Add j-cook.py

j-cook.py

@@ -0,0 +1,54 @@
+import csv
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import linregress
+
+r2max = 0.2
+# Specify the path to your CSV file
+csv_file_path = 'test-data/500-0.1.csv'
+custom_delimiter = "\t"
+
+# Read the CSV file and convert numbers from strings with commas to floats
+with open(csv_file_path, 'r') as file:
+    # Create a CSV reader object with the specified delimiter
+    csv_reader = csv.reader(file, delimiter=custom_delimiter)
+
+    # Convert numbers from strings with commas to floats
+    data = np.array([[float(cell.replace(',', '.')) for cell in row] for row in csv_reader])
+
+# Treat stress and strain values to be positive
+data[:, 0] = np.abs(data[:, 0])
+data[:, 1] = np.abs(data[:, 1])
+
+
+# Use linregress to perform linear regression
+elasticMax = 10
+r_value = 0
+
+while r_value < r2max and len(data[:, 0]):
+    slope, intercept, r_value, p_value, std_err = linregress(data[:elasticMax, 0], data[:elasticMax, 1])
+    print(r_value)
+    elasticMax += 10
+    
+    
+
+# Create the fitted line using the slope and intercept
+fitted_line = slope * data[:, 0] + intercept
+
+
+# Plot data
+x_values = data[:, 0]
+y_values = data[:, 1]
+
+
+    
+plt.plot(data[:, 0], data[:, 1], label='Orignial data', color='blue')
+plt.plot(data[:, 0], fitted_line, label='Fitted Line', color='red')
+
+# Add labels and legend
+plt.xlabel('Stress')
+plt.ylabel('Strain')
+plt.legend()
+
+# Show the plot
+plt.show()