From 42ea96435af4aa16d2fb319b842a89fbd887dd85 Mon Sep 17 00:00:00 2001
From: Matteo Bachetti <matteo@matteobachetti.it>
Date: Mon, 11 Mar 2024 11:31:44 +0100
Subject: [PATCH] Test with missing data

---
 stingray/pulse/accelsearch.py            |  6 +++++-
 stingray/pulse/tests/test_accelsearch.py | 24 ++++++++++++++++++++++++
 2 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/stingray/pulse/accelsearch.py b/stingray/pulse/accelsearch.py
index d7c90cd09..8183e7c94 100644
--- a/stingray/pulse/accelsearch.py
+++ b/stingray/pulse/accelsearch.py
@@ -354,7 +354,11 @@ def accelsearch(
 
     dt = times[1] - times[0]
     n_photons = np.sum(signal)
-    if gti is not None:
+    if gti is not None and isinstance(gti, Iterable) and len(gti) > 1:
+        warnings.warn(
+            "Data contain multiple GTIs. Bad time intervals will be "
+            "filled with the mean of the signal."
+        )
         gti = np.asarray(gti)
         # Fill in the data with a constant outside GTIs
         gti_mask = create_gti_mask(times, gti)
diff --git a/stingray/pulse/tests/test_accelsearch.py b/stingray/pulse/tests/test_accelsearch.py
index fe8ec6c88..8fd3d54ca 100644
--- a/stingray/pulse/tests/test_accelsearch.py
+++ b/stingray/pulse/tests/test_accelsearch.py
@@ -107,3 +107,27 @@ def test_noisy_neg_fdot(self):
             -self.fdot * self.rescale_fdot,
             atol=2 * self.dfdot * self.rescale_fdot,
         )
+
+    def test_signal_with_gaps(self):
+        with pytest.warns(UserWarning, match="Data contain multiple GTIs."):
+            candidate_table = accelsearch(
+                self.times,
+                self.signal,
+                zmax=10,
+                candidate_file="bubu.csv",
+                delta_z=0.5,
+                gti=[[self.tstart, self.tstart + 10], [self.tstart + 20, self.tstop]],
+                debug=True,
+                interbin=True,
+                nproc=1,
+            )
+        best = np.argmax(candidate_table["power"])
+        assert np.isclose(candidate_table["frequency"][best], self.freq, atol=5 * self.df)
+
+        print(candidate_table["fdot"][best] * self.rescale_fdot, self.fdot * self.rescale_fdot)
+
+        assert np.isclose(
+            candidate_table["fdot"][best] * self.rescale_fdot,
+            self.fdot * self.rescale_fdot,
+            atol=2 * self.dfdot * self.rescale_fdot,
+        )