diff --git a/notebook.ipynb b/notebook.ipynb
index dedf5a8877f9af46e8fcb87ff1627a8245401e8f..07ed0d214a781dccbf79030b202e43b466d07c4b 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -7,36 +7,13 @@
     "# X-Lap in Action"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import logging\n",
-    "logger = logging.getLogger()\n",
-    "logger.setLevel(logging.DEBUG)\n",
-    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
-    "import ipywidgets as widgets\n",
-    "from xlap.parse import evaluate, evaluate_side, parse_config\n",
-    "import xlap.analyse.jitter as jitter\n",
-    "from xlap.analyse.cdf import multi_cdf\n",
-    "from xlap.analyse.regress import linear as linear_regression\n",
-    "from xlap.analyse.trace import traces\n",
-    "from xlap.analyse.correlation import correlation, multi_correlation\n",
-    "from xlap.analyse.latency import analyse\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import copy\n",
-    "%matplotlib inline\n",
-    "colors = [\"#E69F00\", \"#009E73\", \"#56B4E9\",  \"#CC79A7\", \"#D55E00\"]"
+    "%matplotlib inline"
    ]
   },
   {
@@ -52,18 +29,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from xlap.parse import evaluate, parse_config\n",
     "config = parse_config()\n",
     "data_files = {\n",
     "    \"sender\": \"rtn2018/20180417_testbed/\",\n",
     "    \"receiver\": \"rtn2018/20180417_testbed/\"\n",
     "}\n",
-    "original1 = evaluate(data_files[\"sender\"] + \"sender-1000000.csv\", data_files[\"receiver\"] + \"receiver-1000000.csv\", config=config, kind=0)\n",
-    "original1.name = \"1GHz\"\n",
-    "original2 = evaluate(data_files[\"sender\"] + \"sender-2000000.csv\", data_files[\"receiver\"] + \"receiver-2000000.csv\", config=config, kind=0)\n",
-    "original2.name = \"2GHz\"\n",
-    "original3 = evaluate(data_files[\"sender\"] + \"sender-3000000.csv\", data_files[\"receiver\"] + \"receiver-3000000.csv\", config=config, kind=0)\n",
-    "original3.name = \"3GHz\"\n",
-    "dfs = [original1, original2, original3]"
+    "df_1GHz = evaluate(data_files[\"sender\"] + \"sender-1000000.csv\", data_files[\"receiver\"] + \"receiver-1000000.csv\", config=config, kind=0)\n",
+    "df_1GHz.name = \"1GHz\"\n",
+    "df_2GHz = evaluate(data_files[\"sender\"] + \"sender-2000000.csv\", data_files[\"receiver\"] + \"receiver-2000000.csv\", config=config, kind=0)\n",
+    "df_2GHz.name = \"2GHz\"\n",
+    "df_3GHz = evaluate(data_files[\"sender\"] + \"sender-3000000.csv\", data_files[\"receiver\"] + \"receiver-3000000.csv\", config=config, kind=0)\n",
+    "df_3GHz.name = \"3GHz\"\n",
+    "dfs = [df_1GHz, df_2GHz, df_3GHz]"
    ]
   },
   {
@@ -79,7 +57,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "traces(original1, config, global_xaxis=True)"
+    "from xlap.analyse.trace import traces\n",
+    "traces(df_1GHz, config, global_xaxis=True)"
    ]
   },
   {
@@ -95,11 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def multi_trace_jitter(dfs, config):\n",
-    "    for df in dfs:\n",
-    "        print(\"############################ {} ############################\".format(df.name))\n",
-    "        jitter.trace_jitter(df, config=config, threshold=200)\n",
-    "        \n",
+    "from xlap.analyse.jitter import multi_trace_jitter\n",
     "multi_trace_jitter(dfs, config)"
    ]
   },
@@ -116,14 +91,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from xlap.analyse.cdf import multi_cdf\n",
+    "from xlap.analyse.util import colors\n",
+    "import copy\n",
     "cfg = copy.deepcopy(config)\n",
-    "d = cfg[\"durations\"]\n",
-    "\n",
-    "l=(\"Decoding\",\"ReceiverIPC\",\"HandlePacket\", \"Feedback\", \"SenderIPC\",\"SenderEnqueued\",\"Enqueue\")\n",
-    "for e in l:\n",
-    "    if e in l:\n",
-    "        del d[e]\n",
-    "\n",
+    "list(map(cfg[\"durations\"].pop, (\"Decoding\",\n",
+    "                                \"ReceiverIPC\",\n",
+    "                                \"HandlePacket\", \n",
+    "                                \"Feedback\", \n",
+    "                                \"SenderIPC\",\n",
+    "                                \"SenderEnqueued\",\n",
+    "                                \"Enqueue\")))\n",
     "multi_cdf(dfs, cfg, colors=colors)"
    ]
   },
@@ -140,6 +118,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from xlap.analyse.correlation import multi_correlation\n",
     "multi_correlation(dfs, config, colors=colors, figsize=(3.0,2.0), cols=4)"
    ]
   },
@@ -156,7 +135,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d = analyse(original1, config)"
+    "from xlap.analyse.latency import analyse\n",
+    "d = analyse(df_1GHz, config)"
    ]
   },
   {
@@ -204,25 +184,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from scipy import stats\n",
-    "from xlap.analyse.util import extract_durations\n",
-    "import numpy as np\n",
-    "\n",
-    "def timing_behaviour(df1, df2, config, confidence=0.9):\n",
-    "    durations = [x + \"_D\" for x in extract_durations(config)]\n",
-    "    \n",
-    "    norm = lambda x: x / np.max(x)\n",
-    "    \n",
-    "    for duration in durations:\n",
-    "        rvs1 = norm(df1[duration])\n",
-    "        rvs2 = norm(df2[duration])\n",
-    "        stat, pvalue = stats.ks_2samp(rvs1, rvs2)\n",
-    "        result = \"CANNOT REJECT\"\n",
-    "        if pvalue < 1 - confidence:\n",
-    "            result = \"REJECT\"\n",
-    "        print(duration.ljust(20), \"{:.6f}\".format(pvalue), result, sep=\"\\t\\t\")\n",
-    "\n",
-    "timing_behaviour(original1, original2, config)"
+    "from xlap.analyse.timing import timing_behaviour\n",
+    "timing_behaviour(df_1GHz, df_2GHz, config)"
    ]
   },
   {
@@ -231,7 +194,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "timing_behaviour(original1, original3, config)"
+    "timing_behaviour(df_1GHz, df_3GHz, config)"
    ]
   },
   {
@@ -240,7 +203,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "timing_behaviour(original2, original3, config)"
+    "timing_behaviour(df_2GHz, df_3GHz, config)"
    ]
   }
  ],
diff --git a/xlap/analyse/jitter.py b/xlap/analyse/jitter.py
index 3e6d8ed3a8ff846d6a47142efc5a2348077cc542..ad5dba37c8067cfabc2ba2bb093ff44feb1fad67 100644
--- a/xlap/analyse/jitter.py
+++ b/xlap/analyse/jitter.py
@@ -38,10 +38,15 @@ def trace_jitter(data_frame, config=None, threshold=None, export=False, file_nam
     if threshold is None:
         threshold = get_outlier_threshold(data_frame["EndToEnd_D"].describe())
     df_no_outliers = data_frame[data_frame["EndToEnd_D"] <= threshold]
-    fig = plt.gcf()
+    ax, fig = box(df_no_outliers, (0, threshold), export, file_name, figsize)
     fig.set_size_inches(figsize[0], figsize[1])
-    box(df_no_outliers, (0, threshold), export, file_name, figsize)
     print("{} / {} are no outliers.".format(len(df_no_outliers), len(data_frame)))
     fig.canvas.set_window_title('Jitter Analysis')
     plt.show()
+    plt.close()
     return None
+
+def multi_trace_jitter(dfs, config):
+    for df in dfs:
+        print("############################ {} ############################".format(df.name))
+        trace_jitter(df, config=config, threshold=200)
diff --git a/xlap/analyse/timing.py b/xlap/analyse/timing.py
new file mode 100644
index 0000000000000000000000000000000000000000..4686e91a8f9735d81a320af5c4234c719d38ff59
--- /dev/null
+++ b/xlap/analyse/timing.py
@@ -0,0 +1,16 @@
+from scipy import stats
+from xlap.analyse.util import extract_durations
+import numpy as np
+
+def timing_behaviour(df1, df2, config, confidence=0.9):
+    durations = [x + "_D" for x in extract_durations(config)]
+
+    norm = lambda x: x / np.max(x)
+
+    for duration in durations:
+        rvs1 = norm(df1[duration])
+        rvs2 = norm(df2[duration])
+        stat, pvalue = stats.ks_2samp(rvs1, rvs2)
+        result = ("REJECT" if pvalue < 1 - confidence else
+                  "CANNOT REJECT")
+        print(duration.ljust(20), "{:.6f}".format(pvalue), result, sep="\t\t")
diff --git a/xlap/analyse/trace.py b/xlap/analyse/trace.py
index 66972352b252770675e5c7b26a2948123f8ad01c..aee64d940d376b80f101966e9f7399129acd9e04 100644
--- a/xlap/analyse/trace.py
+++ b/xlap/analyse/trace.py
@@ -92,6 +92,6 @@ def traces(df, config, figsize=(10, 4.5), global_xaxis=False):
     if global_xaxis:
         t_max = df["EndToEnd_D"].max()
 
-    @interact(seq_no=widgets.IntSlider(min=1, max=len(df), step=1, value=47))
+    @interact(seq_no=widgets.IntSlider(min=1, max=len(df), step=1, value=47,description="Seq. No."))
     def _f(seq_no):
         trace(df.iloc[seq_no], config, figsize=figsize, t_max=t_max)
diff --git a/xlap/analyse/util.py b/xlap/analyse/util.py
index edaa704ecced99d99f5d459f9b2ab9ad056417cb..8e924e934e7181043e0c9235461197be87569c25 100644
--- a/xlap/analyse/util.py
+++ b/xlap/analyse/util.py
@@ -2,6 +2,8 @@ import matplotlib.pyplot as plt
 import numpy as np
 import math
 
+colors = ["#E69F00", "#009E73", "#56B4E9",  "#CC79A7", "#D55E00"]
+
 def cdf(values, ax=None, grid=False, **kwargs):
     if ax is None:
         ax = plt
@@ -54,7 +56,7 @@ def box(data_frame, xlim=None, export=False, file_name=None, figsize=(8, 4.5)):
     plt.tight_layout()
     if export and file_name is not None:
         fig.savefig(file_name)
-
+    return ax, fig
 
 def describe_table(df):
     stats = df.describe()