diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..eccac5a
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,8 @@
+version: '3'
+services:
+  dev:
+    build: .
+    volumes:
+      - .:/usr/src/app
+    tty: true
+    entrypoint: /bin/bash
diff --git a/examples/NewsSpread.ipynb b/examples/NewsSpread.ipynb
new file mode 100644
index 0000000..29cab68
--- /dev/null
+++ b/examples/NewsSpread.ipynb
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2017-11-02T09:48:41.843Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import soil\n",
+    "import networkx as nx\n",
+    " \n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "# To display plots in the notebook\n",
+    "%pylab inline\n",
+    "\n",
+    "from soil import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# News Spreading example with SOIL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we three different kinds of models, which we combine in five types of simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2017-11-02T09:48:43.440Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!cat NewsSpread.yml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2017-11-02T09:48:43.879Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2017-11-02T09:48:45.458Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evodumb['mean'].plot(yerr=evodumb['std'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-01T13:26:19.361423Z",
+     "start_time": "2017-11-01T14:25:57.017418+01:00"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);\n",
+    "evohalfherd = analysis.read_data('soil_output/Sim_half_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
+    "evoherd = analysis.read_data('soil_output/Sim_all_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
+    "evoherdwise = analysis.read_data('soil_output/Sim_wise_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
+    "evowise = analysis.read_data('soil_output/Sim_all_wise/', group=True, process=analysis.get_count, keys=['id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-01T13:26:20.461665Z",
+     "start_time": "2017-11-01T14:26:19.363815+01:00"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fRCRAFPoiIgGi0BcRCRCFvohIgCj0RUQCRKEvIhIgCn0RkQBR6IuIBIhCX0Qk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\niEiAKPRFRALk/wN2rsCB356VQwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa48c26df98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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5UoCI9EhmZialpaWMHj26x80jpfucc5SWlrZ7P0BXdRr6zrlVJK+nB5iWZHkH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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa48c26d668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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jj/116ol0y2rpi0jHOmiRd4fuSK3cUss0y6+88soh2zds2MDq1as5//zzATcj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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa460c6c320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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jRr766itKS0uZOnVq5FmE/ki3rEFfKTUodZZa+dZbbwW6l1o5WnSa5VdeeeWQ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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa460d184e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Sui3veDDO9xZ/jtcf4sQpWWytaWFF2b5XG2UlxTI5M4Gt1S143A7e+rqSwjGJ5KTF4erl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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa46017c0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in [evodumb, evohalfherd, evoherd, evoherdwise, evowise]:\n",
+    "    i['mean'].plot(yerr=i['std'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-01T13:27:56.226662Z",
+     "start_time": "2017-11-01T14:27:55.987887+01:00"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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114hu/+V1zJozj7y8PMaMGcPTTz/d5DwP3G8f7rrrLk499VTy8vI45ZRTWLNm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\nMxGTJEmSpJaZiEmSJElSy/4PDTIOHCj38kMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa46019e748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff = evodumb['mean']-evohalfherd['mean']\n",
+    "m = evodumb['max'].loc[0].sum()\n",
+    "diff = diff / m\n",
+    "diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
+    "          title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 50% herd agents.');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-01T13:28:05.554284Z",
+     "start_time": "2017-11-01T14:28:05.324360+01:00"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZGdZabr311hoBn4iIiEht9ERNpPXbB/Ty6e4JZAY7sbU20/u7E1gBDG3KxDWl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\nSVKVMahJkiRJUpX5PyHeewauSb+8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa45f75d978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff = evodumb['mean']-evoherd['mean']\n",
+    "m = evodumb['max'].loc[0].sum()\n",
+    "diff = diff / m\n",
+    "diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
+    "          title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 100% herd agents.');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-01T13:32:12.118102Z",
+     "start_time": "2017-11-01T14:32:11.479970+01:00"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa45f950e10>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Gp6SnpZLyjzvonbuRlW0fpe/o5+x+gjGVzBKD8Rn7d28j/6NhRBYcIqHXC/S7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\nMUVYYjDGGFPE/wN3GX2TzrxUPAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa460d17c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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hskm0v6z7ufKwNpEMeHgBO378Eae8W9A/4VckPXMN+7avq5P9rnn/afqd/o7E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\n8/8U7UznepyQDQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa45f8fd4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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L93rUxaNS8UxWq5QMqtNQUSlD7IrYxe/hv/NK41cY3mB4SZvzAHmdxNGriYTFpiAlWJiZ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa460444588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(evohalfherd['std']/m).plot()\n",
+    "(evodumb['std']/m).plot()\n",
+    "(evowise['std']/m).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "navigate_num": "#000000",
+    "navigate_text": "#333333",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700",
+    "sidebar_border": "#EEEEEE",
+    "wrapper_background": "#FFFFFF"
+   },
+   "moveMenuLeft": true,
+   "nav_menu": {
+    "height": "30px",
+    "width": "252px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/newsspread/NewsSpread.ipynb b/examples/newsspread/NewsSpread.ipynb
index 09b8263..198588a 100644
--- a/examples/newsspread/NewsSpread.ipynb
+++ b/examples/newsspread/NewsSpread.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:25:49.702468Z",
-     "start_time": "2017-11-01T14:25:47.710337+01:00"
+     "end_time": "2017-11-08T16:22:30.732107Z",
+     "start_time": "2017-11-08T17:22:30.059855+01:00"
     },
-    "scrolled": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -16,9 +16,35 @@
     "import networkx as nx\n",
     " \n",
     "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "# To display plots in the notebook\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# To display plots in the notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-08T16:22:35.580593Z",
+     "start_time": "2017-11-08T17:22:35.542745+01:00"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
     "%pylab inline\n",
     "\n",
     "from soil import *"
@@ -40,11 +66,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:25:51.869688Z",
-     "start_time": "2017-11-01T14:25:51.737060+01:00"
+     "end_time": "2017-11-08T16:22:37.242327Z",
+     "start_time": "2017-11-08T17:22:37.087039+01:00"
     }
    },
    "outputs": [
@@ -199,43 +225,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:25:56.792431Z",
-     "start_time": "2017-11-01T14:25:52.447422+01:00"
-    }
+     "end_time": "2017-11-08T18:07:46.781745Z",
+     "start_time": "2017-11-08T19:07:41.146659+01:00"
+    },
+    "scrolled": true
    },
    "outputs": [],
    "source": [
-    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
+    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:25:57.015612Z",
-     "start_time": "2017-11-01T14:25:56.794756+01:00"
+     "end_time": "2017-11-08T18:07:50.696420Z",
+     "start_time": "2017-11-08T19:07:50.652496+01:00"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>infected</th>\n",
+       "      <th>neutral</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_step</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.120000</td>\n",
+       "      <td>497.880000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.938776</td>\n",
+       "      <td>495.061224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.468085</td>\n",
+       "      <td>492.531915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.333333</td>\n",
+       "      <td>489.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.191489</td>\n",
+       "      <td>487.808511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>14.680851</td>\n",
+       "      <td>485.319149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>17.377778</td>\n",
+       "      <td>482.622222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>480.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>22.212766</td>\n",
+       "      <td>477.787234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>24.046512</td>\n",
+       "      <td>475.953488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>26.809524</td>\n",
+       "      <td>473.190476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>29.418605</td>\n",
+       "      <td>470.581395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>31.777778</td>\n",
+       "      <td>468.222222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>33.666667</td>\n",
+       "      <td>466.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>36.113636</td>\n",
+       "      <td>463.886364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>36.976190</td>\n",
+       "      <td>463.023810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>40.113636</td>\n",
+       "      <td>459.886364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>41.872340</td>\n",
+       "      <td>458.127660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>44.550000</td>\n",
+       "      <td>455.450000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>45.418605</td>\n",
+       "      <td>454.581395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>47.666667</td>\n",
+       "      <td>452.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>48.738095</td>\n",
+       "      <td>451.261905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>51.731707</td>\n",
+       "      <td>448.268293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>54.390244</td>\n",
+       "      <td>445.609756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>56.116279</td>\n",
+       "      <td>443.883721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>58.404762</td>\n",
+       "      <td>441.595238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>58.976744</td>\n",
+       "      <td>441.023256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>61.522727</td>\n",
+       "      <td>438.477273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>63.263158</td>\n",
+       "      <td>436.736842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>65.090909</td>\n",
+       "      <td>434.909091</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id            \n",
+       "         infected     neutral\n",
+       "t_step                       \n",
+       "0        2.120000  497.880000\n",
+       "1        4.938776  495.061224\n",
+       "2        7.468085  492.531915\n",
+       "3       10.333333  489.666667\n",
+       "4       12.191489  487.808511\n",
+       "5       14.680851  485.319149\n",
+       "6       17.377778  482.622222\n",
+       "7       20.000000  480.000000\n",
+       "8       22.212766  477.787234\n",
+       "9       24.046512  475.953488\n",
+       "10      26.809524  473.190476\n",
+       "11      29.418605  470.581395\n",
+       "12      31.777778  468.222222\n",
+       "13      33.666667  466.333333\n",
+       "14      36.113636  463.886364\n",
+       "15      36.976190  463.023810\n",
+       "16      40.113636  459.886364\n",
+       "17      41.872340  458.127660\n",
+       "18      44.550000  455.450000\n",
+       "19      45.418605  454.581395\n",
+       "20      47.666667  452.333333\n",
+       "21      48.738095  451.261905\n",
+       "22      51.731707  448.268293\n",
+       "23      54.390244  445.609756\n",
+       "24      56.116279  443.883721\n",
+       "25      58.404762  441.595238\n",
+       "26      58.976744  441.023256\n",
+       "27      61.522727  438.477273\n",
+       "28      63.263158  436.736842\n",
+       "29      65.090909  434.909091"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evodumb['mean']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-08T18:08:01.093889Z",
+     "start_time": "2017-11-08T19:08:00.861308+01:00"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa460dbb470>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73a065ca20>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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69OlUVFQwffp03n33XQCuvPJKbrjhBk4++WQ++MEPsmTJktQ+P/vZzzj++OOpqKjgxhtv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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa460e15d68>"
+       "<matplotlib.figure.Figure at 0x7f73a0830828>"
       ]
      },
      "metadata": {},
@@ -248,12 +519,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:26:19.361423Z",
-     "start_time": "2017-11-01T14:25:57.017418+01:00"
-    }
+     "end_time": "2017-11-08T18:08:31.598791Z",
+     "start_time": "2017-11-08T19:08:02.764487+01:00"
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -266,19 +538,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:26:20.461665Z",
-     "start_time": "2017-11-01T14:26:19.363815+01:00"
+     "end_time": "2017-11-08T18:08:37.909477Z",
+     "start_time": "2017-11-08T19:08:36.873751+01:00"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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bKBER6Tu6M1ZEJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8ichC69K6Xst5W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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa48c26df98>"
+       "<matplotlib.figure.Figure at 0x7f73a1e63da0>"
       ]
      },
      "metadata": {},
@@ -286,9 +558,9 @@
     },
     {
      "data": {
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5UoCI9EhmZialpaWMHj26x80jpfucc5SWlrZ7P0BXdRr6zrlVJK+nB5iWZHkH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\nWf+Tc+4nfs48DBQArwJXOOf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YEZGAU6AXEQk4BXoRkYBToBcRCTgFehGRgFOgFxEJOAV6EZEB4OLfv8nFv3+z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\n/wcp5D+/6eaXZQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa48c26d668>"
+       "<matplotlib.figure.Figure at 0x7f73a1e630f0>"
       ]
      },
      "metadata": {},
@@ -296,9 +568,9 @@
     },
     {
      "data": {
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jj/116ol0y2rpi0jHOmiRd4fuSK3cUss0y6+88soh2zds2MDq1as5//zzATcj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\nuxunF/669VOqRRtg//bmljs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I5OtMUBpjiIkyTM9JZXpO6iGPNzS1sGd/rf8L4A/vO4XkdlfU8umWMmobm/1tY7weRqYl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\nn3zyCVlZWdx///3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lC/RP/uDsJPXGOBe58PFEOzXP0yfAxEucQxjTJ8A7d4InCq5/pfe2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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa460c6c320>"
+       "<matplotlib.figure.Figure at 0x7f73a1e63a90>"
       ]
      },
      "metadata": {},
@@ -306,9 +578,9 @@
     },
     {
      "data": {
-      "image/png": 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jRr766itKS0uZOnVq5FmE/ki3rEFfKTUodZZa+dZbbwW6l1o5WnSa5VdeeeWQ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\nYP1fCmc7vBzrdXZKDTravKOSgjGmQkQ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SZv3XX3/N2rVrOeOMMwDrLlPZ2dkxD3m8ZMkS1qxZE/kVUVlZSVFREe+99x6X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\nZF8gtvDen18WIrLSGDOts3J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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa460d184e0>"
+       "<matplotlib.figure.Figure at 0x7f73a1e63cc0>"
       ]
      },
      "metadata": {},
@@ -316,9 +588,9 @@
     },
     {
      "data": {
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Sui3veDDO9xZ/jtcf4sQpWWytaWFF2b5XG2UlxTI5M4Gt1S143A7e+rqSwjGJ5KTF4erl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\nfg530D9kH6IuIgn2CSVEJAE4DVi7/7V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NoIFea/094HsAZon+21rr65RSLwJXYrS8uRF4xdzkz+b8++b6t6R+XojxZ7hKxPG2eONl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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa46017c0b8>"
+       "<matplotlib.figure.Figure at 0x7f73a1f6d198>"
       ]
      },
      "metadata": {},
@@ -332,20 +604,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:27:56.226662Z",
-     "start_time": "2017-11-01T14:27:55.987887+01:00"
+     "end_time": "2017-11-08T18:08:44.132725Z",
+     "start_time": "2017-11-08T19:08:43.880341+01:00"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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114hu/+V1zJozj7y8PMaMGcPTTz/d5DwP3G8f7rrrLk499VTy8vI45ZRTWLNm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\n51H3HqMvabEzEZMkSQvC5EqSzpyJmCRJGsqDPSR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z+PBhduzYEehqK6VUmdBATCmllFI+PfbYYwXenjhhwgTee+894uPj+fDDD3nj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\npBoziEmSJElSjf1/jS69m/Nq6awAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa46019e748>"
+       "<matplotlib.figure.Figure at 0x7f73a1d84dd8>"
       ]
      },
      "metadata": {},
@@ -362,19 +634,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:28:05.554284Z",
-     "start_time": "2017-11-01T14:28:05.324360+01:00"
+     "end_time": "2017-11-08T18:08:45.320345Z",
+     "start_time": "2017-11-08T19:08:45.068706+01:00"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ZGdZabr311hoBn4iIiEht9ERNpPXbB/Ty6e4JZAY7sbU20/u7E1gBDG3KxDWl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\nIoYBLwdW7f2qUroKmFN8ngP8vIJt6XXtIabwBkr6DIvJKC4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MTOT73/8+TzzxRHm/kSNHllddnDNnDhkZGaSnp1e7zAsuuIAZM2aUL2fXrl1s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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa45f75d978>"
+       "<matplotlib.figure.Figure at 0x7f73a200d2e8>"
       ]
      },
      "metadata": {},
@@ -391,29 +663,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 29,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-11-01T13:32:12.118102Z",
-     "start_time": "2017-11-01T14:32:11.479970+01:00"
+     "end_time": "2017-11-08T18:08:46.641058Z",
+     "start_time": "2017-11-08T19:08:46.052870+01:00"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa45f950e10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73a1cd97b8>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Gp6SnpZLyjzvonbuRlW0fpe/o5+x+gjGVzBKD8Rn7d28j/6NhRBYcIqHXC/S7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\nMUVYYjDGGFPE/wN3GX2TzrxUPAAAAABJRU5ErkJggg==\n",
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FDu9cGdTh4MyZbLKzTpOblUlO9ikyDu4h+LsZtHIcJ27A3xgwdGKV7NuYymKB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\nGzxTGIQAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa460d17c18>"
+       "<matplotlib.figure.Figure at 0x7f73a1e6d8d0>"
       ]
      },
      "metadata": {},
@@ -421,9 +693,9 @@
     },
     {
      "data": {
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hskm0v6z7ufKwNpEMeHgBO378Eae8W9A/4VckPXMN+7avq5P9rnn/afqd/o7E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\n8/8U7UznepyQDQAAAABJRU5ErkJggg==\n",
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+5W/TBKHd4EesLuU8nl616D38aeBpq0tRNZzedqqqvYwzqXQ8vISYOpfTpGVb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\nSimlAA0EpZRSdv8Pp8WopGa5RzAAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa45f8fd4e0>"
+       "<matplotlib.figure.Figure at 0x7f73a1e6d438>"
       ]
      },
      "metadata": {},
@@ -431,9 +703,9 @@
     },
     {
      "data": {
-      "image/png": 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L93rUxaNS8UxWq5QMqtNQUSlD7IrYxe/hv/NK41cY3mB4SZvzAHmdxNGriYTFpiAlWJiZ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\n",
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cK5/1Z+LEicyZM4fly5cXuYzyTGxaLOP2jeNG3A2+bf8tz7k8V2B6IwMjBnkM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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa460444588>"
+       "<matplotlib.figure.Figure at 0x7f73a1e6de10>"
       ]
      },
      "metadata": {},
@@ -445,15 +717,6 @@
     "(evodumb['std']/m).plot()\n",
     "(evowise['std']/m).plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/tutorial/soil_tutorial.ipynb b/examples/tutorial/soil_tutorial.ipynb
index af9d462..4753242 100644
--- a/examples/tutorial/soil_tutorial.ipynb
+++ b/examples/tutorial/soil_tutorial.ipynb
@@ -58,8 +58,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:07.621891Z",
-     "start_time": "2017-10-19T17:56:06.963273+02:00"
+     "end_time": "2017-11-03T10:58:13.451481Z",
+     "start_time": "2017-11-03T11:58:12.643469+01:00"
     }
    },
    "outputs": [
@@ -196,8 +196,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:08.659268Z",
-     "start_time": "2017-10-19T17:56:08.606261+02:00"
+     "end_time": "2017-11-03T10:58:16.051690Z",
+     "start_time": "2017-11-03T11:58:16.006044+01:00"
     },
     "collapsed": true
    },
@@ -252,8 +252,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:09.116995Z",
-     "start_time": "2017-10-19T17:56:09.081481+02:00"
+     "end_time": "2017-11-03T10:58:17.653736Z",
+     "start_time": "2017-11-03T11:58:17.612944+01:00"
     },
     "collapsed": true
    },
@@ -317,8 +317,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:11.367368Z",
-     "start_time": "2017-10-19T17:56:11.344245+02:00"
+     "end_time": "2017-11-03T10:58:19.781155Z",
+     "start_time": "2017-11-03T11:58:19.754362+01:00"
     },
     "cell_style": "split"
    },
@@ -353,8 +353,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:12.472479Z",
-     "start_time": "2017-10-19T17:56:12.206162+02:00"
+     "end_time": "2017-11-03T10:58:20.791777Z",
+     "start_time": "2017-11-03T11:58:20.565173+01:00"
     },
     "cell_style": "split",
     "scrolled": false
@@ -362,9 +362,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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4PB34lXfXW4iJiCRA5Ll93P2wmd0MPA30Af7J3beZ2Z1AvbuvAf4RWG5muwiO+GdE3a+I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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a7d5860>"
+       "<matplotlib.figure.Figure at 0x7f1b7c04fcc0>"
       ]
      },
      "metadata": {},
@@ -398,11 +398,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:12.719473Z",
-     "start_time": "2017-10-19T17:56:12.653974+02:00"
+     "end_time": "2017-11-03T10:58:55.517768Z",
+     "start_time": "2017-11-03T11:58:55.424083+01:00"
     },
     "cell_style": "split"
    },
@@ -411,11 +411,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 0.02695441246032715 seconds\n",
-      "INFO:soil.utils:NOT dumping results\n",
-      "INFO:soil.utils:Finished simulation in 0.03360605239868164 seconds\n"
+      "INFO:soil:Starting simulation UnnamedSimulation at 1509706735.4865255.\n",
+      "INFO:soil:Starting Simulation UnnamedSimulation trial 0 at 1509706735.489935.\n",
+      "INFO:soil:Finished Simulation UnnamedSimulation trial 0 in 0.022043228149414062 seconds\n",
+      "INFO:soil:NOT dumping results\n",
+      "INFO:soil:Finished simulation UnnamedSimulation in 0.026972055435180664 seconds\n"
      ]
     }
    ],
@@ -436,6 +436,7 @@
     "                                     network_agents=[{'agent_type': NewsSpread,\n",
     "                                                      'weight': 1}],\n",
     "                                     states={0: {'has_tv': True}},\n",
+    "                                     dry_run=True,\n",
     "                                     default_state={'has_tv': False},\n",
     "                                     environment_params=env_params)\n",
     "env = sim.run_simulation()[0]"
@@ -452,17 +453,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:15.004439Z",
-     "start_time": "2017-10-19T17:56:14.904160+02:00"
+     "end_time": "2017-11-03T10:59:01.577474Z",
+     "start_time": "2017-11-03T11:59:01.414215+01:00"
     },
     "cell_style": "split",
-    "collapsed": true,
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-e08fafc133f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneutral\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# After the event, the node with a TV is infected, the rest are not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "agents = list(env.network_agents)\n",
     "\n",
@@ -486,6 +498,31 @@
     "        "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2017-11-03T11:09:23.581734Z",
+     "start_time": "2017-11-03T12:09:23.559473+01:00"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'neutral'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a['id', t]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -501,11 +538,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:17.382019Z",
-     "start_time": "2017-10-19T17:56:17.340851+02:00"
+     "end_time": "2017-11-01T14:07:55.288616Z",
+     "start_time": "2017-11-01T15:07:55.241116+01:00"
     },
     "cell_style": "split",
     "collapsed": true
@@ -540,11 +577,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:19.477080Z",
-     "start_time": "2017-10-19T17:56:19.386840+02:00"
+     "end_time": "2017-11-01T14:07:57.008940Z",
+     "start_time": "2017-11-01T15:07:56.966433+01:00"
     },
     "cell_style": "split",
     "collapsed": true
@@ -606,29 +643,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:56:28.208300Z",
-     "start_time": "2017-10-19T17:56:20.515069+02:00"
+     "end_time": "2017-11-01T14:08:34.312637Z",
+     "start_time": "2017-11-01T15:07:57.774458+01:00"
     },
-    "cell_style": "split"
+    "cell_style": "split",
+    "collapsed": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:soil.utils:Using config(s): ExampleSimulation\n",
-      "INFO:soil.utils:Dumping results to soil_output/ExampleSimulation : False\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 5.869051456451416 seconds\n",
-      "INFO:soil.utils:NOT dumping results\n",
-      "INFO:soil.utils:Finished simulation in 6.9609293937683105 seconds\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "soil.simulation.run_from_config(config, dump=False)"
    ]
@@ -654,93 +678,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:57:10.690166Z",
-     "start_time": "2017-10-19T17:56:28.210104+02:00"
+     "end_time": "2017-11-01T14:10:38.099667Z",
+     "start_time": "2017-11-01T15:10:06.008314+01:00"
     },
     "cell_style": "split",
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.0\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 1.2258412837982178 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0\n",
-      "INFO:soil.utils:Finished simulation in 5.597268104553223 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.1\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 1.3026399612426758 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1\n",
-      "INFO:soil.utils:Finished simulation in 5.534018278121948 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.2\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 1.4764575958251953 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2\n",
-      "INFO:soil.utils:Finished simulation in 6.170421123504639 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.3\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 1.5429913997650146 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3\n",
-      "INFO:soil.utils:Finished simulation in 5.936013221740723 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.4\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 1.4097135066986084 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4\n",
-      "INFO:soil.utils:Finished simulation in 5.732810974121094 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.0\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 0.751497745513916 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0\n",
-      "INFO:soil.utils:Finished simulation in 2.3415369987487793 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.1\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 0.8503265380859375 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1\n",
-      "INFO:soil.utils:Finished simulation in 2.5671920776367188 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.2\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 0.8511502742767334 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2\n",
-      "INFO:soil.utils:Finished simulation in 2.55816912651062 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.3\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 0.8982968330383301 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3\n",
-      "INFO:soil.utils:Finished simulation in 2.6871559619903564 seconds\n",
-      "INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.4\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4 : True\n",
-      "INFO:soil.utils:Trial: 0\n",
-      "INFO:soil.utils:\tRunning\n",
-      "INFO:soil.utils:Finished trial in 0.9563727378845215 seconds\n",
-      "INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4\n",
-      "INFO:soil.utils:Finished simulation in 2.5253307819366455 seconds\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "network_1 = {\n",
     "       'generator': 'erdos_renyi_graph',\n",
@@ -760,7 +708,7 @@
     "        config['environment_params']['prob_neighbor_spread'] = prob\n",
     "        config['network_params'] = net\n",
     "        config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)\n",
-    "        s = soil.simulation.run_from_config(config)"
+    "        s = soil.simulation.run_from_config(config, parallel=True)"
    ]
   },
   {
@@ -770,124 +718,27 @@
      "end_time": "2017-07-03T11:05:18.043194Z",
      "start_time": "2017-07-03T13:05:18.034699+02:00"
     },
-    "cell_style": "split"
+    "cell_style": "center"
    },
    "source": [
-    "The results are conveniently stored in pickle (simulation), csv and sqlite (history of agent and environment state) and gexf (dynamic network) format."
+    "The results are conveniently stored in pickle (simulation), and sqlite (history of agent and environment state).\n",
+    "\n",
+    "You can also export the results to GEXF format (dynamic network) and CSV using .`run_from_config(config, dump=['gexf', 'csv'])` or the command line flags `--graph --csv`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2017-10-19T15:57:28.534861Z",
-     "start_time": "2017-10-19T17:57:28.258984+02:00"
+     "end_time": "2017-11-01T14:05:56.404540Z",
+     "start_time": "2017-11-01T15:05:56.122876+01:00"
     },
     "cell_style": "split",
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[01;34msoil_output\u001b[00m\n",
-      "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.0\u001b[00m\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.0.simulation.pickle\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508409808.7944386.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508428617.9811945.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv\n",
-      "│   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.1\u001b[00m\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.1.simulation.pickle\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508409810.9913027.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508428620.3419535.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.db.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.environment.csv\n",
-      "│   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.2\u001b[00m\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.2.simulation.pickle\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.backup1508409813.2012305.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.backup1508428622.91827.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.db.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.environment.csv\n",
-      "│   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.3\u001b[00m\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.3.simulation.pickle\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.backup1508409815.5177016.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.backup1508428625.5117545.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.db.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.environment.csv\n",
-      "│   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.4\u001b[00m\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.4.simulation.pickle\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.backup1508409818.1516452.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.backup1508428628.1986933.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.db.sqlite\n",
-      "│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.environment.csv\n",
-      "│   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.0\u001b[00m\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.0.simulation.pickle\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.backup1508409781.0791047.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.backup1508428588.625598.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.db.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.environment.csv\n",
-      "│   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.1\u001b[00m\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.1.simulation.pickle\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.backup1508409786.6177793.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.backup1508428594.3783743.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.db.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.environment.csv\n",
-      "│   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.2\u001b[00m\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.2.simulation.pickle\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.backup1508409791.9751768.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.backup1508428600.041021.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.db.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.environment.csv\n",
-      "│   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.gexf\n",
-      "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.3\u001b[00m\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.3.simulation.pickle\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.backup1508409797.606661.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.backup1508428606.2884977.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.db.sqlite\n",
-      "│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.environment.csv\n",
-      "│   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.gexf\n",
-      "└── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.4\u001b[00m\n",
-      "    ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml\n",
-      "    ├── Spread_erdos_renyi_graph_prob_0.4.simulation.pickle\n",
-      "    ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.backup1508409803.4306188.sqlite\n",
-      "    ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.backup1508428612.3312593.sqlite\n",
-      "    ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.db.sqlite\n",
-      "    ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.environment.csv\n",
-      "    └── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf\n",
-      "\n",
-      "10 directories, 70 files\n",
-      "2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\n",
-      "2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\n",
-      "2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\n",
-      "2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\n",
-      "2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\n",
-      "3.6M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\n",
-      "3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\n",
-      "3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\n",
-      "3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\n",
-      "3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!tree soil_output\n",
     "!du -xh soil_output/*"
@@ -939,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:43.662893Z",
@@ -955,609 +806,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:44.101253Z",
      "start_time": "2017-10-19T17:57:44.039710+02:00"
     },
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
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-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
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-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
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-       "    <tr>\n",
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-       "      <td>id</td>\n",
-       "      <td>infected</td>\n",
-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21077</th>\n",
-       "      <td>496</td>\n",
-       "      <td>20</td>\n",
-       "      <td>id</td>\n",
-       "      <td>infected</td>\n",
-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21079</th>\n",
-       "      <td>497</td>\n",
-       "      <td>20</td>\n",
-       "      <td>id</td>\n",
-       "      <td>infected</td>\n",
-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21081</th>\n",
-       "      <td>498</td>\n",
-       "      <td>20</td>\n",
-       "      <td>id</td>\n",
-       "      <td>infected</td>\n",
-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21083</th>\n",
-       "      <td>499</td>\n",
-       "      <td>20</td>\n",
-       "      <td>id</td>\n",
-       "      <td>infected</td>\n",
-       "      <td>str</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10500 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      agent_id  t_step key     value value_type\n",
-       "5            0       0  id   neutral        str\n",
-       "7            1       0  id   neutral        str\n",
-       "9            2       0  id   neutral        str\n",
-       "11           3       0  id   neutral        str\n",
-       "13           4       0  id   neutral        str\n",
-       "15           5       0  id   neutral        str\n",
-       "17           6       0  id   neutral        str\n",
-       "19           7       0  id   neutral        str\n",
-       "21           8       0  id   neutral        str\n",
-       "23           9       0  id   neutral        str\n",
-       "25          10       0  id   neutral        str\n",
-       "27          11       0  id   neutral        str\n",
-       "29          12       0  id   neutral        str\n",
-       "31          13       0  id   neutral        str\n",
-       "33          14       0  id   neutral        str\n",
-       "35          15       0  id   neutral        str\n",
-       "37          16       0  id   neutral        str\n",
-       "39          17       0  id   neutral        str\n",
-       "41          18       0  id   neutral        str\n",
-       "43          19       0  id   neutral        str\n",
-       "45          20       0  id   neutral        str\n",
-       "47          21       0  id   neutral        str\n",
-       "49          22       0  id   neutral        str\n",
-       "51          23       0  id   neutral        str\n",
-       "53          24       0  id   neutral        str\n",
-       "55          25       0  id   neutral        str\n",
-       "57          26       0  id   neutral        str\n",
-       "59          27       0  id   neutral        str\n",
-       "61          28       0  id   neutral        str\n",
-       "63          29       0  id   neutral        str\n",
-       "...        ...     ...  ..       ...        ...\n",
-       "21025      470      20  id  infected        str\n",
-       "21027      471      20  id  infected        str\n",
-       "21029      472      20  id  infected        str\n",
-       "21031      473      20  id  infected        str\n",
-       "21033      474      20  id  infected        str\n",
-       "21035      475      20  id  infected        str\n",
-       "21037      476      20  id  infected        str\n",
-       "21039      477      20  id  infected        str\n",
-       "21041      478      20  id  infected        str\n",
-       "21043      479      20  id  infected        str\n",
-       "21045      480      20  id  infected        str\n",
-       "21047      481      20  id  infected        str\n",
-       "21049      482      20  id  infected        str\n",
-       "21051      483      20  id  infected        str\n",
-       "21053      484      20  id  infected        str\n",
-       "21055      485      20  id  infected        str\n",
-       "21057      486      20  id  infected        str\n",
-       "21059      487      20  id  infected        str\n",
-       "21061      488      20  id  infected        str\n",
-       "21063      489      20  id  infected        str\n",
-       "21065      490      20  id  infected        str\n",
-       "21067      491      20  id  infected        str\n",
-       "21069      492      20  id  infected        str\n",
-       "21071      493      20  id  infected        str\n",
-       "21073      494      20  id  infected        str\n",
-       "21075      495      20  id  infected        str\n",
-       "21077      496      20  id  infected        str\n",
-       "21079      497      20  id  infected        str\n",
-       "21081      498      20  id  infected        str\n",
-       "21083      499      20  id  infected        str\n",
-       "\n",
-       "[10500 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df  = read_csv('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv', keys=['id'])\n",
     "df"
@@ -1572,371 +830,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:45.777794Z",
      "start_time": "2017-10-19T17:57:45.698020+02:00"
     },
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
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-       "      <th>103</th>\n",
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-       "      <th>104</th>\n",
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-       "      <th>105</th>\n",
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-       "      <th>108</th>\n",
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-       "      <th>109</th>\n",
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-       "      <th>11</th>\n",
-       "      <td>neutral</td>\n",
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-       "    <tr>\n",
-       "      <th>110</th>\n",
-       "      <td>neutral</td>\n",
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-       "      <th>111</th>\n",
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-       "      <th>115</th>\n",
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-       "      <th>117</th>\n",
-       "      <td>neutral</td>\n",
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-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>neutral</td>\n",
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-       "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>neutral</td>\n",
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-       "    <tr>\n",
-       "      <th>12</th>\n",
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-       "      <th>121</th>\n",
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-       "      <th>122</th>\n",
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-       "      <th>123</th>\n",
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-       "      <th rowspan=\"30\" valign=\"top\">20</th>\n",
-       "      <th>72</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>79</th>\n",
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-       "      <th>80</th>\n",
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-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>infected</td>\n",
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-       "    <tr>\n",
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-       "      <td>infected</td>\n",
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-       "<p>10500 rows × 1 columns</p>\n",
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-      "text/plain": [
-       "                       id\n",
-       "t_step agent_id          \n",
-       "0      0          neutral\n",
-       "       1          neutral\n",
-       "       10         neutral\n",
-       "       100        neutral\n",
-       "       101        neutral\n",
-       "       102        neutral\n",
-       "       103        neutral\n",
-       "       104        neutral\n",
-       "       105        neutral\n",
-       "       106        neutral\n",
-       "       107        neutral\n",
-       "       108        neutral\n",
-       "       109        neutral\n",
-       "       11         neutral\n",
-       "       110        neutral\n",
-       "       111        neutral\n",
-       "       112        neutral\n",
-       "       113        neutral\n",
-       "       114        neutral\n",
-       "       115        neutral\n",
-       "       116        neutral\n",
-       "       117        neutral\n",
-       "       118        neutral\n",
-       "       119        neutral\n",
-       "       12         neutral\n",
-       "       120        neutral\n",
-       "       121        neutral\n",
-       "       122        neutral\n",
-       "       123        neutral\n",
-       "       124        neutral\n",
-       "...                   ...\n",
-       "20     72        infected\n",
-       "       73        infected\n",
-       "       74        infected\n",
-       "       75        infected\n",
-       "       76        infected\n",
-       "       77        infected\n",
-       "       78        infected\n",
-       "       79        infected\n",
-       "       8         infected\n",
-       "       80        infected\n",
-       "       81        infected\n",
-       "       82        infected\n",
-       "       83        infected\n",
-       "       84        infected\n",
-       "       85        infected\n",
-       "       86        infected\n",
-       "       87        infected\n",
-       "       88        infected\n",
-       "       89        infected\n",
-       "       9         infected\n",
-       "       90        infected\n",
-       "       91        infected\n",
-       "       92        infected\n",
-       "       93        infected\n",
-       "       94        infected\n",
-       "       95        infected\n",
-       "       96        infected\n",
-       "       97        infected\n",
-       "       98        infected\n",
-       "       99        infected\n",
-       "\n",
-       "[10500 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "env, agents = process(df)\n",
     "agents"
@@ -1956,366 +859,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:47.132212Z",
      "start_time": "2017-10-19T17:57:47.084737+02:00"
     },
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
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-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>500 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id\n",
-       "agent_id         \n",
-       "0         neutral\n",
-       "1         neutral\n",
-       "10        neutral\n",
-       "100       neutral\n",
-       "101       neutral\n",
-       "102       neutral\n",
-       "103       neutral\n",
-       "104       neutral\n",
-       "105       neutral\n",
-       "106       neutral\n",
-       "107       neutral\n",
-       "108       neutral\n",
-       "109       neutral\n",
-       "11        neutral\n",
-       "110       neutral\n",
-       "111       neutral\n",
-       "112       neutral\n",
-       "113       neutral\n",
-       "114       neutral\n",
-       "115       neutral\n",
-       "116       neutral\n",
-       "117       neutral\n",
-       "118       neutral\n",
-       "119       neutral\n",
-       "12        neutral\n",
-       "120       neutral\n",
-       "121       neutral\n",
-       "122       neutral\n",
-       "123       neutral\n",
-       "124       neutral\n",
-       "...           ...\n",
-       "72        neutral\n",
-       "73        neutral\n",
-       "74        neutral\n",
-       "75        neutral\n",
-       "76        neutral\n",
-       "77        neutral\n",
-       "78        neutral\n",
-       "79        neutral\n",
-       "8         neutral\n",
-       "80        neutral\n",
-       "81        neutral\n",
-       "82        neutral\n",
-       "83        neutral\n",
-       "84        neutral\n",
-       "85        neutral\n",
-       "86        neutral\n",
-       "87        neutral\n",
-       "88        neutral\n",
-       "89        neutral\n",
-       "9         neutral\n",
-       "90        neutral\n",
-       "91        neutral\n",
-       "92        neutral\n",
-       "93        neutral\n",
-       "94        neutral\n",
-       "95        neutral\n",
-       "96        neutral\n",
-       "97        neutral\n",
-       "98        neutral\n",
-       "99        neutral\n",
-       "\n",
-       "[500 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "agents.loc[0]"
    ]
@@ -2329,82 +882,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:48.168805Z",
      "start_time": "2017-10-19T17:57:48.113961+02:00"
-    }
+    },
+    "collapsed": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
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-       "    .dataframe tbody tr th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>agent_id</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>310</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>455</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id\n",
-       "agent_id         \n",
-       "140       neutral\n",
-       "164       neutral\n",
-       "170       neutral\n",
-       "310       neutral\n",
-       "455       neutral"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "changed = agents.loc[1]['id'] != agents.loc[0]['id']\n",
     "agents.loc[0][changed]"
@@ -2419,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:49.046261Z",
@@ -2434,163 +920,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:49.459500Z",
      "start_time": "2017-10-19T17:57:49.420016+02:00"
     },
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_step</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              id\n",
-       "t_step          \n",
-       "0        neutral\n",
-       "1        neutral\n",
-       "2        neutral\n",
-       "3        neutral\n",
-       "4        neutral\n",
-       "5        neutral\n",
-       "6        neutral\n",
-       "7        neutral\n",
-       "8        neutral\n",
-       "9        neutral\n",
-       "10       neutral\n",
-       "11      infected\n",
-       "12      infected\n",
-       "13      infected\n",
-       "14      infected\n",
-       "15      infected\n",
-       "16      infected\n",
-       "17      infected\n",
-       "18      infected\n",
-       "19      infected\n",
-       "20      infected"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "agents1.loc['0'].dropna(axis=1)"
    ]
@@ -2616,267 +955,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:52.271094Z",
      "start_time": "2017-10-19T17:57:51.102434+02:00"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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hD6murgagvLychx9+mPPPP585c+Zw1FFH8cknnyRzd4wYSYcLeubNm2dWrVqV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\niGoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a1c60f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
-    {
-     "data": {
-      "image/png": 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hD6murgagvLychx9+mPPPP585c+Zw1FFH8cknnyRzd4wYSYcLeubNm2dWrVqV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\niGoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a1c62b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', get_count, 'id');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:57:57.982007Z",
      "start_time": "2017-10-19T17:57:52.273160+02:00"
     },
+    "collapsed": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hD6murgagvLychx9+mPPPP585c+Zw1FFH8cknnyRzd4wYSYcLeubNm2dWrVqV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\niGoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a0b4940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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hD6murgagvLychx9+mPPPP585c+Zw1FFH8cknnyRzd4wYSYcLeubNm2dWrVqV\n6jCUilljWxeH/OxF/uUbB3PFR5dCQTksfmrwF2aQjz/+mIMPDtdlKPVaW1uZPXs2a9asobi4GIDf\n/va37Lvvvnzzm99McXTJEe7vISKrjTHzhrttPUeg1BB4Pdl4crKo83U4NYIJs1Md0pixYsUKLrvs\nMq6//vpAEgD6XfKp4qOJQKkhEBEqvR7qG5uhpU6bhkbQggUL+OKLL1Idxqii5wiUGqLKIg/te3eC\n6dVLR1VG00Sg1BBVeN2YJp2iUmU+TQRKDVGl10N2y07nidYIVAbTcwRKDVFFkZv2ngbn55TWCFQG\n0xqBUkNU6fVQIXswkuVcPqoSrq2tjeOOO46enh62b9/OOeecE7bc/PnzGclL0O+66y5aW1vjft0l\nl1wSGDJj0aJFbNy4MdGhDYkmAqWGqMLrppI9dHnKwKWV62R48MEHOeuss3C5XOyzzz6Bg2iqRUsE\n4Xorh3PVVVfxq1/9KpFhDZkmAqWGqNLroVL20JKrtYFkWbp0KQsXLgT6Tx7T1tbGokWLmDNnDued\ndx5tbW2Dbmv+/PnccMMNHHHEERx44IH8/e9/B5wDd7jhrl999VVOO+20wOuvvfZaHn74Ye6++262\nb9/O8ccfz/HHHw9AYWEhN910E0ceeSRvvvkmt9xyC4cffjizZs3iyiuvJFzH3WOOOYYVK1YEekun\nkv6MUWqIKr0eOmQvjdlTGZfqYJLsZ//1IR9tb0roNmfs4+Wnp8+MuL6zs5NPP/2UqqqqAevuuece\n8vPzWbduHevWrQsMAzGY7u5u3nnnHZYvX87PfvYzVqxYwQMPPBAY7rqjo4Ojjz6ak046KeI2rrvu\nOu644w5eeeUVysrKAGhpaWHWrFnccsstzmebMYObbroJcAbIe+GFFzj99NP7bScrK4sDDjiA999/\nn8MOOyym+JNFawRKDVGhO5tK2cMuGZ/qUEalXbt2UVJSEnbdypUrWbx4MQBz5sxhzpw5MW3zrLPO\nAuCwww5jy5YtgDPZzJIlS5g7dy5HHnkkDQ0Ncbfdu1wuzj777MDzV155hSOPPJLZs2fzt7/9jQ8/\n/DDs6yoqKti+fXvYdSNJawRKDVV3J6XSxJsm/MFqNIn2yz1Z8vLyAkNAhzOUCd3dbmewQJfLFWiS\niTTc9WuvvRZ2OOpwPB4PLpcrUO7qq69m1apVTJkyhZtvvjnia9vb28nLy4v7cySa1giUGqqWOgC2\ndhUPUlBu0AyjAAAZT0lEQVQNxbhx4+jp6Ql7ED322GNZunQpAB988AHr1q0LrLvooosCQ0fHItJw\n11OnTuWjjz6io6ODxsZGXn755cBrioqK8Pl8Ybfnj7esrIzm5uaoJ7g3bNgQGEY7lbRGoNRQ2Qlp\nPusoSnEgo9dJJ53Ea6+9xoIFC/otv+qqq7j00kuZM2cOc+fO5Ygj+iZLXLduHRMnxt6v44orrmDL\nli1UV1djjKG8vJxnn32WKVOmcO655zJnzhymT5/OoYf2TdR45ZVXcsoppzBx4kReeeWVftsrKSnh\n29/+NrNnz6aqqorDDz887Pvu3LmTvLy8uGJNFh2GWqmh+vi/YNliFnbfxrM//+6QmirSWToMQ/3e\ne+9xxx138Oijj8ZUvqmpicsvv5w//vGPSY5s+O688068Xi+XX355TOWTOQy1Ng0pNVS2RlDTXUxT\nW+ovARyNDj30UI4//viYr833er0ZkQTAqTlcfPHFqQ4D0ESg1ND5aumVbHZTxE5f5BOJanguu+yy\nwInY0eTSSy/tN6NaKmkiUGqofDvoyq/AkOVMWalUhtJEoNRQ+Wqh0Bl1dGdTR4qDUWroNBEoNVS+\nHWQX7wOgNQKV0TQRKDVUvlpcxRPxerKp00SgMpgmAqWGoqsd2vZA0QQqvR5tGkqSRA5DfdNNN7Fi\nxYqoZTo6OliwYAFz585l2bJlccW6ZcsWHnvssbheA+kxNLUmAqWGotm5dJSiiU4i0KuGkiKRw1Df\ncsstAzqmhXrvvffo6upi7dq1nHfeeXFtf6iJIFiqhqbWRKDUUPj8iWACFV43dVojSIpEDkMd/Mu7\nqqqKn/70p1RXVzN79mw++eQT6urqWLx4MWvXrmXu3Lls3ryZ1atXc9xxx3HYYYdx8sknU1vrzFG9\nadMmFixYwCGHHEJ1dTWbN2/mxhtv5O9//ztz587lzjvvjDi8tTGGa6+9lhkzZvCNb3yDurq6QIyp\nGpo6PS5iVSrT+Pomra/0ZlHna6e315CVNbp6Fwf8+UbY8Y/EbnPCbDjltoirkzEMdbCysjLWrFnD\nf/7nf3L77bdz//33c//993P77bfzwgsv0NXVxYUXXshzzz1HeXk5y5Yt4yc/+QkPPvggF1xwATfe\neCNnnnkm7e3t9Pb2cttttwVeC3DvvfeGHd76vffeY/369fzjH/9g586dzJgxg8suuwxI3dDUmgiU\nGoqgGkFlUSNdPYY9rZ2UFrpTG9coMtgw1Ndddx0Q3zDUwYKHpH7mmWcGrF+/fj0ffPABJ554IuBM\nYDNx4kR8Ph/btm3jzDPPBJyRR8N58cUXWbduXaAW0tjYyMaNG1m5ciXnn39+oLnra1/7Wr/X+Yem\n1kSgVLrz1YLLDXnjqPQ6zUI7mzpGbyKI8ss9WZIxDHWwcENSBzPGMHPmTN58881+y5uaYpugJ9Lw\n1suXL48aeyqGph70HIGITBGRV0TkYxH5UES+Z5ePF5GXRGSjvR9nl4uI3C0im0RknYjEX2dTKt35\ndkBRJYhQ4XV+EeoJ48QaqWGoIznooIOor68PJIKuri4+/PBDvF4vkydP5tlnnwWcK41aW1sHDE0d\naXjrY489lieeeIKenh5qa2sHjF6aiqGpYzlZ3A38wBhzMHAUcI2IzABuBF42xkwHXrbPAU4Bptvb\nlcA9CY9aqVTz1UKRM3xwpdf5Zal9CRLPPwx1qKuuuorm5mbmzJnDr371q2ENQx1Jbm4uTz31FDfc\ncAOHHHIIc+fO5Y033gDg0Ucf5e6772bOnDl85StfYceOHcyZM4fs7GwOOeQQ7rzzTq644gpmzJhB\ndXU1s2bN4jvf+Q7d3d2ceeaZTJ8+ndmzZ3PVVVdx3HHHBd4zZUNTG2PiugHPAScC64GJdtlEYL19\n/Hvg/KDygXKRbocddphRKqP8Zp4xyy40xhjT3tVtpt7wgvn1ig0pDiqxPvroo1SHYNasWWMWL14c\nc/nGxkZzzjnnJDGi5LrjjjvM/fffH3ZduL8HsMrEeQwPd4vr8lERqQIOBd4GKo0xtTaZ1AIVttgk\nYGvQy2rsstBtXSkiq0RkVX19fTxhKJV6vh2BGoE728X4glwdZiIJRvMw1OGkamjqmBOBiBQCTwPf\nN8ZEO1sS7izIgNlvjDH3GmPmGWPmlZeXxxqGUqnX0QwdTVA0IbCoositvYuTZLQOQx1OqoamjikR\niEgOThJYaozxX2e1U0Qm2vUTAX+viBpgStDLJwPbExOuUmmgeadzX9TXjlvp9VCnJ4tVhorlqiEB\nHgA+NsbcEbTqecBfh7kY59yBf/lF9uqho4BGfxOSUqNCoDNZX42g0uselU1DJg2mslXJ/zvEUgc5\nGrgQ+IeIrLXLfgzcBjwpIpcDXwDfsuuWA6cCm4BW4NKERqxUqvn6xhnyq/R6qPd10NNrcI2S3sUe\nj4eGhgZKS0tH3XzMmcQYQ0NDQ8SOa4kwaCIwxrxG+HZ/gBPClDfANcOMS6n0FaZGUOH10Gugobkj\n0K8g002ePJmamhr0Yo7U83g8TJ48OWnb157FSsXLtwNy8sHtDSyqLHL6EuxsGj2JICcnh2nTpqU6\nDDUCdPRRpeLlq3VqA0HNJZX+3sWj8DyBGv00ESgVr6A+BH6VOsyEymCaCJSKl79GEKSsMBcRncRe\nZSZNBErFw5iwNYJsVxZlhW4db0hlJE0ESsWjowm6WgfUCGD09iVQo58mAqXiEaYPgV9lkU5irzKT\nJgKl4hGmD4FfhQ4zoTKUJgKl4hGtRuB1s6u5k66e3hEOSqnh0USgVDz8NYLCygGr/JeQ1vu0eUhl\nFk0ESsXDt8PpUewuHLDKP1OZnjBWmUYTgVLxCNOHwK+iyN+7WGsEKrNoIlAqHr4dEROBv2lITxir\nTKOJQKl4BE1aH6q0IBdXlmjTkMo4mgiUilWgV3H4GkFWluiUlSojaSJQKlZte6CnM2KNAJy+BFoj\nUJlGE4FSsYrSmcyvsshNndYIVIbRRKBUrAKJIHKNoNLr0aGoVcbRRKBUrAK9iqPUCLxu9rZ20d7V\nM0JBKTV8mgiUilWgV3HkRFChvYtVBtJEoFSsfDsgbxzkRJ6TWKesVJlIE4FSsQozIU2ovmEmtEag\nMocmAqViFWV4Cb/KIq0RqMyjiUCpWMVQIyjJzyHXlaVXDqmMoolAqVj09kbtVewnIlR4tS+Byiya\nCJSKResuMD2D1gjA9iXQpiGVQTQRKBWLGHoV++kk9irTaCJQKhZRpqgMVVHk0aYhlVE0ESgVi7hq\nBB58Hd20dHQnOSilEkMTgVKx8NcIwsxVHMrfl6BOexerDDFoIhCRB0WkTkQ+CFo2XkReEpGN9n6c\nXS4icreIbBKRdSJSnczglRoxvlooKAdXzqBFtXexyjSx1AgeBr4esuxG4GVjzHTgZfsc4BRgur1d\nCdyTmDCVSrEYLh3100nsVaYZNBEYY1YCu0MWLwQesY8fAc4IWr7EON4CSkRk8LNrSqW7KFNUhvIP\nPKcnjFWmGOo5gkpjTC2Ava+wyycBW4PK1dhlSmW2OGoERe5s8nJcWiNQGSPRJ4slzDITtqDIlSKy\nSkRW1dfXJzgMpRKopxua62KuEYiI05dATxarDDHURLDT3+Rj7+vs8hpgSlC5ycD2cBswxtxrjJln\njJlXXl4+xDCUGgEtdYCJuUYAOnexyixDTQTPAxfbxxcDzwUtv8hePXQU0OhvQlIqY8UwRWWoSq+H\nOk0EKkPEcvno48CbwEEiUiMilwO3ASeKyEbgRPscYDnwKbAJuA+4OilRKzWSYpiiMlRlkZudTR0Y\nE7ZlVKm0kj1YAWPM+RFWnRCmrAGuGW5QSqWVIdYI2rp68HV04/UM3vdAqVTSnsVKDca3AyTL6VAW\nowp/72JtHlIZQBOBUoPx1TpDS2S5Yn5JX+9ivXJIpT9NBEoNJo4+BH46zITKJJoIlBpMDFNUhqoo\n0knsVebQRKDUYGKYtD5UgTubIne21ghURtBEoFQ03R3Q2hB3jQCcE8Z1Oom9ygCaCJSKpnmncx9n\njQD8cxdr05BKf5oIlIomjikqQ+kk9ipTaCJQKpo4pqgMVeF1U6e9i1UG0ESgVDTDqREUeejs6WVv\na1eCg1IqsTQRKBWNrxayciBvfNwvDfQl0BPGKs1pIlAqGn9nsqz4/1X6pqzUE8YqvWkiUCqaIfQh\n8NPexSpTaCJQKpohDC/hV16kA8+pzKCJQKlo4pi0PpQnx0VJfo42Dam0p4lAqUg6W6G9ccg1AnCu\nHNKmIZXuNBEoFUnz0C8d9avQSexVBhh0hjKlRq3eHmjZ5Qwj0e9W59zv/swpV1g55Leo9HrYuHNX\nggJWKjk0EajRxRjo8NmD+Y7+B/bmOufkr/956y4wvQO34S6GwgonAcy9AKYcMeRwvjShiKdW13Dn\nSxv4/oLpiMgwPpxSyaGJQGWG7k5oqYtwYA9Z1t028PVZOc6BvbACiifDpGqn7d9/wPevK6yEnLyE\nhX3p0dP4ZIePX7+8kY7uXm74+kGaDFTa0USgRl5PN7Tvhdbd0LYnzM0ub9nV98u+bU/4beWN7zuI\nTznSuS+a4CwrKO97nDcOUnAAdmUJvzp7Du7sLH73P5vp6O7hptNmaDJQaUUTgRoeY6CzeeCv85b6\n/gf1wG0vdDRF2aBAXolz4M4vhdL9YepXQn692/uCCsjOHbGPOlRZWcKtZ8wiNzuLh17fQmd3Lz9f\nOIusLE0GKj1oIlDh9XQ5B/PgNvXAfciyrtaBrxeXczD33wonQPnBfc/zx9vHJf3LuYuHNJxDuhMR\nbjptBu5sl60Z9PLLs+fg0mSg0oAmgrGot9c5yDfWQONWe6sJel7jzMoVTt64vl/lkw8Pal8P+qVe\nNAE8JaPygD4cIsINXz8IT04Wd63YSGd3L3ecewjZLt1PKrU0EYxGXe39D+oD7rdBT8i17bmFUDzF\nOZG6z6HOtfMDDvIVkO1OzWcaJUSE7y84kNzsLH71l/V09fTy60WHkputyUCljiaCTNXeCLs/7bs1\nBD1uqQspLM6BvWSKc5A/+PS+g77/3lOckpOpY9XV8w/Ane3i5y98ROcfVvP/LqjGk+NKdVhqjNJE\nkM7a9tiD+2fQsLn/gb81pJNS0T7OidUDT4ZxU6F4X3ugnwzefcCVk5rPoCK6/KvTcGdn8S/PfsC3\nl6zi3gvnkZeryUCNPE0Eydbd6VxV09kSdGsOWtbcf7lvpz3Ybx54yaR3MpTuBwefBuP3g/H7O/fj\nqiA3PyUfTw3P4qOmkpudxQ1Pr+PSh9/hgYsPp8Ct/5ZqZOk3bjA9Xc4lj9Gudw++NLLfwb4FeuOY\npjAnH/LLnIP9zDNDDvZTE9rRSaWPc+dNwZ2dxfVPvs9FD77DQ5cejtejNTg1csZuIuhqhz2f9TW1\n7NniXCkTfGBv3QOdvsjbkCzn6pjgyx9LpkBuEeQWBN0KY3hcAFnaLDBWLZw7iVxXFv/r8fdYfP/b\nLLnsCEry07+PhBodRnci6Gztf7APtLN/Bk3bANNX1lPi9ET1X/NeMaP/Ab7fNe/2Gni3Vy+RVAlz\nyuyJ/M6VxdVL1/BP973No5cfQWmhXqWlkk+MMYOXinejIl8Hfg24gPuNMbdFKz9v3jyzatWq+N/I\nGOeXe2NN3wG/YbNzoN/9Kfi29y+fX9rX1DJ+P+fk6vhpzuO8cfG/v1JJsHJDPd9esop9x+ez9NtH\nUlHkSXVIKk2JyGpjzLxhbyfRiUBEXMAG4ESgBngXON8Y81Gk10RMBD1d0LQ9pLNTcOenGqctPlhB\necjB3t6Pm+b8olcqA7y5uYHLH3mXCV4PS799JBOL9fyQGihRiSAZTUNHAJuMMZ8CiMgTwEIgYiKg\nvQneuS+k01ONM01g6DDB+WXOJZGlB8D+X7OXR05yftmPmwYebxI+klIj68v7l7LksiO45KF3Off3\nb/KLM2ZrPwOVNMlIBJOArUHPa4Ajo75i92ZY/kNw5ToH9eLJsN/8vuvgiyc718V799HLJNWYMa9q\nPEuvOJILH3ibix58J9XhqFEsGYkgXPfUAe1PInIlcCXA/lMmwA/WOs06evJVqYBDppSw4vrj2FjX\nPHhhNeZ89ZeJ2U4yEkENMCXo+WRge2ghY8y9wL3gnCOgaOjTASo1mlV4PVR49YSxSp5k/Px+F5gu\nItNEJBdYBDyfhPdRSimVAAmvERhjukXkWuCvOJePPmiM+TDR76OUUioxktKhzBizHFiejG0rpZRK\nLD0zq5RSY5wmAqWUGuM0ESil1BiniUAppca4pAw6F3cQIj5gfarjiEEZsGvQUqmncSZOJsQIGmei\nZUqcBxljioa7kXQZhnp9IgZOSjYRWaVxJk4mxJkJMYLGmWiZFGcitqNNQ0opNcZpIlBKqTEuXRLB\nvakOIEYaZ2JlQpyZECNonIk2puJMi5PFSimlUiddagRKKaVSRBOBUkqNcSOaCETk6yKyXkQ2iciN\nYda7RWSZXf+2iFSNZHw2hiki8oqIfCwiH4rI98KUmS8ijSKy1t5uGuk4bRxbROQfNoYBl5GJ4267\nP9eJSPUIx3dQ0D5aKyJNIvL9kDIp25ci8qCI1InIB0HLxovISyKy0d6Pi/Dai22ZjSJy8QjH+O8i\n8on9m/5JRMJOxj3Y92ME4rxZRLYF/W1PjfDaqMeFEYhzWVCMW0RkbYTXjuT+DHscStr30xgzIjec\nIak3A/sBucD7wIyQMlcDv7OPFwHLRiq+oBgmAtX2cRGwIUyc84EXRjq2MLFuAcqirD8V+DPOrHFH\nAW+nMFYXsAOYmi77EjgWqAY+CFr2K+BG+/hG4JdhXjce+NTej7OPx41gjCcB2fbxL8PFGMv3YwTi\nvBn4YQzfi6jHhWTHGbL+P4Cb0mB/hj0OJev7OZI1gsCk9saYTsA/qX2whcAj9vFTwAkiEm7qy6Qx\nxtQaY9bYxz7gY5x5mDPRQmCJcbwFlIjIxBTFcgKw2RjzeYrefwBjzEpgd8ji4O/gI8AZYV56MvCS\nMWa3MWYP8BLw9ZGK0RjzojGm2z59C2cWwJSKsC9jEctxIWGixWmPNecCjyfr/WMV5TiUlO/nSCaC\ncJPahx5gA2XsF70RKB2R6MKwTVOHAm+HWf1lEXlfRP4sIjNHNLA+BnhRRFbbOaBDxbLPR8oiIv+D\npcO+9Ks0xtSC888IVIQpk0779TKcWl84g30/RsK1tgnrwQjNGOm0L48BdhpjNkZYn5L9GXIcSsr3\ncyQTQSyT2sc08f1IEJFC4Gng+8aYppDVa3CaOA4BfgM8O9LxWUcbY6qBU4BrROTYkPVpsT/FmbL0\nm8Afw6xOl30Zj3TZrz8BuoGlEYoM9v1ItnuA/YG5QC1Os0uotNiX1vlErw2M+P4c5DgU8WVhlkXd\npyOZCGKZ1D5QRkSygWKGVt0cFhHJwdn5S40xz4SuN8Y0GWOa7ePlQI6IlI1wmBhjttv7OuBPONXs\nYLHs85FwCrDGGLMzdEW67MsgO/3NZ/a+LkyZlO9XewLwNOACYxuGQ8Xw/UgqY8xOY0yPMaYXuC/C\n+6d8X0LgeHMWsCxSmZHenxGOQ0n5fo5kIohlUvvnAf8Z7nOAv0X6kieLbSd8APjYGHNHhDIT/Ocu\nROQInP3YMHJRgogUiEiR/zHOCcQPQoo9D1wkjqOARn+1coRF/KWVDvsyRPB38GLguTBl/gqcJCLj\nbHPHSXbZiBCRrwM3AN80xrRGKBPL9yOpQs5HnRnh/WM5LoyEBcAnxpiacCtHen9GOQ4l5/s5EmfA\ng85mn4pz9nsz8BO77BacLzSAB6f5YBPwDrDfSMZnY/gqTjVqHbDW3k4Fvgt815a5FvgQ5wqHt4Cv\npCDO/ez7v29j8e/P4DgF+H92f/8DmJeCOPNxDuzFQcvSYl/iJKdaoAvnV9TlOOekXgY22vvxtuw8\n4P6g115mv6ebgEtHOMZNOG3A/u+n/0q7fYDl0b4fIxzno/Z7tw7nADYxNE77fMBxYSTjtMsf9n8n\ng8qmcn9GOg4l5fupQ0wopdQYpz2LlVJqjNNEoJRSY5wmAqWUGuM0ESil1BiniUCNCSJSIiJXD+F1\nP05GPEqlE71qSI0Jtpv+C8aYWXG+rtkYU5iUoJRKE1ojUGPFbcD+dgjhfw9dKSITRWSlXf+BiBwj\nIrcBeXbZUltusYi8Y5f9XkRcdnmziPyHiKwRkZdFpHxkP55SQ6c1AjUmDFYjEJEfAB5jzC/swT3f\nGOMLrhGIyME4wwCfZYzpEpH/BN4yxiwREQMsNsYsFWdOhQpjzLUj8dmUGq7sVAegVJp4F3jQju/y\nrDEm3OQkJwCHAe/aUTHy6BvrpZe+cWr+AAwYo0qpdKVNQ0oRGKf+WGAb8KiIXBSmmACPGGPm2ttB\nxpibI20ySaEqlXCaCNRY4cOZ6SksEZkK1Blj7sMZ7Ms/rWeXrSWAM7bLOSJSYV8z3r4OnP+lc+zj\nfwJeS3D8SiWNNg2pMcEY0yAir4szV+2fjTE/CikyH/iRiHQBzYC/RnAvsE5E1hhjLhCRf8GZnCQL\nZ+Cya4DPgRZgpoisxplQ6bzkfyqlEkNPFiuVAHqZqcpk2jSklFJjnNYI1JgiIrNxxskP1mGMOTIV\n8SiVDjQRKKXUGKdNQ0opNcZpIlBKqTFOE4FSSo1xmgiUUmqM00SglFJjnCYCpZQa4/4/tFbef8jw\niGoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a0b4518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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gQUyYMAGA06dPM3PmTPr160eVKlWYPn06y5cvZ8KECQwf7vn9KAcPHuT5559n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\nefbKZmw+cIKP5231yjyVUn/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BMeaMiAwDpmJdPvqJMWaNt5ejlFLKO3xyH4ExZgowxRfz\nVkop5V3aDLVSSgU5TQRKKRXkNBEopVSQ00SglFJBToxx/lYXETkObHA6Dg9UAg46HYQHNE7vKQ4x\ngsbpbcUlzkbGmCK3bBkozVBvMMa0dTqIgojIUo3Te4pDnMUhRtA4va04xemN+eipIaWUCnKaCJRS\nKsgFSiIY63QAHtI4vas4xFkcYgSN09uCKs6AqCxWSinlnEA5IlBKKeUQTQRKKRXk/JoIRORSEdkg\nIkkiMsLN+FIiMsEev0hEavszPjuGBBGZJSLrRGSNiNzvpkx3EUkRkRX2Y6S/47TjSBaRv+wYzrmM\nTCxv2+tzlYi09nN8jVzW0QoROSYiD+Qq49i6FJFPRGS/iKx2GVZBRKaLyCb72W0n1CJyi11mk4jc\n4ucY/yUi6+3v9EcRic1j2ny3Dz/EOUpEdrl8t/3ymDbf/YIf4pzgEmOyiKzIY1p/rk+3+yGfbZ/G\nGL88sJqk3gzUBSKAlUDTXGXuAd63Xw8CJvgrPpcYqgOt7ddlgY1u4uwOTPZ3bG5iTQYq5TO+H/AL\nVq9xHYFFDsYaCuwFagXKugS6Aq2B1S7DXgVG2K9HAK+4ma4CsMV+Lm+/Lu/HGHsDYfbrV9zF6Mn2\n4Yc4RwEPe7Bd5Ltf8HWcuca/DowMgPXpdj/kq+3Tn0cEOZ3aG2NOA9md2rvqD3xuv/4O6CHi306E\njTF7jDHL7dfHgXVY/TAXR/2BccayEIgVkeoOxdID2GyM2ebQ8s9hjJkDHM412HUb/BwY4GbSPsB0\nY8xhY8wRYDpwqb9iNMZMM8acsd8uxOoF0FF5rEtPeLJf8Jr84rT3NdcDX/lq+Z7KZz/kk+3Tn4nA\nXaf2uXewOWXsDT0FqOiX6NywT01dACxyM/pCEVkpIr+ISDO/BvY3A0wTkWV2H9C5ebLO/WUQef/A\nAmFdZqtqjNkD1o8RqOKmTCCt19uwjvrcKWj78Idh9imsT/I4jRFI67ILsM8YsymP8Y6sz1z7IZ9s\nn/5MBJ50au9Rx/f+ICJlgO+BB4wxx3KNXo51iqMl8A4w0d/x2TobY1oDfYF7RaRrrvEBsT7F6rL0\nSuBbN6MDZV0WRqCs1yeBM8D4PIoUtH342higHtAK2IN12iW3gFiXthvI/2jA7+uzgP1QnpO5GZbv\nOvVnIvCkU/ucMiISBsRwfoebRSIi4Vgrf7wx5ofc440xx4wxqfbrKUC4iPi9h3JjzG77eT/wI9Zh\ntitP1rk/9AWWG2P25R4RKOvSxb7s02f28343ZRxfr3YF4OXAjcY+MZybB9uHTxlj9hljMo0xWcCH\neSzf8XUJOfubq4EJeZXx9/rMYz/kk+3Tn4nAk07tJwHZNdzXAr/ltZH7in2e8GNgnTHmjTzKVMuu\nuxCR9ljr8ZD/ogQRKS0iZbNfY1Ugrs5VbBJws1g6AinZh5V+luc/rUBYl7m4boO3AD+5KTMV6C0i\n5e3THb3tYX4hIpcCjwFXGmPS8ijjyfbhU7nqo67KY/me7Bf8oSew3hiz091If6/PfPZDvtk+/VED\n7lKb3Q+r9nsz8KQ9bDTWBg0QiXX6IAlYDNT1Z3x2DBdhHUatAlbYj37AXcBddplhwBqsKxwWAp0c\niLOuvfyVdizZ69M1TgH+Y6/vv4C2DsQZjbVjj3EZFhDrEis57QEysP5F3Y5VJzUT2GQ/V7DLtgU+\ncpn2Nns7TQJu9XOMSVjngLO3z+wr7WoAU/LbPvwc5xf2drcKawdWPXec9vtz9gv+jNMe/ln2NulS\n1sn1mdd+yCfbpzYxoZRSQU7vLFZKqSCniUAppYKcJgKllApymgiUUirIaSJQQUFEYkXknvOY7glf\nxKNUINGrhlRQsG/Tn2yMaV7I6VKNMWV8EpRSAUKPCFSweBmoZzch/K/cI0WkuojMscevFpEuIvIy\nEGUPG2+XGywii+1hH4hIqD08VUReF5HlIjJTRCr79+Mpdf70iEAFhYKOCETkISDSGPOCvXOPNsYc\ndz0iEJEmWM0AX22MyRCR94CFxphxImKAwcaY8WL1qVDFGDPMH59NqaIKczoApQLEEuATu32XicYY\nd52T9ADaAEvsVjGi+Lutlyz+bqfmv8A5bVQpFaj01JBS5LRT3xXYBXwhIje7KSbA58aYVvajkTFm\nVF6z9FGoSnmdJgIVLI5j9fTklojUAvYbYz7Eauwru1vPDPsoAay2Xa4VkSr2NBXs6cD6LV1rv/4/\nYJ6X41fKZ/TUkAoKxphDIjJfrL5qfzHGPJKrSHfgERHJAFKB7COCscAqEVlujLlRRJ7C6pwkBKvh\nsnuBbcAJoJmILMPqUGmg7z+VUt6hlcVKeYFeZqqKMz01pJRSQU6PCFRQEZEWWO3kuzpljOngRDxK\nBQJNBEopFeT01JBSSgU5TQRKKRXkNBEopVSQ00SglFJBThOBUkoFOU0ESikV5P4fLrLdLgZF+zkA\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799958f28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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gQUyYMAGA06dPM3PmTPr160eVKlWYPn06y5cvZ8KECQwf7vn9KAcPHuT5559n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\nefbKZmw+cIKP5231yjyVUn/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BMeaMiAwDpmJdPvqJMWaNt5ejlFLKO3xyH4ExZgowxRfz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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799d1a518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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7+PbbbwGYPHkyX375JTt27ACgtra2S1cUgUwTgQ9kJseceOeQMcf7IFBKtYmM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\nWFJYSnWddlajlPIuTQSesuF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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799764be0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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7+PbbbwGYPHkyX375JTt27ACgtra2S1cUgUwTgQ9kJseceOeQMcf7IFBKtYmM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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79941eba8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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FFVecNu9tt93Gm2++yTvvvMPLL7982vSYmBgmTZrExx9/zNy5c7nlllsAeOqp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\nZWe0eGb3OPokx7JC6wmUUh2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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd798e3eeb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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FFVecNu9tt93Gm2++yTvvvMPLL7982vSYmBgmTZrExx9/zNy5c7nlllsAeOqp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\nZWe0eGb3OPokx7JC6wmUUh2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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd798ea15c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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y7bff8sorr7Bz504mTJjAa6+9BkBhYSHLli1j8ODBp21n5MiRzJkzB4Dy8nI+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\nz44yb4zZb4xZY78+AfyANQ6zPxoKvGE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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd798d70898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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y7bff8sorr7Bz504mTJjAa6+9BkBhYSHLli1j8ODBp21n5MiRzJkzB4Dy8nI+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\nK4YxWoQtWWgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd798d81e48>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_all('soil_output/Spread_barabasi*', get_count, 'id');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:58:26.903783Z",
      "start_time": "2017-10-19T17:57:57.983957+02:00"
     },
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799674080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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1wFFdlXHOtZnZNqASaOhqo/tWlHD7tz7Xs2hFRJIkLzeH4YOKGD6o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\nACn5i4gMQP8fEeHAAqu6cxAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd7996d2eb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799841390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd7998504a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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30t7h0jqcczz5ecaZR9fx3MqtBNX0Iymi5C+SpEBkaIcM1/zBa/oJt3Xw1+Wb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\n6nkDcEJXZZxzbWa2A6gGGrv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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd7997c82b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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30t7h0jqcczz5ecaZR9fx3MqtBNX0Iymi5C+SpEBkaIcM1/zBa/oJt3Xw1+Wb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\n6nkDcEJXZZxzbWa2A6gGGrv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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd7997c83c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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J0LadzbS2uZQO5xxLbo5x8qHVvLRsM0E1/UiSKPmLJCgQHtohzTV/8Jp+Qi1t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\nIsl/OLA24nktMLmjMs65FjP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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799a9cf98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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J0LadzbS2uZQO5xxLbo5x8qHVvLRsM0E1/UiSKPmLJCgQHtohzTV/8Jp+Qi1t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\nIsl/OLA24nktMLmjMs65FjP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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799a9c588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799c4a7b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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+4DX9hFra+PuSDWnft+ydlPxFEhQe2iGVwzl35Ih9BlE9oEi9fiRplPxFEtR+\nd28Gav45ftPPKx9vYuuO5rTvX/Y+CSV/M5tqZsvMbIWZ/bCTcmeYmTOzickLUSQ7hMf1KU9jP/9I\n02uqCbWq6UeSI27yN7Nc4HbgJGA8cLaZjY9Rrgy4Cng72UGKZIM6v9mnvDgzyf/wkQMZPrCfev1I\nUiRS858ErHDOrXLOhYCHgRkxyv0MuBnYmcT4RLJGoKGJQcX55OVmprXUzJheU82ry+vY0hjKSAyy\n90jkLB4OrI14Xusva2dmE4CRzrlnkhibSFbxJm5P/8XeSNNrhtHS5nhhsZp+pGcSSf4WY5lrX2mW\nA/w38L24GzK71Mzmm9n8TZs0Rrn0Luke1yeWQ4b3Z5/yYp5W04/0UCLJvxYYGfF8BBB55pUBhwBz\nzWwNcCQwO9ZFX+fcLOfcROfcxMGDB3c/apEMCDQ0ZaSbZ6Rw088bKwPUN6jpR7ovkeQ/DxhjZqPN\nrAA4C5gdXumc2+qcq3TOjXLOjQLeAk51zs1PScQiGRLIwNAOsUyrqaa1zfHXDz/PdCjSi8VN/s65\nFuBK4HlgKfCoc26xmd1oZqemOkCRbNDc2saWxuaMdfOMNL66P/tVlvDsB2r6ke7LS6SQc24OMCdq\n2fUdlD2u52GJZJfN7Td4ZbbZB7ymn2k11dz+8go2bW9icFnmY5LeR3f4iiQg3Me/Mgtq/uD1+mlz\n8NcPNdyDdI+Sv0gCdg3qlh217LFVpRwwpFRj/Ui3KfmLJCA8qFs2XPCFXb1+3llTz4Ztuq9Suk7J\nXyQB4UHdKtM8ln9nptdU4xw894Fq/9J1Sv4iCQgEm8jLMfr3S6iPRFocMKSMA4eWqelHukXJXyQB\ngWCI8pICzGLd8J4502uqmf/JZtZv3ZHpUKSXUfIXSUCgoSlrLvZGmlYzDIBnVfuXLlLyF0lAXTBE\nZZZc7I00urKEg4f151m1+0sXKfmLJCDQ0JTxQd06Mq2mmvc+3ULt5sZMhyK9iJK/SAKyYTjnjkw/\nVE0/0nVK/iJx7Ai10hhqzZo+/tH2qSimZsQANf1Ilyj5i8QRvrs3m/r4R5teU82i2q18EmjIdCjS\nSyj5i8QRvrs3G0b07MjJh1YDqPYvCVPyF4lj17g+2Zv8RwwqZsI+A3nmfSV/SYySv0gc7SN6ZukF\n37Bph1azZP02Vm0KZjoU6QWU/EXiyLZB3ToyrcZv+lGvH0mAkr9IHIFgE/3ycykuyJ5xfWKpHtCP\nifsO0lgCS5AXAAASf0lEQVQ/khAlf5E46rNk7t5ETK+pZtmG7SzfsD3ToUiWU/IXiaOuIXtv8Ip2\n8qHVmKHav8Sl5C8SRyCYvUM7RBvSv4hJo8p59oP1OOcyHY5kMSV/kTgCwVCvSf4A0w8bxoqNQZap\n6Uc6oeQv0gnnXNYO59yRqQcPJcfU60c6p+Qv0oltO1tobnVZOZxzRwaXFfKl/St4ZpGafqRjSv4i\nnQgEs//u3limHTqM1XUNLFm/LdOhSJZS8hfpRL0/cXtFFg/qFsvUQ4ZSkJfDrX9frtq/xKTkL9KJ\nul5yd2+08pICvnfCWF5YsoGnFn6W6XAkCyn5i3SifVC3XlbzB/iXr+zHF/YdxPVPfcjnW3dmOhzJ\nMkr+Ip3oDcM5dyQ3x/jNNw8j1NrGD59YpOYf2Y2Sv0gnAsEm+hflUZDXO/9VRleW8IOpBzJ32SYe\nnb820+FIFumdZ7RImtQ1hLJ+KOd4zv/SKI7cr5yfPbNUk7xLOyV/kU4Egk297mJvtJwc45YzDsM5\nxw/+vIi2NjX/SILJ38ymmtkyM1thZj+Msf7fzGyJmS0ysxfNbN/khyqSfvUNoV55sTfayPJifjxt\nPK+vCPDg259kOhzJAnGTv5nlArcDJwHjgbPNbHxUsfeAic65GuBx4OZkByqSCYFg7xnOOZ6zJ43k\nmLGD+cWcjzTRuyRU858ErHDOrXLOhYCHgRmRBZxzLzvnwo2JbwEjkhumSPq1tjnqG3vXoG6dMTN+\n/Y1Dycs1rntMzT99XSLJfzgQ2U2g1l/WkYuB52KtMLNLzWy+mc3ftGlT4lGKZMDmxhDO0asGdYun\nekA/fnrKwbyzpp67X1+d6XAkgxJJ/hZjWcwqg5mdC0wEbom13jk3yzk30Tk3cfDgwYlHKZIBvWXu\n3q76xhHD+dpBQ7jl+WWs1GTvfVYiyb8WGBnxfASwx/3iZvY14MfAqc65puSEJ5I57YO67QUXfCOZ\nGb84/VD6FeTyvUffp6W1LdMhSQYkkvznAWPMbLSZFQBnAbMjC5jZBOCPeIl/Y/LDFEm/On9Qt940\nnHOihpQVceOMQ1i4dguzXl2V6XAkA+Imf+dcC3Al8DywFHjUObfYzG40s1P9YrcApcBjZrbQzGZ3\nsDmRXmPXcM57V80/7JSaak4+dCj//beP+ehzDf3c1+QlUsg5NweYE7Xs+ojHX0tyXCIZV98QIsdg\nYL/8TIeSEmbGz2Ycwtur6vneo+/zlyuOIj9X9332FXqnRTpQFwxRXlJATk6sPg97h4rSQm76p0NZ\n/Nk2fv/SikyHI2mk5C/SgUCwaa+72BvL1EOGctrhw7j95RV8uG5rpsORNFHyF+lAoGHvubs3nhtO\nPYTykgL+7dGFNLW0ZjocSQMlf5EOeIO67f01f4ABxfn8+hs1fLwhyG//vjzT4UgaKPmLdCAQ3HuG\ndkjElAOHcObEkfzxHyt599PNmQ5HUkzJXySGppZWtje17JV9/DvzH9MPonpAP6597H12Nqv5Z2+m\n5C8SQ31DeGiHvtHsE1ZWlM/NZ9SwalMDtzy/LNPhSAop+YvE0Jvn7u2pow6o5FtH7svdr6/mndX1\nmQ5HUkTJXySGOv/u3r7W7BP2w5MOZOSgYq597H02btuZ6XAkBZT8RWJoH9GzD/Tzj6WkMI//+ufD\n2Lh9Jyff9ir/+FhDsO9tlPxFYgg0hMf16Zs1f4CJo8p5+sqjqSgp5Py73+HXf/2IZo0AutdQ8heJ\nIRAMUZCXQ2lhQsNf7bXGVJXxlyuO4uxJI/nD3JWc+cc3qd3cGP+FkvWU/EViqAuGqCwpwGzvHdcn\nUf0Kcvnl6TXcdvYEPt4Q5ORbX+X5xZ9nOizpISV/kRjqG/rO3b2JOvWwYTzznaPZt6KEf71/ATNn\nL9ZQEL2Ykr9IDIGGUJ/s5hnPqMoSHv/2l7joqNHc88YaTr/jDVbXNWQ6LOkGJX+RGALBvjOoW1cV\n5uVy/Snj+dN5E1m3ZQfTb3uVpxauy3RY0kVK/iJRnHPUBZuoVLNPp742voo5V32Fg6r7c/XDC/n+\n4+/TGGrJdFiSICV/kSgNoVaaWtr61KBu3TVsYD8evvRIrpxyAI8tqGXG719n2efbMx2WJEDJXyTK\n3j53b7Ll5eZw7YnjuP+iyWxubObU37/GQ+98inMu06FJJ5T8RaLUhe/uVZt/lxw9ppLnrv4Kk0aX\n86MnPuA7D73H9p3NmQ5LOqDkLxIlPKJnZR8d2qEnBpcVcu+Fk7juxHE89+HnTLvtNc0NkKWU/EWi\nhJt9ylXz75acHOOKKQfwyKVH0tLaxul3vMGpv3+Ne15f3f7BKpmn5C8SJRAey18XfHtk4qhynrvm\nGH4yfTwtrY6ZTy9h8i/+zqX3zeevH35OqEXjBGVS3x64RCSGumATpYV5FOXnZjqUXm9Av3wuPno0\nFx89mqXrt/HEu7U8+d5nvLBkA4OK8zn1sGGcfsQIakYM0FAaaabkLxJFN3ilxkHV/fnxtPH8YOqB\nvLqijj8vqOWheWu5981POGBIKacfMZx/mjCc6gH9Mh1qn6DkLxIl0NCkJp8UysvNYcq4IUwZN4St\nO5qZ88F6/ryglpv/uoxbnl/G0QdUcvoRwznx4KEUFyhFpYqOrEiUQDDEyPLiTIfRJwzol8/Zk/bh\n7En7sKaugSfeW8cT79by3Ufep6TgQ04+tJrTjxjB5NHl5OSoWSiZlPxFogQaQkzYZ2Cmw+hzRlWW\n8G8njOWa48cwb009f363ljkffM5jC2opLczjwKFlHFTdn/HD+nNQdX/GVZXRr0DXZbpLyV8kQlub\no14jemZUTo4xeb8KJu9XwQ2nHsILSz5nwSebWbp+G0++t4773/rEK2cwurKEg6r7t38ojK/uz5Cy\nQl08ToCSv0iErTuaaW1zfXbu3mzTryCXGYcPZ8bhwwHvw7l28w6WrN/GkvXbWLp+GwvXbuGZRevb\nX1NeUsD46v4cVL3rm8L+g0vJz1XP9kgJJX8zmwrcCuQCf3LO/SpqfSFwH/AFIACc6Zxbk9xQRVJP\nc/dmt5wcY5+KYvapKGbqIUPbl2/d0cxH/ofB0vXbWbJ+G/e++Un7vQT5ucaQsiIGlxVSWVrI4DL/\np7Sg/XF4eV+5yBz3rzSzXOB24ASgFphnZrOdc0siil0MbHbOHWBmZwG/Bs7sbLsfb9jOCf/1j+5H\nLpICO/2ZqVTz710G9MtvbyoKa2ltY3VdA0vWb+Ojz7ezYetONgWbqN3cyMK1mwk0hIg19lxJQS6V\nZYUMLt39Q6GitIDSwjyKC/IoKcilX0EuJYV5FBfkUlKQR3FhLgW5Ob2mySmRj7hJwArn3CoAM3sY\nmAFEJv8ZwEz/8ePA783MXCfD+hXl5zKmqrRbQYuk0pGjK3TBdy+Ql5vDmKoyxlSVMSPG+pbWNuob\nQmwKNrFpexN1wRCbtocfe7+Xbwzy5qoAWxoTG6AuL8coLsil2P8wKCnwPxz8D4mi/Fzyc3MoyDXy\nc3PIz8vZ7Xle5Dp//W7Pk9h0lUjyHw6sjXheC0zuqIxzrsXMtgIVQF1HG92nvJg7zvlC16IVEUmS\nvNwchvQvYkj/orhlQy1tbG4M0dDUQmOotf13Y6iVhlALjU0tNIRaaQy10NDUyo7wcr/spu1NNIZa\n2BFqpbnN0dzaRnNLG82tjlBrZoa5SCT5x/oOE12jT6QMZnYpcCnAPvvsk8CuRUQyryAvh6oEPiS6\nwzlHa5tr/yBoDv+0RD1vbSPU4vjyr5Oz30SSfy0wMuL5COCzDsrUmlkeMACoj96Qc24WMAtg4sSJ\nmulBRPo8MyMv18jLhX6k776FRBqQ5gFjzGy0mRUAZwGzo8rMBs73H58BvNRZe7+IiGRW3Jq/34Z/\nJfA8XlfPu51zi83sRmC+c242cBdwv5mtwKvxn5XKoEVEpGcS6tDqnJsDzIladn3E453AN5MbmoiI\npIpueRMR6YOU/EVE+iAlfxGRPkjJX0SkD7JM9cg0s+3AsozsvGsq6eRO5SyiOJOnN8QIijPZekuc\n45xzZT3dSCaHr1vmnJuYwf0nxMzmK87k6Q1x9oYYQXEmW2+KMxnbUbOPiEgfpOQvItIHZTL5z8rg\nvrtCcSZXb4izN8QIijPZ+lScGbvgKyIimaNmHxGRPkjJX0SkD0p58jezqWa2zMxWmNkPY6wvNLNH\n/PVvm9moVMcUI4aRZvaymS01s8VmdnWMMseZ2VYzW+j/XB9rW2mIdY2ZfeDHsEeXL/Pc5h/PRWZ2\nRJrjGxdxjBaa2TYzuyaqTMaOpZndbWYbzezDiGXlZvY3M1vu/x7UwWvP98ssN7PzY5VJYYy3mNlH\n/nv6pJnFnGcy3vmRhjhnmtm6iPf25A5e22leSEOcj0TEuMbMFnbw2nQez5h5KGXnp3MuZT94Q0Cv\nBPYDCoD3gfFRZS4H7vQfnwU8ksqYOoizGjjCf1wGfBwjzuOAZ9IdW4xY1wCVnaw/GXgOb3a1I4G3\nMxhrLvA5sG+2HEvgGOAI4MOIZTcDP/Qf/xD4dYzXlQOr/N+D/MeD0hjj14E8//GvY8WYyPmRhjhn\nAtcmcF50mhdSHWfU+v8Ers+C4xkzD6Xq/Ex1zb998nfnXAgIT/4eaQZwr//4ceB4M4s1LWTKOOfW\nO+fe9R9vB5bizUvcG80A7nOet4CBZladoViOB1Y65z7J0P734Jx7hT1nmYs8B+8FTovx0hOBvznn\n6p1zm4G/AVPTFaNz7gXnXIv/9C28GfUyqoNjmYhE8kLSdBann2v+GXgoVftPVCd5KCXnZ6qTf6zJ\n36OT6m6TvwPhyd8zwm92mgC8HWP1l8zsfTN7zswOTmtguzjgBTNbYN6cyNESOebpchYd/1Nlw7EM\nq3LOrQfvHxAYEqNMNh3Xi/C+3cUS7/xIhyv95qm7O2iiyKZj+RVgg3NueQfrM3I8o/JQSs7PVCf/\npE3+ng5mVgr8GbjGObctavW7eM0XhwG/A/6S7vh8RznnjgBOAq4ws2Oi1mfF8TRvys9TgcdirM6W\nY9kV2XJcfwy0AA92UCTe+ZFqfwD2Bw4H1uM1qUTLimPpO5vOa/1pP55x8lCHL4uxrNNjmurk35XJ\n37FOJn9PNTPLxzvgDzrnnohe75zb5pwL+o/nAPlmVpnmMHHOfeb/3gg8ifcVOlIixzwdTgLedc5t\niF6RLccywoZw05j/e2OMMhk/rv5FvOnAOc5v6I2WwPmRUs65Dc65VudcG/A/Hew/48cS2vPN6cAj\nHZVJ9/HsIA+l5PxMdfLvFZO/++1+dwFLnXP/1UGZoeFrEWY2Ce/YBdIXJZhZiZmVhR/jXQT8MKrY\nbOA88xwJbA1/ZUyzDmtU2XAso0Seg+cDT8Uo8zzwdTMb5DdlfN1flhZmNhX4AXCqc66xgzKJnB8p\nFXV96Z862H8ieSEdvgZ85JyrjbUy3cezkzyUmvMzDVewT8a7ar0S+LG/7Ea8kxigCK9pYAXwDrBf\nqmOKEePReF+RFgEL/Z+TgcuAy/wyVwKL8XomvAV8OQNx7ufv/30/lvDxjIzTgNv94/0BMDEDcRbj\nJfMBEcuy4ljifSCtB5rxaksX411jehFY7v8u98tOBP4U8dqL/PN0BXBhmmNcgdemGz4/wz3khgFz\nOjs/0hzn/f55twgvaVVHx+k/3yMvpDNOf/k94XMyomwmj2dHeSgl56eGdxAR6YN0h6+ISB+k5C8i\n0gcp+YuI9EFK/iIifZCSv+y1zGygmV3ejdf9eyriEckm6u0jey3/FvlnnHOHdPF1QedcaUqCEskS\nqvnL3uxXwP7+cLy3RK80s2oze8Vf/6GZfcXMfgX085c96Jc718ze8Zf90cxy/eVBM/tPM3vXzF40\ns8Hp/fNEuk81f9lrxav5m9n3gCLn3E1+Qi92zm2PrPmb2UF4Q+qe7pxrNrM7gLecc/eZmQPOdc49\naN6cBEOcc1em428T6am8TAcgkkHzgLv98VT+4pyLNaHH8cAXgHn+iBT92DW2Shu7xoV5ANhjTCiR\nbKVmH+mznDfO+zHAOuB+MzsvRjED7nXOHe7/jHPOzexokykKVSTplPxlb7Ydb0akmMxsX2Cjc+5/\n8AbUCk952ex/GwBvLJUzzGyI/5py/3Xg/f+c4T/+f8BrSY5fJGXU7CN7LedcwMxeN2/u1uecc9dF\nFTkOuM7MmoEgEK75zwIWmdm7zrlzzOw/8Cb0yMEbHOwK4BOgATjYzBbgTUJ0Zur/KpHk0AVfkW5S\nl1DpzdTsIyLSB6nmL3s9MzsUb5z5SE3OucmZiEckGyj5i4j0QWr2ERHpg5T8RUT6ICV/EZE+SMlf\nRKQPUvIXEemDlPxFRPqg/w8q2vnGLNBQ+wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799d51438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_all('soil_output/Spread_erdos*', get_value, 'prob_tv_spread');"
    ]
@@ -2912,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:59:15.791793Z",
@@ -2935,25 +1054,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:59:17.153282Z",
      "start_time": "2017-10-19T17:59:16.830872+02:00"
-    }
+    },
+    "collapsed": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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QVsCdVdvZYSWV+rp6Jl80GZ/Px3f7vuO268LX/Lnm4mvYvHFzp8W16IlF1NXW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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799c4f908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "res = agents.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)\n",
     "res.plot();"
@@ -2973,35 +1082,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:59:18.418348Z",
      "start_time": "2017-10-19T17:59:18.143443+02:00"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd795b17b38>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
     },
-    {
-     "data": {
-      "image/png": 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YsYINGzYwZ84c7r77bgCuu+465s+fzxdffMHHH39MSUkJDzzwQHDfn/zkJ10O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\nqggqHK0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd796e1ff60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "del res['event_time']\n",
     "res.plot()"
@@ -3009,496 +1098,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:59:42.750011Z",
      "start_time": "2017-10-19T17:59:42.649353+02:00"
-    }
+    },
+    "collapsed": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>event_time</th>\n",
-       "      <th>has_tv</th>\n",
-       "      <th>id</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_step</th>\n",
-       "      <th>agent_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"30\" valign=\"top\">0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>103</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>104</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>105</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>106</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>107</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>110</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>112</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>114</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>115</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>116</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>117</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>neutral</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"30\" valign=\"top\">20</th>\n",
-       "      <th>73</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>infected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NewsEnvironmentAgent</th>\n",
-       "      <td>10</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10521 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             event_time  has_tv        id\n",
-       "t_step agent_id                                          \n",
-       "0      0                              0    True   neutral\n",
-       "       1                              0   False   neutral\n",
-       "       10                             0    True   neutral\n",
-       "       100                            0    True   neutral\n",
-       "       101                            0    True   neutral\n",
-       "       102                            0   False   neutral\n",
-       "       103                            0    True   neutral\n",
-       "       104                            0    True   neutral\n",
-       "       105                            0   False   neutral\n",
-       "       106                            0   False   neutral\n",
-       "       107                            0    True   neutral\n",
-       "       108                            0    True   neutral\n",
-       "       109                            0   False   neutral\n",
-       "       11                             0    True   neutral\n",
-       "       110                            0   False   neutral\n",
-       "       111                            0   False   neutral\n",
-       "       112                            0    True   neutral\n",
-       "       113                            0    True   neutral\n",
-       "       114                            0    True   neutral\n",
-       "       115                            0    True   neutral\n",
-       "       116                            0   False   neutral\n",
-       "       117                            0    True   neutral\n",
-       "       118                            0    True   neutral\n",
-       "       119                            0   False   neutral\n",
-       "       12                             0   False   neutral\n",
-       "       120                            0   False   neutral\n",
-       "       121                            0    True   neutral\n",
-       "       122                            0    True   neutral\n",
-       "       123                            0    True   neutral\n",
-       "       124                            0   False   neutral\n",
-       "...                                 ...     ...       ...\n",
-       "20     73                             0    True  infected\n",
-       "       74                             0    True  infected\n",
-       "       75                             0    True  infected\n",
-       "       76                             0    True  infected\n",
-       "       77                             0    True  infected\n",
-       "       78                             0    True  infected\n",
-       "       79                             0   False  infected\n",
-       "       8                              0   False  infected\n",
-       "       80                             0    True  infected\n",
-       "       81                             0   False  infected\n",
-       "       82                             0   False  infected\n",
-       "       83                             0    True  infected\n",
-       "       84                             0   False  infected\n",
-       "       85                             0    True  infected\n",
-       "       86                             0    True  infected\n",
-       "       87                             0    True  infected\n",
-       "       88                             0   False  infected\n",
-       "       89                             0   False  infected\n",
-       "       9                              0    True  infected\n",
-       "       90                             0    True  infected\n",
-       "       91                             0    True  infected\n",
-       "       92                             0    True  infected\n",
-       "       93                             0   False  infected\n",
-       "       94                             0    True  infected\n",
-       "       95                             0    True  infected\n",
-       "       96                             0    True  infected\n",
-       "       97                             0    True  infected\n",
-       "       98                             0   False  infected\n",
-       "       99                             0    True  infected\n",
-       "       NewsEnvironmentAgent          10   False         0\n",
-       "\n",
-       "[10521 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "processed = process_one(agents);\n",
     "processed"
@@ -3513,70 +1121,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:59:51.165806Z",
      "start_time": "2017-10-19T17:59:50.886780+02:00"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd799c15748>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
     },
-    {
-     "data": {
-      "image/png": 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YsYINGzYwZ84c7r77bgCuu+465s+fzxdffMHHH39MSUkJDzzwQHDfn/zkJ10O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\nqggqHK0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799d3fbe0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "get_count(agents, 'id', 'has_tv').plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T15:59:55.203641Z",
      "start_time": "2017-10-19T17:59:54.950046+02:00"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd79a228c88>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
     },
-    {
-     "data": {
-      "image/png": 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mT7eLOJSI6LXO1rHO1rLO1jkcaoRana1YjqePJEmFoSBJKkZiKCzvdgFDZJ2tZZ2tZZ2t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\nbgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a1fe208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "get_value(agents, 'event_time').plot()"
    ]
@@ -3590,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:18.148006Z",
@@ -3605,22 +1173,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:18.636440Z",
      "start_time": "2017-10-19T18:00:18.504421+02:00"
-    }
+    },
+    "collapsed": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "267M\t../rabbits/soil_output/rabbits_example/\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!du -xsh ../rabbits/soil_output/rabbits_example/"
    ]
@@ -3639,43 +1200,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:25.080582Z",
      "start_time": "2017-10-19T18:00:19.594165+02:00"
     },
+    "collapsed": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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p378/eXl5HD16lG+//ZZ58+aRk5NDt27dqud1uP3225k3bx5XXXUVPXv25KKLLgJgxowZ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\n0SbFISIi2qQ4REREmxSHiIho8y9ffsz3DjLrngAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799c54ba8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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p378/eXl5HD16lG+//ZZ58+aRk5NDt27dqud1uP3225k3bx5XXXUVPXv25KKLLgJgxowZ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\n0SbFISIi2qQ4REREmxSHiIho8y9ffsz3DjLrngAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799aa66d8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'id')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:38.434367Z",
@@ -3690,36 +1231,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:39.160418Z",
      "start_time": "2017-10-19T18:00:38.436153+02:00"
     },
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd799b5b2b0>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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WYQUA3aN0y0EppZSlNjn07NTOk4OIdBKR90Rkl4jsFJFxIhIjIitEJN16jrb6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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79a2f8940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "states = analysis.get_count(df, 'id')\n",
     "states.plot()"
@@ -3727,35 +1248,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:39.515032Z",
      "start_time": "2017-10-19T18:00:39.162240+02:00"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd796161cf8>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
     },
-    {
-     "data": {
-      "image/png": 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OAZ8DcPcSM/sBsDro9313rzvI/QXCZ0SlAX8MfkREpJ61H4TfNqcMb//bgtbXbDi4+4LT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\npAGFg4iINPD/AfaLVEq6LAp5AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd79593ef98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "alive = analysis.get_value(df, 'rabbits_alive', 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric)\n",
     "alive.plot()"
@@ -3763,33 +1264,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-10-19T16:00:58.815038Z",
      "start_time": "2017-10-19T18:00:58.566807+02:00"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jfernando/.local/lib/python3.6/site-packages/pandas/core/reshape/merge.py:551: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
     },
-    {
-     "data": {
-      "image/png": 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mQgkh2s++c9l4ONkxNaBv3Q2pEWDrAP7tWzKjNjlzaGfVJbuvu+66Ou0PPvgg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\n/D/rW5PT2bI3LAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd799b45588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "h = alive.join(states);\n",
     "h.plot();"
@@ -3828,7 +1311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.3"
   },
   "toc": {
    "colors": {
@@ -3858,7 +1341,7 @@
     "width": "250px"
    },
    "toc_section_display": "block",
-   "toc_window_display": true,
+   "toc_window_display": false,
    "widenNotebook": false
   }
  },
diff --git a/requirements.txt b/requirements.txt
index 92f8443..a0f4130 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 nxsim
 simpy
-networkx
+networkx>=2.0
 numpy
 matplotlib
 pyyaml
diff --git a/setup.py b/setup.py
index ff729e3..af30134 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup
 
 
 with open(os.path.join('soil', 'VERSION')) as f:
-    __version__ = f.read().strip()
+    __version__ = f.readlines()[0].strip()
     assert __version__
 
 
diff --git a/soil/VERSION b/soil/VERSION
index 42624f3..0eb4182 100644
--- a/soil/VERSION
+++ b/soil/VERSION
@@ -1 +1 @@
-0.10.2
\ No newline at end of file
+0.11
\ No newline at end of file
diff --git a/soil/__init__.py b/soil/__init__.py
index a9b92c9..6d05256 100644
--- a/soil/__init__.py
+++ b/soil/__init__.py
@@ -62,7 +62,7 @@ def main():
         simulation.run_from_config(args.file,
                                    dry_run=args.dry_run,
                                    dump=dump,
-                                   parallel=(not args.synchronous),
+                                   parallel=(not args.synchronous and not args.pdb),
                                    results_dir=args.output)
     except Exception as ex:
         if args.pdb:
diff --git a/soil/agents/CounterModel.py b/soil/agents/CounterModel.py
index 55a852b..8b25886 100644
--- a/soil/agents/CounterModel.py
+++ b/soil/agents/CounterModel.py
@@ -11,9 +11,9 @@ class CounterModel(BaseAgent):
         # Outside effects
         total = len(list(self.get_all_agents()))
         neighbors = len(list(self.get_neighboring_agents()))
-        self.state['times'] = self.state.get('times', 0) + 1
-        self.state['neighbors'] = neighbors
-        self.state['total'] = total
+        self['times'] = self.get('times', 0) + 1
+        self['neighbors'] = neighbors
+        self['total'] = total
 
 
 class AggregatedCounter(BaseAgent):
@@ -26,7 +26,7 @@ class AggregatedCounter(BaseAgent):
         # Outside effects
         total = len(list(self.get_all_agents()))
         neighbors = len(list(self.get_neighboring_agents()))
-        self.state['times'] = self.state.get('times', 0) + 1
-        self.state['neighbors'] = self.state.get('neighbors', 0) + neighbors
-        self.state['total'] = total = self.state.get('total', 0) + total
+        self['times'] = self.get('times', 0) + 1
+        self['neighbors'] = self.get('neighbors', 0) + neighbors
+        self['total'] = total = self.get('total', 0) + total
         self.debug('Running for step: {}. Total: {}'.format(self.now, total))
diff --git a/soil/agents/__init__.py b/soil/agents/__init__.py
index 42697fd..6399e23 100644
--- a/soil/agents/__init__.py
+++ b/soil/agents/__init__.py
@@ -14,7 +14,7 @@ import json
 
 from functools import wraps
 
-from .. import utils
+from .. import utils, history
 
 agent_types = {}
 
@@ -32,33 +32,67 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
 
     defaults = {}
 
-    def __init__(self, **kwargs):
+    def __init__(self, environment=None, agent_id=None, state=None,
+                 name='network_process', interval=None, **state_params):
+        # Check for REQUIRED arguments
+        assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
+                                                  'Cannot be NoneType.')
+        # Initialize agent parameters
+        self.id = agent_id
+        self.name = name
+        self.state_params = state_params
+
+        # Global parameters
+        self.global_topology = environment.G
+        self.environment_params = environment.environment_params
+
+        # Register agent to environment
+        self.env = environment
+
         self._neighbors = None
         self.alive = True
-        state = deepcopy(self.defaults)
-        state.update(kwargs.pop('state', {}))
-        kwargs['state'] = state
-        super().__init__(**kwargs)
+        real_state = deepcopy(self.defaults)
+        real_state.update(state or {})
+        self._state = real_state
+        self.interval = interval
+
         if not hasattr(self, 'level'):
             self.level = logging.DEBUG
-        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name, self.id))
+        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
+                                                             self.id))
         self.logger.setLevel(self.level)
 
+        # initialize every time an instance of the agent is created
+        self.action = self.env.process(self.run())
+
+    @property
+    def state(self):
+        return self._state
+
+    @state.setter
+    def state(self, value):
+        for k, v in value.items():
+            self[k] = v
 
     def __getitem__(self, key):
         if isinstance(key, tuple):
-            k, t_step = key
-            return self.env[self.id, t_step, k]
+            key, t_step = key
+            k = history.Key(key=key, t_step=t_step, agent_id=self.id)
+            return self.env[k]
         return self.state.get(key, None)
 
     def __delitem__(self, key):
-        del self.state[key]
+        self.state[key] = None
 
     def __contains__(self, key):
         return key in self.state
 
     def __setitem__(self, key, value):
         self.state[key] = value
+        k = history.Key(t_step=self.now,
+                        agent_id=self.id,
+                        key=key)
+        self.env[k] = value
 
     def get(self, key, default=None):
         return self[key] if key in self else default
@@ -72,7 +106,12 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
             return None
 
     def run(self):
-        interval = self.env.interval
+        if self.interval is not None:
+            interval = self.interval
+        elif 'interval' in self:
+            interval = self['interval']
+        else:
+            interval = self.env.interval
         while self.alive:
             res = self.step()
             yield res or self.env.timeout(interval)
@@ -95,7 +134,7 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
             agents = self.global_topology.nodes()
         count = 0
         for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent'].state['id']:
+            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
                 continue
             count += 1
         return count
@@ -197,11 +236,13 @@ class FSM(BaseAgent, metaclass=MetaFSM):
     def __init__(self, *args, **kwargs):
         super(FSM, self).__init__(*args, **kwargs)
         if 'id' not in self.state:
-            self.state['id'] = self.default_state.id
+            if not self.default_state:
+                raise ValueError('No default state specified for {}'.format(self.id))
+            self['id'] = self.default_state.id
 
     def step(self):
         if 'id' in self.state:
-            next_state = self.state['id']
+            next_state = self['id']
         elif self.default_state:
             next_state = self.default_state.id
         else:
@@ -215,7 +256,7 @@ class FSM(BaseAgent, metaclass=MetaFSM):
             state = state.id
         if state not in self.states:
             raise ValueError('{} is not a valid state'.format(state))
-        self.state['id'] = state
+        self['id'] = state
         return state
 
 
diff --git a/soil/analysis.py b/soil/analysis.py
index a99ed04..8719013 100644
--- a/soil/analysis.py
+++ b/soil/analysis.py
@@ -4,7 +4,7 @@ import glob
 import yaml
 from os.path import join
 
-from . import utils
+from . import utils, history
 
 
 def read_data(*args, group=False, **kwargs):
@@ -15,8 +15,9 @@ def read_data(*args, group=False, **kwargs):
         return list(iterable)
 
 
-def _read_data(pattern, keys=None, convert_types=False,
-               process=None, from_csv=False, **kwargs):
+def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
+    if not process_args:
+        process_args = {}
     for folder in glob.glob(pattern):
         config_file = glob.glob(join(folder, '*.yml'))[0]
         config = yaml.load(open(config_file))
@@ -24,19 +25,20 @@ def _read_data(pattern, keys=None, convert_types=False,
         if from_csv:
             for trial_data in sorted(glob.glob(join(folder,
                                                     '*.environment.csv'))):
-                df = read_csv(trial_data, convert_types=convert_types)
-                if process:
-                    df = process(df, **kwargs)
+                df = read_csv(trial_data, **kwargs)
                 yield config_file, df, config
         else:
             for trial_data in sorted(glob.glob(join(folder, '*.db.sqlite'))):
-                df = read_sql(trial_data, convert_types=convert_types,
-                              keys=keys)
-                if process:
-                    df = process(df, **kwargs)
+                df = read_sql(trial_data, **kwargs)
                 yield config_file, df, config
 
 
+def read_sql(db, *args, **kwargs):
+    h = history.History(db, backup=False)
+    df = h.read_sql(*args, **kwargs)
+    return df
+
+
 def read_csv(filename, keys=None, convert_types=False, **kwargs):
     '''
     Read a CSV in canonical form: ::
@@ -49,18 +51,7 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs):
         df = convert_types_slow(df)
     if keys:
         df = df[df['key'].isin(keys)]
-    return df
-
-
-def read_sql(filename, keys=None, convert_types=False, limit=-1):
-    condition = ''
-    if keys:
-        k = map(lambda x: "\'{}\'".format(x), keys)
-        condition = 'where key in ({})'.format(','.join(k))
-    query = 'select * from history {} limit {}'.format(condition, limit)
-    df = pd.read_sql_query(query, 'sqlite:///{}'.format(filename))
-    if convert_types:
-        df = convert_types_slow(df)
+    df = process_one(df)
     return df
 
 
@@ -108,8 +99,9 @@ def get_types(df):
     return {k:v[0] for k,v in dtypes.iteritems()}
 
 
-def process_one(df, *keys, columns=['key'], values='value',
-                index=['t_step', 'agent_id'], aggfunc='first', **kwargs):
+def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
+                fill=True, index=['t_step',],
+                aggfunc='first', **kwargs):
     '''
     Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
     a dataframe with a column per key
@@ -119,35 +111,29 @@ def process_one(df, *keys, columns=['key'], values='value',
     if keys:
         df = df[df['key'].isin(keys)]
 
-    dtypes = get_types(df)
-
     df = df.pivot_table(values=values, index=index, columns=columns,
                         aggfunc=aggfunc, **kwargs)
-    df = df.fillna(0).astype(dtypes)
+    if fill:
+        df = fillna(df)
     return df
 
 
-def get_count_processed(df, *keys):
-    if keys:
-        df = df[list(keys)]
-    # p = df.groupby(level=0).apply(pd.Series.value_counts)
-    p = df.unstack().apply(pd.Series.value_counts, axis=1)
-    return p
-
-
 def get_count(df, *keys):
     if keys:
-        df = df[df['key'].isin(keys)]
-    p = df.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)
-    return p
+        df = df[list(keys)]
+    counts = pd.DataFrame()
+    for key in df.columns.levels[0]:
+        g = df[key].apply(pd.Series.value_counts, axis=1).fillna(0)
+        for value, series in g.iteritems():
+            counts[key, value] = series
+    counts.columns = pd.MultiIndex.from_tuples(counts.columns)
+    return counts
 
 
 def get_value(df, *keys, aggfunc='sum'):
     if keys:
-        df = df[df['key'].isin(keys)]
-    p = process_one(df, *keys)
-    p = p.groupby(level='t_step').agg(aggfunc)
-    return p
+        df = df[list(keys)]
+    return df.groupby(axis=1, level=0).agg(aggfunc, axis=1)
 
 
 def plot_all(*args, **kwargs):
@@ -175,4 +161,6 @@ def group_trials(trials, aggfunc=['mean', 'min', 'max', 'std']):
     return pd.concat(trials).groupby(level=0).agg(aggfunc).reorder_levels([2, 0,1] ,axis=1)
 
 
-
+def fillna(df):
+    new_df = df.ffill(axis=0)
+    return new_df
diff --git a/soil/environment.py b/soil/environment.py
index 2809cb9..de2f8b1 100644
--- a/soil/environment.py
+++ b/soil/environment.py
@@ -5,16 +5,26 @@ import csv
 import random
 import simpy
 import tempfile
+import pandas as pd
 from copy import deepcopy
 from networkx.readwrite import json_graph
 
 import networkx as nx
 import nxsim
 
-from . import utils, agents
+from . import utils, agents, analysis, history
 
 
 class SoilEnvironment(nxsim.NetworkEnvironment):
+    """
+    The environment is key in a simulation. It contains the network topology,
+    a reference to network and environment agents, as well as the environment
+    params, which are used as shared state between agents.
+
+    The environment parameters and the state of every agent can be accessed
+    both by using the environment as a dictionary or with the environment's 
+    :meth:`soil.environment.SoilEnvironment.get` method.
+    """
 
     def __init__(self, name=None,
                  network_agents=None,
@@ -38,19 +48,21 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         self._env_agents = {}
         self.dry_run = dry_run
         self.interval = interval
+        self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
+        self.get_path()
+        self._history = history.History(name=self.name if not dry_run else None,
+                                        dir_path=self.dir_path)
         # Add environment agents first, so their events get
         # executed before network agents
-        self['SEED'] = seed or time.time()
-        random.seed(self['SEED'])
-        self.process(self.save_state())
         self.environment_agents = environment_agents or []
         self.network_agents = network_agents or []
-        self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
         if self.dry_run:
             self._db_path = ":memory:"
         else:
             self._db_path = os.path.join(self.get_path(), '{}.db.sqlite'.format(self.name))
         self.create_db(self._db_path)
+        self['SEED'] = seed or time.time()
+        random.seed(self['SEED'])
 
     def create_db(self, db_path=None):
         db_path = db_path or self._db_path
@@ -95,10 +107,8 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         if not network_agents:
             return
         for ix in self.G.nodes():
-            i = ix
-            node = self.G.node[i]
             agent, state = agents._agent_from_distribution(network_agents)
-            self.set_agent(i, agent_type=agent, state=state)
+            self.set_agent(ix, agent_type=agent, state=state)
 
     def set_agent(self, agent_id, agent_type, state=None):
         node = self.G.nodes[agent_id]
@@ -125,16 +135,21 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         return self.G.add_edge(agent1, agent2)
 
     def run(self, *args, **kwargs):
+        self._save_state()
         super().run(*args, **kwargs)
+        self._history.flush_cache()
 
     def _save_state(self, now=None):
         # for agent in self.agents:
         #     agent.save_state()
         utils.logger.debug('Saving state @{}'.format(self.now))
-        with self._db:
-            self._db.executemany("insert into history(agent_id, t_step, key, value, value_type) values (?, ?, ?, ?, ?)", self.state_to_tuples(now=now))
+        self._history.save_records(self.state_to_tuples(now=now))
 
     def save_state(self):
+        '''
+        :DEPRECATED:
+        Periodically save the state of the environment and the agents.
+        '''
         self._save_state()
         while self.peek() != simpy.core.Infinity:
             delay = max(self.peek() - self.now, self.interval)
@@ -149,64 +164,44 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
 
     def __getitem__(self, key):
         if isinstance(key, tuple):
-            values = [("agent_id", key[0]),
-                      ("t_step", key[1]),
-                      ("key", key[2]),
-                      ("value", None),
-                      ("value_type", None)]
-            fields = list(k for k, v in values if v is None)
-            conditions = " and ".join("{}='{}'".format(k, v) for k, v in values if v is not None)
-
-            query = """SELECT {fields} from history""".format(fields=",".join(fields))
-            if conditions:
-                query = """{query} where {conditions}""".format(query=query,
-                                                                conditions=conditions)
-            with self._db:
-                rows = self._db.execute(query).fetchall()
-
-            utils.logger.debug(rows)
-            results = self.rows_to_dict(rows)
-            return results
+            self._history.flush_cache()
+            return self._history[key]
 
         return self.environment_params[key]
 
-    def rows_to_dict(self, rows):
-        if len(rows) < 1:
-            return None
-
-        level = len(rows[0])-2
-
-        if level == 0:
-            if len(rows) != 1:
-                raise ValueError('Cannot convert {} to dictionaries'.format(rows))
-            value, value_type = rows[0]
-            return utils.convert(value, value_type)
-
-        results = {}
-        for row in rows:
-            item = results
-            for i in range(level-1):
-                key = row[i]
-                if key not in item:
-                    item[key] = {}
-                item = item[key]
-            key, value, value_type = row[level-1:]
-            item[key] = utils.convert(value, value_type)
-        return results
-
     def __setitem__(self, key, value):
+        if isinstance(key, tuple):
+            k = history.Key(*key)
+            self._history.save_record(*k,
+                                      value=value)
+            return
         self.environment_params[key] = value
+        self._history.save_record(agent_id='env',
+                                  t_step=self.now,
+                                  key=key,
+                                  value=value)
 
     def __contains__(self, key):
         return key in self.environment_params
 
     def get(self, key, default=None):
+        '''
+        Get the value of an environment attribute in a
+        given point in the simulation (history).
+        If key is an attribute name, this method returns
+        the current value.
+        To get values at other times, use a
+        :meth: `soil.history.Key` tuple.
+        '''
         return self[key] if key in self else default
 
     def get_path(self, dir_path=None):
         dir_path = dir_path or self.dir_path
         if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
+            try:
+                os.makedirs(dir_path)
+            except FileExistsError:
+                pass
         return dir_path
 
     def get_agent(self, agent_id):
@@ -255,17 +250,19 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         if now is None:
             now = self.now
         for k, v in self.environment_params.items():
-            v, v_t = utils.repr(v)
-            yield 'env', now, k, v, v_t
+            yield history.Record(agent_id='env',
+                                 t_step=now,
+                                 key=k,
+                                 value=v)
         for agent in self.agents:
             for k, v in agent.state.items():
-                v, v_t = utils.repr(v)
-                yield agent.id, now, k, v, v_t
+                yield history.Record(agent_id=agent.id,
+                                     t_step=now,
+                                     key=k,
+                                     value=v)
 
     def history_to_tuples(self):
-        with self._db:
-            res = self._db.execute("select agent_id, t_step, key, value, value_type from history ").fetchall()
-        yield from res
+        return self._history.to_tuples()
 
     def history_to_graph(self):
         G = nx.Graph(self.G)
@@ -317,14 +314,10 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
     def __getstate__(self):
         state = self.__dict__.copy()
         state['G'] = json_graph.node_link_data(self.G)
-        state['network_agents'] = agents.serialize_distribution(self.network_agents)
+        state['network_agents'] = agents._serialize_distribution(self.network_agents)
         state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
                                                                  to_string=True)
         del state['_queue']
-        import inspect
-        for k, v in state.items():
-            if inspect.isgeneratorfunction(v):
-                print(k, v, type(v))
         return state
 
     def __setstate__(self, state):
diff --git a/soil/history.py b/soil/history.py
new file mode 100644
index 0000000..73eed13
--- /dev/null
+++ b/soil/history.py
@@ -0,0 +1,231 @@
+import time
+import os
+import pandas as pd
+import sqlite3
+import copy
+from collections import UserDict, Iterable, namedtuple
+
+from . import utils
+
+
+class History:
+    """
+    Store and retrieve values from a sqlite database.
+    """
+
+    def __init__(self, db_path=None, name=None, dir_path=None, backup=True):
+        if db_path is None and name:
+            db_path = os.path.join(dir_path or os.getcwd(), '{}.db.sqlite'.format(name))
+
+        if db_path is None:
+            db_path = ":memory:"
+        else:
+            if backup and os.path.exists(db_path):
+                newname = db_path.replace('db.sqlite', 'backup{}.sqlite'.format(time.time()))
+                os.rename(db_path, newname)
+        self._db_path = db_path
+        if isinstance(db_path, str):
+            self._db = sqlite3.connect(db_path)
+        else:
+            self._db = db_path
+
+        with self._db:
+            self._db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text text)''')
+            self._db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
+            self._db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
+        self._dtypes = {}
+        self._tups = []
+
+    def conversors(self, key):
+        """Get the serializer and deserializer for a given key."""
+        if key not in self._dtypes:
+            self.read_types()
+        return self._dtypes[key]
+
+    @property
+    def dtypes(self):
+        return {k:v[0] for k, v in self._dtypes.items()}
+
+    def save_tuples(self, tuples):
+        self.save_records(Record(*tup) for tup in tuples)
+
+    def save_records(self, records):
+        with self._db:
+            for rec in records:
+                if not isinstance(rec, Record):
+                    rec = Record(*rec)
+                if rec.key not in self._dtypes:
+                    name = utils.name(rec.value)
+                    serializer = utils.serializer(name)
+                    deserializer = utils.deserializer(name)
+                    self._dtypes[rec.key] = (name, serializer, deserializer)
+                    self._db.execute("replace into value_types (key, value_type) values (?, ?)", (rec.key, name))
+                self._db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
+
+    def save_record(self, *args, **kwargs):
+        self._tups.append(Record(*args, **kwargs))
+        if len(self._tups) > 100:
+            self.flush_cache()
+
+    def flush_cache(self):
+        '''
+        Use a cache to save state changes to avoid opening a session for every change.
+        The cache will be flushed at the end of the simulation, and when history is accessed.
+        '''
+        self.save_records(self._tups)
+        self._tups = list()
+
+    def to_tuples(self):
+            self.flush_cache()
+            with self._db:
+                res = self._db.execute("select agent_id, t_step, key, value from history ").fetchall()
+            for r in res:
+                agent_id, t_step, key, value = r
+                _, _ , des = self.conversors(key)
+                yield agent_id, t_step, key, des(value)
+
+    def read_types(self):
+            with self._db:
+                res = self._db.execute("select key, value_type from value_types ").fetchall()
+            for k, v in res:
+                serializer = utils.serializer(v)
+                deserializer = utils.deserializer(v)
+                self._dtypes[k] = (v, serializer, deserializer)
+
+    def __getitem__(self, key):
+        key = Key(*key)
+        agent_ids = [key.agent_id] if key.agent_id is not None else []
+        t_steps = [key.t_step] if key.t_step is not None else []
+        keys = [key.key] if key.key is not None else []
+
+        df = self.read_sql(agent_ids=agent_ids,
+                           t_steps=t_steps,
+                           keys=keys)
+        r = Records(df, filter=key, dtypes=self._dtypes)
+        return r.value()
+
+
+
+    def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
+
+        self.read_types()
+
+        def escape_and_join(v):
+            if v is None:
+                return
+            return ",".join(map(lambda x: "\'{}\'".format(x), v))
+
+        filters = [("key in ({})".format(escape_and_join(keys)), keys),
+                   ("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids)
+        ]
+        filters = list(k[0] for k in filters if k[1])
+
+        last_df = None
+        if t_steps:
+            # Look for the last value before the minimum step in the query
+            min_step = min(t_steps)
+            last_filters = ['t_step < {}'.format(min_step),]
+            last_filters = last_filters + filters
+            condition = ' and '.join(last_filters)
+
+            last_query = '''
+            select h1.*
+            from history h1
+            inner join (
+            select agent_id, key, max(t_step) as t_step
+            from history
+            where {condition}
+            group by agent_id, key
+            ) h2
+            on h1.agent_id = h2.agent_id  and
+               h1.key      = h2.key       and
+               h1.t_step   = h2.t_step
+            '''.format(condition=condition)
+            last_df = pd.read_sql_query(last_query, self._db)
+
+            filters.append("t_step >= '{}' and t_step <= '{}'".format(min_step, max(t_steps)))
+
+        condition = ''
+        if filters:
+            condition = 'where {} '.format(' and '.join(filters))
+        query = 'select * from history {} limit {}'.format(condition, limit)
+        df = pd.read_sql_query(query, self._db)
+        if last_df is not None:
+            df = pd.concat([df, last_df])
+
+        df_p = df.pivot_table(values='value', index=['t_step'],
+                              columns=['key', 'agent_id'],
+                              aggfunc='first')
+
+        for k, v in self._dtypes.items():
+            if k in df_p:
+                dtype, _, deserial = v
+                df_p[k] = df_p[k].fillna(method='ffill').fillna(deserial()).astype(dtype)
+        if t_steps:
+            df_p = df_p.reindex(t_steps, method='ffill')
+        return df_p.ffill()
+
+
+class Records():
+
+    def __init__(self, df, filter=None, dtypes=None):
+        if not filter:
+            filter = Key(agent_id=None,
+                         t_step=None,
+                         key=None)
+        self._df = df
+        self._filter = filter
+        self.dtypes = dtypes or {}
+        super().__init__()
+
+    def mask(self, tup):
+        res = ()
+        for i, k in zip(tup[:-1], self._filter):
+            if k is None:
+                res = res + (i,)
+        res = res + (tup[-1],)
+        return res
+
+    def filter(self, newKey):
+        f = list(self._filter)
+        for ix, i in enumerate(f):
+            if i is None:
+                f[ix] = newKey
+        self._filter = Key(*f)
+
+    @property
+    def resolved(self):
+        return sum(1 for i in self._filter if i is not None) == 3
+
+    def __iter__(self):
+        for column, series in self._df.iteritems():
+            key, agent_id = column
+            for t_step, value in series.iteritems():
+                r = Record(t_step=t_step,
+                           agent_id=agent_id,
+                           key=key,
+                           value=value)
+                yield self.mask(r)
+
+    def value(self):
+        if self.resolved:
+            f = self._filter
+            try:
+                i = self._df[f.key][str(f.agent_id)]
+                ix = i.index.get_loc(f.t_step, method='ffill')
+                return i.iloc[ix]
+            except KeyError:
+                return self.dtypes[f.key][2]()
+        return self
+
+    def __getitem__(self, k):
+        n = copy.copy(self)
+        n.filter(k)
+        return n.value()
+
+    def __len__(self):
+        return len(self._df)
+
+
+Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
+Record = namedtuple('Record', 'agent_id t_step key value')
diff --git a/soil/simulation.py b/soil/simulation.py
index f53540f..da5dbdf 100644
--- a/soil/simulation.py
+++ b/soil/simulation.py
@@ -20,7 +20,7 @@ class SoilSimulation(NetworkSimulation):
     """
     Subclass of nsim.NetworkSimulation with three main differences:
         1) agent type can be specified by name or by class.
-        2) instead of just one type, an network_agents can be used.
+        2) instead of just one type, a network agents distribution can be used.
            The distribution specifies the weight (or probability) of each
            agent type in the topology. This is an example distribution: ::
 
@@ -95,16 +95,16 @@ class SoilSimulation(NetworkSimulation):
     def run_simulation_gen(self, *args, parallel=False, dry_run=False,
                            **kwargs):
         p = Pool()
-        with utils.timer('simulation'):
+        with utils.timer('simulation {}'.format(self.name)):
             if parallel:
-                func = partial(self.run_trial, dry_run=dry_run,
+                func = partial(self.run_trial, dry_run=dry_run or self.dry_run,
                                return_env=not parallel, **kwargs)
                 for i in p.imap_unordered(func, range(self.num_trials)):
                     yield i
             else:
                 for i in range(self.num_trials):
-                    yield self.run_trial(i, dry_run=dry_run, **kwargs)
-            if not dry_run or self.dry_run:
+                    yield self.run_trial(i, dry_run=dry_run or self.dry_run, **kwargs)
+            if not (dry_run or self.dry_run):
                 logger.info('Dumping results to {}'.format(self.dir_path))
                 self.dump_pickle(self.dir_path)
                 self.dump_yaml(self.dir_path)
@@ -192,7 +192,7 @@ class SoilSimulation(NetworkSimulation):
         return state
 
 
-def from_config(config, G=None):
+def from_config(config):
     config = list(utils.load_config(config))
     if len(config) > 1:
         raise AttributeError('Provide only one configuration')
@@ -201,9 +201,10 @@ def from_config(config, G=None):
     return sim
 
 
-def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False,  **kwargs):
+def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False,  **kwargs):
     for config_def in configs:
-        for config, cpath in utils.load_config(config_def):
+        # logger.info("Found {} config(s)".format(len(ls)))
+        for config, _ in utils.load_config(config_def):
             name = config.get('name', 'unnamed')
             logger.info("Using config(s): {name}".format(name=name))
 
@@ -215,4 +216,4 @@ def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=Fa
             dir_path = os.path.join(results_dir, sim_folder)
             sim = SoilSimulation(dir_path=dir_path, dump=dump, **config)
             logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
-            results = sim.run_simulation(**kwargs)
+            sim.run_simulation(**kwargs)
diff --git a/soil/utils.py b/soil/utils.py
index 147d0ca..c6dd75e 100644
--- a/soil/utils.py
+++ b/soil/utils.py
@@ -1,6 +1,7 @@
 import os
 import yaml
 import logging
+import importlib
 from time import time
 from glob import glob
 from random import random
@@ -72,13 +73,22 @@ def timer(name='task', pre="", function=logger.info, to_object=None):
 
 
 def repr(v):
-    if isinstance(v, bool):
-        v = "true" if v else ""
-        return v, bool.__name__
-    return v, type(v).__name__
+    func = serializer(v)
+    tname = name(v)
+    return func(v), tname
 
-def convert(value, type_):
-    import importlib
+
+def name(v):
+    return type(v).__name__
+
+
+def serializer(type_):
+    if type_ == 'bool':
+        return lambda x:  "true" if x else ""
+    return lambda x: x
+
+
+def deserializer(type_):
     try:
         # Check if it's a builtin type
         module = importlib.import_module('builtins')
@@ -88,4 +98,8 @@ def convert(value, type_):
         module, type_ = type_.rsplit(".", 1)
         module = importlib.import_module(module)
         cls = getattr(module, type_)
-    return cls(value)
+    return cls
+
+
+def convert(value, type_):
+    return deserializer(type_)(value)
diff --git a/tests/test.csv b/tests/test.csv
new file mode 100644
index 0000000..8f4a925
--- /dev/null
+++ b/tests/test.csv
@@ -0,0 +1,16 @@
+agent_id,t_step,key,value,value_type
+a0,0,hello,w,str
+a0,1,hello,o,str
+a0,2,hello,r,str
+a0,3,hello,l,str
+a0,4,hello,d,str
+a0,5,hello,!,str
+env,1,started,,bool
+env,2,started,True,bool
+env,7,started,,bool
+a0,0,hello,w,str
+a0,1,hello,o,str
+a0,2,hello,r,str
+a0,3,hello,l,str
+a0,4,hello,d,str
+a0,5,hello,!,str
diff --git a/tests/test_analysis.py b/tests/test_analysis.py
new file mode 100644
index 0000000..a5b1e94
--- /dev/null
+++ b/tests/test_analysis.py
@@ -0,0 +1,90 @@
+from unittest import TestCase
+
+import os
+import pandas as pd
+import yaml
+from functools import partial
+
+from os.path import join
+from soil import simulation, analysis, agents
+
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+
+
+class Ping(agents.FSM):
+
+    defaults = {
+        'count': 0,
+    }
+
+    @agents.default_state
+    @agents.state
+    def even(self):
+        self['count'] += 1
+        return self.odd
+
+    @agents.state
+    def odd(self):
+        self['count'] += 1
+        return self.even
+
+
+class TestAnalysis(TestCase):
+
+    # Code to generate a simple sqlite history
+    def setUp(self):
+        """
+        The initial states should be applied to the agent and the
+        agent should be able to update its state."""
+        config = {
+            'name': 'analysis',
+            'dry_run': True,
+            'seed': 'seed',
+            'network_params': {
+                'generator': 'complete_graph',
+                'n': 2
+            },
+            'agent_type': Ping,
+            'states': [{'interval': 1}, {'interval': 2}],
+            'max_time': 30,
+            'num_trials': 1,
+            'environment_params': {
+            }
+        }
+        s = simulation.from_config(config)
+        self.env = s.run_simulation()[0]
+
+    def test_saved(self):
+        env = self.env
+        assert env.get_agent(0)['count', 0] == 1
+        assert env.get_agent(0)['count', 29] == 30
+        assert env.get_agent(1)['count', 0] == 1
+        assert env.get_agent(1)['count', 29] == 15
+        assert env['env', 29, None]['SEED'] == env['env', 29, 'SEED']
+
+    def test_count(self):
+        env = self.env
+        df = analysis.read_sql(env._history._db)
+        res = analysis.get_count(df, 'SEED', 'id')
+        assert res['SEED']['seedanalysis_trial_0'].iloc[0] == 1
+        assert res['SEED']['seedanalysis_trial_0'].iloc[-1] == 1
+        assert res['id']['odd'].iloc[0] == 2
+        assert res['id']['even'].iloc[0] == 0
+        assert res['id']['odd'].iloc[-1] == 1
+        assert res['id']['even'].iloc[-1] == 1
+
+    def test_value(self):
+        env = self.env
+        df = analysis.read_sql(env._history._db)
+        res_sum = analysis.get_value(df, 'count')
+
+        assert res_sum['count'].iloc[0] == 2
+
+        import numpy as np
+        res_mean = analysis.get_value(df, 'count', aggfunc=np.mean)
+        assert res_mean['count'].iloc[0] == 1
+
+        res_total = analysis.get_value(df)
+
+        res_total['SEED'].iloc[0] == 'seedanalysis_trial_0'
diff --git a/tests/test_history.py b/tests/test_history.py
new file mode 100644
index 0000000..51d4e3e
--- /dev/null
+++ b/tests/test_history.py
@@ -0,0 +1,90 @@
+from unittest import TestCase
+
+import os
+import pandas as pd
+
+from soil import history, analysis
+
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+
+
+class TestHistory(TestCase):
+
+    def test_history(self):
+        """
+        """
+        tuples = (
+            ('a_0', 0, 'id', 'h',  ),
+            ('a_0', 1, 'id', 'e',  ),
+            ('a_0', 2, 'id', 'l',  ),
+            ('a_0', 3, 'id', 'l',  ),
+            ('a_0', 4, 'id', 'o',  ),
+            ('a_1', 0, 'id', 'v',  ),
+            ('a_1', 1, 'id', 'a',  ),
+            ('a_1', 2, 'id', 'l',  ),
+            ('a_1', 3, 'id', 'u',  ),
+            ('a_1', 4, 'id', 'e',  ),
+            ('env', 1, 'prob', 1),
+            ('env', 3, 'prob', 2),
+            ('env', 5, 'prob', 3),
+            ('a_2', 7, 'finished', True),
+        )
+        h = history.History()
+        h.save_tuples(tuples)
+        # assert h['env', 0, 'prob'] == 0
+        for i in range(1, 7):
+            assert h['env', i, 'prob'] == ((i-1)//2)+1
+
+
+        for i, k in zip(range(5), 'hello'):
+            assert h['a_0', i, 'id'] == k
+        for record, value in zip(h['a_0', None, 'id'], 'hello'):
+            t_step, val = record
+            assert val == value
+
+        for i, k in zip(range(5), 'value'):
+            assert h['a_1', i, 'id'] == k
+        for i in range(5, 8):
+            assert h['a_1', i, 'id'] == 'e'
+        for i in range(7):
+            assert h['a_2', i, 'finished'] == False
+        assert h['a_2', 7, 'finished']
+
+    def test_history_gen(self):
+        """
+        """
+        tuples = (
+            ('a_1', 0, 'id', 'v',  ),
+            ('a_1', 1, 'id', 'a',  ),
+            ('a_1', 2, 'id', 'l',  ),
+            ('a_1', 3, 'id', 'u',  ),
+            ('a_1', 4, 'id', 'e',  ),
+            ('env', 1, 'prob', 1),
+            ('env', 2, 'prob', 2),
+            ('env', 3, 'prob', 3),
+            ('a_2', 7, 'finished', True),
+        )
+        h = history.History()
+        h.save_tuples(tuples)
+        for t_step, key, value in h['env', None, None]:
+            assert t_step == value
+            assert key == 'prob'
+
+        records = list(h[None, 7, None])
+        assert len(records) == 3
+        for i in records:
+            agent_id, key, value = i
+            if agent_id == 'a_1':
+                assert key == 'id'
+                assert value == 'e'
+            elif agent_id == 'a_2':
+                assert key == 'finished'
+                assert value == True
+            else:
+                assert key == 'prob'
+                assert value == 3
+
+
+        records = h['a_1', 7, None]
+        assert records['id'] == 'e'
diff --git a/tests/test_main.py b/tests/test_main.py
index 6b22f8e..a5d39c2 100644
--- a/tests/test_main.py
+++ b/tests/test_main.py
@@ -22,6 +22,7 @@ class TestMain(TestCase):
         Raise an exception otherwise.
         """
         config = {
+            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             }
@@ -31,6 +32,7 @@ class TestMain(TestCase):
         assert len(G) == 2
         with self.assertRaises(AttributeError):
             config = {
+            'dry_run': True,
                 'network_params': {
                     'path': join(ROOT, 'unknown.extension')
                 }
@@ -44,6 +46,7 @@ class TestMain(TestCase):
         should be used to generate a network
         """
         config = {
+            'dry_run': True,
             'network_params': {
                 'generator': 'barabasi_albert_graph'
             }
@@ -58,6 +61,7 @@ class TestMain(TestCase):
     def test_empty_simulation(self):
         """A simulation with a base behaviour should do nothing"""
         config = {
+            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -74,11 +78,12 @@ class TestMain(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'CounterAgent',
+            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
             'agent_type': 'CounterModel',
-            'states': [{'neighbors': 10}, {'total': 12}],
+            'states': [{'times': 10}, {'times': 20}],
             'max_time': 2,
             'num_trials': 1,
             'environment_params': {
@@ -86,10 +91,10 @@ class TestMain(TestCase):
         }
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
-        assert env.get_agent(0)['neighbors', 0] == 10
-        assert env.get_agent(0)['neighbors', 1] == 1
-        assert env.get_agent(1)['total', 0] == 12
-        assert env.get_agent(1)['neighbors', 1] == 1
+        assert env.get_agent(0)['times', 0] == 11
+        assert env.get_agent(0)['times', 1] == 12
+        assert env.get_agent(1)['times', 0] == 21
+        assert env.get_agent(1)['times', 1] == 22
 
     def test_counter_agent_history(self):
         """
@@ -97,6 +102,7 @@ class TestMain(TestCase):
         """
         config = {
             'name': 'CounterAgent',
+            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -114,11 +120,10 @@ class TestMain(TestCase):
         env = s.run_simulation(dry_run=True)[0]
         for agent in env.network_agents:
             last = 0
-            assert len(agent[None, None]) == 11
-            for step, total in agent['total', None].items():
-                if step > 0:
-                    assert total == last + 2
-                    last = total
+            assert len(agent[None, None]) == 10
+            for step, total in sorted(agent['total', None]):
+                assert total == last + 2
+                last = total
 
     def test_custom_agent(self):
         """Allow for search of neighbors with a certain state_id"""
@@ -127,6 +132,7 @@ class TestMain(TestCase):
                 self.state['neighbors'] = self.count_agents(state_id=0,
                                                             limit_neighbors=True)
         config = {
+            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -150,7 +156,8 @@ class TestMain(TestCase):
         config['network_params']['path'] = join(EXAMPLES,
                                                 config['network_params']['path'])
         s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
+        s.dry_run = True
+        env = s.run_simulation()[0]
         for a in env.network_agents:
             skill_level = a.state['skill_level']
             if a.id == 'Torvalds':
@@ -174,14 +181,15 @@ class TestMain(TestCase):
         with utils.timer('loading'):
             config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
             s = simulation.from_config(config)
+            s.dry_run = True
         with utils.timer('serializing'):
             serial = s.to_yaml()
         with utils.timer('recovering'):
             recovered = yaml.load(serial)
         with utils.timer('deleting'):
             del recovered['topology']
-            del recovered['dry_run']
             del recovered['load_module']
+            del recovered['dry_run']
         assert config == recovered
 
     def test_configuration_changes(self):
@@ -191,6 +199,7 @@ class TestMain(TestCase):
         """
         config = utils.load_file('examples/complete.yml')[0]
         s = simulation.from_config(config)
+        s.dry_run = True
         for i in range(5):
             s.run_simulation(dry_run=True)
             nconfig = s.to_dict()
@@ -206,17 +215,14 @@ class TestMain(TestCase):
         pass
 
     def test_row_conversion(self):
-        sim = simulation.SoilSimulation()
         env = environment.SoilEnvironment(dry_run=True)
         env['test'] = 'test_value'
-        env._save_state(now=0)
 
         res = list(env.history_to_tuples())
         assert len(res) == len(env.environment_params)
-        assert ('env', 0, 'test', 'test_value', 'str') in res
 
+        env._now = 1
         env['test'] = 'second_value'
-        env._save_state(now=1)
         res = list(env.history_to_tuples())
 
         assert env['env', 0, 'test' ] == 'test_value'