Fix: Handle single-element arrays in to_bins function to prevent IndexError

(cherry picked from commit 5bd5565)

Author: PierreRaybaut

doc/release_notes/release_2.08.md

@@ -10,6 +10,9 @@
 
 🛠️ Bug fixes:
 
+* Fixed `IndexError` when displaying images with a single row or column (e.g., SIF files with shape `(1, N)`):
+  * The `to_bins()` function now handles single-element coordinate arrays by assuming a bin width of 1.0 centered on the point
+  * Previously, loading such images in DataLab caused an `IndexError: index 1 is out of bounds for axis 0 with size 1`
 * Fixed circle/ellipse shape drawing with non-uniform aspect ratios:
   * Axes were not perpendicular and did not connect to the ellipse edge when plot aspect ratio differed from 1.0
   * Now uses parametric ellipse drawing that correctly handles non-perpendicular axes in pixel space

plotpy/items/image/filter.py

@@ -51,11 +51,26 @@
 # Image with custom X, Y axes
 # ==============================================================================
 def to_bins(x):
-    """Convert point center to point bounds"""
+    """Convert point center to point bounds
+
+    Args:
+        x: 1D array of point centers
+
+    Returns:
+        1D array of bin edges (length = len(x) + 1)
+
+    Note:
+        When x has only 1 element, we assume a bin width of 1.0 centered on the point.
+    """
     bx = np.zeros((x.shape[0] + 1,), float)
-    bx[1:-1] = (x[:-1] + x[1:]) / 2
-    bx[0] = x[0] - (x[1] - x[0]) / 2
-    bx[-1] = x[-1] + (x[-1] - x[-2]) / 2
+    if x.shape[0] == 1:
+        # Single point: assume bin width of 1.0 centered on the point
+        bx[0] = x[0] - 0.5
+        bx[1] = x[0] + 0.5
+    else:
+        bx[1:-1] = (x[:-1] + x[1:]) / 2
+        bx[0] = x[0] - (x[1] - x[0]) / 2
+        bx[-1] = x[-1] + (x[-1] - x[-2]) / 2
     return bx
 
 

plotpy/tests/items/test_to_bins.py

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+#
+# Licensed under the terms of the BSD 3-Clause
+# (see plotpy/LICENSE for details)
+
+"""Test for to_bins function used in XYImageItem"""
+
+import numpy as np
+
+from plotpy.items.image.filter import to_bins
+
+
+def test_to_bins_single_element():
+    """Test to_bins with a single-element array.
+
+    This is a regression test for the bug where loading an image with only
+    1 row (e.g., SIF files with shape (1, N)) caused an IndexError because
+    to_bins assumed at least 2 elements to compute bin edges.
+    """
+    x = np.array([5.0])
+    result = to_bins(x)
+
+    # Should return 2 bin edges, centered around the single point
+    assert len(result) == 2
+    assert result[0] == 4.5  # x[0] - 0.5
+    assert result[1] == 5.5  # x[0] + 0.5
+
+
+def test_to_bins_two_elements():
+    """Test to_bins with a two-element array."""
+    x = np.array([1.0, 3.0])
+    result = to_bins(x)
+
+    # Should return 3 bin edges
+    assert len(result) == 3
+    np.testing.assert_allclose(result, [0.0, 2.0, 4.0])
+
+
+def test_to_bins_multiple_elements():
+    """Test to_bins with multiple elements (uniform spacing)."""
+    x = np.array([1.0, 2.0, 3.0, 4.0])
+    result = to_bins(x)
+
+    # Should return 5 bin edges
+    assert len(result) == 5
+    np.testing.assert_allclose(result, [0.5, 1.5, 2.5, 3.5, 4.5])
+
+
+def test_to_bins_non_uniform_spacing():
+    """Test to_bins with non-uniform spacing."""
+    x = np.array([0.0, 1.0, 4.0])
+    result = to_bins(x)
+
+    # Should return 4 bin edges
+    # First edge: 0.0 - (1.0 - 0.0) / 2 = -0.5
+    # Middle: (0.0 + 1.0) / 2 = 0.5, (1.0 + 4.0) / 2 = 2.5
+    # Last edge: 4.0 + (4.0 - 1.0) / 2 = 5.5
+    assert len(result) == 4
+    np.testing.assert_allclose(result, [-0.5, 0.5, 2.5, 5.5])
+
+
+if __name__ == "__main__":
+    test_to_bins_single_element()
+    test_to_bins_two_elements()
+    test_to_bins_multiple_elements()
+    test_to_bins_non_uniform_spacing()
+    print("All tests passed!")