From 43185a5a6f7df499c282d31cafa25467d05f5d59 Mon Sep 17 00:00:00 2001
From: Jan Wodnicki <25946310+janwodnicki@users.noreply.github.com>
Date: Tue, 3 Dec 2024 13:24:34 +0100
Subject: [PATCH] [Fix] Squeeze y_values in signal_interpolate

---
 neurokit2/signal/signal_interpolate.py |  4 +++-
 tests/tests_signal.py                  | 13 ++++++++++++-
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/neurokit2/signal/signal_interpolate.py b/neurokit2/signal/signal_interpolate.py
index c44e8053c1..9574ebbc78 100644
--- a/neurokit2/signal/signal_interpolate.py
+++ b/neurokit2/signal/signal_interpolate.py
@@ -101,6 +101,8 @@ def signal_interpolate(
         x_values = np.squeeze(x_values.values)
     if isinstance(x_new, pd.Series):
         x_new = np.squeeze(x_new.values)
+    if isinstance(y_values, pd.Series):
+        y_values = np.squeeze(y_values.values)
 
     if len(x_values) != len(y_values):
         raise ValueError("x_values and y_values must be of the same length.")
@@ -158,7 +160,7 @@ def signal_interpolate(
             # scipy.interpolate.PchipInterpolator for constant extrapolation akin to the behavior of
             # scipy.interpolate.interp1d with fill_value=([y_values[0]], [y_values[-1]].
             fill_value = ([interpolated[first_index]], [interpolated[last_index]])
-        elif isinstance(fill_value, float) or isinstance(fill_value, int):
+        elif isinstance(fill_value, (float, int)):
             # if only a single integer or float is provided as a fill value, format as a tuple
             fill_value = ([fill_value], [fill_value])
 
diff --git a/tests/tests_signal.py b/tests/tests_signal.py
index 399ee7babd..121ab2496a 100644
--- a/tests/tests_signal.py
+++ b/tests/tests_signal.py
@@ -208,14 +208,25 @@ def test_signal_filter_with_missing():
 
 def test_signal_interpolate():
 
+    # Test with arrays
     x_axis = np.linspace(start=10, stop=30, num=10)
     signal = np.cos(x_axis)
+    x_new = np.arange(1000)
 
-    interpolated = nk.signal_interpolate(x_axis, signal, x_new=np.arange(1000))
+    interpolated = nk.signal_interpolate(x_axis, signal, x_new)
     assert len(interpolated) == 1000
     assert interpolated[0] == signal[0]
     assert interpolated[-1] == signal[-1]
 
+    # Test with Series
+    x_axis = pd.Series(x_axis)
+    signal = pd.Series(signal)
+    x_new = pd.Series(x_new)
+
+    interpolated = nk.signal_interpolate(x_axis, signal, x_new)
+    assert len(interpolated) == 1000
+    assert interpolated[0] == signal.iloc[0]
+    assert interpolated[-1] == signal.iloc[-1]
 
 def test_signal_findpeaks():