diff --git a/mysignal/__init__.py b/mysignal/__init__.py
index 124e462..c3bb296 100644
--- a/mysignal/__init__.py
+++ b/mysignal/__init__.py
@@ -1 +1 @@
-__version__ = '0.1.7'
+__version__ = '0.1.8'
diff --git a/mysignal/phasefreq.py b/mysignal/phasefreq.py
index cb8e4ce..4099d2f 100644
--- a/mysignal/phasefreq.py
+++ b/mysignal/phasefreq.py
@@ -3,6 +3,9 @@ import numpy as np
 import pandas as pd
 import pywt
 
+import matplotlib.pyplot as plt
+
+
 from scipy.fft import fft, fftfreq
 from scipy.signal import find_peaks, hilbert, butter, filtfilt, correlate, sosfiltfilt
 from scipy.stats import linregress
@@ -104,6 +107,30 @@ def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, requ
     return filtered_signals, dominant_freqs, time_filtered
 
 
+def plot_filtered_modes(t, e, e_filtered, e_frequencies, e_time, n_modes):
+
+    fig, ax = plt.subplots(nrows=n_modes+1, ncols=2, figsize=(12, 4 * n_modes), gridspec_kw={'width_ratios': [3, 1]})
+     
+    ax[0, 0].plot(t, e)
+    ax[0, 0].set_title('Signal original')
+    ax[0, 0].set_xlabel('Temps (s)')
+    ax[0, 0].set_ylabel('Amplitude')
+    
+    
+    
+    for i in range(n_modes):
+        ax[i+1, 0].plot(e_time, e_filtered[i])
+        ax[i+1, 0].set_title(f'Mode #{i+1} filtré ({e_frequencies[i]:.2f} Hz)')
+        ax[i+1, 0].set_xlabel('Temps (s)')
+        ax[i+1, 0].set_ylabel('Amplitude')
+    
+        ax[i+1, 1].plot(e_time, e_filtered[i])
+        ax[i+1, 1].set_xlim(left=0.5 * e_time.mean(), right=0.5*e_time.mean() + 10/e_frequencies[i])
+    plt.tight_layout()
+    plt.show()
+
+
+
 def analyze_signal_Hilbert(time, e_signal, s_signal, freq_rtol=0.01):
     """
     Analyzes the dominant frequencies, phase shift, time shift, and periods between two signals e_signal(t) and s_signal(t) from a DataFrame.
@@ -544,4 +571,23 @@ def analyze_signal_wavelet(time, e_signal, s_signal, freq_rtol=0.01):
            'delay': time_shift}
     
     return res
- 
+
+
+def plot_phases(e_time, e_filtered, s_filtered, n_modes, callback=analyze_signal_wavelet):
+    fig, ax = plt.subplots(nrows=n_modes, figsize=(12, 6))
+    
+    
+    for mod in range(n_modes):
+    
+        res = callback(e_time, e_filtered[mod], s_filtered[mod], freq_rtol=0.3)
+    
+        ax[mod].set_title(f'Freq: {e_frequencies[mod]:.3f}, Phase: {res['phase']:.3f}, Delay: {res['delay']:.3f}' )
+        ax[mod].plot(e_time, e_filtered[mod], label='e')
+        ax[mod].plot(e_time, s_filtered[mod], label='s')
+        ax[mod].set_xlim(left=20, right=230)
+    
+        for a in ax:
+            a.legend()
+    
+    plt.tight_layout();
+    plt.show();
\ No newline at end of file