prepared saving opportunity for different file formats

plotting/timeseries/aggregateTimeseries.py

@@ -25,6 +25,7 @@ class TimeSeriesAggregator:
         self.agg_throughput = None
         self.cols_latency = None
         self.cols_throughput = None
+        self.format = 'pdf'
         self.run = -1
         self.files = 0
         self.min_t = 0
@@ -59,6 +60,7 @@ class TimeSeriesAggregator:
         parser.add_argument('--plotAverage', '-pa', action='store_true', default=False, required=False)
         # optional plotting of outliers as scatter points, processed if '-outscat' is given
         parser.add_argument('--outlier_scatter', '-outscat', action='store_true', default=False, required=False)
+        parser.add_argument('--format', '-f', type=str, nargs=1, required=False)
         # fix the parser as main parser object of the TimeSeriesAggregator object
         self.parser = parser
 
@@ -92,6 +94,8 @@ class TimeSeriesAggregator:
         self.plotAverage = args.plotAverage
         # if scatter plot shall be added
         self.outlier_scatter = args.outlier_scatter
+        if args.format is not None:
+            self.format = args.format[0]
 
     def extract_input(self):
         """
@@ -178,7 +182,8 @@ class TimeSeriesAggregator:
         # calculate the overall mean
         overall_mean = self.agg_throughput["Mean"].mean()
         # draw the overall mean as horizontal line
-        ax.axhline(overall_mean, linestyle='--', label='Overall Average', color='black')
+        if self.plotAverage:
+            ax.axhline(overall_mean, linestyle='--', label='Overall Average', color='black')
         # draw the marker with its label on the graph if given
         if self.marker_pos_x is not None:
             ax.axvline(self.marker_pos_x, color='red', linestyle=':', label=self.label_text)
@@ -196,7 +201,7 @@ class TimeSeriesAggregator:
         # indicating saving process
         print("Saving throughput plot to disk: " + output_file)
         # store the created picture
-        plt.savefig(output_file, format='pdf')
+        plt.savefig(output_file, format=self.format)
         plt.close()
 
     def plot_latency(self):
@@ -228,7 +233,8 @@ class TimeSeriesAggregator:
         # calculate the overall mean
         overall_mean = self.agg_latency["Mean"].mean()
         # draw the overall mean as horizontal line
-        ax.axhline(overall_mean, linestyle='--', label='Overall Average', color='black')
+        if self.plotAverage:
+            ax.axhline(overall_mean, linestyle='--', label='Overall Average', color='black')
         # draw the marker with its label on the graph if given
         if self.marker_pos_x is not None:
             ax.axvline(self.marker_pos_x, color='red', linestyle=':', label=str(self.label_text))
@@ -253,14 +259,14 @@ class TimeSeriesAggregator:
                     x, y = zip(*item)
                     plt.scatter(x, y, color=color_dict[key], label="outlier {}".format(key))
         # modify default legend
-        ax.legend(loc='lower center', ncol=3, borderpad=1, prop={'size': 6})
+        ax.legend(loc='lower center', ncol=4, borderpad=1, prop={'size': 6})
         # adjust size of the figure
         plt.plot(figsize=(1000, 600))
 
         # indicating saving process
         print("Saving latency plot to disk: " + output_file)
         # store the created picture
-        plt.savefig(output_file, format='pdf')
+        plt.savefig(output_file, format=self.format)
 
     # @staticmethod
     def remove_outliers(self, df_in, col_name):