Fix parsing depot section

tests/parse/test_parse_vrplib.py

@@ -276,3 +276,27 @@ def test_empty_text():
     """
     actual = parse_vrplib("")
     assert_equal(actual, {})
+
+
+def test_multiple_depot():
+    """
+    Tests if an instance with multiple depots is correctly parsed. This fixes
+    a bug that was introduced in #121 where the depot section only returned the
+    first depot.
+    """
+    instance = "\n".join(
+        [
+            "NAME: Test",
+            "EDGE_WEIGHT_TYPE: EUC_2D",
+            "NODE_COORD_SECTION",
+            "1  20  20",
+            "DEPOT_SECTION",
+            "1",
+            "2",
+            "3",
+            "-1",
+        ]
+    )
+
+    actual = parse_vrplib(instance)
+    assert_equal(actual["depot"], [0, 1, 2])

vrplib/parse/parse_vrplib.py

@@ -111,20 +111,21 @@ def parse_section(
     Parses the data section lines.
     """
     name = lines[0].strip().removesuffix("_SECTION").lower()
-    values = [[infer_type(n) for n in line.split()] for line in lines[1:]]
+    rows = [[infer_type(n) for n in line.split()] for line in lines[1:]]
 
     if name == "edge_weight":
         # Parse edge weights separately as it involves extra processing.
-        data = parse_distances(values, **instance)  # type: ignore
+        data = parse_distances(rows, **instance)  # type: ignore
     elif name == "depot":
         # Remove -1 end token and renormalize depots to start at zero.
-        data = np.array(values[0]) - 1
-    elif any(len(row) != len(values[0]) for row in values):
+        data = np.array(rows)
+        data = data[data != -1] - 1
+    elif any(len(row) != len(rows[0]) for row in rows):
         # This is a ragged array, so we keep it as a nested list, but we
         # remove the indices column.
-        data = [row[1:] for row in values]
+        data = [row[1:] for row in rows]
     else:
-        data = np.array([row[1:] for row in values])
+        data = np.array([row[1:] for row in rows])
 
         if data.ndim > 1 and data.shape[-1] == 1:
             # Squeeze data lines that contain only one column.