Improved README, example notebook. Added interaction for traces.

README.md

 # X-Lap: A Systems Approach for Cross-Layer Profiling and Latency Analysis for Cyber-Physical Networks
 
-## Conventions
-
-* Stampnames:
-
-`_T`: Timestamp (us precision)
-`_C`: Clockstamp
-`_D`: Duration (us precision)
-
-* Reserved Names:
-
-* `Channel`
-* `Sender`
-* `Receiver`
-* `EndToEnd`
+## How to install X-Lap?
 
+Enter the project directory and type `python setup.py install` to install `xlap` on your system.
 
 ## How to use X-lap?
 
@@ -36,13 +24,69 @@ Define time- and cyclestamps:
 
 
 ### Step 2: Use the provided xlap analysis tools
-* Write your xlap.yml file as follows:
+* Write your `xlap.yml` file as follows:
 
-```yaml
-data_files: ads
-stamps: ads
-durations: asd
+```yml
+data_files:
+    sender: sender.csv
+    receiver: receiver.csv
+cycle_reference:
+    sender: StampA
+    receiver: SampB
+time_reference:
+    sender: StampC
+    receiver: StampE
+stamps:
+    StampA:
+        Source: receiver
+        Type: time
+    StampB:
+        Source: receiver
+        Type: time
+    StampC:
+        Source: receiver
+        Type: time
+    StampD:
+        Source: receiver
+        Type: cycle
+    StampE:
+        Source: receiver
+        Type: time
+durations:
+    DurationA:
+        Start: StampA
+        Stop: StampB
+        Source: sender
+    DurationB:
+        Start: StampC
+        Stop: StampD
+        Source: receiver
 ```
 
-* Run one of the following analyses on the data set:
-    * Jitter Analysis (`jitter`)
+* You can run a jitter analysis via `xlap jitter`. The parameter `-e` exports the diagrams as `pdf`.
+
+* The best way to interact with `xlap` is through a [Jupyter Python 3 Notebook](http://jupyter.org/). An example can be found in `notebook.ipynb`.
+
+* `xlap.analyse.trace.traces()` can be used to investigate individual packet traces.
+
+
+## Conventions
+
+* Suffixes (indicate type):
+
+  * `_T`: Timestamp (us precision)
+  * `_C`: Clockstamp
+  * `_D`: Duration (us precision)
+
+
+* Reserved Names (including suffixes as mentioned before):
+
+    * `Channel`
+    * `Sender`
+    * `SenderCycle`
+    * `Receiver`
+    * `ReceiverCycle`
+    * `EndToEnd`
+
+
+* Note that expressions with other suffixes such as `SenderQueueing_T` would be allowed.

xlap/analyse/trace.py

@@ -2,6 +2,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from xlap.analyse.util import extract_durations
+from ipywidgets import interact
+import ipywidgets as widgets
 
 
 def _create_line(config):
@@ -62,3 +64,11 @@ def trace(data_frame, config, export=False, file_name="TraceJitter.pdf"):
     if export:
         plt.savefig(file_name)
     plt.show()
+
+def traces(df, config):
+    """
+    Display a slider to select sequence numbers and the respective trace.
+    """
+    @interact(seq_no=widgets.IntSlider(min=1,max=len(df),step=1,value=47))
+    def _f(seq_no):
+        trace(df.ix[seq_no],config)

xlap/parse.py

@@ -73,7 +73,7 @@ def evaluate(sender_file, receiver_file, config, kind=0):
     # Generate Durations
     for name, duration in config["durations"].items():
         diff = df[duration["Stop"] + "_C"] - df[duration["Start"] + "_C"]
-        df[name + "Cycles"] = diff
+        df[name + "_C"] = diff
         df[name + "_D"] = diff * df[duration["Source"].capitalize() + "Cycle_D"]
 
     df["EndToEnd_D"] = df["Sender_D"] + df["Receiver_D"]