diff --git a/visu/test.py b/visu/test.py
new file mode 100644
index 0000000..bbd17c0
--- /dev/null
+++ b/visu/test.py
@@ -0,0 +1,85 @@
+from numpy import *
+from scipy import *
+from scipy.signal import *
+from pylab import *
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.signal import butter, lfilter, freqz
+import os
+
+def butter_lowpass(cutoff, fs, order=5):
+    nyq = 0.5 * fs
+    normal_cutoff = cutoff / nyq
+    b, a = butter(order, normal_cutoff, btype='low', analog=False)
+    return b, a
+
+def butter_lowpass_filter(data, cutoff, fs, order=5):
+    b, a = butter_lowpass(cutoff, fs, order=order)
+    y = lfilter(b, a, data)
+    return y
+
+file_path = "Dataset/Shear_Wave_Rot00_CSV_Data/Shear_x001-x101_y011_Rot00.csv"
+files_path = ['Shear_x001-x101_y{:03d}_Rot00.csv'.format(i) for i in range(10, 62)]
+df = pd.read_csv(file_path)
+
+print(df.head())
+"""
+for file in files_path:
+    file_path = "Dataset/Shear_Wave_Rot00_CSV_Data/" + file
+    df = pd.read_csv(file_path)
+
+    # Créer un DataFrame pour stocker les données transformées
+    transformed_data = pd.DataFrame()
+
+    for index, row in df.iterrows():
+        # Calculate envelope, called m_hat via hilbert transform
+        m_hat = abs(hilbert(row))
+
+        fs = 600.0
+        cutoff = 3.667
+        filtered_m_hat = butter_lowpass_filter(m_hat, cutoff, fs)
+
+        # Ajouter les données transformées au DataFrame
+        transformed_data = pd.concat([transformed_data, pd.DataFrame(filtered_m_hat).T], ignore_index=True)
+
+    # Créer le nom du fichier de sortie en ajoutant "_transform" à la fin du nom du fichier original
+    output_file_path = "Dataset/Shear_transform/" + os.path.splitext(file)[0] + "_transform.csv"
+
+    # Écrire le DataFrame transformé dans le fichier de sortie
+    transformed_data.to_csv(output_file_path, index=False)
+"""
+
+
+
+
+first_row = df.iloc[0]
+# Calculate envelope, called m_hat via hilbert transform
+m_hat = abs(hilbert(first_row))
+
+fs = 600.0
+cutoff = 3.667
+filtered_m_hat = butter_lowpass_filter(m_hat, cutoff, fs)
+
+# Create a new figure for the first plot
+plt.figure()
+plt.plot(m_hat)
+plt.title('Plot of the m_hat')
+
+# Create a new figure for the second plot
+plt.figure()
+plt.plot(first_row)
+plt.title('Plot of the pre_m_hat')
+
+plt.show()
+"""
+# Plot x
+plot(t, x)
+plot(t, m_hat)
+axis('tight')
+xlabel('Time (seconds)')
+ylabel('Amplitude')
+title('X, with calculated envelope')
+legend(['x', 'm_hat'])
+ylim(-2,2)
+show()
+"""