This is on top of #2225. The goal is to be able to skip specific tests. Many core test sets have just one test that uses subprocess or threads. Others have a few tests that are failing due to upstream problems in emscripten. I went through and switched to skipping specific tests in as many cases as I could. In modules where most or all tests use threading or subprocess, I gave up and put a comment saying something like 35/50 tests fork and I didn't want to bother to salvage the remaining ones.

In order to accommodate using patterns to skip specific tests (and also extra comments) I switched the format of `python_tests` to a yaml file. This gives us significantly more flexibility in the structure of the file and allows us to use `ruamel.yaml` to update it. We gain a lot flexibility while reducing the length of `make_test_lists.py` modestly from 108 to 78 loc.

In the future, hopefully we should be able to make fixes by deleting one skip pattern and looking into why that particular test fails.

Rework core tests. This is on top of #2225. The goal is to be able to skip specific tests. Many core test sets have just one test that uses subprocess or threads. Others have a few tests that are failing due to upstream problems in emscripten. I went through and switched to skipping specific tests in as many cases as I could. In modules where most or all tests use threading or subprocess, I gave up and put a comment saying something like 35/50 tests fork and I didn't want to bother to salvage the remaining ones.

In order to accommodate using patterns to skip specific tests (and also extra comments) I switched the format of `python_tests` to a yaml file. This gives us significantly more flexibility in the structure of the file and allows us to use `ruamel.yaml` to update it. We gain a lot flexibility while reducing the length of `make_test_lists.py` modestly from 108 to 78 loc.

In the future, hopefully we should be able to make fixes by deleting one skip pattern and looking into why that particular test fails.

from pyodide_build.testing import run_in_pyodide


@run_in_pyodide(
    standalone=True,
    packages=["numcodecs", "numpy"],
    xfail_browsers={
        "chrome": "test_numcodecs triggers a recursion error in chrome",
    },
)
def test_blosc():
    import array

    import numpy as np
    from numcodecs.blosc import Blosc
    from numcodecs.compat import ensure_bytes, ensure_ndarray
    from numcodecs.lz4 import LZ4
    from numcodecs.zstd import Zstd
    from numpy.testing import assert_array_almost_equal, assert_array_equal

    def compare_arrays(arr, res, precision=None):
        # ensure numpy array with matching dtype
        res = ensure_ndarray(res).view(arr.dtype)

        # convert to correct shape
        if arr.flags.f_contiguous:
            order = "F"
        else:
            order = "C"
        res = res.reshape(arr.shape, order=order)

        # exact compare
        if precision is None:
            assert_array_equal(arr, res)

        # fuzzy compare
        else:
            assert_array_almost_equal(arr, res, decimal=precision)

    def check_encode_decode(arr, codec, precision=None):

        # N.B., watch out here with blosc compressor, if the itemsize of
        # the source buffer is different then the results of encoding
        # (i.e., compression) may be different. Hence we *do not* require that
        # the results of encoding be identical for all possible inputs, rather
        # we just require that the results of the encode/decode round-trip can
        # be compared to the original array.

        # encoding should support any object exporting the buffer protocol

        # test encoding of numpy array
        enc = codec.encode(arr)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # test encoding of bytes
        buf = arr.tobytes(order="A")
        enc = codec.encode(buf)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # test encoding of bytearray
        buf = bytearray(arr.tobytes(order="A"))
        enc = codec.encode(buf)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # test encoding of array.array
        buf = array.array("b", arr.tobytes(order="A"))
        enc = codec.encode(buf)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # decoding should support any object exporting the buffer protocol,

        # setup
        enc_bytes = ensure_bytes(enc)

        # test decoding of raw bytes
        dec = codec.decode(enc_bytes)
        compare_arrays(arr, dec, precision=precision)

        # test decoding of bytearray
        dec = codec.decode(bytearray(enc_bytes))
        compare_arrays(arr, dec, precision=precision)

        # test decoding of array.array
        buf = array.array("b", enc_bytes)
        dec = codec.decode(buf)
        compare_arrays(arr, dec, precision=precision)

        # test decoding of numpy array
        buf = np.frombuffer(enc_bytes, dtype="u1")
        dec = codec.decode(buf)
        compare_arrays(arr, dec, precision=precision)

        # test decoding directly into numpy array
        out = np.empty_like(arr)
        codec.decode(enc_bytes, out=out)
        compare_arrays(arr, out, precision=precision)

        # test decoding directly into bytearray
        out = bytearray(arr.nbytes)
        codec.decode(enc_bytes, out=out)
        # noinspection PyTypeChecker
        compare_arrays(arr, out, precision=precision)

    # mix of dtypes: integer, float, bool, string
    # mix of shapes: 1D, 2D, 3D
    # mix of orders: C, F
    arrays = [
        np.arange(1000, dtype="i4"),
        np.linspace(1000, 1001, 1000, dtype="f8"),
        np.random.normal(loc=1000, scale=1, size=(100, 10)),
        np.random.randint(0, 2, size=1000, dtype=bool).reshape(100, 10, order="F"),
        np.random.choice([b"a", b"bb", b"ccc"], size=1000).reshape(10, 10, 10),
        np.random.randint(0, 2**60, size=1000, dtype="u8").view("M8[ns]"),
        np.random.randint(0, 2**60, size=1000, dtype="u8").view("m8[ns]"),
        np.random.randint(0, 2**25, size=1000, dtype="u8").view("M8[m]"),
        np.random.randint(0, 2**25, size=1000, dtype="u8").view("m8[m]"),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "M8[ns]"
        ),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "m8[ns]"
        ),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "M8[m]"
        ),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "m8[m]"
        ),
    ]

    codecs = [
        LZ4(),
        LZ4(acceleration=-1),
        LZ4(acceleration=10),
        Zstd(),
        Zstd(level=-1),
        Zstd(level=10),
        Blosc(shuffle=Blosc.SHUFFLE),
        Blosc(clevel=0, shuffle=Blosc.SHUFFLE),
        Blosc(cname="lz4", shuffle=Blosc.SHUFFLE),
        Blosc(cname="lz4", clevel=1, shuffle=Blosc.NOSHUFFLE),
        Blosc(cname="lz4", clevel=5, shuffle=Blosc.SHUFFLE),
        Blosc(cname="lz4", clevel=9, shuffle=Blosc.BITSHUFFLE),
        Blosc(cname="zlib", clevel=1, shuffle=0),
        Blosc(cname="zstd", clevel=1, shuffle=1),
        Blosc(cname="blosclz", clevel=1, shuffle=2),
        Blosc(shuffle=Blosc.SHUFFLE, blocksize=0),
        Blosc(shuffle=Blosc.SHUFFLE, blocksize=2**8),
        Blosc(cname="lz4", clevel=1, shuffle=Blosc.NOSHUFFLE, blocksize=2**8),
    ]
    for codec in codecs:
        for arr in arrays:
            check_encode_decode(arr, codec)

from pyodide_build.testing import run_in_pyodide


@run_in_pyodide(
    standalone=True,
    packages=["numcodecs", "numpy"],
    xfail_browsers={
        "chrome": "test_numcodecs triggers a recursion error in chrome",
    },
)
def test_blosc():
    import array

    import numpy as np
    from numcodecs.blosc import Blosc
    from numcodecs.compat import ensure_bytes, ensure_ndarray
    from numcodecs.lz4 import LZ4
    from numcodecs.zstd import Zstd
    from numpy.testing import assert_array_almost_equal, assert_array_equal

    def compare_arrays(arr, res, precision=None):
        # ensure numpy array with matching dtype
        res = ensure_ndarray(res).view(arr.dtype)

        # convert to correct shape
        if arr.flags.f_contiguous:
            order = "F"
        else:
            order = "C"
        res = res.reshape(arr.shape, order=order)

        # exact compare
        if precision is None:
            assert_array_equal(arr, res)

        # fuzzy compare
        else:
            assert_array_almost_equal(arr, res, decimal=precision)

    def check_encode_decode(arr, codec, precision=None):

        # N.B., watch out here with blosc compressor, if the itemsize of
        # the source buffer is different then the results of encoding
        # (i.e., compression) may be different. Hence we *do not* require that
        # the results of encoding be identical for all possible inputs, rather
        # we just require that the results of the encode/decode round-trip can
        # be compared to the original array.

        # encoding should support any object exporting the buffer protocol

        # test encoding of numpy array
        enc = codec.encode(arr)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # test encoding of bytes
        buf = arr.tobytes(order="A")
        enc = codec.encode(buf)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # test encoding of bytearray
        buf = bytearray(arr.tobytes(order="A"))
        enc = codec.encode(buf)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # test encoding of array.array
        buf = array.array("b", arr.tobytes(order="A"))
        enc = codec.encode(buf)
        dec = codec.decode(enc)
        compare_arrays(arr, dec, precision=precision)

        # decoding should support any object exporting the buffer protocol,

        # setup
        enc_bytes = ensure_bytes(enc)

        # test decoding of raw bytes
        dec = codec.decode(enc_bytes)
        compare_arrays(arr, dec, precision=precision)

        # test decoding of bytearray
        dec = codec.decode(bytearray(enc_bytes))
        compare_arrays(arr, dec, precision=precision)

        # test decoding of array.array
        buf = array.array("b", enc_bytes)
        dec = codec.decode(buf)
        compare_arrays(arr, dec, precision=precision)

        # test decoding of numpy array
        buf = np.frombuffer(enc_bytes, dtype="u1")
        dec = codec.decode(buf)
        compare_arrays(arr, dec, precision=precision)

        # test decoding directly into numpy array
        out = np.empty_like(arr)
        codec.decode(enc_bytes, out=out)
        compare_arrays(arr, out, precision=precision)

        # test decoding directly into bytearray
        out = bytearray(arr.nbytes)
        codec.decode(enc_bytes, out=out)
        # noinspection PyTypeChecker
        compare_arrays(arr, out, precision=precision)

    # mix of dtypes: integer, float, bool, string
    # mix of shapes: 1D, 2D, 3D
    # mix of orders: C, F
    arrays = [
        np.arange(1000, dtype="i4"),
        np.linspace(1000, 1001, 1000, dtype="f8"),
        np.random.normal(loc=1000, scale=1, size=(100, 10)),
        np.random.randint(0, 2, size=1000, dtype=bool).reshape(100, 10, order="F"),
        np.random.choice([b"a", b"bb", b"ccc"], size=1000).reshape(10, 10, 10),
        np.random.randint(0, 2**60, size=1000, dtype="u8").view("M8[ns]"),
        np.random.randint(0, 2**60, size=1000, dtype="u8").view("m8[ns]"),
        np.random.randint(0, 2**25, size=1000, dtype="u8").view("M8[m]"),
        np.random.randint(0, 2**25, size=1000, dtype="u8").view("m8[m]"),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "M8[ns]"
        ),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "m8[ns]"
        ),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "M8[m]"
        ),
        np.random.randint(-(2**63), -(2**63) + 20, size=1000, dtype="i8").view(
            "m8[m]"
        ),
    ]

    codecs = [
        LZ4(),
        LZ4(acceleration=-1),
        LZ4(acceleration=10),
        Zstd(),
        Zstd(level=-1),
        Zstd(level=10),
        Blosc(shuffle=Blosc.SHUFFLE),
        Blosc(clevel=0, shuffle=Blosc.SHUFFLE),
        Blosc(cname="lz4", shuffle=Blosc.SHUFFLE),
        Blosc(cname="lz4", clevel=1, shuffle=Blosc.NOSHUFFLE),
        Blosc(cname="lz4", clevel=5, shuffle=Blosc.SHUFFLE),
        Blosc(cname="lz4", clevel=9, shuffle=Blosc.BITSHUFFLE),
        Blosc(cname="zlib", clevel=1, shuffle=0),
        Blosc(cname="zstd", clevel=1, shuffle=1),
        Blosc(cname="blosclz", clevel=1, shuffle=2),
        Blosc(shuffle=Blosc.SHUFFLE, blocksize=0),
        Blosc(shuffle=Blosc.SHUFFLE, blocksize=2**8),
        Blosc(cname="lz4", clevel=1, shuffle=Blosc.NOSHUFFLE, blocksize=2**8),
    ]
    for codec in codecs:
        for arr in arrays:
            check_encode_decode(arr, codec)
