numpydantic/tests/test_interface/test_hdf5.py

import json
from datetime import datetime, timezone
from typing import Any

import h5py
import pytest
from pydantic import BaseModel, ValidationError

import numpy as np
from numpydantic import NDArray, Shape
from numpydantic.interface import H5Interface
from numpydantic.interface.hdf5 import H5ArrayPath, H5Proxy
from numpydantic.exceptions import DtypeError, ShapeError

from tests.conftest import ValidationCase


def hdf5_array_case(
    case: ValidationCase, array_func, compound: bool = False
) -> H5ArrayPath:
    """
    Args:
        case:
        array_func: ( the function returned from the `hdf5_array` fixture )

    Returns:

    """
    if issubclass(case.dtype, BaseModel):
        pytest.skip("hdf5 cant support arbitrary python objects")
    return array_func(case.shape, case.dtype, compound)


def _test_hdf5_case(case: ValidationCase, array_func, compound: bool = False) -> None:
    array = hdf5_array_case(case, array_func, compound)
    if case.passes:
        case.model(array=array)
    else:
        with pytest.raises((ValidationError, DtypeError, ShapeError)):
            case.model(array=array)


def test_hdf5_enabled():
    assert H5Interface.enabled()


def test_hdf5_check(interface_type):
    if interface_type[1] is H5Interface:
        if interface_type[0].__name__ == "_hdf5_array":
            interface_type = (interface_type[0](), interface_type[1])
        assert H5Interface.check(interface_type[0])
        if isinstance(interface_type[0], H5ArrayPath):
            # also test that we can instantiate from a tuple like the H5ArrayPath
            assert H5Interface.check((interface_type[0].file, interface_type[0].path))
    else:
        assert not H5Interface.check(interface_type[0])


def test_hdf5_check_not_exists():
    """We should fail a check for a nonexistent hdf5 file"""
    spec = ("./fakefile.h5", "/fake/array")
    assert not H5Interface.check(spec)


def test_hdf5_check_not_hdf5(tmp_path):
    """Files that exist but aren't actually hdf5 files should fail a check"""
    afile = tmp_path / "not_an_hdf.h5"
    with open(afile, "w") as af:
        af.write("hey")

    spec = (afile, "/fake/array")
    assert not H5Interface.check(spec)


@pytest.mark.parametrize("compound", [True, False])
def test_hdf5_shape(shape_cases, hdf5_array, compound):
    _test_hdf5_case(shape_cases, hdf5_array, compound)


@pytest.mark.parametrize("compound", [True, False])
def test_hdf5_dtype(dtype_cases, hdf5_array, compound):
    _test_hdf5_case(dtype_cases, hdf5_array, compound)


def test_hdf5_dataset_not_exists(hdf5_array, model_blank):
    array = hdf5_array()
    with pytest.raises(ValueError) as e:
        model_blank(array=H5ArrayPath(file=array.file, path="/some/random/path"))
        assert "file located" in e
        assert "no array found" in e


def test_assignment(hdf5_array, model_blank):
    array = hdf5_array()

    model = model_blank(array=array)
    model.array[1, 1] = 5
    assert model.array[1, 1] == 5

    model.array[1:3, 2:4] = 10
    assert (model.array[1:3, 2:4] == 10).all()


def test_to_json(hdf5_array, array_model):
    """
    Test serialization of HDF5 arrays to JSON
    Args:
        hdf5_array:

    Returns:

    """
    array = hdf5_array((10, 10), int)
    model = array_model((10, 10), int)

    instance = model(array=array)  # type: BaseModel

    json_str = instance.model_dump_json()
    json_dict = json.loads(json_str)["array"]

    assert json_dict["file"] == str(array.file)
    assert json_dict["path"] == str(array.path)
    assert json_dict["attrs"] == {}
    assert json_dict["array"] == instance.array[:].tolist()


def test_compound_dtype(tmp_path):
    """
    hdf5 proxy indexes compound dtypes as single fields when field is given
    """
    h5f_path = tmp_path / "test.h5"
    dataset_path = "/dataset"
    field = "data"
    dtype = np.dtype([(field, "i8"), ("extra", "f8")])
    data = np.zeros((10, 20), dtype=dtype)
    with h5py.File(h5f_path, "w") as h5f:
        dset = h5f.create_dataset(dataset_path, data=data)
        assert dset.dtype == dtype

    proxy = H5Proxy(h5f_path, dataset_path, field=field)
    assert proxy.dtype == np.dtype("int64")
    assert proxy.shape == (10, 20)
    assert proxy[0, 0] == 0

    class MyModel(BaseModel):
        array: NDArray[Shape["10, 20"], np.int64]

    instance = MyModel(array=(h5f_path, dataset_path, field))
    assert instance.array.dtype == np.dtype("int64")
    assert instance.array.shape == (10, 20)
    assert instance.array[0, 0] == 0

    # set values too
    instance.array[0, :] = 1
    assert all(instance.array[0, :] == 1)
    assert all(instance.array[1, :] == 0)
    instance.array[1] = 2
    assert all(instance.array[1] == 2)


@pytest.mark.parametrize("compound", [True, False])
def test_strings(hdf5_array, compound):
    """
    HDF5 proxy can get and set strings just like any other dtype
    """
    array = hdf5_array((10, 10), str, compound=compound)

    class MyModel(BaseModel):
        array: NDArray[Shape["10, 10"], str]

    instance = MyModel(array=array)
    instance.array[0, 0] = "hey"
    assert instance.array[0, 0] == "hey"
    assert isinstance(instance.array[0, 1], str)

    instance.array[1] = "sup"
    assert all(instance.array[1] == "sup")


@pytest.mark.parametrize("compound", [True, False])
def test_datetime(hdf5_array, compound):
    """
    We can treat S32 byte arrays as datetimes if our type annotation
    says to, including validation, setting and getting values
    """
    array = hdf5_array((10, 10), datetime, compound=compound)

    class MyModel(BaseModel):
        array: NDArray[Any, datetime]

    instance = MyModel(array=array)
    assert isinstance(instance.array[0, 0], np.datetime64)
    assert instance.array[0:5].dtype.type is np.datetime64

    now = datetime.now()

    instance.array[0, 0] = now
    assert instance.array[0, 0] == now
    instance.array[0] = now
    assert all(instance.array[0] == now)