examples/l5pc/L5PC.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimisation of a Neocortical Layer 5 Pyramidal Cell"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015.\n",
    "\n",
    "Author of this script: Werner Van Geit @ Blue Brain Project\n",
    "\n",
    "Choice of parameters, protocols and other settings was done by Etay Hay @ HUJI\n",
    "\n",
    "What's described here is a more advanced use of BluePyOpt. We suggest to first go through the introductary example here: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb\n",
    "\n",
    "**If you use the methods in this notebook, we ask you to cite the following publications when publishing your research:**\n",
    "\n",
    "Van Geit, W., M. Gevaert, G. Chindemi, C. Rössert, J.-D. Courcol, E. Muller, F. Schürmann, I. Segev, and H. Markram (2016, March). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. ArXiv e-prints.\n",
    "http://arxiv.org/abs/1603.00500\n",
    "\n",
    "Markram, H., E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah, C. A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492.\n",
    "http://www.cell.com/abstract/S0092-8674%2815%2901191-5\n",
    "\n",
    "Some of the modules loaded in this script are located in the L5PC example folder: https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first load the bluepyopt python module, the ephys submodule and some helper functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/werner/src/bpopt/examples/l5pc\n",
      "mechanisms/CaDynamics_E2.mod mechanisms/Ca_HVA.mod mechanisms/Ca_LVAst.mod mechanisms/Ih.mod mechanisms/Im.mod mechanisms/K_Pst.mod mechanisms/K_Tst.mod mechanisms/NaTa_t.mod mechanisms/NaTs2_t.mod mechanisms/Nap_Et2.mod mechanisms/ProbAMPANMDA_EMS.mod mechanisms/ProbGABAAB_EMS.mod mechanisms/SK_E2.mod mechanisms/SKv3_1.mod\n",
      "CaDynamics_E2.mod Ca_HVA.mod Ca_LVAst.mod Ih.mod Im.mod K_Pst.mod K_Tst.mod NaTa_t.mod NaTs2_t.mod Nap_Et2.mod ProbAMPANMDA_EMS.mod ProbGABAAB_EMS.mod SK_E2.mod SKv3_1.mod\n",
      "\"/Users/werner/local/nrnnogui/share/nrn/libtool\"  --mode=compile gcc -DHAVE_CONFIG_H  -I. -I.. -I\"/Users/werner/local/nrnnogui/include/nrn\" -I\"/Users/werner/local/nrnnogui/x86_64/lib\"      -g -O2 -c -o mod_func.lo mod_func.c\n",
      "libtool: compile:  gcc -DHAVE_CONFIG_H -I. -I.. -I/Users/werner/local/nrnnogui/include/nrn -I/Users/werner/local/nrnnogui/x86_64/lib -g -O2 -c mod_func.c  -fno-common -DPIC -o .libs/mod_func.o\n",
      "\"/Users/werner/local/nrnnogui/share/nrn/libtool\"  --mode=link gcc -module  -g -O2    -headerpad_max_install_names -o libnrnmech.la -rpath \"/Users/werner/local/nrnnogui/x86_64/lib\"  CaDynamics_E2.lo Ca_HVA.lo Ca_LVAst.lo Ih.lo Im.lo K_Pst.lo K_Tst.lo NaTa_t.lo NaTs2_t.lo Nap_Et2.lo ProbAMPANMDA_EMS.lo ProbGABAAB_EMS.lo SK_E2.lo SKv3_1.lo mod_func.lo  -L\"/Users/werner/local/nrnnogui/x86_64/lib\" -lnrnoc -loc -lmemacs -lnrnmpi -lscopmath -lsparse13 -lreadline -lncurses -L\"/Users/werner/local/nrnnogui/x86_64/lib\" \"/Users/werner/local/nrnnogui/x86_64/lib/libnrniv.la\" -livoc -lneuron_gnu -lmeschach -lsundials -livos      \n",
      "libtool: link: rm -fr  .libs/libnrnmech.0.so .libs/libnrnmech.0.so.dSYM .libs/libnrnmech.la .libs/libnrnmech.lai .libs/libnrnmech.so\n",
      "libtool: link: gcc -Wl,-undefined -Wl,dynamic_lookup -o .libs/libnrnmech.0.so -bundle  .libs/CaDynamics_E2.o .libs/Ca_HVA.o .libs/Ca_LVAst.o .libs/Ih.o .libs/Im.o .libs/K_Pst.o .libs/K_Tst.o .libs/NaTa_t.o .libs/NaTs2_t.o .libs/Nap_Et2.o .libs/ProbAMPANMDA_EMS.o .libs/ProbGABAAB_EMS.o .libs/SK_E2.o .libs/SKv3_1.o .libs/mod_func.o   -L/Users/werner/local/nrnnogui/x86_64/lib /Users/werner/local/nrnnogui/x86_64/lib/libnrnoc.dylib /Users/werner/local/nrnnogui/x86_64/lib/liboc.dylib /Users/werner/local/nrnnogui/x86_64/lib/libmemacs.dylib /Users/werner/local/nrnnogui/x86_64/lib/libnrnmpi.dylib /Users/werner/local/nrnnogui/x86_64/lib/libscopmath.dylib /Users/werner/local/nrnnogui/x86_64/lib/libsparse13.dylib -lreadline -lncurses /Users/werner/local/nrnnogui/x86_64/lib/libnrniv.dylib /Users/werner/local/nrnnogui/x86_64/lib/libivoc.dylib /Users/werner/local/nrnnogui/x86_64/lib/libneuron_gnu.dylib /Users/werner/local/nrnnogui/x86_64/lib/libmeschach.dylib /Users/werner/local/nrnnogui/x86_64/lib/libsundials.dylib /Users/werner/local/nrnnogui/x86_64/lib/libivos.dylib   \n",
      "libtool: link: dsymutil .libs/libnrnmech.0.so || :\n",
      "libtool: link: (cd \".libs\" && rm -f \"libnrnmech.so\" && ln -s \"libnrnmech.0.so\" \"libnrnmech.so\")\n",
      "libtool: link: ( cd \".libs\" && rm -f \"libnrnmech.la\" && ln -s \"../libnrnmech.la\" \"libnrnmech.la\" )\n",
      "Successfully created x86_64/special\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload\n",
    "\n",
    "!nrnivmodl mechanisms\n",
    "import bluepyopt as bpopt\n",
    "import bluepyopt.ephys as ephys\n",
    "\n",
    "import pprint\n",
    "pp = pprint.PrettyPrinter(indent=2)\n",
    "\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enable the code below to enable debug level logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import logging                                                                      \n",
    "# logging.basicConfig()                                                               \n",
    "# logger = logging.getLogger()                                                        \n",
    "# logger.setLevel(logging.DEBUG)   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model description"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Morphology"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're using a complex reconstructed morphology of an L5PC cell. Let's visualise this with the BlueBrain NeuroM software:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: neurom in /usr/local/lib/python2.7/site-packages\n",
      "Requirement already up-to-date: pyyaml>=3.10 in /usr/local/lib/python2.7/site-packages (from neurom)\n",
      "Requirement already up-to-date: enum34>=1.0.4 in /usr/local/lib/python2.7/site-packages (from neurom)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.segments is deprecated. \n",
      "  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n",
      "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.sections is deprecated. \n",
      "  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n",
      "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.bifurcations is deprecated. \n",
      "  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n",
      "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.points is deprecated. \n",
      "  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n",
      "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.ezy is deprecated. \n",
      "  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n",
      "/usr/local/lib/python2.7/site-packages/neurom/io/neurolucida.py:254: UserWarning: This is an experimental reader. There are no guarantees regarding ability to parse Neurolucida .asc files or correctness of output.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
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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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.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 overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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>"
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     "output_type": "display_data"
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n",
      "  def _comm_id_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:29: DeprecationWarning: Comm._iopub_socket_default is deprecated: use @default decorator instead.\n",
      "  def _iopub_socket_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n",
      "  def _kernel_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n",
      "  def _session_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n",
      "  def _topic_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n",
      "  def _kernel_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n",
      "  def _comm_id_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/manager.py:37: DeprecationWarning: CommManager._iopub_socket_default is deprecated: use @default decorator instead.\n",
      "  def _iopub_socket_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n",
      "  def _session_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n",
      "  def _topic_default(self):\n"
     ]
    }
   ],
   "source": [
    "!pip install neurom --upgrade\n",
    "import neurom.viewer\n",
    "neurom.viewer.draw(neurom.load_neuron('morphology/C060114A7.asc'));"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the morphology we create a NrnFileMorphology object. We set 'do_replace_axon' to True to replace the axon with a AIS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "morphology/C060114A7.asc\n"
     ]
    }
   ],
   "source": [
    "morphology = ephys.morphologies.NrnFileMorphology('morphology/C060114A7.asc', do_replace_axon=True)\n",
    "print(str(morphology))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have many parameters in this model, they are stored in a json file: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/config/parameters.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'g_pas', u'e_pas', u'cm', u'Ra', u'v_init', u'celsius', u'ena', u'ek', u'cm', u'ena', u'ek', u'cm', u'ena', u'ek', u'gIhbar_Ih', u'gNaTs2_tbar_NaTs2_t', u'gSKv3_1bar_SKv3_1', u'gImbar_Im', u'gIhbar_Ih', u'gNaTa_tbar_NaTa_t', u'gNap_Et2bar_Nap_Et2', u'gK_Pstbar_K_Pst', u'gK_Tstbar_K_Tst', u'gSK_E2bar_SK_E2', u'gSKv3_1bar_SKv3_1', u'gCa_HVAbar_Ca_HVA', u'gCa_LVAstbar_Ca_LVAst', u'gamma_CaDynamics_E2', u'decay_CaDynamics_E2', u'gNaTs2_tbar_NaTs2_t', u'gSKv3_1bar_SKv3_1', u'gSK_E2bar_SK_E2', u'gCa_HVAbar_Ca_HVA', u'gCa_LVAstbar_Ca_LVAst', u'gamma_CaDynamics_E2', u'decay_CaDynamics_E2', u'gIhbar_Ih']\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "param_configs = json.load(open('config/parameters.json'))\n",
    "print([param_config['param_name'] for param_config in param_configs])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directory that contains this notebook has a module that will load all the parameters in BluePyOpt Parameter objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g_pas.all: ['all'] g_pas = 3e-05\n",
      "e_pas.all: ['all'] e_pas = -75\n",
      "cm.all: ['all'] cm = 1\n",
      "Ra.all: ['all'] Ra = 100\n",
      "v_init: v_init = -65\n",
      "celsius: celsius = 34\n",
      "ena.apical: ['apical'] ena = 50\n",
      "ek.apical: ['apical'] ek = -85\n",
      "cm.apical: ['apical'] cm = 2\n",
      "ena.somatic: ['somatic'] ena = 50\n",
      "ek.somatic: ['somatic'] ek = -85\n",
      "cm.basal: ['basal'] cm = 2\n",
      "ena.axonal: ['axonal'] ena = 50\n",
      "ek.axonal: ['axonal'] ek = -85\n",
      "gIhbar_Ih.basal: ['basal'] gIhbar_Ih = 8e-05\n",
      "gNaTs2_tbar_NaTs2_t.apical: ['apical'] gNaTs2_tbar_NaTs2_t = [0, 0.04]\n",
      "gSKv3_1bar_SKv3_1.apical: ['apical'] gSKv3_1bar_SKv3_1 = [0, 0.04]\n",
      "gImbar_Im.apical: ['apical'] gImbar_Im = [0, 0.001]\n",
      "gIhbar_Ih.apical: ['apical'] gIhbar_Ih = 8e-05\n",
      "gNaTa_tbar_NaTa_t.axonal: ['axonal'] gNaTa_tbar_NaTa_t = [0, 4]\n",
      "gNap_Et2bar_Nap_Et2.axonal: ['axonal'] gNap_Et2bar_Nap_Et2 = [0, 4]\n",
      "gK_Pstbar_K_Pst.axonal: ['axonal'] gK_Pstbar_K_Pst = [0, 1]\n",
      "gK_Tstbar_K_Tst.axonal: ['axonal'] gK_Tstbar_K_Tst = [0, 0.1]\n",
      "gSK_E2bar_SK_E2.axonal: ['axonal'] gSK_E2bar_SK_E2 = [0, 0.1]\n",
      "gSKv3_1bar_SKv3_1.axonal: ['axonal'] gSKv3_1bar_SKv3_1 = [0, 2]\n",
      "gCa_HVAbar_Ca_HVA.axonal: ['axonal'] gCa_HVAbar_Ca_HVA = [0, 0.001]\n",
      "gCa_LVAstbar_Ca_LVAst.axonal: ['axonal'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\n",
      "gamma_CaDynamics_E2.axonal: ['axonal'] gamma_CaDynamics_E2 = [0.0005, 0.05]\n",
      "decay_CaDynamics_E2.axonal: ['axonal'] decay_CaDynamics_E2 = [20, 1000]\n",
      "gNaTs2_tbar_NaTs2_t.somatic: ['somatic'] gNaTs2_tbar_NaTs2_t = [0, 1]\n",
      "gSKv3_1bar_SKv3_1.somatic: ['somatic'] gSKv3_1bar_SKv3_1 = [0, 1]\n",
      "gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.1]\n",
      "gCa_HVAbar_Ca_HVA.somatic: ['somatic'] gCa_HVAbar_Ca_HVA = [0, 0.001]\n",
      "gCa_LVAstbar_Ca_LVAst.somatic: ['somatic'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\n",
      "gamma_CaDynamics_E2.somatic: ['somatic'] gamma_CaDynamics_E2 = [0.0005, 0.05]\n",
      "decay_CaDynamics_E2.somatic: ['somatic'] decay_CaDynamics_E2 = [20, 1000]\n",
      "gIhbar_Ih.somatic: ['somatic'] gIhbar_Ih = 8e-05\n"
     ]
    }
   ],
   "source": [
    "import l5pc_model\n",
    "parameters = l5pc_model.define_parameters()\n",
    "print('\\n'.join('%s' % param for param in parameters))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see there are two types of parameters, parameters with a fixed value and parameters with bounds. The latter will be optimised by the algorithm."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mechanism"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to add all the necessary mechanisms, like ion channels to the model. \n",
    "The configuration of the mechanisms is also stored in a json file, and can be loaded in a similar way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ih.basal: Ih at ['basal']\n",
      "pas.all: pas at ['all']\n",
      "NaTs2_t.somatic: NaTs2_t at ['somatic']\n",
      "SKv3_1.somatic: SKv3_1 at ['somatic']\n",
      "SK_E2.somatic: SK_E2 at ['somatic']\n",
      "CaDynamics_E2.somatic: CaDynamics_E2 at ['somatic']\n",
      "Ca_HVA.somatic: Ca_HVA at ['somatic']\n",
      "Ca_LVAst.somatic: Ca_LVAst at ['somatic']\n",
      "Ih.somatic: Ih at ['somatic']\n",
      "Ih.apical: Ih at ['apical']\n",
      "Im.apical: Im at ['apical']\n",
      "SKv3_1.apical: SKv3_1 at ['apical']\n",
      "NaTs2_t.apical: NaTs2_t at ['apical']\n",
      "Ca_LVAst.axonal: Ca_LVAst at ['axonal']\n",
      "Ca_HVA.axonal: Ca_HVA at ['axonal']\n",
      "CaDynamics_E2.axonal: CaDynamics_E2 at ['axonal']\n",
      "SKv3_1.axonal: SKv3_1 at ['axonal']\n",
      "SK_E2.axonal: SK_E2 at ['axonal']\n",
      "K_Tst.axonal: K_Tst at ['axonal']\n",
      "K_Pst.axonal: K_Pst at ['axonal']\n",
      "Nap_Et2.axonal: Nap_Et2 at ['axonal']\n",
      "NaTa_t.axonal: NaTa_t at ['axonal']\n"
     ]
    }
   ],
   "source": [
    "mechanisms = l5pc_model.define_mechanisms()\n",
    "print('\\n'.join('%s' % mech for mech in mechanisms))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the morphology, mechanisms and parameters we can build the cell model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l5pc:\n",
      "  morphology:\n",
      "    morphology/C060114A7.asc\n",
      "  mechanisms:\n",
      "    Ih.basal: Ih at ['basal']\n",
      "    pas.all: pas at ['all']\n",
      "    NaTs2_t.somatic: NaTs2_t at ['somatic']\n",
      "    SKv3_1.somatic: SKv3_1 at ['somatic']\n",
      "    SK_E2.somatic: SK_E2 at ['somatic']\n",
      "    CaDynamics_E2.somatic: CaDynamics_E2 at ['somatic']\n",
      "    Ca_HVA.somatic: Ca_HVA at ['somatic']\n",
      "    Ca_LVAst.somatic: Ca_LVAst at ['somatic']\n",
      "    Ih.somatic: Ih at ['somatic']\n",
      "    Ih.apical: Ih at ['apical']\n",
      "    Im.apical: Im at ['apical']\n",
      "    SKv3_1.apical: SKv3_1 at ['apical']\n",
      "    NaTs2_t.apical: NaTs2_t at ['apical']\n",
      "    Ca_LVAst.axonal: Ca_LVAst at ['axonal']\n",
      "    Ca_HVA.axonal: Ca_HVA at ['axonal']\n",
      "    CaDynamics_E2.axonal: CaDynamics_E2 at ['axonal']\n",
      "    SKv3_1.axonal: SKv3_1 at ['axonal']\n",
      "    SK_E2.axonal: SK_E2 at ['axonal']\n",
      "    K_Tst.axonal: K_Tst at ['axonal']\n",
      "    K_Pst.axonal: K_Pst at ['axonal']\n",
      "    Nap_Et2.axonal: Nap_Et2 at ['axonal']\n",
      "    NaTa_t.axonal: NaTa_t at ['axonal']\n",
      "  params:\n",
      "    g_pas.all: ['all'] g_pas = 3e-05\n",
      "    e_pas.all: ['all'] e_pas = -75\n",
      "    cm.all: ['all'] cm = 1\n",
      "    Ra.all: ['all'] Ra = 100\n",
      "    v_init: v_init = -65\n",
      "    celsius: celsius = 34\n",
      "    ena.apical: ['apical'] ena = 50\n",
      "    ek.apical: ['apical'] ek = -85\n",
      "    cm.apical: ['apical'] cm = 2\n",
      "    ena.somatic: ['somatic'] ena = 50\n",
      "    ek.somatic: ['somatic'] ek = -85\n",
      "    cm.basal: ['basal'] cm = 2\n",
      "    ena.axonal: ['axonal'] ena = 50\n",
      "    ek.axonal: ['axonal'] ek = -85\n",
      "    gIhbar_Ih.basal: ['basal'] gIhbar_Ih = 8e-05\n",
      "    gNaTs2_tbar_NaTs2_t.apical: ['apical'] gNaTs2_tbar_NaTs2_t = [0, 0.04]\n",
      "    gSKv3_1bar_SKv3_1.apical: ['apical'] gSKv3_1bar_SKv3_1 = [0, 0.04]\n",
      "    gImbar_Im.apical: ['apical'] gImbar_Im = [0, 0.001]\n",
      "    gIhbar_Ih.apical: ['apical'] gIhbar_Ih = 8e-05\n",
      "    gNaTa_tbar_NaTa_t.axonal: ['axonal'] gNaTa_tbar_NaTa_t = [0, 4]\n",
      "    gNap_Et2bar_Nap_Et2.axonal: ['axonal'] gNap_Et2bar_Nap_Et2 = [0, 4]\n",
      "    gK_Pstbar_K_Pst.axonal: ['axonal'] gK_Pstbar_K_Pst = [0, 1]\n",
      "    gK_Tstbar_K_Tst.axonal: ['axonal'] gK_Tstbar_K_Tst = [0, 0.1]\n",
      "    gSK_E2bar_SK_E2.axonal: ['axonal'] gSK_E2bar_SK_E2 = [0, 0.1]\n",
      "    gSKv3_1bar_SKv3_1.axonal: ['axonal'] gSKv3_1bar_SKv3_1 = [0, 2]\n",
      "    gCa_HVAbar_Ca_HVA.axonal: ['axonal'] gCa_HVAbar_Ca_HVA = [0, 0.001]\n",
      "    gCa_LVAstbar_Ca_LVAst.axonal: ['axonal'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\n",
      "    gamma_CaDynamics_E2.axonal: ['axonal'] gamma_CaDynamics_E2 = [0.0005, 0.05]\n",
      "    decay_CaDynamics_E2.axonal: ['axonal'] decay_CaDynamics_E2 = [20, 1000]\n",
      "    gNaTs2_tbar_NaTs2_t.somatic: ['somatic'] gNaTs2_tbar_NaTs2_t = [0, 1]\n",
      "    gSKv3_1bar_SKv3_1.somatic: ['somatic'] gSKv3_1bar_SKv3_1 = [0, 1]\n",
      "    gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.1]\n",
      "    gCa_HVAbar_Ca_HVA.somatic: ['somatic'] gCa_HVAbar_Ca_HVA = [0, 0.001]\n",
      "    gCa_LVAstbar_Ca_LVAst.somatic: ['somatic'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\n",
      "    gamma_CaDynamics_E2.somatic: ['somatic'] gamma_CaDynamics_E2 = [0.0005, 0.05]\n",
      "    decay_CaDynamics_E2.somatic: ['somatic'] decay_CaDynamics_E2 = [20, 1000]\n",
      "    gIhbar_Ih.somatic: ['somatic'] gIhbar_Ih = 8e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "l5pc_cell = ephys.models.CellModel('l5pc', morph=morphology, mechs=mechanisms, params=parameters)\n",
    "print(l5pc_cell)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For use in the cell evaluator later, we need to make a list of the name of the parameters we are going to optimise.\n",
    "These are the parameters that are not frozen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_names = [param.name for param in l5pc_cell.params.values() if not param.frozen]      "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protocols"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a cell model, we can apply protocols to it. The protocols are also stored in a json file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'Step1': {u'stimuli': [{u'delay': 700, u'amp': 0.458, u'duration': 2000, u'totduration': 3000}, {u'delay': 0, u'amp': -0.126, u'duration': 3000, u'totduration': 3000}]}, u'Step3': {u'stimuli': [{u'delay': 700, u'amp': 0.95, u'duration': 2000, u'totduration': 3000}, {u'delay': 0, u'amp': -0.126, u'duration': 3000, u'totduration': 3000}]}, u'Step2': {u'stimuli': [{u'delay': 700, u'amp': 0.562, u'duration': 2000, u'totduration': 3000}, {u'delay': 0, u'amp': -0.126, u'duration': 3000, u'totduration': 3000}]}, u'bAP': {u'stimuli': [{u'delay': 295, u'amp': 1.9, u'duration': 5, u'totduration': 600}], u'extra_recordings': [{u'var': u'v', u'somadistance': 660, u'type': u'somadistance', u'name': u'dend1', u'seclist_name': u'apical'}, {u'var': u'v', u'somadistance': 800, u'type': u'somadistance', u'name': u'dend2', u'seclist_name': u'apical'}]}}\n"
     ]
    }
   ],
   "source": [
    "proto_configs = json.load(open('config/protocols.json'))\n",
    "print(proto_configs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And they can be automatically loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bAP:\n",
      "  stimuli:\n",
      "    Square pulse amp 1.900000 delay 295.000000 duration 5.000000 totdur 600.000000 at somatic[0](0.5)\n",
      "  recordings:\n",
      "    bAP.soma.v: v at somatic[0](0.5)\n",
      "    bAP.dend1.v: v at 660.000000 micron from soma in apical\n",
      "    bAP.dend2.v: v at 800.000000 micron from soma in apical\n",
      "\n",
      "Step3:\n",
      "  stimuli:\n",
      "    Square pulse amp 0.950000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at somatic[0](0.5)\n",
      "    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at somatic[0](0.5)\n",
      "  recordings:\n",
      "    Step3.soma.v: v at somatic[0](0.5)\n",
      "\n",
      "Step2:\n",
      "  stimuli:\n",
      "    Square pulse amp 0.562000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at somatic[0](0.5)\n",
      "    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at somatic[0](0.5)\n",
      "  recordings:\n",
      "    Step2.soma.v: v at somatic[0](0.5)\n",
      "\n",
      "Step1:\n",
      "  stimuli:\n",
      "    Square pulse amp 0.458000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at somatic[0](0.5)\n",
      "    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at somatic[0](0.5)\n",
      "  recordings:\n",
      "    Step1.soma.v: v at somatic[0](0.5)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import l5pc_evaluator\n",
    "fitness_protocols = l5pc_evaluator.define_protocols()\n",
    "print('\\n'.join('%s' % protocol for protocol in fitness_protocols.values()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## eFeatures"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For every protocol we need to define which eFeatures will be used as objectives of the optimisation algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ u'Step1': { u'soma': { u'AHP_depth_abs': [-60.3636, 2.3018],\n",
      "                         u'AHP_depth_abs_slow': [-61.1513, 2.3385],\n",
      "                         u'AHP_slow_time': [0.1599, 0.0483],\n",
      "                         u'AP_height': [25.0141, 3.1463],\n",
      "                         u'AP_width': [3.5312, 0.8592],\n",
      "                         u'ISI_CV': [0.109, 0.1217],\n",
      "                         u'adaptation_index2': [0.0047, 0.0514],\n",
      "                         u'doublet_ISI': [62.75, 9.6667],\n",
      "                         u'mean_frequency': [6, 1.2222],\n",
      "                         u'time_to_first_spike': [27.25, 5.7222]}},\n",
      "  u'Step2': { u'soma': { u'AHP_depth_abs': [-59.9055, 1.8329],\n",
      "                         u'AHP_depth_abs_slow': [-60.2471, 1.8972],\n",
      "                         u'AHP_slow_time': [0.1676, 0.0339],\n",
      "                         u'AP_height': [27.1003, 3.1463],\n",
      "                         u'AP_width': [2.7917, 0.7499],\n",
      "                         u'ISI_CV': [0.0674, 0.075],\n",
      "                         u'adaptation_index2': [0.005, 0.0067],\n",
      "                         u'doublet_ISI': [44.0, 7.1327],\n",
      "                         u'mean_frequency': [8.5, 0.9796],\n",
      "                         u'time_to_first_spike': [19.75, 2.8776]}},\n",
      "  u'Step3': { u'soma': { u'AHP_depth_abs': [-57.0905, 2.3427],\n",
      "                         u'AHP_depth_abs_slow': [-61.1513, 2.3385],\n",
      "                         u'AHP_slow_time': [0.1968, 0.0112],\n",
      "                         u'AP_height': [19.7207, 3.7204],\n",
      "                         u'AP_width': [3.5347, 0.8788],\n",
      "                         u'ISI_CV': [0.0737, 0.0292],\n",
      "                         u'adaptation_index2': [0.0055, 0.0015],\n",
      "                         u'doublet_ISI': [22.75, 4.14],\n",
      "                         u'mean_frequency': [17.5, 0.8],\n",
      "                         u'time_to_first_spike': [10.5, 1.36]}},\n",
      "  u'bAP': { u'dend1': { u'AP_amplitude_from_voltagebase': [45, 10]},\n",
      "            u'dend2': { u'AP_amplitude_from_voltagebase': [36, 9.33]},\n",
      "            u'soma': { u'AP_height': [25.0, 5.0],\n",
      "                       u'AP_width': [2.0, 0.5],\n",
      "                       u'Spikecount': [1.0, 0.01]}}}\n"
     ]
    }
   ],
   "source": [
    "feature_configs = json.load(open('config/features.json'))\n",
    "pp.pprint(feature_configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "objectives:\n",
      "  ( AP_height for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 25.0141 and std 3.1463 and AP threshold override -20 )\n",
      "  ( AHP_slow_time for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.1599 and std 0.0483 and AP threshold override -20 )\n",
      "  ( ISI_CV for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.109 and std 0.1217 and AP threshold override -20 )\n",
      "  ( doublet_ISI for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 62.75 and std 9.6667 and AP threshold override -20 )\n",
      "  ( AHP_depth_abs_slow for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean -61.1513 and std 2.3385 and AP threshold override -20 )\n",
      "  ( AP_width for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 3.5312 and std 0.8592 and AP threshold override -20 )\n",
      "  ( time_to_first_spike for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 27.25 and std 5.7222 and AP threshold override -20 )\n",
      "  ( AHP_depth_abs for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean -60.3636 and std 2.3018 and AP threshold override -20 )\n",
      "  ( adaptation_index2 for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.0047 and std 0.0514 and AP threshold override -20 )\n",
      "  ( mean_frequency for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 6 and std 1.2222 and AP threshold override -20 )\n",
      "  ( AP_height for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 19.7207 and std 3.7204 and AP threshold override -20 )\n",
      "  ( AHP_slow_time for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.1968 and std 0.0112 and AP threshold override -20 )\n",
      "  ( ISI_CV for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.0737 and std 0.0292 and AP threshold override -20 )\n",
      "  ( doublet_ISI for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 22.75 and std 4.14 and AP threshold override -20 )\n",
      "  ( AHP_depth_abs_slow for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean -61.1513 and std 2.3385 and AP threshold override -20 )\n",
      "  ( AP_width for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 3.5347 and std 0.8788 and AP threshold override -20 )\n",
      "  ( time_to_first_spike for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 10.5 and std 1.36 and AP threshold override -20 )\n",
      "  ( AHP_depth_abs for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean -57.0905 and std 2.3427 and AP threshold override -20 )\n",
      "  ( adaptation_index2 for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.0055 and std 0.0015 and AP threshold override -20 )\n",
      "  ( mean_frequency for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 17.5 and std 0.8 and AP threshold override -20 )\n",
      "  ( AP_height for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 27.1003 and std 3.1463 and AP threshold override -20 )\n",
      "  ( AHP_slow_time for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.1676 and std 0.0339 and AP threshold override -20 )\n",
      "  ( ISI_CV for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.0674 and std 0.075 and AP threshold override -20 )\n",
      "  ( doublet_ISI for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 44.0 and std 7.1327 and AP threshold override -20 )\n",
      "  ( AHP_depth_abs_slow for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean -60.2471 and std 1.8972 and AP threshold override -20 )\n",
      "  ( AP_width for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 2.7917 and std 0.7499 and AP threshold override -20 )\n",
      "  ( time_to_first_spike for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 19.75 and std 2.8776 and AP threshold override -20 )\n",
      "  ( AHP_depth_abs for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean -59.9055 and std 1.8329 and AP threshold override -20 )\n",
      "  ( adaptation_index2 for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.005 and std 0.0067 and AP threshold override -20 )\n",
      "  ( mean_frequency for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 8.5 and std 0.9796 and AP threshold override -20 )\n",
      "  ( AP_width for {'': u'bAP.soma.v'} with stim start 295 and end 600, exp mean 2.0 and std 0.5 and AP threshold override -20 )\n",
      "  ( AP_height for {'': u'bAP.soma.v'} with stim start 295 and end 600, exp mean 25.0 and std 5.0 and AP threshold override -20 )\n",
      "  ( Spikecount for {'': u'bAP.soma.v'} with stim start 295 and end 600, exp mean 1.0 and std 0.01 and AP threshold override -20 )\n",
      "  ( AP_amplitude_from_voltagebase for {'': u'bAP.dend1.v'} with stim start 295 and end 600, exp mean 45 and std 10 and AP threshold override -55 )\n",
      "  ( AP_amplitude_from_voltagebase for {'': u'bAP.dend2.v'} with stim start 295 and end 600, exp mean 36 and std 9.33 and AP threshold override -55 )\n"
     ]
    }
   ],
   "source": [
    "fitness_calculator = l5pc_evaluator.define_fitness_calculator(fitness_protocols)\n",
    "print(fitness_calculator)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Simulator"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to define which simulator we will use. In this case it will be Neuron, i.e. the NrnSimulator class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sim = ephys.simulators.NrnSimulator()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluator"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all the components defined above we can build a cell evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "evaluator = ephys.evaluators.CellEvaluator(                                          \n",
    "        cell_model=l5pc_cell,                                                       \n",
    "        param_names=param_names,                                                    \n",
    "        fitness_protocols=fitness_protocols,                                        \n",
    "        fitness_calculator=fitness_calculator,                                      \n",
    "        sim=sim)  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This evaluator can be used to run the protocols. The original parameter values for the Markram et al. 2015 L5PC model are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "release_params = {\n",
    "    'gNaTs2_tbar_NaTs2_t.apical': 0.026145,\n",
    "    'gSKv3_1bar_SKv3_1.apical': 0.004226,\n",
    "    'gImbar_Im.apical': 0.000143,\n",
    "    'gNaTa_tbar_NaTa_t.axonal': 3.137968,\n",
    "    'gK_Tstbar_K_Tst.axonal': 0.089259,\n",
    "    'gamma_CaDynamics_E2.axonal': 0.002910,\n",
    "    'gNap_Et2bar_Nap_Et2.axonal': 0.006827,\n",
    "    'gSK_E2bar_SK_E2.axonal': 0.007104,\n",
    "    'gCa_HVAbar_Ca_HVA.axonal': 0.000990,\n",
    "    'gK_Pstbar_K_Pst.axonal': 0.973538,\n",
    "    'gSKv3_1bar_SKv3_1.axonal': 1.021945,\n",
    "    'decay_CaDynamics_E2.axonal': 287.198731,\n",
    "    'gCa_LVAstbar_Ca_LVAst.axonal': 0.008752,\n",
    "    'gamma_CaDynamics_E2.somatic': 0.000609,\n",
    "    'gSKv3_1bar_SKv3_1.somatic': 0.303472,\n",
    "    'gSK_E2bar_SK_E2.somatic': 0.008407,\n",
    "    'gCa_HVAbar_Ca_HVA.somatic': 0.000994,\n",
    "    'gNaTs2_tbar_NaTs2_t.somatic': 0.983955,\n",
    "    'decay_CaDynamics_E2.somatic': 210.485284,\n",
    "    'gCa_LVAstbar_Ca_LVAst.somatic': 0.000333\n",
    "}\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the responses is as easy as passing the protocols and parameters to the evaluator. (The line below will take some time to execute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "release_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=release_params)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot all the responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.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 overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n",
      "  def _comm_id_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:29: DeprecationWarning: Comm._iopub_socket_default is deprecated: use @default decorator instead.\n",
      "  def _iopub_socket_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n",
      "  def _kernel_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n",
      "  def _session_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n",
      "  def _topic_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n",
      "  def _kernel_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n",
      "  def _comm_id_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n",
      "  def _session_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n",
      "  def _topic_default(self):\n"
     ]
    }
   ],
   "source": [
    "def plot_responses(responses):\n",
    "    fig, axes = plt.subplots(len(responses), figsize=(10,10))\n",
    "    for index, (resp_name, response) in enumerate(sorted(responses.items())):\n",
    "        axes[index].plot(response['time'], response['voltage'], label=resp_name)\n",
    "        axes[index].set_title(resp_name)\n",
    "    fig.tight_layout()\n",
    "    fig.show()\n",
    "plot_responses(release_responses)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running an optimisation of the parameters now has become very easy. \n",
    "Of course running the L5PC optimisation will require quite some computing resources. \n",
    "\n",
    "To show a proof-of-concept, we will only run 2 generations, with 2 offspring individuals per generations.\n",
    "If you want to run all full optimisation, you should run for 100 generations with an offspring size of 100 individuals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "opt = bpopt.optimisations.DEAPOptimisation(                                     \n",
    "    evaluator=evaluator,                                                            \n",
    "    offspring_size=2) \n",
    "final_pop, halloffame, log, hist = opt.run(max_ngen=2, cp_filename='checkpoints/checkpoint.pkl')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first individual in the hall of fame will contain the best solution found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0007314357233762349, 0.03367469445915072, 0.0009391491627785106, 1.5248169507528497, 1.1187461922146316, 0.3599561642296192, 0.0029040787574867947, 0.022169166627303505, 0.8757751873011441, 0.00048095122076782077, 0.008079463408928685, 0.016843185951228554, 234.40541659093483, 0.19334802048394228, 0.3527777415382436, 0.0005121897764114314, 0.00022936710285097395, 0.007891413026508394, 0.03254436076094874, 150.32985893616308]\n"
     ]
    }
   ],
   "source": [
    "print(halloffame[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the raw parameter values. \n",
    "The evaluator object can convert this in a dictionary, so that we can see the parameter names corresponding to these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ u'decay_CaDynamics_E2.axonal': 234.40541659093483,\n",
      "  u'decay_CaDynamics_E2.somatic': 150.32985893616308,\n",
      "  u'gCa_HVAbar_Ca_HVA.axonal': 0.00048095122076782077,\n",
      "  u'gCa_HVAbar_Ca_HVA.somatic': 0.00022936710285097395,\n",
      "  u'gCa_LVAstbar_Ca_LVAst.axonal': 0.008079463408928685,\n",
      "  u'gCa_LVAstbar_Ca_LVAst.somatic': 0.007891413026508394,\n",
      "  u'gImbar_Im.apical': 0.0009391491627785106,\n",
      "  u'gK_Pstbar_K_Pst.axonal': 0.3599561642296192,\n",
      "  u'gK_Tstbar_K_Tst.axonal': 0.0029040787574867947,\n",
      "  u'gNaTa_tbar_NaTa_t.axonal': 1.5248169507528497,\n",
      "  u'gNaTs2_tbar_NaTs2_t.apical': 0.0007314357233762349,\n",
      "  u'gNaTs2_tbar_NaTs2_t.somatic': 0.19334802048394228,\n",
      "  u'gNap_Et2bar_Nap_Et2.axonal': 1.1187461922146316,\n",
      "  u'gSK_E2bar_SK_E2.axonal': 0.022169166627303505,\n",
      "  u'gSK_E2bar_SK_E2.somatic': 0.0005121897764114314,\n",
      "  u'gSKv3_1bar_SKv3_1.apical': 0.03367469445915072,\n",
      "  u'gSKv3_1bar_SKv3_1.axonal': 0.8757751873011441,\n",
      "  u'gSKv3_1bar_SKv3_1.somatic': 0.3527777415382436,\n",
      "  u'gamma_CaDynamics_E2.axonal': 0.016843185951228554,\n",
      "  u'gamma_CaDynamics_E2.somatic': 0.03254436076094874}\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "best_params = evaluator.param_dict(halloffame[0])\n",
    "print(pp.pprint(best_params))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can run the fitness protocols on the model with these parameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=best_params)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can also plot these responses. \n",
    "\n",
    "When you ran the above optimisation with only 2 individuals and 2 generations, this 'best' model will of course be very low quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.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 overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n",
      "  def _comm_id_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:29: DeprecationWarning: Comm._iopub_socket_default is deprecated: use @default decorator instead.\n",
      "  def _iopub_socket_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n",
      "  def _kernel_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n",
      "  def _session_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n",
      "  def _topic_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n",
      "  def _kernel_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n",
      "  def _comm_id_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n",
      "  def _session_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n",
      "  def _topic_default(self):\n"
     ]
    }
   ],
   "source": [
    "plot_responses(best_responses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}