~= refactor (27d0f7e8) · Commits · LARN / X-Lap

notebook.ipynb

+28 −62

Original line number	Diff line number	Diff line
		%% Cell type:markdown id: tags:

		# X-Lap in Action

		%% Cell type:markdown id: tags:

		## Imports

		%% Cell type:code id: tags:

		``` 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.cdf import multi_cdf
		from xlap.analyse.regress import linear as linear_regression
		from xlap.analyse.trace import traces
		from xlap.analyse.correlation import correlation, multi_correlation
		from xlap.analyse.latency import analyse
		import matplotlib.pyplot as plt
		import pandas as pd
		import copy
		%matplotlib inline
		colors = ["#E69F00", "#009E73", "#56B4E9", "#CC79A7", "#D55E00"]
		```

		%% Cell type:markdown id: tags:

		## Data Retrieval

		%% Cell type:code id: tags:

		``` python
		from xlap.parse import evaluate, parse_config
		config = parse_config()
		data_files = {
		"sender": "rtn2018/20180417_testbed/",
		"receiver": "rtn2018/20180417_testbed/"
		}
		original1 = evaluate(data_files["sender"] + "sender-1000000.csv", data_files["receiver"] + "receiver-1000000.csv", config=config, kind=0)
		original1.name = "1GHz"
		original2 = evaluate(data_files["sender"] + "sender-2000000.csv", data_files["receiver"] + "receiver-2000000.csv", config=config, kind=0)
		original2.name = "2GHz"
		original3 = evaluate(data_files["sender"] + "sender-3000000.csv", data_files["receiver"] + "receiver-3000000.csv", config=config, kind=0)
		original3.name = "3GHz"
		dfs = [original1, original2, original3]
		df_1GHz = evaluate(data_files["sender"] + "sender-1000000.csv", data_files["receiver"] + "receiver-1000000.csv", config=config, kind=0)
		df_1GHz.name = "1GHz"
		df_2GHz = evaluate(data_files["sender"] + "sender-2000000.csv", data_files["receiver"] + "receiver-2000000.csv", config=config, kind=0)
		df_2GHz.name = "2GHz"
		df_3GHz = evaluate(data_files["sender"] + "sender-3000000.csv", data_files["receiver"] + "receiver-3000000.csv", config=config, kind=0)
		df_3GHz.name = "3GHz"
		dfs = [df_1GHz, df_2GHz, df_3GHz]
		```

		%% Cell type:markdown id: tags:

		## Traces

		%% Cell type:code id: tags:

		``` python
		traces(original1, config, global_xaxis=True)
		from xlap.analyse.trace import traces
		traces(df_1GHz, config, global_xaxis=True)
		```

		%% Cell type:markdown id: tags:

		## Jitter Analysis

		%% Cell type:code id: tags:

		``` python
		def multi_trace_jitter(dfs, config):
		for df in dfs:
		print("############################ {} ############################".format(df.name))
		jitter.trace_jitter(df, config=config, threshold=200)

		from xlap.analyse.jitter import multi_trace_jitter
		multi_trace_jitter(dfs, config)
		```

		%% Cell type:markdown id: tags:

		## CDFs

		%% Cell type:code id: tags:

		``` python
		from xlap.analyse.cdf import multi_cdf
		from xlap.analyse.util import colors
		import copy
		cfg = copy.deepcopy(config)
		d = cfg["durations"]

		l=("Decoding","ReceiverIPC","HandlePacket", "Feedback", "SenderIPC","SenderEnqueued","Enqueue")
		for e in l:
		if e in l:
		del d[e]

		list(map(cfg["durations"].pop, ("Decoding",
		"ReceiverIPC",
		"HandlePacket",
		"Feedback",
		"SenderIPC",
		"SenderEnqueued",
		"Enqueue")))
		multi_cdf(dfs, cfg, colors=colors)
		```

		%% Cell type:markdown id: tags:

		## Correlation

		%% Cell type:code id: tags:

		``` python
		from xlap.analyse.correlation import multi_correlation
		multi_correlation(dfs, config, colors=colors, figsize=(3.0,2.0), cols=4)
		```

		%% Cell type:markdown id: tags:

		## Latency Criticality

		%% Cell type:code id: tags:

		``` python
		d = analyse(original1, config)
		from xlap.analyse.latency import analyse
		d = analyse(df_1GHz, config)
		```

		%% Cell type:markdown id: tags:

		### Correlations

		%% Cell type:code id: tags:

		``` python
		d.corr.sort_values(ascending=False)
		```

		%% Cell type:markdown id: tags:

		### Control Flow Graph

		%% Cell type:code id: tags:

		``` python
		d.cfg
		```

		%% Cell type:markdown id: tags:

		# Kolmogorov

		%% Cell type:code id: tags:

		``` python
		from scipy import stats
		from xlap.analyse.util import extract_durations
		import numpy as np

		def timing_behaviour(df1, df2, config, confidence=0.9):
		durations = [x + "_D" for x in extract_durations(config)]

		norm = lambda x: x / np.max(x)

		for duration in durations:
		rvs1 = norm(df1[duration])
		rvs2 = norm(df2[duration])
		stat, pvalue = stats.ks_2samp(rvs1, rvs2)
		result = "CANNOT REJECT"
		if pvalue < 1 - confidence:
		result = "REJECT"
		print(duration.ljust(20), "{:.6f}".format(pvalue), result, sep="\t\t")

		timing_behaviour(original1, original2, config)
		from xlap.analyse.timing import timing_behaviour
		timing_behaviour(df_1GHz, df_2GHz, config)
		```

		%% Cell type:code id: tags:

		``` python
		timing_behaviour(original1, original3, config)
		timing_behaviour(df_1GHz, df_3GHz, config)
		```

		%% Cell type:code id: tags:

		``` python
		timing_behaviour(original2, original3, config)
		timing_behaviour(df_2GHz, df_3GHz, config)
		```

xlap/analyse/jitter.py

+7 −2

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		@@ -38,10 +38,15 @@ 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]
		fig = plt.gcf()
		ax, fig = box(df_no_outliers, (0, threshold), export, file_name, figsize)
		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()
		plt.close()
		return None

		def multi_trace_jitter(dfs, config):
		for df in dfs:
		print("############################ {} ############################".format(df.name))
		trace_jitter(df, config=config, threshold=200)

xlap/analyse/timing.py

0 → 100644

+16 −0

Original line number	Diff line number	Diff line
		from scipy import stats
		from xlap.analyse.util import extract_durations
		import numpy as np

		def timing_behaviour(df1, df2, config, confidence=0.9):
		durations = [x + "_D" for x in extract_durations(config)]

		norm = lambda x: x / np.max(x)

		for duration in durations:
		rvs1 = norm(df1[duration])
		rvs2 = norm(df2[duration])
		stat, pvalue = stats.ks_2samp(rvs1, rvs2)
		result = ("REJECT" if pvalue < 1 - confidence else
		"CANNOT REJECT")
		print(duration.ljust(20), "{:.6f}".format(pvalue), result, sep="\t\t")

xlap/analyse/trace.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -92,6 +92,6 @@ def traces(df, config, figsize=(10, 4.5), global_xaxis=False):
		if global_xaxis:
		t_max = df["EndToEnd_D"].max()

		@interact(seq_no=widgets.IntSlider(min=1, max=len(df), step=1, value=47))
		@interact(seq_no=widgets.IntSlider(min=1, max=len(df), step=1, value=47,description="Seq. No."))
		def _f(seq_no):
		trace(df.iloc[seq_no], config, figsize=figsize, t_max=t_max)

xlap/analyse/util.py

+3 −1

Original line number	Diff line number	Diff line
		@@ -2,6 +2,8 @@ import matplotlib.pyplot as plt
		import numpy as np
		import math

		colors = ["#E69F00", "#009E73", "#56B4E9", "#CC79A7", "#D55E00"]

		def cdf(values, ax=None, grid=False, **kwargs):
		if ax is None:
		ax = plt
		@@ -54,7 +56,7 @@ def box(data_frame, xlim=None, export=False, file_name=None, figsize=(8, 4.5)):
		plt.tight_layout()
		if export and file_name is not None:
		fig.savefig(file_name)

		return ax, fig

		def describe_table(df):
		stats = df.describe()