Fix resample_sig for signals starting with NaN

docs/changes.rst

@@ -32,6 +32,9 @@ Unreleased
 **Use Ruff for formatting and linting**
   The ``dev`` extra now includes ``ruff`` instead of ``black`` and ``pylint``, and the code is formatted with a line length of 88 (the default).
 
+**Fix resample_sig for signals starting with NaN**
+  ``resample_sig`` (and the functions that use it, such as ``resample_singlechan`` and ``resample_multichan``) returned only NaN values if the signal started with NaN. Leading NaN values now take the first valid value before resampling, as trailing ones already took the last.
+
 Version 4.3.1 (Feb 2026)
 -----------------------------
 

src/wfdb/processing/basic.py

@@ -53,9 +53,10 @@ def resample_sig(x, fs, fs_target):
         return x, t
 
     new_length = int(x.shape[0] * fs_target / fs)
-    # Resample the array if NaN values are present
+    # Fill NaN values before resampling. Leading and trailing NaN values take
+    # the nearest valid value, since interpolate() leaves leading ones as NaN.
     if np.isnan(x).any():
-        x = pd.Series(x.reshape((-1,))).interpolate().values
+        x = pd.Series(x.reshape((-1,))).interpolate(limit_direction="both").values
     resampled_x, resampled_t = signal.resample(x, num=new_length, t=t)
     assert resampled_x.shape == resampled_t.shape and resampled_x.shape[0] == new_length
     assert np.all(np.diff(resampled_t) > 0)

tests/test_processing.py

@@ -36,6 +36,27 @@ def test_resample_multi(self):
         assert new_sig.shape[0] == expected_length
         assert new_sig.shape[1] == sig.shape[1]
 
+    def test_resample_leading_nan(self):
+        """Leading NaN values take the first valid value before resampling."""
+        x = np.array([np.nan, np.nan, 1.0, 2.0, 3.0, 4.0])
+        filled = np.array([1.0, 1.0, 1.0, 2.0, 3.0, 4.0])
+
+        new_x, _ = processing.resample_sig(x, fs=2, fs_target=4)
+        expected, _ = processing.resample_sig(filled, fs=2, fs_target=4)
+
+        assert np.isfinite(new_x).all()
+        np.testing.assert_allclose(new_x, expected)
+
+    def test_resample_trailing_and_inner_nan(self):
+        """Inner NaN values are interpolated, and trailing ones take the last."""
+        x = np.array([1.0, np.nan, 3.0, 4.0, np.nan, np.nan])
+        filled = np.array([1.0, 2.0, 3.0, 4.0, 4.0, 4.0])
+
+        new_x, _ = processing.resample_sig(x, fs=2, fs_target=4)
+        expected, _ = processing.resample_sig(filled, fs=2, fs_target=4)
+
+        np.testing.assert_allclose(new_x, expected)
+
     def test_normalize_bound(self):
         sig, _ = wfdb.rdsamp("sample-data/100")
         lb = -5