Commit 8e9b5f48 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

Add minimal commandline interface.

parent 37fe812c
# 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:
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:
......@@ -11,7 +17,15 @@ def parse_config():
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))
jitter.analyse(df_data, config, "TraceJitter.pdf")
df_data = evaluate(data_files["sender"], data_files["receiver"],kind=0)
jitter.analyse(df_data, config, "TraceJitter.pdf")
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