diff --git a/src/report/DataSyncReport.java b/src/report/DataSyncReport.java
index 1fde09e235c5cd31357d5c38070d1d8490017744..573f23aa2b7dab651a046d364021d23515a29053 100644
--- a/src/report/DataSyncReport.java
+++ b/src/report/DataSyncReport.java
@@ -127,6 +127,7 @@ public class DataSyncReport extends SamplingReport{
//Write out statistics gathered over all hosts with database
write("sim_time: " + format(getSimTime()) +", "+
"avg_used_mem: " + getAverage(usedDataBasePercentage) +"%, "+
+ "min_used_mem: " + getMinimum(usedDataBasePercentage) +"%, "+
"max_used_mem: " + getMaximum(usedDataBasePercentage) +"%, "+
"med_avg_data_util: " + getMedian(averageDataUtility)+ ", "+
"avg_data_util: " + getAverage(averageDataUtility) + ", "+
diff --git a/src/report/Report.java b/src/report/Report.java
index 1b9be30127baa6e46572ce35c341d9fe89fbe604..479e69888737a25cf797301b667ef0028886e107 100644
--- a/src/report/Report.java
+++ b/src/report/Report.java
@@ -414,6 +414,21 @@ public abstract class Report {
return format(max);
}
+ /**
+ * Returns the minimum of the values in the list
+ * as a formatted string with the given numerical precision.
+ * Return "NaN" for empty lists.
+ *
+ * @param values the list to search the minimum in
+ * @return a formatted string of the minimum in the list
+ */
+ public String getMinimum(List<Double> values){
+ if (values.isEmpty()){
+ return NAN;
+ }
+ double min = Collections.min(values);
+ return format(min);
+ }
/**
* Returns the variance of the values in the List.
*
diff --git a/toolkit/reportSummary/dataSyncAnalysis.py b/toolkit/reportSummary/dataSyncAnalysis.py
index 0cae787ac5343627fd55376813926164e1bd32f8..f714e522849e5dda85b6ee434f5538392a8aa1d2 100644
--- a/toolkit/reportSummary/dataSyncAnalysis.py
+++ b/toolkit/reportSummary/dataSyncAnalysis.py
@@ -15,10 +15,10 @@ import matplotlib.ticker as ticker
# (2) a path to save the resulting graphic to.
#
# Data sync stats for scenario realisticScenario
-# sim_time: 60.00, avg_used_mem: 0.00%, max_used_mem: 0.00%, med_avg_data_util: 0.99, avg_data_util: 0.99, med_avg_data_age: 21.15, avg_data_age: 24.56, med_max_data_age: 21.15, med_avg_data_dist: 97.41, avg_data_dist: 90.05, med_max_data_dist: 97.41
+# sim_time: 60.00, avg_used_mem: 0.00%, min_used_mem: 0.00%, max_used_mem: 0.00%, med_avg_data_util: 0.99, avg_data_util: 0.99, med_avg_data_age: 21.15, avg_data_age: 24.56, med_max_data_age: 21.15, med_avg_data_dist: 97.41, avg_data_dist: 90.05, med_max_data_dist: 97.41
# avg_ratio_map: 0.00%, avg_ratio_marker: 29.41%, avg_ratio_skill: 47.06%, avg_ratio_res: 23.53%
#
-# sim_time: 120.10, avg_used_mem: 0.00%, max_used_mem: 0.00%, med_avg_data_util: 0.99, avg_data_util: 0.98, med_avg_data_age: 42.55, avg_data_age: 54.16, med_max_data_age: 42.55, med_avg_data_dist: 117.17, avg_data_dist: 146.08, med_max_data_dist: 117.17
+# sim_time: 120.10, avg_used_mem: 0.00%, min_used_mem: 0.00%, max_used_mem: 0.00%, med_avg_data_util: 0.99, avg_data_util: 0.98, med_avg_data_age: 42.55, avg_data_age: 54.16, med_max_data_age: 42.55, med_avg_data_dist: 117.17, avg_data_dist: 146.08, med_max_data_dist: 117.17
# avg_ratio_map: 0.00%, avg_ratio_marker: 23.64%, avg_ratio_skill: 32.73%, avg_ratio_res: 43.64%
# ...
@@ -30,6 +30,7 @@ def main(analysisFileName, graphicFileName):
# Interpret lines to find memory consumption, data utility, data distance and data age metrics over time
timePoints = []
averageMemoryConsumption = []
+ minimumMemoryConsumption = []
maximumMemoryConsumption = []
medianAverageDataUtility = []
averageAverageDataUtility = []
@@ -42,7 +43,7 @@ def main(analysisFileName, graphicFileName):
nextTimePoint = 0
for line in report:
match = re.match("sim_time: (\d+.\d+), "
- "avg_used_mem: (\d+.\d+)%, ""max_used_mem: (\d+.\d+)%, "
+ "avg_used_mem: (\d+.\d+)%, min_used_mem: (\d+.\d+)%, max_used_mem: (\d+.\d+)%, "
"med_avg_data_util: (\d+.\d+), avg_data_util: (\d+.\d+), "
"med_avg_data_age: (\d+.\d+), avg_data_age: (\d+.\d+), med_max_data_age: (\d+.\d+), "
"med_avg_data_dist: (\d+.\d+), avg_data_dist: (\d+.\d+), med_max_data_dist: (\d+.\d+)", line)
@@ -57,15 +58,16 @@ def main(analysisFileName, graphicFileName):
# Make sure to use correct unit (minutes, kilometres)
timePoints.append(timePoint)
averageMemoryConsumption.append(float(match.group(2)))
- maximumMemoryConsumption.append(float(match.group(3)))
- medianAverageDataUtility.append(float(match.group(4)))
- averageAverageDataUtility.append(float(match.group(5)))
- medianAverageDataAge.append(float(match.group(6)) / 60)
- averageAverageDataAge.append(float(match.group(7)) / 60)
- medianMaximumDataAge.append(float(match.group(8)) / 60)
- medianAverageDataDistance.append(float(match.group(9)) / 1000)
- averageAverageDataDistance.append(float(match.group(10)) / 1000)
- medianMaximumDataDistance.append(float(match.group(11)) / 1000)
+ minimumMemoryConsumption.append(float(match.group(3)))
+ maximumMemoryConsumption.append(float(match.group(4)))
+ medianAverageDataUtility.append(float(match.group(5)))
+ averageAverageDataUtility.append(float(match.group(6)))
+ medianAverageDataAge.append(float(match.group(7)) / 60)
+ averageAverageDataAge.append(float(match.group(8)) / 60)
+ medianMaximumDataAge.append(float(match.group(9)) / 60)
+ medianAverageDataDistance.append(float(match.group(10)) / 1000)
+ averageAverageDataDistance.append(float(match.group(11)) / 1000)
+ medianMaximumDataDistance.append(float(match.group(12)) / 1000)
# Create four graphics over time: one each for memory consumption, data utility, data distance and data age
fig = plt.figure(figsize=(12,12))
@@ -88,7 +90,9 @@ def main(analysisFileName, graphicFileName):
addSubplot(3, title='Memory Consumption',
ylabel='Used memory',
- plots=[(averageMemoryConsumption, 'Average'), (maximumMemoryConsumption, 'Maximum')])
+ plots=[(averageMemoryConsumption, 'Average'),
+ (maximumMemoryConsumption, 'Maximum'),
+ (minimumMemoryConsumption, 'Minimum')])
addSubplot(2, title='Average Data Utility in Local Database',
ylabel='Average data utility',
plots=[(medianAverageDataUtility, 'Median'), (averageAverageDataUtility, 'Average')])