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- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/splitData.py
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- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/mainScript.py
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/graphs.py
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/main.py
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/gui.py
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/__pycache__/gui.cpython-312.pyc
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/__pycache__/gui.cpython-311.pyc
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/__pycache__/graphs.cpython-312.pyc
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/__pycache__/graphs.cpython-311.pyc
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/__pycache__/mainScript.cpython-312.pyc
- /BioCode/A1_Programming_finale_version/A1_1_PythonCode/__pycache__/mainScript.cpython-311.pyc
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- /BioCode/A1_Programming_finale_version/Data For Bioprogramming/original_data_for_bioprogramming/AgarAgar1Gruppe9,29.04.2024.014Data.csv
- /BioCode/A1_Programming_finale_version/Data For Bioprogramming/original_data_for_bioprogramming/.DS_Store
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BioCode/A1_Programming_finale_version/.Rhistory

+q()

BioCode/A1_Programming_finale_version/A1_1_PythonCode/graphs.py

+import matplotlib.pyplot as plt
+import re
+
+
+class Graphs:
+
+    def __init__(self, filename, dataframe) -> None:
+        self.filename = filename
+        self.df_visualize = dataframe
+
+        print(self.df_visualize.columns)
+
+    def wildcard_for_columnname(self, columnname) -> str:
+        pattern = re.compile(rf'{columnname}.*')
+        matching_columns = [col for col in self.df_visualize.columns if pattern.match(col)]
+        if matching_columns:
+            return matching_columns[0]  # Return the first match instead of a list
+        else:
+            raise ValueError(f"No match found for column: {columnname}")
+
+    def plotting(self):
+
+            
+           
+        # Assuming self.wildcard_for_columnname is a method that returns the appropriate column name based on a pattern
+
+        var_spannung_y_var = self.wildcard_for_columnname("Sigma")
+        var_dehnung_x_var = self.wildcard_for_columnname("Epsilon")
+        var_tip_displacement_x_var = self.wildcard_for_columnname("Tip_Displacement")
+
+        # Create a figure with 2 subplots, smaller size
+        fig, axs = plt.subplots(2, 1, figsize=(10, 10))
+
+        # First subplot: Spannungs-Dehnungs-Diagramm
+        axs[0].plot(self.df_visualize[var_dehnung_x_var], self.df_visualize[var_spannung_y_var], label='Sigma / Epsilon')
+        axs[0].set_title('Spannungs-Dehnungs/Compression-Diagramm /')
+        axs[0].set_ylabel('Sigma (Spannung/Compression)')
+        axs[0].set_xlabel('Epsilon (Dehnung)')
+        axs[0].grid()
+        axs[0].legend()
+
+        # Second subplot: Force-Tip_Displacement
+        axs[1].plot(self.df_visualize[var_tip_displacement_x_var], self.df_visualize["Force (N)"], label='Force/ Tip Displacement')
+        axs[1].set_title('Force-Tip_Displacement')
+        axs[1].set_ylabel('Force (N)')
+        axs[1].set_xlabel('Tip Displacement (m)')
+        axs[1].grid()
+        axs[1].legend()
+
+        # Adjust layout to prevent overlap
+        plt.tight_layout()
+
+        # Show the plot
+        plt.show()
+
+
+    def run(self):
+        self.plotting()

BioCode/A1_Programming_finale_version/A1_1_PythonCode/gui.py

+import tkinter as tk
+from tkinter import filedialog
+
+from mainScript import MainScript
+
+
+class Gui:
+   
+    def __init__(self):
+        self.root = tk.Tk()
+        self.root.title("Main Window")
+        self.root.geometry("400x400")
+        pass
+
+    def selectFileInFolder(self):
+        self.filename = filedialog.askopenfilename(
+            title="Browse File"
+        )
+        self.instanceOfMainskript = MainScript(self.filename)
+        self.instanceOfMainskript.run()
+
+
+    def close(self):
+        self.root.quit()
+
+    def functionWithButton(self):
+# Configure grid
+        self.root.columnconfigure(0, weight=1)
+        self.root.columnconfigure(1, weight=1)
+        self.root.columnconfigure(2, weight=1)
+        self.root.rowconfigure(0, weight=1)
+        self.root.rowconfigure(1, weight=1)
+        self.root.rowconfigure(2, weight=1)
+        self.root.rowconfigure(3, weight=1)
+
+        button = tk.Button(self.root, text='Browse for file!', command=self.selectFileInFolder, height=2, width=20)
+        button.grid(column=1, row=1, pady=10)
+
+        exitButton = tk.Button(self.root, text='Exit', command=self.close, height=2, width=20)
+        exitButton.grid(column=1, row=2, pady=10)
+
+
+    def run(self):
+        self.functionWithButton()
+        self.root.mainloop()
+
+
+        # x,x,x,x,x

BioCode/A1_Programming_finale_version/A1_1_PythonCode/main.py

+from gui import Gui
+
+
+class Main:
+    
+    def __init__(self):
+        self.gui_Instance = Gui()
+
+    def run(self):
+        self.gui_Instance.run()
+
+
+instanceOfMain = Main()
+instanceOfMain.run()     

BioCode/A1_Programming_finale_version/A1_1_PythonCode/mainScript.py

+
+import pandas as pd  # Import the pandas library for data manipulation
+import numpy as np  # Import the numpy library for numerical operations
+import re  # Import the re library for regular expressions
+
+from graphs import Graphs  # Import the Graphs class from the graphs module
+from splitData import SplitColumn  # Import the SplitColumn class from the splitData module
+
+import csv
+
+class MainScript:
+
+    def __init__(self, filename):  # Initialization method
+        self.filename = filename  # Store the filename
+
+    def createDataframe(self) -> pd.DataFrame:
+        with open(self.filename, 'r', encoding='unicode_escape') as file:  # Open the file with the given filename
+            first_line = file.readline()  # Read the first line to determine the delimiter
+        
+        # Determine the delimiter
+        if ';' in first_line:  # Check if the delimiter is a semicolon
+            delimiter = ';'  # Set the delimiter to semicolon
+        elif ',' in first_line:  # Check if the delimiter is a comma
+            delimiter = ','  # Set the delimiter to comma
+        else:
+            raise ValueError("Unknown delimiter in the CSV file.")  # Raise an error if the delimiter is unknown
+        
+        # Read the CSV file with the determined delimiter
+        df = pd.read_csv(self.filename, sep=delimiter, encoding='unicode_escape')  # Read the CSV file into a DataFrame
+        return df  # Return the DataFrame
+
+    def wildcard_for_columnname(self, columnname) -> str:
+        pattern = re.compile(rf'{columnname}.*')  # Compile a regular expression pattern for the column name
+        matching_columns = [col for col in self.dataframe.columns if pattern.match(col)]  # Find matching columns
+        if matching_columns:
+            return matching_columns[0]  # Return the first matching column name
+        else:
+            raise ValueError(f"No match found for column: {columnname}")  # Raise an error if no match is found
+
+    def delete_unnecessary_data(self):
+        print("Before delete_unnecessary_data:")  # Print message before deleting data
+        print(self.dataframe)  # Print the DataFrame
+
+        def delete_not_compress_values():
+            print(self.dataframe.columns)  # Print the DataFrame columns
+            print(self.dataframe["Cycle"])  # Print the "Cycle" column
+            self.dataframe = self.dataframe[self.dataframe["Cycle"] == "1-Compress"]  # Keep only rows where "Cycle" is "1-Compress"
+
+        def delete_negative_force_values():
+            force_val = self.wildcard_for_columnname("Force")  # Get the force column name
+            self.dataframe[force_val] = pd.to_numeric(self.dataframe[force_val], errors='coerce')  # Convert force values to numeric
+            # Keep the first row even if its force value is negative or NaN
+            self.dataframe = self.dataframe[(self.dataframe.index == 0) | (self.dataframe[force_val] >= 0) | self.dataframe[force_val].isna()]  # Filter rows
+
+        delete_not_compress_values()  # Delete non-compress values
+        delete_negative_force_values()  # Delete negative force values
+
+        print("After delete_unnecessary_data:")  # Print message after deleting data
+        print(self.dataframe)  # Print the DataFrame
+        self.dataframe = self.dataframe.reset_index(drop=True)  # Reset the DataFrame index
+
+    def umwandlung_der_micro_newton_values(self):
+        print("Before umwandlung_der_micro_newton_values:")  # Print message before conversion
+        print(self.dataframe)  # Print the DataFrame
+
+        var_force_col = self.wildcard_for_columnname("Force")  # Get the force column name
+        var_current_size_col = self.wildcard_for_columnname("Current Size")  # Get the current size column name
+        var_tip_displacement_col = self.wildcard_for_columnname("Tip Displacement")  # Get the tip displacement column name
+
+        def umwandlung_force():
+            self.dataframe["Force (N)"] = self.dataframe[var_force_col] / np.power(10, 6)  # Convert force values to Newtons
+
+        def umwandlung_current_size():
+            self.dataframe[var_current_size_col] = pd.to_numeric(self.dataframe[var_current_size_col], errors='coerce')  # Convert current size values to numeric
+            self.dataframe["Current_Size (m)"] = self.dataframe[var_current_size_col] / np.power(10, 6)  # Convert current size to meters
+            self.dataframe["Radius (m)"] = (self.dataframe[var_current_size_col] / 2) / np.power(10, 6)  # Calculate radius in meters
+
+        def umwandlung_tip_displacement():
+            self.dataframe[var_tip_displacement_col] = pd.to_numeric(self.dataframe[var_tip_displacement_col], errors='coerce')  # Convert tip displacement values to numeric
+            self.dataframe["Tip_Displacement_in_m (um / 10^6)"] = self.dataframe[var_tip_displacement_col] / np.power(10, 6)  # Convert tip displacement to meters
+
+        umwandlung_force()  # Perform force conversion
+        umwandlung_current_size()  # Perform current size conversion
+        umwandlung_tip_displacement()  # Perform tip displacement conversion
+
+        print("After umwandlung_der_micro_newton_values:")  # Print message after conversion
+        print(self.dataframe)  # Print the DataFrame
+
+    def mathematical_operations(self):
+        print("Before mathematical_operations:")  # Print message before mathematical operations
+        print(self.dataframe)  # Print the DataFrame
+
+        def function_calc_theta_winkel():
+            var_tip_displacement_col = self.wildcard_for_columnname("Tip_Displacement")  # Get the tip displacement column name
+            var_radius_col = self.wildcard_for_columnname("Radius")  # Get the radius column name
+            argument_for_arccos = (self.dataframe[var_radius_col] - self.dataframe[var_tip_displacement_col]) / self.dataframe[var_radius_col]  # Calculate argument for arccos
+            self.dataframe["Theta"] = np.arccos(argument_for_arccos)  # Calculate theta using arccos
+            print("Argument for arccos:", argument_for_arccos)  # Print argument for arccos
+            print("Theta:", np.degrees(self.dataframe["Theta"]))  # Print theta in degrees
+
+        def function_calc_alpha():
+            var_tip_displacement_col = self.wildcard_for_columnname("Tip Displacement")  # Get the tip displacement column name
+            var_radius_col = self.wildcard_for_columnname("Radius")  # Get the radius column name
+            var_theta = self.wildcard_for_columnname("Theta")  # Get the theta column name
+            self.dataframe["Alpha"] = (self.dataframe[var_radius_col] - self.dataframe[var_tip_displacement_col]) * np.tan(self.dataframe[var_theta])  # Calculate alpha
+            print(self.dataframe["Alpha"])  # Print alpha
+
+        def calc_two_parts():
+            def function_calc_left_part():
+                var_radius_col = self.wildcard_for_columnname("Radius")  # Get the radius column name
+                var_alpha_col = self.wildcard_for_columnname("Alpha")  # Get the alpha column name
+                var_upper_part = 2 * (1 + 0.5) * np.power(self.dataframe[var_radius_col], 2)  # Calculate the upper part of the left part
+                var_lower_part = np.power(np.power(self.dataframe[var_alpha_col], 2) + 4 * np.power(self.dataframe[var_radius_col], 2), 1.5)  # Calculate the lower part of the left part
+                return var_upper_part / var_lower_part  # Return the left part
+
+            def function_calc_right_part():
+                var_radius_col = self.wildcard_for_columnname("Radius")  # Get the radius column name
+                var_alpha_col = self.wildcard_for_columnname("Alpha")  # Get the alpha column name
+                v = 0.5  # Define the Poisson's ratio
+                var_upper_part = 1 - np.power(v, 2)  # Calculate the upper part of the right part
+                var_lower_part = np.power(np.power(self.dataframe[var_alpha_col], 2) + 4 * np.power(self.dataframe[var_radius_col], 2), 0.5)  # Calculate the lower part of the right part
+                return var_upper_part / var_lower_part  # Return the right part
+
+            left_part_for_calc = function_calc_left_part()  # Calculate the left part
+            right_part_for_calc = function_calc_right_part()  # Calculate the right part
+            self.dataframe["function_f_a"] = left_part_for_calc + right_part_for_calc  # Calculate the function f_a
+            print(self.dataframe["function_f_a"])  # Print the function f_a
+
+        def function_for_e_calc():
+            def function_calc_left_part():
+                var_radius_col = self.wildcard_for_columnname("Radius")  # Get the radius column name
+                var_alpha_col = self.wildcard_for_columnname("Alpha")  # Get the alpha column name
+                var_force_col = self.wildcard_for_columnname("Force")  # Get the force column name
+                var_tip_displacement_col = self.wildcard_for_columnname("Tip Displacement")  # Get the tip displacement column name
+                v = 0.5  # Define the Poisson's ratio
+                var_upper_part = 3 * (1 - np.power(v, 2)) * self.dataframe[var_force_col]  # Calculate the upper part of the left part
+                var_lower_part = 4 * self.dataframe[var_tip_displacement_col] * self.dataframe[var_alpha_col]  # Calculate the lower part of the left part
+                return var_upper_part / var_lower_part  # Return the left part
+
+            def function_calc_right_part():
+                var_radius_col = self.wildcard_for_columnname("Radius")  # Get the radius column name
+                var_alpha_col = self.wildcard_for_columnname("Alpha")  # Get the alpha column name
+                var_force_col = self.wildcard_for_columnname("Force")  # Get the force column name
+                var_tip_displacement_col = self.wildcard_for_columnname("Tip Displacement")  # Get the tip displacement column name
+                var_f_a_col = self.wildcard_for_columnname("function_f_a")  # Get the function f_a column name
+                var_upper_part = self.dataframe[var_f_a_col] * self.dataframe[var_force_col]  # Calculate the upper part of the right part
+                var_lower_part = np.pi * self.dataframe[var_tip_displacement_col]  # Calculate the lower part of the right part
+                return var_upper_part / var_lower_part  # Return the right part
+
+            left_part_for_calc = function_calc_left_part()  # Calculate the left part
+            right_part_for_calc = function_calc_right_part()  # Calculate the right part
+            self.dataframe["E Modul"] = left_part_for_calc - right_part_for_calc  # Calculate the E modulus
+            print(self.dataframe["E Modul"])  # Print the E modulus
+
+        function_calc_theta_winkel()  # Calculate theta
+        function_calc_alpha()  # Calculate alpha
+        calc_two_parts()  # Calculate the two parts
+        function_for_e_calc()  # Calculate the E modulus
+
+        print("After mathematical_operations:")  # Print message after mathematical operations
+        print(self.dataframe)  # Print the DataFrame
+
+    def e_modul_spannung_dehnung(self):
+        print("Before e_modul_spannung_dehnung:")  # Print message before E modulus, stress, and strain calculations
+        print(self.dataframe)  # Print the DataFrame
+
+        def calc_area():
+            var_radius = self.wildcard_for_columnname("Radius")  # Get the radius column name
+            self.dataframe["Flaeche (m)"] = np.pi * np.power(self.dataframe[var_radius], 2)  # Calculate the area
+
+        def calc_delta():
+            var_area = self.wildcard_for_columnname("Flaeche")  # Get the area column name
+            self.dataframe["Sigma (Spannung) [N /m hoch 2]"] = self.dataframe["Force (N)"] / self.dataframe[var_area]  # Calculate the stress
+
+        def calc_E():
+            var_current_size = self.wildcard_for_columnname("Current_Size")  # Get the current size column name
+            print(self.dataframe)  # Print the DataFrame
+            print(self.dataframe[var_current_size])  # Print the current size column
+            self.dataframe["Epsilon (Dehnung)"] = ((self.dataframe.at[0, var_current_size] - self.dataframe[var_current_size]) / self.dataframe.at[0, var_current_size]) * 100  # Calculate the strain
+
+        def calc_e_modul():
+            var_delta_col = self.wildcard_for_columnname("Sigma")  # Get the stress column name
+            var_e_col = self.wildcard_for_columnname("Epsilon")  # Get the strain column name
+            self.dataframe[var_delta_col] = pd.to_numeric(self.dataframe[var_delta_col], errors='coerce')  # Convert stress values to numeric
+            self.dataframe[var_e_col] = pd.to_numeric(self.dataframe[var_e_col], errors='coerce')  # Convert strain values to numeric
+            self.dataframe["E_Modul (Spannung / Dehnung)"] = self.dataframe[var_delta_col] / self.dataframe[var_e_col]  # Calculate the E modulus
+
+        calc_area()  # Calculate the area
+        calc_delta()  # Calculate the stress
+        calc_E()  # Calculate the strain
+        calc_e_modul()  # Calculate the E modulus
+
+        print("After e_modul_spannung_dehnung:")  # Print message after E modulus, stress, and strain calculations
+        print(self.dataframe)  # Print the DataFrame
+
+    def run(self):
+        self.original_dataframe = self.createDataframe()  # Create the original DataFrame
+        self.dataframe = self.original_dataframe.copy()  # Make a copy of the original DataFrame
+
+        # self.split_data_instance = SplitColumn(self.original_dataframe)  # Initialize SplitColumn class with the original DataFrame
+        # self.dataframe_for_biocalc = self.split_data_instance.run()  # Run the SplitColumn instance
+
+        self.var_len_of_dataframe = len(self.dataframe)  # Get the length of the DataFrame
+
+        self.delete_unnecessary_data()  # Delete unnecessary data
+        self.umwandlung_der_micro_newton_values()  # Convert micro newton values
+        
+        self.mathematical_operations()  # Perform mathematical operations
+        self.e_modul_spannung_dehnung()  # Calculate E modulus, stress, and strain
+       
+        self.dataframe.to_csv(self.filename, index=False)  # Save the processed DataFrame to CSV
+        new_filename = self.filename + '_original_data.csv'
+        print(self.filename)
+        self.original_dataframe.to_csv(new_filename, index=False)
+
+        self.graphs_instance = Graphs(self.filename, self.dataframe)  # Initialize Graphs class with filename and DataFrame
+        self.graphs_instance.run()  # Run the Graphs instance to generate graphs
+