From e7bdc70bee981c94bfca1c42e17840e81bd97b58 Mon Sep 17 00:00:00 2001
From: matinnuhamunada <matinnu@biosustain.dtu.dk>
Date: Sun, 1 Aug 2021 20:34:05 +0200
Subject: [PATCH] linked hhist vhist

---
 figures/bio_sinta.html           |  10 +-
 notebooks/01_data_cleaning.ipynb | 266 +++++++++++++++----------------
 2 files changed, 137 insertions(+), 139 deletions(-)

diff --git a/figures/bio_sinta.html b/figures/bio_sinta.html
index cd8712d..e778d43 100644
--- a/figures/bio_sinta.html
+++ b/figures/bio_sinta.html
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-                  var render_items = [{"docid":"289f3993-564e-4286-a453-bdbd56dc7e41","root_ids":["1542"],"roots":{"1542":"e8f8b628-3b5f-4d59-99e3-2fa92b16b451"}}];
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diff --git a/notebooks/01_data_cleaning.ipynb b/notebooks/01_data_cleaning.ipynb
index 8701657..be5bf95 100644
--- a/notebooks/01_data_cleaning.ipynb
+++ b/notebooks/01_data_cleaning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "id": "263ffa95",
    "metadata": {},
    "outputs": [],
@@ -16,9 +16,10 @@
     "\n",
     "#from bokeh.io import output_file, show\n",
     "from bokeh.io import output_notebook, show, save\n",
-    "from bokeh.plotting import figure, ColumnDataSource, output_file, save\n",
-    "from bokeh.models import Band, Div, RangeSlider, Spinner, CategoricalColorMapper\n",
+    "from bokeh.plotting import figure, ColumnDataSource, output_file, save, curdoc\n",
+    "from bokeh.models import Band, Div, RangeSlider, Spinner, CategoricalColorMapper, Range1d\n",
     "from bokeh.palettes import d3\n",
+    "from bokeh.layouts import gridplot\n",
     "\n",
     "import numpy as np"
    ]
@@ -439,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "id": "22046093",
    "metadata": {},
    "outputs": [
@@ -452,7 +453,7 @@
        "\n",
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Mikroba']\",\"['Mikrobiologi/ fungi']\",\"['Bioteknologi Mikroba']\",\"['Medical Microbiology', 'Microbial Physiology', 'and Genetic Engineering']\",\"['Bioengineering']\",\"['Bioengineering']\",\"['Bioengineering']\",\"['Bioengineering']\",\"['Aquaculture', 'Microbiology']\",\"['Ecology', 'Wetland Ecology']\",\"['Biologi', 'Entomologi', 'Pertanian']\",\"['Biology']\",\"['Ekologi Tumbuhan', 'biodiversitas']\",\"['forest ecology', 'ecosystem services', 'spatial modelling']\",\"['Aplied Plant Taxonomy for Forestry Engineering']\",\"['forestry and agriculture']\",\"['Ekologi']\",\"['No verified email']\",\"['Developmental biology']\",\"['Biologi', 'Fisiologi']\",\"['developmental biology']\",\"['animal reproduction and development', 'toxicology']\",\"['Entomology']\",\"['Biology']\",\"['cell biology', 'cancer biology', 'reproductive biology', 'developmental biology']\",\"['Cell dan developmental biology']\",\"['Molecular Biology & Synthetic Biology']\",\"['biology', 'genetics']\",\"['Molecular Biology']\",\"['Medical microbiology']\",\"['Microbiology', 'virology']\",\"[]\",\"['decision making', 'policy analysis', 'strategic planning']\",\"['Entomology']\",\"['Entomology', 'Applied Biology']\",\"['sosial ekonomi pertanian']\",\"['Payment for Ecosystem Services', 'Watershed Management', 'Forestry Policy']\",\"['Manajemen Sumber Daya Hayati']\",\"['Forestry']\",\"['Manajemen Sumber Daya Hayati']\",\"['Manajemen Sumber Daya Hayati']\",\"[]\",\"['Ekonomi Pertanian']\",\"['Bioresources management']\",\"['Resilience thinking', 'Political economy of food', 'Human Geography', 'actor-network theory', 'post-structuralism']\",\"['Plant sciences & biotechnology']\",\"['Plant developmental biology', 'plant cell and tissue culture']\",\"['Sains dan Bioteknologi Tumbuhan']\",\"['Sains dan Bioteknologi Tumbuhan']\",\"['Agarwood', 'Duckweed', 'Plant Physiology', 'Saponin', 'Tissue Culture']\",\"[]\",\"['Computer Science', 'Artificial Intelligence', 'Machine 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\":\"score_extracted\"}},\"id\":\"28907\",\"type\":\"Scatter\"},{\"attributes\":{\"source\":{\"id\":\"1006\"}},\"id\":\"28909\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"28962\"}},\"id\":\"28966\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"29712\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"29713\",\"type\":\"AllLabels\"},{\"attributes\":{\"bottom\":{\"value\":0},\"fill_alpha\":{\"value\":0.4},\"left\":{\"field\":\"left\"},\"line_alpha\":{\"value\":0.4},\"line_color\":{\"value\":\"gray\"},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"28928\",\"type\":\"Quad\"},{\"attributes\":{},\"id\":\"29715\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"29716\",\"type\":\"AllLabels\"},{\"attributes\":{\"data\":{\"left\":{\"__ndarray__\":\"AAAAAAAAOkAAAAAAAAA8QAAAAAAAAD5AAAAAAAAAQEAAAAAAAABBQAAAAAAAAEJAAAAAAAAAQ0AAAAAAAABEQAAAAAAAAEVAAAAAAAAARkAAAAAAAABHQAAAAAAAAEhAAAAAAAAASUAAAAAAAABKQAAAAAAAAEtAAAAAAAAATEAAAAAAAABNQAAAAAAAAE5AAAAAAAAAT0AAAAAAAABQQAAAAAAAgFBAAAAAAAAAUUA=\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[22]},\"right\":{\"__ndarray__\":\"AAAAAAAAPEAAAAAAAAA+QAAAAAAAAEBAAAAAAAAAQUAAAAAAAABCQAAAAAAAAENAAAAAAAAAREAAAAAAAABFQAAAAAAAAEZAAAAAAAAAR0AAAAAAAABIQAAAAAAAAElAAAAAAAAASkAAAAAAAABLQAAAAAAAAExAAAAAAAAATUAAAAAAAABOQAAAAAAAAE9AAAAAAAAAUEAAAAAAAIBQQAAAAAAAAFFAAAAAAACAUUA=\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[22]},\"top\":[0,0,1,0,2,2,4,2,0,1,4,1,2,4,4,1,3,2,3,1,1,1]},\"selected\":{\"id\":\"29753\"},\"selection_policy\":{\"id\":\"29754\"}},\"id\":\"28927\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"28972\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.3\"}};\n",
+       "  var render_items = [{\"docid\":\"2a42f771-b647-4840-bf00-8721569d768a\",\"root_ids\":[\"28972\"],\"roots\":{\"28972\":\"b3423c08-81c9-4b55-a2d7-31a6240f6f1f\"}}];\n",
        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "\n",
        "  }\n",
@@ -492,18 +493,31 @@
      },
      "metadata": {
       "application/vnd.bokehjs_exec.v0+json": {
-       "id": "1008"
+       "id": "28972"
       }
      },
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Start : This command cannot be run due to the error: The system cannot find the file specified.\n",
+      "At line:1 char:1\n",
+      "+ Start \"file:///home/matinnu/others/SINTA_data_mining/figures/bio_sint ...\n",
+      "+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "    + CategoryInfo          : InvalidOperation: (:) [Start-Process], InvalidOperationException\n",
+      "    + FullyQualifiedErrorId : InvalidOperationException,Microsoft.PowerShell.Commands.StartProcessCommand\n",
+      " \n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'/home/matinnu/others/SINTA_data_mining/figures/bio_sinta.html'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,33 +534,87 @@
     "]\n",
     "\n",
     "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save\"\n",
-    "p = figure(plot_width=600, plot_height=600, tooltips=TOOLTIPS,\n",
+    "p = figure(plot_width=500, plot_height=500, tooltips=TOOLTIPS,\n",
     "           title=\"Life Sciences SINTA\", x_axis_label=\"Estimated Age\",\n",
-    "           y_axis_label=\"SINTA Score\")\n",
+    "           y_axis_label=\"SINTA Score\",\n",
+    "           toolbar_location=\"above\",)\n",
+    "\n",
+    "ph = figure(toolbar_location=None, width=p.width, height=200,# y_range=(0, 18),\n",
+    "            min_border=10, min_border_left=50, y_axis_location=\"right\")\n",
+    "ph.xgrid.grid_line_color = None\n",
+    "ph.yaxis.major_label_orientation = np.pi/4\n",
+    "ph.background_fill_color = \"#fafafa\"\n",
+    "\n",
+    "pv = figure(toolbar_location=None, width=200, height=p.height,\n",
+    "            min_border=10, y_axis_location=\"right\")\n",
+    "pv.ygrid.grid_line_color = None\n",
+    "pv.xaxis.major_label_orientation = np.pi/4\n",
+    "pv.background_fill_color = \"#fafafa\"\n",
     "\n",
     "palette= d3['Category10'][len(univ)]\n",
+    "p.x_range = Range1d(25, 71)\n",
+    "p.y_range = Range1d(-3, 95)\n",
+    "ph.x_range = p.x_range\n",
+    "pv.y_range = p.y_range \n",
     "\n",
     "for num, s in enumerate(source):\n",
-    "    p.scatter('est_age', 'score_extracted', size=12, fill_alpha=0.5, source=s,\n",
+    "    r = p.scatter('est_age', 'score_extracted', size=12, fill_alpha=0.5, source=s,\n",
     "         #color={'field': 'Instansi', 'transform': color_map}, legend_label='UGM')\n",
     "          color=palette[num], legend_label=univ[num].upper())\n",
+    "    \n",
+    "    x = df_all[num].est_age\n",
+    "    y = df_all[num].score_extracted\n",
+    "    \n",
+    "    # create the horizontal histogram\n",
+    "    hhist, hedges = np.histogram(x, bins=22, range=(26, 70))\n",
+    "    hzeros = np.zeros(len(hedges)-1)\n",
+    "    hmax = max(hhist)*1.1\n",
+    "    \n",
+    "    LINE_ARGS = dict(color=\"#3A5785\", line_color=None)\n",
+    "    \n",
+    "    #s.data['hleft'] = hedges[:-1]\n",
+    "    #s.data['hright'] = hedges[1:]\n",
+    "    #s.data['htop'] = hhist\n",
+    "    #ph = figure(toolbar_location=None, width=p.width, height=200, x_range=p.x_range,\n",
+    "    #            y_range=(0, hmax), min_border=10, min_border_left=50, y_axis_location=\"right\")\n",
+    "\n",
+    "    #ph.quad(source=s, bottom=0, left='hleft', right='hright', top='hhist', color=palette[num], line_color=\"gray\", alpha=0.2)\n",
     "\n",
+    "    ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hhist, color=\"gray\", line_color=\"gray\", alpha=0.4)\n",
+    "    #hh1 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.5, **LINE_ARGS)\n",
+    "    #hh2 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.1, **LINE_ARGS)\n",
+    "\n",
+    "    # create the vertical histogram\n",
+    "    vhist, vedges = np.histogram(y, bins=18, range=(0, 90))\n",
+    "    vzeros = np.zeros(len(vedges)-1)\n",
+    "    vmax = max(vhist)*1.1\n",
+    "\n",
+    "    #pv = figure(toolbar_location=None, width=200, height=p.height, x_range=(0, vmax),\n",
+    "    #            y_range=p.y_range, min_border=10, y_axis_location=\"right\")\n",
+    "\n",
+    "\n",
+    "    pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vhist, color=\"gray\", line_color=\"gray\", alpha=0.4)\n",
+    "    #vh1 = pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vzeros, alpha=0.5, **LINE_ARGS)\n",
+    "    #vh2 = pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vzeros, alpha=0.1, **LINE_ARGS)\n",
+    "\n",
+    "    layout = gridplot([[p, pv], [ph, None]], merge_tools=False)\n",
+    "\n",
+    "curdoc().add_root(layout)\n",
+    "curdoc().title = \"Selection Histogram\"\n",
     "p.legend.location = \"top_left\"\n",
     "p.legend.click_policy=\"hide\"\n",
     "\n",
-    "show(p)\n",
+    "show(layout)\n",
     "\n",
     "# set output to static HTML file\n",
     "output_file(filename=\"../figures/bio_sinta.html\", title=\"Static HTML file\")\n",
-    "save(p)"
+    "save(layout)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e605a5cb",
+   "cell_type": "raw",
+   "id": "0bc281d2-1486-4f52-ac23-e58987715d1d",
    "metadata": {},
-   "outputs": [],
    "source": [
     "x1 = df_all[0].est_age\n",
     "y1 = df_all[0].score_extracted\n",
@@ -559,144 +627,74 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a07ae220",
+   "cell_type": "raw",
+   "id": "50ae0ce0-d799-4608-b331-f5485405d92e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "  <div class=\"bk-root\" id=\"b0885dfb-02b3-45bd-8cb4-06fa08715793\" data-root-id=\"1542\"></div>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function embed_document(root) {\n",
-       "    \n",
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-       "  var render_items = [{\"docid\":\"61c2b067-7a04-47bb-ae18-cc370e1cc1fa\",\"root_ids\":[\"1542\"],\"roots\":{\"1542\":\"b0885dfb-02b3-45bd-8cb4-06fa08715793\"}}];\n",
-       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "\n",
-       "  }\n",
-       "  if (root.Bokeh !== undefined) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (root.Bokeh !== undefined) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else {\n",
-       "        attempts++;\n",
-       "        if (attempts > 100) {\n",
-       "          clearInterval(timer);\n",
-       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
-       "        }\n",
-       "      }\n",
-       "    }, 10, root)\n",
-       "  }\n",
-       "})(window);"
-      ],
-      "application/vnd.bokehjs_exec.v0+json": ""
-     },
-     "metadata": {
-      "application/vnd.bokehjs_exec.v0+json": {
-       "id": "1542"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Start : This command cannot be run due to the error: The system cannot find the file specified.\n",
-      "At line:1 char:1\n",
-      "+ Start \"file:///home/matinnu/others/SINTA_data_mining/figures/bio_sint ...\n",
-      "+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "    + CategoryInfo          : InvalidOperation: (:) [Start-Process], InvalidOperationException\n",
-      "    + FullyQualifiedErrorId : InvalidOperationException,Microsoft.PowerShell.Commands.StartProcessCommand\n",
-      " \n"
-     ]
-    }
-   ],
    "source": [
     "from bokeh.layouts import gridplot\n",
     "from bokeh.models import BoxSelectTool, LassoSelectTool\n",
     "from bokeh.plotting import curdoc, figure\n",
     "\n",
     "# create three normal population samples with different parameters\n",
-    "\n",
-    "x1 = df_all[0].est_age\n",
-    "y1 = df_all[0].score_extracted\n",
-    "\n",
-    "x2 = df_all[1].est_age\n",
-    "y2 = df_all[1].score_extracted\n",
-    "\n",
-    "x3 = df_all[2].est_age\n",
-    "y3 = df_all[2].score_extracted\n",
-    "\n",
-    "x = np.concatenate((x1, x2, x3))\n",
-    "y = np.concatenate((y1, y2, y3))\n",
-    "\n",
-    "TOOLS=\"pan,wheel_zoom,box_select,lasso_select,reset\"\n",
-    "\n",
     "# create the scatter plot\n",
     "p = figure(tools=TOOLS, width=600, height=600, min_border=10, min_border_left=50,\n",
     "           toolbar_location=\"above\",\n",
     "           #toolbar_location=\"above\", x_axis_location=None, y_axis_location=None,\n",
     "           title=\"Distribution Histograms\")\n",
     "p.background_fill_color = \"#fafafa\"\n",
-    "#p.select(BoxSelectTool).select_every_mousemove = False\n",
-    "#p.select(LassoSelectTool).select_every_mousemove = False\n",
-    "\n",
-    "r = p.scatter(x, y, size=10, color=\"#3A5785\", alpha=0.6)\n",
-    "\n",
-    "# create the horizontal histogram\n",
-    "hhist, hedges = np.histogram(x, bins=8)\n",
-    "hzeros = np.zeros(len(hedges)-1)\n",
-    "hmax = max(hhist)*1.1\n",
-    "\n",
-    "LINE_ARGS = dict(color=\"#3A5785\", line_color=None)\n",
-    "\n",
-    "ph = figure(toolbar_location=None, width=p.width, height=200, x_range=p.x_range,\n",
-    "            y_range=(0, hmax), min_border=10, min_border_left=50, y_axis_location=\"right\")\n",
+    "TOOLS=\"pan,wheel_zoom,box_select,lasso_select,reset\"\n",
+    "ph = figure(toolbar_location=None, width=p.width, height=200, x_range=(25, 71),  y_range=(0, 50),\n",
+    "            min_border=10, min_border_left=50, y_axis_location=\"right\")\n",
     "ph.xgrid.grid_line_color = None\n",
     "ph.yaxis.major_label_orientation = np.pi/4\n",
     "ph.background_fill_color = \"#fafafa\"\n",
     "\n",
-    "ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hhist, color=\"white\", line_color=\"#3A5785\")\n",
-    "hh1 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.5, **LINE_ARGS)\n",
-    "hh2 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.1, **LINE_ARGS)\n",
-    "\n",
-    "# create the vertical histogram\n",
-    "vhist, vedges = np.histogram(y, bins=10)\n",
-    "vzeros = np.zeros(len(vedges)-1)\n",
-    "vmax = max(vhist)*1.1\n",
-    "\n",
-    "pv = figure(toolbar_location=None, width=200, height=p.height, x_range=(0, vmax),\n",
-    "            y_range=p.y_range, min_border=10, y_axis_location=\"right\")\n",
     "pv.ygrid.grid_line_color = None\n",
     "pv.xaxis.major_label_orientation = np.pi/4\n",
     "pv.background_fill_color = \"#fafafa\"\n",
     "\n",
-    "pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vhist, color=\"white\", line_color=\"#3A5785\")\n",
-    "vh1 = pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vzeros, alpha=0.5, **LINE_ARGS)\n",
-    "vh2 = pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vzeros, alpha=0.1, **LINE_ARGS)\n",
+    "pv = figure(toolbar_location=None, width=200, height=p.height,\n",
+    "            min_border=10, y_axis_location=\"right\")\n",
+    "\n",
+    "for i in df_all:\n",
+    "\n",
+    "    x = i.est_age\n",
+    "    y = i.score_extracted\n",
+    "\n",
+    "    #p.select(BoxSelectTool).select_every_mousemove = False\n",
+    "    #p.select(LassoSelectTool).select_every_mousemove = False\n",
+    "\n",
+    "    r = p.scatter(x, y, size=10, color=\"#3A5785\", alpha=0.6)\n",
+    "\n",
+    "    # create the horizontal histogram\n",
+    "    hhist, hedges = np.histogram(x, bins=8, range=(25, 71))\n",
+    "    hzeros = np.zeros(len(hedges)-1)\n",
+    "    hmax = max(hhist)*1.1\n",
+    "\n",
+    "    LINE_ARGS = dict(color=\"#3A5785\", line_color=None)\n",
+    "\n",
+    "    #ph = figure(toolbar_location=None, width=p.width, height=200, x_range=p.x_range,\n",
+    "    #            y_range=(0, hmax), min_border=10, min_border_left=50, y_axis_location=\"right\")\n",
+    "\n",
+    "\n",
+    "    ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hhist, color=\"red\", line_color=\"#3A5785\", alpha=0.5)\n",
+    "    hh1 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.5, **LINE_ARGS)\n",
+    "    hh2 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.1, **LINE_ARGS)\n",
+    "\n",
+    "    # create the vertical histogram\n",
+    "    vhist, vedges = np.histogram(y, bins=10, range=(0, 90))\n",
+    "    vzeros = np.zeros(len(vedges)-1)\n",
+    "    vmax = max(vhist)*1.1\n",
+    "\n",
+    "    #pv = figure(toolbar_location=None, width=200, height=p.height, x_range=(0, vmax),\n",
+    "    #            y_range=p.y_range, min_border=10, y_axis_location=\"right\")\n",
+    "\n",
+    "\n",
+    "    pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vhist, color=\"red\", line_color=\"#3A5785\", alpha=0.5)\n",
+    "    vh1 = pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vzeros, alpha=0.5, **LINE_ARGS)\n",
+    "    vh2 = pv.quad(left=0, bottom=vedges[:-1], top=vedges[1:], right=vzeros, alpha=0.1, **LINE_ARGS)\n",
     "\n",
-    "layout = gridplot([[p, pv], [ph, None]], merge_tools=False)\n",
+    "    layout = gridplot([[p, pv], [ph, None]], merge_tools=False)\n",
     "\n",
     "curdoc().add_root(layout)\n",
     "curdoc().title = \"Selection Histogram\"\n",