rsHRF_Mediated_Brain_Dynamics.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Modeling fMRI BOLD signal dynamics with the Virtual Brain using the Individual Region-Specific HRF)\n",
    "\n",
    "This notebook illustrates the workflow for creating a virtual brain similar to https://github.com/mlion0200/educase_TVB_braintumor. \n",
    "However, here we investigate the effect of incorporating the Individual Region-Specific HRF instead of the standard model for the HRF. The TVB BOLD simulation considers a constant HRF for all the regions of the parcellation, and across individuals. This reduces the specificity of the model. In the present study, we intend to personalize the models to a greater depth by retrieving the local HRF for each empirical recording.\n",
    "The relevant additions to the TVB can be found at https://github.com/AmoghJohri/tvb-root/tree/amogh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading libraries\n",
    "import os\n",
    "import sys\n",
    "import csv\n",
    "import scipy\n",
    "import nitime\n",
    "import warnings\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import scipy.stats as stat\n",
    "import multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "from nitime.utils import percent_change\n",
    "from nitime.timeseries import TimeSeries\n",
    "from nitime.analysis import CorrelationAnalyzer, CoherenceAnalyzer\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following two steps are required so that python does not search for tvb. imports in the installed version\n",
    "path = os.getcwd() + \"/tvb-root/scientific_library\"\n",
    "sys.path.insert(0, path)\n",
    "\n",
    "from tvb.simulator.lab import *\n",
    "from tvb.simulator.plot.tools import *\n",
    "from tvb.analyzers import fmri_balloon\n",
    "from tvb.datatypes.time_series import TimeSeriesRegion\n",
    "from tvb.simulator.models.wong_wang_exc_inh import ReducedWongWangExcInh\n",
    "\n",
    "LOG = get_logger('BrainTumor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "We show how to simulate neural mass model with the reduced Wong-Wang model, using the individual subject connectivity. The global model parameters of the Reduced Wong-Wang model are individually optimized to improve prediction accuracy of functional connectivity.\n",
    "Original publication can be found here: https://www.eneuro.org/content/5/3/ENEURO.0083-18.2018\n",
    "\n",
    "![Workflow](https://www.eneuro.org/content/eneuro/5/3/ENEURO.0083-18.2018/F1.large.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hemodynamic Response Function Retrieval\n",
    "Instead of using a constant HRF, we retrieve the same through empirical region-wise BOLD time-series data (separately for each subject, and each brain region of the subject). For more information, visit: https://www.nitrc.org/projects/rshrf.\n",
    "\n",
    "<hr style=\"border:2px solid gray\"> </hr>\n",
    "\n",
    "![Resting-State HRF Retrieval](https://guorongwu.github.io/HRF/deconv.jpg)\n",
    "\n",
    "<hr style=\"border:2px solid gray\"> </hr>\n",
    "Here, we carry out the steps 1 and 2 as shown in the figure. The region-wise empirical time-series forms the input and the HRF is retrieved from the same. The consequent neural simulations are then converted to BOLD through the retrieved HRF and downsampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Files\n",
    "For the purpose of this demonstration, we have a singular relevant directory **Input** with all that we are going to require.\n",
    "\n",
    "The revelant files are:\n",
    "* **connectivity.h5** - This contains all the relevant data for initializing a brain-model in TVB. The same has also been provided in .txt format in the Input directory. These correspond to:\n",
    "    * *areas.txt*\n",
    "    * *average_orientations.txt*\n",
    "    * *centres.txt*\n",
    "    * *cortical.txt*\n",
    "    * *hemisphere.txt*\n",
    "    * *tract_lengths.txt*\n",
    "    * *weights.txt*\n",
    "* **FC.txt** - This contains the empirical functional connectivity matrix for the subject.\n",
    "* **ROIts.txt** - This contains the empirical region-wise BOLD time-series for the subject (input for retrieval of HRF).\n",
    "\n",
    "All the other files are irrelevant to the current tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Connectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
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       "        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",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
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       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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       "\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
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       "    this.root.append(canvas_div);\n",
       "\n",
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       "    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",
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
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       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "        icon_img.addClass('ui-corner-all');\n",
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       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
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       "\n",
       "    // Add hover states to the ui-buttons\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
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       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
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       "    properties['type'] = type;\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
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       "    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",
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       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
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       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        cursor = 'default';\n",
       "        break;\n",
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       "        break;\n",
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       "        break;\n",
       "    }\n",
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       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
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       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
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       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Structural connectivity for subject 1')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot Structural Connectivity Info: A 2D plot for visualizing the Connectivity.weights matrix\n",
    "%pylab nbagg\n",
    "conn=connectivity.Connectivity.from_file(os.getcwd() + \"/Input/connectivity.h5\")\n",
    "conn.configure()\n",
    "\n",
    "# plot of structural connectivity\n",
    "plot_connectivity(connectivity = conn, plot_tracts=False) \n",
    "plt.xlabel(\"Regions\")\n",
    "plt.ylabel(\"Regions\")\n",
    "connectivity_title = \"Structural connectivity for subject \" + \"1\"\n",
    "plt.title(connectivity_title, fontweight=\"bold\", fontsize=\"12\", y = 1.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Empirical Functional Connectivity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get empirical functional connectivity from FC.txt\n",
    "em_fc_matrix = np.loadtxt(os.getcwd() + \"/Input/FC.txt\")\n",
    "\n",
    "# indexes of all the values above the diagonal.\n",
    "uidx = np.triu_indices(68, 1)\n",
    "\n",
    "# Fisher-Z transform the correlations, important for standardization\n",
    "em_fc_z = np.arctanh(em_fc_matrix)\n",
    "# get the upper triangle since it is symmetric along diagonal\n",
    "em_fc = em_fc_z[uidx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure the Simulator\n",
    "Reduced Wong-Wang model with an excitatory and an inhibitory population, mutually coupled.\n",
    "Following Deco et al 2014.\n",
    "Table below shows parameters defined in Reduced Wong-Wang model, default value and description."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Parameter Name | Default Value   | Description |\n",
    "|--------------|-------------|-----------|\n",
    "| a_e            | 310           | Excitatory population input gain parameter (n/C)       |\n",
    "| b_e     | 125         | Excitatory population input shift parameter (Hz)     |\n",
    "| d_e     | 0.16        | Excitatory population input scaling parameter (s)   |\n",
    "| gamma_e | 0.641/1000  | Excitatory population kinetic parameter      |\n",
    "| tau_e   | 100     | Excitatory population NMDA decay time constant (ms)      |\n",
    "| w_p   | 1.4     | Excitatory population recurrence weight      |\n",
    "| J_N   | 0.15     | NMDA current (nA)      |\n",
    "| W_e   | 1.0     | Excitatory population external input scaling weight      |\n",
    "| a_i   | 615     | Inhibitory population input gain parameter (n/C)      |\n",
    "| b_i   | 177     | Inhibitory population input shift parameter (Hz)      |\n",
    "| d_i   | 0.087     | Inhibitory population input scaling parameter (s)      |\n",
    "| gamma_i   | 1.0/1000     | Inhibitory population kinetic parameter      |\n",
    "| tau_i   | 10     | Inhibitory population NMDA decay time constant (ms)      |\n",
    "| J_i   | 1.0     | Local inhibitory current (nA)      |\n",
    "| I_o   | 0.382     | Effective external input (nA)      |\n",
    "| G   | 2.0     | Global coupling scaling (To be optimized)      |\n",
    "| lamda   | 0.0     | Inhibitory global coupling scaling      |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the Reduced Wong-Wang model\n",
    "# adjust the parameters to your needs, otherwise it will use the default value shown above\n",
    "rww = ReducedWongWangExcInh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparitive Analysis\n",
    "For the sake of comparision, we have branched out two experiments from this point.\n",
    "1. Using TVB's default BOLD simulation (employing a constant HRF function.\n",
    "2. Using region-specific HRF for each region for the convolution.\n",
    "\n",
    "We shall proceed in the same order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default BOLD Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the simulator\n",
    "# adjust the simulation_length to your needs/ available computation time\n",
    "# in the demo data, time resolution of 2100\n",
    "# a simulation_length of 420,000\n",
    "# the BOLD monitor in the simulator applies an HRF with convolution during simulation to generate BOLD time series\n",
    "sim = simulator.Simulator(\n",
    "    model=rww,\n",
    "    connectivity=conn,\n",
    "    coupling=coupling.Linear(),\n",
    "    integrator=integrators.HeunStochastic(dt=1, noise=noise.Additive(nsig=np.array([1e-5]))), \n",
    "    monitors=(\n",
    "        monitors.TemporalAverage(period=2100.0),\n",
    "        monitors.Bold(period=2100)),\n",
    "    simulation_length=420000\n",
    ").configure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# each iteration for the parameter space exploration corresponds to one full-execution of this function\n",
    "def run_sim(global_coupling):\n",
    "    # this is the parameter being adjusted for each iteration of simulation\n",
    "    sim.coupling.a = global_coupling \n",
    "    \n",
    "    # running the BOLD simulation\n",
    "    (tavg_time, tavg_data), (bold_time, bold_data) = sim.run() \n",
    "    \n",
    "    # obtaining the BOLD time-series\n",
    "    tsr = bold_data[:, 0, :, 0].T\n",
    "    \n",
    "    # Time-series object for analysis\n",
    "    T = TimeSeries(tsr, sampling_interval=2.1)\n",
    "    T.metadata['roi'] = sim.connectivity.region_labels\n",
    "    \n",
    "    # Initialize the correlation analyzer\n",
    "    C = CorrelationAnalyzer(T)\n",
    "    sim_fc = np.arctanh(C.corrcoef)[uidx]\n",
    "    \n",
    "    # Calculate the link-wise Pearson correlation between individual’s \n",
    "    # upper triangular part of the simulated and empirical functional connectivity matrix\n",
    "    pearson_corr, _ = stat.pearsonr(sim_fc, em_fc)\n",
    "\n",
    "    return (global_coupling, pearson_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Space Exploration (Global Coupling Factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define global coupling range to explore in simulation\n",
    "# in the original study a range from 0.01 to 3 with steps of 0.015 was explored\n",
    "# NOTE: Too many steps will take very long time when running the script on a local computer\n",
    "# adjust the range of G, or the step size to reduce simulation time\n",
    "gc_range = np.arange(0.01, 3, 0.29)\n",
    "gc_range = np.expand_dims(gc_range, axis = 0).T\n",
    "\n",
    "# run simulation in parallel - be sure that your computer has enough cores\n",
    "n_cores = 4 # specify number of cores which should be used in parallel \n",
    "p = mp.Pool(processes=n_cores)\n",
    "\n",
    "results = p.map(run_sim, gc_range)\n",
    "p.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'G Vs Correlation')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot \n",
    "%pylab nbagg\n",
    "g = []\n",
    "PCorr = []\n",
    "for result in results:\n",
    "    g.append(result[0])\n",
    "    PCorr.append(result[1])\n",
    "\n",
    "plt.plot(g, PCorr)\n",
    "plt.xlabel('G')\n",
    "plt.ylabel('Corr')\n",
    "plt.title('G Vs Correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimized G for subject: 0.59\n",
      "The optimized Pearson's R: 0.358\n"
     ]
    }
   ],
   "source": [
    "optimized_g = g[PCorr.index(max(PCorr))]\n",
    "print(\"The optimized G for subject: \" + str(optimized_g))\n",
    "print(\"The optimized Pearson's R: \" + str(max(PCorr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOLD Simulation with RestingStateHRF Kernel\n",
    "For more information about the underlying details, refer to exploring_the_rsHRF_BOLD_Monitor.ipynb (in the same repository)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the simulator (similar as above)\n",
    "'''hrf_kernel has been set to RestingStateHRF where the roiTS (empirical BOLD time-series) \n",
    "has been provided as a numpy array'''\n",
    "''' retrieval of HRF requires the information of HRF length (HRF_length) and BOLD Repetition Time (TR), \n",
    "the same has been provided while maintaining regularity with the simulator\n",
    "NOTE: RestingStateHRF class takes input in seconds, where as the simulator takes input in milliseconds'''\n",
    "sim = simulator.Simulator(\n",
    "    model=rww,\n",
    "    connectivity=conn,\n",
    "    coupling=coupling.Linear(),\n",
    "    integrator=integrators.HeunStochastic(dt=1, noise=noise.Additive(nsig=np.array([1e-5]))), \n",
    "    monitors=(\n",
    "        monitors.TemporalAverage(period=2100.0),\n",
    "        monitors.Bold(period=2100, hrf_length = 24, hrf_kernel = equations.RestingStateHRF(roiTS=np.loadtxt(os.getcwd() + \"/Input/ROIts.txt\"), TR = 2.1, HRF_length = 24.))),\n",
    "    simulation_length=420000\n",
    ").configure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# parameter space exploration for G\n",
    "s = p.map(run_sim, gc_range)\n",
    "p.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // 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",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'G Vs Correlation')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot \n",
    "%pylab nbagg\n",
    "g = []\n",
    "PCorr = []\n",
    "for result in results:\n",
    "    g.append(result[0])\n",
    "    PCorr.append(result[1])\n",
    "\n",
    "plt.plot(g, PCorr)\n",
    "plt.xlabel('G')\n",
    "plt.ylabel('Corr')\n",
    "plt.title('G Vs Correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimized G for subject: 0.3\n",
      "The optimized Pearson's R: 0.359\n"
     ]
    }
   ],
   "source": [
    "optimized_g = g[PCorr.index(max(PCorr))]\n",
    "print(\"The optimized G for subject: \" + str(optimized_g))\n",
    "print(\"The optimized Pearson's R: \" + str(max(PCorr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,\n",
    "Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)\n",
    "    The Virtual Brain: a simulator of primate brain network dynamics.\n",
    "Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)\n",
    "\n",
    "> Guo-Rong Wu, Daniele Marinazzo. “Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications.” PeerJ PrePrints, 2015."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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 },
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}