Commit cdb8a8c6 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

Improved README, example notebook. Added interaction for traces.

parent e9da367f

README.md

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# 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.

notebook.ipynb

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xlap/analyse/trace.py

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@@ -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

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@@ -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"]