new plot

simrunner/data_config.json

 {
   "data_set_1":{
-    "--posixDataDir": "protokoll",
+    "--posixDataDir": "fehler_test",
     "-n": "15",
     "-s": "2M",
     "-i": "0.1s",
     "-N": "4"},
   "data_set_2":{
     "--posixDataDir": "protokoll",
-    "-q": "20",
+    "-q": "error test parameter",
     "-n": "20",
     "-s": "1M",
     "-i": "0.1s",

simrunner/simrunner.py

@@ -44,7 +44,7 @@ def run_sim(e_list, d_path, s_path):
         print(output)
         print(errors)
         if return_code > 0:
-            print(return_code)
+            print("return code: ", return_code)
 
 
 # --- MAIN ---

visualizer/README.md

-Install the controller and its dependencies 
+Install the visualizer and its dependencies 
 by following these instructions:
 
 Please make sure that you are using Python 3.9 or higher.
 
 Please make sure that you have installed setuptools for python3
 if not:
-   sudo apt install python3-setuptools
+   apt install python3-setuptools
    or
    pip install setuptools
 
-1. Navigate to /detectorwritesim/controller
+1. Navigate to /detectorwritesim/visualizer
 2. Create a new Python3.9 or higher virtual environment:
    python3 -m venv visualizerenv
 3. Activate the environment:
@@ -18,6 +18,6 @@ if not:
    python3 setup.py install
 
 Now you have completed the setup process, 
-you can start the controller by entering the following command:
+you can start the visualizer by entering the following command:
 
 visualizer_run
\ No newline at end of file

visualizer/data_folder_path.json

-"/home/user/Projekt/detectorwritesim/output1"
\ No newline at end of file

visualizer/setup.py

@@ -11,6 +11,7 @@ setup(
         'pandas',
         'seaborn',
         'matplotlib',
+        'scipy'
     ],
     entry_points={
         'console_scripts': [

visualizer/visualizer/SeabornPlotter.py

 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+from scipy.stats import iqr
 
 
 class SeabornPlotter:
@@ -22,10 +23,11 @@ class SeabornPlotter:
         y_bins = [y_start + (y_step * i) for i in range(int((y_end - y_start) / y_step) + 1)]
 
         f, ax = plt.subplots(figsize=(16, 20))
-        sns.scatterplot(x=x, y=y, s=5, color=".15", hue=list(self.dataframe.index.get_level_values(0)))
-        sns.histplot(x=x, y=y, bins=[x_bins, y_bins], pthresh=.1, cmap="coolwarm")
-        ax.set_xscale('log')
+        sns.violinplot(x=x, y=y, s=5, color=".15", palette="light:b", inner="points", hue=list(self.dataframe.index.get_level_values(0)))
+        #sns.histplot(x=x, y=y, bins=[x_bins, y_bins], pthresh=.1, cmap="coolwarm")
+        #ax.set_xscale('log')
         ax.set_yscale('log')
 
         plt.tight_layout()
         plt.show()
+