Merge pull request #1055 from janwodnicki/fix-signal-interpolate-with…

…-y-as-series

[Fix] Squeeze y_values in signal_interpolate

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])
 

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():