bullshit in DataFrameCreator.py but i have to save

simrunner/simrunner.py

@@ -139,4 +139,3 @@ event_list = create_event_list()
 
 # Run detectorSimulation
 run_sim(event_list, data_path, simulation_path, stat_path)
-

visualizer/visualizer/DataFrameCreator.py

@@ -6,39 +6,73 @@ class DataFrameCreator:
     def __init__(self, f_path):
         self.folder_path = f_path
 
-    def create_dataframes_data(self):
-        dfs = {}
-        for root, _, folder in os.walk(self.folder_path):
-            if os.path.basename(root) == "statistics":
-                print("this was statistic")
-                continue
-            for filename in folder:
-                if filename.endswith('.dat'):
-                    file_path = os.path.join(root, filename)
-                    dataframe_name = os.path.splitext(filename)[0]
-                    dataframe_type = dataframe_name.split('-')[1]
-                    dataframe = pd.read_csv(file_path, delimiter=" ", header=0)
-
-                    if dataframe_type in dfs:
-                        inner_dict = dfs[dataframe_type]
-                        if dataframe_name in inner_dict:
-                            # DataFrame hinzufügen
-                            inner_dict[dataframe_name] = pd.concat([inner_dict[dataframe_name], dataframe],
-                                                                   ignore_index=True)
-                        else:
-                            inner_dict[dataframe_name] = dataframe
-                    else:
-                        dfs[dataframe_type] = {
-                            dataframe_name: dataframe
-                        }
-
+    @staticmethod
+    def _merge_dataframes(dfs):
         merged_dfs = {}
         for outer_key in dfs:
             merged_dataframe = pd.concat(dfs[outer_key].values(), keys=dfs[outer_key].keys())
             merged_dataframe_sorted = merged_dataframe.sort_index()
             merged_dfs[outer_key] = merged_dataframe_sorted
+        return merged_dfs
 
+    def read_data(self, root, _, folder):
+        dataframe, dataframe_type, dataframe_name = "", "", ""
+        for filename in folder:
+            if filename.endswith('.dat'):
+                file_path = os.path.join(root, filename)
+                dataframe_name = os.path.splitext(filename)[0]
+                dataframe_type = dataframe_name.split('-')[1]
+                dataframe = pd.read_csv(file_path, delimiter=" ", header=0)
+        return dataframe, dataframe_type, dataframe_name
+
+    def create_dataframe_long(self, root, _, folder):
+        dfs = {}
+        for filename in folder:
+            if filename.endswith('.dat'):
+                dataframe, dataframe_type, dataframe_name = self.read_data(root, _, folder)
+                if dataframe_type in dfs:
+                    inner_dict = dfs[dataframe_type]
+                    if dataframe_name in inner_dict:
+                        inner_dict[dataframe_name] = pd.concat([inner_dict[dataframe_name], dataframe],
+                                                               ignore_index=True)
+                    else:
+                        inner_dict[dataframe_name] = dataframe
+                else:
+                    dfs[dataframe_type] = {
+                        dataframe_name: dataframe
+                    }
+        print(dfs)
+        return dfs
+
+    def create_dataframe_statistics(self, root, _, folder):
+        print("statistics" + root + folder)
+
+    def create_dataframes_various(self, root, _, folder):
+        dfs = {}
+
+        dataframe, dataframe_type, dataframe_name = self.read_data(root, _, folder)
+        if dataframe_type in dfs:
+            inner_dict = dfs[dataframe_type]
+            if dataframe_name in inner_dict:
+                inner_dict[dataframe_name] = pd.concat([inner_dict[dataframe_name], dataframe], ignore_index=True)
+            else:
+                inner_dict[dataframe_name] = dataframe
+        else:
+            dfs[dataframe_type] = {
+                dataframe_name: dataframe
+            }
+        merged_dfs = self._merge_dataframes(dfs)
         return merged_dfs
 
+    def create(self):
+        various_frames = {}
+        for root, _, folder in os.walk(self.folder_path):
+            if os.path.basename(root) == "statistics":
+                statistic_frames = self.create_dataframe_statistics(root, _, folder)
+            elif os.path.basename(root) == "Data_Set_Long":
+                long_frames = self.create_dataframe_long(root, _, folder)
+            else:
+                self.create_dataframes_various(root, _, folder)
 
+        return various_frames
 

visualizer/visualizer/SeabornPlotter.py

@@ -14,45 +14,63 @@ class SeabornPlotter:
         self.name = name
         self.current_plot = None
 
-    def plot(self):
-        df = self.dataframe
-        x = df['size']
-        y = df['duration']
-        title = "Procedure: {}".format(self.name)
+    @staticmethod
+    def _add_logo(logo_path, logo_size, logo_x, logo_y):
+        current_directory = os.path.dirname(os.path.abspath(__file__))
+        logo_path = os.path.join(current_directory, logo_path)
+        logo_img = plt.imread(logo_path)
+        imagebox = OffsetImage(logo_img, zoom=logo_size)
+        ab = AnnotationBbox(imagebox, (logo_x, logo_y), xycoords='axes fraction', frameon=False)
+        plt.gca().add_artist(ab)
 
-        # Bin Grenzen festlegen
+    @staticmethod
+    def _prepare_bins(df):
         n = df['size'].nunique()
         bins_x = np.zeros(n + 1, dtype=np.float64)
         bins_x[0] = 0.75 * (1 << 4)
         for i in range(1, n + 1):
             bins_x[i] = bins_x[i - 1] * 2
         bins_y = np.logspace(-6, 0, num=101)
+        return bins_x, bins_y
 
-        # Achsen und Plot definieren
+    @staticmethod
+    def plot_histogram_various(x, y, title, bins_x, bins_y):
         f, ax = plt.subplots(figsize=(16, 9))
-        sns.histplot(x=x, y=y, color="blue", bins=[bins_x, bins_y],)
-
-        # Setze die Skala der x-Achse auf logarithmisch
+        sns.histplot(x=x, y=y, color="blue", bins=[bins_x, bins_y])
         ax.set_xscale('log')
         ax.set_yscale('log')
+        ax.set_xlabel("Data size [Bytes]")
+        ax.set_ylabel("Time/Event [s]")
+        plt.title(title)
+        plt.grid(True)
+        plt.xticks(rotation=45)
 
-        # Setze die Beschriftungen
+    @staticmethod
+    def plot_histogram_long(x, y, title, bins_x, bins_y):
+        f, ax = plt.subplots(figsize=(16, 9))
+        sns.histplot(x=x, y=y, color="blue", bins=[bins_x, bins_y])
+        ax.set_xscale('log')
+        ax.set_yscale('log')
         ax.set_xlabel("Data size [Bytes]")
         ax.set_ylabel("Time/Event [s]")
         plt.title(title)
         plt.grid(True)
         plt.xticks(rotation=45)
-        current_directory = os.path.dirname(os.path.abspath(__file__))
-        logo_path = os.path.join(current_directory, "desy_logo.png")
-        logo_img = plt.imread(logo_path)
-        logo_size = 0.15
-        logo_x = 1.065
-        logo_y = 1.04
-        imagebox = OffsetImage(logo_img, zoom=logo_size)
-        ab = AnnotationBbox(imagebox, (logo_x, logo_y), xycoords='axes fraction', frameon=False)
-        plt.gca().add_artist(ab)
 
-        # Speichern und Anzeigen
+    def plot(self):
+        df = self.dataframe
+
+        # if self.name
+        x = df['size']
+        y = df['duration']
+        title = "Procedure: {}".format(self.name)
+
+        bins_x, bins_y = self._prepare_bins(df)
+        self.plot_histogram_various(x, y, title, bins_x, bins_y)
+
+        self._add_logo("desy_logo.png", logo_size=0.15, logo_x=1.065, logo_y=1.04)
+
+        # Save and View
         plt.savefig(title + ".svg", format='svg')
         plt.show()
 

visualizer/visualizer/main.py

@@ -16,12 +16,12 @@ def main():
 
     # Import Data
     creator = DataFrameCreator(data_path)
-    dataframes = creator.create_dataframes_data()
+    dataframes = creator.create()
     for frame in dataframes.keys():
         if not dataframes[frame].empty:
             # Setup Plotter
             frame_name = frame
-
+            print(frame_name)
             plotter = SeabornPlotter(dataframes[frame], frame_name)
 
             # Plotter run