Add minimal commandline interface.

# X-Lap: A Systems Approach for Cross-Layer Profiling and Latency Analysis for Cyber-Physical Networks

## How to use X-lap?

### Step 0: Define your relevant timestamps across a packet trace.

Define time- and cyclestamps:

* Where data enters the sender app.

* Where data leaves the sender app.

* Where data enters the receiver app.

* Where data leaves the receiver app.

* Define additional cyclestamps wherever appropriate.

* Define interesting durations inside xlap.yml.

### Step 1: Instrument your code and generate traces

* Define a Sender and Receiver Application (send arbitrary data around).

* Initialize the xlap facility.

* Add stamping code where-ever appropriate.

* There are different mechanisms for adding timestamps.

### Step 2: Use the provided xlap analysis tools

* Write your xlap.yml file as follows:

```yaml

data_files: ads

stamps: ads

durations: asd

```

* Run one of the following analyses on the data set:

    * Jitter Analysis (`jitter`)

import argparse

import yaml

from xlap.parse import evaluate

import xlap.analyse.jitter as jitter

tasks = {

    "jitter": None

}

def parse_config():

    contents = None

    with open("xlap.yml") as f:

def main():

    config = parse_config()

    data_files = config["data_files"]

    df_data = evaluate(data_files["sender"], data_files["receiver"],kind=0)

    parser = argparse.ArgumentParser()

    parser.add_argument("tasks", metavar="T", type=str, nargs="+", help="analysis tasks to execute")

    args = parser.parse_args()

    for command in args.tasks:

        if command not in tasks.keys():

            print("{} is not a known analysis task.".format(command))

            continue

        df_data = evaluate(data_files["sender"], data_files["receiver"],kind=0)

        jitter.analyse(df_data, config, "TraceJitter.pdf")