add tiledb xarray test

tests/test_tiledb.py

@@ -0,0 +1,61 @@
+import os
+import shutil
+
+import numpy as np
+import pytest
+import tiledb
+import xarray as xr
+
+from polytope.engine.hullslicer import HullSlicer
+from polytope.polytope import Polytope, Request
+from polytope.shapes import Box
+
+
+class TestTileDB:
+
+    def setup_method(self):
+        cfg = tiledb.Ctx().config()
+        cfg.update({'py.init_buffer_bytes': 1024**2 * 50})
+        tiledb.default_ctx(cfg)
+
+        # Create a TileDB object
+
+        array_dense_1 = os.path.expanduser("~/array_dense_1")
+        if os.path.exists(array_dense_1):
+            shutil.rmtree(array_dense_1)
+
+        d1 = tiledb.Dim(name="d1", domain=(0, 99), tile=2, dtype=np.int32)
+        d2 = tiledb.Dim(name="d2", domain=(0, 99), tile=2, dtype=np.int32)
+
+        # Create a domain using the two dimensions
+        dom1 = tiledb.Domain(d1, d2)
+
+        # Create an attribute
+        a = tiledb.Attr(name="a", dtype=np.int32)
+
+        # Create the array schema, setting `sparse=False` to indicate a dense array
+        schema1 = tiledb.ArraySchema(domain=dom1, sparse=False, attrs=[a])
+
+        # Create the array on disk (it will initially be empty)
+        tiledb.Array.create(array_dense_1, schema1)
+
+        data = np.random.randint(1, 100, (100, 100))
+
+        A = tiledb.open(array_dense_1, 'w')
+
+        A[:] = data
+
+        xr_array = xr.open_dataset(array_dense_1, engine="tiledb")
+
+        # Extract using Polytope
+
+        slicer = HullSlicer()
+        self.poly_API = Polytope(datacube=xr_array.a, engine=slicer)
+
+    @pytest.mark.skip(reason="TileDB dependency")
+    def test_tiledb(self):
+
+        box1 = Box(["d1", "d2"], [3, 10], [6, 11])
+        request = Request(box1)
+        result = self.poly_API.retrieve(request)
+        assert len(result.leaves) == 8