{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Hippocampal Subfield Volumes Computed By ASHS & Freesurfer\n",
    "\n",
    "<font color='red'>CAUTION: Dummy data with pseudo volumes</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "SUBJECTS_DIR = '../tests/subjects'\n",
    "SUBJECT = 'bert'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Volume Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_basename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bert</td>\n",
       "      <td>ASHS</td>\n",
       "      <td>bert_right_corr_nogray_volumes.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bert</td>\n",
       "      <td>ASHS</td>\n",
       "      <td>bert_left_heur_volumes.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bert</td>\n",
       "      <td>ASHS</td>\n",
       "      <td>bert_left_corr_usegray_volumes.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bert</td>\n",
       "      <td>ASHS</td>\n",
       "      <td>bert_left_corr_nogray_volumes.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bert</td>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>lh.hippoSfVolumes-T1.v10.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bert</td>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>lh.hippoSfVolumes-T1-T2.v10.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject source_type                     source_basename\n",
       "0    bert        ASHS  bert_right_corr_nogray_volumes.txt\n",
       "1    bert        ASHS          bert_left_heur_volumes.txt\n",
       "2    bert        ASHS  bert_left_corr_usegray_volumes.txt\n",
       "3    bert        ASHS   bert_left_corr_nogray_volumes.txt\n",
       "4    bert  Freesurfer        lh.hippoSfVolumes-T1.v10.txt\n",
       "5    bert  Freesurfer     lh.hippoSfVolumes-T1-T2.v10.txt"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os, pandas\n",
    "from freesurfer_volume_reader import SubfieldVolumeFile, ashs, freesurfer\n",
    "\n",
    "def read_volume_file(volume_file: SubfieldVolumeFile) -> pandas.DataFrame:\n",
    "    volume_frame = volume_file.read_volumes_dataframe()\n",
    "    volume_frame['source_basename'] = os.path.basename(volume_file.absolute_path)\n",
    "    return volume_frame\n",
    "\n",
    "def find_read_volume_files(source_type: str, volume_type) -> pandas.DataFrame:\n",
    "    volume_files = filter(lambda f: f.subject == SUBJECT, volume_type.find(SUBJECTS_DIR))\n",
    "    frame = pandas.concat(map(read_volume_file, volume_files))\n",
    "    frame['source_type'] = source_type\n",
    "    return frame\n",
    "\n",
    "volume_frame = pandas.concat([find_read_volume_files('ASHS', ashs.HippocampalSubfieldsVolumeFile),\n",
    "                              find_read_volume_files('Freesurfer', freesurfer.HippocampalSubfieldsVolumeFile)],\n",
    "                             sort=False)\n",
    "volume_frame[['subject', 'source_type', 'source_basename']].drop_duplicates().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Subfields Provided by Both ASHS & Freesurfer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_type</th>\n",
       "      <th>subfield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>CA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>CA2+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>ERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>PHC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>PRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ASHS</td>\n",
       "      <td>SUB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>Hippocampal_tail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>subiculum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>CA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>hippocampal-fissure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>presubiculum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>parasubiculum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>molecular_layer_HP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>GC-ML-DG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>CA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>CA4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>fimbria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>HATA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Freesurfer</td>\n",
       "      <td>Whole_hippocampus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source_type             subfield\n",
       "0         ASHS                  CA1\n",
       "1         ASHS                CA2+3\n",
       "2         ASHS                   DG\n",
       "3         ASHS                  ERC\n",
       "4         ASHS                  PHC\n",
       "5         ASHS                  PRC\n",
       "6         ASHS                  SUB\n",
       "7   Freesurfer     Hippocampal_tail\n",
       "8   Freesurfer            subiculum\n",
       "9   Freesurfer                  CA1\n",
       "10  Freesurfer  hippocampal-fissure\n",
       "11  Freesurfer         presubiculum\n",
       "12  Freesurfer        parasubiculum\n",
       "13  Freesurfer   molecular_layer_HP\n",
       "14  Freesurfer             GC-ML-DG\n",
       "15  Freesurfer                  CA3\n",
       "16  Freesurfer                  CA4\n",
       "17  Freesurfer              fimbria\n",
       "18  Freesurfer                 HATA\n",
       "19  Freesurfer    Whole_hippocampus"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume_frame[['source_type', 'subfield']].drop_duplicates().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CA1', 'subiculum'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume_frame['subfield'].replace('SUB', 'subiculum', inplace=True)\n",
    "\n",
    "common_subfields = set.intersection(*(set(volume_frame[volume_frame['source_type'] == src]['subfield'].unique())\n",
    "                                      for src in volume_frame['source_type'].unique()))\n",
    "common_subfields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>segmentation_mode</th>\n",
       "      <th>ASHS T1 &amp; T2 heur</th>\n",
       "      <th>ASHS T1 &amp; T2 nogray</th>\n",
       "      <th>ASHS T1 &amp; T2 usegray</th>\n",
       "      <th>Freesurfer T1</th>\n",
       "      <th>Freesurfer T1 &amp; T2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subfield</th>\n",
       "      <th>hemisphere</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CA1</th>\n",
       "      <th>left</th>\n",
       "      <td>678.903</td>\n",
       "      <td>678.901</td>\n",
       "      <td>678.902</td>\n",
       "      <td>34.567891</td>\n",
       "      <td>44.567891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>NaN</td>\n",
       "      <td>678.903</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">subiculum</th>\n",
       "      <th>left</th>\n",
       "      <td>457.781</td>\n",
       "      <td>457.789</td>\n",
       "      <td>457.780</td>\n",
       "      <td>234.567891</td>\n",
       "      <td>244.567891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>NaN</td>\n",
       "      <td>457.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "segmentation_mode     ASHS T1 & T2 heur  ASHS T1 & T2 nogray  \\\n",
       "subfield  hemisphere                                           \n",
       "CA1       left                  678.903              678.901   \n",
       "          right                     NaN              678.903   \n",
       "subiculum left                  457.781              457.789   \n",
       "          right                     NaN              457.781   \n",
       "\n",
       "segmentation_mode     ASHS T1 & T2 usegray  Freesurfer T1  Freesurfer T1 & T2  \n",
       "subfield  hemisphere                                                           \n",
       "CA1       left                     678.902      34.567891           44.567891  \n",
       "          right                        NaN            NaN                 NaN  \n",
       "subiculum left                     457.780     234.567891          244.567891  \n",
       "          right                        NaN            NaN                 NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_subfields_frame = volume_frame[volume_frame['subfield'].isin(common_subfields)].copy()\n",
    "\n",
    "def generate_segmentation_mode(row) -> str:\n",
    "    if row['source_type'] == 'ASHS':\n",
    "        return 'ASHS T1 & T2 {}'.format(row['correction'] or 'heur')\n",
    "    if row['source_type'] == 'Freesurfer':\n",
    "        abrev_analysis_id = row['analysis_id'].split('_')[0][:7] if row['analysis_id'] else None\n",
    "        return 'Freesurfer ' + ' & '.join(filter(None, ['T1' if row['T1_input'] else None, abrev_analysis_id]))\n",
    "    raise TypeError(row['source_type'])\n",
    "\n",
    "\n",
    "common_subfields_frame['segmentation_mode'] = common_subfields_frame.apply(generate_segmentation_mode, axis=1)\n",
    "\n",
    "common_subfields_frame.pivot_table(values='volume_mm^3',\n",
    "                                   index=['subfield', 'hemisphere'],\n",
    "                                   columns='segmentation_mode')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>CAUTION: Dummy data with pseudo volumes</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"754.4\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hippocampal Subfield Volumes of Subject bert')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_subfields_frame['subfield_hemisphere'] = common_subfields_frame.apply(\n",
    "    lambda row: row['subfield'] + ' ' + row['hemisphere'], axis=1)\n",
    "\n",
    "import seaborn\n",
    "%matplotlib notebook\n",
    "ax = seaborn.barplot(data=common_subfields_frame.sort_values(['subfield_hemisphere', 'segmentation_mode']),\n",
    "                     x='subfield_hemisphere',\n",
    "                     y='volume_mm^3',\n",
    "                     hue='segmentation_mode')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=60)\n",
    "ax.set_title('Hippocampal Subfield Volumes of Subject {}'.format(SUBJECT))"
   ]
  }
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