deap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import string\n",
    "import numpy as np\n",
    "from deap import base, creator, tools\n",
    "from baseball_simulation import test, run_player_scenario_mc, run_player_scenario_mh, run_player_scenario_mc_standalone, Player\n",
    "\n",
    "import multiprocessing\n",
    "pool = multiprocessing.Pool()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0\n",
      "1.0205078125 1.2300000000000004\n",
      "1.0205078125 1.1400000000000006\n"
     ]
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup a runner for the genetic algorithm\n",
    "class Runner:\n",
    "    \n",
    "    def __init__(self, toolbox):\n",
    "        self.toolbox = toolbox\n",
    "        self.set_parameters(10, 5, 2)\n",
    "        \n",
    "    def set_parameters(self, population_size, iterations, n_matings):\n",
    "        self.iterations = iterations\n",
    "        self.population_size = population_size\n",
    "        self.n_matings = n_matings\n",
    "        \n",
    "    def set_fitness(self, population):\n",
    "        fitnesses = [ \n",
    "            (individual, self.toolbox.evaluate(individual)) \n",
    "            for individual in population \n",
    "        ]\n",
    "\n",
    "        for individual, fitness in fitnesses:\n",
    "            individual.fitness.values = (fitness,)\n",
    "            \n",
    "    def get_offspring(self, population):\n",
    "        n = len(population)\n",
    "        for _ in range(self.n_matings):\n",
    "            i1, i2 = np.random.choice(range(n), size=2, replace=False)\n",
    "\n",
    "            offspring1, offspring2 = \\\n",
    "                self.toolbox.mate(population[i1], population[i2])\n",
    "            \n",
    "            yield self.toolbox.mutate(offspring1)[0]\n",
    "            yield self.toolbox.mutate(offspring2)[0]\n",
    "    \n",
    "    @staticmethod\n",
    "    def pull_stats(population, iteration=1):\n",
    "        fitnesses = [ individual.fitness.values[0] for individual in population ]\n",
    "        return {\n",
    "            'i': iteration,\n",
    "            'mu': np.mean(fitnesses),\n",
    "            'std': np.std(fitnesses),\n",
    "            'max': np.max(fitnesses),\n",
    "            'min': np.min(fitnesses)\n",
    "        }  \n",
    "    \n",
    "    def Run(self):\n",
    "        population = self.toolbox.population(n=self.population_size)\n",
    "        print('Size of population {}'.format(len(population)))\n",
    "        self.set_fitness(population)\n",
    "        \n",
    "        stats = []\n",
    "        for iteration in list(range(1, self.iterations + 1)):\n",
    "            current_population = list(map(self.toolbox.clone, population))\n",
    "            offspring = list(self.get_offspring(current_population))            \n",
    "            for child in offspring:\n",
    "                current_population.append(child)\n",
    "            \n",
    "            ## reset fitness,\n",
    "            self.set_fitness(current_population)\n",
    "            \n",
    "            population[:] = self.toolbox.select(current_population, len(population))\n",
    "            stats.append(\n",
    "                Runner.pull_stats(population, iteration))\n",
    "            \n",
    "        return stats, population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n",
    "creator.create(\"Individual\", list, fitness=creator.FitnessMin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## globals,\n",
    "\n",
    "random.seed(11);\n",
    "np.random.seed(121);\n",
    "\n",
    "\n",
    "INDIVIDUAL_SIZE = 10\n",
    "POPULATION_SIZE = 20 #200\n",
    "N_ITERATIONS = 100\n",
    "N_MATINGS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "toolbox = base.Toolbox()\n",
    "\n",
    "## permutation setup for individual,\n",
    "toolbox.register(\"indices\", random.sample, range(INDIVIDUAL_SIZE), INDIVIDUAL_SIZE)\n",
    "toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.indices)\n",
    "\n",
    "## population setup,\n",
    "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "players =[Player([0.7, 0.3, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.5, 0.0, 0.0, 0.0, 0.0, 0.5]),\n",
    "          Player([0.5, 0.5, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.4, 0.0, 0.0, 0.0, 0.0, 0.6]),\n",
    "          Player([0.5, 0.5, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.6, 0.4, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.4, 0.0, 0.0, 0.0, 0.0, 0.6]),\n",
    "          Player([0.8, 0.0, 0.0, 0.0, 0.0, 0.2]),\n",
    "          Player([0.5, 0.5, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.7, 0.3, 0.0, 0.0, 0.0, 0.0])]\n",
    "\n",
    "players =[Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.5, 0.0, 0.0, 0.0, 0.0, 0.5]),\n",
    "          Player([0.5, 0.5, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.4, 0.0, 0.0, 0.0, 0.0, 0.6]),\n",
    "          Player([0.5, 0.5, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.6, 0.4, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.4, 0.0, 0.0, 0.0, 0.0, 0.6]),\n",
    "          Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([0.5, 0.5, 0.0, 0.0, 0.0, 0.0]),\n",
    "          Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]\n",
    "\n",
    "initial_order = [0,1,2,3,4,5,6,7,8,9]\n",
    "\n",
    "toolbox.register(\"evaluate\", run_player_scenario_mc, players)\n",
    "toolbox.register(\"map\", pool.map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do a single run and show results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    Inning Runs Outs Inning Runs\n",
       " 4        4   17    1           5\n",
       " 8        4   20    1           5\n",
       " 12       4   17    1           5\n",
       " 16       4   18    0           5\n",
       " 20       4   19    0           5\n",
       " ..     ...  ...  ...         ...\n",
       " 384      4   20    2           5\n",
       " 388      4   20    1           5\n",
       " 392      4   18    1           5\n",
       " 396      4   18    2           5\n",
       " 400      4   20    2           5\n",
       " \n",
       " [100 rows x 4 columns], 19.14)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = run_player_scenario_mc_standalone(players,initial_order)\n",
    "df = df[df.Inning == 4]\n",
    "df, df['Runs'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setup as an ordered crossover and some other parameters\n",
    "toolbox.register(\"mate\", tools.cxOrdered)\n",
    "toolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb=0.01)\n",
    "toolbox.register(\"select\", tools.selTournament, tournsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of population 20\n"
     ]
    }
   ],
   "source": [
    "#Do the run\n",
    "a = Runner(toolbox)\n",
    "a.set_parameters(POPULATION_SIZE, N_ITERATIONS, N_MATINGS)\n",
    "stats, population = a.Run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 0.4800000000000004, [8, 4, 7, 2, 6, 3, 0, 1, 9, 5]),\n",
       " (10, 0.5300000000000011, [8, 4, 7, 2, 6, 3, 0, 1, 9, 5]),\n",
       " (13, 0.5500000000000007, [2, 9, 5, 7, 1, 3, 0, 6, 8, 4]),\n",
       " (1, 0.620000000000001, [5, 8, 0, 2, 3, 4, 1, 9, 6, 7]),\n",
       " (9, 0.620000000000001, [2, 9, 5, 7, 1, 3, 0, 6, 8, 4])]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitnesses = sorted([ \n",
    "    (i, toolbox.evaluate(individual), individual) \n",
    "    for i, individual in enumerate(population) \n",
    "], key=lambda x: x[1])\n",
    "\n",
    "fitnesses[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
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       "\n",
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       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"1500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "\n",
    "plt.scatter([ s['min'] for s in stats ], [ s['max'] for s in stats ], marker='*', s=[ (s['std'] + 1) / 20 for s in stats ])\n",
    "\n",
    "plt.title('min by max')\n",
    "plt.xlabel('min')\n",
    "plt.ylabel('max')\n",
    "\n",
    "plt.plot(stats[0]['min'], stats[0]['max'], marker='.', color='yellow')\n",
    "plt.plot(stats[-1]['min'], stats[-1]['max'], marker='.', color='red')\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.scatter([ s['i'] for s in stats ], [ s['mu'] for s in stats ], marker='*', s=[ (s['std'] + 1)*10 for s in stats ])\n",
    "\n",
    "plt.title('average by iteration')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('average')\n",
    "\n",
    "plt.plot(stats[0]['i'], stats[0]['mu'], marker='.', color='yellow')\n",
    "plt.plot(stats[-1]['i'], stats[-1]['mu'], marker='.', color='red')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.39\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook \n",
    "\n",
    "df = run_player_scenario_mc_standalone(players,fitnesses[0][2])\n",
    "ax = df[df.Inning == 4]['Runs'].hist() \n",
    "ax.set_xlabel('Number of Runs') \n",
    "ax.set_title('Histogram of Number of Runs over Four Innings (1000 Monte Carlo Runs)') \n",
    "print(df[df.Inning == 4]['Runs'].sum()/df[df.Inning == 4]['Runs'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P 1B  2B  3B  HR  SO  BO \n",
      "8 0.5 0.5 0.0 0.0 0.0 0.0\n",
      "4 0.5 0.5 0.0 0.0 0.0 0.0\n",
      "7 1.0 0.0 0.0 0.0 0.0 0.0\n",
      "2 0.5 0.5 0.0 0.0 0.0 0.0\n",
      "6 0.4 0.0 0.0 0.0 0.0 0.6\n",
      "3 0.4 0.0 0.0 0.0 0.0 0.6\n",
      "0 1.0 0.0 0.0 0.0 0.0 0.0\n",
      "1 0.5 0.0 0.0 0.0 0.0 0.5\n",
      "9 1.0 0.0 0.0 0.0 0.0 0.0\n",
      "5 0.6 0.4 0.0 0.0 0.0 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('P', '1B ', '2B ', '3B ', 'HR ', 'SO ', 'BO ')\n",
    "[print(i, players[i].p1b, players[i].p2b, players[i].p3b, players[i].phr, players[i].pso, players[i].pbo) for i in fitnesses[0][2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
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 "nbformat": 4,
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