diff --git a/examples/sample_simulation_onesided.ipynb b/examples/sample_simulation_onesided.ipynb
new file mode 100644
index 0000000..92b9f43
--- /dev/null
+++ b/examples/sample_simulation_onesided.ipynb
@@ -0,0 +1,599 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc": "true"
+ },
+ "source": [
+ "# Table of Contents\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-02-12T22:52:48.190265Z",
+ "start_time": "2018-02-12T22:52:48.180428Z"
+ },
+ "init_cell": true,
+ "run_control": {
+ "frozen": false,
+ "read_only": false
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "import pyximport; pyximport.install(\n",
+ " setup_args={\"include_dirs\":np.get_include()},\n",
+ " reload_support=True)\n",
+ "from looplib import loopviz, looptools, simlef_onesided, simlef\n",
+ "import os, sys, glob, shelve, time\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-02-12T22:52:50.160493Z",
+ "start_time": "2018-02-12T22:52:50.148936Z"
+ },
+ "collapsed": true,
+ "init_cell": true,
+ "run_control": {
+ "frozen": false,
+ "read_only": false
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "from importlib import reload\n",
+ "reload(loopviz)\n",
+ "\n",
+ "import seaborn as sns\n",
+ "sns.set_style('white')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Single loop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-02-12T22:46:15.259908Z",
+ "start_time": "2018-02-12T22:46:15.183650Z"
+ },
+ "run_control": {
+ "frozen": false,
+ "read_only": false
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "b'proc' 10 14.42531723487518 300.0\n",
+ "b'proc' 20 29.17314452271834 300.0\n",
+ "b'proc' 30 42.35714889983879 300.0\n",
+ "b'proc' 40 58.698682882642885 300.0\n",
+ "b'proc' 50 83.69413743473946 300.0\n",
+ "b'proc' 60 98.5425529576151 300.0\n",
+ "b'proc' 70 110.9386937914856 300.0\n",
+ "b'proc' 80 125.38561281927699 300.0\n",
+ "b'proc' 90 142.39904709021866 300.0\n",
+ "b'proc' 100 160.15653925400454 300.0\n",
+ "b'proc' 110 174.81909704289907 300.0\n",
+ "b'proc' 120 187.57329988734992 300.0\n",
+ "b'proc' 130 204.1817992906797 300.0\n",
+ "b'proc' 140 220.48335416667496 300.0\n",
+ "b'proc' 150 241.45508377532553 300.0\n",
+ "b'proc' 160 257.5729812563208 300.0\n",
+ "b'proc' 170 280.5868988495545 300.0\n",
+ "b'proc' 180 300.13706833845833 300.0\n",
+ "b'proc' 190 316.43943597346436 300.0\n",
+ "b'proc' 200 335.44325649027036 300.0\n",
+ "b'proc' 210 357.6266668753823 300.0\n",
+ "b'proc' 220 377.33141785532865 300.0\n",
+ "b'proc' 230 393.7953217776738 300.0\n",
+ "b'proc' 240 413.2838234704751 300.0\n",
+ "b'proc' 250 504.64991628473337 300.0\n",
+ "b'proc' 260 519.5394595126398 300.0\n",
+ "b'proc' 270 536.3830424677202 300.0\n",
+ "b'proc' 280 559.8394817237349 300.0\n",
+ "b'proc' 290 579.5379699680042 300.0\n",
+ "b'proc' 300 604.8271515720439 300.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "p = {}\n",
+ "p['L'] = 100\n",
+ "p['N'] = 1\n",
+ "p['R_OFF'] = 1.0 / 100\n",
+ "p['R_EXTEND'] = float(1.0)\n",
+ "p['R_SHRINK'] = 0#float(.4)\n",
+ "p['R_SWITCH'] = 0.03\n",
+ "\n",
+ "p['T_MAX_LIFETIMES'] = 3.0\n",
+ "p['T_MAX'] = p['T_MAX_LIFETIMES'] / p['R_OFF']\n",
+ "p['N_SNAPSHOTS'] = 300\n",
+ "p['PROCESS_NAME'] = b'proc'\n",
+ "\n",
+ "l_sites, r_sites, leading_legs, ts = simlef_onesided.simulate(p)\n",
+ "#l_sites, r_sites, ts = simlef.simulate(p)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-02-12T22:46:16.026154Z",
+ "start_time": "2018-02-12T22:46:15.793953Z"
+ },
+ "run_control": {
+ "frozen": false,
+ "read_only": false
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 100.0)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
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+ "