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[test] Added more test of datasets, datastores and groups
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‎tests/test_dataset.py‎

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# Copyright 2024-2025 Open Quantum Design
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# %%
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import numpy as np
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import pytest
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from pydantic import TypeAdapter
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from oqd_dataschema.base import CastDataset, Dataset, DTypes, condataset
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########################################################################################
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class TestDatasetDtype:
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@pytest.mark.parametrize(
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("dtype", "np_dtype"),
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[
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("bool", np.dtypes.BoolDType),
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("int16", np.dtypes.Int16DType),
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("int32", np.dtypes.Int32DType),
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("int64", np.dtypes.Int64DType),
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("uint16", np.dtypes.UInt16DType),
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("uint32", np.dtypes.UInt32DType),
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("uint64", np.dtypes.UInt64DType),
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("float16", np.dtypes.Float16DType),
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("float32", np.dtypes.Float32DType),
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("float64", np.dtypes.Float64DType),
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("complex64", np.dtypes.Complex64DType),
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("complex128", np.dtypes.Complex128DType),
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("str", np.dtypes.StrDType),
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("bytes", np.dtypes.BytesDType),
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("string", np.dtypes.StringDType),
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],
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)
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def test_dtypes(self, dtype, np_dtype):
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ds = Dataset(dtype=dtype, shape=(100,))
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data = np.random.rand(100).astype(np_dtype)
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ds.data = data
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@pytest.mark.xfail(raises=ValueError)
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@pytest.mark.parametrize("dtype", list(DTypes.names()))
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def test_unmatched_dtype_data(self, dtype):
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ds = Dataset(dtype=dtype, shape=(100,))
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data = np.random.rand(100).astype("O")
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ds.data = data
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@pytest.mark.parametrize("dtype", list(DTypes.names()))
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def test_flexible_dtype(self, dtype):
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ds = Dataset(dtype=None, shape=(100,))
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data = np.random.rand(100).astype(DTypes.get(dtype).value)
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ds.data = data
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assert ds.dtype == DTypes(type(ds.data.dtype)).name.lower()
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def test_dtype_mutation(self):
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ds = Dataset(dtype="float32", shape=(100,))
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ds.dtype = "float64"
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data = np.random.rand(100)
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ds.data = data
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class TestDatasetShape:
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@pytest.mark.xfail(raises=ValueError)
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@pytest.mark.parametrize(
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"shape",
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[
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(0,),
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(1,),
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(99,),
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(1, 1),
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],
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)
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def test_unmatched_shape_data(self, shape):
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ds = Dataset(dtype="float64", shape=(100,))
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data = np.random.rand(*shape)
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ds.data = data
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@pytest.mark.parametrize(
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("shape", "data_shape"),
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[
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((None,), (0,)),
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((None,), (1,)),
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((None,), (100,)),
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((None, 0), (0, 0)),
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((None, 1), (1, 1)),
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((None, None), (1, 1)),
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((None, None), (10, 100)),
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((None, None, 1), (1, 1, 1)),
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],
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)
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def test_flexible_shape(self, shape, data_shape):
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ds = Dataset(dtype="float64", shape=shape)
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data = np.random.rand(*data_shape)
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ds.data = data
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assert ds.shape == ds.data.shape
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def test_shape_mutation(self):
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ds = Dataset(dtype="float64", shape=(1,))
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ds.shape = (100,)
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data = np.random.rand(100)
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ds.data = data
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class TestCastDataset:
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@pytest.fixture
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def adapter(self):
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return TypeAdapter(CastDataset)
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@pytest.mark.parametrize(
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("data", "dtype", "shape"),
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[
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(np.random.rand(100), "float64", (100,)),
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(np.random.rand(10).astype("str"), "str", (10,)),
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(np.random.rand(1, 10, 100).astype("bytes"), "bytes", (1, 10, 100)),
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],
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)
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def test_cast(self, adapter, data, shape, dtype):
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ds = adapter.validate_python(data)
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assert ds.shape == shape and ds.dtype == dtype
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class TestConstrainedDataset:
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@pytest.mark.parametrize(
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("cds", "data"),
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[
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(condataset(dtype_constraint="float64"), np.random.rand(10)),
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(condataset(dtype_constraint="str"), np.random.rand(10).astype(str)),
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(
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condataset(dtype_constraint=("float16", "float32", "float64")),
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np.random.rand(10),
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),
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(
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condataset(dtype_constraint=("float16", "float32", "float64")),
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np.random.rand(10).astype("float16"),
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),
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(
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condataset(dtype_constraint=("float16", "float32", "float64")),
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np.random.rand(10).astype("float32"),
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),
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],
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)
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def test_constrained_dataset_dtype(self, cds, data):
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adapter = TypeAdapter(cds)
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adapter.validate_python(data)
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@pytest.mark.xfail(raises=ValueError)
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@pytest.mark.parametrize(
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("cds", "data"),
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[
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(condataset(dtype_constraint="float64"), np.random.rand(10).astype(str)),
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(condataset(dtype_constraint="str"), np.random.rand(10)),
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(
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condataset(dtype_constraint=("float16", "float32", "float64")),
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np.random.rand(10).astype(str),
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),
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],
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)
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def test_violate_dtype_constraint(self, cds, data):
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adapter = TypeAdapter(cds)
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adapter.validate_python(data)
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@pytest.mark.parametrize(
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("cds", "data"),
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[
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(condataset(min_dim=1, max_dim=1), np.random.rand(10)),
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(condataset(min_dim=0, max_dim=1), np.random.rand(10)),
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(condataset(max_dim=2), np.random.rand(10)),
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(condataset(max_dim=3), np.random.rand(10, 10, 10)),
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(condataset(min_dim=2), np.random.rand(10, 10)),
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(condataset(min_dim=2), np.random.rand(10, 10, 10, 10, 10)),
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(condataset(min_dim=2, max_dim=4), np.random.rand(10, 10, 10, 10)),
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(condataset(min_dim=2, max_dim=4), np.random.rand(10, 10, 10)),
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(condataset(min_dim=2, max_dim=4), np.random.rand(10, 10)),
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],
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)
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def test_constrained_dataset_dimension(self, cds, data):
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adapter = TypeAdapter(cds)
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adapter.validate_python(data)
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@pytest.mark.xfail(raises=ValueError)
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@pytest.mark.parametrize(
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("cds", "data"),
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[
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(condataset(min_dim=1, max_dim=1), np.random.rand(10, 10)),
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(condataset(min_dim=2, max_dim=3), np.random.rand(10)),
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(condataset(min_dim=2, max_dim=3), np.random.rand(10, 10, 10, 10)),
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],
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)
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def test_violate_dimension_constraint(self, cds, data):
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adapter = TypeAdapter(cds)
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adapter.validate_python(data)
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@pytest.mark.parametrize(
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("cds", "data"),
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[
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(condataset(shape_constraint=(None,)), np.random.rand(10)),
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(condataset(shape_constraint=(10,)), np.random.rand(10)),
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(condataset(shape_constraint=(None, None)), np.random.rand(1, 2)),
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(condataset(shape_constraint=(1, None)), np.random.rand(1, 2)),
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(condataset(shape_constraint=(1, 2)), np.random.rand(1, 2)),
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(condataset(shape_constraint=(1, None, 3)), np.random.rand(1, 10, 3)),
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],
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)
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def test_constrained_dataset_shape(self, cds, data):
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adapter = TypeAdapter(cds)
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adapter.validate_python(data)
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@pytest.mark.xfail(raises=ValueError)
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@pytest.mark.parametrize(
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("cds", "data"),
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[
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(condataset(shape_constraint=(1,)), np.random.rand(10)),
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(condataset(shape_constraint=(None,)), np.random.rand(10, 10)),
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(condataset(shape_constraint=(None, 1)), np.random.rand(10, 10)),
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(condataset(shape_constraint=(None, 1)), np.random.rand(1, 10)),
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],
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)
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def test_violate_shape_constraint(self, cds, data):
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adapter = TypeAdapter(cds)
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adapter.validate_python(data)

‎tests/test_datastore.py‎

Lines changed: 81 additions & 28 deletions
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# limitations under the License.
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# %%
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import pathlib
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import uuid
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from typing import Dict, Optional
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import numpy as np
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import pytest
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from oqd_dataschema.base import Dataset, DTypes
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from oqd_dataschema.datastore import Datastore
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from oqd_dataschema.groups import (
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SinaraRawDataGroup,
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)
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from oqd_dataschema import Datastore, GroupBase
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from oqd_dataschema.base import Dataset
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# %%
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@pytest.mark.parametrize(
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"dtype",
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[
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"int32",
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"int64",
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"float32",
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"float64",
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"complex64",
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"complex128",
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],
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_Group = type(
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f"_Group_{uuid.uuid4()}".replace("-", ""),
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(GroupBase,),
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{
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"__annotations__": {
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"x": Dataset,
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"y": Dict[str, Dataset],
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"z": Optional[Dataset],
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},
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"y": {},
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"z": None,
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},
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)
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def test_serialize_deserialize(dtype):
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data = np.ones([10, 10]).astype(dtype)
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dataset = SinaraRawDataGroup(camera_images=Dataset(data=data))
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data = Datastore(groups={"test": dataset})
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filepath = pathlib.Path("test.h5")
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data.model_dump_hdf5(filepath)
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data_reload = Datastore.model_validate_hdf5(filepath)
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class TestDatastore:
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@pytest.mark.parametrize(
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("dtype", "np_dtype"),
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[
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("bool", np.dtypes.BoolDType),
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("int16", np.dtypes.Int16DType),
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("int32", np.dtypes.Int32DType),
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("int64", np.dtypes.Int64DType),
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("uint16", np.dtypes.UInt16DType),
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("uint32", np.dtypes.UInt32DType),
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("uint64", np.dtypes.UInt64DType),
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("float16", np.dtypes.Float16DType),
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("float32", np.dtypes.Float32DType),
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("float64", np.dtypes.Float64DType),
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("complex64", np.dtypes.Complex64DType),
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("complex128", np.dtypes.Complex128DType),
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("str", np.dtypes.StrDType),
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("bytes", np.dtypes.BytesDType),
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("string", np.dtypes.StringDType),
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],
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)
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def test_serialize_deserialize_dtypes(self, dtype, np_dtype, tmp_path):
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f = tmp_path / f"tmp{uuid.uuid4()}.h5"
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datastore = Datastore(
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groups={"g1": _Group(x=Dataset(data=np.random.rand(1).astype(np_dtype)))}
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)
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assert (
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type(data_reload.groups["test"].camera_images.data.dtype)
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is DTypes.get(dtype).value
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datastore.model_dump_hdf5(f)
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Datastore.model_validate_hdf5(f)
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@pytest.mark.parametrize(
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("x", "y", "z"),
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[
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(
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Dataset(data=np.random.rand(10)),
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{},
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None,
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),
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(
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Dataset(data=np.random.rand(10)),
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{"f1": Dataset(data=np.random.rand(10))},
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None,
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),
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(
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Dataset(data=np.random.rand(10)),
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{"f1": Dataset(data=np.random.rand(10))},
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Dataset(data=np.random.rand(10)),
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),
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(
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Dataset(data=np.random.rand(10)),
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{
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"f1": Dataset(data=np.random.rand(10)),
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"f2": Dataset(data=np.random.rand(10)),
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},
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Dataset(data=np.random.rand(10)),
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),
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],
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)
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def test_serialize_deserialize_dataset_types(self, x, y, z, tmp_path):
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f = tmp_path / f"tmp{uuid.uuid4()}.h5"
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datastore = Datastore(groups={"g1": _Group(x=x, y=y, z=z)})
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# %%
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datastore.model_dump_hdf5(f)
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Datastore.model_validate_hdf5(f)

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