Merge branch 'master' into packet-exchange

measure-analysis/measure-analysis.py

@@ -13,11 +13,11 @@ import matplotlib.pyplot as plt
 
 class MeasureSetHandler:
 
-    report_description = ("This report was generated with the measure-analysis.py script. json formated measures "
-                         "were imported in the measures folder using the import functionnality of the script. This report was then "
-                         "generated using these measures. Metadatas are included with the measures, such as the kernel "
+    report_description = ("This report was generated with the measure-analysis.py script.\nJSON formated measures "
+                         "were imported in the measures folder using the import functionnality of the script, this report was then "
+                         "generated using these measures.\nMetadata is included with the measures, such as the kernel "
                          "version used, the boot parameters passed, various others parameters specific to the measure, etc... "
-                         "Measures measuring the same propriety are grouped together in tables and graphs, and are identified "
+                         "\nMeasures measuring the same propriety are grouped together in tables and graphs, and are identified "
                          "by their diverging metadatas. This is useful to analyse the effect of specific parameters on the  "
                          "measured propriety.")
                          
@@ -134,9 +134,6 @@ class MeasureSetHandler:
 
     def generate_graphs(self, metadata_masks):
 
-        # List of colors to use to superimpose the different plots
-        colors = ['red', 'blue', 'green', 'purple', 'yellow']
-
         # For each type of measure set
         for mtype in self.measure_sets:
 
@@ -169,9 +166,9 @@ class MeasureSetHandler:
                     metadata_infos.append(", ".join(metadata_info))
 
                     if props_type == 'histogram':
-                        subplots.append(measure.generate_histogram(i, colors[j], metadata_info))
+                        subplots.append(measure.generate_histogram(i, metadata_info))
                     else:
-                        subplots.append(measure.generate_chrono_graph(i, colors[j], metadata_info))
+                        subplots.append(measure.generate_chrono_graph(i, metadata_info))
 
                 fig, ax = plt.gcf(), plt.gca()
 
@@ -501,7 +498,7 @@ class MeasureSet:
 
             self.add_chronological(props_names, props)
 
-    def generate_histogram(self, i, color, name):
+    def generate_histogram(self, i, name):
 
         bins = [i for i in range(self.min[i], self.max[i] + 1)]
         vals = self.props[i][self.min[i] + self.middle : self.max[i] + self.middle + 1]
@@ -513,10 +510,10 @@ class MeasureSet:
 
         return plt.bar(bins, vals, alpha=0.4)
 
-    def generate_chrono_graph(self, i, color, name):
+    def generate_chrono_graph(self, i, name):
         prop = self.props[i]
         x = [i for i in range(len(prop))]
-        return plt.plot(x, prop, color=color)
+        return plt.plot(x, prop)
 
     def generate_table(self, headers=True, values=True, metadata_mask=[], props_lens=[]):
 

measure-analysis/measure-report.md

 ## Measurements
 
-This report was generated with the measure-analysis.py script. json formated measures were imported in the measures folder using the import functionnality of the script. This report was then generated using these measures. Metadatas are included with the measures, such as the kernel version used, the boot parameters passed, various others parameters specific to the measure, etc... Measures measuring the same propriety are grouped together in tables and graphs, and are identified by their diverging metadatas. This is useful to analyse the effect of specific parameters on the  measured propriety.
+This report was generated with the measure-analysis.py script.
+JSON formated measures were imported in the measures folder using the import functionnality of the script, this report was then generated using these measures.
+Metadata is included with the measures, such as the kernel version used, the boot parameters passed, various others parameters specific to the measure, etc... 
+Measures measuring the same propriety are grouped together in tables and graphs, and are identified by their diverging metadatas. This is useful to analyse the effect of specific parameters on the  measured propriety.
 
 ### Abbreviations used