damage_calcs.ipynb

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  {
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   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# TO FIX in nokka parser\n",
    "# stat display order\n",
    "# stat combination rules (also fix the elemental min/max damage combination)\n",
    "# max HP\n",
    "# \"Atlus\" suffix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cb_factor(boss, ranged, pr):\n",
    "    alpha = 1./8 if boss else 1./4\n",
    "    if ranged: alpha /= 2.\n",
    "    alpha *= min(max(1-pr, 0), 1) # crushing blow dmg decreased by PR but cannot be increased with -PR\n",
    "    #alpha *= 2./(nplayers+1) # crushing blow penalized with many players\n",
    "    return alpha\n",
    "\n",
    "# Apply crushing blow and return the remaining life\n",
    "# life: the starting life value\n",
    "# N: the number of players\n",
    "# boss: True if boss\n",
    "# ranged: True if ranged attack\n",
    "# pr: physical resist\n",
    "# chance: chance of CB\n",
    "def apply_cb(life, N, boss=False, ranged=False, pr=0, chance=1):\n",
    "    cbgood = np.random.binomial(1,chance)\n",
    "    if cbgood:\n",
    "        return (1-cb_factor(boss, ranged, pr)*2./(N+1))*life\n",
    "    else:\n",
    "        return life\n",
    "\n",
    "# Apply crushing blow and the damage, return the remaining life\n",
    "def apply_hit(life, damage, N, boss=False, ranged=False, pr=0, cb_chance=1):\n",
    "    return apply_cb(life, N, boss, ranged, pr, chance=cb_chance) - damage*max(1-pr,0)\n",
    "\n",
    "# Return number of hits to kill a monster\n",
    "def hits_to_kill(life_p1, damage, N, boss=False, ranged=False, pr=0, cb_chance=1):\n",
    "    damage *= max(1-pr,0)\n",
    "    if cb_chance == 0:\n",
    "        return 1.0*life_p1*0.5*(N+1)/damage\n",
    "    beta = 1-cb_factor(boss, ranged, pr)*2./(N+1)*cb_chance\n",
    "    #return -np.log((1-beta)*life/damage + 1)/np.log(beta)\n",
    "    return -np.log(cb_factor(boss,ranged,pr)*cb_chance*life_p1/damage +1)/np.log(beta)\n",
    "\n",
    "# 1 - beta = cb_factor(boss, ranged, pr)*2./(N+1)*cb_chance\n",
    "# (1-beta)*p1_life*0.5*(N+1) = cb_factor(boss, ranged, pr)*cb_chance*p1_life\n",
    "# return -np.log(cb_factor(boss,ranged,pr)*cb_chance*p1_life/damage +1)/np.log(beta)\n",
    "\n",
    "# formula from madscientist post\n",
    "def ms_formula(L, average_damage, nr_players, boss=False, p=0, p_cb=1, p_hit=1):\n",
    "    e_cb = 1./8 if boss else 1./4\n",
    "    x = 1-p_hit*p_cb*(1-p)*e_cb*2/(nr_players+1)\n",
    "    y = p_hit*average_damage*(1-p)\n",
    "    # n = (np.log(y)-np.log(L*(1-p)+y))/np.log(x)\n",
    "    # above formula kept in original form, but equivalent to\n",
    "    # n = -np.log(L*(1-p)/y+1)/np.log(x)\n",
    "    # substitute for y\n",
    "    n = -np.log(L/(p_hit*average_damage)+1)/np.log(x)\n",
    "    # this form makes it more obvious what went wrong when comparing to hits_to_kill formula\n",
    "    return n\n",
    "\n",
    "# my formula, but it takes life @ Px instead of life @ P1\n",
    "def M81_formula(life, damage, N, boss=False, ranged=False, pr=0, cb_chance=1):\n",
    "    return hits_to_kill(life*2./(N+1), damage, N, boss=boss, ranged=ranged, pr=pr, cb_chance=cb_chance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "                // 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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nplayers = [1,2,3,4,5,6,7,8]\n",
    "nhits = 50\n",
    "cbs_per_second = 11.8\n",
    "monster_hp = 10000 #493701\n",
    "dps_mode = False\n",
    "if not dps_mode:\n",
    "    cbs_per_second = 1.0\n",
    "t = np.array(range(nhits+1))/cbs_per_second\n",
    "plt.figure()\n",
    "\n",
    "for n in nplayers:\n",
    "    life = np.zeros(nhits+1)\n",
    "    starting_life = monster_hp*0.5*(1+n)\n",
    "    life[0] = starting_life\n",
    "    for i in range(nhits):\n",
    "        life[i+1] = apply_cb(life[i], n, boss=True, ranged=False)\n",
    "\n",
    "\n",
    "    if not dps_mode:\n",
    "        plt.step(t[1:], -1.0*np.diff(life)/np.diff(t), where='mid')\n",
    "        #plt.step(t, life, where='mid')\n",
    "    else:\n",
    "        plt.plot(t[1:], -1.0*np.diff(life)/np.diff(t))\n",
    "if not dps_mode:\n",
    "    plt.xlabel('strike #')\n",
    "    plt.ylabel('damage')\n",
    "else:\n",
    "    plt.xlabel('t [s]')\n",
    "    plt.ylabel('dps')\n",
    "plt.title('Crushing blow damage to baal')\n",
    "plt.legend(['P'+str(n) for n in nplayers])\n",
    "#plt.xticks(np.arange(0,101,step=10))\n",
    "plt.grid(axis='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "damage: 1489.18\n",
      "M81 formula: 13.42\n",
      "MadScientist formula: 246.03\n",
      "No crushing blow (life/damage): 6.72\n"
     ]
    }
   ],
   "source": [
    "#damage = 29070\n",
    "damage = 39*(1+3.3)*(1+3+2+2.88)\n",
    "print('damage: {:.2f}'.format(damage))\n",
    "print('M81 formula: {:.2f}'.format(M81_formula(monster_hp*0.5*(1+1), damage, 1, boss=False, ranged=True, cb_chance=0.001, pr=0.5)))\n",
    "print('MadScientist formula: {:.2f}'.format(ms_formula(monster_hp*0.5*(8+1), damage, 8, boss=True, p_cb=0.5, p_hit=1)))\n",
    "print('No crushing blow (life/damage): {:.2f}'.format(monster_hp*0.5*(1+1)/(damage)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'damage' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-d63df45c3084>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mnhits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[0mmlife\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[0mmlife\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapply_hit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmlife\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdamage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNplayers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcb_chance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m         \u001b[0mnhits\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mnhits_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnhits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'damage' is not defined"
     ]
    }
   ],
   "source": [
    "trials = 10000\n",
    "Nplayers = 8\n",
    "nhits_list = []\n",
    "for trial in range(trials):\n",
    "    mlife = monster_hp*0.5*(1+Nplayers)\n",
    "    nhits = 0\n",
    "    while mlife > 0:\n",
    "        mlife = apply_hit(mlife, damage, Nplayers, boss=True, cb_chance=0.5)\n",
    "        nhits += 1\n",
    "    nhits_list.append(nhits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-6ed9ee8c2722>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-5-6ed9ee8c2722>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    print np.average(nhits_list)\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(nhits_list)\n",
    "print np.average(nhits_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation result: 52.18\n",
      "M81 formula: 51.72\n",
      "MadScientist formula: 310.97\n",
      "No crushing blow (life/damage): 76.42\n"
     ]
    }
   ],
   "source": [
    "print 'simulation result: {:.2f}'.format(np.average(nhits_list))\n",
    "print 'M81 formula: {:.2f}'.format(M81_formula(monster_hp*0.5*(8+1), damage, 8, boss=True, cb_chance=0.5))\n",
    "print 'MadScientist formula: {:.2f}'.format(ms_formula(monster_hp*0.5*(8+1), damage, 8, boss=True, p_cb=0.5, p_hit=1))\n",
    "print 'No crushing blow (life/damage): {:.2f}'.format(monster_hp*0.5*(8+1)/(damage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137.25\n",
      "eth tomb reaver\n",
      "631.35 227.7 1035.0\n",
      "90\n",
      "3.6\n",
      "ribcracker\n",
      "447.9 360.0 535.8\n",
      "death colossus sword\n",
      "662.025\n"
     ]
    }
   ],
   "source": [
    "import abc\n",
    "\n",
    "class MagicProperties(object):\n",
    "    def __init__(self, ias=0, ed=0, cb=0, ds=0, maxdmg=0, mindmg=0):\n",
    "        self.ias = ias\n",
    "        self.ed = ed\n",
    "        self.cb = cb\n",
    "        self.ds = ds\n",
    "        self.maxdmg = maxdmg\n",
    "        self.mindmg = mindmg\n",
    "        \n",
    "    def __add__(self, mp):\n",
    "        return MagicProperties(ias=self.ias+mp.ias,\n",
    "                               ed=self.ed+mp.ed,\n",
    "                               cb=self.cb+mp.cb,\n",
    "                               ds=self.ds+mp.ds,\n",
    "                               maxdmg=self.maxdmg+mp.maxdmg,\n",
    "                               mindmg=self.mindmg+mp.mindmg)\n",
    "\n",
    "class Item(object):\n",
    "    def __init__(self):\n",
    "        self.mp = MagicProperties()\n",
    "        \n",
    "    # create an item with the given magic properties\n",
    "    @classmethod\n",
    "    def create(cls, mp, *args, **kwargs):\n",
    "        item = cls(*args, **kwargs)\n",
    "        item.mp = mp\n",
    "        return item\n",
    "    \n",
    "class Socketable(Item):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        Item.__init__(self)\n",
    "        self.mp = MagicProperties(*args, **kwargs)\n",
    "\n",
    "class Equipable(Item):\n",
    "    pass\n",
    "\n",
    "class Weapon(Equipable):\n",
    "    def __init__(self, eth=False, sockets=0):\n",
    "        Equipable.__init__(self)\n",
    "        self.socketables = []\n",
    "        self.sockets = sockets\n",
    "        self.eth = eth\n",
    "        \n",
    "    def add_socketable(self, socketable):\n",
    "        if len(self.socketables) < self.sockets:\n",
    "            self.socketables.append(socketable)\n",
    "\n",
    "    def avg_damage(self):\n",
    "        return (self.max_damage()+self.min_damage())/2.0\n",
    "    \n",
    "    def max_damage(self):\n",
    "        return self.compute_dmg(self.maxdmg, self.total_mp().maxdmg)\n",
    "    \n",
    "    def min_damage(self):\n",
    "        return self.compute_dmg(self.mindmg, self.total_mp().mindmg)\n",
    "    \n",
    "    def compute_dmg(self, basedmg, adds_dmg):\n",
    "        dmg = basedmg\n",
    "        if self.eth: dmg *= 1.5\n",
    "        dmg *= (1+self.total_mp().ed)\n",
    "        dmg += adds_dmg\n",
    "        return dmg\n",
    "    \n",
    "    def total_mp(self):\n",
    "        return reduce(lambda x,y: x+y, [s.mp for s in self.socketables], self.mp)\n",
    "    \n",
    "    def total_ed(self):\n",
    "        return self.total_mp().ed\n",
    "    \n",
    "    def total_ias(self):\n",
    "        return self.total_mp().ias\n",
    "\n",
    "class Staff(Weapon):\n",
    "    wtype='STF'\n",
    "    \n",
    "class TwoHandSword(Weapon):\n",
    "    wtype='2HS'\n",
    "    \n",
    "class HandToHand(Weapon):\n",
    "    wtype='HTH'\n",
    "    \n",
    "class CrypticAxe(Staff):\n",
    "    mindmg = 33\n",
    "    maxdmg = 150\n",
    "    wsm = 10\n",
    "        \n",
    "class Stalagmite(Staff):\n",
    "    mindmg = 75\n",
    "    maxdmg = 107\n",
    "    wsm = 10\n",
    "\n",
    "class ColossusSword(TwoHandSword):\n",
    "    mindmg = 61\n",
    "    maxdmg = 121\n",
    "    wsm = 10\n",
    "    \n",
    "class ColossusBlade(TwoHandSword):\n",
    "    mindmg = 58\n",
    "    maxdmg = 115\n",
    "    wsm = 5\n",
    "    \n",
    "class Runeword(MagicProperties):\n",
    "    pass\n",
    "    \n",
    "class Death(Runeword):\n",
    "    def __init__(self, ed=3.85):\n",
    "        MagicProperties.__init__(self)\n",
    "        self.ed = ed\n",
    "        self.cb = 0.5\n",
    "        self.ds = 0\n",
    "        \n",
    "class BreathOfTheDying(Runeword):\n",
    "    def __init__(self, ed=4.0):\n",
    "        MagicProperties.__init__(self)\n",
    "        self.ed = ed\n",
    "        self.ias = 60\n",
    "    \n",
    "class TombReaver(CrypticAxe):\n",
    "    def __init__(self, ed=2.8, sockets=3, eth=False):\n",
    "        CrypticAxe.__init__(self, eth=eth, sockets=sockets)\n",
    "        self.mp.ed = ed\n",
    "        self.mp.ias = 60\n",
    "        \n",
    "class Ribcracker(Stalagmite):\n",
    "    def __init__(self, ed=3.0, sockets=1, eth=False):\n",
    "        Stalagmite.__init__(self, eth=eth, sockets=sockets)\n",
    "        self.mp.ed = ed\n",
    "        self.mp.ias = 50\n",
    "        self.mp.mindmg = 30\n",
    "        self.mp.maxdmg = 65\n",
    "        \n",
    "\n",
    "rjof = Socketable(ed=0.4, ias=15)\n",
    "vjoc = Socketable(maxdmg=30)\n",
    "ber = Socketable(cb=20)\n",
    "    \n",
    "ca = CrypticAxe(eth=True)\n",
    "print ca.avg_damage()\n",
    "\n",
    "etr = TombReaver(eth=True)\n",
    "etr.add_socketable(rjof)\n",
    "etr.add_socketable(rjof)\n",
    "\n",
    "print 'eth tomb reaver'\n",
    "print etr.avg_damage(), etr.min_damage(), etr.max_damage()\n",
    "print etr.total_ias()\n",
    "print etr.total_ed()\n",
    "print 'ribcracker'\n",
    "rb = Ribcracker(eth=False)\n",
    "rb.add_socketable(rjof)\n",
    "print rb.avg_damage(), rb.min_damage(), rb.max_damage()\n",
    "\n",
    "print 'death colossus sword'\n",
    "death_cs = ColossusSword.create(Death(), eth=True)\n",
    "print death_cs.avg_damage()\n",
    "\n",
    "ebotd_cb = ColossusBlade.create(BreathOfTheDying(), eth=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.0951113137\n",
      "29.027238308811075\n",
      "39.83106393730772\n"
     ]
    }
   ],
   "source": [
    "off_weapon_ed = 2.0+2+3 # str, might, fort\n",
    "insight_damage = 91.5*1.5*(1+2.6)*(1+off_weapon_ed)*1.46\n",
    "obed_damage = 91.5*1.5*(1+3.7)*(1+off_weapon_ed)\n",
    "obed_fast_damage = 77*1.5*(1+3.7)*(1+2+2.0)\n",
    "reaper_damage = 77*1.5*(1+2.4)*(1+off_weapon_ed)*1.48\n",
    "#print insight_damage, obed_damage\n",
    "NNplayers = 8\n",
    "mhp = 25000.\n",
    "#print 1.0*mhp/insight_damage\n",
    "print hits_to_kill(mhp, insight_damage, NNplayers, boss=True, cb_chance=0.0, pr=0.33)\n",
    "print hits_to_kill(mhp, obed_damage, NNplayers, boss=True, cb_chance=0.4, pr=0.33)\n",
    "print hits_to_kill(mhp, obed_fast_damage, NNplayers, boss=True, cb_chance=0.4, pr=0.33)*11./14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4649.568"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reaper_damage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9581795817958179"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7.79/8.13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare the damage that a particular setup does with and without CB across number of players, the results are a bit surprising. The ratio of hits required to kill without CB to hits required to kill with CB *decreases* with player count. That means that if it took 10 hits to kill a monster without CB and 9 to kill him with CB at P1, it could take 45 and 43, respectively at P8. The setup with CB will obviously always be better in this case (more damage = better), but the way the scaling works is the surprising part. The scaling does change very little with player count, ~5% at the most across the P1-P8 range.\n",
    "\n",
    "Another interesting thing is that this ratio approaches a constant as the number of players approaches infinity, meaning that CB is no more or less effective at high player counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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, fig.canvas.height);\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'normalized avg dmg per hit')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at hits required to kill hell baal across player count\n",
    "n = np.array(range(8))+1\n",
    "mlife = np.logspace(3,6,num=50)\n",
    "plt.figure()\n",
    "mlifes = [10e3, 20e3, 50e3, 100e3, 200e3, 500e3]\n",
    "cb_chances = np.array(range(11))/10.0\n",
    "#for mlife in mlifes:\n",
    "#for ni in n:\n",
    "for cb_chance in cb_chances:\n",
    "    #plt.plot(n, hits_to_kill(mlife, obed_damage, n, boss=True, cb_chance=0.5, pr=0.0)/hits_to_kill(mlife, obed_damage, n, boss=True, cb_chance=0.0, pr=0.0))\n",
    "    #plt.plot(mlife/obed_damage, hits_to_kill(mlife, obed_damage, ni, boss=True, cb_chance=0.0, pr=0.0)/hits_to_kill(mlife, obed_damage, ni, boss=True, cb_chance=0.5, pr=0.0))\n",
    "    plt.plot(mlife/reaper_damage, hits_to_kill(mlife, reaper_damage, 8, boss=False, cb_chance=0, pr=0.0)/hits_to_kill(mlife, reaper_damage, 8, boss=True, cb_chance=cb_chance, pr=0.0))\n",
    "#plt.legend(['{:.2f}'.format(x/obed_damage) for x in mlifes])\n",
    "#plt.legend(n)\n",
    "plt.legend(cb_chances/4)\n",
    "#plt.title('Ratio of hits to kill without CB to with CB\\n50% CB chance, 0% PR, champ/unique/boss')\n",
    "plt.title('Damage per hit\\nEach line is CB factor = 0.25*chance/(2 if boss)/(2 if ranged)\\nShown at P8 and 0% PR')\n",
    "plt.xlabel('P1 monster life divided by player damage per hit')\n",
    "#plt.xlabel('number of players')\n",
    "#plt.ylabel('hits to kill without CB / hits to kill with CB')\n",
    "plt.ylabel('normalized avg dmg per hit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inf thresh fort 4363.87875 88.97617436419016 23.110694640049392\n",
      "inf thresh treach 121.56432572009629 26.71743422419699\n",
      "inf ca fort 77.49912421912056 21.708438156616403\n"
     ]
    }
   ],
   "source": [
    "# lister calcs\n",
    "# infinity thresher\n",
    "inf_thresh_fort_dmg = 77*1.5*(1+3.25)*(1+2.39+2.5+3.0)\n",
    "inf_thresh_treach_dmg = 77*1.5*(1+3.25)*(1+2.39+2.5)\n",
    "inf_ca_fort_dmg = 91.5*1.5*(1+3.25)*(1+2.39+2.5+3.0)\n",
    "inf_thresh_fort_hits = hits_to_kill(56000., inf_thresh_fort_dmg, 8, boss=True, cb_chance=0.4, pr=0.5)\n",
    "inf_thresh_treach_hits = hits_to_kill(56000., inf_thresh_treach_dmg, 8, boss=True, cb_chance=0.4, pr=0.5)\n",
    "inf_ca_fort_hits = hits_to_kill(56000., inf_ca_fort_dmg, 8, boss=True, cb_chance=0.4, pr=0.5)\n",
    "inf_thresh_fort_time = inf_thresh_fort_hits/3.85\n",
    "inf_thresh_treach_time = inf_thresh_treach_hits/4.55\n",
    "inf_ca_fort_time = inf_ca_fort_hits/3.57\n",
    "print 'inf thresh fort', inf_thresh_fort_dmg, inf_thresh_fort_hits, inf_thresh_fort_time\n",
    "print 'inf thresh treach', inf_thresh_treach_hits, inf_thresh_treach_time\n",
    "print 'inf ca fort', inf_ca_fort_hits, inf_ca_fort_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from an interactive python session\n",
    ">>> def par(y):\n",
    "...     return 120*y/(120+y)\n",
    "...\n",
    ">>> par(100)\n",
    "54\n",
    ">>> par(101)\n",
    "54\n",
    ">>> par(103)\n",
    "55\n",
    ">>> 120/3\n",
    "40\n",
    ">>> 120/43\n",
    "2\n",
    ">>> par(2760)\n",
    "115\n",
    ">>> par(100000)\n",
    "119\n",
    ">>> par(10000)\n",
    "118\n",
    ">>> par(20000)\n",
    "119\n",
    ">>> import numpy as np\n",
    ">>> np.log(1)\n",
    "0.0\n",
    ">>> LoverD = 10.0\n",
    ">>> CB=0.5*0.25\n",
    ">>> def crap(LoverD, CB, N):\n",
    "...     return -0.5*(N+1)*LoverD*np.log(1-2.*CB/(N+1))/np.log(CB*LoverD+1)\n",
    "...\n",
    ">>> N = np.array(range(8))+1\n",
    ">>> plt.figure()\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "NameError: name 'plt' is not defined\n",
    ">>> import matplotlib.pyplot as plt\n",
    ">>> plt.figure()\n",
    "<Figure size 640x480 with 0 Axes>\n",
    ">>> plt.plot(N, crap(10.0, 0.5*0.25, N))\n",
    "[<matplotlib.lines.Line2D object at 0x0000000006DF7E10>]\n",
    ">>> plt.show()\n",
    ">>> plt.figure()\n",
    "<Figure size 640x480 with 0 Axes>\n",
    ">>> plt.plot(N, crap(1.73, 0.5*0.25, N))\n",
    "[<matplotlib.lines.Line2D object at 0x0000000007056240>]\n",
    ">>> plt.show()\n",
    ">>> N2 = np.array(range(1000))+1\n",
    ">>> plt.figure()\n",
    "<Figure size 640x480 with 0 Axes>\n",
    ">>> plt.plot(N2, crap(1.73, 0.5*0.25, N2))\n",
    "[<matplotlib.lines.Line2D object at 0x000000000714BE80>]\n",
    ">>> plt.show()\n",
    ">>> plt.figure()\n",
    "<Figure size 640x480 with 0 Axes>\n",
    ">>> plt.plot(N2, crap(85., 0.5*0.25, N2))\n",
    "[<matplotlib.lines.Line2D object at 0x0000000007375668>]\n",
    ">>> plt.show()\n",
    ">>>"
   ]
  }
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