1 files changed, 104 insertions, 0 deletions
diff --git a/src/silx/io/test/test_sliceh5.py b/src/silx/io/test/test_sliceh5.py
new file mode 100644
index 0000000..8ccf14a
--- /dev/null
+++ b/src/silx/io/test/test_sliceh5.py
@@ -0,0 +1,104 @@
+# /*##########################################################################
+# Copyright (C) 2022-2023 European Synchrotron Radiation Facility
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# ############################################################################*/
+import contextlib
+from io import BytesIO
+
+import h5py
+import numpy
+import pytest
+
+import silx.io
+from silx.io import commonh5
+from silx.io._sliceh5 import DatasetSlice, _combine_indices
+
+
+@contextlib.contextmanager
+def h5py_file(filename, mode):
+    with BytesIO() as buffer:
+        with h5py.File(buffer, mode) as h5file:
+            yield h5file
+
+
+@pytest.fixture(params=[commonh5.File, h5py_file])
+def temp_h5file(request):
+    temp_file_context = request.param
+    with temp_file_context("tempfile.h5", "w") as h5file:
+        yield h5file
+
+
+@pytest.mark.parametrize("indices", [1, slice(None), (1, slice(1, 4))])
+def test_datasetslice(temp_h5file, indices):
+    data = numpy.arange(50).reshape(10, 5)
+    ref_data = numpy.array(data[indices], copy=False)
+
+    h5dataset = temp_h5file.create_group("group").create_dataset("dataset", data=data)
+
+    with DatasetSlice(h5dataset, indices, attrs={}) as dset:
+        assert silx.io.is_dataset(dset)
+        assert dset.file == temp_h5file
+        assert dset.shape == ref_data.shape
+        assert dset.size == ref_data.size
+        assert dset.dtype == ref_data.dtype
+        assert len(dset) == len(ref_data)
+        assert numpy.array_equal(dset[()], ref_data)
+        assert dset.name == h5dataset.name
+
+
+def test_datasetslice_on_external_link(tmp_path):
+    data = numpy.arange(10).reshape(5, 2)
+
+    external_filename = str(tmp_path / "external.h5")
+    ext_dataset_name = "/external_data"
+    with h5py.File(external_filename, "w") as h5file:
+        h5file[ext_dataset_name] = data
+
+    with h5py.File(tmp_path / "test.h5", "w") as h5file:
+        h5file["group/data"] = h5py.ExternalLink(external_filename, ext_dataset_name)
+
+        with DatasetSlice(h5file["group/data"], slice(None), attrs={}) as dset:
+            assert dset.name == ext_dataset_name
+            assert numpy.array_equal(dset[()], data)
+
+
+@pytest.mark.parametrize(
+    "shape,outer_indices,indices",
+    [
+        ((2, 5, 10), (-1, slice(None), slice(None)), slice(None)),
+        ((2, 5, 10), (-1, slice(None), slice(None)), Ellipsis),
+        # negative strides
+        ((5, 10), (slice(1, 5, 2), slice(2, 8)), (slice(2, 3), slice(4, None, -2))),
+        (
+            (5, 10),
+            (slice(4, None, -1), slice(9, 3, -2)),
+            (slice(1, 3), slice(3, 0, -1)),
+        ),
+        ((5, 10), (slice(1, 8, 2), slice(None)), slice(2, 8)),  # slice overflow
+    ],
+)
+def test_combine_indices(shape, outer_indices, indices):
+    data = numpy.arange(numpy.prod(shape)).reshape(shape)
+    ref_data = data[outer_indices][indices]
+
+    combined_indices = _combine_indices(shape, outer_indices, indices)
+
+    assert numpy.array_equal(data[combined_indices], ref_data)