lingvar/kings_r.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>265</li>\n",
       "\t<li>265</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 265\n",
       "\\item 265\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 265\n",
       "2. 265\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 265 265"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Reges_I.11.41</th><th scope=col>Reges_I.14.29</th><th scope=col>Reges_I.15.7</th><th scope=col>Reges_I.15.31</th><th scope=col>Reges_I.16.5</th><th scope=col>Reges_I.16.14</th><th scope=col>Reges_I.16.20</th><th scope=col>Reges_I.16.27</th><th scope=col>Reges_I.22.40</th><th scope=col>Reges_I.22.46</th><th scope=col>Reges_II.1.18</th><th scope=col>Reges_II.8.23</th><th scope=col>Reges_II.8.26</th><th scope=col>Reges_II.10.34</th><th scope=col>Reges_II.12.20</th><th scope=col>Reges_II.13.8</th><th scope=col>Reges_II.14.2</th><th scope=col>Reges_II.14.18</th><th scope=col>Reges_II.15.2</th><th scope=col>Reges_II.15.3</th><th scope=col>Reges_II.15.6</th><th scope=col>Reges_II.15.11</th><th scope=col>Reges_II.15.21</th><th scope=col>Reges_II.15.22</th><th scope=col>Reges_II.15.26</th><th scope=col>Reges_II.15.31</th><th scope=col>Reges_II.15.33</th><th scope=col>Reges_II.15.34</th><th scope=col>Reges_II.15.36</th><th scope=col>Reges_II.16.19</th><th scope=col>Reges_II.18.2</th><th scope=col>Reges_II.18.3</th><th scope=col>Reges_IIr.19.1</th><th scope=col>Reges_IIr.19.2</th><th scope=col>Reges_IIr.19.3</th><th scope=col>Reges_IIr.19.4</th><th scope=col>Reges_IIr.19.5</th><th scope=col>Reges_IIr.19.6</th><th scope=col>Reges_IIr.19.7</th><th scope=col>Reges_IIr.19.8</th><th scope=col>Reges_IIr.19.9</th><th scope=col>Reges_IIr.19.10</th><th scope=col>Reges_IIr.19.11</th><th scope=col>Reges_IIr.19.12</th><th scope=col>Reges_IIr.19.13</th><th scope=col>Reges_IIr.19.14</th><th scope=col>Reges_IIr.19.15</th><th scope=col>Reges_IIr.19.16</th><th scope=col>Reges_IIr.19.17</th><th scope=col>Reges_IIr.19.18</th><th scope=col>Reges_IIr.19.19</th><th scope=col>Reges_IIr.19.20</th><th scope=col>Reges_IIr.19.21</th><th scope=col>Reges_IIr.19.22</th><th scope=col>Reges_IIr.19.23</th><th scope=col>Reges_IIr.19.24</th><th scope=col>Reges_IIr.19.25</th><th scope=col>Reges_IIr.19.26</th><th scope=col>Reges_IIr.19.27</th><th scope=col>Reges_IIr.19.28</th><th scope=col>Reges_IIr.19.29</th><th scope=col>Reges_IIr.19.30</th><th scope=col>Reges_IIr.19.31</th><th scope=col>Reges_IIr.19.32</th><th scope=col>Reges_IIr.19.33</th><th scope=col>Reges_IIr.19.34</th><th scope=col>Reges_IIr.19.35</th><th scope=col>Reges_IIr.19.36</th><th scope=col>Reges_IIr.19.37</th><th scope=col>Reges_IIr.20.1</th><th scope=col>Reges_IIr.20.2</th><th scope=col>Reges_IIr.20.3</th><th scope=col>Reges_IIr.20.5</th><th scope=col>Reges_IIr.20.7</th><th scope=col>Reges_IIr.20.12</th><th scope=col>Reges_IIr.20.13</th><th scope=col>Reges_IIr.20.14</th><th scope=col>Reges_IIr.20.15</th><th scope=col>Reges_IIr.20.16</th><th scope=col>Reges_IIr.20.17</th><th scope=col>Reges_IIr.20.18</th><th scope=col>Reges_IIr.20.19</th><th scope=col>Reges_IIr.20.21</th><th scope=col>Reges_IIr.21.1</th><th scope=col>Reges_IIr.21.2</th><th scope=col>Reges_IIr.21.3</th><th scope=col>Reges_IIr.21.4</th><th scope=col>Reges_IIr.21.5</th><th scope=col>Reges_IIr.21.6</th><th scope=col>Reges_IIr.21.7</th><th scope=col>Reges_IIr.21.8</th><th scope=col>Reges_IIr.21.9</th><th scope=col>Reges_IIr.21.10</th><th scope=col>Reges_IIr.21.17</th><th scope=col>Reges_IIr.21.18</th><th scope=col>Reges_IIr.21.19</th><th scope=col>Reges_IIr.21.20</th><th scope=col>Reges_IIr.21.24</th><th scope=col>Reges_IIr.21.25</th><th scope=col>Reges_IIr.22.2</th><th scope=col>Reges_IIr.22.5</th><th scope=col>Reges_IIr.22.8</th><th scope=col>Reges_IIr.22.10</th><th scope=col>Reges_IIr.22.11</th><th scope=col>Reges_IIr.22.12</th><th scope=col>Reges_IIr.22.13</th><th scope=col>Reges_IIr.22.14</th><th scope=col>Reges_IIr.22.15</th><th scope=col>Reges_IIr.22.16</th><th scope=col>Reges_IIr.22.17</th><th scope=col>Reges_IIr.22.18</th><th scope=col>Reges_IIr.22.19</th><th scope=col>Reges_IIr.22.20</th><th scope=col>Reges_IIr.23.1</th><th scope=col>Reges_IIr.23.2</th><th scope=col>Reges_IIr.23.3</th><th scope=col>Reges_IIr.23.23</th><th scope=col>Reges_IIr.23.28</th><th scope=col>Reges_IIr.23.31</th><th scope=col>Reges_IIr.23.32</th><th scope=col>Reges_IIr.23.36</th><th scope=col>Reges_IIr.23.37</th><th scope=col>Reges_IIr.24.5</th><th scope=col>Reges_IIr.24.6</th><th scope=col>Reges_IIr.24.9</th><th scope=col>Reges_IIr.24.18</th><th scope=col>Reges_IIr.24.19</th><th scope=col>Reges_IIr.24.20</th><th scope=col>Reges_IIr.25.1</th><th scope=col>Reges_IIr.25.2</th><th scope=col>Reges_IIr.25.3</th><th scope=col>Reges_IIr.25.4</th><th scope=col>Reges_IIr.25.5</th><th scope=col>Reges_IIr.25.6</th><th scope=col>Reges_IIr.25.8</th><th scope=col>Reges_IIr.25.9</th><th scope=col>Reges_IIr.25.10</th><th scope=col>Reges_IIr.25.11</th><th scope=col>Reges_IIr.25.12</th><th scope=col>Reges_IIr.25.13</th><th scope=col>Reges_IIr.25.14</th><th scope=col>Reges_IIr.25.16</th><th scope=col>Reges_IIr.25.18</th><th scope=col>Reges_IIr.25.19</th><th scope=col>Reges_IIr.25.20</th><th 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scope=col>Chronica_II.33.5</th><th scope=col>Chronica_II.33.6</th><th scope=col>Chronica_II.33.7</th><th scope=col>Chronica_II.33.8</th><th scope=col>Chronica_II.33.9</th><th scope=col>Chronica_II.33.20</th><th scope=col>Chronica_II.33.25</th><th scope=col>Chronica_II.34.2</th><th scope=col>Chronica_II.34.10</th><th scope=col>Chronica_II.34.15</th><th scope=col>Chronica_II.34.18</th><th scope=col>Chronica_II.34.19</th><th scope=col>Chronica_II.34.20</th><th scope=col>Chronica_II.34.21</th><th scope=col>Chronica_II.34.22</th><th scope=col>Chronica_II.34.23</th><th scope=col>Chronica_II.34.24</th><th scope=col>Chronica_II.34.25</th><th scope=col>Chronica_II.34.26</th><th scope=col>Chronica_II.34.27</th><th scope=col>Chronica_II.34.28</th><th scope=col>Chronica_II.34.29</th><th scope=col>Chronica_II.34.30</th><th scope=col>Chronica_II.34.31</th><th scope=col>Chronica_II.35.19</th><th scope=col>Chronica_II.36.5</th><th scope=col>Chronica_II.36.11</th></tr></thead>\n",
       "<tbody>\n",
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       "\t<tr><th scope=row>Reges_I.15.7</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>Reges_I.15.31</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>Reges_I.16.5</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>Reges_I.16.14</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & Reges_I.11.41 & Reges_I.14.29 & Reges_I.15.7 & Reges_I.15.31 & Reges_I.16.5 & Reges_I.16.14 & Reges_I.16.20 & Reges_I.16.27 & Reges_I.22.40 & Reges_I.22.46 & Reges_II.1.18 & Reges_II.8.23 & Reges_II.8.26 & Reges_II.10.34 & Reges_II.12.20 & Reges_II.13.8 & Reges_II.14.2 & Reges_II.14.18 & Reges_II.15.2 & Reges_II.15.3 & Reges_II.15.6 & Reges_II.15.11 & Reges_II.15.21 & Reges_II.15.22 & Reges_II.15.26 & Reges_II.15.31 & Reges_II.15.33 & Reges_II.15.34 & Reges_II.15.36 & Reges_II.16.19 & Reges_II.18.2 & Reges_II.18.3 & Reges_IIr.19.1 & Reges_IIr.19.2 & Reges_IIr.19.3 & Reges_IIr.19.4 & Reges_IIr.19.5 & Reges_IIr.19.6 & Reges_IIr.19.7 & Reges_IIr.19.8 & Reges_IIr.19.9 & Reges_IIr.19.10 & Reges_IIr.19.11 & Reges_IIr.19.12 & Reges_IIr.19.13 & Reges_IIr.19.14 & Reges_IIr.19.15 & Reges_IIr.19.16 & Reges_IIr.19.17 & Reges_IIr.19.18 & Reges_IIr.19.19 & Reges_IIr.19.20 & Reges_IIr.19.21 & Reges_IIr.19.22 & Reges_IIr.19.23 & Reges_IIr.19.24 & Reges_IIr.19.25 & Reges_IIr.19.26 & Reges_IIr.19.27 & Reges_IIr.19.28 & Reges_IIr.19.29 & Reges_IIr.19.30 & Reges_IIr.19.31 & Reges_IIr.19.32 & Reges_IIr.19.33 & Reges_IIr.19.34 & Reges_IIr.19.35 & Reges_IIr.19.36 & Reges_IIr.19.37 & Reges_IIr.20.1 & Reges_IIr.20.2 & Reges_IIr.20.3 & Reges_IIr.20.5 & Reges_IIr.20.7 & Reges_IIr.20.12 & Reges_IIr.20.13 & Reges_IIr.20.14 & Reges_IIr.20.15 & Reges_IIr.20.16 & Reges_IIr.20.17 & Reges_IIr.20.18 & Reges_IIr.20.19 & Reges_IIr.20.21 & Reges_IIr.21.1 & Reges_IIr.21.2 & Reges_IIr.21.3 & Reges_IIr.21.4 & Reges_IIr.21.5 & Reges_IIr.21.6 & Reges_IIr.21.7 & Reges_IIr.21.8 & Reges_IIr.21.9 & Reges_IIr.21.10 & Reges_IIr.21.17 & Reges_IIr.21.18 & Reges_IIr.21.19 & Reges_IIr.21.20 & Reges_IIr.21.24 & Reges_IIr.21.25 & Reges_IIr.22.2 & Reges_IIr.22.5 & Reges_IIr.22.8 & Reges_IIr.22.10 & Reges_IIr.22.11 & Reges_IIr.22.12 & Reges_IIr.22.13 & Reges_IIr.22.14 & Reges_IIr.22.15 & Reges_IIr.22.16 & Reges_IIr.22.17 & Reges_IIr.22.18 & Reges_IIr.22.19 & Reges_IIr.22.20 & Reges_IIr.23.1 & Reges_IIr.23.2 & Reges_IIr.23.3 & Reges_IIr.23.23 & Reges_IIr.23.28 & Reges_IIr.23.31 & Reges_IIr.23.32 & Reges_IIr.23.36 & Reges_IIr.23.37 & Reges_IIr.24.5 & Reges_IIr.24.6 & Reges_IIr.24.9 & Reges_IIr.24.18 & Reges_IIr.24.19 & Reges_IIr.24.20 & Reges_IIr.25.1 & Reges_IIr.25.2 & Reges_IIr.25.3 & Reges_IIr.25.4 & Reges_IIr.25.5 & Reges_IIr.25.6 & Reges_IIr.25.8 & Reges_IIr.25.9 & Reges_IIr.25.10 & Reges_IIr.25.11 & Reges_IIr.25.12 & Reges_IIr.25.13 & Reges_IIr.25.14 & Reges_IIr.25.16 & Reges_IIr.25.18 & Reges_IIr.25.19 & Reges_IIr.25.20 & Reges_IIr.25.21 & Reges_IIr.25.24 & Reges_IIr.25.25 & Reges_IIr.25.27 & Reges_IIr.25.28 & Reges_IIr.25.29 & Reges_IIr.25.30 & Jesaia.37.1 & Jesaia.37.2 & Jesaia.37.3 & Jesaia.37.4 & Jesaia.37.5 & Jesaia.37.6 & Jesaia.37.7 & Jesaia.37.8 & Jesaia.37.9 & Jesaia.37.10 & Jesaia.37.11 & Jesaia.37.12 & Jesaia.37.13 & Jesaia.37.14 & Jesaia.37.16 & Jesaia.37.17 & Jesaia.37.18 & Jesaia.37.19 & Jesaia.37.20 & Jesaia.37.21 & Jesaia.37.22 & Jesaia.37.23 & Jesaia.37.24 & Jesaia.37.25 & Jesaia.37.26 & Jesaia.37.27 & Jesaia.37.28 & Jesaia.37.29 & Jesaia.37.30 & Jesaia.37.31 & Jesaia.37.32 & Jesaia.37.33 & Jesaia.37.34 & Jesaia.37.35 & Jesaia.37.36 & Jesaia.37.37 & Jesaia.37.38 & Jesaia.38.1 & Jesaia.38.2 & Jesaia.38.3 & Jesaia.38.5 & Jesaia.38.21 & Jesaia.39.1 & Jesaia.39.2 & Jesaia.39.3 & Jesaia.39.4 & Jesaia.39.5 & Jesaia.39.6 & Jesaia.39.7 & Jesaia.39.8 & Jeremia.39.9 & Jeremia.40.9 & Jeremia.41.1 & Jeremia.52.1 & Jeremia.52.2 & Jeremia.52.3 & Jeremia.52.4 & Jeremia.52.5 & Jeremia.52.6 & Jeremia.52.7 & Jeremia.52.8 & Jeremia.52.9 & Jeremia.52.12 & Jeremia.52.13 & Jeremia.52.14 & Jeremia.52.15 & Jeremia.52.16 & Jeremia.52.17 & Jeremia.52.18 & Jeremia.52.20 & Jeremia.52.24 & Jeremia.52.25 & Jeremia.52.26 & Jeremia.52.27 & Jeremia.52.31 & Jeremia.52.32 & Jeremia.52.33 & Jeremia.52.34 & Haggai.1.3 & Chronica_II.26.4 & Chronica_II.27.1 & Chronica_II.29.2 & Chronica_II.33.1 & Chronica_II.33.2 & Chronica_II.33.3 & Chronica_II.33.4 & Chronica_II.33.5 & Chronica_II.33.6 & Chronica_II.33.7 & Chronica_II.33.8 & Chronica_II.33.9 & Chronica_II.33.20 & Chronica_II.33.25 & Chronica_II.34.2 & Chronica_II.34.10 & Chronica_II.34.15 & Chronica_II.34.18 & Chronica_II.34.19 & Chronica_II.34.20 & Chronica_II.34.21 & Chronica_II.34.22 & Chronica_II.34.23 & Chronica_II.34.24 & Chronica_II.34.25 & Chronica_II.34.26 & Chronica_II.34.27 & Chronica_II.34.28 & Chronica_II.34.29 & Chronica_II.34.30 & Chronica_II.34.31 & Chronica_II.35.19 & Chronica_II.36.5 & Chronica_II.36.11\\\\\n",
       "\\hline\n",
       "\tReges_I.11.41 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tReges_I.14.29 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tReges_I.15.7 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tReges_I.15.31 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tReges_I.16.5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tReges_I.16.14 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "              Reges_I.11.41 Reges_I.14.29 Reges_I.15.7 Reges_I.15.31\n",
       "Reges_I.11.41             0             0            0             0\n",
       "Reges_I.14.29             0             0            0             0\n",
       "Reges_I.15.7              0             0            0             0\n",
       "Reges_I.15.31             0             0            0             0\n",
       "Reges_I.16.5              0             0            0             0\n",
       "Reges_I.16.14             0             0            0             0\n",
       "              Reges_I.16.5 Reges_I.16.14 Reges_I.16.20 Reges_I.16.27\n",
       "Reges_I.11.41            0             0             0             0\n",
       "Reges_I.14.29            0             0             0             0\n",
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       "Reges_I.16.5             0             0             0             0\n",
       "Reges_I.16.14            0             0             0             0\n",
       "              Reges_I.22.40 Reges_I.22.46 Reges_II.1.18 Reges_II.8.23\n",
       "Reges_I.11.41             0             0             0             0\n",
       "Reges_I.14.29             0             0             0             0\n",
       "Reges_I.15.7              0             0             0             0\n",
       "Reges_I.15.31             0             0             0             0\n",
       "Reges_I.16.5              0             0             0             0\n",
       "Reges_I.16.14             0             0             0             0\n",
       "              Reges_II.8.26 Reges_II.10.34 Reges_II.12.20 Reges_II.13.8\n",
       "Reges_I.11.41             0              0              0             0\n",
       "Reges_I.14.29             0              0              0             0\n",
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       "Reges_I.16.14             0              0              0             0\n",
       "              Reges_II.14.2 Reges_II.14.18 Reges_II.15.2 Reges_II.15.3\n",
       "Reges_I.11.41             0              0             0             0\n",
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       "Reges_I.16.14             0              0             0             0\n",
       "              Reges_II.15.6 Reges_II.15.11 Reges_II.15.21 Reges_II.15.22\n",
       "Reges_I.11.41             0              0              0              0\n",
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       "Reges_I.16.14             0              0              0              0\n",
       "              Reges_II.15.26 Reges_II.15.31 Reges_II.15.33 Reges_II.15.34\n",
       "Reges_I.11.41              0              0              0              0\n",
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       "Reges_I.16.14              0              0              0              0\n",
       "              Reges_II.15.36 Reges_II.16.19 Reges_II.18.2 Reges_II.18.3\n",
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       "Reges_I.16.14              0              0             0             0\n",
       "              Reges_IIr.19.1 Reges_IIr.19.2 Reges_IIr.19.3 Reges_IIr.19.4\n",
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       "              Reges_IIr.19.5 Reges_IIr.19.6 Reges_IIr.19.7 Reges_IIr.19.8\n",
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       "              Reges_IIr.19.9 Reges_IIr.19.10 Reges_IIr.19.11 Reges_IIr.19.12\n",
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       "              Reges_IIr.19.13 Reges_IIr.19.14 Reges_IIr.19.15 Reges_IIr.19.16\n",
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       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.19.17 Reges_IIr.19.18 Reges_IIr.19.19 Reges_IIr.19.20\n",
       "Reges_I.11.41               0               0               0               0\n",
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       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.19.21 Reges_IIr.19.22 Reges_IIr.19.23 Reges_IIr.19.24\n",
       "Reges_I.11.41               0               0               0               0\n",
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       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.19.25 Reges_IIr.19.26 Reges_IIr.19.27 Reges_IIr.19.28\n",
       "Reges_I.11.41               0               0               0               0\n",
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       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.19.29 Reges_IIr.19.30 Reges_IIr.19.31 Reges_IIr.19.32\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
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       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.19.33 Reges_IIr.19.34 Reges_IIr.19.35 Reges_IIr.19.36\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
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       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.19.37 Reges_IIr.20.1 Reges_IIr.20.2 Reges_IIr.20.3\n",
       "Reges_I.11.41               0              0              0              0\n",
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       "Reges_I.16.5                0              0              0              0\n",
       "Reges_I.16.14               0              0              0              0\n",
       "              Reges_IIr.20.5 Reges_IIr.20.7 Reges_IIr.20.12 Reges_IIr.20.13\n",
       "Reges_I.11.41              0              0               0               0\n",
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       "Reges_I.16.5               0              0               0               0\n",
       "Reges_I.16.14              0              0               0               0\n",
       "              Reges_IIr.20.14 Reges_IIr.20.15 Reges_IIr.20.16 Reges_IIr.20.17\n",
       "Reges_I.11.41               0               0               0               0\n",
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       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.20.18 Reges_IIr.20.19 Reges_IIr.20.21 Reges_IIr.21.1\n",
       "Reges_I.11.41               0               0               0              0\n",
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       "Reges_I.16.14               0               0               0              0\n",
       "              Reges_IIr.21.2 Reges_IIr.21.3 Reges_IIr.21.4 Reges_IIr.21.5\n",
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       "Reges_I.16.14              0              0              0              0\n",
       "              Reges_IIr.21.6 Reges_IIr.21.7 Reges_IIr.21.8 Reges_IIr.21.9\n",
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       "              Reges_IIr.21.10 Reges_IIr.21.17 Reges_IIr.21.18 Reges_IIr.21.19\n",
       "Reges_I.11.41               0               0               0               0\n",
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       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.21.20 Reges_IIr.21.24 Reges_IIr.21.25 Reges_IIr.22.2\n",
       "Reges_I.11.41               0               0               0              0\n",
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       "Reges_I.16.5                0               0               0              0\n",
       "Reges_I.16.14               0               0               0              0\n",
       "              Reges_IIr.22.5 Reges_IIr.22.8 Reges_IIr.22.10 Reges_IIr.22.11\n",
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       "Reges_I.16.14              0              0               0               0\n",
       "              Reges_IIr.22.12 Reges_IIr.22.13 Reges_IIr.22.14 Reges_IIr.22.15\n",
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       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.22.16 Reges_IIr.22.17 Reges_IIr.22.18 Reges_IIr.22.19\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
       "Reges_I.15.7                0               0               0               0\n",
       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.22.20 Reges_IIr.23.1 Reges_IIr.23.2 Reges_IIr.23.3\n",
       "Reges_I.11.41               0              0              0              0\n",
       "Reges_I.14.29               0              0              0              0\n",
       "Reges_I.15.7                0              0              0              0\n",
       "Reges_I.15.31               0              0              0              0\n",
       "Reges_I.16.5                0              0              0              0\n",
       "Reges_I.16.14               0              0              0              0\n",
       "              Reges_IIr.23.23 Reges_IIr.23.28 Reges_IIr.23.31 Reges_IIr.23.32\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
       "Reges_I.15.7                0               0               0               0\n",
       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.23.36 Reges_IIr.23.37 Reges_IIr.24.5 Reges_IIr.24.6\n",
       "Reges_I.11.41               0               0              0              0\n",
       "Reges_I.14.29               0               0              0              0\n",
       "Reges_I.15.7                0               0              0              0\n",
       "Reges_I.15.31               0               0              0              0\n",
       "Reges_I.16.5                0               0              0              0\n",
       "Reges_I.16.14               0               0              0              0\n",
       "              Reges_IIr.24.9 Reges_IIr.24.18 Reges_IIr.24.19 Reges_IIr.24.20\n",
       "Reges_I.11.41              0               0               0               0\n",
       "Reges_I.14.29              0               0               0               0\n",
       "Reges_I.15.7               0               0               0               0\n",
       "Reges_I.15.31              0               0               0               0\n",
       "Reges_I.16.5               0               0               0               0\n",
       "Reges_I.16.14              0               0               0               0\n",
       "              Reges_IIr.25.1 Reges_IIr.25.2 Reges_IIr.25.3 Reges_IIr.25.4\n",
       "Reges_I.11.41              0              0              0              0\n",
       "Reges_I.14.29              0              0              0              0\n",
       "Reges_I.15.7               0              0              0              0\n",
       "Reges_I.15.31              0              0              0              0\n",
       "Reges_I.16.5               0              0              0              0\n",
       "Reges_I.16.14              0              0              0              0\n",
       "              Reges_IIr.25.5 Reges_IIr.25.6 Reges_IIr.25.8 Reges_IIr.25.9\n",
       "Reges_I.11.41              0              0              0              0\n",
       "Reges_I.14.29              0              0              0              0\n",
       "Reges_I.15.7               0              0              0              0\n",
       "Reges_I.15.31              0              0              0              0\n",
       "Reges_I.16.5               0              0              0              0\n",
       "Reges_I.16.14              0              0              0              0\n",
       "              Reges_IIr.25.10 Reges_IIr.25.11 Reges_IIr.25.12 Reges_IIr.25.13\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
       "Reges_I.15.7                0               0               0               0\n",
       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.25.14 Reges_IIr.25.16 Reges_IIr.25.18 Reges_IIr.25.19\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
       "Reges_I.15.7                0               0               0               0\n",
       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.25.20 Reges_IIr.25.21 Reges_IIr.25.24 Reges_IIr.25.25\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
       "Reges_I.15.7                0               0               0               0\n",
       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Reges_IIr.25.27 Reges_IIr.25.28 Reges_IIr.25.29 Reges_IIr.25.30\n",
       "Reges_I.11.41               0               0               0               0\n",
       "Reges_I.14.29               0               0               0               0\n",
       "Reges_I.15.7                0               0               0               0\n",
       "Reges_I.15.31               0               0               0               0\n",
       "Reges_I.16.5                0               0               0               0\n",
       "Reges_I.16.14               0               0               0               0\n",
       "              Jesaia.37.1 Jesaia.37.2 Jesaia.37.3 Jesaia.37.4 Jesaia.37.5\n",
       "Reges_I.11.41           0           0           0           0           0\n",
       "Reges_I.14.29           0           0           0           0           0\n",
       "Reges_I.15.7            0           0           0           0           0\n",
       "Reges_I.15.31           0           0           0           0           0\n",
       "Reges_I.16.5            0           0           0           0           0\n",
       "Reges_I.16.14           0           0           0           0           0\n",
       "              Jesaia.37.6 Jesaia.37.7 Jesaia.37.8 Jesaia.37.9 Jesaia.37.10\n",
       "Reges_I.11.41           0           0           0           0            0\n",
       "Reges_I.14.29           0           0           0           0            0\n",
       "Reges_I.15.7            0           0           0           0            0\n",
       "Reges_I.15.31           0           0           0           0            0\n",
       "Reges_I.16.5            0           0           0           0            0\n",
       "Reges_I.16.14           0           0           0           0            0\n",
       "              Jesaia.37.11 Jesaia.37.12 Jesaia.37.13 Jesaia.37.14 Jesaia.37.16\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jesaia.37.17 Jesaia.37.18 Jesaia.37.19 Jesaia.37.20 Jesaia.37.21\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jesaia.37.22 Jesaia.37.23 Jesaia.37.24 Jesaia.37.25 Jesaia.37.26\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jesaia.37.27 Jesaia.37.28 Jesaia.37.29 Jesaia.37.30 Jesaia.37.31\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jesaia.37.32 Jesaia.37.33 Jesaia.37.34 Jesaia.37.35 Jesaia.37.36\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jesaia.37.37 Jesaia.37.38 Jesaia.38.1 Jesaia.38.2 Jesaia.38.3\n",
       "Reges_I.11.41            0            0           0           0           0\n",
       "Reges_I.14.29            0            0           0           0           0\n",
       "Reges_I.15.7             0            0           0           0           0\n",
       "Reges_I.15.31            0            0           0           0           0\n",
       "Reges_I.16.5             0            0           0           0           0\n",
       "Reges_I.16.14            0            0           0           0           0\n",
       "              Jesaia.38.5 Jesaia.38.21 Jesaia.39.1 Jesaia.39.2 Jesaia.39.3\n",
       "Reges_I.11.41           0            0           0           0           0\n",
       "Reges_I.14.29           0            0           0           0           0\n",
       "Reges_I.15.7            0            0           0           0           0\n",
       "Reges_I.15.31           0            0           0           0           0\n",
       "Reges_I.16.5            0            0           0           0           0\n",
       "Reges_I.16.14           0            0           0           0           0\n",
       "              Jesaia.39.4 Jesaia.39.5 Jesaia.39.6 Jesaia.39.7 Jesaia.39.8\n",
       "Reges_I.11.41           0           0           0           0           0\n",
       "Reges_I.14.29           0           0           0           0           0\n",
       "Reges_I.15.7            0           0           0           0           0\n",
       "Reges_I.15.31           0           0           0           0           0\n",
       "Reges_I.16.5            0           0           0           0           0\n",
       "Reges_I.16.14           0           0           0           0           0\n",
       "              Jeremia.39.9 Jeremia.40.9 Jeremia.41.1 Jeremia.52.1 Jeremia.52.2\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jeremia.52.3 Jeremia.52.4 Jeremia.52.5 Jeremia.52.6 Jeremia.52.7\n",
       "Reges_I.11.41            0            0            0            0            0\n",
       "Reges_I.14.29            0            0            0            0            0\n",
       "Reges_I.15.7             0            0            0            0            0\n",
       "Reges_I.15.31            0            0            0            0            0\n",
       "Reges_I.16.5             0            0            0            0            0\n",
       "Reges_I.16.14            0            0            0            0            0\n",
       "              Jeremia.52.8 Jeremia.52.9 Jeremia.52.12 Jeremia.52.13\n",
       "Reges_I.11.41            0            0             0             0\n",
       "Reges_I.14.29            0            0             0             0\n",
       "Reges_I.15.7             0            0             0             0\n",
       "Reges_I.15.31            0            0             0             0\n",
       "Reges_I.16.5             0            0             0             0\n",
       "Reges_I.16.14            0            0             0             0\n",
       "              Jeremia.52.14 Jeremia.52.15 Jeremia.52.16 Jeremia.52.17\n",
       "Reges_I.11.41             0             0             0             0\n",
       "Reges_I.14.29             0             0             0             0\n",
       "Reges_I.15.7              0             0             0             0\n",
       "Reges_I.15.31             0             0             0             0\n",
       "Reges_I.16.5              0             0             0             0\n",
       "Reges_I.16.14             0             0             0             0\n",
       "              Jeremia.52.18 Jeremia.52.20 Jeremia.52.24 Jeremia.52.25\n",
       "Reges_I.11.41             0             0             0             0\n",
       "Reges_I.14.29             0             0             0             0\n",
       "Reges_I.15.7              0             0             0             0\n",
       "Reges_I.15.31             0             0             0             0\n",
       "Reges_I.16.5              0             0             0             0\n",
       "Reges_I.16.14             0             0             0             0\n",
       "              Jeremia.52.26 Jeremia.52.27 Jeremia.52.31 Jeremia.52.32\n",
       "Reges_I.11.41             0             0             0             0\n",
       "Reges_I.14.29             0             0             0             0\n",
       "Reges_I.15.7              0             0             0             0\n",
       "Reges_I.15.31             0             0             0             0\n",
       "Reges_I.16.5              0             0             0             0\n",
       "Reges_I.16.14             0             0             0             0\n",
       "              Jeremia.52.33 Jeremia.52.34 Haggai.1.3 Chronica_II.26.4\n",
       "Reges_I.11.41             0             0          0                0\n",
       "Reges_I.14.29             0             0          0                0\n",
       "Reges_I.15.7              0             0          0                0\n",
       "Reges_I.15.31             0             0          0                0\n",
       "Reges_I.16.5              0             0          0                0\n",
       "Reges_I.16.14             0             0          0                0\n",
       "              Chronica_II.27.1 Chronica_II.29.2 Chronica_II.33.1\n",
       "Reges_I.11.41                0                0                0\n",
       "Reges_I.14.29                0                0                0\n",
       "Reges_I.15.7                 0                0                0\n",
       "Reges_I.15.31                0                0                0\n",
       "Reges_I.16.5                 0                0                0\n",
       "Reges_I.16.14                0                0                0\n",
       "              Chronica_II.33.2 Chronica_II.33.3 Chronica_II.33.4\n",
       "Reges_I.11.41                0                0                0\n",
       "Reges_I.14.29                0                0                0\n",
       "Reges_I.15.7                 0                0                0\n",
       "Reges_I.15.31                0                0                0\n",
       "Reges_I.16.5                 0                0                0\n",
       "Reges_I.16.14                0                0                0\n",
       "              Chronica_II.33.5 Chronica_II.33.6 Chronica_II.33.7\n",
       "Reges_I.11.41                0                0                0\n",
       "Reges_I.14.29                0                0                0\n",
       "Reges_I.15.7                 0                0                0\n",
       "Reges_I.15.31                0                0                0\n",
       "Reges_I.16.5                 0                0                0\n",
       "Reges_I.16.14                0                0                0\n",
       "              Chronica_II.33.8 Chronica_II.33.9 Chronica_II.33.20\n",
       "Reges_I.11.41                0                0                 0\n",
       "Reges_I.14.29                0                0                 0\n",
       "Reges_I.15.7                 0                0                 0\n",
       "Reges_I.15.31                0                0                 0\n",
       "Reges_I.16.5                 0                0                 0\n",
       "Reges_I.16.14                0                0                 0\n",
       "              Chronica_II.33.25 Chronica_II.34.2 Chronica_II.34.10\n",
       "Reges_I.11.41                 0                0                 0\n",
       "Reges_I.14.29                 0                0                 0\n",
       "Reges_I.15.7                  0                0                 0\n",
       "Reges_I.15.31                 0                0                 0\n",
       "Reges_I.16.5                  0                0                 0\n",
       "Reges_I.16.14                 0                0                 0\n",
       "              Chronica_II.34.15 Chronica_II.34.18 Chronica_II.34.19\n",
       "Reges_I.11.41                 0                 0                 0\n",
       "Reges_I.14.29                 0                 0                 0\n",
       "Reges_I.15.7                  0                 0                 0\n",
       "Reges_I.15.31                 0                 0                 0\n",
       "Reges_I.16.5                  0                 0                 0\n",
       "Reges_I.16.14                 0                 0                 0\n",
       "              Chronica_II.34.20 Chronica_II.34.21 Chronica_II.34.22\n",
       "Reges_I.11.41                 0                 0                 0\n",
       "Reges_I.14.29                 0                 0                 0\n",
       "Reges_I.15.7                  0                 0                 0\n",
       "Reges_I.15.31                 0                 0                 0\n",
       "Reges_I.16.5                  0                 0                 0\n",
       "Reges_I.16.14                 0                 0                 0\n",
       "              Chronica_II.34.23 Chronica_II.34.24 Chronica_II.34.25\n",
       "Reges_I.11.41                 0                 0                 0\n",
       "Reges_I.14.29                 0                 0                 0\n",
       "Reges_I.15.7                  0                 0                 0\n",
       "Reges_I.15.31                 0                 0                 0\n",
       "Reges_I.16.5                  0                 0                 0\n",
       "Reges_I.16.14                 0                 0                 0\n",
       "              Chronica_II.34.26 Chronica_II.34.27 Chronica_II.34.28\n",
       "Reges_I.11.41                 0                 0                 0\n",
       "Reges_I.14.29                 0                 0                 0\n",
       "Reges_I.15.7                  0                 0                 0\n",
       "Reges_I.15.31                 0                 0                 0\n",
       "Reges_I.16.5                  0                 0                 0\n",
       "Reges_I.16.14                 0                 0                 0\n",
       "              Chronica_II.34.29 Chronica_II.34.30 Chronica_II.34.31\n",
       "Reges_I.11.41                 0                 0                 0\n",
       "Reges_I.14.29                 0                 0                 0\n",
       "Reges_I.15.7                  0                 0                 0\n",
       "Reges_I.15.31                 0                 0                 0\n",
       "Reges_I.16.5                  0                 0                 0\n",
       "Reges_I.16.14                 0                 0                 0\n",
       "              Chronica_II.35.19 Chronica_II.36.5 Chronica_II.36.11\n",
       "Reges_I.11.41                 0                0                 0\n",
       "Reges_I.14.29                 0                0                 0\n",
       "Reges_I.15.7                  0                0                 0\n",
       "Reges_I.15.31                 0                0                 0\n",
       "Reges_I.16.5                  0                0                 0\n",
       "Reges_I.16.14                 0                0                 0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = read.table(\n",
    "    'new_matrix.tsv', \n",
    "    sep=\"\\t\", \n",
    "    header=TRUE, \n",
    "    comment.char=\"\",\n",
    "    quote=\"\",\n",
    "    as.is = FALSE,\n",
    ")\n",
    "rownames(data) = data[, 1]\n",
    "data[, 1] = NULL\n",
    "dim(data)\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In install.packages(\"lattice\", repos = REPO): installation of package ‘lattice’ had non-zero exit status"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded source packages are in\n",
      "\t‘/private/var/folders/mr/v68m5kjn375c46fmlw70r4wh0000gn/T/RtmpOyRtnL/downloaded_packages’\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updating HTML index of packages in '.Library'\n",
      "Making 'packages.html' ... done\n"
     ]
    }
   ],
   "source": [
    "REPO = 'http://cran.us.r-project.org'\n",
    "#install.packages('gplots', repos=REPO)\n",
    "install.packages('lattice', repos=REPO)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require(gplots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "library(lattice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levelplot(as.matrix(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table width=\"100%\" summary=\"page for heatmap.2 {gplots}\"><tr><td>heatmap.2 {gplots}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
       "\n",
       "<h2> Enhanced Heat Map </h2>\n",
       "\n",
       "<h3>Description</h3>\n",
       "\n",
       "<p>A heat map is a false color image (basically\n",
       "<code>image(t(x))</code>) with a dendrogram added to the left side\n",
       "and/or to the top.  Typically, reordering of the rows and columns\n",
       "according to some set of values (row or column means) within the\n",
       "restrictions imposed by the dendrogram is carried out.\n",
       "</p>\n",
       "<p>This heatmap provides a number of extensions to the standard R\n",
       "<code>heatmap</code> function.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Usage</h3>\n",
       "\n",
       "<pre>\n",
       "heatmap.2 (x,\n",
       "\n",
       "           # dendrogram control\n",
       "           Rowv = TRUE,\n",
       "           Colv=if(symm)\"Rowv\" else TRUE,\n",
       "           distfun = dist,\n",
       "           hclustfun = hclust,\n",
       "           dendrogram = c(\"both\",\"row\",\"column\",\"none\"),\n",
       "           reorderfun = function(d, w) reorder(d, w),\n",
       "           symm = FALSE,\n",
       "\n",
       "           # data scaling\n",
       "           scale = c(\"none\",\"row\", \"column\"),\n",
       "           na.rm=TRUE,\n",
       "\n",
       "           # image plot\n",
       "           revC = identical(Colv, \"Rowv\"),\n",
       "           add.expr,\n",
       "\n",
       "           # mapping data to colors\n",
       "           breaks,\n",
       "           symbreaks=any(x &lt; 0, na.rm=TRUE) || scale!=\"none\",\n",
       "\n",
       "           # colors\n",
       "           col=\"heat.colors\",\n",
       "\n",
       "           # block sepration\n",
       "           colsep,\n",
       "           rowsep,\n",
       "           sepcolor=\"white\",\n",
       "           sepwidth=c(0.05,0.05),\n",
       "\n",
       "           # cell labeling\n",
       "           cellnote,\n",
       "           notecex=1.0,\n",
       "           notecol=\"cyan\",\n",
       "           na.color=par(\"bg\"),\n",
       "\n",
       "           # level trace\n",
       "           trace=c(\"column\",\"row\",\"both\",\"none\"),\n",
       "           tracecol=\"cyan\",\n",
       "           hline=median(breaks),\n",
       "           vline=median(breaks),\n",
       "           linecol=tracecol,\n",
       "\n",
       "           # Row/Column Labeling\n",
       "           margins = c(5, 5),\n",
       "           ColSideColors,\n",
       "           RowSideColors,\n",
       "           cexRow = 0.2 + 1/log10(nr),\n",
       "           cexCol = 0.2 + 1/log10(nc),\n",
       "           labRow = NULL,\n",
       "           labCol = NULL,\n",
       "           srtRow = NULL,\n",
       "           srtCol = NULL,\n",
       "           adjRow = c(0,NA),\n",
       "           adjCol = c(NA,0),\n",
       "           offsetRow = 0.5,\n",
       "           offsetCol = 0.5,\n",
       "           colRow = NULL,\n",
       "           colCol = NULL,\n",
       "\n",
       "           # color key + density info\n",
       "           key = TRUE,\n",
       "           keysize = 1.5,\n",
       "           density.info=c(\"histogram\",\"density\",\"none\"),\n",
       "           denscol=tracecol,\n",
       "           symkey = any(x &lt; 0, na.rm=TRUE) || symbreaks,\n",
       "           densadj = 0.25,\n",
       "           key.title = NULL,\n",
       "           key.xlab = NULL,\n",
       "           key.ylab = NULL,\n",
       "           key.xtickfun = NULL,\n",
       "           key.ytickfun = NULL,\n",
       "           key.par=list(),\n",
       "\n",
       "           # plot labels\n",
       "           main = NULL,\n",
       "           xlab = NULL,\n",
       "           ylab = NULL,\n",
       "\n",
       "           # plot layout\n",
       "           lmat = NULL,\n",
       "           lhei = NULL,\n",
       "           lwid = NULL,\n",
       "\n",
       "           # extras\n",
       "           extrafun=NULL,\n",
       "           ...\n",
       "           )\n",
       "           </pre>\n",
       "\n",
       "\n",
       "<h3>Arguments</h3>\n",
       "\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>x</code></td>\n",
       "<td>\n",
       "<p>numeric matrix of the values to be plotted. </p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>Rowv</code></td>\n",
       "<td>\n",
       "<p>determines if and how the <em>row</em> dendrogram should be\n",
       "reordered.\tBy default, it is TRUE, which implies dendrogram is\n",
       "computed and reordered based on row means. If NULL or FALSE, then no\n",
       "dendrogram is computed and no reordering is done. If a\n",
       "<code>dendrogram</code>, then it is used &quot;as-is&quot;, ie\n",
       "without any reordering. If a vector of integers, then dendrogram is\n",
       "computed and reordered based on the order of the vector.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>Colv</code></td>\n",
       "<td>\n",
       "<p>determines if and how the <em>column</em> dendrogram should\n",
       "be reordered.\tHas the options as the <code>Rowv</code> argument above and\n",
       "<em>additionally</em> when <code>x</code> is a square matrix,\n",
       "<code>Colv=\"Rowv\"</code> means that columns should be treated identically\n",
       "to the rows.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>distfun</code></td>\n",
       "<td>\n",
       "<p>function used to compute the distance (dissimilarity)\n",
       "between both rows and columns.  Defaults to <code>dist</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>hclustfun</code></td>\n",
       "<td>\n",
       "<p>function used to compute the hierarchical clustering\n",
       "when <code>Rowv</code> or <code>Colv</code> are not dendrograms.  Defaults to\n",
       "<code>hclust</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>dendrogram</code></td>\n",
       "<td>\n",
       "<p>character string indicating whether to draw 'none',\n",
       "'row', 'column' or 'both' dendrograms.  Defaults to 'both'. However,\n",
       "if Rowv (or Colv) is FALSE or NULL and dendrogram is 'both', then a\n",
       "warning is issued and Rowv (or Colv) arguments are honoured.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>reorderfun</code></td>\n",
       "<td>\n",
       "<p><code>function(d, w)</code> of dendrogram and weights for\n",
       "reordering the row and column dendrograms.  The default uses\n",
       "<code>stats{reorder.dendrogram}</code> </p>\n",
       "</td></tr></table>\n",
       "<p>.\n",
       "</p>\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>symm</code></td>\n",
       "<td>\n",
       "<p>logical indicating if <code>x</code> should be treated\n",
       "<b>symm</b>etrically; can only be true when <code>x</code> is a\n",
       "square matrix.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>scale</code></td>\n",
       "<td>\n",
       "<p>character indicating if the values should be centered and\n",
       "scaled in either the row direction or the column direction, or\n",
       "none.  The default is <code>\"none\"</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>na.rm</code></td>\n",
       "<td>\n",
       "<p>logical indicating whether <code>NA</code>'s should be removed.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>revC</code></td>\n",
       "<td>\n",
       "<p>logical indicating if the column order should be\n",
       "<code>rev</code>ersed for plotting, such that e.g., for the\n",
       "symmetric case, the symmetry axis is as usual.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>add.expr</code></td>\n",
       "<td>\n",
       "<p>expression that will be evaluated after the call to\n",
       "<code>image</code>.  Can be used to add components to the plot.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>breaks</code></td>\n",
       "<td>\n",
       "<p>(optional) Either a numeric vector indicating the\n",
       "splitting points for binning <code>x</code> into colors, or a integer\n",
       "number of break points to be used, in which case the break points\n",
       "will be spaced equally between <code>min(x)</code> and <code>max(x)</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>symbreaks</code></td>\n",
       "<td>\n",
       "<p>Boolean indicating whether breaks should be\n",
       "made symmetric about 0. Defaults to <code>TRUE</code> if the data includes\n",
       "negative values, and to <code>FALSE</code> otherwise.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>col</code></td>\n",
       "<td>\n",
       "<p>colors used for the image. Defaults to heat colors\n",
       "(<code>heat.colors</code>).</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>colsep, rowsep, sepcolor</code></td>\n",
       "<td>\n",
       "<p>(optional) vector of integers\n",
       "indicating which columns or rows should be separated from the\n",
       "preceding columns or rows by a narrow space of color\n",
       "<code>sepcolor</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>sepwidth</code></td>\n",
       "<td>\n",
       "<p>(optional) Vector of length 2 giving the width\n",
       "(colsep) or height (rowsep) the separator box drawn by colsep and\n",
       "rowsep as a function of the width (colsep) or height (rowsep) of a\n",
       "cell. Defaults to <code>c(0.05, 0.05)</code></p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>cellnote</code></td>\n",
       "<td>\n",
       "<p>(optional) matrix of character strings which will be\n",
       "placed within each color cell, e.g. p-value symbols.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>notecex</code></td>\n",
       "<td>\n",
       "<p>(optional) numeric scaling factor for <code>cellnote</code>\n",
       "items.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>notecol</code></td>\n",
       "<td>\n",
       "<p>(optional) character string specifying the color for\n",
       "<code>cellnote</code> text.  Defaults to &quot;cyan&quot;.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>na.color</code></td>\n",
       "<td>\n",
       "<p>Color to use for missing value (<code>NA</code>). Defaults\n",
       "to the plot background color.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>trace</code></td>\n",
       "<td>\n",
       "<p>character string indicating whether a solid &quot;trace&quot; line\n",
       "should be drawn across 'row's or down 'column's, 'both' or 'none'.\n",
       "The distance of the line from the center of each color-cell is\n",
       "proportional to the size of the measurement. Defaults to 'column'.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>tracecol</code></td>\n",
       "<td>\n",
       "<p>character string giving the color for &quot;trace&quot;\n",
       "line. Defaults to &quot;cyan&quot;.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>hline, vline, linecol</code></td>\n",
       "<td>\n",
       "<p>Vector of values within cells where a\n",
       "horizontal or vertical dotted line should be drawn.  The color of\n",
       "the line is controlled by <code>linecol</code>.  Horizontal  lines are only\n",
       "plotted if <code>trace</code> is 'row' or 'both'.  Vertical lines are only\n",
       "drawn if <code>trace</code> 'column' or 'both'.   <code>hline</code> and\n",
       "<code>vline</code> default to the median of the breaks, <code>linecol</code>\n",
       "defaults to the value of <code>tracecol</code>.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>margins</code></td>\n",
       "<td>\n",
       "<p>numeric vector of length 2 containing the margins\n",
       "(see <code>par(mar= *)</code>) for column and row names,\n",
       "respectively.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>ColSideColors</code></td>\n",
       "<td>\n",
       "<p>(optional) character vector of length\n",
       "<code>ncol(x)</code> containing the color names for a horizontal side bar\n",
       "that may be used to annotate the columns of <code>x</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>RowSideColors</code></td>\n",
       "<td>\n",
       "<p>(optional) character vector of length\n",
       "<code>nrow(x)</code> containing the color names for a vertical side bar\n",
       "that may be used to annotate the rows of <code>x</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>cexRow, cexCol</code></td>\n",
       "<td>\n",
       "<p>positive numbers, used as <code>cex.axis</code> in\n",
       "for the row or column axis labeling.  The defaults currently only\n",
       "use number of rows or columns, respectively.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>labRow, labCol</code></td>\n",
       "<td>\n",
       "<p>character vectors with row and column labels to\n",
       "use; these default to <code>rownames(x)</code> or <code>colnames(x)</code>,\n",
       "respectively.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>srtRow, srtCol</code></td>\n",
       "<td>\n",
       "<p>angle of row/column labels, in degrees from\n",
       "horizontal</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>adjRow, adjCol</code></td>\n",
       "<td>\n",
       "<p>2-element vector giving the (left-right,\n",
       "top-bottom) justification of row/column labels (relative to the text\n",
       "orientation).</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>offsetRow, offsetCol</code></td>\n",
       "<td>\n",
       "<p>Number of character-width spaces to\n",
       "place between row/column labels and the edge of the plotting\n",
       "region.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>colRow, colCol</code></td>\n",
       "<td>\n",
       "<p>color of row/column labels, either a scalar to\n",
       "set the color of all labels the same, or a vector providing the\n",
       "colors of each label item</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>key</code></td>\n",
       "<td>\n",
       "<p>logical indicating whether a color-key should be shown.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>keysize</code></td>\n",
       "<td>\n",
       "<p>numeric value indicating the size of the key</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>density.info</code></td>\n",
       "<td>\n",
       "<p>character string indicating whether to superimpose\n",
       "a 'histogram', a 'density' plot, or no plot ('none') on the\n",
       "color-key.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>denscol</code></td>\n",
       "<td>\n",
       "<p>character string giving the color for the density\n",
       "display specified by <code>density.info</code>, defaults to the same value\n",
       "as <code>tracecol</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>symkey</code></td>\n",
       "<td>\n",
       "<p>Boolean indicating whether the color key should be\n",
       "made symmetric about 0. Defaults to <code>TRUE</code> if the data includes\n",
       "negative values, and to <code>FALSE</code> otherwise.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>densadj</code></td>\n",
       "<td>\n",
       "<p>Numeric scaling value for tuning the kernel width when\n",
       "a density plot is drawn on the color key.  (See the <code>adjust</code>\n",
       "parameter for the <code>density</code> function for details.)  Defaults to\n",
       "0.25.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>key.title</code></td>\n",
       "<td>\n",
       "<p>main title of the color key. If set to NA no title\n",
       "will be plotted.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>key.xlab</code></td>\n",
       "<td>\n",
       "<p>x axis label of the color key. If set to NA no label\n",
       "will be plotted.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>key.ylab</code></td>\n",
       "<td>\n",
       "<p>y axis label of the color key. If set to NA no label\n",
       "will be plotted.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>key.xtickfun</code></td>\n",
       "<td>\n",
       "<p>function computing tick location and labels for\n",
       "the xaxis of the color key. Returns a named list containing\n",
       "parameters that can be passed to <code>axis</code>. See examples.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>key.ytickfun</code></td>\n",
       "<td>\n",
       "<p>function computing tick location and labels for\n",
       "the y axis of the color key. Returns a named list containing\n",
       "parameters that can be passed to <code>axis</code>.  See examples.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>key.par</code></td>\n",
       "<td>\n",
       "<p>graphical parameters for the color key. Named list that\n",
       "can be passed to <code>par</code>.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>main, xlab, ylab</code></td>\n",
       "<td>\n",
       "<p>main, x- and y-axis titles; defaults to none.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>lmat, lhei, lwid</code></td>\n",
       "<td>\n",
       "<p>visual layout: position matrix, column height,\n",
       "column width.  See below for details</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>extrafun</code></td>\n",
       "<td>\n",
       "<p>A function to be called after all other work. See\n",
       "examples.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>...</code></td>\n",
       "<td>\n",
       "<p>additional arguments passed on to <code>image</code> </p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "<h3>Details</h3>\n",
       "\n",
       "<p>If either <code>Rowv</code> or <code>Colv</code> are dendrograms they are honored\n",
       "(and not reordered).\tOtherwise, dendrograms are computed as\n",
       "<code>dd &lt;- as.dendrogram(hclustfun(distfun(X)))</code> where <code>X</code> is\n",
       "either <code>x</code> or <code>t(x)</code>.<br />\n",
       "</p>\n",
       "<p>If either is a vector (of &ldquo;weights&rdquo;) then the appropriate\n",
       "dendrogram is reordered according to the supplied values subject to\n",
       "the constraints imposed by the dendrogram, by <code>reorder(dd,\n",
       "    Rowv)</code>, in the row case.\n",
       "\n",
       "If either is missing, as by default, then the ordering of the\n",
       "corresponding dendrogram is by the mean value of the rows/columns,\n",
       "i.e., in the case of rows, <code>Rowv &lt;- rowMeans(x, na.rm=na.rm)</code>.\n",
       "\n",
       "If either is <code>NULL</code>, <em>no reordering</em> will be done for\n",
       "the corresponding side.\n",
       "</p>\n",
       "<p>If <code>scale=\"row\"</code> (or <code>scale=\"col\"</code>) the rows (columns) are\n",
       "scaled to have mean zero and standard deviation one.  There is some\n",
       "empirical evidence from genomic plotting that this is useful.\n",
       "</p>\n",
       "<p>The default colors range from red to white (<code>heat.colors</code>) and\n",
       "are not pretty.  Consider using enhancements such as the\n",
       "<span class=\"pkg\">RColorBrewer</span> package,\n",
       "<a href=\"http://cran.r-project.org/web/packages/RColorBrewer\">http://cran.r-project.org/web/packages/RColorBrewer</a>\n",
       "to select better colors.\n",
       "</p>\n",
       "<p>By default four components will be displayed in the plot.  At the top\n",
       "left is the color key, top right is the column dendrogram, bottom left\n",
       "is the row dendrogram, bottom right is the image plot.  When\n",
       "RowSideColor or ColSideColor are provided, an additional row or column\n",
       "is inserted in the appropriate location.   This layout can be\n",
       "overriden by specifiying appropriate values for <code>lmat</code>,\n",
       "<code>lwid</code>, and <code>lhei</code>.  <code>lmat</code> controls the relative\n",
       "postition of each element, while <code>lwid</code> controls the column\n",
       "width, and <code>lhei</code> controls the row height. See the help page for\n",
       "<code>layout</code> for details on how to use these\n",
       "arguments.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Value</h3>\n",
       "\n",
       "<p>Invisibly, a list with components\n",
       "</p>\n",
       "<table summary=\"R valueblock\">\n",
       "<tr valign=\"top\"><td><code>rowInd</code></td>\n",
       "<td>\n",
       "<p>row index permutation vector as returned by\n",
       "<code>order.dendrogram</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>colInd</code></td>\n",
       "<td>\n",
       "<p>column index permutation vector.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>call</code></td>\n",
       "<td>\n",
       "<p>the matched call</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>rowMeans, rowSDs</code></td>\n",
       "<td>\n",
       "<p>mean and standard deviation of each row: only\n",
       "present if <code>scale=\"row\"</code></p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>colMeans, colSDs</code></td>\n",
       "<td>\n",
       "<p>mean and standard deviation of each column: only\n",
       "present if <code>scale=\"column\"</code></p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>carpet</code></td>\n",
       "<td>\n",
       "<p>reordered and scaled 'x' values used generate the main\n",
       "'carpet'</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>rowDendrogram</code></td>\n",
       "<td>\n",
       "<p>row dendrogram, if present</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>colDendrogram</code></td>\n",
       "<td>\n",
       "<p>column dendrogram, if present</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>breaks</code></td>\n",
       "<td>\n",
       "<p>values used for color break points</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>col</code></td>\n",
       "<td>\n",
       "<p>colors used</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>vline</code></td>\n",
       "<td>\n",
       "<p>center-line value used for column trace, present only if\n",
       "<code>trace=\"both\"</code> or <code>trace=\"column\"</code> </p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>hline</code></td>\n",
       "<td>\n",
       "<p>center-line value used for row trace, present only if\n",
       "<code>trace=\"both\"</code> or <code>trace=\"row\"</code> </p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>colorTable</code></td>\n",
       "<td>\n",
       "<p>A three-column data frame providing the lower and upper\n",
       "bound and color for each bin</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "<h3>Note</h3>\n",
       "\n",
       "<p>The original rows and columns are reordered to match the dendrograms\n",
       "<code>Rowv</code> and <code>Colv</code> (if present).\n",
       "</p>\n",
       "<p><code>heatmap.2()</code> uses <code>layout</code> to arragent the plot\n",
       "elements. Consequentially, it can <b>not</b> be used in a multi\n",
       "column/row layout using <code>layout(...)</code>,\n",
       "<code>par(mfrow=...)</code> or <code>(mfcol=...)</code>.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Author(s)</h3>\n",
       "\n",
       "<p>Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber,\n",
       "G. Warnes, revisions.</p>\n",
       "\n",
       "\n",
       "<h3>See Also</h3>\n",
       "\n",
       "<p><code>image</code>, <code>hclust</code></p>\n",
       "\n",
       "\n",
       "<h3>Examples</h3>\n",
       "\n",
       "<pre>\n",
       " data(mtcars)\n",
       " x  &lt;- as.matrix(mtcars)\n",
       " rc &lt;- rainbow(nrow(x), start=0, end=.3)\n",
       " cc &lt;- rainbow(ncol(x), start=0, end=.3)\n",
       "\n",
       " ##\n",
       " ## demonstrate the effect of row and column dendrogram options\n",
       " ##\n",
       " heatmap.2(x)                    ## default - dendrogram plotted and reordering done.\n",
       " heatmap.2(x, dendrogram=\"none\") ##  no dendrogram plotted, but reordering done.\n",
       " heatmap.2(x, dendrogram=\"row\")  ## row dendrogram plotted and row reordering done.\n",
       " heatmap.2(x, dendrogram=\"col\")  ## col dendrogram plotted and col reordering done.\n",
       "\n",
       " heatmap.2(x, keysize=2)         ## default - dendrogram plotted and reordering done.\n",
       "\n",
       " heatmap.2(x, Rowv=FALSE, dendrogram=\"both\") ## generates a warning!\n",
       " heatmap.2(x, Rowv=NULL, dendrogram=\"both\")  ## generates a warning!\n",
       " heatmap.2(x, Colv=FALSE, dendrogram=\"both\") ## generates a warning!\n",
       "\n",
       " ## Reorder dendrogram by branch means rather than sums\n",
       " heatmap.2(x, reorderfun=function(d, w) reorder(d, w, agglo.FUN = mean) )\n",
       "\n",
       " ## plot a sub-cluster using the same color coding as for the full heatmap\n",
       " full &lt;- heatmap.2(x)\n",
       " heatmap.2(x, Colv=full$colDendrogram[[2]], breaks=full$breaks)  # column subset\n",
       " heatmap.2(x, Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # row subset\n",
       " heatmap.2(x, Colv=full$colDendrogram[[2]],\n",
       "              Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # both\n",
       "\n",
       " ## Show effect of row and column label rotation\n",
       " heatmap.2(x, srtCol=NULL)\n",
       " heatmap.2(x, srtCol=0,   adjCol = c(0.5,1) )\n",
       " heatmap.2(x, srtCol=45,  adjCol = c(1,1)   )\n",
       " heatmap.2(x, srtCol=135, adjCol = c(1,0)   )\n",
       " heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )\n",
       " heatmap.2(x, srtCol=225, adjCol = c(0,0)   ) ## not very useful\n",
       " heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )\n",
       " heatmap.2(x, srtCol=315, adjCol = c(0,1)   )\n",
       " heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )\n",
       "\n",
       " heatmap.2(x, srtRow=45, adjRow=c(0, 1) )\n",
       " heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )\n",
       " heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )\n",
       "\n",
       "\n",
       " ## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is\n",
       " ## not also present)\n",
       " heatmap.2(x, offsetRow=0, offsetCol=0)\n",
       " heatmap.2(x, offsetRow=1, offsetCol=1)\n",
       " heatmap.2(x, offsetRow=2, offsetCol=2)\n",
       " heatmap.2(x, offsetRow=-1, offsetCol=-1)\n",
       "\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)\n",
       "\n",
       "\n",
       " ## Show how to use 'extrafun' to replace the 'key' with a scatterplot\n",
       " lmat &lt;- rbind( c(5,3,4), c(2,1,4) )\n",
       " lhei &lt;- c(1.5, 4)\n",
       " lwid &lt;- c(1.5, 4, 0.75)\n",
       "\n",
       " myplot &lt;- function() {\n",
       "             oldpar &lt;- par(\"mar\")\n",
       "             par(mar=c(5.1, 4.1, 0.5, 0.5))\n",
       "             plot(mpg ~ hp, data=x)\n",
       "           }\n",
       "\n",
       " heatmap.2(x, lmat=lmat, lhei=lhei, lwid=lwid, key=FALSE, extrafun=myplot)\n",
       "\n",
       " ## show how to customize the color key\n",
       " heatmap.2(x,\n",
       "           key.title=NA, # no title\n",
       "           key.xlab=NA,  # no xlab\n",
       "           key.par=list(mgp=c(1.5, 0.5, 0),\n",
       "                        mar=c(2.5, 2.5, 1, 0)),\n",
       "           key.xtickfun=function() {\n",
       "                 breaks &lt;- parent.frame()$breaks\n",
       "                 return(list(\n",
       "                      at=parent.frame()$scale01(c(breaks[1],\n",
       "                                                  breaks[length(breaks)])),\n",
       "                      labels=c(as.character(breaks[1]),\n",
       "                               as.character(breaks[length(breaks)]))\n",
       "                      ))\n",
       "           })\n",
       "\n",
       " heatmap.2(x,\n",
       "          breaks=256,\n",
       "          key.title=NA,\n",
       "          key.xlab=NA,\n",
       "          key.par=list(mgp=c(1.5, 0.5, 0),\n",
       "                       mar=c(1, 2.5, 1, 0)),\n",
       "          key.xtickfun=function() {\n",
       "               cex &lt;- par(\"cex\")*par(\"cex.axis\")\n",
       "               side &lt;- 1\n",
       "               line &lt;- 0\n",
       "               col &lt;- par(\"col.axis\")\n",
       "               font &lt;- par(\"font.axis\")\n",
       "               mtext(\"low\", side=side, at=0, adj=0,\n",
       "                     line=line, cex=cex, col=col, font=font)\n",
       "               mtext(\"high\", side=side, at=1, adj=1,\n",
       "                     line=line, cex=cex, col=col, font=font)\n",
       "               return(list(labels=FALSE, tick=FALSE))\n",
       "          })\n",
       "\n",
       "\n",
       " ##\n",
       " ## Show effect of z-score scaling within columns, blue-red color scale\n",
       " ##\n",
       " hv &lt;- heatmap.2(x, col=bluered, scale=\"column\", tracecol=\"#303030\")\n",
       "\n",
       " ###\n",
       " ## Look at the return values\n",
       " ###\n",
       " names(hv)\n",
       "\n",
       " ## Show the mapping of z-score values to color bins\n",
       " hv$colorTable\n",
       "\n",
       " ## Extract the range associated with white\n",
       " hv$colorTable[hv$colorTable[,\"color\"]==\"#FFFFFF\",]\n",
       "\n",
       " ## Determine the original data values that map to white\n",
       " whiteBin &lt;- unlist(hv$colorTable[hv$colorTable[,\"color\"]==\"#FFFFFF\",1:2])\n",
       " rbind(whiteBin[1] * hv$colSDs + hv$colMeans,\n",
       "       whiteBin[2] * hv$colSDs + hv$colMeans )\n",
       " ##\n",
       " ## A more decorative heatmap, with z-score scaling along columns\n",
       " ##\n",
       " hv &lt;- heatmap.2(x, col=cm.colors(255), scale=\"column\",\n",
       "\t       RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),\n",
       "\t       xlab=\"specification variables\", ylab= \"Car Models\",\n",
       "\t       main=\"heatmap(&lt;Mtcars data&gt;, ..., scale=\\\"column\\\")\",\n",
       "         tracecol=\"green\", density=\"density\")\n",
       " ## Note that the breakpoints are now symmetric about 0\n",
       "\n",
       " ## Color the labels to match RowSideColors and ColSideColors\n",
       " hv &lt;- heatmap.2(x, col=cm.colors(255), scale=\"column\",\n",
       "         RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),\n",
       "\t       xlab=\"specification variables\", ylab= \"Car Models\",\n",
       "\t       main=\"heatmap(&lt;Mtcars data&gt;, ..., scale=\\\"column\\\")\",\n",
       "         tracecol=\"green\", density=\"density\", colRow=rc, colCol=cc,\n",
       "         srtCol=45, adjCol=c(0.5,1))\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       " data(attitude)\n",
       " round(Ca &lt;- cor(attitude), 2)\n",
       " symnum(Ca) # simple graphic\n",
       "\n",
       " # with reorder\n",
       " heatmap.2(Ca, \t\t symm=TRUE, margin=c(6, 6), trace=\"none\" )\n",
       "\n",
       " # without reorder\n",
       " heatmap.2(Ca, Rowv=FALSE, symm=TRUE, margin=c(6, 6), trace=\"none\" )\n",
       "\n",
       " ## Place the color key below the image plot\n",
       " heatmap.2(x, lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(1.5, 4, 2 ) )\n",
       "\n",
       " ## Place the color key to the top right of the image plot\n",
       " heatmap.2(x, lmat=rbind( c(0, 3, 4), c(2,1,0 ) ), lwid=c(1.5, 4, 2 ) )\n",
       "\n",
       " ## For variable clustering, rather use distance based on cor():\n",
       " data(USJudgeRatings)\n",
       " symnum( cU &lt;- cor(USJudgeRatings) )\n",
       "\n",
       " hU &lt;- heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=topo.colors(16),\n",
       "              distfun=function(c) as.dist(1 - c), trace=\"none\")\n",
       "\n",
       " ## The Correlation matrix with same reordering:\n",
       " hM &lt;- format(round(cU, 2))\n",
       " hM\n",
       "\n",
       " # now with the correlation matrix on the plot itself\n",
       "\n",
       " heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=rev(heat.colors(16)),\n",
       "             distfun=function(c) as.dist(1 - c), trace=\"none\",\n",
       "             cellnote=hM)\n",
       "\n",
       " ## genechip data examples\n",
       " ## Not run: \n",
       " library(affy)\n",
       " data(SpikeIn)\n",
       " pms &lt;- SpikeIn@pm\n",
       "\n",
       " # just the data, scaled across rows\n",
       " heatmap.2(pms, col=rev(heat.colors(16)), main=\"SpikeIn@pm\",\n",
       "              xlab=\"Relative Concentration\", ylab=\"Probeset\",\n",
       "              scale=\"row\")\n",
       "\n",
       " # fold change vs \"12.50\" sample\n",
       " data &lt;- pms / pms[, \"12.50\"]\n",
       " data &lt;- ifelse(data&gt;1, data, -1/data)\n",
       " heatmap.2(data, breaks=16, col=redgreen, tracecol=\"blue\",\n",
       "               main=\"SpikeIn@pm Fold Changes\\nrelative to 12.50 sample\",\n",
       "               xlab=\"Relative Concentration\", ylab=\"Probeset\")\n",
       " \n",
       "## End(Not run)\n",
       "\n",
       "</pre>\n",
       "\n",
       "<hr /><div style=\"text-align: center;\">[Package <em>gplots</em> version 2.17.0 ]</div>"
      ],
      "text/latex": [
       "\\inputencoding{utf8}\n",
       "\\HeaderA{heatmap.2}{ Enhanced Heat Map }{heatmap.2}\n",
       "\\keyword{hplot}{heatmap.2}\n",
       "%\n",
       "\\begin{Description}\\relax\n",
       "A heat map is a false color image (basically\n",
       "\\code{\\LinkA{image}{image}(t(x))}) with a dendrogram added to the left side\n",
       "and/or to the top.  Typically, reordering of the rows and columns\n",
       "according to some set of values (row or column means) within the\n",
       "restrictions imposed by the dendrogram is carried out.\n",
       "\n",
       "This heatmap provides a number of extensions to the standard R\n",
       "\\code{\\LinkA{heatmap}{heatmap}} function.\n",
       "\\end{Description}\n",
       "%\n",
       "\\begin{Usage}\n",
       "\\begin{verbatim}\n",
       "heatmap.2 (x,\n",
       "\n",
       "           # dendrogram control\n",
       "           Rowv = TRUE,\n",
       "           Colv=if(symm)\"Rowv\" else TRUE,\n",
       "           distfun = dist,\n",
       "           hclustfun = hclust,\n",
       "           dendrogram = c(\"both\",\"row\",\"column\",\"none\"),\n",
       "           reorderfun = function(d, w) reorder(d, w),\n",
       "           symm = FALSE,\n",
       "\n",
       "           # data scaling\n",
       "           scale = c(\"none\",\"row\", \"column\"),\n",
       "           na.rm=TRUE,\n",
       "\n",
       "           # image plot\n",
       "           revC = identical(Colv, \"Rowv\"),\n",
       "           add.expr,\n",
       "\n",
       "           # mapping data to colors\n",
       "           breaks,\n",
       "           symbreaks=any(x < 0, na.rm=TRUE) || scale!=\"none\",\n",
       "\n",
       "           # colors\n",
       "           col=\"heat.colors\",\n",
       "\n",
       "           # block sepration\n",
       "           colsep,\n",
       "           rowsep,\n",
       "           sepcolor=\"white\",\n",
       "           sepwidth=c(0.05,0.05),\n",
       "\n",
       "           # cell labeling\n",
       "           cellnote,\n",
       "           notecex=1.0,\n",
       "           notecol=\"cyan\",\n",
       "           na.color=par(\"bg\"),\n",
       "\n",
       "           # level trace\n",
       "           trace=c(\"column\",\"row\",\"both\",\"none\"),\n",
       "           tracecol=\"cyan\",\n",
       "           hline=median(breaks),\n",
       "           vline=median(breaks),\n",
       "           linecol=tracecol,\n",
       "\n",
       "           # Row/Column Labeling\n",
       "           margins = c(5, 5),\n",
       "           ColSideColors,\n",
       "           RowSideColors,\n",
       "           cexRow = 0.2 + 1/log10(nr),\n",
       "           cexCol = 0.2 + 1/log10(nc),\n",
       "           labRow = NULL,\n",
       "           labCol = NULL,\n",
       "           srtRow = NULL,\n",
       "           srtCol = NULL,\n",
       "           adjRow = c(0,NA),\n",
       "           adjCol = c(NA,0),\n",
       "           offsetRow = 0.5,\n",
       "           offsetCol = 0.5,\n",
       "           colRow = NULL,\n",
       "           colCol = NULL,\n",
       "\n",
       "           # color key + density info\n",
       "           key = TRUE,\n",
       "           keysize = 1.5,\n",
       "           density.info=c(\"histogram\",\"density\",\"none\"),\n",
       "           denscol=tracecol,\n",
       "           symkey = any(x < 0, na.rm=TRUE) || symbreaks,\n",
       "           densadj = 0.25,\n",
       "           key.title = NULL,\n",
       "           key.xlab = NULL,\n",
       "           key.ylab = NULL,\n",
       "           key.xtickfun = NULL,\n",
       "           key.ytickfun = NULL,\n",
       "           key.par=list(),\n",
       "\n",
       "           # plot labels\n",
       "           main = NULL,\n",
       "           xlab = NULL,\n",
       "           ylab = NULL,\n",
       "\n",
       "           # plot layout\n",
       "           lmat = NULL,\n",
       "           lhei = NULL,\n",
       "           lwid = NULL,\n",
       "\n",
       "           # extras\n",
       "           extrafun=NULL,\n",
       "           ...\n",
       "           )\n",
       "           \n",
       "\\end{verbatim}\n",
       "\\end{Usage}\n",
       "%\n",
       "\\begin{Arguments}\n",
       "\\begin{ldescription}\n",
       "\n",
       "\\item[\\code{x}] numeric matrix of the values to be plotted. \n",
       "\\item[\\code{Rowv}] determines if and how the \\emph{row} dendrogram should be\n",
       "reordered.\tBy default, it is TRUE, which implies dendrogram is\n",
       "computed and reordered based on row means. If NULL or FALSE, then no\n",
       "dendrogram is computed and no reordering is done. If a\n",
       "\\code{\\LinkA{dendrogram}{dendrogram}}, then it is used \"as-is\", ie\n",
       "without any reordering. If a vector of integers, then dendrogram is\n",
       "computed and reordered based on the order of the vector.\n",
       "\\item[\\code{Colv}] determines if and how the \\emph{column} dendrogram should\n",
       "be reordered.\tHas the options as the \\code{Rowv} argument above and\n",
       "\\emph{additionally} when \\code{x} is a square matrix,\n",
       "\\code{Colv=\"Rowv\"} means that columns should be treated identically\n",
       "to the rows.\n",
       "\\item[\\code{distfun}] function used to compute the distance (dissimilarity)\n",
       "between both rows and columns.  Defaults to \\code{\\LinkA{dist}{dist}}.\n",
       "\\item[\\code{hclustfun}] function used to compute the hierarchical clustering\n",
       "when \\code{Rowv} or \\code{Colv} are not dendrograms.  Defaults to\n",
       "\\code{\\LinkA{hclust}{hclust}}.\n",
       "\\item[\\code{dendrogram}] character string indicating whether to draw 'none',\n",
       "'row', 'column' or 'both' dendrograms.  Defaults to 'both'. However,\n",
       "if Rowv (or Colv) is FALSE or NULL and dendrogram is 'both', then a\n",
       "warning is issued and Rowv (or Colv) arguments are honoured.\n",
       "\\item[\\code{reorderfun}] \\code{function(d, w)} of dendrogram and weights for\n",
       "reordering the row and column dendrograms.  The default uses\n",
       "\\code{\\LinkA{stats}{stats}\\{reorder.dendrogram\\}} .\n",
       "\\item[\\code{symm}] logical indicating if \\code{x} should be treated\n",
       "\\bold{symm}etrically; can only be true when \\code{x} is a\n",
       "square matrix.\n",
       "\n",
       "\\item[\\code{scale}] character indicating if the values should be centered and\n",
       "scaled in either the row direction or the column direction, or\n",
       "none.  The default is \\code{\"none\"}.\n",
       "\\item[\\code{na.rm}] logical indicating whether \\code{NA}'s should be removed.\n",
       "\n",
       "\\item[\\code{revC}] logical indicating if the column order should be\n",
       "\\code{\\LinkA{rev}{rev}}ersed for plotting, such that e.g., for the\n",
       "symmetric case, the symmetry axis is as usual.\n",
       "\\item[\\code{add.expr}] expression that will be evaluated after the call to\n",
       "\\code{image}.  Can be used to add components to the plot.\n",
       "\\item[\\code{breaks}] (optional) Either a numeric vector indicating the\n",
       "splitting points for binning \\code{x} into colors, or a integer\n",
       "number of break points to be used, in which case the break points\n",
       "will be spaced equally between \\code{min(x)} and \\code{max(x)}.\n",
       "\\item[\\code{symbreaks}] Boolean indicating whether breaks should be\n",
       "made symmetric about 0. Defaults to \\code{TRUE} if the data includes\n",
       "negative values, and to \\code{FALSE} otherwise.\n",
       "\\item[\\code{col}] colors used for the image. Defaults to heat colors\n",
       "(\\code{heat.colors}).\n",
       "\n",
       "\\item[\\code{colsep, rowsep, sepcolor}] (optional) vector of integers\n",
       "indicating which columns or rows should be separated from the\n",
       "preceding columns or rows by a narrow space of color\n",
       "\\code{sepcolor}.\n",
       "\\item[\\code{sepwidth}] (optional) Vector of length 2 giving the width\n",
       "(colsep) or height (rowsep) the separator box drawn by colsep and\n",
       "rowsep as a function of the width (colsep) or height (rowsep) of a\n",
       "cell. Defaults to \\code{c(0.05, 0.05)}\n",
       "\n",
       "\\item[\\code{cellnote}] (optional) matrix of character strings which will be\n",
       "placed within each color cell, e.g. p-value symbols.\n",
       "\\item[\\code{notecex}] (optional) numeric scaling factor for \\code{cellnote}\n",
       "items.\n",
       "\\item[\\code{notecol}] (optional) character string specifying the color for\n",
       "\\code{cellnote} text.  Defaults to \"cyan\".\n",
       "\\item[\\code{na.color}] Color to use for missing value (\\code{NA}). Defaults\n",
       "to the plot background color.\n",
       "\n",
       "\\item[\\code{trace}] character string indicating whether a solid \"trace\" line\n",
       "should be drawn across 'row's or down 'column's, 'both' or 'none'.\n",
       "The distance of the line from the center of each color-cell is\n",
       "proportional to the size of the measurement. Defaults to 'column'.\n",
       "\\item[\\code{tracecol}] character string giving the color for \"trace\"\n",
       "line. Defaults to \"cyan\".\n",
       "\\item[\\code{hline, vline, linecol}] Vector of values within cells where a\n",
       "horizontal or vertical dotted line should be drawn.  The color of\n",
       "the line is controlled by \\code{linecol}.  Horizontal  lines are only\n",
       "plotted if \\code{trace} is 'row' or 'both'.  Vertical lines are only\n",
       "drawn if \\code{trace} 'column' or 'both'.   \\code{hline} and\n",
       "\\code{vline} default to the median of the breaks, \\code{linecol}\n",
       "defaults to the value of \\code{tracecol}.\n",
       "\n",
       "\\item[\\code{margins}] numeric vector of length 2 containing the margins\n",
       "(see \\code{\\LinkA{par}{par}(mar= *)}) for column and row names,\n",
       "respectively.\n",
       "\\item[\\code{ColSideColors}] (optional) character vector of length\n",
       "\\code{ncol(x)} containing the color names for a horizontal side bar\n",
       "that may be used to annotate the columns of \\code{x}.\n",
       "\\item[\\code{RowSideColors}] (optional) character vector of length\n",
       "\\code{nrow(x)} containing the color names for a vertical side bar\n",
       "that may be used to annotate the rows of \\code{x}.\n",
       "\\item[\\code{cexRow, cexCol}] positive numbers, used as \\code{cex.axis} in\n",
       "for the row or column axis labeling.  The defaults currently only\n",
       "use number of rows or columns, respectively.\n",
       "\\item[\\code{labRow, labCol}] character vectors with row and column labels to\n",
       "use; these default to \\code{rownames(x)} or \\code{colnames(x)},\n",
       "respectively.\n",
       "\\item[\\code{srtRow, srtCol}] angle of row/column labels, in degrees from\n",
       "horizontal\n",
       "\\item[\\code{adjRow, adjCol}] 2-element vector giving the (left-right,\n",
       "top-bottom) justification of row/column labels (relative to the text\n",
       "orientation).\n",
       "\\item[\\code{offsetRow, offsetCol}] Number of character-width spaces to\n",
       "place between row/column labels and the edge of the plotting\n",
       "region.\n",
       "\\item[\\code{colRow, colCol}] color of row/column labels, either a scalar to\n",
       "set the color of all labels the same, or a vector providing the\n",
       "colors of each label item\n",
       "\n",
       "\\item[\\code{key}] logical indicating whether a color-key should be shown.\n",
       "\\item[\\code{keysize}] numeric value indicating the size of the key\n",
       "\\item[\\code{density.info}] character string indicating whether to superimpose\n",
       "a 'histogram', a 'density' plot, or no plot ('none') on the\n",
       "color-key.\n",
       "\\item[\\code{denscol}] character string giving the color for the density\n",
       "display specified by \\code{density.info}, defaults to the same value\n",
       "as \\code{tracecol}.\n",
       "\\item[\\code{symkey}] Boolean indicating whether the color key should be\n",
       "made symmetric about 0. Defaults to \\code{TRUE} if the data includes\n",
       "negative values, and to \\code{FALSE} otherwise.\n",
       "\\item[\\code{densadj}] Numeric scaling value for tuning the kernel width when\n",
       "a density plot is drawn on the color key.  (See the \\code{adjust}\n",
       "parameter for the \\code{density} function for details.)  Defaults to\n",
       "0.25.\n",
       "\\item[\\code{key.title}] main title of the color key. If set to NA no title\n",
       "will be plotted.\n",
       "\\item[\\code{key.xlab}] x axis label of the color key. If set to NA no label\n",
       "will be plotted.\n",
       "\\item[\\code{key.ylab}] y axis label of the color key. If set to NA no label\n",
       "will be plotted.\n",
       "\\item[\\code{key.xtickfun}] function computing tick location and labels for\n",
       "the xaxis of the color key. Returns a named list containing\n",
       "parameters that can be passed to \\code{axis}. See examples.\n",
       "\\item[\\code{key.ytickfun}] function computing tick location and labels for\n",
       "the y axis of the color key. Returns a named list containing\n",
       "parameters that can be passed to \\code{axis}.  See examples.\n",
       "\\item[\\code{key.par}] graphical parameters for the color key. Named list that\n",
       "can be passed to \\code{par}.\n",
       "\n",
       "\\item[\\code{main, xlab, ylab}] main, x- and y-axis titles; defaults to none.\n",
       "\n",
       "\\item[\\code{lmat, lhei, lwid}] visual layout: position matrix, column height,\n",
       "column width.  See below for details\n",
       "\\item[\\code{extrafun}] A function to be called after all other work. See\n",
       "examples.\n",
       "\\item[\\code{...}] additional arguments passed on to \\code{\\LinkA{image}{image}} \n",
       "\\end{ldescription}\n",
       "\\end{Arguments}\n",
       "%\n",
       "\\begin{Details}\\relax\n",
       "If either \\code{Rowv} or \\code{Colv} are dendrograms they are honored\n",
       "(and not reordered).\tOtherwise, dendrograms are computed as\n",
       "\\code{dd <- as.dendrogram(hclustfun(distfun(X)))} where \\code{X} is\n",
       "either \\code{x} or \\code{t(x)}.\\\\{}\n",
       "\n",
       "If either is a vector (of ``weights'') then the appropriate\n",
       "dendrogram is reordered according to the supplied values subject to\n",
       "the constraints imposed by the dendrogram, by \\code{\\LinkA{reorder}{reorder}(dd,\n",
       "    Rowv)}, in the row case.\n",
       "\n",
       "If either is missing, as by default, then the ordering of the\n",
       "corresponding dendrogram is by the mean value of the rows/columns,\n",
       "i.e., in the case of rows, \\code{Rowv <- rowMeans(x, na.rm=na.rm)}.\n",
       "\n",
       "If either is \\code{\\LinkA{NULL}{NULL}}, \\emph{no reordering} will be done for\n",
       "the corresponding side.\n",
       "\n",
       "If \\code{scale=\"row\"} (or \\code{scale=\"col\"}) the rows (columns) are\n",
       "scaled to have mean zero and standard deviation one.  There is some\n",
       "empirical evidence from genomic plotting that this is useful.\n",
       "\n",
       "The default colors range from red to white (\\code{heat.colors}) and\n",
       "are not pretty.  Consider using enhancements such as the\n",
       "\\pkg{RColorBrewer} package,\n",
       "\\url{http://cran.r-project.org/web/packages/RColorBrewer}\n",
       "to select better colors.\n",
       "\n",
       "By default four components will be displayed in the plot.  At the top\n",
       "left is the color key, top right is the column dendrogram, bottom left\n",
       "is the row dendrogram, bottom right is the image plot.  When\n",
       "RowSideColor or ColSideColor are provided, an additional row or column\n",
       "is inserted in the appropriate location.   This layout can be\n",
       "overriden by specifiying appropriate values for \\code{lmat},\n",
       "\\code{lwid}, and \\code{lhei}.  \\code{lmat} controls the relative\n",
       "postition of each element, while \\code{lwid} controls the column\n",
       "width, and \\code{lhei} controls the row height. See the help page for\n",
       "\\code{\\LinkA{layout}{layout}} for details on how to use these\n",
       "arguments.\n",
       "\\end{Details}\n",
       "%\n",
       "\\begin{Value}\n",
       "Invisibly, a list with components\n",
       "\\begin{ldescription}\n",
       "\\item[\\code{rowInd}] row index permutation vector as returned by\n",
       "\\code{\\LinkA{order.dendrogram}{order.dendrogram}}.\n",
       "\\item[\\code{colInd}] column index permutation vector.\n",
       "\\item[\\code{call}] the matched call\n",
       "\\item[\\code{rowMeans, rowSDs}] mean and standard deviation of each row: only\n",
       "present if \\code{scale=\"row\"}\n",
       "\\item[\\code{colMeans, colSDs}] mean and standard deviation of each column: only\n",
       "present if \\code{scale=\"column\"}\n",
       "\\item[\\code{carpet}] reordered and scaled 'x' values used generate the main\n",
       "'carpet'\n",
       "\\item[\\code{rowDendrogram}] row dendrogram, if present\n",
       "\\item[\\code{colDendrogram}] column dendrogram, if present\n",
       "\\item[\\code{breaks}] values used for color break points\n",
       "\\item[\\code{col}] colors used\n",
       "\\item[\\code{vline}] center-line value used for column trace, present only if\n",
       "\\code{trace=\"both\"} or \\code{trace=\"column\"} \n",
       "\\item[\\code{hline}] center-line value used for row trace, present only if\n",
       "\\code{trace=\"both\"} or \\code{trace=\"row\"} \n",
       "\\item[\\code{colorTable}] A three-column data frame providing the lower and upper\n",
       "bound and color for each bin\n",
       "\\end{ldescription}\n",
       "\\end{Value}\n",
       "%\n",
       "\\begin{Note}\\relax\n",
       "The original rows and columns are reordered to match the dendrograms\n",
       "\\code{Rowv} and \\code{Colv} (if present).\n",
       "\n",
       "\\code{heatmap.2()} uses \\code{\\LinkA{layout}{layout}} to arragent the plot\n",
       "elements. Consequentially, it can \\bold{not} be used in a multi\n",
       "column/row layout using \\code{\\LinkA{layout}{layout}(...)},\n",
       "\\code{\\LinkA{par}{par}(mfrow=...)} or \\code{(mfcol=...)}.\n",
       "\\end{Note}\n",
       "%\n",
       "\\begin{Author}\\relax\n",
       "Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber,\n",
       "G. Warnes, revisions.\n",
       "\\end{Author}\n",
       "%\n",
       "\\begin{SeeAlso}\\relax\n",
       "\\code{\\LinkA{image}{image}}, \\code{\\LinkA{hclust}{hclust}}\n",
       "\\end{SeeAlso}\n",
       "%\n",
       "\\begin{Examples}\n",
       "\\begin{ExampleCode}\n",
       " data(mtcars)\n",
       " x  <- as.matrix(mtcars)\n",
       " rc <- rainbow(nrow(x), start=0, end=.3)\n",
       " cc <- rainbow(ncol(x), start=0, end=.3)\n",
       "\n",
       " ##\n",
       " ## demonstrate the effect of row and column dendrogram options\n",
       " ##\n",
       " heatmap.2(x)                    ## default - dendrogram plotted and reordering done.\n",
       " heatmap.2(x, dendrogram=\"none\") ##  no dendrogram plotted, but reordering done.\n",
       " heatmap.2(x, dendrogram=\"row\")  ## row dendrogram plotted and row reordering done.\n",
       " heatmap.2(x, dendrogram=\"col\")  ## col dendrogram plotted and col reordering done.\n",
       "\n",
       " heatmap.2(x, keysize=2)         ## default - dendrogram plotted and reordering done.\n",
       "\n",
       " heatmap.2(x, Rowv=FALSE, dendrogram=\"both\") ## generates a warning!\n",
       " heatmap.2(x, Rowv=NULL, dendrogram=\"both\")  ## generates a warning!\n",
       " heatmap.2(x, Colv=FALSE, dendrogram=\"both\") ## generates a warning!\n",
       "\n",
       " ## Reorder dendrogram by branch means rather than sums\n",
       " heatmap.2(x, reorderfun=function(d, w) reorder(d, w, agglo.FUN = mean) )\n",
       "\n",
       " ## plot a sub-cluster using the same color coding as for the full heatmap\n",
       " full <- heatmap.2(x)\n",
       " heatmap.2(x, Colv=full$colDendrogram[[2]], breaks=full$breaks)  # column subset\n",
       " heatmap.2(x, Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # row subset\n",
       " heatmap.2(x, Colv=full$colDendrogram[[2]],\n",
       "              Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # both\n",
       "\n",
       " ## Show effect of row and column label rotation\n",
       " heatmap.2(x, srtCol=NULL)\n",
       " heatmap.2(x, srtCol=0,   adjCol = c(0.5,1) )\n",
       " heatmap.2(x, srtCol=45,  adjCol = c(1,1)   )\n",
       " heatmap.2(x, srtCol=135, adjCol = c(1,0)   )\n",
       " heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )\n",
       " heatmap.2(x, srtCol=225, adjCol = c(0,0)   ) ## not very useful\n",
       " heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )\n",
       " heatmap.2(x, srtCol=315, adjCol = c(0,1)   )\n",
       " heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )\n",
       "\n",
       " heatmap.2(x, srtRow=45, adjRow=c(0, 1) )\n",
       " heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )\n",
       " heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )\n",
       "\n",
       "\n",
       " ## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is\n",
       " ## not also present)\n",
       " heatmap.2(x, offsetRow=0, offsetCol=0)\n",
       " heatmap.2(x, offsetRow=1, offsetCol=1)\n",
       " heatmap.2(x, offsetRow=2, offsetCol=2)\n",
       " heatmap.2(x, offsetRow=-1, offsetCol=-1)\n",
       "\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)\n",
       " heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)\n",
       "\n",
       "\n",
       " ## Show how to use 'extrafun' to replace the 'key' with a scatterplot\n",
       " lmat <- rbind( c(5,3,4), c(2,1,4) )\n",
       " lhei <- c(1.5, 4)\n",
       " lwid <- c(1.5, 4, 0.75)\n",
       "\n",
       " myplot <- function() {\n",
       "             oldpar <- par(\"mar\")\n",
       "             par(mar=c(5.1, 4.1, 0.5, 0.5))\n",
       "             plot(mpg ~ hp, data=x)\n",
       "           }\n",
       "\n",
       " heatmap.2(x, lmat=lmat, lhei=lhei, lwid=lwid, key=FALSE, extrafun=myplot)\n",
       "\n",
       " ## show how to customize the color key\n",
       " heatmap.2(x,\n",
       "           key.title=NA, # no title\n",
       "           key.xlab=NA,  # no xlab\n",
       "           key.par=list(mgp=c(1.5, 0.5, 0),\n",
       "                        mar=c(2.5, 2.5, 1, 0)),\n",
       "           key.xtickfun=function() {\n",
       "                 breaks <- parent.frame()$breaks\n",
       "                 return(list(\n",
       "                      at=parent.frame()$scale01(c(breaks[1],\n",
       "                                                  breaks[length(breaks)])),\n",
       "                      labels=c(as.character(breaks[1]),\n",
       "                               as.character(breaks[length(breaks)]))\n",
       "                      ))\n",
       "           })\n",
       "\n",
       " heatmap.2(x,\n",
       "          breaks=256,\n",
       "          key.title=NA,\n",
       "          key.xlab=NA,\n",
       "          key.par=list(mgp=c(1.5, 0.5, 0),\n",
       "                       mar=c(1, 2.5, 1, 0)),\n",
       "          key.xtickfun=function() {\n",
       "               cex <- par(\"cex\")*par(\"cex.axis\")\n",
       "               side <- 1\n",
       "               line <- 0\n",
       "               col <- par(\"col.axis\")\n",
       "               font <- par(\"font.axis\")\n",
       "               mtext(\"low\", side=side, at=0, adj=0,\n",
       "                     line=line, cex=cex, col=col, font=font)\n",
       "               mtext(\"high\", side=side, at=1, adj=1,\n",
       "                     line=line, cex=cex, col=col, font=font)\n",
       "               return(list(labels=FALSE, tick=FALSE))\n",
       "          })\n",
       "\n",
       "\n",
       " ##\n",
       " ## Show effect of z-score scaling within columns, blue-red color scale\n",
       " ##\n",
       " hv <- heatmap.2(x, col=bluered, scale=\"column\", tracecol=\"#303030\")\n",
       "\n",
       " ###\n",
       " ## Look at the return values\n",
       " ###\n",
       " names(hv)\n",
       "\n",
       " ## Show the mapping of z-score values to color bins\n",
       " hv$colorTable\n",
       "\n",
       " ## Extract the range associated with white\n",
       " hv$colorTable[hv$colorTable[,\"color\"]==\"#FFFFFF\",]\n",
       "\n",
       " ## Determine the original data values that map to white\n",
       " whiteBin <- unlist(hv$colorTable[hv$colorTable[,\"color\"]==\"#FFFFFF\",1:2])\n",
       " rbind(whiteBin[1] * hv$colSDs + hv$colMeans,\n",
       "       whiteBin[2] * hv$colSDs + hv$colMeans )\n",
       " ##\n",
       " ## A more decorative heatmap, with z-score scaling along columns\n",
       " ##\n",
       " hv <- heatmap.2(x, col=cm.colors(255), scale=\"column\",\n",
       "\t       RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),\n",
       "\t       xlab=\"specification variables\", ylab= \"Car Models\",\n",
       "\t       main=\"heatmap(<Mtcars data>, ..., scale=\\\"column\\\")\",\n",
       "         tracecol=\"green\", density=\"density\")\n",
       " ## Note that the breakpoints are now symmetric about 0\n",
       "\n",
       " ## Color the labels to match RowSideColors and ColSideColors\n",
       " hv <- heatmap.2(x, col=cm.colors(255), scale=\"column\",\n",
       "         RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),\n",
       "\t       xlab=\"specification variables\", ylab= \"Car Models\",\n",
       "\t       main=\"heatmap(<Mtcars data>, ..., scale=\\\"column\\\")\",\n",
       "         tracecol=\"green\", density=\"density\", colRow=rc, colCol=cc,\n",
       "         srtCol=45, adjCol=c(0.5,1))\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       " data(attitude)\n",
       " round(Ca <- cor(attitude), 2)\n",
       " symnum(Ca) # simple graphic\n",
       "\n",
       " # with reorder\n",
       " heatmap.2(Ca, \t\t symm=TRUE, margin=c(6, 6), trace=\"none\" )\n",
       "\n",
       " # without reorder\n",
       " heatmap.2(Ca, Rowv=FALSE, symm=TRUE, margin=c(6, 6), trace=\"none\" )\n",
       "\n",
       " ## Place the color key below the image plot\n",
       " heatmap.2(x, lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(1.5, 4, 2 ) )\n",
       "\n",
       " ## Place the color key to the top right of the image plot\n",
       " heatmap.2(x, lmat=rbind( c(0, 3, 4), c(2,1,0 ) ), lwid=c(1.5, 4, 2 ) )\n",
       "\n",
       " ## For variable clustering, rather use distance based on cor():\n",
       " data(USJudgeRatings)\n",
       " symnum( cU <- cor(USJudgeRatings) )\n",
       "\n",
       " hU <- heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=topo.colors(16),\n",
       "              distfun=function(c) as.dist(1 - c), trace=\"none\")\n",
       "\n",
       " ## The Correlation matrix with same reordering:\n",
       " hM <- format(round(cU, 2))\n",
       " hM\n",
       "\n",
       " # now with the correlation matrix on the plot itself\n",
       "\n",
       " heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=rev(heat.colors(16)),\n",
       "             distfun=function(c) as.dist(1 - c), trace=\"none\",\n",
       "             cellnote=hM)\n",
       "\n",
       " ## genechip data examples\n",
       " ## Not run: \n",
       " library(affy)\n",
       " data(SpikeIn)\n",
       " pms <- SpikeIn@pm\n",
       "\n",
       " # just the data, scaled across rows\n",
       " heatmap.2(pms, col=rev(heat.colors(16)), main=\"SpikeIn@pm\",\n",
       "              xlab=\"Relative Concentration\", ylab=\"Probeset\",\n",
       "              scale=\"row\")\n",
       "\n",
       " # fold change vs \"12.50\" sample\n",
       " data <- pms / pms[, \"12.50\"]\n",
       " data <- ifelse(data>1, data, -1/data)\n",
       " heatmap.2(data, breaks=16, col=redgreen, tracecol=\"blue\",\n",
       "               main=\"SpikeIn@pm Fold Changes\\nrelative to 12.50 sample\",\n",
       "               xlab=\"Relative Concentration\", ylab=\"Probeset\")\n",
       " \n",
       "## End(Not run)\n",
       "\n",
       "\\end{ExampleCode}\n",
       "\\end{Examples}"
      ],
      "text/plain": [
       "heatmap.2                package:gplots                R Documentation\n",
       "\n",
       "_\bE_\bn_\bh_\ba_\bn_\bc_\be_\bd _\bH_\be_\ba_\bt _\bM_\ba_\bp\n",
       "\n",
       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
       "\n",
       "     A heat map is a false color image (basically ‘image(t(x))’) with a\n",
       "     dendrogram added to the left side and/or to the top.  Typically,\n",
       "     reordering of the rows and columns according to some set of values\n",
       "     (row or column means) within the restrictions imposed by the\n",
       "     dendrogram is carried out.\n",
       "\n",
       "     This heatmap provides a number of extensions to the standard R\n",
       "     ‘heatmap’ function.\n",
       "\n",
       "_\bU_\bs_\ba_\bg_\be:\n",
       "\n",
       "     heatmap.2 (x,\n",
       "     \n",
       "                # dendrogram control\n",
       "                Rowv = TRUE,\n",
       "                Colv=if(symm)\"Rowv\" else TRUE,\n",
       "                distfun = dist,\n",
       "                hclustfun = hclust,\n",
       "                dendrogram = c(\"both\",\"row\",\"column\",\"none\"),\n",
       "                reorderfun = function(d, w) reorder(d, w),\n",
       "                symm = FALSE,\n",
       "     \n",
       "                # data scaling\n",
       "                scale = c(\"none\",\"row\", \"column\"),\n",
       "                na.rm=TRUE,\n",
       "     \n",
       "                # image plot\n",
       "                revC = identical(Colv, \"Rowv\"),\n",
       "                add.expr,\n",
       "     \n",
       "                # mapping data to colors\n",
       "                breaks,\n",
       "                symbreaks=any(x < 0, na.rm=TRUE) || scale!=\"none\",\n",
       "     \n",
       "                # colors\n",
       "                col=\"heat.colors\",\n",
       "     \n",
       "                # block sepration\n",
       "                colsep,\n",
       "                rowsep,\n",
       "                sepcolor=\"white\",\n",
       "                sepwidth=c(0.05,0.05),\n",
       "     \n",
       "                # cell labeling\n",
       "                cellnote,\n",
       "                notecex=1.0,\n",
       "                notecol=\"cyan\",\n",
       "                na.color=par(\"bg\"),\n",
       "     \n",
       "                # level trace\n",
       "                trace=c(\"column\",\"row\",\"both\",\"none\"),\n",
       "                tracecol=\"cyan\",\n",
       "                hline=median(breaks),\n",
       "                vline=median(breaks),\n",
       "                linecol=tracecol,\n",
       "     \n",
       "                # Row/Column Labeling\n",
       "                margins = c(5, 5),\n",
       "                ColSideColors,\n",
       "                RowSideColors,\n",
       "                cexRow = 0.2 + 1/log10(nr),\n",
       "                cexCol = 0.2 + 1/log10(nc),\n",
       "                labRow = NULL,\n",
       "                labCol = NULL,\n",
       "                srtRow = NULL,\n",
       "                srtCol = NULL,\n",
       "                adjRow = c(0,NA),\n",
       "                adjCol = c(NA,0),\n",
       "                offsetRow = 0.5,\n",
       "                offsetCol = 0.5,\n",
       "                colRow = NULL,\n",
       "                colCol = NULL,\n",
       "     \n",
       "                # color key + density info\n",
       "                key = TRUE,\n",
       "                keysize = 1.5,\n",
       "                density.info=c(\"histogram\",\"density\",\"none\"),\n",
       "                denscol=tracecol,\n",
       "                symkey = any(x < 0, na.rm=TRUE) || symbreaks,\n",
       "                densadj = 0.25,\n",
       "                key.title = NULL,\n",
       "                key.xlab = NULL,\n",
       "                key.ylab = NULL,\n",
       "                key.xtickfun = NULL,\n",
       "                key.ytickfun = NULL,\n",
       "                key.par=list(),\n",
       "     \n",
       "                # plot labels\n",
       "                main = NULL,\n",
       "                xlab = NULL,\n",
       "                ylab = NULL,\n",
       "     \n",
       "                # plot layout\n",
       "                lmat = NULL,\n",
       "                lhei = NULL,\n",
       "                lwid = NULL,\n",
       "     \n",
       "                # extras\n",
       "                extrafun=NULL,\n",
       "                ...\n",
       "                )\n",
       "     \n",
       "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n",
       "\n",
       "       x: numeric matrix of the values to be plotted.\n",
       "\n",
       "    Rowv: determines if and how the _row_ dendrogram should be\n",
       "          reordered.  By default, it is TRUE, which implies dendrogram\n",
       "          is computed and reordered based on row means. If NULL or\n",
       "          FALSE, then no dendrogram is computed and no reordering is\n",
       "          done. If a ‘dendrogram’, then it is used \"as-is\", ie without\n",
       "          any reordering. If a vector of integers, then dendrogram is\n",
       "          computed and reordered based on the order of the vector.\n",
       "\n",
       "    Colv: determines if and how the _column_ dendrogram should be\n",
       "          reordered.  Has the options as the ‘Rowv’ argument above and\n",
       "          _additionally_ when ‘x’ is a square matrix, ‘Colv=\"Rowv\"’\n",
       "          means that columns should be treated identically to the rows.\n",
       "\n",
       " distfun: function used to compute the distance (dissimilarity) between\n",
       "          both rows and columns.  Defaults to ‘dist’.\n",
       "\n",
       "hclustfun: function used to compute the hierarchical clustering when\n",
       "          ‘Rowv’ or ‘Colv’ are not dendrograms.  Defaults to ‘hclust’.\n",
       "\n",
       "dendrogram: character string indicating whether to draw 'none', 'row',\n",
       "          'column' or 'both' dendrograms.  Defaults to 'both'. However,\n",
       "          if Rowv (or Colv) is FALSE or NULL and dendrogram is 'both',\n",
       "          then a warning is issued and Rowv (or Colv) arguments are\n",
       "          honoured.\n",
       "\n",
       "reorderfun: ‘function(d, w)’ of dendrogram and weights for reordering\n",
       "          the row and column dendrograms.  The default uses\n",
       "          ‘stats{reorder.dendrogram}’\n",
       "     .\n",
       "\n",
       "    symm: logical indicating if ‘x’ should be treated *symm*etrically;\n",
       "          can only be true when ‘x’ is a square matrix.\n",
       "\n",
       "   scale: character indicating if the values should be centered and\n",
       "          scaled in either the row direction or the column direction,\n",
       "          or none.  The default is ‘\"none\"’.\n",
       "\n",
       "   na.rm: logical indicating whether ‘NA’'s should be removed.\n",
       "\n",
       "    revC: logical indicating if the column order should be ‘rev’ersed\n",
       "          for plotting, such that e.g., for the symmetric case, the\n",
       "          symmetry axis is as usual.\n",
       "\n",
       "add.expr: expression that will be evaluated after the call to ‘image’.\n",
       "          Can be used to add components to the plot.\n",
       "\n",
       "  breaks: (optional) Either a numeric vector indicating the splitting\n",
       "          points for binning ‘x’ into colors, or a integer number of\n",
       "          break points to be used, in which case the break points will\n",
       "          be spaced equally between ‘min(x)’ and ‘max(x)’.\n",
       "\n",
       "symbreaks: Boolean indicating whether breaks should be made symmetric\n",
       "          about 0. Defaults to ‘TRUE’ if the data includes negative\n",
       "          values, and to ‘FALSE’ otherwise.\n",
       "\n",
       "     col: colors used for the image. Defaults to heat colors\n",
       "          (‘heat.colors’).\n",
       "\n",
       "colsep, rowsep, sepcolor: (optional) vector of integers indicating\n",
       "          which columns or rows should be separated from the preceding\n",
       "          columns or rows by a narrow space of color ‘sepcolor’.\n",
       "\n",
       "sepwidth: (optional) Vector of length 2 giving the width (colsep) or\n",
       "          height (rowsep) the separator box drawn by colsep and rowsep\n",
       "          as a function of the width (colsep) or height (rowsep) of a\n",
       "          cell. Defaults to ‘c(0.05, 0.05)’\n",
       "\n",
       "cellnote: (optional) matrix of character strings which will be placed\n",
       "          within each color cell, e.g. p-value symbols.\n",
       "\n",
       " notecex: (optional) numeric scaling factor for ‘cellnote’ items.\n",
       "\n",
       " notecol: (optional) character string specifying the color for\n",
       "          ‘cellnote’ text.  Defaults to \"cyan\".\n",
       "\n",
       "na.color: Color to use for missing value (‘NA’). Defaults to the plot\n",
       "          background color.\n",
       "\n",
       "   trace: character string indicating whether a solid \"trace\" line\n",
       "          should be drawn across 'row's or down 'column's, 'both' or\n",
       "          'none'.  The distance of the line from the center of each\n",
       "          color-cell is proportional to the size of the measurement.\n",
       "          Defaults to 'column'.\n",
       "\n",
       "tracecol: character string giving the color for \"trace\" line. Defaults\n",
       "          to \"cyan\".\n",
       "\n",
       "hline, vline, linecol: Vector of values within cells where a horizontal\n",
       "          or vertical dotted line should be drawn.  The color of the\n",
       "          line is controlled by ‘linecol’.  Horizontal lines are only\n",
       "          plotted if ‘trace’ is 'row' or 'both'.  Vertical lines are\n",
       "          only drawn if ‘trace’ 'column' or 'both'.  ‘hline’ and\n",
       "          ‘vline’ default to the median of the breaks, ‘linecol’\n",
       "          defaults to the value of ‘tracecol’.\n",
       "\n",
       " margins: numeric vector of length 2 containing the margins (see\n",
       "          ‘par(mar= *)’) for column and row names, respectively.\n",
       "\n",
       "ColSideColors: (optional) character vector of length ‘ncol(x)’\n",
       "          containing the color names for a horizontal side bar that may\n",
       "          be used to annotate the columns of ‘x’.\n",
       "\n",
       "RowSideColors: (optional) character vector of length ‘nrow(x)’\n",
       "          containing the color names for a vertical side bar that may\n",
       "          be used to annotate the rows of ‘x’.\n",
       "\n",
       "cexRow, cexCol: positive numbers, used as ‘cex.axis’ in for the row or\n",
       "          column axis labeling.  The defaults currently only use number\n",
       "          of rows or columns, respectively.\n",
       "\n",
       "labRow, labCol: character vectors with row and column labels to use;\n",
       "          these default to ‘rownames(x)’ or ‘colnames(x)’,\n",
       "          respectively.\n",
       "\n",
       "srtRow, srtCol: angle of row/column labels, in degrees from horizontal\n",
       "\n",
       "adjRow, adjCol: 2-element vector giving the (left-right, top-bottom)\n",
       "          justification of row/column labels (relative to the text\n",
       "          orientation).\n",
       "\n",
       "offsetRow, offsetCol: Number of character-width spaces to place between\n",
       "          row/column labels and the edge of the plotting region.\n",
       "\n",
       "colRow, colCol: color of row/column labels, either a scalar to set the\n",
       "          color of all labels the same, or a vector providing the\n",
       "          colors of each label item\n",
       "\n",
       "     key: logical indicating whether a color-key should be shown.\n",
       "\n",
       " keysize: numeric value indicating the size of the key\n",
       "\n",
       "density.info: character string indicating whether to superimpose a\n",
       "          'histogram', a 'density' plot, or no plot ('none') on the\n",
       "          color-key.\n",
       "\n",
       " denscol: character string giving the color for the density display\n",
       "          specified by ‘density.info’, defaults to the same value as\n",
       "          ‘tracecol’.\n",
       "\n",
       "  symkey: Boolean indicating whether the color key should be made\n",
       "          symmetric about 0. Defaults to ‘TRUE’ if the data includes\n",
       "          negative values, and to ‘FALSE’ otherwise.\n",
       "\n",
       " densadj: Numeric scaling value for tuning the kernel width when a\n",
       "          density plot is drawn on the color key.  (See the ‘adjust’\n",
       "          parameter for the ‘density’ function for details.)  Defaults\n",
       "          to 0.25.\n",
       "\n",
       "key.title: main title of the color key. If set to NA no title will be\n",
       "          plotted.\n",
       "\n",
       "key.xlab: x axis label of the color key. If set to NA no label will be\n",
       "          plotted.\n",
       "\n",
       "key.ylab: y axis label of the color key. If set to NA no label will be\n",
       "          plotted.\n",
       "\n",
       "key.xtickfun: function computing tick location and labels for the xaxis\n",
       "          of the color key. Returns a named list containing parameters\n",
       "          that can be passed to ‘axis’. See examples.\n",
       "\n",
       "key.ytickfun: function computing tick location and labels for the y\n",
       "          axis of the color key. Returns a named list containing\n",
       "          parameters that can be passed to ‘axis’.  See examples.\n",
       "\n",
       " key.par: graphical parameters for the color key. Named list that can\n",
       "          be passed to ‘par’.\n",
       "\n",
       "main, xlab, ylab: main, x- and y-axis titles; defaults to none.\n",
       "\n",
       "lmat, lhei, lwid: visual layout: position matrix, column height, column\n",
       "          width.  See below for details\n",
       "\n",
       "extrafun: A function to be called after all other work. See examples.\n",
       "\n",
       "     ...: additional arguments passed on to ‘image’\n",
       "\n",
       "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n",
       "\n",
       "     If either ‘Rowv’ or ‘Colv’ are dendrograms they are honored (and\n",
       "     not reordered).  Otherwise, dendrograms are computed as ‘dd <-\n",
       "     as.dendrogram(hclustfun(distfun(X)))’ where ‘X’ is either ‘x’ or\n",
       "     ‘t(x)’.\n",
       "     If either is a vector (of “weights”) then the appropriate\n",
       "     dendrogram is reordered according to the supplied values subject\n",
       "     to the constraints imposed by the dendrogram, by ‘reorder(dd,\n",
       "     Rowv)’, in the row case.\n",
       "\n",
       "     If either is missing, as by default, then the ordering of the\n",
       "     corresponding dendrogram is by the mean value of the rows/columns,\n",
       "     i.e., in the case of rows, ‘Rowv <- rowMeans(x, na.rm=na.rm)’.\n",
       "\n",
       "     If either is ‘NULL’, _no reordering_ will be done for the\n",
       "     corresponding side.\n",
       "\n",
       "     If ‘scale=\"row\"’ (or ‘scale=\"col\"’) the rows (columns) are scaled\n",
       "     to have mean zero and standard deviation one.  There is some\n",
       "     empirical evidence from genomic plotting that this is useful.\n",
       "\n",
       "     The default colors range from red to white (‘heat.colors’) and are\n",
       "     not pretty.  Consider using enhancements such as the\n",
       "     ‘RColorBrewer’ package, <URL:\n",
       "     http://cran.r-project.org/web/packages/RColorBrewer> to select\n",
       "     better colors.\n",
       "\n",
       "     By default four components will be displayed in the plot.  At the\n",
       "     top left is the color key, top right is the column dendrogram,\n",
       "     bottom left is the row dendrogram, bottom right is the image plot.\n",
       "     When RowSideColor or ColSideColor are provided, an additional row\n",
       "     or column is inserted in the appropriate location.  This layout\n",
       "     can be overriden by specifiying appropriate values for ‘lmat’,\n",
       "     ‘lwid’, and ‘lhei’.  ‘lmat’ controls the relative postition of\n",
       "     each element, while ‘lwid’ controls the column width, and ‘lhei’\n",
       "     controls the row height. See the help page for ‘layout’ for\n",
       "     details on how to use these arguments.\n",
       "\n",
       "_\bV_\ba_\bl_\bu_\be:\n",
       "\n",
       "     Invisibly, a list with components\n",
       "\n",
       "  rowInd: row index permutation vector as returned by\n",
       "          ‘order.dendrogram’.\n",
       "\n",
       "  colInd: column index permutation vector.\n",
       "\n",
       "    call: the matched call\n",
       "\n",
       "rowMeans, rowSDs: mean and standard deviation of each row: only present\n",
       "          if ‘scale=\"row\"’\n",
       "\n",
       "colMeans, colSDs: mean and standard deviation of each column: only\n",
       "          present if ‘scale=\"column\"’\n",
       "\n",
       "  carpet: reordered and scaled 'x' values used generate the main\n",
       "          'carpet'\n",
       "\n",
       "rowDendrogram: row dendrogram, if present\n",
       "\n",
       "colDendrogram: column dendrogram, if present\n",
       "\n",
       "  breaks: values used for color break points\n",
       "\n",
       "     col: colors used\n",
       "\n",
       "   vline: center-line value used for column trace, present only if\n",
       "          ‘trace=\"both\"’ or ‘trace=\"column\"’\n",
       "\n",
       "   hline: center-line value used for row trace, present only if\n",
       "          ‘trace=\"both\"’ or ‘trace=\"row\"’\n",
       "\n",
       "colorTable: A three-column data frame providing the lower and upper\n",
       "          bound and color for each bin\n",
       "\n",
       "_\bN_\bo_\bt_\be:\n",
       "\n",
       "     The original rows and columns are reordered to match the\n",
       "     dendrograms ‘Rowv’ and ‘Colv’ (if present).\n",
       "\n",
       "     ‘heatmap.2()’ uses ‘layout’ to arragent the plot elements.\n",
       "     Consequentially, it can *not* be used in a multi column/row layout\n",
       "     using ‘layout(...)’, ‘par(mfrow=...)’ or ‘(mfcol=...)’.\n",
       "\n",
       "_\bA_\bu_\bt_\bh_\bo_\br(_\bs):\n",
       "\n",
       "     Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber, G.\n",
       "     Warnes, revisions.\n",
       "\n",
       "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n",
       "\n",
       "     ‘image’, ‘hclust’\n",
       "\n",
       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
       "\n",
       "      data(mtcars)\n",
       "      x  <- as.matrix(mtcars)\n",
       "      rc <- rainbow(nrow(x), start=0, end=.3)\n",
       "      cc <- rainbow(ncol(x), start=0, end=.3)\n",
       "     \n",
       "      ##\n",
       "      ## demonstrate the effect of row and column dendrogram options\n",
       "      ##\n",
       "      heatmap.2(x)                    ## default - dendrogram plotted and reordering done.\n",
       "      heatmap.2(x, dendrogram=\"none\") ##  no dendrogram plotted, but reordering done.\n",
       "      heatmap.2(x, dendrogram=\"row\")  ## row dendrogram plotted and row reordering done.\n",
       "      heatmap.2(x, dendrogram=\"col\")  ## col dendrogram plotted and col reordering done.\n",
       "     \n",
       "      heatmap.2(x, keysize=2)         ## default - dendrogram plotted and reordering done.\n",
       "     \n",
       "      heatmap.2(x, Rowv=FALSE, dendrogram=\"both\") ## generates a warning!\n",
       "      heatmap.2(x, Rowv=NULL, dendrogram=\"both\")  ## generates a warning!\n",
       "      heatmap.2(x, Colv=FALSE, dendrogram=\"both\") ## generates a warning!\n",
       "     \n",
       "      ## Reorder dendrogram by branch means rather than sums\n",
       "      heatmap.2(x, reorderfun=function(d, w) reorder(d, w, agglo.FUN = mean) )\n",
       "     \n",
       "      ## plot a sub-cluster using the same color coding as for the full heatmap\n",
       "      full <- heatmap.2(x)\n",
       "      heatmap.2(x, Colv=full$colDendrogram[[2]], breaks=full$breaks)  # column subset\n",
       "      heatmap.2(x, Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # row subset\n",
       "      heatmap.2(x, Colv=full$colDendrogram[[2]],\n",
       "                   Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # both\n",
       "     \n",
       "      ## Show effect of row and column label rotation\n",
       "      heatmap.2(x, srtCol=NULL)\n",
       "      heatmap.2(x, srtCol=0,   adjCol = c(0.5,1) )\n",
       "      heatmap.2(x, srtCol=45,  adjCol = c(1,1)   )\n",
       "      heatmap.2(x, srtCol=135, adjCol = c(1,0)   )\n",
       "      heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )\n",
       "      heatmap.2(x, srtCol=225, adjCol = c(0,0)   ) ## not very useful\n",
       "      heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )\n",
       "      heatmap.2(x, srtCol=315, adjCol = c(0,1)   )\n",
       "      heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )\n",
       "     \n",
       "      heatmap.2(x, srtRow=45, adjRow=c(0, 1) )\n",
       "      heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )\n",
       "      heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )\n",
       "     \n",
       "     \n",
       "      ## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is\n",
       "      ## not also present)\n",
       "      heatmap.2(x, offsetRow=0, offsetCol=0)\n",
       "      heatmap.2(x, offsetRow=1, offsetCol=1)\n",
       "      heatmap.2(x, offsetRow=2, offsetCol=2)\n",
       "      heatmap.2(x, offsetRow=-1, offsetCol=-1)\n",
       "     \n",
       "      heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)\n",
       "      heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)\n",
       "      heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)\n",
       "      heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)\n",
       "     \n",
       "     \n",
       "      ## Show how to use 'extrafun' to replace the 'key' with a scatterplot\n",
       "      lmat <- rbind( c(5,3,4), c(2,1,4) )\n",
       "      lhei <- c(1.5, 4)\n",
       "      lwid <- c(1.5, 4, 0.75)\n",
       "     \n",
       "      myplot <- function() {\n",
       "                  oldpar <- par(\"mar\")\n",
       "                  par(mar=c(5.1, 4.1, 0.5, 0.5))\n",
       "                  plot(mpg ~ hp, data=x)\n",
       "                }\n",
       "     \n",
       "      heatmap.2(x, lmat=lmat, lhei=lhei, lwid=lwid, key=FALSE, extrafun=myplot)\n",
       "     \n",
       "      ## show how to customize the color key\n",
       "      heatmap.2(x,\n",
       "                key.title=NA, # no title\n",
       "                key.xlab=NA,  # no xlab\n",
       "                key.par=list(mgp=c(1.5, 0.5, 0),\n",
       "                             mar=c(2.5, 2.5, 1, 0)),\n",
       "                key.xtickfun=function() {\n",
       "                      breaks <- parent.frame()$breaks\n",
       "                      return(list(\n",
       "                           at=parent.frame()$scale01(c(breaks[1],\n",
       "                                                       breaks[length(breaks)])),\n",
       "                           labels=c(as.character(breaks[1]),\n",
       "                                    as.character(breaks[length(breaks)]))\n",
       "                           ))\n",
       "                })\n",
       "     \n",
       "      heatmap.2(x,\n",
       "               breaks=256,\n",
       "               key.title=NA,\n",
       "               key.xlab=NA,\n",
       "               key.par=list(mgp=c(1.5, 0.5, 0),\n",
       "                            mar=c(1, 2.5, 1, 0)),\n",
       "               key.xtickfun=function() {\n",
       "                    cex <- par(\"cex\")*par(\"cex.axis\")\n",
       "                    side <- 1\n",
       "                    line <- 0\n",
       "                    col <- par(\"col.axis\")\n",
       "                    font <- par(\"font.axis\")\n",
       "                    mtext(\"low\", side=side, at=0, adj=0,\n",
       "                          line=line, cex=cex, col=col, font=font)\n",
       "                    mtext(\"high\", side=side, at=1, adj=1,\n",
       "                          line=line, cex=cex, col=col, font=font)\n",
       "                    return(list(labels=FALSE, tick=FALSE))\n",
       "               })\n",
       "     \n",
       "     \n",
       "      ##\n",
       "      ## Show effect of z-score scaling within columns, blue-red color scale\n",
       "      ##\n",
       "      hv <- heatmap.2(x, col=bluered, scale=\"column\", tracecol=\"#303030\")\n",
       "     \n",
       "      ###\n",
       "      ## Look at the return values\n",
       "      ###\n",
       "      names(hv)\n",
       "     \n",
       "      ## Show the mapping of z-score values to color bins\n",
       "      hv$colorTable\n",
       "     \n",
       "      ## Extract the range associated with white\n",
       "      hv$colorTable[hv$colorTable[,\"color\"]==\"#FFFFFF\",]\n",
       "     \n",
       "      ## Determine the original data values that map to white\n",
       "      whiteBin <- unlist(hv$colorTable[hv$colorTable[,\"color\"]==\"#FFFFFF\",1:2])\n",
       "      rbind(whiteBin[1] * hv$colSDs + hv$colMeans,\n",
       "            whiteBin[2] * hv$colSDs + hv$colMeans )\n",
       "      ##\n",
       "      ## A more decorative heatmap, with z-score scaling along columns\n",
       "      ##\n",
       "      hv <- heatmap.2(x, col=cm.colors(255), scale=\"column\",\n",
       "                    RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),\n",
       "                    xlab=\"specification variables\", ylab= \"Car Models\",\n",
       "                    main=\"heatmap(<Mtcars data>, ..., scale=\\\"column\\\")\",\n",
       "              tracecol=\"green\", density=\"density\")\n",
       "      ## Note that the breakpoints are now symmetric about 0\n",
       "     \n",
       "      ## Color the labels to match RowSideColors and ColSideColors\n",
       "      hv <- heatmap.2(x, col=cm.colors(255), scale=\"column\",\n",
       "              RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),\n",
       "                    xlab=\"specification variables\", ylab= \"Car Models\",\n",
       "                    main=\"heatmap(<Mtcars data>, ..., scale=\\\"column\\\")\",\n",
       "              tracecol=\"green\", density=\"density\", colRow=rc, colCol=cc,\n",
       "              srtCol=45, adjCol=c(0.5,1))\n",
       "     \n",
       "     \n",
       "     \n",
       "     \n",
       "     \n",
       "      data(attitude)\n",
       "      round(Ca <- cor(attitude), 2)\n",
       "      symnum(Ca) # simple graphic\n",
       "     \n",
       "      # with reorder\n",
       "      heatmap.2(Ca,           symm=TRUE, margin=c(6, 6), trace=\"none\" )\n",
       "     \n",
       "      # without reorder\n",
       "      heatmap.2(Ca, Rowv=FALSE, symm=TRUE, margin=c(6, 6), trace=\"none\" )\n",
       "     \n",
       "      ## Place the color key below the image plot\n",
       "      heatmap.2(x, lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(1.5, 4, 2 ) )\n",
       "     \n",
       "      ## Place the color key to the top right of the image plot\n",
       "      heatmap.2(x, lmat=rbind( c(0, 3, 4), c(2,1,0 ) ), lwid=c(1.5, 4, 2 ) )\n",
       "     \n",
       "      ## For variable clustering, rather use distance based on cor():\n",
       "      data(USJudgeRatings)\n",
       "      symnum( cU <- cor(USJudgeRatings) )\n",
       "     \n",
       "      hU <- heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=topo.colors(16),\n",
       "                   distfun=function(c) as.dist(1 - c), trace=\"none\")\n",
       "     \n",
       "      ## The Correlation matrix with same reordering:\n",
       "      hM <- format(round(cU, 2))\n",
       "      hM\n",
       "     \n",
       "      # now with the correlation matrix on the plot itself\n",
       "     \n",
       "      heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=rev(heat.colors(16)),\n",
       "                  distfun=function(c) as.dist(1 - c), trace=\"none\",\n",
       "                  cellnote=hM)\n",
       "     \n",
       "      ## genechip data examples\n",
       "      ## Not run:\n",
       "     \n",
       "      library(affy)\n",
       "      data(SpikeIn)\n",
       "      pms <- SpikeIn@pm\n",
       "     \n",
       "      # just the data, scaled across rows\n",
       "      heatmap.2(pms, col=rev(heat.colors(16)), main=\"SpikeIn@pm\",\n",
       "                   xlab=\"Relative Concentration\", ylab=\"Probeset\",\n",
       "                   scale=\"row\")\n",
       "     \n",
       "      # fold change vs \"12.50\" sample\n",
       "      data <- pms / pms[, \"12.50\"]\n",
       "      data <- ifelse(data>1, data, -1/data)\n",
       "      heatmap.2(data, breaks=16, col=redgreen, tracecol=\"blue\",\n",
       "                    main=\"SpikeIn@pm Fold Changes\\nrelative to 12.50 sample\",\n",
       "                    xlab=\"Relative Concentration\", ylab=\"Probeset\")\n",
       "     ## End(Not run)\n",
       "     "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "help(heatmap.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = read.table(\n",
    "    'these_crossrefs.ncol', \n",
    "    sep=\"\\t\", \n",
    "    header=FALSE, \n",
    "    comment.char=\"\",\n",
    "    quote=\"\",\n",
    "    as.is = FALSE,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>250</li>\n",
       "\t<li>2</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 250\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 250\n",
       "2. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 250   2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>V1</th><th scope=col>V2</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Reges_IIr.19.1-Jesaia.37.1</td><td>100</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Reges_IIr.19.2-Jesaia.37.2</td><td>92</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Reges_IIr.19.3-Jesaia.37.3</td><td>100</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Reges_IIr.19.4-Jesaia.37.4</td><td>99</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Reges_IIr.19.5-Jesaia.37.5</td><td>100</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Reges_IIr.19.6-Jesaia.37.6</td><td>100</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & V1 & V2\\\\\n",
       "\\hline\n",
       "\t1 & Reges_IIr.19.1-Jesaia.37.1 & 100\\\\\n",
       "\t2 & Reges_IIr.19.2-Jesaia.37.2 & 92\\\\\n",
       "\t3 & Reges_IIr.19.3-Jesaia.37.3 & 100\\\\\n",
       "\t4 & Reges_IIr.19.4-Jesaia.37.4 & 99\\\\\n",
       "\t5 & Reges_IIr.19.5-Jesaia.37.5 & 100\\\\\n",
       "\t6 & Reges_IIr.19.6-Jesaia.37.6 & 100\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                          V1  V2\n",
       "1 Reges_IIr.19.1-Jesaia.37.1 100\n",
       "2 Reges_IIr.19.2-Jesaia.37.2  92\n",
       "3 Reges_IIr.19.3-Jesaia.37.3 100\n",
       "4 Reges_IIr.19.4-Jesaia.37.4  99\n",
       "5 Reges_IIr.19.5-Jesaia.37.5 100\n",
       "6 Reges_IIr.19.6-Jesaia.37.6 100"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim(data)\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot(data$V2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table width=\"100%\" summary=\"page for barplot {graphics}\"><tr><td>barplot {graphics}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
       "\n",
       "<h2>Bar Plots</h2>\n",
       "\n",
       "<h3>Description</h3>\n",
       "\n",
       "<p>Creates a bar plot with vertical or horizontal bars.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Usage</h3>\n",
       "\n",
       "<pre>\n",
       "barplot(height, ...)\n",
       "\n",
       "## Default S3 method:\n",
       "barplot(height, width = 1, space = NULL,\n",
       "        names.arg = NULL, legend.text = NULL, beside = FALSE,\n",
       "        horiz = FALSE, density = NULL, angle = 45,\n",
       "        col = NULL, border = par(\"fg\"),\n",
       "        main = NULL, sub = NULL, xlab = NULL, ylab = NULL,\n",
       "        xlim = NULL, ylim = NULL, xpd = TRUE, log = \"\",\n",
       "        axes = TRUE, axisnames = TRUE,\n",
       "        cex.axis = par(\"cex.axis\"), cex.names = par(\"cex.axis\"),\n",
       "        inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0,\n",
       "        add = FALSE, args.legend = NULL, ...)\n",
       "</pre>\n",
       "\n",
       "\n",
       "<h3>Arguments</h3>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>height</code></td>\n",
       "<td>\n",
       "<p>either a vector or matrix of values describing the\n",
       "bars which make up the plot.  If <code>height</code> is a vector, the\n",
       "plot consists of a sequence of rectangular bars with heights\n",
       "given by the values in the vector.  If <code>height</code> is a matrix\n",
       "and <code>beside</code> is <code>FALSE</code> then each bar of the plot\n",
       "corresponds to a column of <code>height</code>, with the values in the\n",
       "column giving the heights of stacked sub-bars making up the\n",
       "bar.  If <code>height</code> is a matrix and <code>beside</code> is\n",
       "<code>TRUE</code>, then the values in each column are juxtaposed\n",
       "rather than stacked.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>width</code></td>\n",
       "<td>\n",
       "<p>optional vector of bar widths. Re-cycled to length the\n",
       "number of bars drawn.  Specifying a single value will have no\n",
       "visible effect unless <code>xlim</code> is specified.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>space</code></td>\n",
       "<td>\n",
       "<p>the amount of space (as a fraction of the average bar\n",
       "width) left before each bar.  May be given as a single number or\n",
       "one number per bar.  If <code>height</code> is a matrix and\n",
       "<code>beside</code> is <code>TRUE</code>, <code>space</code> may be specified by\n",
       "two numbers, where the first is the space between bars in the\n",
       "same group, and the second the space between the groups.  If not\n",
       "given explicitly, it defaults to <code>c(0,1)</code> if <code>height</code>\n",
       "is a matrix and <code>beside</code> is <code>TRUE</code>, and to 0.2\n",
       "otherwise.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>names.arg</code></td>\n",
       "<td>\n",
       "<p>a vector of names to be plotted below each bar or\n",
       "group of bars.  If this argument is omitted, then the names are\n",
       "taken from the <code>names</code> attribute of <code>height</code> if this\n",
       "is a vector, or the column names if it is a matrix.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>legend.text</code></td>\n",
       "<td>\n",
       "<p>a vector of text used to construct a legend for\n",
       "the plot, or a logical indicating whether a legend should be\n",
       "included.  This is only useful when <code>height</code> is a matrix.\n",
       "In that case given legend labels should correspond to the rows of\n",
       "<code>height</code>; if <code>legend.text</code> is true, the row names of\n",
       "<code>height</code> will be used as labels if they are non-null.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>beside</code></td>\n",
       "<td>\n",
       "<p>a logical value.  If <code>FALSE</code>, the columns of\n",
       "<code>height</code> are portrayed as stacked bars, and if <code>TRUE</code>\n",
       "the columns are portrayed as juxtaposed bars.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>horiz</code></td>\n",
       "<td>\n",
       "<p>a logical value.  If <code>FALSE</code>, the bars are drawn\n",
       "vertically with the first bar to the left.  If <code>TRUE</code>, the\n",
       "bars are drawn horizontally with the first at the bottom.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>density</code></td>\n",
       "<td>\n",
       "<p>a vector giving the density of shading lines, in\n",
       "lines per inch, for the bars or bar components.\n",
       "The default value of <code>NULL</code> means that no shading lines\n",
       "are drawn. Non-positive values of <code>density</code> also inhibit the\n",
       "drawing of shading lines.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>angle</code></td>\n",
       "<td>\n",
       "<p>the slope of shading lines, given as an angle in\n",
       "degrees (counter-clockwise), for the bars or bar components.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>col</code></td>\n",
       "<td>\n",
       "<p>a vector of colors for the bars or bar components.\n",
       "By default, grey is used if <code>height</code> is a vector, and a\n",
       "gamma-corrected grey palette if <code>height</code> is a matrix.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>border</code></td>\n",
       "<td>\n",
       "<p>the color to be used for the border of the bars.\n",
       "Use <code>border = NA</code> to omit borders.  If there are shading\n",
       "lines, <code>border = TRUE</code> means use the same colour for\n",
       "the border as for the shading lines.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>main,sub</code></td>\n",
       "<td>\n",
       "<p>overall and sub title for the plot.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>xlab</code></td>\n",
       "<td>\n",
       "<p>a label for the x axis.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>ylab</code></td>\n",
       "<td>\n",
       "<p>a label for the y axis.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>xlim</code></td>\n",
       "<td>\n",
       "<p>limits for the x axis.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>ylim</code></td>\n",
       "<td>\n",
       "<p>limits for the y axis.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>xpd</code></td>\n",
       "<td>\n",
       "<p>logical. Should bars be allowed to go outside region?</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>log</code></td>\n",
       "<td>\n",
       "<p>string specifying if axis scales should be logarithmic; see\n",
       "<code>plot.default</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>axes</code></td>\n",
       "<td>\n",
       "<p>logical.  If <code>TRUE</code>, a vertical (or horizontal, if\n",
       "<code>horiz</code> is true) axis is drawn.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>axisnames</code></td>\n",
       "<td>\n",
       "<p>logical.  If <code>TRUE</code>, and if there are\n",
       "<code>names.arg</code> (see above), the\n",
       "other axis is drawn (with <code>lty = 0</code>) and labeled.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>cex.axis</code></td>\n",
       "<td>\n",
       "<p>expansion factor for numeric axis labels.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>cex.names</code></td>\n",
       "<td>\n",
       "<p>expansion factor for axis names (bar labels).</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>inside</code></td>\n",
       "<td>\n",
       "<p>logical.  If <code>TRUE</code>, the lines which divide\n",
       "adjacent (non-stacked!) bars will be drawn.  Only applies when\n",
       "<code>space = 0</code> (which it partly is when <code>beside = TRUE</code>).</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>plot</code></td>\n",
       "<td>\n",
       "<p>logical.  If <code>FALSE</code>, nothing is plotted.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>axis.lty</code></td>\n",
       "<td>\n",
       "<p>the graphics parameter <code>lty</code> applied to the axis\n",
       "and tick marks of the categorical (default horizontal) axis.  Note\n",
       "that by default the axis is suppressed.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>offset</code></td>\n",
       "<td>\n",
       "<p>a vector indicating how much the bars should be shifted\n",
       "relative to the x axis.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>add</code></td>\n",
       "<td>\n",
       "<p>logical specifying if bars should be added to an already\n",
       "existing plot; defaults to <code>FALSE</code>.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>args.legend</code></td>\n",
       "<td>\n",
       "<p>list of additional arguments to pass to\n",
       "<code>legend()</code>; names of the list are used as argument\n",
       "names.  Only used if <code>legend.text</code> is supplied.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>...</code></td>\n",
       "<td>\n",
       "<p>arguments to be passed to/from other methods.  For the\n",
       "default method these can include further arguments (such as\n",
       "<code>axes</code>, <code>asp</code> and <code>main</code>) and\n",
       "graphical parameters (see <code>par</code>) which are passed to\n",
       "<code>plot.window()</code>, <code>title()</code> and\n",
       "<code>axis</code>.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "<h3>Details</h3>\n",
       "\n",
       "<p>This is a generic function, it currently only has a default method.\n",
       "A formula interface may be added eventually.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Value</h3>\n",
       "\n",
       "<p>A numeric vector (or matrix, when <code>beside = TRUE</code>), say\n",
       "<code>mp</code>, giving the coordinates of <em>all</em> the bar midpoints\n",
       "drawn, useful for adding to the graph.\n",
       "</p>\n",
       "<p>If <code>beside</code> is true, use <code>colMeans(mp)</code> for the\n",
       "midpoints of each <em>group</em> of bars, see example.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>References</h3>\n",
       "\n",
       "<p>Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)\n",
       "<em>The New S Language</em>.\n",
       "Wadsworth &amp; Brooks/Cole.\n",
       "</p>\n",
       "<p>Murrell, P. (2005) <em>R Graphics</em>. Chapman &amp; Hall/CRC Press.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>See Also</h3>\n",
       "\n",
       "<p><code>plot(..., type = \"h\")</code>, <code>dotchart</code>,\n",
       "<code>hist</code>.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Examples</h3>\n",
       "\n",
       "<pre>\n",
       "require(grDevices) # for colours\n",
       "tN &lt;- table(Ni &lt;- stats::rpois(100, lambda = 5))\n",
       "r &lt;- barplot(tN, col = rainbow(20))\n",
       "#- type = \"h\" plotting *is* 'bar'plot\n",
       "lines(r, tN, type = \"h\", col = \"red\", lwd = 2)\n",
       "\n",
       "barplot(tN, space = 1.5, axisnames = FALSE,\n",
       "        sub = \"barplot(..., space= 1.5, axisnames = FALSE)\")\n",
       "\n",
       "barplot(VADeaths, plot = FALSE)\n",
       "barplot(VADeaths, plot = FALSE, beside = TRUE)\n",
       "\n",
       "mp &lt;- barplot(VADeaths) # default\n",
       "tot &lt;- colMeans(VADeaths)\n",
       "text(mp, tot + 3, format(tot), xpd = TRUE, col = \"blue\")\n",
       "barplot(VADeaths, beside = TRUE,\n",
       "        col = c(\"lightblue\", \"mistyrose\", \"lightcyan\",\n",
       "                \"lavender\", \"cornsilk\"),\n",
       "        legend = rownames(VADeaths), ylim = c(0, 100))\n",
       "title(main = \"Death Rates in Virginia\", font.main = 4)\n",
       "\n",
       "hh &lt;- t(VADeaths)[, 5:1]\n",
       "mybarcol &lt;- \"gray20\"\n",
       "mp &lt;- barplot(hh, beside = TRUE,\n",
       "        col = c(\"lightblue\", \"mistyrose\",\n",
       "                \"lightcyan\", \"lavender\"),\n",
       "        legend = colnames(VADeaths), ylim = c(0,100),\n",
       "        main = \"Death Rates in Virginia\", font.main = 4,\n",
       "        sub = \"Faked upper 2*sigma error bars\", col.sub = mybarcol,\n",
       "        cex.names = 1.5)\n",
       "segments(mp, hh, mp, hh + 2*sqrt(1000*hh/100), col = mybarcol, lwd = 1.5)\n",
       "stopifnot(dim(mp) == dim(hh))  # corresponding matrices\n",
       "mtext(side = 1, at = colMeans(mp), line = -2,\n",
       "      text = paste(\"Mean\", formatC(colMeans(hh))), col = \"red\")\n",
       "\n",
       "# Bar shading example\n",
       "barplot(VADeaths, angle = 15+10*1:5, density = 20, col = \"black\",\n",
       "        legend = rownames(VADeaths))\n",
       "title(main = list(\"Death Rates in Virginia\", font = 4))\n",
       "\n",
       "# border :\n",
       "barplot(VADeaths, border = \"dark blue\") \n",
       "\n",
       "\n",
       "# log scales (not much sense here):\n",
       "barplot(tN, col = heat.colors(12), log = \"y\")\n",
       "barplot(tN, col = gray.colors(20), log = \"xy\")\n",
       "\n",
       "# args.legend\n",
       "barplot(height = cbind(x = c(465, 91) / 465 * 100,\n",
       "                       y = c(840, 200) / 840 * 100,\n",
       "                       z = c(37, 17) / 37 * 100),\n",
       "        beside = FALSE,\n",
       "        width = c(465, 840, 37),\n",
       "        col = c(1, 2),\n",
       "        legend.text = c(\"A\", \"B\"),\n",
       "        args.legend = list(x = \"topleft\"))\n",
       "</pre>\n",
       "\n",
       "<hr /><div style=\"text-align: center;\">[Package <em>graphics</em> version 3.2.2 ]</div>"
      ],
      "text/latex": [
       "\\inputencoding{utf8}\n",
       "\\HeaderA{barplot}{Bar Plots}{barplot}\n",
       "\\methaliasA{barplot.default}{barplot}{barplot.default}\n",
       "\\keyword{hplot}{barplot}\n",
       "%\n",
       "\\begin{Description}\\relax\n",
       "Creates a bar plot with vertical or horizontal bars.\n",
       "\\end{Description}\n",
       "%\n",
       "\\begin{Usage}\n",
       "\\begin{verbatim}\n",
       "barplot(height, ...)\n",
       "\n",
       "## Default S3 method:\n",
       "barplot(height, width = 1, space = NULL,\n",
       "        names.arg = NULL, legend.text = NULL, beside = FALSE,\n",
       "        horiz = FALSE, density = NULL, angle = 45,\n",
       "        col = NULL, border = par(\"fg\"),\n",
       "        main = NULL, sub = NULL, xlab = NULL, ylab = NULL,\n",
       "        xlim = NULL, ylim = NULL, xpd = TRUE, log = \"\",\n",
       "        axes = TRUE, axisnames = TRUE,\n",
       "        cex.axis = par(\"cex.axis\"), cex.names = par(\"cex.axis\"),\n",
       "        inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0,\n",
       "        add = FALSE, args.legend = NULL, ...)\n",
       "\\end{verbatim}\n",
       "\\end{Usage}\n",
       "%\n",
       "\\begin{Arguments}\n",
       "\\begin{ldescription}\n",
       "\\item[\\code{height}] either a vector or matrix of values describing the\n",
       "bars which make up the plot.  If \\code{height} is a vector, the\n",
       "plot consists of a sequence of rectangular bars with heights\n",
       "given by the values in the vector.  If \\code{height} is a matrix\n",
       "and \\code{beside} is \\code{FALSE} then each bar of the plot\n",
       "corresponds to a column of \\code{height}, with the values in the\n",
       "column giving the heights of stacked sub-bars making up the\n",
       "bar.  If \\code{height} is a matrix and \\code{beside} is\n",
       "\\code{TRUE}, then the values in each column are juxtaposed\n",
       "rather than stacked.\n",
       "\\item[\\code{width}] optional vector of bar widths. Re-cycled to length the\n",
       "number of bars drawn.  Specifying a single value will have no\n",
       "visible effect unless \\code{xlim} is specified.\n",
       "\\item[\\code{space}] the amount of space (as a fraction of the average bar\n",
       "width) left before each bar.  May be given as a single number or\n",
       "one number per bar.  If \\code{height} is a matrix and\n",
       "\\code{beside} is \\code{TRUE}, \\code{space} may be specified by\n",
       "two numbers, where the first is the space between bars in the\n",
       "same group, and the second the space between the groups.  If not\n",
       "given explicitly, it defaults to \\code{c(0,1)} if \\code{height}\n",
       "is a matrix and \\code{beside} is \\code{TRUE}, and to 0.2\n",
       "otherwise.\n",
       "\\item[\\code{names.arg}] a vector of names to be plotted below each bar or\n",
       "group of bars.  If this argument is omitted, then the names are\n",
       "taken from the \\code{names} attribute of \\code{height} if this\n",
       "is a vector, or the column names if it is a matrix.\n",
       "\\item[\\code{legend.text}] a vector of text used to construct a legend for\n",
       "the plot, or a logical indicating whether a legend should be\n",
       "included.  This is only useful when \\code{height} is a matrix.\n",
       "In that case given legend labels should correspond to the rows of\n",
       "\\code{height}; if \\code{legend.text} is true, the row names of\n",
       "\\code{height} will be used as labels if they are non-null.\n",
       "\\item[\\code{beside}] a logical value.  If \\code{FALSE}, the columns of\n",
       "\\code{height} are portrayed as stacked bars, and if \\code{TRUE}\n",
       "the columns are portrayed as juxtaposed bars.\n",
       "\\item[\\code{horiz}] a logical value.  If \\code{FALSE}, the bars are drawn\n",
       "vertically with the first bar to the left.  If \\code{TRUE}, the\n",
       "bars are drawn horizontally with the first at the bottom.\n",
       "\\item[\\code{density}] a vector giving the density of shading lines, in\n",
       "lines per inch, for the bars or bar components.\n",
       "The default value of \\code{NULL} means that no shading lines\n",
       "are drawn. Non-positive values of \\code{density} also inhibit the\n",
       "drawing of shading lines.\n",
       "\\item[\\code{angle}] the slope of shading lines, given as an angle in\n",
       "degrees (counter-clockwise), for the bars or bar components.\n",
       "\\item[\\code{col}] a vector of colors for the bars or bar components.\n",
       "By default, grey is used if \\code{height} is a vector, and a\n",
       "gamma-corrected grey palette if \\code{height} is a matrix.\n",
       "\\item[\\code{border}] the color to be used for the border of the bars.\n",
       "Use \\code{border = NA} to omit borders.  If there are shading\n",
       "lines, \\code{border = TRUE} means use the same colour for\n",
       "the border as for the shading lines.\n",
       "\\item[\\code{main,sub}] overall and sub title for the plot.\n",
       "\\item[\\code{xlab}] a label for the x axis.\n",
       "\\item[\\code{ylab}] a label for the y axis.\n",
       "\\item[\\code{xlim}] limits for the x axis.\n",
       "\\item[\\code{ylim}] limits for the y axis.\n",
       "\\item[\\code{xpd}] logical. Should bars be allowed to go outside region?\n",
       "\\item[\\code{log}] string specifying if axis scales should be logarithmic; see\n",
       "\\code{\\LinkA{plot.default}{plot.default}}.\n",
       "\\item[\\code{axes}] logical.  If \\code{TRUE}, a vertical (or horizontal, if\n",
       "\\code{horiz} is true) axis is drawn.\n",
       "\\item[\\code{axisnames}] logical.  If \\code{TRUE}, and if there are\n",
       "\\code{names.arg} (see above), the\n",
       "other axis is drawn (with \\code{lty = 0}) and labeled.\n",
       "\\item[\\code{cex.axis}] expansion factor for numeric axis labels.\n",
       "\\item[\\code{cex.names}] expansion factor for axis names (bar labels).\n",
       "\\item[\\code{inside}] logical.  If \\code{TRUE}, the lines which divide\n",
       "adjacent (non-stacked!) bars will be drawn.  Only applies when\n",
       "\\code{space = 0} (which it partly is when \\code{beside = TRUE}).\n",
       "\n",
       "\n",
       "\\item[\\code{plot}] logical.  If \\code{FALSE}, nothing is plotted.\n",
       "\\item[\\code{axis.lty}] the graphics parameter \\code{lty} applied to the axis\n",
       "and tick marks of the categorical (default horizontal) axis.  Note\n",
       "that by default the axis is suppressed.\n",
       "\\item[\\code{offset}] a vector indicating how much the bars should be shifted\n",
       "relative to the x axis.\n",
       "\\item[\\code{add}] logical specifying if bars should be added to an already\n",
       "existing plot; defaults to \\code{FALSE}.\n",
       "\\item[\\code{args.legend}] list of additional arguments to pass to\n",
       "\\code{\\LinkA{legend}{legend}()}; names of the list are used as argument\n",
       "names.  Only used if \\code{legend.text} is supplied.\n",
       "\\item[\\code{...}] arguments to be passed to/from other methods.  For the\n",
       "default method these can include further arguments (such as\n",
       "\\code{axes}, \\code{asp} and \\code{main}) and\n",
       "\\LinkA{graphical parameters}{graphical parameters} (see \\code{\\LinkA{par}{par}}) which are passed to\n",
       "\\code{\\LinkA{plot.window}{plot.window}()}, \\code{\\LinkA{title}{title}()} and\n",
       "\\code{\\LinkA{axis}{axis}}.\n",
       "\\end{ldescription}\n",
       "\\end{Arguments}\n",
       "%\n",
       "\\begin{Details}\\relax\n",
       "This is a generic function, it currently only has a default method.\n",
       "A formula interface may be added eventually.\n",
       "\\end{Details}\n",
       "%\n",
       "\\begin{Value}\n",
       "A numeric vector (or matrix, when \\code{beside = TRUE}), say\n",
       "\\code{mp}, giving the coordinates of \\emph{all} the bar midpoints\n",
       "drawn, useful for adding to the graph.\n",
       "\n",
       "If \\code{beside} is true, use \\code{colMeans(mp)} for the\n",
       "midpoints of each \\emph{group} of bars, see example.\n",
       "\\end{Value}\n",
       "%\n",
       "\\begin{References}\\relax\n",
       "Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)\n",
       "\\emph{The New S Language}.\n",
       "Wadsworth \\& Brooks/Cole.\n",
       "\n",
       "Murrell, P. (2005) \\emph{R Graphics}. Chapman \\& Hall/CRC Press.\n",
       "\\end{References}\n",
       "%\n",
       "\\begin{SeeAlso}\\relax\n",
       "\\code{\\LinkA{plot}{plot}(..., type = \"h\")}, \\code{\\LinkA{dotchart}{dotchart}},\n",
       "\\code{\\LinkA{hist}{hist}}.\n",
       "\\end{SeeAlso}\n",
       "%\n",
       "\\begin{Examples}\n",
       "\\begin{ExampleCode}\n",
       "require(grDevices) # for colours\n",
       "tN <- table(Ni <- stats::rpois(100, lambda = 5))\n",
       "r <- barplot(tN, col = rainbow(20))\n",
       "#- type = \"h\" plotting *is* 'bar'plot\n",
       "lines(r, tN, type = \"h\", col = \"red\", lwd = 2)\n",
       "\n",
       "barplot(tN, space = 1.5, axisnames = FALSE,\n",
       "        sub = \"barplot(..., space= 1.5, axisnames = FALSE)\")\n",
       "\n",
       "barplot(VADeaths, plot = FALSE)\n",
       "barplot(VADeaths, plot = FALSE, beside = TRUE)\n",
       "\n",
       "mp <- barplot(VADeaths) # default\n",
       "tot <- colMeans(VADeaths)\n",
       "text(mp, tot + 3, format(tot), xpd = TRUE, col = \"blue\")\n",
       "barplot(VADeaths, beside = TRUE,\n",
       "        col = c(\"lightblue\", \"mistyrose\", \"lightcyan\",\n",
       "                \"lavender\", \"cornsilk\"),\n",
       "        legend = rownames(VADeaths), ylim = c(0, 100))\n",
       "title(main = \"Death Rates in Virginia\", font.main = 4)\n",
       "\n",
       "hh <- t(VADeaths)[, 5:1]\n",
       "mybarcol <- \"gray20\"\n",
       "mp <- barplot(hh, beside = TRUE,\n",
       "        col = c(\"lightblue\", \"mistyrose\",\n",
       "                \"lightcyan\", \"lavender\"),\n",
       "        legend = colnames(VADeaths), ylim = c(0,100),\n",
       "        main = \"Death Rates in Virginia\", font.main = 4,\n",
       "        sub = \"Faked upper 2*sigma error bars\", col.sub = mybarcol,\n",
       "        cex.names = 1.5)\n",
       "segments(mp, hh, mp, hh + 2*sqrt(1000*hh/100), col = mybarcol, lwd = 1.5)\n",
       "stopifnot(dim(mp) == dim(hh))  # corresponding matrices\n",
       "mtext(side = 1, at = colMeans(mp), line = -2,\n",
       "      text = paste(\"Mean\", formatC(colMeans(hh))), col = \"red\")\n",
       "\n",
       "# Bar shading example\n",
       "barplot(VADeaths, angle = 15+10*1:5, density = 20, col = \"black\",\n",
       "        legend = rownames(VADeaths))\n",
       "title(main = list(\"Death Rates in Virginia\", font = 4))\n",
       "\n",
       "# border :\n",
       "barplot(VADeaths, border = \"dark blue\") \n",
       "\n",
       "\n",
       "# log scales (not much sense here):\n",
       "barplot(tN, col = heat.colors(12), log = \"y\")\n",
       "barplot(tN, col = gray.colors(20), log = \"xy\")\n",
       "\n",
       "# args.legend\n",
       "barplot(height = cbind(x = c(465, 91) / 465 * 100,\n",
       "                       y = c(840, 200) / 840 * 100,\n",
       "                       z = c(37, 17) / 37 * 100),\n",
       "        beside = FALSE,\n",
       "        width = c(465, 840, 37),\n",
       "        col = c(1, 2),\n",
       "        legend.text = c(\"A\", \"B\"),\n",
       "        args.legend = list(x = \"topleft\"))\n",
       "\\end{ExampleCode}\n",
       "\\end{Examples}"
      ],
      "text/plain": [
       "barplot                package:graphics                R Documentation\n",
       "\n",
       "_\bB_\ba_\br _\bP_\bl_\bo_\bt_\bs\n",
       "\n",
       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
       "\n",
       "     Creates a bar plot with vertical or horizontal bars.\n",
       "\n",
       "_\bU_\bs_\ba_\bg_\be:\n",
       "\n",
       "     barplot(height, ...)\n",
       "     \n",
       "     ## Default S3 method:\n",
       "     barplot(height, width = 1, space = NULL,\n",
       "             names.arg = NULL, legend.text = NULL, beside = FALSE,\n",
       "             horiz = FALSE, density = NULL, angle = 45,\n",
       "             col = NULL, border = par(\"fg\"),\n",
       "             main = NULL, sub = NULL, xlab = NULL, ylab = NULL,\n",
       "             xlim = NULL, ylim = NULL, xpd = TRUE, log = \"\",\n",
       "             axes = TRUE, axisnames = TRUE,\n",
       "             cex.axis = par(\"cex.axis\"), cex.names = par(\"cex.axis\"),\n",
       "             inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0,\n",
       "             add = FALSE, args.legend = NULL, ...)\n",
       "     \n",
       "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n",
       "\n",
       "  height: either a vector or matrix of values describing the bars which\n",
       "          make up the plot.  If ‘height’ is a vector, the plot consists\n",
       "          of a sequence of rectangular bars with heights given by the\n",
       "          values in the vector.  If ‘height’ is a matrix and ‘beside’\n",
       "          is ‘FALSE’ then each bar of the plot corresponds to a column\n",
       "          of ‘height’, with the values in the column giving the heights\n",
       "          of stacked sub-bars making up the bar.  If ‘height’ is a\n",
       "          matrix and ‘beside’ is ‘TRUE’, then the values in each column\n",
       "          are juxtaposed rather than stacked.\n",
       "\n",
       "   width: optional vector of bar widths. Re-cycled to length the number\n",
       "          of bars drawn.  Specifying a single value will have no\n",
       "          visible effect unless ‘xlim’ is specified.\n",
       "\n",
       "   space: the amount of space (as a fraction of the average bar width)\n",
       "          left before each bar.  May be given as a single number or one\n",
       "          number per bar.  If ‘height’ is a matrix and ‘beside’ is\n",
       "          ‘TRUE’, ‘space’ may be specified by two numbers, where the\n",
       "          first is the space between bars in the same group, and the\n",
       "          second the space between the groups.  If not given\n",
       "          explicitly, it defaults to ‘c(0,1)’ if ‘height’ is a matrix\n",
       "          and ‘beside’ is ‘TRUE’, and to 0.2 otherwise.\n",
       "\n",
       "names.arg: a vector of names to be plotted below each bar or group of\n",
       "          bars.  If this argument is omitted, then the names are taken\n",
       "          from the ‘names’ attribute of ‘height’ if this is a vector,\n",
       "          or the column names if it is a matrix.\n",
       "\n",
       "legend.text: a vector of text used to construct a legend for the plot,\n",
       "          or a logical indicating whether a legend should be included.\n",
       "          This is only useful when ‘height’ is a matrix.  In that case\n",
       "          given legend labels should correspond to the rows of\n",
       "          ‘height’; if ‘legend.text’ is true, the row names of ‘height’\n",
       "          will be used as labels if they are non-null.\n",
       "\n",
       "  beside: a logical value.  If ‘FALSE’, the columns of ‘height’ are\n",
       "          portrayed as stacked bars, and if ‘TRUE’ the columns are\n",
       "          portrayed as juxtaposed bars.\n",
       "\n",
       "   horiz: a logical value.  If ‘FALSE’, the bars are drawn vertically\n",
       "          with the first bar to the left.  If ‘TRUE’, the bars are\n",
       "          drawn horizontally with the first at the bottom.\n",
       "\n",
       " density: a vector giving the density of shading lines, in lines per\n",
       "          inch, for the bars or bar components.  The default value of\n",
       "          ‘NULL’ means that no shading lines are drawn. Non-positive\n",
       "          values of ‘density’ also inhibit the drawing of shading\n",
       "          lines.\n",
       "\n",
       "   angle: the slope of shading lines, given as an angle in degrees\n",
       "          (counter-clockwise), for the bars or bar components.\n",
       "\n",
       "     col: a vector of colors for the bars or bar components.  By\n",
       "          default, grey is used if ‘height’ is a vector, and a\n",
       "          gamma-corrected grey palette if ‘height’ is a matrix.\n",
       "\n",
       "  border: the color to be used for the border of the bars.  Use ‘border\n",
       "          = NA’ to omit borders.  If there are shading lines, ‘border =\n",
       "          TRUE’ means use the same colour for the border as for the\n",
       "          shading lines.\n",
       "\n",
       "main,sub: overall and sub title for the plot.\n",
       "\n",
       "    xlab: a label for the x axis.\n",
       "\n",
       "    ylab: a label for the y axis.\n",
       "\n",
       "    xlim: limits for the x axis.\n",
       "\n",
       "    ylim: limits for the y axis.\n",
       "\n",
       "     xpd: logical. Should bars be allowed to go outside region?\n",
       "\n",
       "     log: string specifying if axis scales should be logarithmic; see\n",
       "          ‘plot.default’.\n",
       "\n",
       "    axes: logical.  If ‘TRUE’, a vertical (or horizontal, if ‘horiz’ is\n",
       "          true) axis is drawn.\n",
       "\n",
       "axisnames: logical.  If ‘TRUE’, and if there are ‘names.arg’ (see\n",
       "          above), the other axis is drawn (with ‘lty = 0’) and labeled.\n",
       "\n",
       "cex.axis: expansion factor for numeric axis labels.\n",
       "\n",
       "cex.names: expansion factor for axis names (bar labels).\n",
       "\n",
       "  inside: logical.  If ‘TRUE’, the lines which divide adjacent\n",
       "          (non-stacked!) bars will be drawn.  Only applies when ‘space\n",
       "          = 0’ (which it partly is when ‘beside = TRUE’).\n",
       "\n",
       "    plot: logical.  If ‘FALSE’, nothing is plotted.\n",
       "\n",
       "axis.lty: the graphics parameter ‘lty’ applied to the axis and tick\n",
       "          marks of the categorical (default horizontal) axis.  Note\n",
       "          that by default the axis is suppressed.\n",
       "\n",
       "  offset: a vector indicating how much the bars should be shifted\n",
       "          relative to the x axis.\n",
       "\n",
       "     add: logical specifying if bars should be added to an already\n",
       "          existing plot; defaults to ‘FALSE’.\n",
       "\n",
       "args.legend: list of additional arguments to pass to ‘legend()’; names\n",
       "          of the list are used as argument names.  Only used if\n",
       "          ‘legend.text’ is supplied.\n",
       "\n",
       "     ...: arguments to be passed to/from other methods.  For the\n",
       "          default method these can include further arguments (such as\n",
       "          ‘axes’, ‘asp’ and ‘main’) and graphical parameters (see\n",
       "          ‘par’) which are passed to ‘plot.window()’, ‘title()’ and\n",
       "          ‘axis’.\n",
       "\n",
       "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n",
       "\n",
       "     This is a generic function, it currently only has a default\n",
       "     method.  A formula interface may be added eventually.\n",
       "\n",
       "_\bV_\ba_\bl_\bu_\be:\n",
       "\n",
       "     A numeric vector (or matrix, when ‘beside = TRUE’), say ‘mp’,\n",
       "     giving the coordinates of _all_ the bar midpoints drawn, useful\n",
       "     for adding to the graph.\n",
       "\n",
       "     If ‘beside’ is true, use ‘colMeans(mp)’ for the midpoints of each\n",
       "     _group_ of bars, see example.\n",
       "\n",
       "_\bR_\be_\bf_\be_\br_\be_\bn_\bc_\be_\bs:\n",
       "\n",
       "     Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S\n",
       "     Language_.  Wadsworth & Brooks/Cole.\n",
       "\n",
       "     Murrell, P. (2005) _R Graphics_. Chapman & Hall/CRC Press.\n",
       "\n",
       "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n",
       "\n",
       "     ‘plot(..., type = \"h\")’, ‘dotchart’, ‘hist’.\n",
       "\n",
       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
       "\n",
       "     require(grDevices) # for colours\n",
       "     tN <- table(Ni <- stats::rpois(100, lambda = 5))\n",
       "     r <- barplot(tN, col = rainbow(20))\n",
       "     #- type = \"h\" plotting *is* 'bar'plot\n",
       "     lines(r, tN, type = \"h\", col = \"red\", lwd = 2)\n",
       "     \n",
       "     barplot(tN, space = 1.5, axisnames = FALSE,\n",
       "             sub = \"barplot(..., space= 1.5, axisnames = FALSE)\")\n",
       "     \n",
       "     barplot(VADeaths, plot = FALSE)\n",
       "     barplot(VADeaths, plot = FALSE, beside = TRUE)\n",
       "     \n",
       "     mp <- barplot(VADeaths) # default\n",
       "     tot <- colMeans(VADeaths)\n",
       "     text(mp, tot + 3, format(tot), xpd = TRUE, col = \"blue\")\n",
       "     barplot(VADeaths, beside = TRUE,\n",
       "             col = c(\"lightblue\", \"mistyrose\", \"lightcyan\",\n",
       "                     \"lavender\", \"cornsilk\"),\n",
       "             legend = rownames(VADeaths), ylim = c(0, 100))\n",
       "     title(main = \"Death Rates in Virginia\", font.main = 4)\n",
       "     \n",
       "     hh <- t(VADeaths)[, 5:1]\n",
       "     mybarcol <- \"gray20\"\n",
       "     mp <- barplot(hh, beside = TRUE,\n",
       "             col = c(\"lightblue\", \"mistyrose\",\n",
       "                     \"lightcyan\", \"lavender\"),\n",
       "             legend = colnames(VADeaths), ylim = c(0,100),\n",
       "             main = \"Death Rates in Virginia\", font.main = 4,\n",
       "             sub = \"Faked upper 2*sigma error bars\", col.sub = mybarcol,\n",
       "             cex.names = 1.5)\n",
       "     segments(mp, hh, mp, hh + 2*sqrt(1000*hh/100), col = mybarcol, lwd = 1.5)\n",
       "     stopifnot(dim(mp) == dim(hh))  # corresponding matrices\n",
       "     mtext(side = 1, at = colMeans(mp), line = -2,\n",
       "           text = paste(\"Mean\", formatC(colMeans(hh))), col = \"red\")\n",
       "     \n",
       "     # Bar shading example\n",
       "     barplot(VADeaths, angle = 15+10*1:5, density = 20, col = \"black\",\n",
       "             legend = rownames(VADeaths))\n",
       "     title(main = list(\"Death Rates in Virginia\", font = 4))\n",
       "     \n",
       "     # border :\n",
       "     barplot(VADeaths, border = \"dark blue\") \n",
       "     \n",
       "     \n",
       "     # log scales (not much sense here):\n",
       "     barplot(tN, col = heat.colors(12), log = \"y\")\n",
       "     barplot(tN, col = gray.colors(20), log = \"xy\")\n",
       "     \n",
       "     # args.legend\n",
       "     barplot(height = cbind(x = c(465, 91) / 465 * 100,\n",
       "                            y = c(840, 200) / 840 * 100,\n",
       "                            z = c(37, 17) / 37 * 100),\n",
       "             beside = FALSE,\n",
       "             width = c(465, 840, 37),\n",
       "             col = c(1, 2),\n",
       "             legend.text = c(\"A\", \"B\"),\n",
       "             args.legend = list(x = \"topleft\"))\n",
       "     "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "help(barplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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