add test for graph construction resolver and correct mask handling

tests/test_graph_construction.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 import pytest
+import torch
 from utils import generate_dummy_adata
 
 import nichepca as npc
@@ -123,7 +124,7 @@ def test_construct_multi_sample_graph():
         sub_ad = adata_2[mask].copy()
         sub_ad.uns["graph"] = adata_2.uns["graph"][batch].copy()
         npc.ne.aggregate(sub_ad)
-        adata_2[mask].X = sub_ad.X.copy()
+        adata_2.X[mask] = sub_ad.X.copy()
 
     # manual variant
     adata_3.X = npc.utils.to_numpy(adata_3.X).astype(np.float32)
@@ -166,3 +167,36 @@ def test_squidpy_conversion():
 
     assert np.all(edge_index == edge_index_new)
     assert np.all(edge_weight == edge_weight_new)
+
+
+def test_resolve_graph_constructor():
+    adata = generate_dummy_adata()
+
+    knn = 10
+    edge_index_1, edge_weight_1 = npc.gc.knn_graph(adata, knn=knn, return_graph=True)
+    edge_index_2, edge_weight_2 = npc.gc.resolve_graph_constructor(knn=knn)(
+        adata, return_graph=True
+    )
+
+    assert torch.all(edge_index_1 == edge_index_2)
+    assert torch.all(edge_weight_1 == edge_weight_2)
+
+    radius = 0.1
+    edge_index_1, edge_weight_1 = npc.gc.distance_graph(
+        adata, radius=radius, return_graph=True
+    )
+    edge_index_2, edge_weight_2 = npc.gc.resolve_graph_constructor(radius=radius)(
+        adata, return_graph=True
+    )
+
+    assert torch.all(edge_index_1 == edge_index_2)
+    assert torch.all(edge_weight_1 == edge_weight_2)
+
+    edge_index_1, edge_weight_1 = npc.gc.delaunay_graph(adata, return_graph=True)
+    edge_index_2, edge_weight_2 = npc.gc.resolve_graph_constructor(delaunay=True)(
+        adata, return_graph=True
+    )
+    print(type(edge_index_1), type(edge_index_2))
+
+    assert torch.all(edge_index_1 == edge_index_2)
+    assert torch.all(edge_weight_1 == edge_weight_2)