Plot broadcast distribution

Script can be called like this: python [...]/toolkit/reportSummary/broadcastAnalysis.py [...]\reports\broadcastMessageAnalysis.txt [...]reports\graphics\broadcastAnalysis.png

It generates images like this:

We do not need a seperate script for delay as the multicast graphics MR !130 (merged) includes that. That one can be called with:

python [...]/toolkit/reportSummary/delayDistributionAnalysis.py [...]/reports/messageDelayAnalysis.txt BROADCAST 2 reports/BroadcastMessageDelay2.png

This MR is based on !130 (merged) and therefore blocked by that.

toolkit/reportSummary/broadcastAnalysis.py

+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import sys
+import re
+from readFileUtilities import findNextLineContaining
+
+# Script that translates broadcast analysis into plots of average and minimum message distribution over time.
+# Takes as arguments
+# (1) a broadcast analysis file
+# (2) path to save the graphic to.
+#
+# The broadcast analysis file should have a format like:
+#
+# Reached people by broadcasts of prio 2 by time after creation:
+# time	 avg	 min
+# 300		22.75		0
+# 600		156.19		0
+# 900		380.53		0
+# ...
+#
+# Reached people by broadcasts of prio 3 by time after creation:
+# time	 avg	 min
+# 300		22.13		0
+# ...
+
+# In a broadcast analysis file given as a collection of lines, finds all lines concerning the specified priority.
+def findLinesConcerningPriority(all_lines, priority):
+    correctPriorityLine = findNextLineContaining(
+        all_lines, 0, "Reached people by broadcasts of prio {} by time after creation:".format(priority))
+    nextPriorityLine = findNextLineContaining(all_lines, correctPriorityLine + 1, "prio")
+    return all_lines[correctPriorityLine:nextPriorityLine]
+
+# Draws two functions over the same x values.
+# Labels are selected as appropriate for broadcast analysis.
+def drawPlots(x, y_minimum, y_average, priority):
+    plt.title('Broadcast distribution\n Priority {}'.format(priority))
+    plt.xlabel('Minutes since message creation')
+    plt.ylabel('Reached people')
+    plt.plot(x, y_minimum, '.-', label='Minimum')
+    plt.plot(x, y_average, '.-',  label='Average')
+    plt.legend(loc='upper left')
+    plt.grid(True)
+    axes = plt.gca()
+    axes.set_xlim(xmin = 0)
+    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)
+
+# Read broadcast analysis from file
+with open(sys.argv[1]) as file_name:
+    analysis = file_name.readlines()
+
+# Only look at priorities 2, 5 and 9
+prio2Analysis = findLinesConcerningPriority(analysis, 2)
+prio5Analysis = findLinesConcerningPriority(analysis, 5)
+prio9Analysis = findLinesConcerningPriority(analysis, 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)
+
+# Save to file
+plt.tight_layout()
+plt.savefig(sys.argv[2])