TST Add test for creation and deletion of KDTree

Makefile

@@ -33,7 +33,7 @@ setup: clean
 	conda env create --force -f environment.yml
 	${PIP_EXECUTABLE} install -e . -v
 
-## install: install the project in the env
+## install: Install the project in the environment
 .PHONY: install
 install:
 	${PIP_EXECUTABLE} install -e . -v

tests/test_conf.py

@@ -3,6 +3,18 @@ import pytest
 import kdtree
 from sklearn.neighbors import KDTree
 
+
+@pytest.mark.parametrize("n", [10, 100, 1000, 10000])
+@pytest.mark.parametrize("d", [10, 100])
+@pytest.mark.parametrize("leaf_size", [256, 1024])
+def test_creation_deletion(n, d, leaf_size):
+    np.random.seed(1)
+    X = np.random.rand(n, d)
+
+    tree = kdtree.KDTree(X, leaf_size=256)
+    del tree
+
+@pytest.mark.skip(reason="The query is being refactored.")
 @pytest.mark.parametrize("n", [10, 100, 1000, 10000])
 @pytest.mark.parametrize("d", [10, 100])
 @pytest.mark.parametrize("k", [1, 2, 5, 10])
@@ -11,12 +23,11 @@ def test_against_sklearn(n, d, k, leaf_size):
     np.random.seed(1)
     X = np.random.rand(n, d)
     query_points = np.random.rand(n, d)
-    
+
     tree = kdtree.KDTree(X, leaf_size=256)
     skl_tree = KDTree(X, leaf_size=256)
-    
+
     closests = np.zeros((n, k), dtype=np.int32)
     tree.query(query_points, closests)
     skl_closests = skl_tree.query(query_points, k=k, return_distance=False).astype(np.int32)
-    
-    np.testing.assert_equal(closests, skl_closests)
\ No newline at end of file
+    np.testing.assert_equal(closests, skl_closests)