From 5b47050819ea64cf78b6609bbe7399a3fd2f89b4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <devel@sciunto.org>
Date: Tue, 6 Jan 2026 16:22:11 +0100
Subject: [PATCH] upd

---
 mysignal/__init__.py  |  2 +-
 mysignal/phasefreq.py | 21 +++++++--------------
 2 files changed, 8 insertions(+), 15 deletions(-)

diff --git a/mysignal/__init__.py b/mysignal/__init__.py
index e23e22c..b75ec92 100644
--- a/mysignal/__init__.py
+++ b/mysignal/__init__.py
@@ -1 +1 @@
-__version__ = '0.1.29'
+__version__ = '0.1.30'
diff --git a/mysignal/phasefreq.py b/mysignal/phasefreq.py
index 38759de..33a151a 100644
--- a/mysignal/phasefreq.py
+++ b/mysignal/phasefreq.py
@@ -597,26 +597,16 @@ def get_phases(e_time, e_filtered, s_filtered, n_modes, callback=analyze_signal_
         for mod in range(n_modes)
     )
 
-    return pd.DataFrame(results)
-
-#    res = []
-#
-#    for mod in range(n_modes):
-#        output = callback(e_time, e_filtered[mod], s_filtered[mod], freq_rtol=0.3)
-#        res.append({
-#                    'mode': mod,
-#                    'freq': output['freq_e'],
-#                    'phase': output['phase'],
-#                    'delay': output['delay']
-#            })
-#    return pd.DataFrame(res)
+    df = pd.DataFrame(results)
+    df['puls'] = 2 * np.pi * df['freq']
+    return df
 
 
 def plot_phases(e_time, e_filtered, e_frequencies, s_filtered, n_modes, df, output=None):
     """
     """
 
-    fig, ax = plt.subplots(nrows=n_modes, ncols=2, figsize=(12, 2 * n_modes), gridspec_kw={'width_ratios': [3, 1]})
+    fig, ax = plt.subplots(nrows=n_modes, ncols=3, figsize=(12, 2 * n_modes), gridspec_kw={'width_ratios': [2, 1, 1]})
 
     for mod in range(n_modes):
         freq = df[df['mode'] == mod]['freq'].iloc[0]
@@ -633,6 +623,9 @@ def plot_phases(e_time, e_filtered, e_frequencies, s_filtered, n_modes, df, outp
         ax[mod, 1].plot(e_time, s_filtered[mod], label='s')
         ax[mod, 1].set_xlim(left=0.5 * e_time.mean(), right=0.5*e_time.mean() + 10/e_frequencies[mod])
 
+        ax[mod, 2].plot(e_time, e_filtered[mod] / e_filtered[mod].max(), label='e')
+        ax[mod, 2].plot(e_time, s_filtered[mod] / s_filtered[mod].max(), label='s')
+        ax[mod, 2].set_xlim(left=0.5 * e_time.mean(), right=0.5*e_time.mean() + 2/e_frequencies[mod])
         for a in ax[:, 0]:
             a.legend()