From ada94fe3754bb60574173ca9aacdf1b3f770383a Mon Sep 17 00:00:00 2001
From: Jean Cury <jean.cury@normalesup.org>
Date: Wed, 17 Jan 2018 20:17:10 +0100
Subject: [PATCH] Add notebook with main figures

---
 Figure_Final.ipynb | 4737 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 4737 insertions(+)
 create mode 100644 Figure_Final.ipynb

diff --git a/Figure_Final.ipynb b/Figure_Final.ipynb
new file mode 100644
index 0000000..6c8fd4c
--- /dev/null
+++ b/Figure_Final.ipynb
@@ -0,0 +1,4737 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": "true"
+   },
+   "source": [
+    "# Table of Contents\n",
+    " <p><div class=\"lev1\"><a href=\"#Host-range-expansion-and-genetic-plasticity-drive-the-trade-off-between-integrative-and-extrachromosomal-mobile-genetic-elements\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Host range expansion and genetic plasticity drive the trade-off between integrative and extrachromosomal mobile genetic elements</a></div><div class=\"lev1\"><a href=\"#Import-and-defition\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Import and defition</a></div><div class=\"lev2\"><a href=\"#Functions\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Functions</a></div><div class=\"lev2\"><a href=\"#Variable-(dic-color,-name,-etc..)\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Variable (dic color, name, etc..)</a></div><div class=\"lev2\"><a href=\"#Add-color-and-property-to-the-elements-for-plotting\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Add color and property to the elements for plotting</a></div><div class=\"lev1\"><a href=\"#Figure-1-:-distribution-of-GC-diff,-size,-repeats\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Figure 1 : distribution of GC diff, size, repeats</a></div><div class=\"lev1\"><a href=\"#Figure-2:-Comparison-of-function\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Figure 2: Comparison of function</a></div><div class=\"lev1\"><a href=\"#Figure-3\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Figure 3</a></div><div class=\"lev2\"><a href=\"#Supp-mat:-no-MPF\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Supp mat: no MPF</a></div><div class=\"lev1\"><a href=\"#Figure-4\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Figure 4</a></div><div class=\"lev2\"><a href=\"#Supp-mat\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Supp mat</a></div><div class=\"lev1\"><a href=\"#Figure-5\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Figure 5</a></div><div class=\"lev1\"><a href=\"#Figure-6\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Figure 6</a></div><div class=\"lev2\"><a href=\"#Binomial-test-on-the-rectangle-zone\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span>Binomial test on the rectangle zone</a></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 4,
+        "hidden": false,
+        "row": 0,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Host range expansion and genetic plasticity drive the trade-off between integrative and extrachromosomal mobile genetic elements\n",
+    "\n",
+    "The goal here, is to provide the code necessary to reproduce the figure and stats used in the corresponding article."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": false,
+        "row": 0,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Import and defition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import scipy.stats as ss\n",
+    "from scipy.stats import binom_test\n",
+    "%matplotlib inline\n",
+    "sns.set_style(\"ticks\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_conj = pd.read_table(\"Tables/Table_2_Table_count_BIG_TABLE_typeT_1116B_ok_community_repeats.txt\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 10,
+        "hidden": false,
+        "row": 4,
+        "width": 12
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ATB_res</th>\n",
+       "      <th>CELLULAR PROCESSES AND SIGNALING</th>\n",
+       "      <th>DDE_Transposase</th>\n",
+       "      <th>Eex</th>\n",
+       "      <th>INFORMATION STORAGE AND PROCESSING</th>\n",
+       "      <th>Integrase</th>\n",
+       "      <th>METABOLISM</th>\n",
+       "      <th>MOB</th>\n",
+       "      <th>MPF</th>\n",
+       "      <th>N_prot</th>\n",
+       "      <th>...</th>\n",
+       "      <th>End_ICE</th>\n",
+       "      <th>N_repeats</th>\n",
+       "      <th>Close.dir</th>\n",
+       "      <th>Close.inv</th>\n",
+       "      <th>Distant.dir</th>\n",
+       "      <th>Distant.inv</th>\n",
+       "      <th>Overlap.dir</th>\n",
+       "      <th>Palindrome.inv</th>\n",
+       "      <th>Tandem.dir</th>\n",
+       "      <th>N_repeats_norm</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICE_ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BUAM001.B.00001.P004_typeT_1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>301592</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUAM001.B.00002.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>43581</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUAM001.B.00003.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>43581</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUCE001.B.00003.C001_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2265989</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUCE001.B.00006.C001_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2422691</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              ATB_res  CELLULAR PROCESSES AND SIGNALING  \\\n",
+       "ICE_ID                                                                    \n",
+       "BUAM001.B.00001.P004_typeT_1      1.0                              42.0   \n",
+       "BUAM001.B.00002.P004_typeT_1      0.0                              18.0   \n",
+       "BUAM001.B.00003.P004_typeT_1      0.0                              18.0   \n",
+       "BUCE001.B.00003.C001_typeT_1      0.0                              13.0   \n",
+       "BUCE001.B.00006.C001_typeT_1      0.0                              15.0   \n",
+       "\n",
+       "                              DDE_Transposase  Eex  \\\n",
+       "ICE_ID                                               \n",
+       "BUAM001.B.00001.P004_typeT_1              6.0  0.0   \n",
+       "BUAM001.B.00002.P004_typeT_1              0.0  0.0   \n",
+       "BUAM001.B.00003.P004_typeT_1              0.0  0.0   \n",
+       "BUCE001.B.00003.C001_typeT_1              2.0  1.0   \n",
+       "BUCE001.B.00006.C001_typeT_1              0.0  0.0   \n",
+       "\n",
+       "                              INFORMATION STORAGE AND PROCESSING  Integrase  \\\n",
+       "ICE_ID                                                                        \n",
+       "BUAM001.B.00001.P004_typeT_1                                35.0        6.0   \n",
+       "BUAM001.B.00002.P004_typeT_1                                 6.0        0.0   \n",
+       "BUAM001.B.00003.P004_typeT_1                                 6.0        0.0   \n",
+       "BUCE001.B.00003.C001_typeT_1                                12.0        1.0   \n",
+       "BUCE001.B.00006.C001_typeT_1                                13.0        1.0   \n",
+       "\n",
+       "                              METABOLISM  MOB  MPF  N_prot       ...        \\\n",
+       "ICE_ID                                                           ...         \n",
+       "BUAM001.B.00001.P004_typeT_1         9.0  1.0  4.0   266.0       ...         \n",
+       "BUAM001.B.00002.P004_typeT_1         0.0  1.0  8.0    47.0       ...         \n",
+       "BUAM001.B.00003.P004_typeT_1         0.0  1.0  8.0    47.0       ...         \n",
+       "BUCE001.B.00003.C001_typeT_1        12.0  1.0  8.0    52.0       ...         \n",
+       "BUCE001.B.00006.C001_typeT_1         4.0  1.0  8.0    55.0       ...         \n",
+       "\n",
+       "                              End_ICE  N_repeats  Close.dir  Close.inv  \\\n",
+       "ICE_ID                                                                   \n",
+       "BUAM001.B.00001.P004_typeT_1   301592       30.0        5.0        1.0   \n",
+       "BUAM001.B.00002.P004_typeT_1    43581       11.0        1.0        0.0   \n",
+       "BUAM001.B.00003.P004_typeT_1    43581       11.0        1.0        0.0   \n",
+       "BUCE001.B.00003.C001_typeT_1  2265989        4.0        3.0        0.0   \n",
+       "BUCE001.B.00006.C001_typeT_1  2422691        1.0        0.0        0.0   \n",
+       "\n",
+       "                              Distant.dir  Distant.inv  Overlap.dir  \\\n",
+       "ICE_ID                                                                \n",
+       "BUAM001.B.00001.P004_typeT_1         13.0          5.0          6.0   \n",
+       "BUAM001.B.00002.P004_typeT_1          7.0          2.0          1.0   \n",
+       "BUAM001.B.00003.P004_typeT_1          7.0          2.0          1.0   \n",
+       "BUCE001.B.00003.C001_typeT_1          1.0          0.0          0.0   \n",
+       "BUCE001.B.00006.C001_typeT_1          1.0          0.0          0.0   \n",
+       "\n",
+       "                              Palindrome.inv  Tandem.dir  N_repeats_norm  \n",
+       "ICE_ID                                                                    \n",
+       "BUAM001.B.00001.P004_typeT_1             0.0         0.0        0.000099  \n",
+       "BUAM001.B.00002.P004_typeT_1             0.0         0.0        0.000252  \n",
+       "BUAM001.B.00003.P004_typeT_1             0.0         0.0        0.000252  \n",
+       "BUCE001.B.00003.C001_typeT_1             0.0         0.0        0.000073  \n",
+       "BUCE001.B.00006.C001_typeT_1             0.0         0.0        0.000016  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_conj.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_func_count = pd.read_table(\"Tables/TABLE_COUNT_FUNCTION_per_ICECP_1116a_typeT.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 7,
+        "hidden": false,
+        "row": 14,
+        "width": 7
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ICE_ID</th>\n",
+       "      <th>replicon_type</th>\n",
+       "      <th>size_replicon</th>\n",
+       "      <th>N_prot</th>\n",
+       "      <th>Class</th>\n",
+       "      <th>Function</th>\n",
+       "      <th>Function_count</th>\n",
+       "      <th>Function_freq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>P</td>\n",
+       "      <td>301592.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>Betaproteobacteria</td>\n",
+       "      <td>ATB_res</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>P</td>\n",
+       "      <td>301592.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>Betaproteobacteria</td>\n",
+       "      <td>Integrase</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.022556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>P</td>\n",
+       "      <td>301592.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>Betaproteobacteria</td>\n",
+       "      <td>METABOLISM</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.033835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>P</td>\n",
+       "      <td>301592.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>Betaproteobacteria</td>\n",
+       "      <td>integron</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>P</td>\n",
+       "      <td>301592.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>Betaproteobacteria</td>\n",
+       "      <td>Partition_System</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003759</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         ICE_ID replicon_type  size_replicon  N_prot  \\\n",
+       "0  BUAM001.B.00001.P004_typeT_1             P       301592.0   266.0   \n",
+       "1  BUAM001.B.00001.P004_typeT_1             P       301592.0   266.0   \n",
+       "2  BUAM001.B.00001.P004_typeT_1             P       301592.0   266.0   \n",
+       "3  BUAM001.B.00001.P004_typeT_1             P       301592.0   266.0   \n",
+       "4  BUAM001.B.00001.P004_typeT_1             P       301592.0   266.0   \n",
+       "\n",
+       "                Class          Function  Function_count  Function_freq  \n",
+       "0  Betaproteobacteria           ATB_res             1.0       0.003759  \n",
+       "1  Betaproteobacteria         Integrase             6.0       0.022556  \n",
+       "2  Betaproteobacteria        METABOLISM             9.0       0.033835  \n",
+       "3  Betaproteobacteria          integron             0.0       0.000000  \n",
+       "4  Betaproteobacteria  Partition_System             1.0       0.003759  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_func_count.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_grr = pd.read_table(\"Tables/Table_3_Table_GRR_full.txt\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 7,
+        "hidden": false,
+        "row": 21,
+        "width": 12
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ICE_ID_1</th>\n",
+       "      <th>ICE_ID_2</th>\n",
+       "      <th>GRR</th>\n",
+       "      <th>dist_phylo</th>\n",
+       "      <th>replicon_type_1</th>\n",
+       "      <th>replicon_type_2</th>\n",
+       "      <th>RepType_comparison</th>\n",
+       "      <th>Mahalanobis_Pval_1</th>\n",
+       "      <th>Mahalanobis_Pval_2</th>\n",
+       "      <th>Mah_P_inf_ice</th>\n",
+       "      <th>GRR_cat</th>\n",
+       "      <th>dist_phylo_cat</th>\n",
+       "      <th>Inc_type_1</th>\n",
+       "      <th>Inc_type_2</th>\n",
+       "      <th>Same_inc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>5.368511</td>\n",
+       "      <td>0.004952</td>\n",
+       "      <td>P</td>\n",
+       "      <td>P</td>\n",
+       "      <td>CP-CP</td>\n",
+       "      <td>0.051966</td>\n",
+       "      <td>0.979681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.003162</td>\n",
+       "      <td>IncP</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BUAM001.B.00003.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>5.368511</td>\n",
+       "      <td>0.004952</td>\n",
+       "      <td>P</td>\n",
+       "      <td>P</td>\n",
+       "      <td>CP-CP</td>\n",
+       "      <td>0.058989</td>\n",
+       "      <td>0.979681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.003162</td>\n",
+       "      <td>IncP</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BUCE001.B.00003.C001_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>2.866250</td>\n",
+       "      <td>0.028665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>P</td>\n",
+       "      <td>ICE-CP</td>\n",
+       "      <td>0.737052</td>\n",
+       "      <td>0.979681</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BUCE001.B.00006.C001_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>1.642364</td>\n",
+       "      <td>0.027069</td>\n",
+       "      <td>C</td>\n",
+       "      <td>P</td>\n",
+       "      <td>ICE-CP</td>\n",
+       "      <td>0.876676</td>\n",
+       "      <td>0.979681</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BUCE002.B.00006.C002_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>1.421587</td>\n",
+       "      <td>0.029150</td>\n",
+       "      <td>C</td>\n",
+       "      <td>P</td>\n",
+       "      <td>ICE-CP</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.979681</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       ICE_ID_1                      ICE_ID_2       GRR  \\\n",
+       "0  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1  5.368511   \n",
+       "1  BUAM001.B.00003.P004_typeT_1  BUAM001.B.00001.P004_typeT_1  5.368511   \n",
+       "2  BUCE001.B.00003.C001_typeT_1  BUAM001.B.00001.P004_typeT_1  2.866250   \n",
+       "3  BUCE001.B.00006.C001_typeT_1  BUAM001.B.00001.P004_typeT_1  1.642364   \n",
+       "4  BUCE002.B.00006.C002_typeT_1  BUAM001.B.00001.P004_typeT_1  1.421587   \n",
+       "\n",
+       "   dist_phylo replicon_type_1 replicon_type_2 RepType_comparison  \\\n",
+       "0    0.004952               P               P              CP-CP   \n",
+       "1    0.004952               P               P              CP-CP   \n",
+       "2    0.028665               C               P             ICE-CP   \n",
+       "3    0.027069               C               P             ICE-CP   \n",
+       "4    0.029150               C               P             ICE-CP   \n",
+       "\n",
+       "   Mahalanobis_Pval_1  Mahalanobis_Pval_2  Mah_P_inf_ice  GRR_cat  \\\n",
+       "0            0.051966            0.979681            NaN      0.0   \n",
+       "1            0.058989            0.979681            NaN      0.0   \n",
+       "2            0.737052            0.979681            1.0      0.0   \n",
+       "3            0.876676            0.979681            1.0      0.0   \n",
+       "4            1.000000            0.979681            0.0      0.0   \n",
+       "\n",
+       "   dist_phylo_cat Inc_type_1 Inc_type_2 Same_inc  \n",
+       "0        0.003162       IncP         ND    False  \n",
+       "1        0.003162       IncP         ND    False  \n",
+       "2        0.010000         ND         ND     True  \n",
+       "3        0.010000         ND         ND     True  \n",
+       "4        0.010000         ND         ND     True  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_grr.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_bbh = pd.read_table(\"Tables/Table_bbh_all.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 7,
+        "hidden": false,
+        "row": 28,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>element_ID_1</th>\n",
+       "      <th>element_ID_2</th>\n",
+       "      <th>ICE_ID_1</th>\n",
+       "      <th>ICE_ID_2</th>\n",
+       "      <th>N_prot_ICE_1</th>\n",
+       "      <th>N_prot_ICE_2</th>\n",
+       "      <th>perId</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BUAM001.B.00002.P004_00042</td>\n",
+       "      <td>BUAM001.B.00001.P004_00065</td>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>266</td>\n",
+       "      <td>28.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BUAM001.B.00002.P004_00040</td>\n",
+       "      <td>BUAM001.B.00001.P004_00064</td>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>266</td>\n",
+       "      <td>34.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BUAM001.B.00002.P004_00035</td>\n",
+       "      <td>BUAM001.B.00001.P004_00061</td>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>266</td>\n",
+       "      <td>33.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BUAM001.B.00002.P004_00002</td>\n",
+       "      <td>BUAM001.B.00001.P004_00040</td>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>266</td>\n",
+       "      <td>34.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BUAM001.B.00002.P004_00015</td>\n",
+       "      <td>BUAM001.B.00001.P004_00027</td>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>266</td>\n",
+       "      <td>27.85</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 element_ID_1                element_ID_2  \\\n",
+       "0  BUAM001.B.00002.P004_00042  BUAM001.B.00001.P004_00065   \n",
+       "1  BUAM001.B.00002.P004_00040  BUAM001.B.00001.P004_00064   \n",
+       "2  BUAM001.B.00002.P004_00035  BUAM001.B.00001.P004_00061   \n",
+       "3  BUAM001.B.00002.P004_00002  BUAM001.B.00001.P004_00040   \n",
+       "4  BUAM001.B.00002.P004_00015  BUAM001.B.00001.P004_00027   \n",
+       "\n",
+       "                       ICE_ID_1                      ICE_ID_2  N_prot_ICE_1  \\\n",
+       "0  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1            47   \n",
+       "1  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1            47   \n",
+       "2  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1            47   \n",
+       "3  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1            47   \n",
+       "4  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1            47   \n",
+       "\n",
+       "   N_prot_ICE_2  perId  \n",
+       "0           266  28.04  \n",
+       "1           266  34.05  \n",
+       "2           266  33.05  \n",
+       "3           266  34.91  \n",
+       "4           266  27.85  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_bbh.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mkdir Figures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 4,
+        "hidden": false,
+        "row": 0,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "## Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fisher_RR(table):\n",
+    "    \"\"\"Take 2x2 table and compute fisher exact test, relative risk and confidence interval\"\"\"\n",
+    "    oddratio, p = ss.fisher_exact(table)\n",
+    "    p_ICE = (table.iloc[:,0]/table.sum(axis=1)).xs(\"C\", level=\"replicon_type\").values[0]\n",
+    "    p_CP = (table.iloc[:,0]/table.sum(axis=1)).xs(\"P\", level=\"replicon_type\").values[0]    \n",
+    "    rr = p_ICE / p_CP\n",
+    "    var_log_rr = 1/table.iloc[0,0] - 1/table.iloc[0].sum() + 1/table.iloc[1,0] - 1/table.iloc[1].sum()\n",
+    "    ic_l = np.exp(np.log(rr)-1.96*np.sqrt(var_log_rr))\n",
+    "    ic_u = np.exp(np.log(rr)+1.96*np.sqrt(var_log_rr))\n",
+    "    return pd.Series({\"pvalue\":p, \"RR\":rr, \"IC95_lower\":ic_l, \"IC95_upper\":ic_u, \"p_ICE\": p_ICE, \"p_CP\": p_CP})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def Bonferroni_Holm(table, alpha=0.05):\n",
+    "    \"\"\"Given a table with a pvalue column from a computed Relative Risk, \n",
+    "    it will compte the sequential bonferroni test.\n",
+    "    \n",
+    "    This is a inplace operation (add a column)\"\"\"\n",
+    "    \n",
+    "    table.sort_values(\"pvalue\", inplace=1)\n",
+    "    N_func = len(table)\n",
+    "    table[\"significant_level\"] = [alpha/(N_func+1-k) for k in range(1, N_func+1)]\n",
+    "    ns_idx = table.where((table.pvalue>table.significant_level).diff()).dropna().index\n",
+    "    table.loc[ns_idx, \"is_significant\"] = False\n",
+    "    table.iloc[0, -1] = True\n",
+    "    table.is_significant.fillna(method=\"ffill\", inplace=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def mosaic_plot(df, dic_color_row, row_labels=None, col_labels=None, alpha_label=None, top_label=\"Size\", legend_top=False,\n",
+    "                x_label=None, y_label=None, pad=0.01, color_ylabel=False, ax=None, order=\"Size\"):\n",
+    "    \"\"\" \n",
+    "\n",
+    "    From a contingency table NxM, plot a mosaic plot with the values inside. There should be a double-index for rows\n",
+    "    e.g.\n",
+    "                                         3   4   1   0   2  5\n",
+    "        Index_1          Index_2                       \n",
+    "        AA               C               0   0   0   2   3  0\n",
+    "                         P               6   0   0  13   0  0\n",
+    "        BB               C               0   2   0   0   0  0\n",
+    "                         P              45   1  10  10   1  0\n",
+    "        CC               C               0   6  35  15  29  0\n",
+    "                         P               1   1   0   2   0  0\n",
+    "        DD               C               0  56   0   3   0  0\n",
+    "                         P              30   4   2   0   1  9\n",
+    "\n",
+    "    order: how columns are order, by default, from the biggest to the smallest in term of category. Possible values are \n",
+    "        - \"Size\" [default]\n",
+    "        - \"Normal\" : as the columns are order in the input df\n",
+    "        - list of column names to reorder the column\n",
+    "    top_label: Size of each columns. The label can be changed to adapt to your value. \n",
+    "               If `False`, nothing is displayed and the secondary legend is set on top with legend_top=True, or right otherwise.  \n",
+    "    \"\"\"\n",
+    "\n",
+    "    is_multi = len(df.index.names) == 2\n",
+    "    if ax == None:\n",
+    "        fig, ax = plt.subplots(1,1, figsize=(len(df.columns), len(df.index.get_level_values(0).unique())))\n",
+    "    \n",
+    "    size_col = df.sum().sort_values(ascending=False)\n",
+    "    prop_com = size_col.div(size_col.sum())\n",
+    "\n",
+    "    if order == \"Size\":\n",
+    "        df = df[size_col.index.values]\n",
+    "    elif order == \"Normal\":\n",
+    "        prop_com = prop_com[df.columns]\n",
+    "        size_col = size_col[df.columns]\n",
+    "    else:\n",
+    "        df = df[order]\n",
+    "        prop_com = prop_com[order]\n",
+    "        size_col = size_col[order]\n",
+    "    \n",
+    "    if is_multi:\n",
+    "        inner_index = df.index.get_level_values(1).unique()\n",
+    "        prop_ii0 = (df.swaplevel().loc[inner_index[0]]/(df.swaplevel().loc[inner_index[0]]+df.swaplevel().loc[inner_index[1]])).fillna(0)\n",
+    "        alpha_ii = 0.5\n",
+    "        true_y_labels = df.index.levels[0]\n",
+    "    else:\n",
+    "        alpha_ii = 1\n",
+    "        true_y_labels = df.index\n",
+    "        \n",
+    "    Yt = (df.groupby(level=0).sum().iloc[:,0].div(df.groupby(level=0).sum().iloc[:,0].sum())+pad).cumsum() - pad\n",
+    "    Ytt = df.groupby(level=0).sum().iloc[:,0].div(df.groupby(level=0).sum().iloc[:,0].sum())\n",
+    "\n",
+    "    x = 0    \n",
+    "    for j in df.groupby(level=0).sum().iteritems():\n",
+    "        bot = 0\n",
+    "        S = float(j[1].sum())\n",
+    "        for lab, k in j[1].iteritems():\n",
+    "            bars = []\n",
+    "            ax.bar(x, k/S, width=prop_com[j[0]], bottom=bot, color=dic_color_row[lab], alpha=alpha_ii, lw=0, align=\"edge\")\n",
+    "            if is_multi:\n",
+    "                ax.bar(x, k/S, width=prop_com[j[0]]*prop_ii0.loc[lab, j[0]], bottom=bot, color=dic_color_row[lab], lw=0, alpha=1, align=\"edge\")\n",
+    "            bot += k/S + pad\n",
+    "        x += prop_com[j[0]] + pad\n",
+    "\n",
+    "    ## Aesthetic of the plot and ticks\n",
+    "    # Y-axis\n",
+    "    if row_labels == None:\n",
+    "        row_labels = Yt.index\n",
+    "    ax.set_yticks(Yt - Ytt/2)\n",
+    "    ax.set_yticklabels(row_labels)\n",
+    "\n",
+    "    ax.set_ylim(0, 1 + (len(j[1]) - 1) * pad)\n",
+    "    if y_label == None:\n",
+    "        y_label = df.index.names[0]\n",
+    "    ax.set_ylabel(y_label)\n",
+    "\n",
+    "    # X-axis\n",
+    "    if col_labels == None:\n",
+    "        col_labels = prop_com.index\n",
+    "    xticks = (prop_com + pad).cumsum() - pad - prop_com/2.\n",
+    "    ax.set_xticks(xticks)\n",
+    "    ax.set_xticklabels(col_labels)\n",
+    "    ax.set_xlim(0, prop_com.sum() + pad * (len(prop_com)-1))\n",
+    "\n",
+    "    if x_label == None:\n",
+    "        x_label = df.columns.name\n",
+    "    ax.set_xlabel(x_label)\n",
+    "  \n",
+    "    # Top label\n",
+    "    if top_label:\n",
+    "        ax2 = ax.twiny()\n",
+    "        ax2.set_xlim(*ax.get_xlim())\n",
+    "        ax2.set_xticks(xticks) \n",
+    "        ax2.set_xticklabels(size_col.values.astype(int))\n",
+    "        ax2.set_xlabel(top_label)\n",
+    "        ax2.tick_params(top=False, right=False, pad=0, length=0)\n",
+    "    \n",
+    "    # Ticks and axis settings\n",
+    "    \n",
+    "    ax.tick_params(top=False, right=False, pad=5)\n",
+    "    sns.despine(left=0, bottom=False, right=0, top=0, offset=3)\n",
+    "\n",
+    "    # Legend\n",
+    "    if is_multi: \n",
+    "        if alpha_label == None:\n",
+    "            alpha_label = inner_index\n",
+    "        bars = [ax.bar(np.nan, np.nan, color=\"0.2\", alpha=[1, 0.5][b]) for b in range(2)]\n",
+    "        if legend_top and not top_label:\n",
+    "            plt.legend(bars, alpha_label, loc=\"lower center\", bbox_to_anchor=(0.5, 1), ncol=2)\n",
+    "        else:\n",
+    "            plt.legend(bars, alpha_label, loc='center left', bbox_to_anchor=(1, 0.5), ncol=1, handletextpad=0)\n",
+    "    \n",
+    "        plt.tight_layout(rect=[0, 0, .9, 0.95])\n",
+    "    if color_ylabel:\n",
+    "        for tick, label in zip(ax.get_yticklabels(), true_y_labels):\n",
+    "            tick.set_bbox(dict( pad=5, facecolor=dic_color_row[label]))\n",
+    "            tick.set_color(\"w\")\n",
+    "            tick.set_fontweight(\"bold\")\n",
+    "\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def histplot_2D(data, ax, bins, cmap=plt.cm.inferno_r):\n",
+    "\n",
+    "    counts, _, _ = np.histogram2d(data.GRR,\n",
+    "                                  data.perId,\n",
+    "                                  bins=bins)\n",
+    "    p = ax.pcolormesh(bins, bins, counts,\n",
+    "                       vmax=1e4,\n",
+    "                       vmin=1e0,\n",
+    "                       cmap=cmap,\n",
+    "                       norm=plt.matplotlib.colors.LogNorm())\n",
+    "    return counts, p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def binom_test_maha(df, H0=None):\n",
+    "    \"\"\"\n",
+    "    Take as input a dataframe with the column `Mah_P_inf_ice`\n",
+    "    Return the pvalue of the binomial test on the ratio of True False match the H0 hypothesis. If H0 is None, return the proportion (no test)\"\"\"\n",
+    "    \n",
+    "    if isinstance(df, pd.Series):\n",
+    "        df = df.to_frame(name=\"Mah_P_inf_ice\")\n",
+    "    vc = df.Mah_P_inf_ice.value_counts()\n",
+    "    try:\n",
+    "        T = vc.loc[1]\n",
+    "    except KeyError:\n",
+    "        T = 0\n",
+    "    Total = float(vc.sum())\n",
+    "    if H0 != None:\n",
+    "        return binom_test(T, Total, H0)\n",
+    "    else:\n",
+    "        if T != 0:\n",
+    "            return T/Total\n",
+    "        else:\n",
+    "            return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def spearman(grp, col1, col2):\n",
+    "    rho, p = ss.spearmanr(grp[col1], grp[col2])\n",
+    "    return pd.Series({\"Rho\":rho, \"p-value\":p})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 7,
+        "height": 4,
+        "hidden": false,
+        "row": 14,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "## Variable (dic color, name, etc..)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dic_replicon_color = {\"P\":\"#3ABAFA\", \"C\": \"#E0BA0A\"} # Blue, Yellow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dic_name = {u'ATB_res': \"Antibiotic resistance\",\n",
+    "             u'Eex': \"Entry Exclusion\",\n",
+    "             u'Integrase': \"Integrase\",\n",
+    "              \"DDE_Transposase\": \"DDE Transposase\",\n",
+    "             u'MOB': \"Relaxase\",\n",
+    "             u'Partition_System': \"Partition\",\n",
+    "             'RMS': \"Restriction Modification\",\n",
+    "             'Replication': \"Replication\",\n",
+    "             u'integron': \"Integron\",\n",
+    "             u'Solitary_RMS': \"Methylase\",\n",
+    "             u't4cp': \"T4 Coupling Protein\",\n",
+    "             u'virb4': \"virB4\",\n",
+    "             \"no_function\":\"Not annotated\",\n",
+    "             'INFORMATION STORAGE AND PROCESSING': \"DNA Processing\",\n",
+    "             'POORLY CHARACTERIZED': \"Poorly characterized\",\n",
+    "             'METABOLISM': \"Metabolism\",\n",
+    "             'CELLULAR PROCESSES AND SIGNALING': \"Cellular process\"}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "list_func = [u'Partition_System',  u'Replication',\n",
+    "             u'Integrase', \"DDE_Transposase\",\n",
+    "             u'ATB_res', u'integron', u'Eex', u'RMS', \"Solitary_RMS\",\n",
+    "             # u'MOB', u'virb4',  u't4cp', \n",
+    "             'METABOLISM', 'CELLULAR PROCESSES AND SIGNALING', 'INFORMATION STORAGE AND PROCESSING',\n",
+    "             'POORLY CHARACTERIZED','no_function']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dic_community_color_all = {1: '#f41a1c',\n",
+    "                           2: '#37b428',\n",
+    "                           3: '#ab42ba',\n",
+    "                           4: '#ff9100',\n",
+    "                           5: '#3f71e0',\n",
+    "                           6: '#ff3a8a'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dic_community_color_noMPF_ALL = {1: '#fb5253',\n",
+    "                                 2: '#7ce37c',\n",
+    "                                 3: '#c26ce0',\n",
+    "                                 4: '#f9a046',\n",
+    "                                 5: '#5d99fe',\n",
+    "                                 6: '#f77cb0'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dic_int_stab2 = {\"11_All\":'#fe3d00',\n",
+    "                 \"10_Integrase\":'#e7c200',\n",
+    "                 \"00_Nothing\":\"#3d2159\",\n",
+    "                 \"01_Stabilization\":\"#01aae8\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dic_proteo_color = {\"Gammaproteobacteria\": '#fb4f52',\n",
+    "                    \"Betaproteobacteria\": '#75cb4d',\n",
+    "                    \"Deltaproteobacteria\": '#9b42ba',\n",
+    "                    \"Epsilonproteobacteria\": '#f7ba31',\n",
+    "                    \"Alphaproteobacteria\": '#3977df',}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 4,
+        "hidden": false,
+        "row": 28,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "## Add color and property to the elements for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_conj[\"replicon_type_2\"] = df_conj.replicon_type.map({\"C\":\"ICE\", \"P\":\"CP\"})\n",
+    "df_conj[\"color_type\"] = df_conj.replicon_type.map(dic_replicon_color)\n",
+    "# Color species\n",
+    "dic_species = {j:i for i,j in enumerate(df_conj.species_2.unique())}\n",
+    "df_conj[\"species_ID\"] = df_conj.species_2.map(dic_species)\n",
+    "df_conj[\"color_species\"] = [\"#{0:02x}{1:02x}{2:02x}\".format(int(r),int(g),int(b)) \n",
+    "                               for r,g,b,a in plt.cm.Dark2(df_conj[df_conj.species_2.isnull()==False].species_ID/len(dic_species.keys()))*255]\n",
+    "#color genus\n",
+    "df_conj[\"genus\"] = df_conj.species_2.apply(lambda x: x.split()[0])\n",
+    "dic_genus = {\n",
+    "            \"Klebsiella\": \"#2d8ef2\",\n",
+    "            \"Pseudomonas\": \"#6964b6\",\n",
+    "            \"Campylobacter\": \"#d09d2a\",\n",
+    "            \"Salmonella\": \"#ba5cd6\",\n",
+    "            \"Legionella\":\"#218f05\",\n",
+    "            \"Enterobacter\": \"#1f9ea2\",\n",
+    "            \"Burkholderia\": \"#8dd525\",\n",
+    "            \"Escherichia\": \"#cb2154\",\n",
+    "            \"Helicobacter\": \"#b18000\",\n",
+    "            \"Fusobacterium\": \"#1d706e\",\n",
+    "            \"Ralstonia\": \"#6d478b\",\n",
+    "            \"Rhodopseudomonas\": \"#4975f4\",\n",
+    "            \"Serratia\": \"#52b305\",\n",
+    "            \"Granulicella\": \"#02f57c\",\n",
+    "            \"Ilyobacter\": \"#b0413b\",\n",
+    "            \"Sedimenticola\": \"#8688f9\",\n",
+    "            }\n",
+    "df_conj[\"color_genus\"] = df_conj.genus.map(dic_genus)\n",
+    "#color Class\n",
+    "df_conj[\"color_class\"] = df_conj.Class.map(dic_proteo_color)\n",
+    "df_conj.color_class.fillna(\"0.4\", inplace=1) # Fusobacteriales & Acidobacteriales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 10,
+        "hidden": false,
+        "row": 35,
+        "width": 12
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ATB_res</th>\n",
+       "      <th>CELLULAR PROCESSES AND SIGNALING</th>\n",
+       "      <th>DDE_Transposase</th>\n",
+       "      <th>Eex</th>\n",
+       "      <th>INFORMATION STORAGE AND PROCESSING</th>\n",
+       "      <th>Integrase</th>\n",
+       "      <th>METABOLISM</th>\n",
+       "      <th>MOB</th>\n",
+       "      <th>MPF</th>\n",
+       "      <th>N_prot</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Palindrome.inv</th>\n",
+       "      <th>Tandem.dir</th>\n",
+       "      <th>N_repeats_norm</th>\n",
+       "      <th>replicon_type_2</th>\n",
+       "      <th>color_type</th>\n",
+       "      <th>species_ID</th>\n",
+       "      <th>color_species</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>color_genus</th>\n",
+       "      <th>color_class</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICE_ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BUAM001.B.00001.P004_typeT_1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000099</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>0</td>\n",
+       "      <td>#1b9e77</td>\n",
+       "      <td>Burkholderia</td>\n",
+       "      <td>#8dd525</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUAM001.B.00002.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000252</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#3a9363</td>\n",
+       "      <td>Burkholderia</td>\n",
+       "      <td>#8dd525</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUAM001.B.00003.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000252</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>0</td>\n",
+       "      <td>#1b9e77</td>\n",
+       "      <td>Burkholderia</td>\n",
+       "      <td>#8dd525</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUCE001.B.00003.C001_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000073</td>\n",
+       "      <td>ICE</td>\n",
+       "      <td>#E0BA0A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#3a9363</td>\n",
+       "      <td>Burkholderia</td>\n",
+       "      <td>#8dd525</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUCE001.B.00006.C001_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>ICE</td>\n",
+       "      <td>#E0BA0A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#3a9363</td>\n",
+       "      <td>Burkholderia</td>\n",
+       "      <td>#8dd525</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 56 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              ATB_res  CELLULAR PROCESSES AND SIGNALING  \\\n",
+       "ICE_ID                                                                    \n",
+       "BUAM001.B.00001.P004_typeT_1      1.0                              42.0   \n",
+       "BUAM001.B.00002.P004_typeT_1      0.0                              18.0   \n",
+       "BUAM001.B.00003.P004_typeT_1      0.0                              18.0   \n",
+       "BUCE001.B.00003.C001_typeT_1      0.0                              13.0   \n",
+       "BUCE001.B.00006.C001_typeT_1      0.0                              15.0   \n",
+       "\n",
+       "                              DDE_Transposase  Eex  \\\n",
+       "ICE_ID                                               \n",
+       "BUAM001.B.00001.P004_typeT_1              6.0  0.0   \n",
+       "BUAM001.B.00002.P004_typeT_1              0.0  0.0   \n",
+       "BUAM001.B.00003.P004_typeT_1              0.0  0.0   \n",
+       "BUCE001.B.00003.C001_typeT_1              2.0  1.0   \n",
+       "BUCE001.B.00006.C001_typeT_1              0.0  0.0   \n",
+       "\n",
+       "                              INFORMATION STORAGE AND PROCESSING  Integrase  \\\n",
+       "ICE_ID                                                                        \n",
+       "BUAM001.B.00001.P004_typeT_1                                35.0        6.0   \n",
+       "BUAM001.B.00002.P004_typeT_1                                 6.0        0.0   \n",
+       "BUAM001.B.00003.P004_typeT_1                                 6.0        0.0   \n",
+       "BUCE001.B.00003.C001_typeT_1                                12.0        1.0   \n",
+       "BUCE001.B.00006.C001_typeT_1                                13.0        1.0   \n",
+       "\n",
+       "                              METABOLISM  MOB  MPF  N_prot     ...       \\\n",
+       "ICE_ID                                                         ...        \n",
+       "BUAM001.B.00001.P004_typeT_1         9.0  1.0  4.0   266.0     ...        \n",
+       "BUAM001.B.00002.P004_typeT_1         0.0  1.0  8.0    47.0     ...        \n",
+       "BUAM001.B.00003.P004_typeT_1         0.0  1.0  8.0    47.0     ...        \n",
+       "BUCE001.B.00003.C001_typeT_1        12.0  1.0  8.0    52.0     ...        \n",
+       "BUCE001.B.00006.C001_typeT_1         4.0  1.0  8.0    55.0     ...        \n",
+       "\n",
+       "                              Palindrome.inv  Tandem.dir  N_repeats_norm  \\\n",
+       "ICE_ID                                                                     \n",
+       "BUAM001.B.00001.P004_typeT_1             0.0         0.0        0.000099   \n",
+       "BUAM001.B.00002.P004_typeT_1             0.0         0.0        0.000252   \n",
+       "BUAM001.B.00003.P004_typeT_1             0.0         0.0        0.000252   \n",
+       "BUCE001.B.00003.C001_typeT_1             0.0         0.0        0.000073   \n",
+       "BUCE001.B.00006.C001_typeT_1             0.0         0.0        0.000016   \n",
+       "\n",
+       "                              replicon_type_2  color_type  species_ID  \\\n",
+       "ICE_ID                                                                  \n",
+       "BUAM001.B.00001.P004_typeT_1               CP     #3ABAFA           0   \n",
+       "BUAM001.B.00002.P004_typeT_1               CP     #3ABAFA           1   \n",
+       "BUAM001.B.00003.P004_typeT_1               CP     #3ABAFA           0   \n",
+       "BUCE001.B.00003.C001_typeT_1              ICE     #E0BA0A           1   \n",
+       "BUCE001.B.00006.C001_typeT_1              ICE     #E0BA0A           1   \n",
+       "\n",
+       "                              color_species         genus  color_genus  \\\n",
+       "ICE_ID                                                                   \n",
+       "BUAM001.B.00001.P004_typeT_1        #1b9e77  Burkholderia      #8dd525   \n",
+       "BUAM001.B.00002.P004_typeT_1        #3a9363  Burkholderia      #8dd525   \n",
+       "BUAM001.B.00003.P004_typeT_1        #1b9e77  Burkholderia      #8dd525   \n",
+       "BUCE001.B.00003.C001_typeT_1        #3a9363  Burkholderia      #8dd525   \n",
+       "BUCE001.B.00006.C001_typeT_1        #3a9363  Burkholderia      #8dd525   \n",
+       "\n",
+       "                              color_class  \n",
+       "ICE_ID                                     \n",
+       "BUAM001.B.00001.P004_typeT_1      #75cb4d  \n",
+       "BUAM001.B.00002.P004_typeT_1      #75cb4d  \n",
+       "BUAM001.B.00003.P004_typeT_1      #75cb4d  \n",
+       "BUCE001.B.00003.C001_typeT_1      #75cb4d  \n",
+       "BUCE001.B.00006.C001_typeT_1      #75cb4d  \n",
+       "\n",
+       "[5 rows x 56 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_conj.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 4,
+        "hidden": false,
+        "row": 45,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Figure 1 : distribution of GC diff, size, repeats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 13,
+        "hidden": false,
+        "row": 45,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zUpfS+ffff8nIyCjQbjQaiYyMfOR8vP39TZFssuw7kKOFey5nwt3EfP0S9oEgCISEhDBp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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1091c3d90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(3.307, 3.)) # simple page : 3.307 / double : 7.008\n",
+    "\n",
+    "ax_gc =        plt.subplot2grid((2,2), (0,0))\n",
+    "ax_size =      plt.subplot2grid((2,2), (1,0))#, sharex=ax_size)\n",
+    "ax_size_host = plt.subplot2grid((2,2), (1,1))\n",
+    "ax_rep =       plt.subplot2grid((2,2), (0,1))\n",
+    "\n",
+    "# Violin plot\n",
+    "\n",
+    "sns.violinplot(x=\"replicon_type\", y=\"size_ICE\", data=df_conj, cut=0,  scale=\"width\", saturation=1,\n",
+    "               palette=dic_replicon_color, ax=ax_size)\n",
+    "sns.violinplot(x=\"replicon_type\", y=\"GC_diff\", data=df_conj, cut=0,  scale=\"width\", saturation=1,\n",
+    "               palette=dic_replicon_color, ax=ax_gc)\n",
+    "\n",
+    "sns.violinplot(x=\"replicon_type\", y=\"N_repeats_norm\", data=df_conj, cut=0,  scale=\"width\", saturation=1,\n",
+    "               palette=dic_replicon_color, ax=ax_rep)\n",
+    "# Stats\n",
+    "## size\n",
+    "s, p_size = ss.ranksums(df_conj[df_conj.replicon_type==\"C\"].size_ICE,\n",
+    "                        df_conj[df_conj.replicon_type==\"P\"].size_ICE)\n",
+    "if p_size < 0.05:\n",
+    "    ax_size.hlines(500*1e3, 0.1, 0.35, lw=0.5)\n",
+    "    ax_size.hlines(500*1e3, 0.65, 0.9, lw=0.5)\n",
+    "    ax_size.text(0.5, 490*1e3, \"*\"*int((-np.log10(p_size))), va=\"center\", ha=\"center\", fontsize=7)\n",
+    "## GC\n",
+    "s, p_gc = ss.ranksums(df_conj[df_conj.replicon_type==\"C\"].GC_diff, df_conj[df_conj.replicon_type==\"P\"].GC_diff)    \n",
+    "if p_gc < 0.05:\n",
+    "    ax_gc.hlines(10, 0.1, 0.35, lw=0.5)\n",
+    "    ax_gc.hlines(10, 0.65, 0.9, lw=0.5)\n",
+    "    ax_gc.text(0.5, 9.5, \"*\"*int(min(3,(-np.log10(p_gc)))), va=\"center\", ha=\"center\", fontsize=7)\n",
+    "\n",
+    "## Repeats\n",
+    "s, p_rep = ss.ranksums(df_conj[df_conj.replicon_type==\"C\"].N_repeats_norm, df_conj[df_conj.replicon_type==\"P\"].N_repeats_norm)    \n",
+    "if p_rep < 0.05:\n",
+    "    ax_rep.hlines(.0014, 0.1, 0.35, lw=0.5)\n",
+    "    ax_rep.hlines(.0014, 0.65, 0.9, lw=0.5)\n",
+    "    ax_rep.text(0.5, .00135, \"*\"*int(min(3,(-np.log10(p_rep)))), va=\"center\", ha=\"center\", fontsize=7)\n",
+    "    \n",
+    "pad = 3\n",
+    "### PLOT GC ###\n",
+    "ax_gc.set_ylim(-16, 16)\n",
+    "ax_gc.set_xlabel(\"\")\n",
+    "ax_gc.set_xticklabels(\"\")\n",
+    "ax_gc.set_yticks([-10, 0, 10])\n",
+    "ax_gc.set_ylabel(\"${\\mathdefault{GC_{elt} - GC_{Host}}}$ (%)\", fontsize=7, labelpad=0)\n",
+    "ax_gc.hlines(0, *ax_gc.get_xlim(), color=\".1\", lw=0.5, zorder=-2)\n",
+    "ax_gc.tick_params(axis=\"both\", direction=\"in\", right=\"off\", top=\"off\", width=0.5, pad=pad, labelsize=6)\n",
+    "\n",
+    "### PLOT SIZE ###\n",
+    "#ax_size.bar(0.4, df_conj.size_ICE.std()/df_conj.size_ICE.mean(), width=0.1, fc=\"k\")\n",
+    "for i, c in enumerate([\"P\", \"C\"]):\n",
+    "    ax_size.text(i,\n",
+    "                 0.18e5,\n",
+    "               df_conj.groupby(\"replicon_type\").size().loc[c], \n",
+    "               color=dic_replicon_color[c], \n",
+    "               fontweight=\"bold\", \n",
+    "               fontsize=10, ha=\"center\", va=\"top\")\n",
+    "\n",
+    "ax_size.set_yscale(\"log\")\n",
+    "ax_size.set_ylim(1e4, 1e6)\n",
+    "ax_size.set_yticks([.1e5, 1e5, 10e5])\n",
+    "ax_size.set_yticks([.1e5, 1e5, 10e5])\n",
+    "ax_size.set_yticklabels([0.1, 1, 10])\n",
+    "# ax_size.set_ylim(0,600*1000)\n",
+    "# ax_size.set_yticks(ax_size.get_yticks()[::2])\n",
+    "# ax_size.set_yticklabels((ax_size.get_yticks()/1000.).astype(int))\n",
+    "ax_size.set_ylabel(\"Element size\\n(\" + r\"$\\mathdefault{\\times10^{5}}$ bp)\", fontsize=7, labelpad=-7)\n",
+    "ax_size.set_xlabel(\"Replicon type\", fontsize=7, labelpad=1)\n",
+    "ax_size.set_xticklabels([\"CP\", \"ICE\"])\n",
+    "ax_size.tick_params(axis=\"both\", which=\"both\", direction=\"in\", right=\"off\", bottom=\"off\", width=0.5, pad=pad, labelsize=6)\n",
+    "\n",
+    "### PLOT REPEATS ###\n",
+    "ax_rep.yaxis.tick_right()\n",
+    "ax_rep.yaxis.set_label_position(\"right\")\n",
+    "ax_rep.set_xlabel(\"\", visible=False)\n",
+    "ax_rep.set_ylabel(\"Repeat density\\n(\" + r\"$\\mathdefault{\\times10^{-4}}$)\", fontsize=7, labelpad=2)\n",
+    "ax_rep.set_yticks([0, 5e-4, 10e-4, 15e-4])\n",
+    "ax_rep.set_yticklabels([0, 5, 10, 15])\n",
+    "ax_rep.set_ylim(0)\n",
+    "ax_rep.set_xticklabels(\"\", visible=False)\n",
+    "ax_rep.tick_params(axis=\"both\", which=\"both\", direction=\"in\", left=\"off\", top=\"off\", width=0.5, pad=pad, labelsize=6)\n",
+    "\n",
+    "### PLOT Size HOST ###\n",
+    "\n",
+    "fg = sns.regplot(data=df_conj[df_conj.replicon_type==\"P\"],\n",
+    "                y=\"size_ICE\",\n",
+    "                x=\"genome_size_corr\",\n",
+    "                truncate=True,\n",
+    "                color=dic_replicon_color[\"P\"],\n",
+    "                #legend=False,\n",
+    "                scatter_kws={\"alpha\":0.5, \"s\":10},\n",
+    "                #size=3.307,\n",
+    "                ax=ax_size_host)\n",
+    "fg = sns.regplot(data=df_conj[df_conj.replicon_type==\"C\"],\n",
+    "                y=\"size_ICE\",\n",
+    "                x=\"genome_size_corr\",\n",
+    "                truncate=True,\n",
+    "                color=dic_replicon_color[\"C\"],\n",
+    "                #legend=False,\n",
+    "                scatter_kws={\"alpha\":0.5},\n",
+    "                #size=3.307,\n",
+    "                ax=ax_size_host)\n",
+    "ax_size_host.tick_params(axis=\"both\", which=\"both\",  direction=\"in\", left=\"off\", top=\"off\", width=0.5, pad=pad, labelsize=6 )\n",
+    "\n",
+    "ax_size_host.set_yscale(\"log\")\n",
+    "ax_size_host.set_ylim(1e4, 1e6)\n",
+    "ax_size_host.set_yticks([.1e5, 1e5, 10e5])\n",
+    "ax_size_host.set_yticks([.1e5, 1e5, 10e5])\n",
+    "ax_size_host.set_yticklabels([0.1, 1, 10])\n",
+    "# ax_size_host.set_ylim(0,600*1000)\n",
+    "# ax_size_host.set_yticks(ax_size_host.get_yticks()[::2])\n",
+    "# ax_size_host.set_yticklabels((ax_size_host.get_yticks()/1000.).astype(int))\n",
+    "ax_size_host.set_ylabel(\"Element size\\n(\" + r\"$\\mathdefault{\\times10^{5}}$ bp)\", fontsize=7, labelpad=0)\n",
+    "ax_size_host.yaxis.tick_right()\n",
+    "ax_size_host.yaxis.set_label_position(\"right\")\n",
+    "\n",
+    "ax_size_host.set_xscale(\"log\")\n",
+    "ax_size_host.set_xlim(1e6, 1e7)\n",
+    "ax_size_host.set_xticks([1e6, 5e6, 1e7])\n",
+    "ax_size_host.set_xticklabels([1, 5, 10])\n",
+    "ax_size_host.set_xlabel(\"Genome size (\" + r\"$\\mathdefault{\\times10^{6}}$ bp)\", fontsize=7, labelpad=0)\n",
+    "\n",
+    "# correlation labels\n",
+    "rho_ice, p_ice = ss.spearmanr(df_conj[df_conj.replicon_type==\"C\"].genome_size_corr, df_conj[df_conj.replicon_type==\"C\"].size_ICE)\n",
+    "rho_cp, p_cp = ss.spearmanr(df_conj[df_conj.replicon_type==\"P\"].genome_size_corr, df_conj[df_conj.replicon_type==\"P\"].size_ICE)\n",
+    "ax_size_host.text(0.05, 0.8,\n",
+    "           r\"$\\rho$ = {:.2f}\".format(rho_ice)+\"\\n\"+\"p<{:.0g}\".format(p_ice),\n",
+    "           transform=ax_size_host.transAxes, fontsize=7, color=dic_replicon_color[\"C\"],)# fontweight=\"bold\")\n",
+    "ax_size_host.text(0.05, 0.6,\n",
+    "           r\"$\\rho$ = {:.2f}\".format(rho_cp)+\"\\n\"+\"p<{:.0g}\".format(p_cp),\n",
+    "           transform=ax_size_host.transAxes, fontsize=7, color=dic_replicon_color[\"P\"],)# fontweight=\"bold\")\n",
+    "\n",
+    "### ABCD labels\n",
+    "ax_gc.text(0.84, 0.86, \"A\", transform=ax_gc.transAxes, fontsize=11, fontweight=\"bold\", zorder=10)\n",
+    "ax_size.text(0.84, 0.86, \"B\", transform=ax_size.transAxes, fontsize=11, fontweight=\"bold\", zorder=10)\n",
+    "ax_rep.text(0.84, 0.86, \"D\", transform=ax_rep.transAxes, fontsize=11, fontweight=\"bold\", zorder=10)\n",
+    "ax_size_host.text(0.84, 0.86, \"C\", transform=ax_size_host.transAxes, fontsize=11, fontweight=\"bold\", zorder=10)\n",
+    "\n",
+    "plt.tight_layout(rect=[-0.05,-0.05,1.05,1.05], h_pad=0, w_pad=0.2)\n",
+    "\n",
+    "plt.savefig(\"Figures/Figure_1_ICE_size_GC_per_type_host_size_repeats.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 4,
+        "hidden": false,
+        "row": 45,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Figure 2: Comparison of function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "crosstab_intstab = pd.crosstab(df_conj.replicon_type, [df_conj.Integrase.astype(bool),\n",
+    "                                     df_conj.Replication.astype(bool) | df_conj.Partition_System.astype(bool)\n",
+    "                                         ]).unstack().to_frame(name=\"Count\").reset_index().rename(columns={\"col_1\":\"Par_OR_rep\"})\n",
+    "\n",
+    "crosstab_intstab[\"IPR\"] = crosstab_intstab.apply(lambda x: \"{}\\n{}\".format(x.replace({False:\"-\", True:\"+\"})[\"Integrase\"], \n",
+    "                                                          x.replace({False:\"-\", True:\"+\"})[\"Par_OR_rep\"]), axis=1)\n",
+    "\n",
+    "\n",
+    "crosstab_intstab.set_index([\"Integrase\", \"Par_OR_rep\"], inplace=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 10,
+        "hidden": false,
+        "row": 49,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>replicon_type</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>IPR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Integrase</th>\n",
+       "      <th>Par_OR_rep</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">False</th>\n",
+       "      <th>False</th>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-\\n-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>P</td>\n",
+       "      <td>19</td>\n",
+       "      <td>-\\n-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-\\n+</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>P</td>\n",
+       "      <td>67</td>\n",
+       "      <td>-\\n+</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">True</th>\n",
+       "      <th>False</th>\n",
+       "      <td>C</td>\n",
+       "      <td>85</td>\n",
+       "      <td>+\\n-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>P</td>\n",
+       "      <td>4</td>\n",
+       "      <td>+\\n-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>C</td>\n",
+       "      <td>59</td>\n",
+       "      <td>+\\n+</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>P</td>\n",
+       "      <td>46</td>\n",
+       "      <td>+\\n+</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     replicon_type  Count   IPR\n",
+       "Integrase Par_OR_rep                           \n",
+       "False     False                  C      5  -\\n-\n",
+       "          False                  P     19  -\\n-\n",
+       "          True                   C      2  -\\n+\n",
+       "          True                   P     67  -\\n+\n",
+       "True      False                  C     85  +\\n-\n",
+       "          False                  P      4  +\\n-\n",
+       "          True                   C     59  +\\n+\n",
+       "          True                   P     46  +\\n+"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crosstab_intstab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "N_prot_elt = df_func_count.drop_duplicates(subset=\"ICE_ID\").groupby(\"replicon_type\").N_prot.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_func_count_ct = pd.crosstab(df_func_count.Function,\n",
+    "                         df_func_count.replicon_type,\n",
+    "                         values=df_func_count.Function_count, aggfunc=sum).stack().to_frame(name=\"Count\")\n",
+    "\n",
+    "\n",
+    "df_func_count_ct[\"No_count\"] = N_prot_elt - df_func_count_ct.Count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 8,
+        "hidden": false,
+        "row": 49,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Count</th>\n",
+       "      <th>No_count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Function</th>\n",
+       "      <th>replicon_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">ATB_res</th>\n",
+       "      <th>C</th>\n",
+       "      <td>49.0</td>\n",
+       "      <td>7752.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>131.0</td>\n",
+       "      <td>10737.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">CELLULAR PROCESSES AND SIGNALING</th>\n",
+       "      <th>C</th>\n",
+       "      <td>2084.0</td>\n",
+       "      <td>5717.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>2146.0</td>\n",
+       "      <td>8722.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DDE_Transposase</th>\n",
+       "      <th>C</th>\n",
+       "      <td>106.0</td>\n",
+       "      <td>7695.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 Count  No_count\n",
+       "Function                         replicon_type                  \n",
+       "ATB_res                          C                49.0    7752.0\n",
+       "                                 P               131.0   10737.0\n",
+       "CELLULAR PROCESSES AND SIGNALING C              2084.0    5717.0\n",
+       "                                 P              2146.0    8722.0\n",
+       "DDE_Transposase                  C               106.0    7695.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_func_count_ct.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_RR_pa = df_func_count_ct.groupby(level=\"Function\").apply(fisher_RR).loc[list_func[:-1]]\n",
+    "df_RR = pd.concat([df_RR_pa.drop([\"Partition_System\", \"Replication\", \"Integrase\"])])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Bonferroni_Holm(df_RR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 18,
+        "hidden": false,
+        "row": 58,
+        "width": 6
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IC95_lower</th>\n",
+       "      <th>IC95_upper</th>\n",
+       "      <th>RR</th>\n",
+       "      <th>p_CP</th>\n",
+       "      <th>p_ICE</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>significant_level</th>\n",
+       "      <th>is_significant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Function</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POORLY CHARACTERIZED</th>\n",
+       "      <td>0.727949</td>\n",
+       "      <td>0.770588</td>\n",
+       "      <td>0.748965</td>\n",
+       "      <td>0.610508</td>\n",
+       "      <td>0.457249</td>\n",
+       "      <td>1.307403e-95</td>\n",
+       "      <td>0.005000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>METABOLISM</th>\n",
+       "      <td>2.471286</td>\n",
+       "      <td>3.072825</td>\n",
+       "      <td>2.755691</td>\n",
+       "      <td>0.041866</td>\n",
+       "      <td>0.115370</td>\n",
+       "      <td>3.164798e-80</td>\n",
+       "      <td>0.005556</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CELLULAR PROCESSES AND SIGNALING</th>\n",
+       "      <td>1.283328</td>\n",
+       "      <td>1.426254</td>\n",
+       "      <td>1.352905</td>\n",
+       "      <td>0.197460</td>\n",
+       "      <td>0.267145</td>\n",
+       "      <td>6.043482e-29</td>\n",
+       "      <td>0.006250</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DDE_Transposase</th>\n",
+       "      <td>0.343051</td>\n",
+       "      <td>0.527950</td>\n",
+       "      <td>0.425575</td>\n",
+       "      <td>0.031929</td>\n",
+       "      <td>0.013588</td>\n",
+       "      <td>1.117967e-16</td>\n",
+       "      <td>0.007143</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eex</th>\n",
+       "      <td>0.090180</td>\n",
+       "      <td>0.297887</td>\n",
+       "      <td>0.163901</td>\n",
+       "      <td>0.009385</td>\n",
+       "      <td>0.001538</td>\n",
+       "      <td>2.325508e-13</td>\n",
+       "      <td>0.008333</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>integron</th>\n",
+       "      <td>0.006771</td>\n",
+       "      <td>0.365623</td>\n",
+       "      <td>0.049756</td>\n",
+       "      <td>0.002576</td>\n",
+       "      <td>0.000128</td>\n",
+       "      <td>4.309689e-06</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ATB_res</th>\n",
+       "      <td>0.375788</td>\n",
+       "      <td>0.722612</td>\n",
+       "      <td>0.521104</td>\n",
+       "      <td>0.012054</td>\n",
+       "      <td>0.006281</td>\n",
+       "      <td>5.228033e-05</td>\n",
+       "      <td>0.012500</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RMS</th>\n",
+       "      <td>1.500194</td>\n",
+       "      <td>5.175012</td>\n",
+       "      <td>2.786309</td>\n",
+       "      <td>0.001380</td>\n",
+       "      <td>0.003846</td>\n",
+       "      <td>1.204868e-03</td>\n",
+       "      <td>0.016667</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INFORMATION STORAGE AND PROCESSING</th>\n",
+       "      <td>0.997227</td>\n",
+       "      <td>1.141785</td>\n",
+       "      <td>1.067061</td>\n",
+       "      <td>0.150166</td>\n",
+       "      <td>0.160236</td>\n",
+       "      <td>6.166292e-02</td>\n",
+       "      <td>0.025000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Solitary_RMS</th>\n",
+       "      <td>0.395344</td>\n",
+       "      <td>1.917712</td>\n",
+       "      <td>0.870722</td>\n",
+       "      <td>0.001472</td>\n",
+       "      <td>0.001282</td>\n",
+       "      <td>8.433189e-01</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    IC95_lower  IC95_upper        RR  \\\n",
+       "Function                                                               \n",
+       "POORLY CHARACTERIZED                  0.727949    0.770588  0.748965   \n",
+       "METABOLISM                            2.471286    3.072825  2.755691   \n",
+       "CELLULAR PROCESSES AND SIGNALING      1.283328    1.426254  1.352905   \n",
+       "DDE_Transposase                       0.343051    0.527950  0.425575   \n",
+       "Eex                                   0.090180    0.297887  0.163901   \n",
+       "integron                              0.006771    0.365623  0.049756   \n",
+       "ATB_res                               0.375788    0.722612  0.521104   \n",
+       "RMS                                   1.500194    5.175012  2.786309   \n",
+       "INFORMATION STORAGE AND PROCESSING    0.997227    1.141785  1.067061   \n",
+       "Solitary_RMS                          0.395344    1.917712  0.870722   \n",
+       "\n",
+       "                                        p_CP     p_ICE        pvalue  \\\n",
+       "Function                                                               \n",
+       "POORLY CHARACTERIZED                0.610508  0.457249  1.307403e-95   \n",
+       "METABOLISM                          0.041866  0.115370  3.164798e-80   \n",
+       "CELLULAR PROCESSES AND SIGNALING    0.197460  0.267145  6.043482e-29   \n",
+       "DDE_Transposase                     0.031929  0.013588  1.117967e-16   \n",
+       "Eex                                 0.009385  0.001538  2.325508e-13   \n",
+       "integron                            0.002576  0.000128  4.309689e-06   \n",
+       "ATB_res                             0.012054  0.006281  5.228033e-05   \n",
+       "RMS                                 0.001380  0.003846  1.204868e-03   \n",
+       "INFORMATION STORAGE AND PROCESSING  0.150166  0.160236  6.166292e-02   \n",
+       "Solitary_RMS                        0.001472  0.001282  8.433189e-01   \n",
+       "\n",
+       "                                    significant_level is_significant  \n",
+       "Function                                                              \n",
+       "POORLY CHARACTERIZED                         0.005000           True  \n",
+       "METABOLISM                                   0.005556           True  \n",
+       "CELLULAR PROCESSES AND SIGNALING             0.006250           True  \n",
+       "DDE_Transposase                              0.007143           True  \n",
+       "Eex                                          0.008333           True  \n",
+       "integron                                     0.010000           True  \n",
+       "ATB_res                                      0.012500           True  \n",
+       "RMS                                          0.016667           True  \n",
+       "INFORMATION STORAGE AND PROCESSING           0.025000          False  \n",
+       "Solitary_RMS                                 0.050000          False  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_RR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 18,
+        "hidden": false,
+        "row": 59,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9Ru/evenQoQMvv/wyycnJ3Lx5U7ftxIkTZclYIUQ5tboF6erqSkpKCu7u7ty6dQsnJycA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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115031690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(3.307, 3.3))\n",
+    "\n",
+    "fg = sns.barplot(data=crosstab_intstab, \n",
+    "                    x=\"IPR\",\n",
+    "                    y=\"Count\",\n",
+    "                    hue=\"replicon_type\",\n",
+    "                    #legend=False,\n",
+    "                    order=[\"+\\n-\", \"+\\n+\", \"-\\n+\", \"-\\n-\"],\n",
+    "                    palette={\"P\":\"#3ABAFA\", \"C\": \"#E0BA0A\"},\n",
+    "                    #size=3.307, aspect=1,\n",
+    "                    saturation=1, ax=ax1)\n",
+    "sns.despine(trim=0, offset=3, ax=ax1, bottom=True)\n",
+    "\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"Count\", fontsize=7, labelpad=2)\n",
+    "ax1.set_xticklabels(\"\")\n",
+    "ax1.set_yticks(range(0,101,25))\n",
+    "ax1.set_yticklabels(range(0,101,25), fontsize=6)\n",
+    "ax1.tick_params(axis=\"x\", length=0, labelsize=6)\n",
+    "ax1.tick_params(axis='y', direction=\"in\", pad=2, length=3)\n",
+    "ax1.legend(ax1.patches[3:5], [\"ICE\", \"CP\"], loc=\"upper center\", bbox_to_anchor=(0.5, 1.1), ncol=2, fontsize=7)\n",
+    "table = ax1.table(cellText=[[\"+\", \"+\", \"-\", \"-\"],\n",
+    "                            [\"-\", \"+\", \"+\", \"-\"]], \n",
+    "                    rowLabels=[\"Integrase\", \"Rep or Par\"],\n",
+    "                    bbox=[0, -0.3, 1, 0.275],\n",
+    "                    cellLoc='center',\n",
+    "                  )\n",
+    "\n",
+    "table.auto_set_font_size(False)\n",
+    "for k, v in table.get_celld().iteritems():\n",
+    "    if k[1]!=-1: # if not row name\n",
+    "        v.set_fontsize(12)\n",
+    "        v.get_text().set_weight(\"bold\")\n",
+    "    else:\n",
+    "        v.set_fontsize(7)\n",
+    "        \n",
+    "[(cell.set_linewidth(0)) for cell in table.properties()['child_artists']]\n",
+    "\n",
+    "\n",
+    "## Right plot\n",
+    "color = [[\"0.5\", \"#3ABAFA\"],\n",
+    "         [\"0.5\", \"#E0BA0A\"]]\n",
+    "N_func = len(list_func[3:])-1\n",
+    "for i, r in enumerate(df_RR.loc[list_func[3:-1]].itertuples()):\n",
+    "    ax2.bar(i, np.log10(r.RR),color=color[r.RR>1][r.is_significant])\n",
+    "ax2.set_ylim(-1.5,1)\n",
+    "\n",
+    "ax2.set_yticks(np.log10([1/30., 1/10., 1/5., 1/2., 2, 5, 10]))\n",
+    "\n",
+    "ax2.set_xticks(np.arange(0, N_func)+0.4)\n",
+    "sns.despine(trim=True, offset=3, ax=ax2)\n",
+    "ax2.set_yticklabels([\"30\", \"10\", \"5\", \"2\", \"2\", \"5\", \"10\"], fontsize=6)\n",
+    "\n",
+    "_ = ax2.set_xticklabels([dic_name[n] for n in list_func[3:-1]], rotation=30, va=\"baseline\", ha=\"right\", fontsize=6, stretch='condensed')\n",
+    "\n",
+    "ax2.set_ylabel('Relative ratio', fontsize=7, labelpad=2)\n",
+    "ax2.tick_params(axis=\"y\", direction=\"in\", pad=2, length=3)\n",
+    "ax2.tick_params(axis=\"x\", direction=\"in\", pad=5, length=3)\n",
+    "\n",
+    "#p_ice = ax2.axvspan(N_func, N_func+.1, ymin=0.52, facecolor='#E0BA0A')\n",
+    "ax2.axvspan(N_func-3.4, N_func-0.25, 0.88, 0.91, facecolor='#E0BA0A')\n",
+    "#p_cp = ax2.axvspan(N_func, N_func+.1, ymax=0.48, facecolor='#3ABAFA')\n",
+    "ax2.axvspan(N_func-3.2, N_func-0.25, 0.03, 0.05, facecolor='#3ABAFA', clip_on=False)\n",
+    "\n",
+    "\n",
+    "ax2.text(N_func-.25, .95, 'More likely in ICE', transform=ax2.get_xaxis_transform(), va=\"center\", ha=\"right\", fontsize=7)\n",
+    "ax2.text(N_func-.25, .105, 'More likely in CP', transform=ax2.get_xaxis_transform(), va=\"center\", ha=\"right\", fontsize=7)\n",
+    "\n",
+    "ax1.annotate(\"A\", xy=(-0.2, 1.1), xycoords=\"axes fraction\", fontweight=\"bold\", fontsize=13, va='top', ha=\"left\")\n",
+    "ax2.annotate(\"B\", xy=(-0.2, 1.1), xycoords=\"axes fraction\", fontweight=\"bold\", fontsize=13, va='top', ha=\"left\")\n",
+    "\n",
+    "plt.tight_layout(rect=[-0.01,-0.05,1,1.01], h_pad=3.5)\n",
+    "plt.savefig(\"Figures/Figure_2_Continuum_ICE_CP_RR.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": false,
+        "row": 76,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Figure 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mat_GRR = df_grr.set_index([\"ICE_ID_1\", \"ICE_ID_2\"]).sort_index().GRR.unstack()\n",
+    "mat_GRR = mat_GRR.loc[df_conj.index, df_conj.index]\n",
+    "mat_GRR.fillna(mat_GRR.T, inplace=1) # get the mtric symmetric\n",
+    "mat_GRR.fillna(100, inplace=1) # fill diagonal with 100 (self hits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    64\n",
+       "5    63\n",
+       "4    59\n",
+       "2    41\n",
+       "3    33\n",
+       "6    27\n",
+       "Name: Community_noMPF_ALL, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_conj.Community_noMPF_ALL.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 12,
+        "hidden": false,
+        "row": 76,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AxMtWSuZ/aragWMw4EgoREPlgeueAcmQ+kEUx6HbbrALysbPFok8kkGMZobtL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\n0GeRomoEkATkTFCuo0AgWaoqimxdp9SxjJvBL6QwrRaRIAnbK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WQN0LbVYxm8KMzlYTRB\nQNenS4OrzCtUe5YdnZu2J+sZhW9FJoCaqIBY6FrQ13C8gnvA5DwI7wkTbgYogJsJjxlu1xsOWnRM\nndFnZF4NFj53z/JPkanhTy2MzzLBLdESfMbu30+LW+LCV0+iNVtibK6PdrfIscs/zw27P0RnJU+H\n24+7s0/Jd5jSvok/lcYzMN4lm2tSsd1mxIMvlZCtieCwWvKNI1KGawr4ACZP7we2G8EK0PfOAEv+\n8fdbtrNtK80tY+HbFo4Z2hM8KE7Bi0J+ghRIqh52f1kJQ8tSI2aJaL1pY9Jpk46tNBpHkYhIJ5py\nSttGCEuV2Xbqp6LC92Pb162XMhIAppkitAEPMj2zVEqumhonTGNrTQLBCyNWqdSscpr0sDRaHjuJ\nQIs2izQOkXmltk+wTQorXEcoSI0YUNNuav4Oz90L7e+1dvhYKq0eO5SNWOrQIu3AEcmhRqHQta34\n/Wb2wfxf/x6mIDpg3BpydgVL+Dz1egcA1SbBPvn1tNsFslaF1eVRjHH62MktYAmf/fOr6fZyjM/2\nMMVtYVrzGrJWhWarTLNdwkbyYm4/xSNQKqlxNghyRFOTKvljW8pUFpyf31+qLwGukQrWHRTBhbUH\nqli2qJvGICV2oRotD+16PviBE8rkAaj79qOUWWOdCOpOaegMFlXtM+Fm8+XgN6FUMat1jpdyRS1r\nGO5Sy2xl9JsGglxrwUbp55hwHc5xkhCkQcaqrGrtNUnjkTJ0fpnxrMKykrc3p82+VGnLNUIvqZZX\nbTqm32947c3zClOcAzOCXpdw7mbqZ3hvhSnQ+v+ae8hcNkRM8sZiniY7rtFaVXi3tBO9jkpV3VBu\nYcDL8M7ATljCp80pYgufVYVR3Nm9C71eE18f9RaXrT+ArqqiHbTKYBeNfhhjKgcKcRId6seuDsO1\nn243Xp/B8ZERrLJYRJbLWP1GZo9mKQpsqlZf4FGuVsPgfeFLVQCwVI5uHs9DDhSVFtLcFMT1+WqZ\nYWMVJUWOjVlMMMhaachiNYSzTA4UIxvrwIAK/zECyxNtrEYcq04bNQsJhjZWg55P5Iz2Mg1srLXC\nHSBgPhoUjqs0eas8fBvrYMuDlFuCUuB1Nla0LdZIWQ0KGmrboBqH4dlYdfysDEK1pI6hLRo2SA2t\n6ZVKkY21NsTL4KuQxdJ7srF+fvxLoY31pdKeAORXF1jRO5qKb7O50ETXig7G77OBG/b9F14tT+CH\nbx6G51vc/xf/n5s2zOKPnRN5oXtXXl63M5M7utjQn8d3oGV1OTzf2EvIYN4yXwCyUm1AwtI4Pjhx\n2x0AHxnBGvf0W1AuR+l3hqYRpoUGD4/IZJQDSkqlIYXam3pji2JAn+Z5yj5WKiM8O6bJiGIkaESx\nrAL6i+XEzCtZLifztIZ9V2FCMrB/UqkgfYnf06uEh7aBNohjDYWPjkwIAvTjGlzEt6qD94GQa1V/\nJ5ZgabTcgHCdULMPw3m0QDbp/Yz42qQRidlYQV2DIFVV4Ckbq+ZkrY3ZDcvskBhmpfugq+Cq0C4t\nwBVfhNCZV1qw15pSahIbwv1rtzO10jB0zBC2g9wPN/9uNpYTOI10/sFOGXZt6mfehF+xh+szmzNo\nzxY57bdnk8lU+c0nf8jfvTmH//P7v+OkPV+gsyXP7s2beGjKU3x55SxKnkN3v6TSapPRL5EE/gpV\n1bYa/m60nVqxYwjM4eKjI1htG1wXP6fqJ1lBaQ2IwkpkzlXLbTuiDXQdpOuEWVPCCzJRdDaWtr26\nTjQN1/bCBHuTzLgqeyvjJpqThHQTba/hejJheJLQxyYQpL6v+j4cxiorElq61Ij0DQ5a2wbhx5wz\nKjTKD7/fU0prbTaZaVbQ55PLIfX0u9b+myRcggwroWtM1YVYyci0Uss960eOrqhP9XbnMIGgEnG6\n6tRl4bpIg79W1JphHCfqt5sB+htvFyC8r0yH1iAa6+iOPjKOum4TJ78NwOoX9mbRpMe5at2R9FSa\n2LdjA9+f/AvmlE+j2a1w/qovUPYcpnRspNku8f3JT/Dd9Yfy5ZWzeGCPZ+j2C8zIXESl2QrrkiWG\niFkGd29gc03UWCXDNwXsIPL3oyNYAw1SVH2kFUxpDfuX8CVUzSmhYWP1PBJpA4GQNjB8gI0b0ORh\nNbfV9rmkt7jnDSpYY9p1MC3Xv83/hx6PmlTOQQ6pTSC1poDBth30eKaN1g+m0VI0Lji3Jfbc2qln\nbfWBxPYGmSnUUuNpE4JJoef59eQpoR3V0ED1dlHj8ePULhukXz39TWFV1f071gCwJggLLHgunaVm\nBioZ3qrabO7PUXBdWl0Vm7umvw17dNT2moE2uv1CWPHVrgTn5vtqllcDTSsY+93o+qca644J4dhI\ny1KF/YKKn2F2lfaoa/o8IxBfOpocRHvdReRV19De89rMqyRPtxO05djJJCz6WI2gNc1MRtG2ZZSG\nqovyCdepL3ESG4jAfW7VaGqDoFG4VWNilPrldZlXNRprLD2VGi3N8MjHMGgkgQ4TiP5X2vQgzqBG\nJgF9XobGKssVdU9VjTHRmr1tjKcW5kYl1lh6r2o8dhyIa6yDoaOtn2zAF/jkbw8EYBcLLnv383hS\nMCHXwyYrz03rjmR0ywC25TMqU8ASMmC1qnD52hnsmdtAZ2ueC9/5bNAR8NwoBjpRY3UcRZ2of2cy\naeZVgI+MYNUPoXRtpBDY+ebIXppBkYA4VpB/L6IYV1+qQHJzKus4Svjqci+m4LVV5pQwKOV0+JSo\nqgqsQliqEms1QU0cTFsFRLGkHFelwMmhg9WrAZ+pZQXMTQlwXfBU/KGsVEMHjpUJjpkkYAcTuo3W\nDZXJZdt1Qk6WK3GhZ1QBiDnaao5TG0mghHFkL1Zjon775Ups+7rSKNo8EKyLtUEUSSArlcBBqKsd\nJLxsk6CFbG3mkvndIKvJTG4w0ff0eAJaHrIBzYC0PF740YH4NiDAqsBKB0UFWIL/ticiPPCycIu1\nO1YJnhNglyReRh3/xfm3ccglf684NiBu1tK0mtKPlktfmUs010EtUsG67ZH3h8FOtZWQAwWE4+C8\ntV5dZNeFblUcTuSbA4eQQDQ3Ibu6kQHJivAl5YmjyLy5Qd1Q2Qz+pHHY67tUw739IRGHv7FTUQIS\nxPVt7IwFjctqFdHTq4haVq9PfBBFSz4iyE5AedpknK7Nqr1gmqadOHZ7G15nlyojHTYYjxk1hYQi\nuXZDO5r2YMtKFdGcVcKjrx8xapRqSz9Qwbfs6qrv/6hRictr4fUocWBS55k2Ty3EhCWwmptV4cea\n6bw1ZTf811bEihyG20g/JhjN9Trvv27bmMMsOEgNM770PHUvCaHIz/U0Wfr4XZuNVF0BhWL4ApQD\nA2o8fYkf3Hdh28Vi+L/ftVmV9da2atuObMghYXpklph0+4vR+AVCTQ4UaDd5C4wQOVmthuW8w35q\nH0PACSCl5Kj7j2CM9yq/fOkZjp4+h1sX/5hlpYl8Iruaky/7FlddcRffnXcml11/D3etncH6q/ek\nefkmJty3PvmCDycMbwfCh0Kwer2P83jpjff3IJl9kH39yvPseYj2NkVKDUjfR7guzu+XI3NNKnRJ\nhx6Vyriv9IImrRYCe+2m6E1dUSFDlmXhV6thNUs5eQKsXK1SX7VB35eI1uaIoDpBgMrN3YPaAp0/\nvB560YUINNbgpvW7NofLRC4XES8HmVeyTI0QirzUocYLyEoZWc0q4eFLJRRAPZxBNVMCHti6/puF\n+QzEBGjG1X4cwppTXjzrSjvVwFZE3rZFbZCjXLEq7lgJthdC4JcqWFnj9pbKCSelCnfSvxWvLDX/\nmxVkAbx4CFlQaFB1VIROQ3UR/JjzUpTLke3RtuOFHmudjLatpt26/dB270fE7MGLIBxjM3BfpyGX\nywgtPPVLJcg8hMhzH6aqaqGrmbp8P4yLPnr6HH6x9HGOPvTLUPX4Z8dmTGklNz11BC2tm7hp5mdA\nCJpzm/BXrmLtlyfB69RgiNjs2m13AHwoBOuc7BTmZKcMb+NTD4ETPgb5LPxmBVz3axgYIm4S+Py7\ny5UGlG8ONVbZq3IKRS6HLJbwDtxLVRHo7YX2VrVjxqW06yialq+LpvlaC7UscDNYOYmsqps3TFdd\ntUZl82Qz0U0lhRKm0g/LatdCtLZHZZSTsOckxPK3jBjUyLGE66qyLxk3iGdVL4LQsZCosVpBiFRE\nkC0tOywvo2Jkg1r1NRqrSLDliubmxOUmpEENGFLvQVwjlT7oqqvZrBqvGo3VnjQB7+3VyvYp/dA+\nKoUVxcYG56q4X5WjzGpuDrlf9b7SI/ofwyasx8DJKlNC4AnXdkdtfglZ9k0mrnIlckYFLzzTxFAX\n/O/peygwlUCgUVqBg1ELbwthBaF8QbwxEJGkmPZoHWoHyjxlFPyLVSoI+h3ObjIuwnW4dfGPOfpT\np3DP4gfxpMQWgi9cNY+HLl3EmX//D3z/tpv5v+/MYf3Vu9Ds2LTetSnhgjMsbtlw2x0A21eew6mH\nwFdnKO/9mm44Yip8Y/aWt9eI9QgCbcFXN6mIlqn/hbpRPmLTmw8dRpAlaouQlAK8I0IIxtmR7dTM\nmm4WHkVdYmiwxBbpD+uzo+BDobEOC7ZQgnVTH5x+D1Q8+PFXYI/Rw24iDBUavROip79OYwjJeH0Z\nPSSej1X1Ay9+VWmslWrEHWC0q1JazemsFRGEaGh6NJuYpzjaSSQvj04imiJaUWquGa40VNjVYHGR\nHxaEjP/QUGCZqcLScDwN1d6Q6ZcJ+2otVPpGCFLthtokMNTD0pUAABZeSURBVKQTy0h7btTGcPFe\nXu7mttlszIyQvL1kWWkiVD3WVPuY4LSwptqHU4ClxV0B9d1kV+gBqHrk7AbMZh/y+22ksf28YqeM\nhdYm+ONqKAXpml/6IZxz/7CbCMNiNinnTyxrByPExRKhxgrgO1aU4ur5UfiPnmKZnKdGWqb0ghhY\nXWY7JBzRKYte/UfK5OX6I0QU3gWh1i30crM/DfBhF6oQt8k2Eh5mhtpgQrV2/XvlQxVWROgc/ras\n+rAp/QIYSnAPtv69ar06dG04+5nbDCVUASzBJ7KrwbGxhQiFazUH05tUMsL0prcjjdWxKSQVyARi\nXBuDfXYQbD8a64R29e1YcNspStAuXQmLnoTewUs+A5BxkX1l2CmwnW7cHJY2thwHWSjgbOhBDgzg\nF4rYvcq7IotFKgeOI1ssQUWVzvZ7ehGtLaqdinJOiFIpHrxfqSpngq4/BaEDQhZLQThWvWYihyhf\nLfqLSCGwsln8Uim0H2IbYS86s8coMQ0oU4YRTkTAlBXTuPXLwYTJw6nDvBo9mMN5YPW5JAnDkC9V\nhnZEvVw4Tixlsrj7aNxV7wQe9GhfYVuqBI3+Hdo2wR4/Dm/nUfDH5cPup/SlonPUmr4lwIuIYUKE\ncbnmS0HGTAYigQsiLJcOUVpyLE5aNNT4TKdaKOgrlZDfoa5/2kEWhAeKpib8/oH6l03ANXHyZd9i\nTGkln184D6cA1Ry88J3bOfiaixldLHHqxfMYGG8xyq/g57OsuHNf+FRtJ+XwNesdRLhuPxprNhAW\nh+0FbU2wrgdm7QuXfWF4+2snQ6WKGCgqguhMRn2C2FF/VAsil0M4Dv6YUfhjRiFG7QSgyFXGj0WO\nasM7aG/lBGtrUamKvnKwhF5bYcVTOC07mt4LSwm+gCe07jN6p+TlwafnoHHgeeFLQYdayWoVkc2o\n/w1tG+IadUxbFeqBjWncus3Y2GfVR1jx78Tr1GB5LaThhQZ1DmEqak18Z/A7LCoYfPyM4gANC/3V\nOp5qfgPgOni5uFYlkoSQcdy6fpfjrF+x8asdu5qXlNRmJjOzqlqNEcFEERGEyxp2LwinE0JE16k2\nZdZwlslyJeqvL5HFYpQgYyJwxF11xV0A/PSSRXzzH3/MTy9ZxMHXnMcfvn0b1Wabq6++kyPO+C2+\no2Z5X/zWr5M7mmqsH1KUA01lTbeysQLcdyZ8ak/oyENn49hPICBtdhD9BeXBz2aiUsNBlID97ial\n1TU3wxoVjycdh5bl3cje/oCVysfZmEFWAiYsxwmKDwpEvlkJacsKGI8Cj31Y6tkKYgWblAaWxF3Z\n2Z3IMaDR9u+vIW1bFcIrFGNlpNXBIuEYaia2jZC+CiuzoiltWGwPVFxqEP4lfakeLB3XqoV4czNy\nYCD8TkKj5SZEEIak+x16qTOuIdx133S2U/2t2vT8y0rDc5349mZWl9mWZeGv34izYZNK+DDiO0XN\n/6HmGTJoaQ3YxmprVUz/GVdVbQhCqUIBZllxz7vx8gjtxsb2wlZef2mrYoVSSuVD0IKmVmPVkQJE\nsavhsUBVT9XxuahZkMi4kM0oU5bmhtWzgEpF3ccBl61pYvnuvDNpad3EmX//DwDcAYwulph5zjk8\nd+ed/PX55yJ8yayFS1h25C78/PIjmf/T+msuP2KO3u1HsG4I6Nbf7opCMpavh8kdMLZlaMFKEL83\naiyidwBZLOH3qEBtq61NsUS1NMPGTvy+fqzdJqmdfJ/yuDzZnn5kc3ukpeiYwO6eIL0x4BbVgdmF\nYhRXWDSm94YpINGJMn4MrNvY8BzKB++Bs/hPodDwC8VIwymVlaDxrCghwHSU1GQoiYA4A4BqNUws\n8Pr6o2J5A4YGWymr38F3kn7RaHkMAX1hLGtKx2nW9C9sM4mK8KB9Ea+sVPy0CQkCZrKBbgfpY7W3\n4ff01W9rhnMFsaxmfK9eF/K06j7pcCujioOm2tN8AmY4W903hFl0sqzinpWgj0rp1I6fFvR+d2/U\nRD6iMwzLt+swu6D8O6iwK80qhi5KWanGEgSEVAkKfTvbtPyml659x2OVwM+CdXgn8ukOjjj9bLoP\ntvEdeO7bh9JcfZ3+nRtMgncgbXQ42H5MAcvXQ7EC+4yH5kBL2z2ICFjbM/T+5UpYWgUp600BlkVl\nbAuiJR/Fowaef2mJoBaWuskrO7crAWrbyhQghCpdUWsK0BEAeiqvp7qDmQIq1UFNAZUWFXuoBY1w\n1UMoHAfRnFPHrSGibmQKUBlJxrS5GjxcWjvU9swRNgXIgG1KUxaaEI4TaV5mWe5Y2qf61BZXrHMm\nNcIQ5NFhP8PaZFbcXDCY9jWEEykxqcKrYQozok3U8QYxBejr7zpDmgLUvWE44gJh38gUIByHa+bd\nhXAcHr3wOuZ/40EevfA6cg/sxI3fuINym8MfL7qNv5z7J5W12N7KYWf/d3JH9bMw1GcHwfajsZY9\neOgFFXJ175nQXVAOrGf+rH4PheBh8ttyiCYXq6svTnHm+7idA6GmIjYHmoBl0TN5HNk/BVqKL3E3\n96qpvllGww/SZA2bWsT8Ew+ADwWAn/CAd3UP+nbPrRmIa3q+VHR/lsBy3ZCsOplwWHdDmv/ExgDL\nqq+xZPZT/07q+2DLTZgaoD5+mLgQzAiMB91v4BCzO/vUOh3k34gLtKY9kctB/zDuGWPfsHyL5ieo\nVhEZNz5WCVEBKqNr+AIjtJEP9yUxnNAs7V8wkgWGA1mtctfaGSAE33jzRJrsCg940xkYb/HY5oMR\nvuRv3prBvbv9BzOZSmViB8++k4e/rG1IUmtrbnzQHUO4bj+CFeDO51Ww/pwDINMKj/8P3PLMsHYV\nQX64vaZTPYBBdgkE9h/Lgk2b1TQ2mzU0Qpex/7mBsMSH5yktVVcBcBzFDxBk5ER8mhYiYB0Kl1nB\nVNV2Y7ZFE9ILsqEawHpjtbKxZtzIxqrjKvv6lPYSpLsOZmNV5204ihwnCA8LtjFtrHoKORwbay07\nfhJM0mhqXgK19bWgYdynXLM+TMmMaauD2FgBFdWRZFM1j2kcV5bLSpBq00RzLor20JpmTeieOUsI\nQ+G0jdWMEtH9C2ysepmsVIdnYzW4KMJxzLihszS87nqZ40T8r0PYWEXGZd01U8jnNtK9aAzdwXFG\nVSssu/LjzLpyCc9859PMYCo/uPVGLvrEcex0zx5wbN2l2i5C/EYS25dglcAdz6vPe4Tf3aNKs1gC\ndh6Lv+pdZEFpLSKXU4KpV2mxfqkU2hv9Ygkxeidkfz8IoWj5+vsDYukqslDELxTUVNHzVEVLiMpU\nOw6+QYcXC49J8EYLIfAHe2v7virJEkyPYxSB5cDW53n4wXEkxDTDsDxIYKeTVR+/JFROuxdpo7K3\nV3G8GkxZsrdX2Q/1dxInAMN4iAqFuN2yWg37E0tpDWn6nMRKpc5uk6i+9U7Y51hiRW1bwbGciRNU\n5d1X6hLa6/aNqgxEVRaQPtV165UJQ2qSlMA04xuC0PMgm1X3j2Y9q7ET6/91/LQsl0MeiVBImmaL\ncOITja/XuTn8bQWMarrqbbTCQvYPhPehpRUKL+600/uF6bpSkv/zRvyVq8i7jjKjOTZ+Pgu+z7Ij\nd6E1v5bKxA4u+sRx/PLFJznq8BMaj+lHCNuXYN0KiFyT8ka35JEbu5Rn13jDyz4f7+C9cd/agNVv\nhxEDwrIY2LWN5u5RSoPwPEQ+rwqq2RYim8Xy/VAjFk0Bu5Vp+wuWEUwfw9jGhAwr0ZpXjFmNzkM/\nFHra68e1WxVzaZCwCBFGCGjHmclVamWzUX56WLAveNlUq6otTYGo+x18J3IFNGWH5AoQuZwSrjq0\nSGpN2rClat5bwxtfyxVAoah4EWqJo4UV7Q9hHSyEhbdmLaIzm+zoCr9FTCiLIHdf9cUOQ7yEbYU1\nu4S+tprQJOizlXGRhvNK+jJ8CYeaazCjEZpcJ3AgAfHMPa0xG+xWluZxgLBEtTBioVWEQQVcF8uN\nz5SEbtOItdUlfUQ2gyhXmHDfetZ+eRKtP9xIzq5Q8FxW3LkvX/zWr/n55UfysUtf4Nl38oy6aw+O\nOvwEfvnsT4FrYte76pZ5Z9eXBr0nNDx3aN6P7QEfDsG65Fvv+yGOmTOG0fuNQrx2KeN6inQMJKTe\nVYBd2ihmC9z71VuHbNPB53Crh52qPv/z31NHvtM1kBLK1TYGXmvn/528N5P6N7F7oRPH9/jM2pfZ\ns9dGWj7lKd30rfiruv3dYpZJK/eLLStmS/zz2f/CGNHYPjmS8CsOA//5KeTmF9lvoqBkOTy46yHs\nWujCkj5T+jfg+B5/vfE11o3K43t5rNcuYWxvXFh72QFW/M3C962fDj4zrF7sMmzarF6yFc/m31ce\nzJIBmDGmwP4bR9WfHxbd9kQUyS9ADh+LLnv3YR/bx6LbmkSmWmVPfpa8jXRZ9tYU9h19AJbvM3VN\nDx0DFXrz3fzi+J/QM7o7cb/hwKoKpvxuHL2FPcl8YxdO320vuMxY7/uMOutoDs8WuPfPi5nJYqwv\nCvZ+YSzWzvHEiwkTJrzn42/JPh82jIhg/bd/+zfuvPNOPE9VIT3uuOM4++yzR6JpAI4//ngeeeSR\nrWpjycbmJNPPdok/dK0H9v6gu7FVeGU17Dfxg+7FlqFzRQ+MGbxg4rbAa5v+h31HH/C+HmPZwKt8\nonnLlYYf/ehHI9ib7QdbLVjXrVvHddddx6OPPkpbWxuFQoHTTjuNPfbYg9mzZ49EH7daqKZIkSLF\ntsRWC9auri6q1Sp9fX20tbWRy+W49tpryWQyzJ49m89+9rMsXboUIQQLFy5k2rRpdHZ2csUVV/Du\nu+8C8LWvfY1Zs2bR29vLpZdeyvLly8lkMlx00UXMmDGDqVOn8uqrr1IoFLjqqqt49dVX8X2f0047\njRNPPJFVq1Yxf/58KpUKtm1zySWXcPDBB2/14KRIkSLFlmCrBevUqVNDATpt2jSmT5/OnDlz2Guv\nvQDo6OjgkUce4ZlnnmH+/Pn8/Oc/Z+HChRx33HEceeSRdHV1cdJJJ3HQQQdx2223scsuu3DzzTez\natUqLrjgAmbMmBE6gu644w722Wcfrr76agqFAqeeeioHHHAADz/8MHPmzOHUU09l2bJl/O53v0sF\na4oUKT4wjIiNdcGCBZx33nksWbKE559/npNPPplrrrkGIQSnnHIKALNmzeLb3/42mzdvZsmSJbzx\nxhvceqtyEHmexxtvvMHSpUu5/vrrAZg8eTKPPvpo7DiLFy+mWCyGy/v7+3nttdeYMWMG8+bN4/e/\n/z0zZ87k9NNPj+23fn2DOjwpUqRI8T5gqwXrs88+y8DAAEcddRTHH388xx9/PP/6r/8a2kVtk+9U\nSuwg9Ofee++lra0NUHbaMWPG4DhOLExp5cqVTJ48Ofzf932uv/56pk2bBsDGjRtpb2/HdV1++ctf\n8txzz/HEE0/wyCOPcPfdd4f7/eQnP9na00yRIkWKYWOruQJyuRw33nhjaC+VUvL6668zdaryJD72\n2GMAPPnkk+y22260trYyffp07r9fEVSvWrWKY489lu7ubg455BB+8YtfAPD2229zxhlnqCD1IKbv\nk5/8JA888ACgbLsnnHACb7zxBgsWLOCpp55i7ty5XH755bz88suxPp500klbe5opUqRIMWxstcY6\nffp0zjvvPM4999ww3Oqwww7ja1/7Go899hjLli3jwQcfJJfLcd111wFw2WWXccUVV3DssccipeTq\nq6+mo6ODr3/963znO9/huOOOw7Isrr322pgWe/7553PllVdyzDHH4Ps+559/PlOnTuWss85i/vz5\nPPDAA9i2zZVXXhnr47hx47b2NFOkSJFi2BgRG+vcuXOZO3du4rpLLrmE0aPjdanGjRvH7bffXrdt\nS0sL//RP/1S3/JVXXgnXa+FsYvLkyTz44INb0vUUKVKkGHG8r7SB77W2UIoUKVLsCHhfU1qffvrp\n97P59w5JwBikP4NsOFwyHlnz/X5C6p6b51C/pFGHoi0TGt1WkPEhk7Ff9WcWv2YNGnof+xobVFnf\n38Y7DbVsOAeGhvvpwZHSHKiok1szNnWn1+h8Ei9mCkDI91oLeDvF4V/6GLPPmcBFt7QgJIjwZgy5\nm6LfwqeUNcggGmyGBFdIBBKvYjfcJlaLepC2hvO737I5Yd0a/ur6cVhIbAkCieV7OMGVbLI8Luza\nUDcGG/uy3PDkgbFmm6XPJb3rsKTc8n69p30EPX4rX9go6Zj+bezMAAcefXV0HgG5iON7+Jagqdvm\nWz/IB6XJ49fIz5RGsF/1vx0kQoIvRbiup8nmi94qZn19PBdtXkcS/HAiGDUmsd9TByRWsFclYRug\naHHR9+CGXfcEGdwHUo1L2a0gLblV42JXBb5vM+/tt7lp0h51GwkpQEhK2VJsn9ZHkrkNPmr4QEhY\n+vr6uPHGG1m6dCmO49DS0sJFF11EuVzmlltu4b777hvxY0pb4GUdssKL32AN4Fa2IBdcDuP3cLdr\n8NuXAimgyTU3EICDplHxfItMApG/W5GUa66440PLlpzrVqBqCSQOFasNKWyjxFfAcAVUg2/PFWRF\nNfmaVQYpAjiCMDnIcr5UxFcZO3GMFUyKPJmwbEthXHOpRHWOYGyM8XE9J6QY3CoIsIQkYzVubIue\nk48AtrlglVJy7rnnMn36dH72s59hWRZ/+MMfOO+885g/f35ql02RIsV2j20uWH/729+yfv16Lrjg\ngnDZwQcfzKJFi+js7AyX/dd//Rc33HADpVKJnp4evvnNb3LUUUfx9NNP8/3vfx/Lsmhvb2fRokXk\n83kuvvhi3n77bUDFraaxqylSpPigsM2LCb7yyisccEA91dmnP/1pxo8fH/5///33893vfpeHH36Y\nhQsXcscddwBw6623cu211/LQQw8xa9YsXnrpJRYvXoyUkocffpi7776bF154IdZ2mtKaIkWKbYlt\nrrFaloU/jIJmixYt4plnnuFXv/oVL774IgNBjaUjjzySc845hyOOOILZs2dz6KGHsm7dOhYuXMhX\nvvIVZs6cyfz582NtpSmtKVKk2JbY5hrr/vvvz0sv1ZdpuP3220PhCXDKKafw4osvsv/++/PVr341\nTGs9//zzueeee5g0aRKLFi3iBz/4AePHj+eJJ57g1FNPZcWKFcydO5e+vr6wrdQskCJFim2JbS5Y\nP/7xjzN27FhuuukmvKBQ2rJly7j//vvp6uoCoLu7m1WrVnHhhRcyY8YMFi9eHGq5xxxzDAB/+7d/\nyxlnnMFLL73E448/zoIFC5g1axaXXnop+XyeNWvWhMdMU1pTpEixLfGBhFvdfvvtfO973+OYY47B\ndV3a2tq47bbbKBZVEbT29nZOPPFEjj76aMaMGcNnPvMZSqUShUKBefPmccEFF+A4Ds3NzSxYsIDJ\nkyfz9NNPM2fOHDKZDJ/73OfYe+/tu3RJihQptl98IIK1vb2da665JnHdvffeC8D8+fNjttKzzjoL\ngJkzZzJz5sy6/W644Yb3oacpUqRI8d6xzU0BKVKkSLGj4yOT0poiRYoU2wqpxpoiRYoUI4xUsKZI\nkSLFCCMVrClSpEgxwkgFa4oUKVKMMFLBmiJFihQjjFSwpkiRIsUIIxWsKVKkSDHCSAVrihQpUoww\nUsGaIkWKFCOM/wWkEMWLRVgxjwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115782710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure Heatmap\n",
+    "order_sort_communities = {j:i for i,j in enumerate(df_conj.Community_ALL.value_counts().sort_values(ascending=False).index)}\n",
+    "df_conj[\"community_rank_size\"] = df_conj.Community_ALL.map(order_sort_communities)\n",
+    "df_conj.sort_values([\"community_rank_size\", \"Class\", \"species\"], inplace=1)\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3.307, 3.307/1.1))\n",
+    "\n",
+    "pad, height = 0.00, 0.075\n",
+    "margin = 0.01\n",
+    "\n",
+    "\n",
+    "# Heatmap\n",
+    "size_community = df_conj.groupby(\"Community_ALL\").replicon_type_2.size()\n",
+    "m = ax.matshow(mat_GRR.loc[df_conj.index, df_conj.index], cmap=plt.cm.viridis, aspect='auto', interpolation='nearest')\n",
+    "cbar = plt.colorbar(m, ax=ax, fraction=0.1, pad=0.02)\n",
+    "cbar.set_ticks(range(0, 101, 20))\n",
+    "cbar.ax.yaxis.set_tick_params(pad=0, length=2, labelsize=8)\n",
+    "cbar.set_label(\"wGRR\", fontsize=9, labelpad=0)\n",
+    "\n",
+    "sns.despine(ax=ax, bottom=True, left=True)\n",
+    "ax.set_xticklabels(\"\")\n",
+    "ax.set_yticklabels(\"\")\n",
+    "ax.tick_params(length=0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig.subplots_adjust(right=1-height-0.02, \n",
+    "                    left=height  + 0.08, # for label \n",
+    "                    top=1-height - 0.01 - margin, \n",
+    "                    bottom=height+ 0.00 + margin,\n",
+    "                    wspace=0.2)\n",
+    "\n",
+    "# Color bar on top\n",
+    "\n",
+    "ratio_types = (df_conj.groupby(\"Community_ALL\").replicon_type_2.value_counts(normalize=True).unstack()\n",
+    "               .loc[size_community.sort_values(ascending=False).sort_values(ascending=False).index])\n",
+    "ratio_types.fillna(0, inplace=1)\n",
+    "box = ax.get_position()\n",
+    "cax = fig.add_axes([box.xmin, box.ymax+pad, box.width, height], zorder=10)\n",
+    "cax.set_xticklabels(\"\")\n",
+    "cax.set_yticks([0.9])\n",
+    "cax.set_yticklabels([\"90%\"], fontsize=9)\n",
+    "cax.tick_params(length=2, pad=.5, axis=\"y\")\n",
+    "cax.tick_params(length=0, axis=\"x\")\n",
+    "cax.text(-0.01, 0.6, \"CP\", color=dic_replicon_color[\"P\"], rotation=0, va=\"bottom\", ha=\"right\", transform=cax.transAxes, fontsize=8, fontweight=\"bold\")\n",
+    "cax.text(-0.01, 0.4, \"ICE\", color=dic_replicon_color[\"C\"], rotation=0, va=\"top\", ha=\"right\", transform=cax.transAxes, fontsize=8, fontweight=\"bold\")\n",
+    "cax.hlines(0.55, -0.1, -0.01, transform=cax.transAxes, clip_on=False, lw=1)\n",
+    "\n",
+    "prev = 0\n",
+    "for i, rt in enumerate(ratio_types.itertuples()):\n",
+    "    cax.bar(prev, rt.ICE, size_community[rt.Index], color=dic_replicon_color[\"C\"], lw=0, edgecolor=dic_replicon_color[\"C\"])\n",
+    "    cax.bar(prev, rt.CP, size_community[rt.Index], bottom=rt.ICE, color=dic_replicon_color[\"P\"], lw=0, edgecolor=dic_replicon_color[\"P\"])\n",
+    "    prev += size_community[rt.Index]\n",
+    "    if i < len(size_community)-1:\n",
+    "        cax.vlines(prev, *cax.get_ylim(), lw=0.5, color=\"0.15\")\n",
+    "cax.hlines(0.9, *cax.get_xlim(), linestyle=\"--\", lw=0.5)\n",
+    "cax.set_xlim(ax.get_xlim())\n",
+    "cax.set_ylim(0, 1)\n",
+    "# prev = 0\n",
+    "# for rt in df_conj.reset_index().replicon_type.iteritems():\n",
+    "#     cax.bar(prev, 1, 1, color=dic_replicon_color[rt[1]], lw=2, edgecolor=dic_replicon_color[rt[1]])\n",
+    "#     prev += 1\n",
+    "# cax.set_xlim(ax.get_xlim())\n",
+    "cax.yaxis.set_label_position(\"right\")\n",
+    "sns.despine(ax=cax, bottom=True, left=True, right=False, trim=True)\n",
+    "\n",
+    "#color bar on bottom\n",
+    "cax_bot = fig.add_axes([box.xmin, box.ymin-pad-height, box.width, height], zorder=10)\n",
+    "cax_bot.set_xticklabels(\"\")\n",
+    "\n",
+    "cax_bot.set_yticks([0.25, 0.75])\n",
+    "cax_bot.set_yticklabels([\"Class\", \"Species\"], fontsize=8)\n",
+    "cax_bot.tick_params(length=1, pad=1, direction=\"out\", axis=\"y\")\n",
+    "cax_bot.tick_params(axis=\"x\", length=0)\n",
+    "#cax_bot.set_ylabel(\"Species\\nGenus\", rotation=0, va=\"top\", ha=\"right\", labelpad=0, fontsize=8.5)\n",
+    "\n",
+    "prv_com = None\n",
+    "for i, rt in enumerate(df_conj.reset_index().itertuples()):\n",
+    "    cax_bot.bar(i, 0.5, 1, color=rt.color_class, lw=0.1, edgecolor=rt.color_class)\n",
+    "    cax_bot.bar(i, 0.5, 1, bottom=0.5, color=rt.color_species, lw=0.1, edgecolor=rt.color_species)\n",
+    "    if rt.Community_ALL != prv_com:\n",
+    "        cax_bot.vlines(i, 0,1, lw=0.5, color=\"0.15\", zorder=20)\n",
+    "        prv_com = rt.Community_ALL\n",
+    "        \n",
+    "cax_bot.set_xlim(ax.get_xlim())\n",
+    "cax_bot.set_ylim(0,1)\n",
+    "sns.despine(ax=cax_bot, bottom=True, left=True)\n",
+    "\n",
+    "\n",
+    "# color bar left\n",
+    "cax2 = fig.add_axes([box.xmin-pad-height, box.ymin, height, box.height])\n",
+    "cax2.set_xticklabels(\"\")\n",
+    "cax2.set_yticklabels(\"\")\n",
+    "cax2.tick_params(length=0)\n",
+    "cax2.set_ylabel(\"Louvain's groups\", labelpad=-5, fontsize=9)\n",
+    "prev = 0\n",
+    "for com in df_conj.Community_ALL.value_counts().sort_values(ascending=False).iteritems():\n",
+    "    cax2.barh(prev, 1, com[1], color=dic_community_color_all[com[0]], lw=0.2, edgecolor=dic_community_color_all[com[0]] )\n",
+    "    cax2.text(0.5, prev+(com[1])/2., str(com[0]), color=\"white\", fontweight=\"bold\", ha=\"center\", va=\"center\")\n",
+    "    prev += com[1]\n",
+    "    cax2.hlines(prev, 0, 1, lw=0.5, color=\"0.15\")\n",
+    "    \n",
+    "cax2.set_ylim(ax.get_ylim())\n",
+    "sns.despine(ax=cax2, bottom=True, left=True)\n",
+    "\n",
+    "# ax_net.text(0.05, 1.05, \"A\",transform=ax_net.transAxes, fontsize=15, fontweight=\"bold\")\n",
+    "# ax_net.text(1., 1.05, \"B\",transform=ax_net.transAxes, fontsize=15, fontweight=\"bold\")\n",
+    "#plt.tight_layout()\n",
+    "plt.savefig(\"Figures/Figure_3_Community_heatmap_Louvain.pdf\")\n",
+    "plt.savefig(\"Figures/Figure_3_Community_heatmap_Louvain.png\", dpi=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Supp mat: no MPF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_nompf_grr_all = pd.read_table(\"Tables/Table_4_Table_GRR_full_noMPF.txt\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ICE_ID_1</th>\n",
+       "      <th>ICE_ID_2</th>\n",
+       "      <th>GRR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00001.P004_typeT_1</td>\n",
+       "      <td>3.666111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUAM001.B.00003.P004_typeT_1</td>\n",
+       "      <td>94.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUCE001.B.00003.C001_typeT_1</td>\n",
+       "      <td>0.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUCE001.B.00006.C001_typeT_1</td>\n",
+       "      <td>0.803056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BUAM001.B.00002.P004_typeT_1</td>\n",
+       "      <td>BUCE002.B.00006.C002_typeT_1</td>\n",
+       "      <td>0.832222</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       ICE_ID_1                      ICE_ID_2        GRR\n",
+       "0  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00001.P004_typeT_1   3.666111\n",
+       "1  BUAM001.B.00002.P004_typeT_1  BUAM001.B.00003.P004_typeT_1  94.444444\n",
+       "2  BUAM001.B.00002.P004_typeT_1  BUCE001.B.00003.C001_typeT_1   0.900000\n",
+       "3  BUAM001.B.00002.P004_typeT_1  BUCE001.B.00006.C001_typeT_1   0.803056\n",
+       "4  BUAM001.B.00002.P004_typeT_1  BUCE002.B.00006.C002_typeT_1   0.832222"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_nompf_grr_all.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mat_GRR_noMPF = df_nompf_grr_all.set_index([\"ICE_ID_1\", \"ICE_ID_2\"]).sort_index().GRR.unstack()\n",
+    "mat_GRR_noMPF = mat_GRR_noMPF.loc[df_conj.index, df_conj.index]\n",
+    "mat_GRR_noMPF.fillna(mat_GRR_noMPF.T, inplace=1) # get the matrix symmetric\n",
+    "mat_GRR_noMPF.fillna(0, inplace=1) # fill diagonal with 0 (GRR have been filtered above 5%, so bad hist form separate cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Community_ALL\n",
+       "1    67\n",
+       "2    64\n",
+       "3    32\n",
+       "4    45\n",
+       "5    62\n",
+       "6    17\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "size_community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ATB_res</th>\n",
+       "      <th>CELLULAR PROCESSES AND SIGNALING</th>\n",
+       "      <th>DDE_Transposase</th>\n",
+       "      <th>Eex</th>\n",
+       "      <th>INFORMATION STORAGE AND PROCESSING</th>\n",
+       "      <th>Integrase</th>\n",
+       "      <th>METABOLISM</th>\n",
+       "      <th>MOB</th>\n",
+       "      <th>MPF</th>\n",
+       "      <th>N_prot</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Tandem.dir</th>\n",
+       "      <th>N_repeats_norm</th>\n",
+       "      <th>replicon_type_2</th>\n",
+       "      <th>color_type</th>\n",
+       "      <th>species_ID</th>\n",
+       "      <th>color_species</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>color_genus</th>\n",
+       "      <th>color_class</th>\n",
+       "      <th>community_rank_size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICE_ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BUPS002.B.00034.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000403</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>6</td>\n",
+       "      <td>#d45f09</td>\n",
+       "      <td>Burkholderia</td>\n",
+       "      <td>#8dd525</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RASO001.B.00005.P003_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>34</td>\n",
+       "      <td>#a8781b</td>\n",
+       "      <td>Ralstonia</td>\n",
+       "      <td>#6d478b</td>\n",
+       "      <td>#75cb4d</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENAS001.B.00003.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000154</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>14</td>\n",
+       "      <td>#a750a0</td>\n",
+       "      <td>Enterobacter</td>\n",
+       "      <td>#1f9ea2</td>\n",
+       "      <td>#fb4f52</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENCL003.B.00001.P003_typeT_1</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>14</td>\n",
+       "      <td>#a750a0</td>\n",
+       "      <td>Enterobacter</td>\n",
+       "      <td>#1f9ea2</td>\n",
+       "      <td>#fb4f52</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENCL004.B.00008.P004_typeT_1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000117</td>\n",
+       "      <td>CP</td>\n",
+       "      <td>#3ABAFA</td>\n",
+       "      <td>15</td>\n",
+       "      <td>#bd4298</td>\n",
+       "      <td>Enterobacter</td>\n",
+       "      <td>#1f9ea2</td>\n",
+       "      <td>#fb4f52</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 57 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              ATB_res  CELLULAR PROCESSES AND SIGNALING  \\\n",
+       "ICE_ID                                                                    \n",
+       "BUPS002.B.00034.P004_typeT_1      0.0                              15.0   \n",
+       "RASO001.B.00005.P003_typeT_1      0.0                              14.0   \n",
+       "ENAS001.B.00003.P004_typeT_1      0.0                              19.0   \n",
+       "ENCL003.B.00001.P003_typeT_1      6.0                              21.0   \n",
+       "ENCL004.B.00008.P004_typeT_1      0.0                              12.0   \n",
+       "\n",
+       "                              DDE_Transposase  Eex  \\\n",
+       "ICE_ID                                               \n",
+       "BUPS002.B.00034.P004_typeT_1              1.0  1.0   \n",
+       "RASO001.B.00005.P003_typeT_1              2.0  0.0   \n",
+       "ENAS001.B.00003.P004_typeT_1              5.0  1.0   \n",
+       "ENCL003.B.00001.P003_typeT_1             13.0  1.0   \n",
+       "ENCL004.B.00008.P004_typeT_1              4.0  1.0   \n",
+       "\n",
+       "                              INFORMATION STORAGE AND PROCESSING  Integrase  \\\n",
+       "ICE_ID                                                                        \n",
+       "BUPS002.B.00034.P004_typeT_1                                 7.0        0.0   \n",
+       "RASO001.B.00005.P003_typeT_1                                12.0        1.0   \n",
+       "ENAS001.B.00003.P004_typeT_1                                15.0        1.0   \n",
+       "ENCL003.B.00001.P003_typeT_1                                32.0        3.0   \n",
+       "ENCL004.B.00008.P004_typeT_1                                11.0        0.0   \n",
+       "\n",
+       "                              METABOLISM  MOB   MPF  N_prot  \\\n",
+       "ICE_ID                                                        \n",
+       "BUPS002.B.00034.P004_typeT_1         0.0  1.0   8.0    56.0   \n",
+       "RASO001.B.00005.P003_typeT_1         3.0  1.0   7.0    90.0   \n",
+       "ENAS001.B.00003.P004_typeT_1         4.0  1.0   6.0    74.0   \n",
+       "ENCL003.B.00001.P003_typeT_1         0.0  2.0  11.0   111.0   \n",
+       "ENCL004.B.00008.P004_typeT_1         1.0  1.0   6.0    52.0   \n",
+       "\n",
+       "                                     ...           Tandem.dir  N_repeats_norm  \\\n",
+       "ICE_ID                               ...                                        \n",
+       "BUPS002.B.00034.P004_typeT_1         ...                  0.0        0.000403   \n",
+       "RASO001.B.00005.P003_typeT_1         ...                  0.0        0.000017   \n",
+       "ENAS001.B.00003.P004_typeT_1         ...                  0.0        0.000154   \n",
+       "ENCL003.B.00001.P003_typeT_1         ...                  0.0        0.000082   \n",
+       "ENCL004.B.00008.P004_typeT_1         ...                  0.0        0.000117   \n",
+       "\n",
+       "                              replicon_type_2  color_type  species_ID  \\\n",
+       "ICE_ID                                                                  \n",
+       "BUPS002.B.00034.P004_typeT_1               CP     #3ABAFA           6   \n",
+       "RASO001.B.00005.P003_typeT_1               CP     #3ABAFA          34   \n",
+       "ENAS001.B.00003.P004_typeT_1               CP     #3ABAFA          14   \n",
+       "ENCL003.B.00001.P003_typeT_1               CP     #3ABAFA          14   \n",
+       "ENCL004.B.00008.P004_typeT_1               CP     #3ABAFA          15   \n",
+       "\n",
+       "                              color_species         genus  color_genus  \\\n",
+       "ICE_ID                                                                   \n",
+       "BUPS002.B.00034.P004_typeT_1        #d45f09  Burkholderia      #8dd525   \n",
+       "RASO001.B.00005.P003_typeT_1        #a8781b     Ralstonia      #6d478b   \n",
+       "ENAS001.B.00003.P004_typeT_1        #a750a0  Enterobacter      #1f9ea2   \n",
+       "ENCL003.B.00001.P003_typeT_1        #a750a0  Enterobacter      #1f9ea2   \n",
+       "ENCL004.B.00008.P004_typeT_1        #bd4298  Enterobacter      #1f9ea2   \n",
+       "\n",
+       "                              color_class  community_rank_size  \n",
+       "ICE_ID                                                          \n",
+       "BUPS002.B.00034.P004_typeT_1      #75cb4d                    0  \n",
+       "RASO001.B.00005.P003_typeT_1      #75cb4d                    0  \n",
+       "ENAS001.B.00003.P004_typeT_1      #fb4f52                    0  \n",
+       "ENCL003.B.00001.P003_typeT_1      #fb4f52                    0  \n",
+       "ENCL004.B.00008.P004_typeT_1      #fb4f52                    0  \n",
+       "\n",
+       "[5 rows x 57 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_conj.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kulwxzsaYWaF20A3khtZYIJJ1vxGs2VFl8qp2ZtHTOfPmrxgHx6oDRhDFtLWN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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1154c63d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure Heatmap\n",
+    "order_sort_communities_noMPF_ALL = {j:i for i,j in enumerate(df_conj.Community_noMPF_ALL.value_counts().sort_values(ascending=False).index)}\n",
+    "df_conj[\"community_rank_size_noMPF_ALL\"] = df_conj.Community_noMPF_ALL.map(order_sort_communities_noMPF_ALL)\n",
+    "df_conj.sort_values([\"community_rank_size_noMPF_ALL\", \"Class\", \"species\"], inplace=1)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3.307, 3.307/1.1))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Heatmap\n",
+    "size_community = df_conj.groupby(\"Community_noMPF_ALL\").replicon_type_2.size()\n",
+    "\n",
+    "m = ax.matshow(mat_GRR_noMPF.loc[df_conj.index, df_conj.index], cmap=plt.cm.viridis, aspect='auto')\n",
+    "cbar = plt.colorbar(m, ax=ax, fraction=0.1, pad=0.02)\n",
+    "cbar.set_ticks(range(0, 101, 20))\n",
+    "cbar.ax.yaxis.set_tick_params(pad=0, length=2, labelsize=8)\n",
+    "cbar.set_label(\"wGRR\", fontsize=9, labelpad=0)\n",
+    "\n",
+    "sns.despine(ax=ax, bottom=True, left=True)\n",
+    "ax.set_xticklabels(\"\")\n",
+    "ax.set_yticklabels(\"\")\n",
+    "ax.tick_params(length=0)\n",
+    "\n",
+    "\n",
+    "pad, height = 0.00, 0.075\n",
+    "margin = 0.01\n",
+    "\n",
+    "fig.subplots_adjust(right=1-height-0.02, \n",
+    "                    left=height  + 0.08, # for label \n",
+    "                    top=1-height - 0.01 - margin, \n",
+    "                    bottom=height+ 0.00 + margin,\n",
+    "                    wspace=0.2)\n",
+    "\n",
+    "# Color bar on top\n",
+    "\n",
+    "ratio_types = (df_conj.groupby(\"Community_noMPF_ALL\").replicon_type_2.value_counts(normalize=True).unstack()\n",
+    "               .loc[size_community.sort_values(ascending=False).sort_values(ascending=False).index])\n",
+    "ratio_types.fillna(0, inplace=1)\n",
+    "box = ax.get_position()\n",
+    "cax = fig.add_axes([box.xmin, box.ymax+pad, box.width, height], zorder=10)\n",
+    "cax.set_xticklabels(\"\")\n",
+    "cax.set_yticks([0.9])\n",
+    "cax.set_yticklabels([\"90%\"], fontsize=9)\n",
+    "cax.tick_params(length=2, pad=.5, axis=\"y\")\n",
+    "cax.tick_params(length=0, axis=\"x\")\n",
+    "cax.text(-0.01, 0.6, \"CP\", color=dic_replicon_color[\"P\"], rotation=0, va=\"bottom\", ha=\"right\", transform=cax.transAxes, fontsize=8, fontweight=\"bold\")\n",
+    "cax.text(-0.01, 0.4, \"ICE\", color=dic_replicon_color[\"C\"], rotation=0, va=\"top\", ha=\"right\", transform=cax.transAxes, fontsize=8, fontweight=\"bold\")\n",
+    "cax.hlines(0.55, -0.1, -0.01, transform=cax.transAxes, clip_on=False, lw=1)\n",
+    "\n",
+    "prev = 0\n",
+    "for i, rt in enumerate(ratio_types.itertuples()):\n",
+    "    cax.bar(prev, rt.ICE, size_community[rt.Index], color=dic_replicon_color[\"C\"], lw=0, edgecolor=dic_replicon_color[\"C\"])\n",
+    "    cax.bar(prev, rt.CP, size_community[rt.Index], bottom=rt.ICE, color=dic_replicon_color[\"P\"], lw=0, edgecolor=dic_replicon_color[\"P\"])\n",
+    "    prev += size_community[rt.Index]\n",
+    "    if i < len(size_community)-1:\n",
+    "        cax.vlines(prev, *cax.get_ylim(), lw=0.5, color=\"0.15\")\n",
+    "cax.hlines(0.9, *cax.get_xlim(), linestyle=\"--\", lw=0.5)\n",
+    "cax.set_xlim(ax.get_xlim())\n",
+    "cax.set_ylim(0, 1)\n",
+    "\n",
+    "cax.yaxis.set_label_position(\"right\")\n",
+    "sns.despine(ax=cax, bottom=True, left=True, right=False, trim=True)\n",
+    "\n",
+    "#color bar on bottom\n",
+    "cax_bot = fig.add_axes([box.xmin, box.ymin-pad-height, box.width, height], zorder=10)\n",
+    "cax_bot.set_xticklabels(\"\")\n",
+    "\n",
+    "cax_bot.set_yticks([0.25, 0.75])\n",
+    "cax_bot.set_yticklabels([\"Class\", \"Species\"], fontsize=8)\n",
+    "cax_bot.tick_params(length=1, pad=1, direction=\"out\", axis=\"y\")\n",
+    "cax_bot.tick_params(axis=\"x\", length=0)\n",
+    "#cax_bot.set_ylabel(\"Species\\nGenus\", rotation=0, va=\"top\", ha=\"right\", labelpad=0, fontsize=8.5)\n",
+    "prev = 0\n",
+    "prv_com = None\n",
+    "for i, rt in enumerate(df_conj.reset_index().itertuples()):\n",
+    "    cax_bot.bar(prev, 0.5, 1, color=rt.color_class, lw=0.1, edgecolor=rt.color_class)\n",
+    "    cax_bot.bar(prev, 0.5, 1, bottom=0.5, color=rt.color_species, lw=0.1, edgecolor=rt.color_species)\n",
+    "    prev += 1\n",
+    "    if rt.Community_noMPF_ALL != prv_com:\n",
+    "        cax_bot.vlines(i, 0,1, lw=0.5, color=\"0.15\", zorder=20)\n",
+    "        prv_com = rt.Community_noMPF_ALL\n",
+    "        \n",
+    "cax_bot.set_xlim(ax.get_xlim())\n",
+    "cax_bot.set_ylim(0,1)\n",
+    "sns.despine(ax=cax_bot, bottom=True, left=True)\n",
+    "\n",
+    "\n",
+    "# color bar left\n",
+    "cax2 = fig.add_axes([box.xmin-pad-height, box.ymin, height, box.height])\n",
+    "cax2.set_xticklabels(\"\")\n",
+    "cax2.set_yticklabels(\"\")\n",
+    "cax2.tick_params(length=0)\n",
+    "cax2.set_ylabel(\"Louvain's groups\", labelpad=-5, fontsize=9)\n",
+    "prev = 0\n",
+    "for com in df_conj.Community_noMPF_ALL.value_counts().sort_values(ascending=False).iteritems():\n",
+    "    cax2.barh(prev, 1, com[1], color=dic_community_color_noMPF_ALL[com[0]], lw=0.2, edgecolor=dic_community_color_noMPF_ALL[com[0]] )\n",
+    "    cax2.text(0.5, prev+(com[1])/2., \"N\"+str(com[0]), color=\"white\", fontweight=\"bold\", ha=\"center\", va=\"center\")\n",
+    "    prev += com[1]\n",
+    "    cax2.hlines(prev, 0, 1, lw=0.5, color=\"0.15\")\n",
+    "    \n",
+    "cax2.set_ylim(ax.get_ylim())\n",
+    "sns.despine(ax=cax2, bottom=True, left=True)\n",
+    "\n",
+    "# ax_net.text(0.05, 1.05, \"A\",transform=ax_net.transAxes, fontsize=15, fontweight=\"bold\")\n",
+    "# ax_net.text(1., 1.05, \"B\",transform=ax_net.transAxes, fontsize=15, fontweight=\"bold\")\n",
+    "#plt.tight_layout()\n",
+    "plt.savefig(\"Figures/suppFigure_3_Community_heatmap_Louvain_noMPF.pdf\")\n",
+    "plt.savefig(\"Figures/suppFigure_3_Community_heatmap_Louvain_noMPF.png\", dpi=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gammaproteobacteria      0.693380\n",
+       "Epsilonproteobacteria    0.170732\n",
+       "Betaproteobacteria       0.090592\n",
+       "Fusobacteriales          0.027875\n",
+       "Alphaproteobacteria      0.013937\n",
+       "Acidobacteriales         0.003484\n",
+       "Name: Class, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_conj.Class.value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 4,
+        "hidden": false,
+        "row": 77,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Figure 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_conj[\"Int_par_or_rep\"] = df_conj.astype(bool).apply(lambda x: \"{}_{}\".format(x[\"Integrase\"], x[\"Partition_System\"] or x[\"Replication\"]), axis=1)\n",
+    "\n",
+    "df_conj[\"Int_par_or_rep2\"] = df_conj.Int_par_or_rep.map({\"True_True\":\"11_All\", \"False_False\":\"00_Nothing\", \"True_False\":\"10_Integrase\", \"False_True\":\"01_Stabilization\"})\n",
+    "\n",
+    "df_stab_int_ct = pd.crosstab([df_conj.Int_par_or_rep2, df_conj.replicon_type], df_conj.Community_ALL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 10,
+        "hidden": false,
+        "row": 80,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Community_ALL</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Int_par_or_rep2</th>\n",
+       "      <th>replicon_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">00_Nothing</th>\n",
+       "      <th>C</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">01_Stabilization</th>\n",
+       "      <th>C</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>38</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">10_Integrase</th>\n",
+       "      <th>C</th>\n",
+       "      <td>0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>29</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">11_All</th>\n",
+       "      <th>C</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>56</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>28</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Community_ALL                    1   2   3   4   5  6\n",
+       "Int_par_or_rep2  replicon_type                       \n",
+       "00_Nothing       C               0   0   3   2   0  0\n",
+       "                 P               1   0   0  13   0  5\n",
+       "01_Stabilization C               0   0   0   0   2  0\n",
+       "                 P              38  10   0  10   1  8\n",
+       "10_Integrase     C               0  41  29  15   0  0\n",
+       "                 P               0   1   0   2   0  1\n",
+       "11_All           C               0   0   0   3  56  0\n",
+       "                 P              28  12   0   0   3  3"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_stab_int_ct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 9,
+        "hidden": false,
+        "row": 81,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ts/5eLX/wyF6lfn7kr729Wp+Yo0r2eEVEarIq1+OtzpKTk0lJSSl3n91ux2rV7zkRUfB6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\nImIyBa+IiMn+H4c5MMQ+OTWQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114b6c5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.rc_context(sns.set(font_scale=0.7, style=\"ticks\")):\n",
+    "    fig, ax = plt.subplots(1, 1, figsize=(3.42, 2.2))\n",
+    "    mosaic_plot(df_stab_int_ct.sort_index(ascending=[True, False]),\n",
+    "                dic_color_row=dic_int_stab2,\n",
+    "                row_labels=[\"Nothing\", \"Partition\\nor Replication\", \"Integration\", \"All\"],\n",
+    "                alpha_label=[\"CP\", \"ICE\"],\n",
+    "                y_label=\"\",\n",
+    "                top_label=False,\n",
+    "                pad=0.02,\n",
+    "               order=range(1,7), \n",
+    "               color_ylabel=True,\n",
+    "               ax=ax)\n",
+    "    ax.tick_params(axis=\"x\", pad=0.1, length=2)\n",
+    "    plt.xlabel(\"Louvain's groups\", labelpad=0)\n",
+    "    plt.tight_layout(rect=[0,0,0.9,1])\n",
+    "    plt.savefig(\"Figures/Figure_4_Mosaic_Communities_Function.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Supp mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ct_int_par_rep_noMPF_ALL = pd.crosstab([df_conj.replicon_type, df_conj.Integrase.astype(bool),\n",
+    "                             df_conj.Partition_System.astype(bool) | df_conj.Replication.astype(bool)],\n",
+    "                             [df_conj.Community_noMPF_ALL]).sort_index(ascending=[True, False, True]).reset_index()\n",
+    "\n",
+    "ct_int_par_rep_noMPF_ALL[\"Int_rep_par\"] = ct_int_par_rep_noMPF_ALL.apply(lambda x: \"{}_{}\".format(x[\"Integrase\"], x[\"row_2\"]), axis=1)\n",
+    "\n",
+    "ct_int_par_rep_noMPF_melt_ALL = pd.melt(ct_int_par_rep_noMPF_ALL, id_vars=[\"replicon_type\", \"Integrase\", \"row_2\", \"Int_rep_par\"])\n",
+    "\n",
+    "ct_int_par_rep_noMPF_ALL[\"Int_par_or_rep2\"] = ct_int_par_rep_noMPF_ALL.Int_rep_par.map({\"True_True\":\"11_All\", \"False_False\":\"00_Nothing\", \"True_False\":\"10_Integrase\", \"False_True\":\"01_Stabilization\"})\n",
+    "\n",
+    "\n",
+    "ct_int_par_rep_noMPF2_ALL = ct_int_par_rep_noMPF_ALL.set_index([\"Int_par_or_rep2\", \"replicon_type\"]).select_dtypes([int])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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APw8AAEWdWbPmNa9/ulo7/iSgA9WECRMUHx+vwYMHq3jx4t7tTZs2Vc+ePa94rsvlUnp6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\nlYiIiIiD1FCJiIiIOOj/A8nNaiEPp8lgAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1158698d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#\n",
+    "fig = plt.figure(figsize=(6,5))\n",
+    "ax1 = plt.subplot2grid((2,1), (0, 0))\n",
+    "ax2 = plt.subplot2grid((2,1), (1, 0))\n",
+    "\n",
+    "CT_noMPF_ALL_ALL = df_conj.groupby([\"Community_noMPF_ALL\"]).Community_ALL.value_counts(normalize=True).unstack()\n",
+    "CT_noMPF_ALL_ALL.plot(kind=\"bar\", \n",
+    "                      stacked=True, \n",
+    "                      color=dic_community_color_all.values(),\n",
+    "                      legend=False,\n",
+    "                      ax=ax1)\n",
+    "\n",
+    "ax1.legend(loc=\"center left\", bbox_to_anchor=[1, 0.5], title=\"Louvain's\\ngroups\")\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=0)\n",
+    "ax1.set_xlabel(\"\")\n",
+    "ax1.set_ylabel(\"Frequency\")\n",
+    "ax1.tick_params(top=False, right=False)\n",
+    "\n",
+    "mosaic_plot(ct_int_par_rep_noMPF2_ALL.sort_index(ascending=[True, False]),\n",
+    "            dic_int_stab2,\n",
+    "            ax=ax2,\n",
+    "           row_labels=[\"Nothing\", \"Partition\\nor Replication\", \"Integration\", \"All\"],\n",
+    "           y_label=\"\",\n",
+    "           top_label=\"Size of groups\",\n",
+    "           alpha_label=[\"CP\", \"ICE\"],\n",
+    "           order=\"Normal\",\n",
+    "           x_label=\"Louvain's groups without MPF genes\",\n",
+    "           color_ylabel=True)\n",
+    "\n",
+    "ax1.tick_params(pad=1)\n",
+    "ax2.tick_params(pad=1, right=\"off\")\n",
+    "ax2.set_xticklabels([\"N\"+str(i) for i in range(1,7)])\n",
+    "ax1.set_xticklabels([\"N\"+str(i) for i in range(1,7)])\n",
+    "plt.tight_layout(rect=[0,0,.9,1])\n",
+    "plt.savefig(\"Figures/supp_stack_barplot_Communities_Communities_noMPF.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 4,
+        "hidden": false,
+        "row": 88,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Figure 5 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_bbh_grr = df_bbh.merge(df_grr, on=[\"ICE_ID_1\", \"ICE_ID_2\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Rho</th>\n",
+       "      <th>p-value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RepType_comparison</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CP-CP</th>\n",
+       "      <td>0.833618</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICE-CP</th>\n",
+       "      <td>0.660781</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICE-ICE</th>\n",
+       "      <td>0.906369</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Rho  p-value\n",
+       "RepType_comparison                   \n",
+       "CP-CP               0.833618      0.0\n",
+       "ICE-CP              0.660781      0.0\n",
+       "ICE-ICE             0.906369      0.0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_bbh_grr.groupby(\"RepType_comparison\").apply(spearman, \"GRR\", \"perId\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 13,
+        "hidden": false,
+        "row": 90,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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B7ceT7soXTw4wb++67Z1vYto6k51FHSnucbQoa0bBEarb2Ni05tHJ28iSVXJc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\nePV/6NAhrF+/ngqmQPl/UbKONvsuAWoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117b6f250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig = plt.figure(figsize=(3.307,3.307))\n",
+    "ax  = plt.subplot2grid((2,2), (0,0))\n",
+    "ax1 = plt.subplot2grid((2,2), (0,1))\n",
+    "ax2 = plt.subplot2grid((2,2), (1,0))\n",
+    "ax3 = plt.subplot2grid((2,2), (1,1))\n",
+    "\n",
+    "bins_grr_bbh = np.linspace(0, 100, 100)\n",
+    "\n",
+    "c, p = histplot_2D(df_bbh_grr, ax, bins_grr_bbh)\n",
+    "ax.hlines(30, 78, 101, zorder=10, lw=0.9, color=\"#01aae8\")\n",
+    "ax.hlines( 0, 79.5, 101, zorder=10, lw=0.9, color=\"#01aae8\")\n",
+    "ax.vlines( 80, -2,  30, zorder=10, clip_on=False, lw=0.9, color=\"#01aae8\")\n",
+    "ax.vlines(101, 0,  30, zorder=10, lw=0.9, color=\"#01aae8\")\n",
+    "ax.text(77.5, 30, \"30\", ha=\"right\", va=\"center\", fontsize=7.5, color=\"0.2\")\n",
+    "ax.text(80, -2.5, \"80\", ha=\"center\", va=\"top\", fontsize=7.5, color=\"0.2\")\n",
+    "#sns.despine(trim=True, ax=ax)\n",
+    "ax.set_xlim(0,102)\n",
+    "ax.set_ylim(-1,100)\n",
+    "\n",
+    "histplot_2D(df_bbh_grr[(df_bbh_grr.RepType_comparison==\"ICE-ICE\") & (df_bbh_grr.GRR<30) & (df_bbh_grr.perId>80)], ax1, bins_grr_bbh)\n",
+    "histplot_2D(df_bbh_grr[(df_bbh_grr.RepType_comparison==\"CP-CP\") & (df_bbh_grr.GRR<30) & (df_bbh_grr.perId>80)], ax3, bins_grr_bbh)\n",
+    "histplot_2D(df_bbh_grr[(df_bbh_grr.RepType_comparison==\"ICE-CP\") & (df_bbh_grr.GRR<30) & (df_bbh_grr.perId>80)], ax2, bins_grr_bbh)\n",
+    "for a in [ax1, ax2, ax3]:\n",
+    "    plt.setp(a.spines.values(), color=\"#01aae8\")\n",
+    "\n",
+    "ax1.set_xlim(79.5,100.5)\n",
+    "ax2.set_xlim(79.5,100.5)\n",
+    "ax3.set_xlim(79.5,100.5)\n",
+    "\n",
+    "ax1.set_ylim(-0.5,30.5)\n",
+    "ax2.set_ylim(-0.5,30.5)\n",
+    "ax3.set_ylim(-0.5,30.5)\n",
+    "\n",
+    "for i,a in enumerate([ax, ax1, ax2, ax3]):\n",
+    "    a.tick_params(pad=1, direction=\"in\", length=2, top=False, right=False, zorder=10, labelsize=\"small\")\n",
+    "    a.plot((80,100), (0,30), ls=\"--\", color=\"#01aae8\", lw=0.9)\n",
+    "    a.annotate(\"ABCD\"[i], xy=(0.05,0.95), xycoords=\"axes fraction\", fontweight=\"bold\", fontsize=14, va='top')\n",
+    "\n",
+    "#ax.set_ylabel(\"wGRR (%)\", labelpad=0.5)\n",
+    "ax.annotate(\"wGRR (%)\", (0.03,0.45), xycoords=\"figure fraction\", ha=\"center\", va=\"center\", rotation=90)\n",
+    "\n",
+    "#ax.set_xlabel(\"Protein Identity (%)\")\n",
+    "ax.set_xlabel(\"\")\n",
+    "\n",
+    "ax1.text(1, 0.5, \"ICE-ICE\", rotation=270, ha=\"left\", va=\"center\", transform=ax1.transAxes)\n",
+    "ax3.text(1, 0.5, \"CP-CP\", rotation=270, ha=\"left\", va=\"center\", transform=ax3.transAxes)\n",
+    "ax2.text(0, 0.5, \"ICE-CP\", rotation=90, ha=\"right\", va=\"center\", transform=ax2.transAxes)\n",
+    "\n",
+    "for a in [ax1, ax3]:\n",
+    "    a.set_yticks([0,15,30])\n",
+    "    a.set_yticklabels([0,\"\", 30])\n",
+    "    a.yaxis.set_label_position(\"right\")\n",
+    "    a.yaxis.set_ticks_position(\"right\")\n",
+    "ax2.set_yticks([0,30])\n",
+    "ax.set_xticks([0,50,100])\n",
+    "ax1.set_xticks([80,90,100])\n",
+    "ax2.set_xticks([80,90,100])\n",
+    "ax3.set_xticks([80,90,100])\n",
+    "ax3.annotate(\"Protein Identity (%)\", (0.5,0), xycoords=\"figure fraction\", ha=\"center\", va=\"bottom\")\n",
+    "\n",
+    "plt.tight_layout()    \n",
+    "fig.subplots_adjust(top=0.83, hspace=0.1, wspace=0.1)\n",
+    "box = ax.get_position()\n",
+    "box1 = ax1.get_position()\n",
+    "pad, height = 0.03, 0.03\n",
+    "cax = fig.add_axes([box.xmin, box.ymax+pad, box1.xmax-box.xmin, height])\n",
+    "cb = plt.colorbar(p, cax=cax, orientation=\"horizontal\")\n",
+    "cax.xaxis.tick_top()\n",
+    "cax.set_xlabel(\"Count\")\n",
+    "cax.xaxis.set_label_position(\"top\")\n",
+    "cax.tick_params(pad=-5, labelsize=\"small\", length=2, direction=\"out\", zorder=10)\n",
+    "\n",
+    "plt.savefig(\"Figures/Figure_5_Prot_ID_GRR_distri_2.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": false,
+        "row": 90,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Figure 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Null hypothesis for ICE-CP comparisons\n",
+    "H0 = binom_test_maha(df_grr.dropna(subset=[\"Mah_P_inf_ice\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bins for ICE-ICE and CP-CP comparisons\n",
+    "bins = 36\n",
+    "xbins = np.linspace(0,100, bins)\n",
+    "ybins = np.array([0]+list(np.logspace(-5, 1, bins-1)))\n",
+    "\n",
+    "# Bins for ICE-CP comparison\n",
+    "bins_CP = 15 # multiple of 7 + 1 to have square in log-log scale\n",
+    "xbins_CP = np.linspace(0, 100, bins_CP)\n",
+    "ybins_CP = np.array([0]+list(np.logspace(-5, 1, bins_CP-1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Compute test for each bin for ICE-CP comparison\n",
+    "df_grr[\"GRR_cat\"] = pd.cut(df_grr.GRR, xbins_CP, labels=xbins_CP[:-1])\n",
+    "df_grr[\"dist_phylo_cat\"] = pd.cut(df_grr.dist_phylo, ybins_CP, labels=ybins_CP[:-1])\n",
+    "\n",
+    "tmp = df_grr.dropna(subset=[\"dist_phylo\", \"Mah_P_inf_ice\"]).groupby([\"dist_phylo_cat\", \"GRR_cat\"]).agg({\"Mah_P_inf_ice\":{\"Mean_p\": np.mean,\n",
+    "                                                                                                                    \"P_binomial\":lambda x: binom_test_maha(x, H0)}})\n",
+    "tmp.columns = tmp.columns.droplevel()\n",
+    "# significant if pval < 0.01 \n",
+    "# Final value is H0 when not significant for representation purpose (so it's grey).\n",
+    "tmp[\"final_val\"] = [i.Mean_p if i.P_binomial<1e-2 else H0 for i in tmp.itertuples()] \n",
+    "P = (pd.DataFrame(index=ybins_CP[:-1], columns=xbins_CP[:-1]).stack(dropna=0).reset_index().drop(0, axis=1)\n",
+    "      .merge(tmp.reset_index(), left_on=[\"level_0\", \"level_1\"], right_on=[\"dist_phylo_cat\", \"GRR_cat\"], how=\"outer\")).set_index([\"level_0\", \"level_1\"]).final_val.unstack()\n",
+    "# Mask array of the final values\n",
+    "ma = np.ma.array(P.values, mask=np.isnan(P.values))\n",
+    "\n",
+    "# #No MPF\n",
+    "# df_nompf_grr_dp2[\"GRR_cat\"] = pd.cut(df_nompf_grr_dp2.GRR, xbins_CP, labels=xbins_CP[:-1])\n",
+    "# df_nompf_grr_dp2[\"dist_phylo_cat\"] = pd.cut(df_nompf_grr_dp2.dist_phylo, ybins_CP, labels=ybins_CP[:-1])\n",
+    "\n",
+    "# tmp = df_nompf_grr_dp2.dropna(subset=[\"dist_phylo\", \"Mah_P_inf_ice\"]).groupby([\"dist_phylo_cat\", \"GRR_cat\"]).agg({\"Mah_P_inf_ice\":{\"Mean_p\": np.mean,\n",
+    "#                                                                                                                     \"P_binomial\":lambda x: binom_test_maha(x, H0)}})\n",
+    "# tmp.columns = tmp.columns.droplevel()\n",
+    "# tmp[\"final_val\"] = [i.Mean_p if i.P_binomial<1e-2 else H0 for i in tmp.itertuples()]\n",
+    "# P = (pd.DataFrame(index=ybins_CP[:-1], columns=xbins_CP[:-1]).stack(dropna=0).reset_index().drop(0, axis=1)\n",
+    "#       .merge(tmp.reset_index(), left_on=[\"level_0\", \"level_1\"], right_on=[\"dist_phylo_cat\", \"GRR_cat\"], how=\"outer\")).set_index([\"level_0\", \"level_1\"]).final_val.unstack()\n",
+    "# ma_noMPF = np.ma.array(P.values, mask=np.isnan(P.values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 9,
+        "hidden": false,
+        "row": 92,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IJBIoFArk5eUJOlfbmkxMeaR3Pw9z9d9lTn+pPeXZ/dul1757BV5Q5LXF0ADj+ayJikGY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\nBEFU4P8Ac3VERtVPsr8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1143e1b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure Final with MPF\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(7.08, 2.5), sharex=True, sharey=True)\n",
+    "\n",
+    "axs = [ax1, ax3, ax2]\n",
+    "\n",
+    "for g, ax in zip(df_grr.groupby(\"RepType_comparison\", sort=False), axs):\n",
+    "    if g[0]!=\"ICE-CP\":\n",
+    "\n",
+    "        counts, _, _ = np.histogram2d(g[1].dropna(subset=[\"dist_phylo\"]).GRR,\n",
+    "                                      g[1].dropna(subset=[\"dist_phylo\"]).dist_phylo,\n",
+    "                                      bins=(xbins, ybins))\n",
+    "        p = ax.pcolormesh(xbins, ybins, counts.T,\n",
+    "                           vmax=1e4,\n",
+    "                           vmin=1e0,\n",
+    "                           cmap=plt.cm.inferno_r,\n",
+    "                           norm=plt.matplotlib.colors.LogNorm())\n",
+    "    else:\n",
+    "        p2 = ax.pcolormesh(xbins_CP, ybins_CP, ma,\n",
+    "                           vmax=H0+0.5,\n",
+    "                           vmin=H0-0.5,\n",
+    "                           cmap=plt.cm.coolwarm,)\n",
+    "                           #)norm=plt.matplotlib.colors.BoundaryNorm(bounds, cmap.N))\n",
+    "\n",
+    "    ax.set_yscale(\"log\")\n",
+    "    ax.set_ylim(6e-6,8)\n",
+    "    ax.set_xlim(-5,105)\n",
+    "    ax.set_xlabel(\"wGRR ({})\".format(g[0]), fontsize=9)\n",
+    "\n",
+    "    ax.tick_params(direction=\"in\", which=\"both\", pad=4)\n",
+    "\n",
+    "ax1.set_ylabel(\"Phylogenetic distance (subst/aa)\")\n",
+    "# Add rectangle\n",
+    "rect1 = plt.matplotlib.patches.Rectangle( (20, 0.1), 80, 4, linewidth=1, edgecolor='k', facecolor='none')\n",
+    "rect2 = plt.matplotlib.patches.Rectangle( (20, 0.1), 80, 4, linewidth=1, edgecolor='k', facecolor='none')\n",
+    "rect3 = plt.matplotlib.patches.Rectangle( (20, 0.1), 80, 4, linewidth=1, edgecolor='k', facecolor='none')\n",
+    "\n",
+    "ax1.add_patch(rect1)\n",
+    "ax2.add_patch(rect2)\n",
+    "ax3.add_patch(rect3)\n",
+    "\n",
+    "ax1.annotate(\"A\", xy=(-0.15,1), xycoords=\"axes fraction\", fontweight=\"bold\", fontsize=14, va='top', ha=\"left\")\n",
+    "ax3.annotate(\"B\", xy=(-0.15, 1), xycoords=\"axes fraction\", fontweight=\"bold\", fontsize=14, va='top', ha=\"left\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "fig.subplots_adjust(right  = 0.8,\n",
+    "                    #top    = 0.85,\n",
+    "                    bottom = 0.18,\n",
+    "                    wspace=0.0,)\n",
+    "\n",
+    "box3 = ax3.get_position()\n",
+    "shift = 0.09\n",
+    "ax3.set_position([box3.xmin+shift, box3.ymin, box3.width, box3.height] )\n",
+    "\n",
+    "box3 = ax3.get_position()\n",
+    "box1 = ax1.get_position()\n",
+    "box2 = ax2.get_position()\n",
+    "pad, width, height = 0.01, 0.01, 0.02\n",
+    "# cax = fig.add_axes([box1.xmin, box1.ymax+pad, box2.xmax - box1.xmin, height]) \n",
+    "cax = fig.add_axes([box2.xmax+pad, box2.ymin, width, box2.height]) \n",
+    "cb = plt.colorbar(p, cax=cax, orientation=\"vertical\")\n",
+    "cb.ax.yaxis.set_ticks(np.linspace(0,1,5 ))\n",
+    "cb.ax.yaxis.set_ticklabels([r\"$\\mathdefault{{10^{{{}}}}}$\".format(i) for i in range(0,5,1)], fontsize=8)\n",
+    "cb.ax.yaxis.set_tick_params(pad=0,length=2)\n",
+    "cb.ax.set_xlabel(\"Count\", fontsize=9, labelpad=10)\n",
+    "cb.ax.yaxis.tick_right()\n",
+    "cb.ax.yaxis.set_label_position(\"right\")\n",
+    "\n",
+    "#cax2 = fig.add_axes([box3.xmin, box3.ymax+pad, box3.width, height])\n",
+    "cax2 = fig.add_axes([box3.xmax+pad, box3.ymin, width, box3.height])\n",
+    "\n",
+    "cb2 = plt.colorbar(p2, cax=cax2, orientation=\"vertical\")\n",
+    "cb2.ax.yaxis.set_ticks([0.1, 0.5, 0.9])\n",
+    "cb2.ax.yaxis.set_ticklabels([\"Less than\\nexpected\", \"Not\\nSignificant\", \"More than\\nexpected\"],\n",
+    "                            fontsize=7.5,\n",
+    "                            ha=\"left\", \n",
+    "                            va=\"center\") # p-value ICE smaller/bigger than CP\n",
+    "cb2.set_label(\"Proportion of ICEs more distant than CPs\", fontsize=8, labelpad=3)\n",
+    "cb2.ax.yaxis.set_tick_params(pad=1, length=0)\n",
+    "cb2.ax.yaxis.set_label_position(\"right\")\n",
+    "cb2.ax.yaxis.set_ticks_position(\"right\")\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"Figures/Figure_6_GRR_dist_phylo_ICE_CP_typeT_maha.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": false,
+        "row": 94,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "## Binomial test on the rectangle zone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1019,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.19239384495520062"
+      ]
+     },
+     "execution_count": 1019,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_grr.Mah_P_inf_ice.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df_grr.RepType_comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "rect = df_grr.dropna(subset=['dist_phylo']).query(\"(GRR>20) & (dist_phylo>0.1) & (RepType_comparison=='ICE-CP')\") # Don't have phylogenetic distance outside proteo\n",
+    "no_rect = df_grr.dropna(subset=['dist_phylo']).query(\"((GRR<=20) | (dist_phylo<=0.1) )& (RepType_comparison=='ICE-CP')\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": false,
+        "row": 98,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The proportion of ICE more distant to their host in: \n",
+      "- outside the rectangle: 0.182508112635, pval = 0.000504692337583,\n",
+      "- in the entire dataset: 0.192393844955 (H0)\n",
+      "- inside the rectangle: 0.234636871508, pval = 0.15500907102\n"
+     ]
+    }
+   ],
+   "source": [
+    "print \"\"\"The proportion of ICE more distant to their host in: \n",
+    "- outside the rectangle: {}, pval = {},\n",
+    "- in the entire dataset: {} (H0)\n",
+    "- inside the rectangle: {}, pval = {}\"\"\".format(binom_test_maha(no_rect), binom_test_maha(no_rect, H0), H0, binom_test_maha(rect), binom_test_maha(rect, H0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "binom_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.8462972518313682e-68"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " binom_test_maha(no_rect, binom_test_maha(rect))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    19.000000\n",
+       "mean      0.315990\n",
+       "std       0.392139\n",
+       "min       0.000000\n",
+       "25%       0.000000\n",
+       "50%       0.086957\n",
+       "75%       0.657143\n",
+       "max       1.000000\n",
+       "Name: Mah_P_inf_ice, dtype: float64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rect.groupby([\"GRR_cat\", \"dist_phylo_cat\"]).Mah_P_inf_ice.mean().describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    47.000000\n",
+       "mean      0.267248\n",
+       "std       0.301059\n",
+       "min       0.000000\n",
+       "25%       0.000000\n",
+       "50%       0.165084\n",
+       "75%       0.400000\n",
+       "max       1.000000\n",
+       "Name: Mah_P_inf_ice, dtype: float64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_rect.groupby([\"GRR_cat\", \"dist_phylo_cat\"]).Mah_P_inf_ice.mean().describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.049567331233219797, 0.96046718051588931)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.ranksums(rect.dropna(subset=[\"Mah_P_inf_ice\"]).groupby([\"GRR_cat\", \"dist_phylo_cat\"]).Mah_P_inf_ice.mean().values,\n",
+    "            no_rect.dropna(subset=[\"Mah_P_inf_ice\"]).groupby([\"GRR_cat\", \"dist_phylo_cat\"]).Mah_P_inf_ice.mean().values,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "diff_rect = rect.apply(lambda x: x[\"Mahalanobis_Pval_1\"] - x[\"Mahalanobis_Pval_2\"] if x[\"replicon_type_1\"==\"C\"] else x[\"Mahalanobis_Pval_2\"] - x[\"Mahalanobis_Pval_1\"], axis=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true,
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "diff_norect = no_rect.apply(lambda x: x[\"Mahalanobis_Pval_1\"] - x[\"Mahalanobis_Pval_2\"] if x[\"replicon_type_1\"==\"C\"] else x[\"Mahalanobis_Pval_2\"] - x[\"Mahalanobis_Pval_1\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-9.5210335296102535, 1.7146515630899331e-21)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.ranksums(diff_rect, diff_norect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "run_control": {
+     "marked": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HSx8tU6kiY/D5jkIUW9G0ZY3Jsgyv19h/Kz7fcTidyaZdLS3qAN66dSumTJkS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\nyMCIESNqfL783czKDh06hPT0dKxd+wq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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x104dbadd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(font_scale=1, style=\"ticks\")\n",
+    "plt.figure(figsize=(3.7,3.7))\n",
+    "plt.violinplot([diff_norect.values, diff_rect.values], showmedians=True,showextrema=False)\n",
+    "_  = plt.xticks([1,2], [\"outside\\nrectangle\", \"inside\\nrectangle\"])\n",
+    "plt.ylabel(r\"$\\mathdefault{pvalue_{ICE} - pvalue_{CP}}$\")\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"Figures/supp_FigureS7_Distance_distri_rectangle.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "grid_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  },
+  "toc": {
+   "toc_cell": true,
+   "toc_number_sections": true,
+   "toc_section_display": "block",
+   "toc_threshold": 6,
+   "toc_window_display": false
+  },
+  "toc_position": {
+   "height": "255px",
+   "left": "1277.03px",
+   "right": "20px",
+   "top": "177px",
+   "width": "388px"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
GitLab