Commit 116a01bd authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

More appealing API for use in Juypter.

parent d4a0d13b

notebook.ipynb

+32 −1
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# X-Lap in Action



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## Imports



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``` python

import logging

logger = logging.getLogger()

logger.setLevel(logging.DEBUG)

from ipywidgets import interact, interactive, fixed, interact_manual

import ipywidgets as widgets

from xlap.parse import evaluate, evaluate_side, parse_config

import xlap.analyse.jitter as jitter

from xlap.analyse.regress import linear as linear_regression

from xlap.analyse.trace import traces

from xlap.analyse.correlation import correlation

from xlap.analyse.latency import analyse

import pandas as pd

%matplotlib inline

```



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## Data Retrieval



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``` python

config = parse_config()

data_files = config["data_files"]

original = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)[:4094]

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

```



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## Traces



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``` python

traces(original, config)

```



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## Jitter Analysis



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``` python

df = jitter.prep(original, config=config)

jitter.trace_jitter(df, threshold=500)

```



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## Correlation



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``` python

correlation(df[df["EndToEnd_D"] < 500], config)

```



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## Latency Criticality



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``` python

d = analyse(original, config)

```



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### Correlations



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``` python

d.corr.sort_values(ascending=False)

```



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### Control Flow Graph



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``` python

d.cfg

```



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``` python

```

xlap/analyse/latency.py

+66 −57
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import pandas as pd

import matplotlib.pyplot as plt

from xlap.analyse.util import get_outlier_threshold, extract_durations, box

from scipy.stats.stats import pearsonr

import numpy as np

import sys

import graphviz





class LatencyAnalysis():

    def __init__(self, cfg=None, hdb=None):

        self.cfg = cfg



        correlations = []

        labels = []

        for x in hdb:

            correlations += [x["Correlation"]]

            labels += ["{} -> {}".format(x["Start"], x["End"])]



        corr = pd.Series(correlations, index=labels)



        self.corr = corr





def _get_thread_for_event(config, e):

    name = str(e)[:-2]

    try:
@@ -112,13 +126,13 @@ def _plot_controlflow_graph(df, hdb): 
    generate the control flow graph using dot

    """

    t_columns = [x for x in df.columns if x.endswith("_T")]

    d = graphviz.Digraph(filename="graph", format="pdf")

    graph = graphviz.Digraph(filename="graph", format="pdf")

    for event1 in df[t_columns]:

        d.node(str(event1)[:-2])

        graph.node(str(event1)[:-2])

    for edge in hdb:

        d.edge(edge["Start"][:-2], edge["End"][:-2])

    d.render()  # saves to graph.pdf in local folder

    return d

        graph.edge(edge["Start"][:-2], edge["End"][:-2])

    graph.render()  # saves to graph.pdf in local folder

    return graph





# Taken from: http://composition.al/blog/2015/11/29/a-better-way-to-add-labels-to-bar-charts-with-matplotlib/
@@ -147,14 +161,11 @@ def autolabel(rects, ax, labels): 
        ax.text(label_position, rect.get_y(), labels[i], ha=align, va='bottom', rotation=0, color=color)





def _plot_critical_regions(df, hdb):

def _plot_critical_regions(hdb):

    """

    plot regions, sorted by latency criticality

    """

    plt.rcParams["font.family"] = "serif"

    for region in sorted(hdb, key=lambda x: -x['Correlation']):

        print("%-10f %10s -> %10s" % (region['Correlation'], region['Start'], region['End']), file=sys.stderr)



    relevant = sorted([x for x in hdb if x['Correlation'] > 0], key=lambda x: -x['Correlation'], reverse=True)

    x = np.arange(len(relevant))
@@ -198,7 +209,5 @@ def _plot_critical_regions(df, hdb): 
                hdb += [{'Start': str(event1), 'End': str(event2), 'Correlation': correlation}]



    cfg = _plot_controlflow_graph(df, hdb)

        _plot_critical_regions(df, hdb)

        return {

            "cfg": cfg

        }
 
    _plot_critical_regions(hdb)

    return LatencyAnalysis(cfg=cfg, hdb=hdb)

xlap/command_line.py

+2 −1
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@@ -43,7 +43,8 @@ def main(): 
                print(output)

        elif command == "latency":

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

            latency.analyse(df_data, config)

            a = latency.analyse(df_data, config)

            print(a.corr.sort_values(ascending=False))

        else:

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