Commit 123504b6 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

Add multi-correlation plots. Add CDF plots.

parent 5123d833

xlap/analyse/cdf.py

0 → 100644
+36 −0
Original line number Diff line number Diff line 
import math

import numpy as np

from .util import cdf, extract_durations

import matplotlib.pyplot as plt



def multi_cdf(dfs, config, export=False, file_name="CDF.pdf"):

    durations = [x + "_D" for x in extract_durations(config)]

    durations.remove("EndToEnd_D")



    cols = 3

    rows = int(math.ceil(len(durations) / cols))

    items = len(durations)

    fig, axes = plt.subplots(nrows=rows, ncols=cols)

    fig.set_size_inches(4 * cols, 4 * rows, forward=True)



    names = []

    for df in dfs:

        names.append(df.name)



    for idx, duration in enumerate(durations):

        if items > cols:

            ax = axes[idx // cols, idx % cols]

        else:

            ax = axes[idx]

        for df in dfs:

            cdf(df[duration], grid=True, ax=ax)



        ax.legend(names)

        ax.set_xlabel("{} [us]".format(duration))

        ax.set_ylabel("CDF")

    plt.subplots_adjust(wspace=0.3,hspace=0.3)



    if export and file_name is not None:

        fig.savefig(file_name)

    plt.tight_layout()

    plt.show()

xlap/analyse/correlation.py

+41 −12
Original line number Diff line number Diff line
@@ -3,25 +3,54 @@ import matplotlib.pyplot as plt 
from xlap.analyse.util import extract_durations





def correlation(data_frame, config, export=False, file_name="Correlation.pdf"):

def corr(df, duration, grid=False, ax=None, color="black", marker="+"):

    df.plot.scatter(ax=ax,

                    y="EndToEnd_D",

                    x=duration,

                    grid=grid,

                    loglog=True,

                    marker=marker,

                    color=color)



def corr_multi(dfs, duration, **kwargs):

    names = []

    for df in dfs:

        names.append(df.name)



    colors = ["green","blue","orange"]

    markers = ["v", "^", ">", "<"]

    for idf, df in enumerate(dfs):

        corr(df, duration, color=colors[idf % len(colors)],

             marker=markers[idf % len(markers)], **kwargs)

    if len(names) > 1:

        kwargs["ax"].legend(names)

    kwargs["ax"].set_xlabel("{} [us]".format(duration))





def multi_correlation(dfs, config):

    durations = [x + "_D" for x in extract_durations(config)]

    durations.remove("EndToEnd_D")



    cols = 4

    cols = 2

    rows = int(math.ceil(len(durations) / cols))

    items = len(durations)

    fig, axes = plt.subplots(nrows=rows, ncols=cols)

    fig.set_size_inches(4 * cols, 3.5 * rows, forward=True)

    fig.set_size_inches(5.5 * cols, 5.5 * rows, forward=True)





    for idx, duration in enumerate(durations):

        if items > cols:

            ax = axes[idx // cols, idx % cols]

        else:

            ax = axes[idx]

        corr_multi(dfs, duration, grid=True, ax=ax)



    i = 0

    for duration in durations:

        ax = data_frame.plot.scatter(ax=axes[i // cols, i % cols], y="EndToEnd_D", x=duration, grid=True, marker="+",

                                     color="black")

        ax.set_ylabel("EndToEnd [us]")

        ax.margins(0.1, 0.1)

        ax.set_xlabel("{} [us]".format(duration.replace("_D", "")))

        i += 1

    plt.subplots_adjust(wspace=0.3,hspace=0.3)



def correlation(df, config, export=False, file_name="Correlation.pdf"):

    res = multi_correlation([df], config)

    if export and file_name is not None:

        fig.savefig(file_name)

    plt.tight_layout()

    plt.show()

    return res

xlap/analyse/jitter.py

+3 −1
Original line number Diff line number Diff line
@@ -43,4 +43,6 @@ def trace_jitter(data_frame, threshold=None, export=False, file_name=None): 




def prep(df, config):

    return df[[x + "_D" for x in extract_durations(config)]]
 
    res = df[[x + "_D" for x in extract_durations(config)]]

    res.name = df.name

    return res

xlap/analyse/util.py

+17 −2
Original line number Diff line number Diff line 
import matplotlib.pyplot as plt

from matplotlib import pyplot as plt

import numpy as np

import math





def cdf(values, grid=False, ax=None):

    # empirical CDF

    def F(x,data):

        return float(len(data[data <= x]))/len(data)



    vF = np.vectorize(F, excluded=['data'])



    if ax is None:

        ax = plt



    ax.semilogx(np.sort(values),vF(x=np.sort(values), data=values))

    if grid:

        ax.grid()



def hist(df):

    return df.hist(cumulative=True, normed=1, bins=200)