rework

visualizer/visualizer/SeabornPlotter.py

+import os
 import numpy as np
-import matplotlib.pyplot as plt
 import seaborn as sns
+import matplotlib.pyplot as plt
 from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-import os
+
 sns.set_theme()
 
 
@@ -10,7 +11,7 @@ class SeabornPlotter:
     def __init__(self, dataframe, name, data_path):
         self.dataframe = dataframe
         self.name = name
-        self.path = os.path.join(data_path, "Plots")
+        self.path = os.path.join(data_path, 'Plots')
 
     def _make_folder(self, folder_name):
         folder = os.path.join(self.path, folder_name)
@@ -43,15 +44,15 @@ class SeabornPlotter:
         folder = self._make_folder('Various_Events')
         x = df['size']
         y = df['duration']
-        title = "Various Procedure: {}".format(self.name)
+        title = f'Various Procedure: {self.name}'
 
         # Calculate the bins
         bins_x, bins_y = self._prepare_bins(df)
 
         # Set up the plot
         f, ax = plt.subplots(figsize=(16, 9))
-        sns.histplot(x=x, y=y, color="blue", bins=[bins_x, bins_y])
-        self._add_logo("desy_logo.png", logo_size=0.1, logo_x=1.065, logo_y=0)
+        sns.histplot(x=x, y=y, color='blue', bins=[bins_x, bins_y])
+        self._add_logo('desy_logo.png', logo_size=0.1, logo_x=1.065, logo_y=0)
 
         # Calculate the mean for x and y
         mean_x_values = df.groupby('size')['size'].mean()
@@ -64,16 +65,16 @@ class SeabornPlotter:
         ax.set_xscale('log')
         ax.set_yscale('log')
         ax.set_xlabel("Data size [Bytes]")
-        ax.set_ylabel("Time/Event [s]")
+        ax.set_ylabel('Time/Event [s]')
         plt.title(title)
         plt.grid(True)
         plt.xticks(rotation=45)
         plt.legend()
 
         # Save the plot as an SVG and close
-        plt.savefig(os.path.join(folder, f"{title}.svg"), format='svg')
+        plt.savefig(os.path.join(folder, f'{title}.svg'), format='svg')
         plt.close()
-        print(title + "Plot done!")
+        print(title + 'Plot done!')
 
     def plot_scatter_long(self):
         # Extract required data and set up the folder for saving the plot
@@ -81,7 +82,7 @@ class SeabornPlotter:
         folder = self._make_folder('Long_Events')
         x = df['seconds_since_start']
         y = df['duration']
-        title = "Long Procedure: {}".format(self.name)
+        title = f'Long Procedure: {self.name}'
 
         # Set up the plot
         f, ax = plt.subplots(figsize=(16, 9))
@@ -136,7 +137,7 @@ class SeabornPlotter:
         # Loop through each DataFrames
         for df_key, df in dataframes.items():
             operations = df['Group'].unique()
-            title = df_key.replace(".dat", "")
+            title = df_key.replace('.dat', '')
             # Number of measures: 'min', 'max', 'mean', 'med', '10%', '90%'
             num_measures = 6
 
@@ -155,7 +156,7 @@ class SeabornPlotter:
             global_max_speed = max(df['speed'].max() for df in dataframes.values())
 
             # Add a logo to the plot
-            self._add_logo("desy_logo.png", logo_size=0.1, logo_x=1.08, logo_y=0)
+            self._add_logo('desy_logo.png', logo_size=0.1, logo_x=1.08, logo_y=0)
 
             # Create subplots for each measure
             for measure_idx, measure in enumerate(['min', 'max', 'mean', 'med', '10%', '90%']):
@@ -164,7 +165,8 @@ class SeabornPlotter:
                 ax = axes[row_idx, col_idx]
 
                 # Create a barplot for the current measure
-                sns.barplot(data=df, x='Group', y='speed', hue='thread', hue_order=df['thread'].unique(), order=operations, errorbar=None, ax=ax, width=0.95, palette='cool')
+                sns.barplot(data=df, x='Group', y='speed', hue='thread', hue_order=df['thread'].unique(),
+                            order=operations, errorbar=None, ax=ax, width=0.95, palette='cool')
 
                 # Set axis labels and formatting
                 ax.set_yscale('log')
@@ -179,6 +181,6 @@ class SeabornPlotter:
 
             # Save the plot as an SVG and close
             plt.tight_layout()
-            plt.savefig(os.path.join(folder, f"{title}.svg"), format='svg')
+            plt.savefig(os.path.join(folder, f'{title}.svg'), format='svg')
             plt.close()
-            print(title + " Plot done!")
+            print(title + ' Plot done!')

visualizer/visualizer/main.py

@@ -31,9 +31,7 @@ def long(l_dataframes, statistics_path):
         l_plotter.plot_scatter_long()
 
 
-# --------------Main------------
 def main():
-
     # Parse Arguments
     parser = argparse.ArgumentParser()
     parser.add_argument("-d", "--data_path", required=True, help="path to Data from Sim")