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Commit e5fd2741 authored by Andreas Schmidt's avatar Andreas Schmidt
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+= thesis AS fixes

parent cd40ae35
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Showing with 112 additions and 140 deletions
xlap.c
View file @ e5fd2741
#define XLAP
#include "xlap.h"
......
xlap/analyse/cdf.py
View file @ e5fd2741
......@@ -3,15 +3,14 @@ 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")
def multi_cdf(dfs, config, export=False, file_name="CDF.pdf", figsize= (4,4), cols=3, colors=None):
durations = extract_durations(config)
durations.remove("EndToEnd")
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)
fig.set_size_inches(figsize[0] * cols, figsize[1] * rows, forward=True)
names = []
for df in dfs:
......@@ -22,16 +21,19 @@ def multi_cdf(dfs, config, export=False, file_name="CDF.pdf"):
ax = axes[idx // cols, idx % cols]
else:
ax = axes[idx]
for df in dfs:
cdf(df[duration], grid=True, ax=ax)
for i, df in enumerate(dfs):
kwargs = dict()
if colors is not None:
kwargs["color"] = colors[i]
cdf(df[duration + "_D"], grid=True, ax=ax, **kwargs)
if(idx == 0):
ax.legend(names)
ax.set_ylabel("CDF")
ax.set_xlabel("{} [us]".format(duration))
ax.set_ylabel("CDF")
plt.subplots_adjust(wspace=0.3,hspace=0.3)
plt.tight_layout()
if export and file_name is not None:
fig.savefig(file_name)
plt.tight_layout()
plt.show()
xlap/analyse/correlation.py
View file @ e5fd2741
......@@ -6,50 +6,52 @@ from xlap.analyse.util import extract_durations
def corr(df, duration, grid=False, ax=None, color="black", marker="+"):
df.plot.scatter(ax=ax,
y="EndToEnd_D",
x=duration,
x=duration + "_D",
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","purple","red","pink"]
markers = ["v", "^", ">", "<", "+"]
def corr_multi(dfs, duration, colors=None, **kwargs):
if colors is None:
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, export=False, file_name="MultiCorrelation.pdf"):
durations = [x + "_D" for x in extract_durations(config)]
durations.remove("EndToEnd_D")
def multi_correlation(dfs, config, export=False, file_name="MultiCorrelation.pdf", figsize=(5.5,5.5), cols=2, colors=None, **kwargs):
durations = extract_durations(config)
durations.remove("EndToEnd")
cols = 2
rows = int(math.ceil(len(durations) / cols))
items = len(durations)
fig, axes = plt.subplots(nrows=rows, ncols=cols)
fig.set_size_inches(5.5 * cols, 5.5 * rows, forward=True)
fig.set_size_inches(figsize[0] * cols, figsize[1] * rows, forward=True)
names = []
for df in dfs:
if hasattr(df, "name"):
names.append(df.name)
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)
corr_multi(dfs, duration, colors=colors, grid=True, ax=ax)
if len(names) > 1 and idx == 0:
ax.legend(names)
ax.set_ylabel("EndToEnd" if idx == 0 else "")
plt.subplots_adjust(wspace=0.3,hspace=0.3)
plt.tight_layout()
if export and file_name is not None:
fig.savefig(file_name)
plt.tight_layout()
plt.show()
def correlation(df, config, export=False, file_name="SingleCorrelation.pdf"):
return multi_correlation([df], config, export, file_name)
def correlation(df, config, export=False, file_name="SingleCorrelation.pdf", **kwargs):
return multi_correlation([df], config, export, file_name, **kwargs)
xlap/analyse/jitter.py
View file @ e5fd2741
......@@ -3,12 +3,13 @@ import matplotlib.pyplot as plt
from xlap.analyse.util import get_outlier_threshold, extract_durations, box
def jitter_causes(df, durations, export=False, file_name=None):
def jitter_causes(df, config, export=False, file_name=None, figsize=(8,3), color="black"):
stats = df["EndToEnd_D"].describe()
durations = extract_durations(config)
threshold = get_outlier_threshold(stats)
outliers = df[df["EndToEnd_D"] > threshold]
reasons = [d + "_D" for d in durations.keys()]
reasons = [d + "_D" for d in durations if d != "EndToEnd"]
df_reasons = pd.DataFrame(index=outliers.index)
......@@ -18,17 +19,17 @@ def jitter_causes(df, durations, export=False, file_name=None):
df_reasons[reason] = outliers[outliers[reason] > reason_threshold].notnull()
df_sum = df_reasons.sum().sort_values(ascending=False)
ax = df_sum.plot.bar(x="Reason", y="Frequency", rot=45, grid=True, legend=False, color="black")
ax = df_sum.plot.bar(x="Reason", y="Frequency", rot=45, grid=True, legend=False, color=color)
fig = ax.get_figure()
plt.ylabel("Frequency")
ax.set_xticklabels(list(map(lambda x: x.get_text().replace("_D", ""), ax.get_xticklabels())))
fig.set_size_inches(8, 3, forward=True)
fig.set_size_inches(figsize[0], figsize[1])
plt.tight_layout()
if export:
fig.savefig(file_name)
print("Outliers:", len(outliers), ";", "Threshold[us]:", threshold)
def trace_jitter(data_frame, config=None, threshold=None, export=False, file_name=None):
def trace_jitter(data_frame, config=None, threshold=None, export=False, file_name=None, figsize=(8, 4.5)):
"""
Displays (and saves) a stacked boxplot of durations.
"""
......@@ -37,8 +38,10 @@ def trace_jitter(data_frame, config=None, threshold=None, export=False, file_nam
if threshold is None:
threshold = get_outlier_threshold(data_frame["EndToEnd_D"].describe())
df_no_outliers = data_frame[data_frame["EndToEnd_D"] <= threshold]
box(df_no_outliers, (0, threshold), export, file_name)
print("{} / {} are no outliers.".format(len(df_no_outliers), len(data_frame)))
fig = plt.gcf()
fig.set_size_inches(figsize[0], figsize[1])
box(df_no_outliers, (0, threshold), export, file_name, figsize)
print("{} / {} are no outliers.".format(len(df_no_outliers), len(data_frame)))
fig.canvas.set_window_title('Jitter Analysis')
plt.show()
return None
xlap/analyse/latency.py
View file @ e5fd2741
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr
from scipy.stats.stats import pearsonr, spearmanr
import numpy as np
import sys
import graphviz
class LatencyAnalysis():
def __init__(self, cfg=None, hdb=None):
plt.rcParams['ps.useafm'] = True
plt.rcParams['pdf.use14corefonts'] = True
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = 'sans-serif'
#plt.rcParams['font.sans-serif'] = 'Latin Modern Sans'
#plt.rcParams['font.monospace'] = 'FreeMono'
self.cfg = cfg
self.hdb = hdb
correlations = []
labels = []
......@@ -25,6 +20,28 @@ class LatencyAnalysis():
corr = pd.Series(correlations, index=labels)
self.corr = corr
def plot_critical_regions(self, figsize=(5,5), export=False, file_name="latency-criticality.pdf"):
"""
plot regions, sorted by latency criticality
"""
hdb = self.hdb
relevant = sorted([x for x in hdb if x['Correlation'] > 0], key=lambda x: -x['Correlation'], reverse=True)
x = np.arange(len(relevant))
correlations = list(map(lambda x: x['Correlation'], relevant))
ticks = list(map(lambda x: "<%s, %s>" % (x['Start'][:-2], x['End'][:-2]), relevant))
fig, ax = plt.subplots(figsize=figsize)
rects = ax.barh(x, correlations, align="center", tick_label="")
autolabel(rects, ax, ticks)
plt.xlabel("Latency Criticality")
plt.tight_layout()
if export:
plt.savefig(file_name)
plt.show()
plt.close()
def _get_thread_for_event(config, e):
......@@ -158,64 +175,15 @@ def autolabel(rects, ax, labels):
# If we can fit the label above the column, do that;
# otherwise, put it inside the column.
if p_width > 0.50: # arbitrary; 95% looked good to me.
#label_position = width - (x_width) + 0.7
label_position = width - (x_width * 0.01)
label_position = width - (x_width) + 0.75
color = "white"
align = "right"
else:
label_position = width + (x_width * 0.002)
mono = {'family': 'monospace'}
ax.text(label_position, rect.get_y(), labels[i], ha=align, va='bottom', rotation=0, color=color, fontdict=mono)
def _plot_critical_regions(hdb):
"""
plot regions, sorted by latency criticality
"""
relevant = sorted([x for x in hdb if x['Correlation'] > 0], key=lambda x: -x['Correlation'], reverse=True)
x = np.arange(len(relevant))
correlations = list(map(lambda x: x['Correlation'], relevant))
ticks = list(map(lambda x: "<%s,%s>" % (x['Start'][:-2], x['End'][:-2]), relevant))
fig, ax = plt.subplots()
rects = ax.barh(x, correlations, align="center", tick_label="")
autolabel(rects, ax, ticks)
plt.xlabel('Latency criticality')
plt.tight_layout()
plt.savefig("latency-criticality.pdf")
plt.close()
def get_durations(df, config):
hb = []
events = [column for column in df.columns if column.endswith("_T")]
for event1 in df[events]:
for event2 in df[events]:
if str(event1) == str(event2):
continue
if _happens_before(df, event1, event2, config):
hb += [{'Start': str(event1), 'End': str(event2)}]
label_position = width + (x_width * 0.01)
hdb = []
e2e = list(df['EndToEnd_D'])
for event1 in df[events]:
for event2 in df[events]:
if str(event1) == str(event2):
continue
# if _locally_happens_directly_before(df, event1, event2, hb, config):
if _happens_directly_before(df, event1, event2, hb):
# compute the correlation between e2e latency and event1-event2 latency
l3 = list(df[event2] - df[event1])
ax.text(label_position, rect.get_y(), labels[i], ha=align, va='bottom', rotation=0, color=color, family="monospace")
hdb += [{'Start': str(event1), 'Stop': str(event2), 'Source': 'cfa'}]
return hdb
def get_durations_2(dfs, config):
df = dfs[0] + dfs[1]
return get_durations(df, config)
def analyse(df, config):
hb = []
......@@ -240,12 +208,11 @@ def analyse(df, config):
# compute the correlation between e2e latency and event1-event2 latency
l3 = list(df[event2] - df[event1])
if any(map(lambda x: x != 0, l3)):
correlation = pearsonr(l3, e2e)[0]
correlation = spearmanr(l3, e2e)[0]
else:
correlation = 0
hdb += [{'Start': str(event1), 'End': str(event2), 'Correlation': correlation}]
cfg = _plot_controlflow_graph(df, hdb)
_plot_critical_regions(hdb)
return LatencyAnalysis(cfg=cfg, hdb=hdb)
xlap/analyse/trace.py
View file @ e5fd2741
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.lines import Line2D
from xlap.analyse.util import extract_durations
from ipywidgets import interact
import ipywidgets as widgets
colors = {
"sender": "#CA0020",
"receiver": "#0571B0",
"EndToEnd": "black"
}
linestyles = {
"sender": (0,(1,1,3,1,1,1)),
"receiver": (0,(1,1)),
"EndToEnd": (0,())
}
def _create_line(config):
tr = config["time_reference"]
color = {
"sender": "#AAAAAA",
"receiver": "#888888",
"e2e": "black"
}
def _creator(duration_name):
if duration_name == "EndToEnd":
return [tr["sender"]["Start"] + "_T", tr["receiver"]["Stop"] + "_T", color["e2e"], "EndToEnd"]
return [tr["sender"]["Start"] + "_T", tr["receiver"]["Stop"] + "_T", colors["EndToEnd"], linestyles["EndToEnd"], "EndToEnd"]
elif duration_name == "Sender":
return [tr["sender"]["Start"] + "_T", tr["sender"]["Stop"] + "_T", color["sender"], "Sender"]
return [tr["sender"]["Start"] + "_T", tr["sender"]["Stop"] + "_T", colors["sender"], linestyles["sender"], "Sender"]
elif duration_name == "Receiver":
return [tr["receiver"]["Start"] + "_T", tr["receiver"]["Stop"] + "_T", color["receiver"], "Receiver"]
return [tr["receiver"]["Start"] + "_T", tr["receiver"]["Stop"] + "_T", colors["receiver"], linestyles["receiver"], "Receiver"]
else:
duration = config["durations"][duration_name]
return [duration["Start"] + "_T", duration["Stop"] + "_T", color[duration["Source"]], duration_name]
return [duration["Start"] + "_T", duration["Stop"] + "_T", colors[duration["Source"]], linestyles[duration["Source"]], duration_name]
return _creator
def trace(data_frame, config, export=False, file_name="TraceJitter.pdf"):
def trace(data_frame, config, export=False, file_name="PacketTrace.pdf", figsize=(8, 4.5), bbox_to_anchor=(1.05, 1), loc=2, t_max=None):
"""
:param data_frame:
......@@ -38,7 +48,7 @@ def trace(data_frame, config, export=False, file_name="TraceJitter.pdf"):
:return:
"""
fig, ax = plt.subplots(figsize=(8, 4.5))
fig, ax = plt.subplots(figsize=figsize)
plt.grid()
line_creator = _create_line(config)
......@@ -54,23 +64,34 @@ def trace(data_frame, config, export=False, file_name="TraceJitter.pdf"):
starts = data_frame[series[0]] - base
ends = data_frame[series[1]] - base
plt.hlines(range(n), starts, ends, series[2], linewidths=[5])
plt.hlines(range(n), starts, ends, series[2], series[3], linewidths=[5])
plt.xlabel("Time [us]")
if t_max is not None:
ax.set_xlim([0,t_max])
fig.canvas.draw()
ax.set_yticklabels(series[3])
ax.set_yticklabels(series[4])
ax.yaxis.set_ticks(np.arange(0, n, 1))
sides = ["sender","receiver","EndToEnd"]
handles = list(map(lambda x: Line2D([0], [0], color=colors[x], linestyle=linestyles[x], lw=5, label=x.title() if x != "EndToEnd" else x), sides))
plt.legend(handles=handles,bbox_to_anchor=bbox_to_anchor, loc=loc, borderaxespad=0.)
plt.tight_layout()
if export:
plt.savefig(file_name)
plt.show()
def traces(df, config):
def traces(df, config, figsize=(10, 4.5), global_xaxis=False):
"""
Display a slider to select sequence numbers and the respective trace.
"""
t_max = None
if global_xaxis:
t_max = df["EndToEnd_D"].max()
@interact(seq_no=widgets.IntSlider(min=1, max=len(df), step=1, value=47))
def _f(seq_no):
trace(df.ix[seq_no], config)
trace(df.iloc[seq_no], config, figsize=figsize, t_max=t_max)
xlap/analyse/util.py
View file @ e5fd2741
......@@ -37,7 +37,7 @@ def extract_durations(config):
return ["EndToEnd", "Sender"] + durations_send + ["Receiver"] + durations_recv
def box(data_frame, xlim=None, export=False, file_name=None):
def box(data_frame, xlim=None, export=False, file_name=None, figsize=(8, 4.5)):
"""
Display a boxplot for the durations contained in data_frame.
:param data_frame:
......@@ -45,13 +45,13 @@ def box(data_frame, xlim=None, export=False, file_name=None):
:param file_name:
:return:
"""
ax = data_frame.plot.box(vert=False, grid=True)
ax = data_frame.plot.box(vert=False, grid=True,figsize=figsize)
fig = ax.get_figure()
ax.set_yticklabels(list(map(lambda x: x.get_text().replace("_D", ""), ax.get_yticklabels())))
plt.xlabel("Time [us]")
if xlim is not None:
plt.xlim(xlim)
fig.set_size_inches(8, 4.5, forward=True)
plt.tight_layout()
if export and file_name is not None:
fig.savefig(file_name)
......
xlap/command_line.py
View file @ e5fd2741
......@@ -3,14 +3,10 @@ import argparse
from xlap.parse import evaluate, parse_config
import xlap.analyse.jitter as jitter
import xlap.analyse.latency as latency
import xlap.analyse.diff as difference
import xlap.analyse.e2e as e2e
tasks = {
"jitter": None,
"latency": None,
"difference": None,
"e2e": None,
"capture": None
}
......@@ -45,30 +41,10 @@ def main():
f.write("\n")
else:
print(output)
elif command == "e2e":
khz1 = 2000000
khz2 = 3000000
path="../publications/rtn-18/eval/20180419_energy/"
df_data1 = evaluate(path+"sender-"+str(khz1)+".csv", path+"receiver-"+str(khz1)+".csv", config=config, kind=0)
df_data2 = evaluate(path+"sender-"+str(khz2)+".csv", path+"receiver-"+str(khz2)+".csv", config=config, kind=0)
e2e.analyse(df_data1, df_data2, config)
elif command == "latency":
df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)
a = latency.analyse(df_data, config)
print(a.corr.sort_values(ascending=False))
elif command == "difference":
path="../publications/rtn-18/eval/20180420_"
khz = 3000000
df_data1 = evaluate(path+"base1/sender-"+str(khz)+".csv", path+"base1/receiver-"+str(khz)+".csv", config=config, kind=0)
# sanity check:
#df_data2 = evaluate("../prrt/out/s.csv", "../prrt/out/r.csv", config=config, kind=0)
# same setup, different measurement run:
#df_data2 = evaluate("../prrt/out/s+same.csv", "../prrt/out/r+same.csv", config=config, kind=0)
# different setup:
#df_data2 = evaluate("../prrt/out/s+send.csv", "../prrt/out/r+send.csv", config=config, kind=0)
df_data2 = evaluate(path+"base2/sender-"+str(khz)+".csv", path+"base2/receiver-"+str(khz)+".csv", config=config, kind=0)
#df_data2 = evaluate(path+"changed/sender-"+str(khz)+".csv", path+"changed/receiver-"+str(khz)+".csv", config=config, kind=0)
difference.analyse(df_data1, df_data2, config)
else:
df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)
......
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