This resolves #2189.

> build isolation would be a bit difficult to use in our case, as for instance when building scipy we need the patched numpy on the host and not the numpy version specified in pyproject.toml (which would be unpatched)

This is indeed the case, certain packages cannot be isolated. My strategy is to make a list of packages that shouldn't be isolated and add symlinks from the isolated build environment into the `.artifacts` directory to "unisolate" them. Then we remove the unisolated package requirements from the list of packages to install, in case pesky constraints aren't satisfied. In particular, packages that expect to be used with `pypa/build` often feel free to put very specific constraints on their build dependencies (often asking them to be == to a particular version). Specific version constraints is good for build reproducibility and with build isolation doesn't cost anything. So we just ignore the constraints. Hopefully nothing goes wrong.

In particular, any package that does stuff both at build time and at runtime and requires synchronization between the build time and run time environments needs the unisolation. This includes cffi with `_cffi_backend.so`, and of course numpy and scipy. pycparser needs to be unisolated because it is a dependency of cffi.

Currently I have also unisolated pythran and cython, though these are build time only tools and do not really need to be unisolated. Cython I unisolated specifically because numcodecs needs it but it isn't in the numcodecs build dependencies. Pythran I unisolated because of a problem with the scipy build which I don't fully understand (some problem with long double feature detection). I believe both of these are removable, particularly once #2132 is finished.
Use pypa/build. This resolves #2189.

> build isolation would be a bit difficult to use in our case, as for instance when building scipy we need the patched numpy on the host and not the numpy version specified in pyproject.toml (which would be unpatched)

This is indeed the case, certain packages cannot be isolated. My strategy is to make a list of packages that shouldn't be isolated and add symlinks from the isolated build environment into the `.artifacts` directory to "unisolate" them. Then we remove the unisolated package requirements from the list of packages to install, in case pesky constraints aren't satisfied. In particular, packages that expect to be used with `pypa/build` often feel free to put very specific constraints on their build dependencies (often asking them to be == to a particular version). Specific version constraints is good for build reproducibility and with build isolation doesn't cost anything. So we just ignore the constraints. Hopefully nothing goes wrong.

In particular, any package that does stuff both at build time and at runtime and requires synchronization between the build time and run time environments needs the unisolation. This includes cffi with `_cffi_backend.so`, and of course numpy and scipy. pycparser needs to be unisolated because it is a dependency of cffi.

Currently I have also unisolated pythran and cython, though these are build time only tools and do not really need to be unisolated. Cython I unisolated specifically because numcodecs needs it but it isn't in the numcodecs build dependencies. Pythran I unisolated because of a problem with the scipy build which I don't fully understand (some problem with long double feature detection). I believe both of these are removable, particularly once #2132 is finished.
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)
