This fixes a bug in the previous version where if the code does not use top level await but returns a generator or coroutine, the code would try to await it. This sometimes causes strange errors or other unexpected behaviors.

Instead, I switched to a new method of returning by raising an exception. This makes it so the coroutines produced by top level await are returned via a separate channel from return values from the code so we won't confuse them anymore.

I added a new class Executor. On creation, the Executor is given the source code and config options and produces an AST. The caller can then manipulate the AST as they see fit before calling `compile` and then `run` or `run_async`. This passes to the user the most powerful possible interface, and should make it so that Executor is helpful even in complicated use cases.

I put the main parse/compile logic into a separate generator to avoid storing any of the config options on the Executor class. This helps separate out control logic from config options and limit the scope of variables as far as possible.
ENH Improvements to eval_code. This fixes a bug in the previous version where if the code does not use top level await but returns a generator or coroutine, the code would try to await it. This sometimes causes strange errors or other unexpected behaviors.

Instead, I switched to a new method of returning by raising an exception. This makes it so the coroutines produced by top level await are returned via a separate channel from return values from the code so we won't confuse them anymore.

I added a new class Executor. On creation, the Executor is given the source code and config options and produces an AST. The caller can then manipulate the AST as they see fit before calling `compile` and then `run` or `run_async`. This passes to the user the most powerful possible interface, and should make it so that Executor is helpful even in complicated use cases.

I put the main parse/compile logic into a separate generator to avoid storing any of the config options on the Executor class. This helps separate out control logic from config options and limit the scope of variables as far as possible.
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 numpy as np
    from numcodecs.blosc import Blosc
    from numcodecs.zstd import Zstd
    from numcodecs.lz4 import LZ4
    from numcodecs.compat import ensure_bytes, ensure_ndarray
    from numpy.testing import assert_array_almost_equal, assert_array_equal
    import array

    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(cname="snappy", 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 numpy as np
    from numcodecs.blosc import Blosc
    from numcodecs.zstd import Zstd
    from numcodecs.lz4 import LZ4
    from numcodecs.compat import ensure_bytes, ensure_ndarray
    from numpy.testing import assert_array_almost_equal, assert_array_equal
    import array

    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(cname="snappy", 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)
