Figure_1_Sampling_of_Phenotypes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducing Fig. 1\n",
    "\n",
    "This notebook exemplifies how to reproduce Figure 1 of the article. The annotations from all screens will be downloaded and parsed to build statistics on phenotypes, which will be displayed using Bokeh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependencies\n",
    "\n",
    " * [Matplotlib](http://matplotlib.org/)\n",
    " * [NumPy](http://www.numpy.org/)\n",
    " * [Pandas](http://pandas.pydata.org/)\n",
    " * [Bokeh](https://bokeh.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "\n",
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       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
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       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
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       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
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       "\n",
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       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
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       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
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       "\n",
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       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
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       "  }\n",
       "\n",
       "  \n",
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       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
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       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
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       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
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       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
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       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
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       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
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       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
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       "        console.warn(\"failed to load library \" + url);\n",
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       "\n",
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       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
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       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8e1fdc26-93d2-44b0-b0d6-663404a24403' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.11.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.11.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.11.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.11.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"8e1fdc26-93d2-44b0-b0d6-663404a24403\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import omero\n",
    "from idr import connection\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.plotting import output_notebook\n",
    "from bokeh.plotting import show\n",
    "from bokeh.plotting import save\n",
    "from bokeh.models.formatters import TickFormatter\n",
    "from bokeh.models.formatters import String\n",
    "from bokeh.models.formatters import List\n",
    "from bokeh.models.formatters import Dict\n",
    "from bokeh.models.formatters import Int\n",
    "from bokeh.models import FixedTicker\n",
    "from bokeh.models import HoverTool\n",
    "import bokeh.palettes as bpal\n",
    "\n",
    "output_notebook()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getBulkAnnotationAsDf(screenID, conn):\n",
    "    ofId = None\n",
    "    sc = conn.getObject('Screen', screenID)\n",
    "    for ann in sc.listAnnotations():\n",
    "        if isinstance(ann, omero.gateway.FileAnnotationWrapper):\n",
    "            if (ann.getFile().getName() == 'bulk_annotations'):\n",
    "                if (ann.getFile().getSize() > 1476250900):\n",
    "                    print \"that's a big file...\"\n",
    "                    return None\n",
    "                ofId = ann.getFile().getId()\n",
    "                break\n",
    "\n",
    "    if ofId is None:\n",
    "        return None\n",
    "\n",
    "    original_file = omero.model.OriginalFileI(ofId, False)\n",
    "\n",
    "    table = conn.c.sf.sharedResources().openTable(original_file)\n",
    "    try:\n",
    "        rowCount = table.getNumberOfRows()\n",
    "\n",
    "        column_names = [col.name for col in table.getHeaders()]\n",
    "\n",
    "        black_list = []\n",
    "        column_indices = []\n",
    "        for column_name in column_names:\n",
    "            if column_name in black_list:\n",
    "                continue\n",
    "            column_indices.append(column_names.index(column_name))\n",
    "\n",
    "        table_data = table.slice(column_indices, None)\n",
    "    finally:\n",
    "        table.close()\n",
    "\n",
    "    data = []\n",
    "    for index in range(rowCount):\n",
    "        row_values = [column.values[index] for column in table_data.columns]\n",
    "        data.append(row_values)\n",
    "\n",
    "    dfAnn = DataFrame(data)\n",
    "    dfAnn.columns = column_names\n",
    "    return dfAnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def appendPhInfo(phall,screen,df):\n",
    "    \"\"\"\n",
    "    extract all phenotypes information from given bulk annotation file and\n",
    "    append it to the phall\n",
    "    \"\"\"\n",
    "    phcol = df.columns[[('Phenotype' in s) and ('Term Accession' in s) for s in df.columns ]]\n",
    "    for s in phcol:\n",
    "        ph = df[s].unique()\n",
    "        if ph[0]!='':\n",
    "            ph = ph[0]            \n",
    "            desc = df[s.replace('Accession', 'Name')].unique()[0]\n",
    "        else:\n",
    "            ph = ph[1]\n",
    "            desc = df[s.replace('Accession', 'Name')].unique()[1]\n",
    "        dfph = df[df[s]!='']\n",
    "        try:\n",
    "            phall[ph]['n'] = phall[ph]['n']+len(dfph)\n",
    "            if not (screen in phall[ph]['screens']): \n",
    "                phall[ph]['screens'].append(screen)\n",
    "\n",
    "        except Exception as e: \n",
    "#             print(e)\n",
    "            phcur = {'n':len(dfph),'screens':[screen],'desc':desc}\n",
    "            phall[ph] = phcur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the IDR server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to IDR...\n"
     ]
    }
   ],
   "source": [
    "conn = connection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and display figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n",
      "[============================================================] 100.0% ...Iterating through screens\r"
     ]
    }
   ],
   "source": [
    "screens = list(conn.getObjects(\"Screen\"))\n",
    "screen_count = len(screens)\n",
    "print screen_count\n",
    "\n",
    "phall={}\n",
    "# List of screens used for paper\n",
    "screen_ids = [3, 102, 51, 202, 597, 253, 201, 154, 751, 206, 251, 803, 1351, 1501, 1551, 1601, 1602, 1603, 1202, 1101, 1302, 1201, 1251, 1151, 1203, 1204, 1651, 1652, 1653, 1654]\n",
    "print \"Iterating through screens...\"\n",
    "for sc in screens:\n",
    "    sc_id = sc.getId()\n",
    "    print 'loading ' + str(sc_id)\n",
    "    if sc_id in screen_ids:\n",
    "        df = getBulkAnnotationAsDf(sc_id, conn)\n",
    "        if df is not None:\n",
    "            appendPhInfo(phall, sc.getName(), df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disconnect when done loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load grouping of phenotypes for ordering and coloring them\n",
    "Categories were chosen manually offline (see [paper](https://doi.org/10.1101/089359))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfColor = read_csv('./includes/CMPOAccessionToPhenotypeCategories.csv')\n",
    "colors = {}\n",
    "for i, grp in enumerate(dfColor.CmpoPhenotypeCategory.unique()):\n",
    "    colors[grp] = bpal.Set3_10[i%10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pass:CMPO_0000458\n",
      "pass:CMPO_0000390\n",
      "pass:CMPO_0000018\n",
      "pass:CMPO_0000446\n",
      "pass:CMPO_0000447\n",
      "pass:CMPO_0000444\n",
      "pass:CMPO_0000445\n",
      "pass:CMPO_0000442\n",
      "pass:CMPO_0000443\n",
      "pass:CMPO_0000440\n",
      "pass:CMPO_0000441\n",
      "pass:CMPO_0000450\n",
      "pass:CMPO_0000453\n",
      "pass:CMPO_0000454\n",
      "pass:CMPO_0000457\n",
      "pass:CMPO_0000113\n",
      "pass:CMPO_0000422\n",
      "pass:CMPO_0000023\n"
     ]
    }
   ],
   "source": [
    "#add the information to the data and sort it\n",
    "for ph in phall.keys():\n",
    "    try:\n",
    "        phall[ph]['group'] = dfColor[dfColor['CmpoAcc']==ph]['CmpoPhenotypeCategory'].values[0]\n",
    "        phall[ph]['groupColor'] = colors[phall[ph]['group']]\n",
    "        phall[ph]['FigureCmpoName'] = dfColor[dfColor['CmpoAcc']==ph]['FigureCmpoName'].values[0]\n",
    "        \n",
    "    except:\n",
    "        print 'pass:'+ph\n",
    "        del phall[ph]\n",
    "\n",
    "phalls = sorted(phall.values(), key=lambda x: x['group'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"ec713a69-00a7-4861-bac1-cb73f5c46939\"></div>\n",
       "</div>"
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       "  function embed_document(root) {\n",
       "    var docs_json = 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1\"},\"id\":\"2ccaa313-9129-40b2-8100-d73f9a8432f7\",\"type\":\"Title\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"fab25230-ab40-4d68-b039-e6ea8809b01e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"fe2968af-dea6-40d7-93ee-369c99d2fe72\",\"type\":\"LogTicker\"}},\"id\":\"fc0732d1-8028-4a22-8ee9-4f91ea035d60\",\"type\":\"Grid\"}],\"root_ids\":[\"fab25230-ab40-4d68-b039-e6ea8809b01e\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.11\"}};\n",
       "    var render_items = [{\"docid\":\"846d7532-a55b-4381-a284-fa21b5af2e31\",\"elementid\":\"ec713a69-00a7-4861-bac1-cb73f5c46939\",\"modelid\":\"fab25230-ab40-4d68-b039-e6ea8809b01e\"}];\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\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",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "fab25230-ab40-4d68-b039-e6ea8809b01e"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOOLS = \"pan,wheel_zoom,reset\"\n",
    "\n",
    "phenotypes = figure(title = \"Fig 1\",\n",
    "                    tools=TOOLS,\n",
    "                    y_axis_type=\"log\",\n",
    "                    width=900,\n",
    "                    toolbar_location=\"above\")\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        ph=[ph['FigureCmpoName'] for ph in phalls],\n",
    "        n=[ph['n'] for ph in phalls],\n",
    "        names=[ph['screens'] for ph in phalls],\n",
    "        desc=[ph['desc'] for ph in phalls],\n",
    "        x=[2*x for x in range(len(phall.keys()))],\n",
    "        r=[1*len(ph['screens']) for ph in phalls],\n",
    "        color=[ph['groupColor'] for ph in phalls],\n",
    "        groupName=[ph['group'] for ph in phalls]\n",
    "    ))\n",
    "\n",
    "label_data = {2*i:x for i,x in enumerate([ph['FigureCmpoName'] for ph in phalls])}\n",
    "cir = phenotypes.circle('x', 'n', radius='r', source=source, color='color')\n",
    "\n",
    "\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "            (\"Term\", \"@ph\"),\n",
    "            (\"Description\", \"@desc\"),\n",
    "            (\"Number of samples\", \"@n\"),\n",
    "            (\"Screens name\", \"@names\"),\n",
    "            (\"group\", \"@groupName\")\n",
    "    ]\n",
    "    )\n",
    "\n",
    "phenotypes.add_tools(hover)\n",
    "phenotypes.xaxis.major_label_orientation = np.pi/4.\n",
    "phenotypes.xaxis.axis_label_text_font_size = \"10pt\"\n",
    "show(phenotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### License\n",
    "\n",
    "Copyright (C) 2016 University of Dundee. All Rights Reserved.\n",
    "\n",
    "This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.\n",
    "\n",
    "This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA."
   ]
  }
 ],
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