From d01be7002e989a7f86ba8b1abbdd8b2d56efc662 Mon Sep 17 00:00:00 2001
From: Britta Heymann <britta_hey@web.de>
Date: Thu, 5 Oct 2017 10:08:35 +0200
Subject: [PATCH] Refactor evaluation scripts
into multiple methods s.t. they can be combined in different ways.
---
toolkit/reportSummary/broadcastAnalysis.py | 56 +++++++++----------
.../delayDistributionAnalysis.py | 52 +++++++++++------
toolkit/reportSummary/multicastAnalysis.py | 46 +++++++--------
.../reportSummary/privateMessageAnalysis.py | 18 ++++--
4 files changed, 102 insertions(+), 70 deletions(-)
diff --git a/toolkit/reportSummary/broadcastAnalysis.py b/toolkit/reportSummary/broadcastAnalysis.py
index bc616e59..07759e99 100644
--- a/toolkit/reportSummary/broadcastAnalysis.py
+++ b/toolkit/reportSummary/broadcastAnalysis.py
@@ -30,6 +30,29 @@ def findLinesConcerningPriority(all_lines, priority):
nextPriorityLine = findNextLineContaining(all_lines, correctPriorityLine + 1, "prio")
return all_lines[correctPriorityLine:nextPriorityLine]
+def parseBroadcastAnalysis(filename, priority):
+ """Parses a broadcast analysis file for the specified priority and returns (in that order) the time points, the
+ average number of reached people, and the minimum number of reached people.
+ """
+ # Read broadcast analysis from file
+ with open(filename) as analysis_file:
+ analysis = analysis_file.readlines()
+ relevantLines = findLinesConcerningPriority(analysis, priority)
+
+ # Interpret lines to find minimum and average number of reached people over time
+ timePoints = []
+ minimum = []
+ average = []
+ for line in relevantLines:
+ match = re.match("(\d+)\s+(\d+.\d+)\s+(\d+)", line)
+ if match is None:
+ continue
+ timePoints.append(float(match.group(1)) / 60)
+ average.append(float(match.group(2)))
+ minimum.append(int(match.group(3)))
+
+ return timePoints, average, minimum
+
# Draws two functions over the same x values.
# Labels are selected as appropriate for broadcast analysis.
def drawPlots(x, y_minimum, y_average, priority):
@@ -45,40 +68,17 @@ def drawPlots(x, y_minimum, y_average, priority):
axes.set_ylim(ymin = 0)
axes.xaxis.set_major_locator(ticker.IndexLocator(base=60, offset=-5))
-
-def plotAnalysis(lines, priority, figure, plotNumber):
- # Interpret lines to find minimum and average number of reached people over time
- timePoints = []
- minimum = []
- average = []
- for line in lines:
- match = re.match("(\d+)\s+(\d+.\d+)\s+(\d+)", line)
- if match is None:
- continue
- timePoints.append(float(match.group(1)) / 60)
- average.append(float(match.group(2)))
- minimum.append(int(match.group(3)))
-
- # Draw plots.
- figure.add_subplot(1, 3, plotNumber)
- drawPlots(timePoints, minimum, average, priority)
-
# Main function of the script. See script description at the top of the file for further information.
def main(analysisFileName, graphicFileName):
- # Read broadcast analysis from file
- with open(analysisFileName) as analysis_file:
- analysis = analysis_file.readlines()
-
# Only look at priorities 2, 5 and 9
- prio2Analysis = findLinesConcerningPriority(analysis, 2)
- prio5Analysis = findLinesConcerningPriority(analysis, 5)
- prio9Analysis = findLinesConcerningPriority(analysis, 9)
+ priorities = [2, 5, 9]
# Draw plots for all those priorities.
fig = plt.figure(figsize=(16, 4))
- plotAnalysis(prio2Analysis, 2, fig, 1)
- plotAnalysis(prio5Analysis, 5, fig, 2)
- plotAnalysis(prio9Analysis, 9, fig, 3)
+ for idx, priority in enumerate(priorities):
+ timePoints, average, minimum = parseBroadcastAnalysis(analysisFileName, priority)
+ fig.add_subplot(1, 3, idx + 1)
+ drawPlots(timePoints, minimum, average, priority)
# Save to file
plt.tight_layout()
diff --git a/toolkit/reportSummary/delayDistributionAnalysis.py b/toolkit/reportSummary/delayDistributionAnalysis.py
index 035ea834..df68989a 100644
--- a/toolkit/reportSummary/delayDistributionAnalysis.py
+++ b/toolkit/reportSummary/delayDistributionAnalysis.py
@@ -29,10 +29,13 @@ from readFileUtilities import findNextLineContaining
messageTypeIntro = "Delay distribution for delivered messages of type {}:"
priorityIntro = "For priority {}:"
-# Main function of the script. See script description at the top of the file for further information.
-def main(analysisFileName, messageType, messagePrio, graphicFileName):
+def parseDelayAnalysis(fileName, messageType, messagePrio):
+ """Parses a delay analysis file for information about the provided message type and priority and
+ returns (in that order) the different delay classes (identified by max delay), the percentage of delivered messages
+ delivered in those intervals, and the cumulative percentages.
+ """
# Read delay analysis from file
- with open(analysisFileName) as analysis_file:
+ with open(fileName) as analysis_file:
analysis = analysis_file.readlines()
# Only look at text for correct type
@@ -46,9 +49,9 @@ def main(analysisFileName, messageType, messagePrio, graphicFileName):
analysis = analysis[correctPriorityLine:nextPriorityLine]
# Find data both for bar chart and cumulative chart
- bins = []
- vals = []
- cumulative = {}
+ delayClasses = []
+ percentageDelivered = []
+ cumulativePercentages = {}
sumOfPercentages = 0
for line in analysis:
match = re.match(".*<\s*(\d+):\s*(\d+.\d+)%", line)
@@ -56,23 +59,25 @@ def main(analysisFileName, messageType, messagePrio, graphicFileName):
continue
maxDelay = int(match.group(1)) / 60
percentage = float(match.group(2))
- bins.append(maxDelay)
- vals.append(percentage)
+ delayClasses.append(maxDelay)
+ percentageDelivered.append(percentage)
sumOfPercentages += percentage
- cumulative[maxDelay] = sumOfPercentages
+ cumulativePercentages[maxDelay] = sumOfPercentages
- # Plot bar chart
- plt.subplot(2,1,1)
- plt.title('Delay distribution of delivered {} messages\nPriority {}'.format(messageType, messagePrio))
- plt.bar(bins, vals)
+ return delayClasses, percentageDelivered, cumulativePercentages
+
+def plotDelayDistribution(title, delayClasses, percentageDelivered):
+ """Plots a delay distribution as a bar chart."""
+ plt.title(title)
+ plt.bar(delayClasses, percentageDelivered)
plt.ylabel('Percentage of messages')
plt.grid(True)
axes = plt.gca()
axes.set_xlim(xmin = 0)
- # Directly below, plot cumulative chart
- plt.subplot(2,1,2)
- plt.plot(bins, [cumulative[bin] for bin in bins])
+def plotCumulativeDelay(delayClasses, cumulativePercentages):
+ """Plots a cumulative delay chart."""
+ plt.plot(delayClasses, [cumulativePercentages[delayClass] for delayClass in delayClasses])
plt.xlabel('Delay in minutes')
plt.ylabel('Cumulative percentage')
plt.grid(True)
@@ -80,6 +85,21 @@ def main(analysisFileName, messageType, messagePrio, graphicFileName):
axes.set_xlim(xmin = 0)
axes.set_ylim(ymin = 0)
+# Main function of the script. See script description at the top of the file for further information.
+def main(analysisFileName, messageType, messagePrio, graphicFileName):
+ delayClasses, percentageDelivered, cumulativePercentages = parseDelayAnalysis(analysisFileName, messageType, messagePrio)
+
+ # Plot bar chart
+ plt.subplot(2,1,1)
+ plotDelayDistribution(
+ title='Delay distribution of delivered {} messages\nPriority {}'.format(messageType, messagePrio),
+ delayClasses=delayClasses,
+ percentageDelivered=percentageDelivered)
+
+ # Directly below, plot cumulative chart
+ plt.subplot(2,1,2)
+ plotCumulativeDelay(delayClasses, cumulativePercentages)
+
# Save to file
plt.savefig(graphicFileName, dpi = 300)
plt.close()
diff --git a/toolkit/reportSummary/multicastAnalysis.py b/toolkit/reportSummary/multicastAnalysis.py
index 3f375019..8e0be53b 100644
--- a/toolkit/reportSummary/multicastAnalysis.py
+++ b/toolkit/reportSummary/multicastAnalysis.py
@@ -15,25 +15,13 @@ import re
# 600 0.0 0.0164942207497068 0.0 0.01223002342392
# 900 0.0 0.0340330430435976 0.0 0.024223002342392
-# Draws two functions over the same x values.
-# Labels are selected as appropiate for multicast analysis.
-def drawPlots(x, y_minimum, y_average, y_minForAll, y_avgForAll):
- plt.title('Multicast delivery rates')
- plt.xlabel('Minutes since message creation')
- plt.ylabel('Delivery rate')
- plt.plot(x, y_minimum, '.-', label='Minimum for existent messages')
- plt.plot(x, y_average, '.-', label='Average for existent messages')
- #Currently not plotted as both minima are constantly zero and if the ever get higher it will be more noticably in
- #the minimum delivery ratio for existent messages, two overlaying graphs make viewers search for the 'missing' one
- #plt.plot(x, y_minForAll, '.-', label='Minimum for all messages ever created')
- plt.plot(x, y_avgForAll, '.-', label='Average for all messages ever created')
- plt.legend(loc='upper left')
- plt.grid(True)
-
-# Main function of the script. See script description at the top of the file for further information.
-def main(analysisFileName, graphicFileName):
+def parseMulticastAnalysis(filename):
+ """Parses a multicast analysis file and returns (in that order) the time points, the minimum delivery rates for
+ existing messages, the average delivery rates for existing messages, and the average delivery rates over all
+ messages ever created.
+ """
# Read multicast analysis from file
- with open(analysisFileName) as analysis_file:
+ with open(filename) as analysis_file:
analysis = analysis_file.readlines()
# Skip first line which only contains explanation.
analysis = analysis[1:]
@@ -41,7 +29,6 @@ def main(analysisFileName, graphicFileName):
# Interpret lines to find minimum and average delivery rates over time
timePoints = []
minimum = []
- minimumForAll = []
average = []
averageForAll = []
for line in analysis:
@@ -51,11 +38,26 @@ def main(analysisFileName, graphicFileName):
timePoints.append(float(match.group(1)) / 60)
minimum.append(float(match.group(2)))
average.append(float(match.group(3)))
- minimumForAll.append(float(match.group(4)))
averageForAll.append(float(match.group(5)))
- # Draw plots.
- drawPlots(timePoints, minimum, average, minimumForAll, averageForAll)
+ return timePoints, minimum, average, averageForAll
+
+# Draws two functions over the same x values.
+# Labels are selected as appropiate for multicast analysis.
+def drawPlots(x, y_minimum, y_average, y_avgForAll):
+ plt.title('Multicast delivery rates')
+ plt.xlabel('Minutes since message creation')
+ plt.ylabel('Delivery rate')
+ plt.plot(x, y_minimum, '.-', label='Minimum for existent messages')
+ plt.plot(x, y_average, '.-', label='Average for existent messages')
+ plt.plot(x, y_avgForAll, '.-', label='Average for all messages ever created')
+ plt.legend(loc='upper left')
+ plt.grid(True)
+
+# Main function of the script. See script description at the top of the file for further information.
+def main(analysisFileName, graphicFileName):
+ timePoints, minimum, average, averageForAll = parseMulticastAnalysis(analysisFileName)
+ drawPlots(timePoints, minimum, average, averageForAll)
# Save to file
plt.savefig(graphicFileName)
diff --git a/toolkit/reportSummary/privateMessageAnalysis.py b/toolkit/reportSummary/privateMessageAnalysis.py
index e327f502..21431ee3 100644
--- a/toolkit/reportSummary/privateMessageAnalysis.py
+++ b/toolkit/reportSummary/privateMessageAnalysis.py
@@ -26,16 +26,21 @@ def getValueFromString(string):
return float(match.group(2))
return int(match.group(2))
-# Main function of the script. See script description at the top of the file for further information.
-def main(analysisFileName, graphicFileName):
- # Read delivery probability from file
- with open(analysisFileName) as analysis_file:
+def parseDeliveryProbabilityReport(fileName):
+ """Parses a delivery probability report file and returns (in that order) the number of created messages,
+ the number of delivered messages, and the delivery probability.
+ """
+ with open(fileName) as analysis_file:
analysis = analysis_file.readlines()
created = getValueFromString(analysis[2])
delivered = getValueFromString(analysis[3])
delivery_prob = getValueFromString(analysis[4])
+ return created, delivered, delivery_prob
+
+def createDeliveryPieChart(created, delivered, delivery_prob):
+ """Creates a graphical presentation of delivery probability."""
values=[delivery_prob, 1-delivery_prob]
labels=["delivered:\n{p:.1f}% ({t})".format(p=delivery_prob*100, t=delivered),
"not delivered:\n{p:.1f}% ({t})".format(p=(1-delivery_prob)*100, t=(created-delivered))]
@@ -49,6 +54,11 @@ def main(analysisFileName, graphicFileName):
plt.figtext(0.6, 0.05, 'Total created messages: %d' % created)
plt.title("Message delivery ratio for one-to-one messages")
+# Main function of the script. See script description at the top of the file for further information.
+def main(analysisFileName, graphicFileName):
+ created, delivered, delivery_prob = parseDeliveryProbabilityReport(analysisFileName)
+ createDeliveryPieChart(created, delivered, delivery_prob)
+
# Save to file
plt.savefig(graphicFileName)
plt.close()
--
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