active_count == 0 else event.vespene_collection_rate / event.workers_active_count\n",
    "        caller.players[event.pid][\"vespene_per_worker_rate\"].append((event.frame, vesp_per_worker))\n",
    "        caller.players[event.pid][\"vespene_cost_active_forces\"].append((event.frame, event.vespene_used_active_forces))\n",
    "        caller.players[event.pid][\"vespene_spend\"].append((event.frame, event.vespene_used_current))\n",
    "        caller.players[event.pid][\"vespene_value_current_technology\"].append((event.frame, event.vespene_used_current_technology))\n",
    "        caller.players[event.pid][\"vespene_value_current_army\"].append((event.frame, event.vespene_used_current_army))\n",
    "        caller.players[event.pid][\"vespene_value_current_economic\"].append((event.frame, event.vespene_used_current_economy))\n",
    "        caller.players[event.pid][\"vespene_queued\"].append((event.frame, event.vespene_used_in_progress))\n",
    "        caller.players[event.pid][\"vespene_queued_technology\"].append((event.frame, event.vespene_used_in_progress_technology))\n",
    "        caller.players[event.pid][\"vespene_queued_army\"].append((event.frame, event.vespene_used_in_progress_technology))\n",
    "        caller.players[event.pid][\"vespene_queued_economic\"].append((event.frame, event.vespene_used_in_progress_economy))\n",
    "    elif type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in VESPENE_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"vespene_event\"].append((event.frame, \"+\", unit))\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in VESPENE_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"vespene_event\"].append((event.frame, \"-\", unit))\n",
    "\n",
    "\n",
    "def handle_resources_events(caller, event):\n",
    "    if type(event) is PlayerStatsEvent:\n",
    "        caller.players[event.pid][\"mineral_destruction\"].append((event.frame, event.minerals_killed))\n",
    "        caller.players[event.pid][\"mineral_destruction_army\"].append((event.frame, event.minerals_killed_army))\n",
    "        caller.players[event.pid][\"mineral_destruction_economic\"].append((event.frame, event.minerals_killed_economy))\n",
    "        caller.players[event.pid][\"mineral_destruction_technology\"].append((event.frame, event.minerals_killed_technology))\n",
    "        caller.players[event.pid][\"mineral_loss\"].append((event.frame, event.minerals_lost))\n",
    "        caller.players[event.pid][\"mineral_loss_army\"].append((event.frame, event.minerals_lost_army))\n",
    "        caller.players[event.pid][\"mineral_loss_economic\"].append((event.frame, event.minerals_lost_economy))\n",
    "        caller.players[event.pid][\"mineral_loss_technology\"].append((event.frame, event.minerals_lost_technology))\n",
    "\n",
    "        caller.players[event.pid][\"vespene_destruction\"].append((event.frame, event.vespene_killed))\n",
    "        caller.players[event.pid][\"vespene_destruction_army\"].append((event.frame, event.vespene_killed_army))\n",
    "        caller.players[event.pid][\"vespene_destruction_economic\"].append((event.frame, event.vespene_killed_economy))\n",
    "        caller.players[event.pid][\"vespene_destruction_technology\"].append((event.frame, event.vespene_killed_technology))\n",
    "        caller.players[event.pid][\"vespene_loss\"].append((event.frame, event.vespene_lost))\n",
    "        caller.players[event.pid][\"vespene_loss_army\"].append((event.frame, event.vespene_lost_army))\n",
    "        caller.players[event.pid][\"vespene_loss_economic\"].append((event.frame, event.vespene_lost_economy))\n",
    "        caller.players[event.pid][\"vespene_loss_technology\"].append((event.frame, event.vespene_lost_technology))\n",
    "\n",
    "\n",
    "def handle_ground_events(caller, event):\n",
    "    if type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in GROUND_UNITS:\n",
    "            count_name = \"_\".join([\"building\", unit, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"ground_building\"].append((event.frame, \"+\", unit))\n",
    "            handle_count(caller, event, count_name, 1)\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in GROUND_UNITS:\n",
    "            count_name = \"_\".join([\"building\", unit, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"ground_building\"].append((event.frame, \"-\", unit))\n",
    "            handle_count(caller, event, count_name, -1)\n",
    "    elif type(event) is UnitTypeChangeEvent:\n",
    "        if event.unit_type_name == \"LurkerDen\":\n",
    "            count_name = \"_\".join([\"building\", event.unit_type_name, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"ground_building\"].append((event.frame, \"*\", event.unit_type_name))\n",
    "            handle_count(caller, event, count_name, 1)\n",
    "\n",
    "\n",
    "def handle_air_events(caller, event):\n",
    "    if type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in AIR_UNITS:\n",
    "            count_name = \"_\".join([\"building\", unit, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"air_building\"].append((event.frame, \"+\", unit))\n",
    "            handle_count(caller, event, count_name, 1)\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in AIR_UNITS:\n",
    "            count_name = \"_\".join([\"building\", unit, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"air_building\"].append((event.frame, \"-\", unit))\n",
    "            handle_count(caller, event, count_name, -1)\n",
    "    elif type(event) is UnitTypeChangeEvent:\n",
    "        if event.unit_type_name == \"GreaterSpire\":\n",
    "            count_name = \"_\".join([\"building\", event.unit_type_name, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"air_building\"].append((event.frame, \"*\", event.unit_type_name))\n",
    "            handle_count(caller, event, count_name, 1)\n",
    "\n",
    "\n",
    "def handle_unit_events(caller, event):\n",
    "    if type(event) is UnitBornEvent:\n",
    "        unit = event.unit_type_name\n",
    "        if unit in ARMY_UNITS:\n",
    "            unit_count_name = \"_\".join([\"unit\", unit, \"count\"])\n",
    "            caller.players[event.control_pid][\"army_event\"].append((event.frame, \"+\", unit))\n",
    "            handle_count(caller, event, unit_count_name, 1)\n",
    "            if unit in ARMY_AIR:\n",
    "                handle_count(caller, event, \"army_air\", 1)\n",
    "            elif unit in ARMY_GROUND:\n",
    "                handle_count(caller, event, \"army_ground\", 1)\n",
    "            handle_count(caller, event, \"army_count\", 1)\n",
    "    elif type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in ARMY_UNITS:\n",
    "            unit_count_name = \"_\".join([\"unit\", unit, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"army_event\"].append((event.frame, \"+\", unit))\n",
    "            handle_count(caller, event, unit_count_name, 1)\n",
    "            if unit in ARMY_AIR:\n",
    "                handle_count(caller, event, \"army_air\", 1)\n",
    "            elif unit in ARMY_GROUND:\n",
    "                handle_count(caller, event, \"army_air\", 1)\n",
    "            handle_count(caller, event, \"army_count\", 1)\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in ARMY_UNITS:\n",
    "            unit_count_name = \"_\".join([\"unit\", unit, \"count\"])\n",
    "            caller.players[event.unit.owner.pid][\"army_event\"].append((event.frame, \"-\", unit))\n",
    "            if unit in ARMY_AIR:\n",
    "                handle_count(caller, event, \"army_air\", -1)\n",
    "            elif unit in ARMY_GROUND:\n",
    "                handle_count(caller, event, \"army_ground\", -1)\n",
    "            handle_count(caller, event, unit_count_name, -1)\n",
    "            handle_count(caller, event, \"army_count\", -1)\n",
    "    elif type(event) is UnitTypeChangeEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if event.unit_type_name in ARMY_UNITS:\n",
    "            unit_count_name = \"_\".join([\"unit\", event.unit_type_name, \"count\"])\n",
    "\n",
    "            caller.players[event.unit.owner.pid][\"army_event\"].append((event.frame, \"*\", unit))\n",
    "\n",
    "            handle_count(caller, event, unit_count_name, 1)\n",
    "\n",
    "\n",
    "def handle_tech_events(caller, event):\n",
    "    if type(event) is UnitDoneEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in TECH_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"tech_building\"].append((event.frame, \"+\", unit))\n",
    "    elif type(event) is UnitDiedEvent:\n",
    "        unit = str(event.unit).split()[0]\n",
    "        if unit in TECH_UNITS:\n",
    "            caller.players[event.unit.owner.pid][\"tech_building\"].append((event.frame, \"-\", unit))\n",
    "    elif type(event) is UnitTypeChangeEvent:\n",
    "        if event.unit_type_name in [\"GreaterSpire\", \"LurkerDen\"]:\n",
    "            caller.players[event.unit.owner.pid][\"tech_building\"].append((event.frame, \"*\", event.unit_type_name))\n",
    "\n",
    "\n",
    "def handle_upgrade_events(caller, event):\n",
    "    if type(event) is UpgradeCompleteEvent and event.frame > 0:\n",
    "        if not event.upgrade_type_name.startswith(\"Spray\"):\n",
    "            caller.players[event.pid][\"upgrades\"].append((event.frame, event.upgrade_type_name))\n",
    "\n",
    "\n",
    "def handle_mineral_events(caller, event):\n",
    "    if type(event) is PlayerStatsEvent:\n",
    "        caller.players[event.pid][\"minerals_available\"].append((event.frame, event.minerals_current))\n",
    "        caller.players[event.pid][\"mineral_collection_rate\"].append((event.frame, event.minerals_collection_rate,))\n",
    "        caller.players[event.pid][\"mineral_cost_active_forces\"].append((event.frame, event.minerals_used_active_forces))\n",
    "        mins_per_worker = 0 if event.workers_active_count == 0 else event.minerals_collection_rate / event.workers_active_count\n",
    "        caller.players[event.pid][\"mineral_per_worker_rate\"].append((event.frame, mins_per_worker))\n",
    "        caller.players[event.pid][\"mineral_spend\"].append((event.frame, event.minerals_used_current))\n",
    "        caller.players[event.pid][\"mineral_value_current_technology\"].append((event.frame, event.minerals_used_current_technology))\n",
    "        caller.players[event.pid][\"mineral_value_current_army\"].append((event.frame, event.minerals_used_current_army))\n",
    "        caller.players[event.pid][\"mineral_value_current_economic\"].append((event.frame, event.minerals_used_current_economy))\n",
    "        caller.players[event.pid][\"mineral_queued\"].append((event.frame, event.minerals_used_in_progress))\n",
    "        caller.players[event.pid][\"mineral_queued_technology\"].append((event.frame, event.minerals_used_in_progress_technology))\n",
    "        caller.players[event.pid][\"mineral_queued_army\"].append((event.frame, event.minerals_used_in_progress_army))\n",
    "        caller.players[event.pid][\"mineral_queued_economic\"].append((event.frame, event.minerals_used_in_progress_economy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aggregate all of our event parsers for use by our ReplayData class\n",
    "\n",
    "handlers = [handle_expansion_events, handle_worker_events, handle_supply_events, handle_mineral_events,\n",
    "            handle_vespene_events, handle_ground_events, handle_air_events, handle_tech_events, handle_upgrade_events,\n",
    "            handle_unit_events]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below we define our class ReplayData for helping us structure and process our replay files\n",
    "\n",
    "class ReplayData:\n",
    "    __parsers__ = handlers\n",
    "\n",
    "    @classmethod\n",
    "    def parse_replay(cls, replay=None, replay_file=None, file_object=None):\n",
    "        \n",
    "        replay_data = ReplayData(replay_file)\n",
    "        try:\n",
    "            # This is the engine that holds some required plugins for parsing\n",
    "            engine = sc2reader.engine.GameEngine(plugins=[ContextLoader(), APMTracker(), SelectionTracker()])\n",
    "               \n",
    "            if replay:\n",
    "                pass\n",
    "            elif replay_file and not file_object:\n",
    "                # Then we are not using ObjectStorage for accessing replay files\n",
    "                replay = sc2reader.load_replay(replay_file, engine=engine)\n",
    "            elif file_object:\n",
    "                # We are using ObjectStorage to access replay files\n",
    "                replay = sc2reader.load_replay(file_object, engine=engine)\n",
    "            else:\n",
    "                pass\n",
    "            \n",
    "            # Get the number of frames (one frame is 1/16 of a second)\n",
    "            replay_data.frames = replay.frames\n",
    "            # Gets the game mode (if available)\n",
    "            replay_data.game_mode = replay.real_type\n",
    "            # Gets the map hash (if we want to download the map, or do map-based analysis)\n",
    "            replay_data.map_hash = replay.map_hash\n",
    "            \n",
    "            # Use the parsers to get data\n",
    "            for event in replay.events:\n",
    "                for parser in cls.__parsers__:\n",
    "                    parser(replay_data, event)\n",
    "            \n",
    "            # Check if there was a winner\n",
    "            if replay.winner is not None:\n",
    "                replay_data.winners = replay.winner.players\n",
    "                replay_data.losers = [p for p in replay.players if p not in replay.winner.players]\n",
    "            else:\n",
    "                replay_data.winners = []\n",
    "                replay_data.losers = []\n",
    "            # Check to see if expansion data is available\n",
    "            print(replay.expansion)\n",
    "            replay_data.expansion = replay.expansion\n",
    "            return replay_data\n",
    "        except:\n",
    "            # print our error and return NoneType object\n",
    "            print_exc()\n",
    "            return None\n",
    "        \n",
    "    def as_dict(self):\n",
    "        return {\n",
    "            \"processed_on\": datetime.utcnow().isoformat(),\n",
    "            \"replay_name\": self.replay,\n",
    "            \"expansion\": self.expansion,\n",
    "            \"frames\": self.frames,\n",
    "            \"mode\": self.game_mode,\n",
    "            \"map\": self.map_hash,\n",
    "            \"matchup\": \"v\".join(sorted([s.detail_data[\"race\"][0].upper() for s in self.winners + self.losers])),\n",
    "            \"winners\": [(s.pid, s.name, s.detail_data['race']) for s in self.winners],\n",
    "            \"losers\": [(s.pid, s.name, s.detail_data['race']) for s in self.losers],\n",
    "            \"stats_names\": [k for k in self.players[1].keys()],\n",
    "            \"stats\": {player: data for player, data in self.players.items()}\n",
    "                }\n",
    "\n",
    "    def __init__(self, replay):\n",
    "        self.players = defaultdict(lambda: defaultdict(list))\n",
    "        self.replay = replay\n",
    "        self.winners = []\n",
    "        self.losers = []\n",
    "        self.expansion = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize and compare replay events\n",
    "\n",
    "The replay file was processed to add event statistics using pandas and\n",
    "helper functions.\n",
    "* Bokeh is used to create Nelson rules charts.\n",
    "* Seaborn is used for box plot charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"a84517cb-58c5-4a74-a015-d627ee5f3265\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
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       "    var toinsert = output_area.element.find(`.${CLASS_NAME.split(' ')[0]}`);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
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       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"a84517cb-58c5-4a74-a015-d627ee5f3265\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"a84517cb-58c5-4a74-a015-d627ee5f3265\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a84517cb-58c5-4a74-a015-d627ee5f3265' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"a84517cb-58c5-4a74-a015-d627ee5f3265\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"a84517cb-58c5-4a74-a015-d627ee5f3265\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"a84517cb-58c5-4a74-a015-d627ee5f3265\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a84517cb-58c5-4a74-a015-d627ee5f3265' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"a84517cb-58c5-4a74-a015-d627ee5f3265\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from collections import defaultdict\n",
    "from traceback import print_exc\n",
    "\n",
    "from bokeh.io import output_notebook, reset_output\n",
    "from bokeh.models import Span, Range1d, Legend, BoxAnnotation, HoverTool, Arrow, NormalHead\n",
    "from bokeh.plotting import figure, show, gridplot, ColumnDataSource\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge(i, last_entry, sign=None, length=3):\n",
    "    if last_entry is not None:\n",
    "        if sign is not None:\n",
    "            # Check to see if this is a continuation\n",
    "            if last_entry[1] == i + length - 1 and last_entry[2] == sign:\n",
    "                return [(last_entry[0], i + length, sign)]\n",
    "            else:\n",
    "                return [last_entry, (i, i + length, sign)]\n",
    "        else:\n",
    "            # Check to see if this is a continuation\n",
    "            if last_entry[1] == i + length - 1:\n",
    "                return [(last_entry[0], i + length)]\n",
    "            else:\n",
    "                return [last_entry, (i, i + length)]\n",
    "    else:\n",
    "        if sign is not None:\n",
    "            return [(i, i + length, sign)]\n",
    "        else:\n",
    "            return [(i, i + length)]\n",
    "                \n",
    "\n",
    "                \n",
    "def detect_nelson_bias(src_data, x_bar):\n",
    "    # Bias is defined as 9 or more consecutive points sitting above or below our x-bar line\n",
    "    bias_ranges = []\n",
    "    length = 9\n",
    "    for i in range(len(src_data) - length):\n",
    "        last_entry = bias_ranges.pop() if len(bias_ranges) > 0 else None\n",
    "        if all([src_data[k] > x_bar for k in range(i, i + length)]):\n",
    "            sign = \"+\"\n",
    "            bias = merge(i, last_entry, sign=sign, length=length)\n",
    "            bias_ranges.extend(bias)\n",
    "        elif all([src_data[k] < x_bar for k in range(i, i+length)]):\n",
    "            sign = \"-\"\n",
    "            bias = merge(i, last_entry, sign=sign, length=length)\n",
    "            bias_ranges.extend(bias)\n",
    "        else:\n",
    "            if last_entry:\n",
    "                bias_ranges.append(last_entry)\n",
    "                \n",
    "    return bias_ranges\n",
    "\n",
    "def detect_nelson_trend(src_data, std):\n",
    "    # Trend is defined as 6 or more consecutive points all increasing or decreasing (or 6 or more consecutive non(increasing, decreasing) where difference between start and end points greater than 1.5 standard deviations )\n",
    "    trend_ranges = []\n",
    "    length = 6\n",
    "    for i in range(len(src_data) - length):\n",
    "        last_entry = trend_ranges.pop() if len(trend_ranges) > 0 else None\n",
    "        if (all(x<y for x, y in zip(src_data[i:i+length], src_data[i+1:i+length]))\n",
    "            or (all(x<=y for x,y in zip(src_data[i:i+length], src_data[i+1:i+length])) \n",
    "                and abs(src_data[i] - src_data[i+length]) >= 1.5*std)):\n",
    "            sign = \"+\"\n",
    "            trend_ranges.extend(merge(i, last_entry, sign=sign, length=length))\n",
    "        elif (all(x>y for x, y in zip(src_data[i:i+length], src_data[i+1:i+length])) \n",
    "            or (all(x>=y for x,y in zip(src_data[i:i+length], src_data[i+1:i+length]))\n",
    "                and abs(src_data[i] - src_data[i+length]) >= 1.5*std)):\n",
    "            sign = \"-\"\n",
    "            trend_ranges.extend(merge(i, last_entry, sign=sign, length=length))\n",
    "        else:\n",
    "            if last_entry:\n",
    "                trend_ranges.append(last_entry)\n",
    "    \n",
    "    return trend_ranges\n",
    "            \n",
    "def detect_nelson_oscillation(src_data):\n",
    "    # Oscillation is defined as 14 or more consecutive points, all alternating in direction\n",
    "    diff = lambda x, y: 1 if y - x > 0 else -1 if y - x  < 0 else None\n",
    "    oscillation_ranges = []\n",
    "    length=14\n",
    "    deltas = []\n",
    "    for i in range(len(src_data) - length):\n",
    "        last_entry = oscillation_ranges.pop() if len(oscillation_ranges) > 0 else None\n",
    "        sign = None\n",
    "        is_oscillating = True\n",
    "        for curr in range(i, i + length - 1):\n",
    "            if sign == None and curr == i:\n",
    "                sign = diff(src_data[curr], src_data[curr + 1])\n",
    "            elif sign is None and curr != i:\n",
    "                is_oscillating = False\n",
    "                break\n",
    "            else:\n",
    "                new_sign = diff(src_data[curr], src_data[curr + 1])\n",
    "                if new_sign is None or new_sign == sign:\n",
    "                    is_oscillating = False\n",
    "                    break\n",
    "                elif new_sign != sign and new_sign is not None:\n",
    "                    sign = new_sign\n",
    "        if is_oscillating:\n",
    "            # check if this is a continuation of a previous oscillation\n",
    "            oscillation_ranges.extend(merge(i, last_entry, length=length))\n",
    "            \n",
    "        else:\n",
    "            if last_entry:\n",
    "                oscillation_ranges.append(last_entry)\n",
    "    \n",
    "    return oscillation_ranges\n",
    "\n",
    "\n",
    "def avg_last_minute(process, pid, time, replay):\n",
    "    data = pd.DataFrame({\n",
    "            \"Data\": [k[1] for k in replay.stats[pid-1][process]]},\n",
    "            index=[int(k[0]/16) for k in replay.stats[pid - 1][process]])\n",
    "    \n",
    "    rolling = data.rolling(6).mean()\n",
    "    pct_change = data.pct_change()\n",
    "    ndx = data.index.get_loc(time, method=\"ffill\")\n",
    "\n",
    "    prev_ndx = max(ndx - 1, 0)\n",
    "    print(ndx, prev_ndx)\n",
    "    r_mean = rolling.get_value(rolling.index[ndx], \"Data\")\n",
    "    prev_mean = rolling.get_value(rolling.index[prev_ndx], \"Data\")\n",
    "    print(r_mean, prev_mean)\n",
    "    print(pct_change)\n",
    "    pcng = pct_change.get_value(rolling.index[ndx], \"Data\")\n",
    "\n",
    "    change = \"⬆️\" if r_mean > prev_mean else \"⬇️\" if r_mean < prev_mean else \"\"\n",
    "    \n",
    "    return r_mean if not pd.isnull(r_mean) else 0, change, pcng if not (pd.isnull(pcng) or pcng != np.Inf) else 0\n",
    "    \n",
    "\n",
    "# Define Nelson Rules Chart Generator\n",
    "def nelson_rules_chart_generator(src, timeseries, player, pid, process_name, unit_name, replay, plot_width=350,fill_color=\"blue\", line_color=\"blue\", line_width=2, annotations=None, fixed_lcl=None, fixed_ucl=None):\n",
    "    # We strip the first two data points (first data point is 0 and second data point should roughly be the same for all games)\n",
    "    x_bar = src[2:].mean()\n",
    "    std = src[2:].std()\n",
    "    ctrl_limits = [x_bar + (k*std) for k in range(-3, 4)]\n",
    "    ctrl_labels = [\"LCL\", \"-2σ\", \"-1σ\", \"x-bar\", \"1σ\", \"2σ\", \"UCL\"]\n",
    "    ctrl_colors = [\"#55597F\", \"#5D6DFF\",\"#A9B2FF\",\"#000000\", \"#FF9E9F\", \"#FF5253\",\"#7F2929\"]\n",
    "    ctrl_dash = [\"solid\", \"dashed\", \"dashed\", \"solid\", \"dashed\", \"dashed\", \"solid\"]\n",
    "    ctrl_legend = [\"{0} - {1:10.4f}\".format(cl[0], cl[1]) for cl in zip(ctrl_labels, ctrl_limits)]\n",
    "    ctrl_width = [3, 2, 2, 3, 2, 2, 3]\n",
    "    \n",
    "    \n",
    "    significant = lambda x: x > ctrl_limits[5] or x < ctrl_limits[1]\n",
    "    \n",
    "    hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"time\", \"@x\"),\n",
    "            (\"value\", \"@y\")\n",
    "        ])\n",
    "    \n",
    "    p = figure(plot_width=plot_width, plot_height=300, x_axis_label=\"Game Time (in seconds)\", y_axis_label=unit_name, tools=[hover], toolbar_location=\"above\")\n",
    "    # Generate control lines\n",
    "    lines = []\n",
    "    source = ColumnDataSource(data=dict(x=[x/16 for x in timeseries], \n",
    "                                        y=src,\n",
    "                                        alpha=[1 if significant(y) and ndx > 2 else 0.7 for ndx, y in enumerate(src)], \n",
    "                                        radius=[6 if significant(y) and ndx > 2 else 4 for ndx, y in enumerate(src)], \n",
    "                                        ))\n",
    "    for ndx, cl in enumerate(ctrl_limits):\n",
    "        limit = cl\n",
    "            \n",
    "        lines.append(p.line([x/16 for x in timeseries], \n",
    "                            [limit]*len(timeseries), \n",
    "                            line_width=ctrl_width[ndx], \n",
    "                            line_dash=ctrl_dash[ndx], \n",
    "                            tags=[ctrl_labels[ndx] if k == 0 else None for k, _ in enumerate(timeseries)],  \n",
    "                            line_color=ctrl_colors[ndx]))\n",
    "        \n",
    "    \n",
    "    p.circle(\"x\", \"y\",\n",
    "             source=source,\n",
    "             alpha=\"alpha\", \n",
    "             radius=\"radius\", \n",
    "             fill_color=fill_color,  \n",
    "             line_width=line_width)\n",
    "    \n",
    "    \n",
    "    # Handle bias\n",
    "    bias_ranges = detect_nelson_bias(src, x_bar)\n",
    "    for rng in bias_ranges:\n",
    "        if rng[2] is \"+\":\n",
    "            p.add_layout(BoxAnnotation(bottom=x_bar, top=ctrl_limits[-1], left=timeseries[rng[0]]/16, right=timeseries[rng[1]]/16, fill_color=\"green\"))\n",
    "        elif rng[2] is \"-\":\n",
    "            p.add_layout(BoxAnnotation(top=x_bar, bottom=ctrl_limits[0], left=timeseries[rng[0]]/16, right=timeseries[rng[1]]/16, fill_color=\"red\"))\n",
    "            \n",
    "    # Handle trends\n",
    "    trend_ranges = detect_nelson_trend(src, std)\n",
    "    for rng in trend_ranges:\n",
    "        if rng[2] is \"+\":\n",
    "            p.add_layout(Arrow(end=NormalHead(line_color=\"goldenrod\",\n",
    "                                              fill_color=\"goldenrod\"),\n",
    "                               x_start=timeseries[rng[0]]/16,\n",
    "                               y_start=src[rng[0]],\n",
    "                               x_end=timeseries[rng[1]]/16,\n",
    "                               y_end=src[rng[1]],\n",
    "                               line_width=4,\n",
    "                               line_alpha=0.6,\n",
    "                               line_dash=\"solid\"))\n",
    "        elif rng[2] is \"-\":\n",
    "            p.add_layout(Arrow(end=NormalHead(line_color=\"#7F0000\",\n",
    "                                              fill_color=\"#7F0000\"),\n",
    "                               x_start=timeseries[rng[0]]/16,\n",
    "                               y_start=src[rng[0]],\n",
    "                               x_end=timeseries[rng[1]]/16,\n",
    "                               y_end=src[rng[1]],\n",
    "                               line_width=4,\n",
    "                               line_alpha=0.6,\n",
    "                               line_dash=\"solid\"))\n",
    "    \n",
    "    p.title.text = \"{0} for {1}\".format(unit_name, player)\n",
    "    p.y_range = p.y_range = Range1d(ctrl_limits[0] - 0.125 * ctrl_limits[0],  1.125 * ctrl_limits[-1])\n",
    "    \n",
    "    legend = Legend(items=list(zip(ctrl_legend, [[l] for l in lines])), location=(10,-30))\n",
    "    p.add_layout(legend, \"right\")\n",
    "    \n",
    "    return p, ctrl_limits, min(src[2:]), max(src[2:]), timeseries[-1]"
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   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotV\n"
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    }
   ],
   "source": [
    "replay_object = ReplayData.parse_replay(replay=replay)\n",
    "replay_dict = replay_object.as_dict()\n",
    "\n",
    "players = {}\n",
    "for player in replay_dict[\"winners\"]:\n",
    "    players[int(player[0])] = {\"full\": \"Winning Player {num}: {name} ({race})\".format(num=player[0], name=player[1], race=player[2]),\n",
    "                               \"short\": \"{name} ({race})\".format(name=player[1], race=player[2]) }\n",
    "for player in replay_dict[\"losers\"]:\n",
    "    players[int(player[0])] = {\"full\": \"Losing Player {num}: {name} ({race})\".format(num=player[0], name=player[1], race=player[2]),\n",
    "                               \"short\": \"{name} ({race})\".format(name=player[1], race=player[2]) }\n",
    "\n",
    "econ = [\"mineral_collection_rate\", \"vespene_collection_rate\", \"workers_active\", \"supply_utilization\", \"worker_supply_ratio\"]\n",
    "units = [\"Minerals per Minute (MPM)\", \"Vespene per Minute (VPM)\", \"Workers\", \"Supply Used / Supply Available\", \"Workers / Supply Used\"]\n",
    "\n",
    "player_charts = defaultdict(dict)\n",
    "player_aggregate = defaultdict(dict)"
   ]
  },
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       "    var render_items = [{\"docid\":\"56e816df-6615-4f9b-8008-b21c29893386\",\"elementid\":\"79f1e495-e3ac-4aa1-8f81-21be0acb68c1\",\"modelid\":\"5f4347c4-c472-4036-9ce2-884a5202bbe6\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "5f4347c4-c472-4036-9ce2-884a5202bbe6"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for pid, player in players.items():\n",
    "    for ndx, process in enumerate(econ):\n",
    "        # Generate charts per player\n",
    "        timeseries = [k[0] for k in replay_dict[\"stats\"][pid][process]]\n",
    "        proc_data = [j[1] for j in replay_dict[\"stats\"][pid][process]]\n",
    "\n",
    "        player_charts[pid][process], limits, v_min, v_max, game_length = nelson_rules_chart_generator(\n",
    "            pd.Series(proc_data),\n",
    "            timeseries,\n",
    "            player[\"full\"],\n",
    "            pid,\n",
    "            process,\n",
    "            units[ndx],\n",
    "            replay,\n",
    "            fixed_lcl=0)\n",
    "        player_aggregate[process][pid] = proc_data\n",
    "\n",
    "grid = [[player_charts[k][measurement] for k in player_charts] for ndx, measurement in enumerate(econ)]\n",
    "\n",
    "show(gridplot(grid, sizing_mode=\"scale_width\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b2e5ae03860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig, axes = plt.subplots(nrows=len(grid),ncols=1,figsize=(15,20))\n",
    "\n",
    "for ndx in range(len(grid)):\n",
    "\n",
    "    sample_max = min([len(player_aggregate[econ[ndx]][j]) for j in player_aggregate[econ[ndx]].keys()])\n",
    "    # Remember, we are removing the first 20 samples (beginning of game should be the same for all, so data is useless)\n",
    "    frame = pd.DataFrame({ units[ndx]: [player_aggregate[econ[ndx]][1][k] for k in range(2,sample_max)] + [player_aggregate[econ[ndx]][2][l] for l in range(2,sample_max)],\n",
    "                           \"Player\": [players[(i//sample_max) + 1][\"short\"] for i in range(0,len(players.keys())*(sample_max - 2))]})\n",
    "    \n",
    "    bp = seaborn.boxplot(x=units[ndx], orient=\"h\", y='Player',  data=frame,  width=0.5, palette=\"muted\", ax=axes[ndx])\n",
    "    bp = seaborn.swarmplot(x=units[ndx], y='Player', data=frame,  color='gold', alpha=0.5, ax=axes[ndx])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Storing Replay Files\n",
    "\n",
    "Now that we have loaded and processed the replay, we can store\n",
    "it for future use and aggregation.\n",
    "\n",
    "The replay_object has an as_dict() method to give us JSON.\n",
    "\n",
    "We'll store this in Cloudant for future use.\n",
    "\n",
    "1. Connect to Cloudant with our IBM Cloud credentials.\n",
    "2. Create 'sc2replays' and 'sc2recents' databases.\n",
    "3. Store the current replay as a document in the 'sc2replays' database.\n",
    "4. Store the current replay as a document in the 'sc2recents' database and remove the older replays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Peek at what is in the object?\n",
    "# This creates a lot of output, but if you are curious just uncomment the following 2 lines:\n",
    "\n",
    "# from pprint import pprint\n",
    "# pprint(replay_object.as_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloudant credentials added for storing replay data as JSON.\n",
      "['bogusdb', 'sc2recents', 'sc2replays']\n",
      "sc2replays entry saved. Latest id: c600ffad339f5f4b451e3971583f28ca\n",
      "sc2recents entry saved. Latest id: 6234bb4ad4e7ee52c985aca97cff367f\n"
     ]
    }
   ],
   "source": [
    "from cloudant.client import Cloudant\n",
    "\n",
    "# Some extra code here helps catch setup errors.\n",
    "try:\n",
    "    sc2replay_creds = credentials_1\n",
    "    print(\"Cloudant credentials added for storing replay data as JSON.\")\n",
    "except NameError:\n",
    "    print('\\n'\n",
    "          'SETUP ERROR: Please follow the directions to add Cloudant credentials to the notebook.\\n'\n",
    "          '             You may need to rename the credentials_* variable.')\n",
    "    raise\n",
    "\n",
    "# Now we need to send this data to 2 databases \"sc2replays for aggregating, and sc2recents.\n",
    "\n",
    "# Connect to cloudant service with IBM Cloud IAM credentials username and apikey.\n",
    "esports = Cloudant.iam(sc2replay_creds[\"username\"], sc2replay_creds[\"apikey\"], connect=True)\n",
    "    \n",
    "print(esports.all_dbs())\n",
    "session = esports.session()\n",
    "\n",
    "if 'sc2replays' not in esports.all_dbs():\n",
    "        esports.create_database('sc2replays')\n",
    "        \n",
    "sc2replays = esports['sc2replays']\n",
    "document = sc2replays.create_document(replay_object.as_dict())\n",
    "if document.exists():\n",
    "    print(\"sc2replays entry saved. Latest id: {0}\".format(document[\"_id\"]))\n",
    "        \n",
    "if 'sc2recents' not in esports.all_dbs():\n",
    "    esports.create_database('sc2recents')\n",
    "        \n",
    "sc2recents = esports['sc2recents']\n",
    "# clear out everything in sc2recents db\n",
    "for d in sc2recents:\n",
    "    d.delete()\n",
    "        \n",
    "document = sc2recents.create_document(replay_object.as_dict())\n",
    "if document.exists():\n",
    "    print(\"sc2recents entry saved. Latest id: {0}\".format(document[\"_id\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# License\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n",
    "not use this file except in compliance with the License. You may obtain\n",
    "a copy of the License at\n",
    "\n",
    "     http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n",
    "WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n",
    "License for the specific language governing permissions and limitations\n",
    "under the License."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
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