From 07e3fc513e0df4c846ee2c81e5fd12f312bb7c9d Mon Sep 17 00:00:00 2001
From: Harris Antony Jos <ha.jos@fz-juelich.de>
Date: Wed, 26 Nov 2025 17:03:10 +0100
Subject: [PATCH] Clipped small negative values in van_rossum_distance to
 prevent Nan from np.sqrt; added test case for same

---
 elephant/spike_train_dissimilarity.py           | 11 ++++++++++-
 elephant/test/test_spike_train_dissimilarity.py | 15 ++++++++++++---
 2 files changed, 22 insertions(+), 4 deletions(-)

diff --git a/elephant/spike_train_dissimilarity.py b/elephant/spike_train_dissimilarity.py
index 3234f8916..2a541186e 100644
--- a/elephant/spike_train_dissimilarity.py
+++ b/elephant/spike_train_dissimilarity.py
@@ -21,7 +21,7 @@
 """
 
 from __future__ import division, print_function, unicode_literals
-
+import warnings
 import numpy as np
 import quantities as pq
 from neo.core import SpikeTrain
@@ -363,6 +363,15 @@ def van_rossum_distance(spiketrains, time_constant=1.0 * pq.s, sort=True):
     for i, j in np.ndindex(k_dist.shape):
         vr_dist[i, j] = (
             k_dist[i, i] + k_dist[j, j] - k_dist[i, j] - k_dist[j, i])
+    
+    # Clip small negative values
+    if np.any(vr_dist < 0):
+        warnings.warn(
+                "van_rossum_distance: very small negative values encountered "
+                "(likely due to floating point error); "
+                "setting them to 0", RuntimeWarning)
+        vr_dist = np.maximum(vr_dist, 0.0)
+    
     return np.sqrt(vr_dist)
 
 
diff --git a/elephant/test/test_spike_train_dissimilarity.py b/elephant/test/test_spike_train_dissimilarity.py
index 4619d4bba..5e9b803c3 100644
--- a/elephant/test/test_spike_train_dissimilarity.py
+++ b/elephant/test/test_spike_train_dissimilarity.py
@@ -14,7 +14,7 @@
 import elephant.kernels as kernels
 from elephant.spike_train_generation import StationaryPoissonProcess
 import elephant.spike_train_dissimilarity as stds
-
+import warnings
 from elephant.datasets import download_datasets
 
 
@@ -47,6 +47,7 @@ def setUp(self):
         self.st23 = StationaryPoissonProcess(rate=30 * Hz, t_start=0 * ms,
                                              t_stop=1000 * ms
                                              ).generate_spiketrain()
+        self.st24 = SpikeTrain([0.1782, 0.2286, 0.2804, 0.4972, 0.5504], units='s',t_stop=4.0)
         self.rd_st_list = [self.st21, self.st22, self.st23]
         self.st31 = SpikeTrain([12.0], units='ms', t_stop=1000.0)
         self.st32 = SpikeTrain([12.0, 12.0], units='ms', t_stop=1000.0)
@@ -73,7 +74,7 @@ def setUp(self):
         self.tau7 = 0.01 * s
         self.q7 = 1.0 / self.tau7
         self.t = np.linspace(0, 200, 20000001) * ms
-
+        self.tau8 = 0.1 * s
     def test_wrong_input(self):
         self.assertRaises(TypeError, stds.victor_purpura_distance,
                           [self.array1, self.array2], self.q3)
@@ -599,7 +600,15 @@ def test_van_rossum_distance(self):
         self.assertEqual(stds.van_rossum_distance(
             [self.st21], self.tau3)[0, 0], 0)
         self.assertEqual(len(stds.van_rossum_distance([], self.tau3)), 0)
-
+        
+        # Check small negative values edge case
+        with warnings.catch_warnings(record='True') as w:
+            warnings.simplefilter("always")
+            result = stds.van_rossum_distance([self.st24, self.st24], self.tau8)
+            self.assertTrue(any("very small negative values encountered" in str(warn.message)
+                       for warn in w))
+        self.assertEqual(result[0,1], 0.0)
+        self.assertFalse(np.any(np.isnan(result)))
 
 if __name__ == '__main__':
     unittest.main()