#######################################################################
# Copyright (c) 2019-present, Blosc Development Team <blosc@blosc.org>
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
#######################################################################

from __future__ import annotations

import builtins
import inspect
import math
import weakref
from abc import abstractmethod
from collections import OrderedDict, namedtuple
from collections.abc import Iterator, Mapping
from contextlib import contextmanager
from functools import reduce
from itertools import islice, product
from typing import TYPE_CHECKING, Any, NamedTuple, Protocol, runtime_checkable

from numpy.exceptions import ComplexWarning

if TYPE_CHECKING:
    from collections.abc import Iterator, Sequence

from dataclasses import asdict

import ndindex
import numpy as np

import blosc2
from blosc2 import SpecialValue, blosc2_ext, compute_chunks_blocks
from blosc2.info import InfoReporter, format_nbytes_info
from blosc2.schunk import SChunk

from .linalg import matmul
from .utils import (
    get_chunks_idx,
    get_local_slice,
    get_selection,
    incomplete_lazyfunc,
    npbinvert,
    nplshift,
    nprshift,
    process_key,
    slice_to_chunktuple,
)

# Upper bound on the number of coordinates the strided-slice sparse-gather
# fast path will build (see NDArray._try_subsample_gather).  Caps both the
# int64 coordinate array (~8 MB at this size) and the per-coordinate scattered
# copies, so a dense slice such as ``a[::2]`` falls back to the bulk path
# instead of materializing a huge coordinate set.
_SUBSAMPLE_GATHER_MAX_COORDS = 1_000_000

# These functions in ufunc_map in ufunc_map_1param are implemented in numexpr and so we call
# those instead (since numexpr uses multithreading it is faster)
ufunc_map = {
    np.add: "+",
    np.subtract: "-",
    np.multiply: "*",
    np.divide: "/",
    np.true_divide: "/",
    np.floor_divide: "//",
    np.power: "**",
    np.less: "<",
    np.less_equal: "<=",
    np.greater: ">",
    np.greater_equal: ">=",
    np.equal: "==",
    np.not_equal: "!=",
    np.bitwise_and: "&",
    np.bitwise_or: "|",
    np.bitwise_xor: "^",
    np.arctan2: "arctan2",
    nplshift: "<<",  # nplshift selected above according to numpy version
    nprshift: ">>",  # nprshift selected above according to numpy version
    np.remainder: "%",
    np.nextafter: "nextafter",
    np.copysign: "copysign",
    np.hypot: "hypot",
    np.maximum: "maximum",
    np.minimum: "minimum",
}

# implemented in numexpr
ufunc_map_1param = {
    np.sqrt: "sqrt",
    np.sin: "sin",
    np.cos: "cos",
    np.tan: "tan",
    np.arcsin: "arcsin",
    np.arccos: "arccos",
    np.arctan: "arctan",
    np.sinh: "sinh",
    np.cosh: "cosh",
    np.tanh: "tanh",
    np.arcsinh: "arcsinh",
    np.arccosh: "arccosh",
    np.arctanh: "arctanh",
    np.exp: "exp",
    np.expm1: "expm1",
    np.log: "log",
    np.log10: "log10",
    np.log1p: "log1p",
    np.log2: "log2",
    np.abs: "abs",
    np.conj: "conj",
    np.real: "real",
    np.imag: "imag",
    npbinvert: "~",  # npbinvert selected above according to numpy version
    np.isnan: "isnan",
    np.isfinite: "isfinite",
    np.isinf: "isinf",
    np.floor: "floor",
    np.ceil: "ceil",
    np.trunc: "trunc",
    np.signbit: "signbit",
    np.round: "round",
}


def normalize_1d_sparse_indices(key, size: int) -> np.ndarray | None:
    if isinstance(key, list):
        indices = np.asarray(key)
    elif isinstance(key, np.ndarray):
        indices = key
    else:
        return None

    if indices.ndim != 1 or not np.issubdtype(indices.dtype, np.integer):
        return None

    indices = np.ascontiguousarray(indices, dtype=np.int64)
    if len(indices) == 0:
        return indices

    negative = indices < 0
    if np.any(negative):
        indices = indices.copy()
        indices[negative] += size
    if np.any((indices < 0) | (indices >= size)):
        raise IndexError("index out of bounds for axis 0")
    return indices


@runtime_checkable
class Array(Protocol):
    """
    A typing protocol for array-like objects with basic array interface.

    This protocol describes the basic interface required by blosc2 arrays.
    It is implemented by blosc2 classes (:ref:`NDArray`, :ref:`NDField`,
    :ref:`LazyArray`, :ref:`C2Array`, :ref:`ProxyNDSource`...)
    and is compatible with NumPy arrays and other array-like containers
    (e.g., PyTorch, TensorFlow, Dask, Zarr, ...).
    """

    @property
    def dtype(self) -> Any:
        """The data type of the array."""
        ...

    @property
    def shape(self) -> tuple[int, ...]:
        """The shape of the array."""
        ...

    def __len__(self) -> int:
        """The length of the array."""
        ...

    def __getitem__(self, key: Any) -> Any:
        """Get items from the array."""
        ...


class FieldsAccessor(Mapping):
    """Read-only mapping of structured field views."""

    def __init__(self, ndarr, field_names: Sequence[str]):
        self._ndarr_ref = weakref.ref(ndarr)
        self._field_names = tuple(field_names)

    def _ndarr(self):
        ndarr = self._ndarr_ref()
        if ndarr is None:
            raise ReferenceError("owning NDArray has been released")
        return ndarr

    def __getitem__(self, key: str) -> Any:
        if key not in self._field_names:
            raise KeyError(key)
        return NDField(self._ndarr(), key)

    def __iter__(self) -> Iterator[str]:
        return iter(self._field_names)

    def __len__(self) -> int:
        return len(self._field_names)

    def __setitem__(self, key: str, value: object) -> None:
        raise TypeError(f'assign through the field view, e.g. array.fields["{key}"][:] = values')

    def copy(self) -> dict[str, Any]:
        ndarr = self._ndarr()
        return {field: NDField(ndarr, field) for field in self._field_names}

    def __or__(self, other: object) -> dict[str, Any]:
        if not isinstance(other, Mapping):
            return NotImplemented
        return self.copy() | dict(other)

    def __ror__(self, other: object) -> dict[str, Any]:
        if not isinstance(other, Mapping):
            return NotImplemented
        return dict(other) | self.copy()

    def __repr__(self) -> str:
        return repr(dict(self))


def is_documented_by(original):
    def wrapper(target):
        target.__doc__ = original.__doc__
        return target

    return wrapper


def is_inside_new_expr() -> bool:
    """
    Whether the current code is being executed during the creation of new expression.
    """
    # Get the current call stack
    stack = inspect.stack()
    return builtins.any(frame_info.function in {"_new_expr", "open_lazyarray"} for frame_info in stack)


def make_key_hashable(key):
    if isinstance(key, slice):
        return (key.start, key.stop, key.step)
    elif isinstance(key, tuple | list):
        return tuple(make_key_hashable(k) for k in key)
    elif isinstance(key, np.ndarray):
        return tuple(key.tolist())
    else:
        return key


def get_ndarray_start_stop(ndim, key, shape):
    # key should be Nones and slices
    none_mask, start, stop, step = [], [], [], []
    for i, s in enumerate(key):
        none_mask.append(s is None)
        if s is not None:
            start.append(s.start if s.start is not None else 0)
            stop.append(s.stop if s.stop is not None else shape[i - np.sum(none_mask)])
            step.append(s.step if s.step is not None else 1)
    # Check that start and stop values do not exceed the shape
    for i in range(ndim):
        if start[i] < 0:
            start[i] = shape[i] + start[i]
        if start[i] > shape[i]:
            start[i] = shape[i]
        if stop[i] < 0:
            stop[i] = shape[i] + stop[i]
        if stop[i] > shape[i]:
            stop[i] = shape[i]

    return start, stop, tuple(step), none_mask


def are_partitions_aligned(shape, chunks, blocks):
    """
    Check if the partitions defined by chunks and blocks are aligned with the shape.

    This function verifies that the shape is aligned with the chunks and the chunks are aligned
    with the blocks.

    Returns
    -------
    bool
        True if the partitions are aligned, False otherwise.
    """
    # Check alignment
    alignment_shape_chunks = builtins.all(s % c == 0 for s, c in zip(shape, chunks, strict=True))
    if not alignment_shape_chunks:
        return False
    return builtins.all(c % b == 0 for c, b in zip(chunks, blocks, strict=True))


def are_partitions_behaved(shape, chunks, blocks):
    """
    Check if the partitions defined by chunks and blocks are well-behaved with respect to the shape.

    This function verifies that partitions are C-contiguous with respect the outer container.

    Returns
    -------
    bool
        True if the partitions are well-behaved, False otherwise.
    """

    # Check C-contiguity among partitions
    def check_contiguity(shape, part):
        ndims = len(shape)
        inner_dim = ndims - 1
        for i, size, unit in zip(reversed(range(ndims)), reversed(shape), reversed(part), strict=True):
            if size > unit:
                if i < inner_dim:
                    if size % unit != 0:
                        return False
                else:
                    if size != unit:
                        return False
                inner_dim = i
        return True

    # Check C-contiguity for blocks inside chunks
    if not check_contiguity(chunks, blocks):
        return False

    # Check C-contiguity for chunks inside shape
    return check_contiguity(shape, chunks)


def get_flat_slices_orig(shape: tuple[int], s: tuple[slice, ...]) -> list[slice]:
    """
    From array with `shape`, get the flattened list of slices corresponding to `s`.

    Parameters
    ----------
    shape: tuple[int]
        The shape of the array.
    s: tuple[slice]
        The slice we want to flatten.

    Returns
    -------
    list[slice]
        A list of slices that correspond to the slice `s`.
    """
    # Note: this has been rewritten to use cython, see get_flat_slices
    # It is kept here for reference
    #
    # Process the slice s to get start and stop indices
    key = np.index_exp[s]
    start = [k.start if k.start is not None else 0 for k in key]
    # For stop, cap the values to the shape (shape may not be an exact multiple of the chunks)
    stop = [builtins.min(k.stop if k.stop is not None else shape[i], shape[i]) for i, k in enumerate(key)]

    # Calculate the strides for each dimension
    strides = np.cumprod((1,) + shape[::-1][:-1])[::-1]

    # Generate the 1-dimensional slices
    slices = []
    current_slice_start = None
    current_slice_end = None
    for idx in np.ndindex(*[stop[i] - start[i] for i in range(len(shape))]):
        flat_idx = builtins.sum((start[i] + idx[i]) * strides[i] for i in range(len(shape)))
        if current_slice_start is None:
            current_slice_start = flat_idx
            current_slice_end = flat_idx
        elif flat_idx == current_slice_end + 1:
            current_slice_end = flat_idx
        else:
            slices.append(slice(current_slice_start, current_slice_end + 1))
            current_slice_start = flat_idx
            current_slice_end = flat_idx

    if current_slice_start is not None:
        slices.append(slice(current_slice_start, current_slice_end + 1))

    return slices


def get_flat_slices(
    shape: tuple[int],
    s: tuple[slice, ...],
    c_order: bool = True,
) -> list[slice]:
    """
    From array with `shape`, get the flattened list of slices corresponding to `s`.

    Parameters
    ----------
    shape: tuple
        The shape of the array.
    s: tuple
        The slice we want to flatten.
    c_order: bool
        Whether to flatten the slices in C order (row-major) or just plain order.
        Default is C order.

    Returns
    -------
    list
        A list of slices that correspond to the slice `s`.
    """
    ndim = len(shape)
    if ndim == 0:
        # this will likely cause failure since expected output is tuple of slices
        # however, the list conversion in the last line causes the process to be killed for some reason if shape = ()
        return ()
    start = [s[i].start if s[i].start is not None else 0 for i in range(ndim)]
    stop = [builtins.min(s[i].stop if s[i].stop is not None else shape[i], shape[i]) for i in range(ndim)]
    # Steps are not used in the computation, so raise an error if they are not None or 1
    if builtins.any(s[i].step not in (None, 1) for i in range(ndim)):
        raise ValueError("steps are not supported in slices")

    # Calculate the strides for each dimension
    # Both methods are equivalent
    # strides = np.cumprod((1,) + shape[::-1][:-1])[::-1]
    strides = [reduce(lambda x, y: x * y, shape[i + 1 :], 1) for i in range(ndim)]

    # Convert lists to numpy arrays
    start = np.array(start, dtype=np.int64)
    stop = np.array(stop, dtype=np.int64)
    strides = np.array(strides, dtype=np.int64)

    if not c_order:
        # Generate just a single 1-dimensional slice
        flat_start = np.sum(start * strides)
        # Compute the size of the slice
        flat_size = math.prod(stop - start)
        return [slice(flat_start, flat_start + flat_size)]

    # Generate and return the 1-dimensional slices in C order
    return list(blosc2_ext.slice_flatter(start, stop, strides))


def reshape(
    src: blosc2.Array,
    shape: tuple | list,
    c_order: bool = True,
    **kwargs: Any,
) -> NDArray:
    """Returns an array containing the same data with a new shape.

    This only works when src.shape is 1-dimensional. Multidim case for src is
    interesting, but not supported yet.

    Parameters
    ----------
    src: :ref:`NDArray` or :ref:`NDField` or :ref:`LazyArray` or :ref:`C2Array`
        The input array.
    shape : tuple or list
        The new shape of the array. It should have the same number of elements
        as the current shape.
    c_order: bool
        Whether to reshape the array in C order (row-major) or insertion order.
        Insertion order means that values will be stored in the array
        following the order of chunks in the source array.
        Default is C order.
    kwargs : dict, optional
        Additional keyword arguments supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A new array with the requested shape.

    Examples
    --------
    >>> import blosc2
    >>> b = blosc2.arange(253)
    >>> c = blosc2.reshape(b, (11, 23))
    >>> print(c.shape)
    (11, 23)
    """

    if src.ndim != 1:
        raise ValueError("reshape only works when src.shape is 1-dimensional")
    # Check if the new shape is valid
    if math.prod(shape) != math.prod(src.shape):
        raise ValueError("total size of new array must be unchanged")

    # Create the new array
    dst = empty(shape, dtype=src.dtype, **kwargs)

    if is_inside_new_expr() or 0 in shape:
        # We already have the dtype and shape, so return immediately
        return dst

    if shape == ():  # get_flat_slices fails for this case so just return directly
        dst[()] = src[()] if src.shape == () else src[0]
        return dst

    # Copy the data chunk by chunk
    for dst_chunk in dst.iterchunks_info():
        dst_slice = tuple(
            slice(c * s, (c + 1) * s) for c, s in zip(dst_chunk.coords, dst.chunks, strict=False)
        )
        # Cap the stop indices in dst_slices to the dst.shape, and create a new list of slices
        dst_slice = tuple(
            slice(s.start, builtins.min(s.stop, sh)) for s, sh in zip(dst_slice, dst.shape, strict=False)
        )
        size_dst_slice = math.prod([s.stop - s.start for s in dst_slice])
        # Find the series of slices in source array that correspond to the destination chunk
        # (assuming the source array is 1-dimensional here)
        # t0 = time()
        # src_slices = get_flat_slices_orig(dst.shape, dst_slice)
        # Use the get_flat_slices which uses a much faster iterator in cython
        src_slices = get_flat_slices(dst.shape, dst_slice, c_order)
        # print(f"Time to get slices: {time() - t0:.3f} s")
        # Compute the size for slices in the source array
        size_src_slices = builtins.sum(s.stop - s.start for s in src_slices)
        if size_src_slices != size_dst_slice:
            raise ValueError("source slice size is not equal to the destination chunk size")
        # Now, assemble the slices for assignment in the destination array
        dst_buf = np.empty(size_dst_slice, dtype=src.dtype)
        dst_buf_len = 0
        for src_slice in src_slices:
            slice_size = src_slice.stop - src_slice.start
            dst_buf_slice = slice(dst_buf_len, dst_buf_len + slice_size)
            dst_buf_len += slice_size
            if hasattr(src, "res_getitem"):
                # Fast path for lazy UDFs (important for e.g. arange or linspace)
                # This essentially avoids the need to create a new,
                # potentially large NumPy array in memory.
                # This is not critical for Linux, but it is for Windows/Mac.
                dst_buf[dst_buf_slice] = src.res_getitem[src_slice]
            else:
                dst_buf[dst_buf_slice] = src[src_slice]
        # Compute the shape of dst_slice
        dst_slice_shape = tuple(s.stop - s.start for s in dst_slice)
        # ... and assign the buffer to the destination array
        dst[dst_slice] = dst_buf.reshape(dst_slice_shape)

    return dst


def _normalize_expr_operand(value: Any) -> Any:
    """Normalize foreign expression operands to the array-like object lazy ops expect."""
    raw_col = getattr(value, "_raw_col", None)
    return raw_col if raw_col is not None else value


def _check_allowed_dtypes(
    value: bool | int | float | str | blosc2.Array,
):
    value = _normalize_expr_operand(value)

    def _is_array_like(v: Any) -> bool:
        try:
            # Try Protocol runtime check first (works when possible)
            if isinstance(v, blosc2.Array):
                return True
        except Exception:
            # Some runtime contexts may raise (or return False) — fall back to duck typing
            pass
        # Structural fallback: common minimal array interface
        return hasattr(v, "shape") and hasattr(v, "dtype") and callable(getattr(v, "__getitem__", None))

    if not (_is_array_like(value) or np.isscalar(value)):
        raise RuntimeError(
            f"Expected blosc2.Array or scalar instances and you provided a '{type(value)}' instance"
        )


def sum(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | complex | bool:
    """
    Return the sum of array elements over a given axis.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array or expression.
    axis: int or tuple of ints, optional
        Axis or axes along which a sum is performed. By default, axis=None,
        sums all the elements of the input array. If axis is negative,
        it counts from the last to the first axis.
    dtype: np.dtype or list str, optional
        The type of the returned array and of the accumulator in which the
        elements are summed. The dtype of :paramref:`ndarr` is used by default unless it has
        an integer dtype of less precision than the default platform integer.
    keepdims: bool, optional
        If set to True, the reduced axes are left in the result
        as dimensions with size one. With this option, the result will broadcast
        correctly against the input array.
    where: array_like of bool, optional
        Elements to include in the reduction. False entries are ignored.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.
    kwargs: dict, optional
        Additional keyword arguments supported by the :func:`empty` constructor.

    Returns
    -------
    sum_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The sum of the elements along the axis.

    References
    ----------
    `np.sum <https://numpy.org/doc/stable/reference/generated/numpy.sum.html>`_

    Examples
    --------
    >>> import blosc2
    >>> nd_array = blosc2.array([[1, 2, 3], [4, 5, 6]])
    >>> # Sum all elements in the array (axis=None)
    >>> total_sum = blosc2.sum(nd_array)
    >>> print("Sum of all elements:", total_sum)
    21
    >>> # Sum along axis 0 (columns)
    >>> sum_axis_0 = blosc2.sum(nd_array, axis=0)
    >>> print("Sum along axis 0 (columns):", sum_axis_0)
    Sum along axis 0 (columns): [5 7 9]
    """
    if where is None:
        return ndarr.sum(axis=axis, dtype=dtype, keepdims=keepdims, **kwargs)
    return ndarr.sum(axis=axis, dtype=dtype, keepdims=keepdims, where=where, **kwargs)


def cumulative_sum(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    include_initial: bool = False,
    **kwargs: Any,
) -> blosc2.Array:
    """
    Calculates the cumulative sum of elements in the input array ndarr.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array or expression.
    axis: int
        Axis along which a cumulative sum must be computed. If array is 1D, axis may be None; otherwise the axis must be specified.
    dtype: dtype
        Data type of the returned array.
    include_initial : bool
        Boolean indicating whether to include the initial value as the first value in the output. Initial value will be zero. Default: False.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.
    kwargs: dict, optional
        Additional keyword arguments supported by the :func:`empty` constructor.

    Returns
    -------
    out: blosc2.Array
        An array containing the cumulative sums. Let N be the size of the axis along which to compute the cumulative sum.
        If include_initial is True, the returned array has the same shape as ndarr, except the size of the axis along which to compute the cumulative sum is N+1.
        If include_initial is False, the returned array has the same shape as ndarr.
    """
    return ndarr.cumulative_sum(axis=axis, dtype=dtype, include_initial=include_initial, **kwargs)


def cumulative_prod(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    include_initial: bool = False,
    **kwargs: Any,
) -> blosc2.Array:
    """
    Calculates the cumulative product of elements in the input array ndarr.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array or expression.
    axis: int
        Axis along which a cumulative product must be computed. If array is 1D, axis may be None; otherwise the axis must be specified.
    dtype: dtype
        Data type of the returned array.
    include_initial : bool
        Boolean indicating whether to include the initial value as the first value in the output. Initial value will be one. Default: False.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.
    kwargs: dict, optional
        Additional keyword arguments supported by the :func:`empty` constructor.

    Returns
    -------
    out: blosc2.Array
        An array containing the cumulative products. Let N be the size of the axis along which to compute the cumulative product.
        If include_initial is True, the returned array has the same shape as ndarr, except the size of the axis along which to compute the cumulative product is N+1.
        If include_initial is False, the returned array has the same shape as ndarr.
    """
    return ndarr.cumulative_prod(axis=axis, dtype=dtype, include_initial=include_initial, **kwargs)


def mean(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | complex | bool:
    """
    Return the arithmetic mean along the specified axis.

    The parameters are documented in the :func:`sum <blosc2.sum>`.

    Returns
    -------
    mean_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The mean of the elements along the axis.

    References
    ----------
    `np.mean <https://numpy.org/doc/stable/reference/generated/numpy.mean.html>`_

    Examples
    --------
    >>> import blosc2
    >>> nd_array = blosc2.array([[1, 2, 3], [4, 5, 6]])
    >>> # Compute the mean of all elements in the array (axis=None)
    >>> overall_mean = blosc2.mean(nd_array)
    >>> print("Mean of all elements:", overall_mean)
    Mean of all elements: 3.5
    """
    if where is None:
        return ndarr.mean(axis=axis, dtype=dtype, keepdims=keepdims, **kwargs)
    return ndarr.mean(axis=axis, dtype=dtype, keepdims=keepdims, where=where, **kwargs)


def std(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    ddof: int = 0,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | bool:
    """
    Return the standard deviation along the specified axis.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array or expression.
    axis: int or tuple of ints, optional
        Axis or axes along which the standard deviation is computed. By default, `axis=None`
        computes the standard deviation of the flattened array.
    dtype: np.dtype or list str, optional
        Type to use in computing the standard deviation. For integer inputs, the
        default is float32; for floating point inputs, it is the same as the input dtype.
    ddof: int, optional
        Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
        where N represents the number of elements. By default, ddof is zero.
    keepdims: bool, optional
        If set to True, the reduced axes are left in the result as
        dimensions with size one. This ensures that the result will broadcast correctly
        against the input array.
    where: array_like of bool, optional
        Elements to include in the reduction. False entries are ignored.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.
    kwargs: dict, optional
        Additional keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    std_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The standard deviation of the elements along the axis.

    References
    ----------
    `np.std <https://numpy.org/doc/stable/reference/generated/numpy.std.html>`_

    Examples
    --------
    >>> import blosc2
    >>> nd_array = blosc2.array([[1, 2, 3], [4, 5, 6]])
    >>> # Compute the standard deviation of the entire array
    >>> std_all = blosc2.std(nd_array)
    >>> print("Standard deviation of the entire array:", std_all)
    Standard deviation of the entire array: 1.707825127659933
    >>> # Compute the standard deviation along axis 0 (columns)
    >>> std_axis0 = blosc2.std(nd_array, axis=0)
    >>> print("Standard deviation along axis 0:", std_axis0)
    Standard deviation along axis 0: [1.5 1.5 1.5]
    """
    if where is None:
        return ndarr.std(axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, **kwargs)
    return ndarr.std(axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, where=where, **kwargs)


def var(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    ddof: int = 0,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | bool:
    """
    Return the variance along the specified axis.

    The parameters are documented in the :func:`std <blosc2.std>`.

    Returns
    -------
    var_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The variance of the elements along the axis.

    References
    ----------
    `np.var <https://numpy.org/doc/stable/reference/generated/numpy.var.html>`_


    Examples
    --------
    >>> import blosc2
    >>> nd_array = blosc2.array([[1, 2, 3], [4, 5, 6]])
    >>> # Compute the variance of the entire array
    >>> var_all = blosc2.var(nd_array)
    >>> print("Variance of the entire array:", var_all)
    Variance of the entire array: 2.9166666666666665
    >>> # Compute the variance along axis 0 (columns)
    >>> var_axis0 = blosc2.var(nd_array, axis=0)
    >>> print("Variance along axis 0:", var_axis0)
    Variance along axis 0: [2.25 2.25 2.25]
    """
    if where is None:
        return ndarr.var(axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, **kwargs)
    return ndarr.var(axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, where=where, **kwargs)


def prod(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    dtype: np.dtype | str = None,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | complex | bool:
    """
    Return the product of array elements over a given axis.

    The parameters are documented in the :func:`sum <blosc2.sum>`.

    Returns
    -------
    product_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The product of the elements along the axis.

    References
    ----------
    `np.prod <https://numpy.org/doc/stable/reference/generated/numpy.prod.html>`_

    Examples
    --------
    >>> import blosc2
    >>> nd_array = blosc2.array([[11, 22, 33], [4, 15, 36]])
    >>> # Compute the product of all elements in the array
    >>> prod_all = blosc2.prod(nd_array)
    >>> print("Product of all elements in the array:", prod_all)
    Product of all elements in the array: 17249760
    >>> # Compute the product along axis 1 (rows)
    >>> prod_axis1 = blosc2.prod(nd_array, axis=1)
    >>> print("Product along axis 1:", prod_axis1)
    Product along axis 1: [7986 2160]
    """
    if where is None:
        return ndarr.prod(axis=axis, dtype=dtype, keepdims=keepdims, **kwargs)
    return ndarr.prod(axis=axis, dtype=dtype, keepdims=keepdims, where=where, **kwargs)


def min(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | complex | bool:
    """
    Return the minimum along a given axis.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array or expression.
    axis: int or tuple of ints, optional
        Axis or axes along which to operate. By default, flattened input is used.
    keepdims: bool, optional
        If set to True, the axes which are reduced are left in the result as
        dimensions with size one. With this option, the result will broadcast correctly
        against the input array.
    where: array_like of bool, optional
        Elements to include in the reduction. False entries are ignored.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    min_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The minimum of the elements along the axis.

    References
    ----------
    `np.min <https://numpy.org/doc/stable/reference/generated/numpy.min.html>`_

    Examples
    --------
    >>> import blosc2
    >>> nd_array = blosc2.array([1, 3, 7, 8, 9, 31])
    >>> min_all = blosc2.min(nd_array)
    >>> print("Minimum of all elements in the array:", min_all)
    Minimum of all elements in the array: 1
    >>> # Compute the minimum along axis 0 with keepdims=True
    >>> min_keepdims = blosc2.min(nd_array, axis=0, keepdims=True)
    >>> print("Minimum along axis 0 with keepdims=True:", min_keepdims)
    Minimum along axis 0 with keepdims=True:  [1]
    """
    if where is None:
        return ndarr.min(axis=axis, keepdims=keepdims, **kwargs)
    return ndarr.min(axis=axis, keepdims=keepdims, where=where, **kwargs)


def max(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    keepdims: bool = False,
    where: blosc2.Array | np.ndarray | None = None,
    **kwargs: Any,
) -> blosc2.Array | int | float | complex | bool:
    """
    Return the maximum along a given axis.

    The parameters are documented in the :func:`min <blosc2.min>`.

    Returns
    -------
    max_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The maximum of the elements along the axis.

    References
    ----------
    `np.max <https://numpy.org/doc/stable/reference/generated/numpy.max.html>`_

    Examples
    --------
    >>> import blosc2
    >>> ndarray = blosc2.array([[11, 2, 36, 24, 5, 69], [73, 81, 49, 6, 73, 0]])
    >>> print("NDArray data:", ndarray[:])
    NDArray data:  [[11  2 36 24  5 69]
                    [73 81 49  6 73  0]]
    >>> # Compute the maximum along axis 0 and 1
    >>> max_along_axis_0 = blosc2.max(ndarray, axis=0)
    >>> print("Maximum along axis 0:", max_along_axis_0)
    Maximum along axis 0: [73 81 49 24 73 69]
    >>> max_along_axis_1 = blosc2.max(ndarray, axis=1)
    >>> print("Maximum along axis 1:", max_along_axis_1)
    Maximum along axis 1: [69 81]
    >>> max_flattened = blosc2.max(ndarray)
    >>> print("Maximum of the flattened array:", max_flattened)
    Maximum of the flattened array: 81
    """
    if where is None:
        return ndarr.max(axis=axis, keepdims=keepdims, **kwargs)
    return ndarr.max(axis=axis, keepdims=keepdims, where=where, **kwargs)


def any(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    keepdims: bool = False,
    **kwargs: Any,
) -> blosc2.Array | bool:
    """
    Test whether any array element along a given axis evaluates to True.

    The parameters are documented in the :func:`min <blosc2.min>`.

    Returns
    -------
    any_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The result of the evaluation along the axis.

    References
    ----------
    `np.any <https://numpy.org/doc/stable/reference/generated/numpy.any.html>`_

    Examples
    --------
    >>> import blosc2
    >>> ndarray = blosc2.array([[1, 0, 0], [0, 1, 0], [0, 0, 0]])
    >>> print("NDArray data:", ndarray[:])
    NDArray data: [[1 0 0]
                    [0 1 0]
                    [0 0 0]]
    >>> any_along_axis_0 = blosc2.any(ndarray, axis=0)
    >>> print("Any along axis 0:", any_along_axis_0)
    Any along axis 0: [True True False]
    >>> any_flattened = blosc2.any(ndarray)
    >>> print("Any in the flattened array:", any_flattened)
    Any in the flattened array: True
    """
    return ndarr.any(axis=axis, keepdims=keepdims, **kwargs)


def argmin(
    ndarr: blosc2.Array, axis: int | None = None, keepdims: bool = False, **kwargs
) -> blosc2.Array | int:
    """
    Returns the indices of the minimum values along a specified axis.

    When the minimum value occurs multiple times, only the indices corresponding to the first occurrence are returned.

    Parameters
    ----------
    x: blosc2.Array
        Input array. Should have a real-valued data type.

    axis: int | None
        Axis along which to search. If None, return index of the minimum value of flattened array. Default: None.

    keepdims: bool
        If True, reduced axis included in the result as singleton dimension. Otherwise, axis not included in the result. Default: False.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.

    Returns
    -------
    out: blosc2.Array
        If axis is None, a zero-dimensional array containing the index of the first occurrence of the minimum value; otherwise, a non-zero-dimensional array containing the indices of the minimum values.
    """
    return ndarr.argmin(axis=axis, keepdims=keepdims, **kwargs)


def argmax(
    ndarr: blosc2.Array, axis: int | None = None, keepdims: bool = False, **kwargs
) -> blosc2.Array | int:
    """
    Returns the indices of the maximum values along a specified axis.

    When the maximum value occurs multiple times, only the indices corresponding to the first occurrence are returned.

    Parameters
    ----------
    x: blosc2.Array
        Input array. Should have a real-valued data type.

    axis: int | None
        Axis along which to search. If None, return index of the maximum value of flattened array. Default: None.

    keepdims: bool
        If True, reduced axis included in the result as singleton dimension. Otherwise, axis not included in the result. Default: False.
    fp_accuracy: :class:`blosc2.FPAccuracy`, optional
        Specifies the floating-point accuracy for reductions on :ref:`LazyExpr`.
        Passed to :func:`LazyExpr.compute` when :paramref:`ndarr` is a LazyExpr.

    Returns
    -------
    out: blosc2.Array
        If axis is None, a zero-dimensional array containing the index of the first occurrence of the maximum value; otherwise, a non-zero-dimensional array containing the indices of the maximum values.
    """
    return ndarr.argmax(axis=axis, keepdims=keepdims, **kwargs)


def all(
    ndarr: blosc2.Array,
    axis: int | tuple[int] | None = None,
    keepdims: bool = False,
    **kwargs: Any,
) -> blosc2.Array | bool:
    """
    Test whether all array elements along a given axis evaluate to True.

    The parameters are documented in the :func:`min <blosc2.min>`.

    Returns
    -------
    all_along_axis: np.ndarray or :ref:`NDArray` or scalar
        The result of the evaluation along the axis.

    References
    ----------
    `np.all <https://numpy.org/doc/stable/reference/generated/numpy.all.html>`_

    Examples
    --------
    >>> import blosc2
    >>> ndarray = blosc2.array([True, True, False, True, True, True])
    >>> # Test if all elements are True along the default axis (flattened array)
    >>> result_flat = blosc2.all(ndarray)
    >>> print("All elements are True (flattened):", result_flat)
    All elements are True (flattened): False
    """
    return ndarr.all(axis=axis, keepdims=keepdims, **kwargs)


def sin(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the trigonometric sine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array containing angles in radians.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the sine of the input angles. The result can be evaluated.

    References
    ----------
    `np.sin <https://numpy.org/doc/stable/reference/generated/numpy.sin.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> angles = np.array([0, np.pi/6, np.pi/4, np.pi/2, np.pi])
    >>> nd_array = blosc2.asarray(angles)
    >>> result_ = blosc2.sin(nd_array)
    >>> result = result_[:]
    >>> print("Angles in radians:", angles)
    Angles in radians: [0.         0.52359878 0.78539816 1.57079633 3.14159265]
    >>> print("Sine of the angles:", result)
    Sine of the angles: [0.00000000e+00 5.00000000e-01 7.07106781e-01 1.00000000e+00
    1.22464680e-16]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "sin", None))


def cos(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Trigonometric cosine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array containing angles in radians.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the cosine of the input angles. The result can be evaluated.

    References
    ----------
    `np.cos <https://numpy.org/doc/stable/reference/generated/numpy.cos.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> angles = np.array([0, np.pi/6, np.pi/4, np.pi/2, np.pi])
    >>> nd_array = blosc2.asarray(angles)
    >>> result_ = blosc2.cos(nd_array)
    >>> result = result_[:]
    >>> print("Angles in radians:", angles)
    Angles in radians: [0.         0.52359878 0.78539816 1.57079633 3.14159265]
    >>> print("Cosine of the angles:", result)
    Cosine of the angles: [ 1.00000000e+00  8.66025404e-01  7.07106781e-01  6.12323400e-17
    -1.00000000e+00]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "cos", None))


def tan(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the trigonometric tangent, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array containing angles in radians.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the tangent of the input angles.
        The result can be evaluated.

    References
    ----------
    `np.tan <https://numpy.org/doc/stable/reference/generated/numpy.tan.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> angles = np.array([0, np.pi/6, np.pi/4, np.pi/2, np.pi])
    >>> nd_array = blosc2.asarray(angles)
    >>> result_ = blosc2.tan(nd_array)
    >>> result = result_[:]
    >>> print("Angles in radians:", angles)
    Angles in radians: [0.         0.52359878 0.78539816 1.57079633 3.14159265]
    >>> print("Tangent of the angles:", result)
    Tangent of the angles: [ 0.00000000e+00  5.77350269e-01  1.00000000e+00  1.63312394e+16
    -1.22464680e-16]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "tan", None))


def sqrt(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the non-negative square-root of an array, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the square root of the input array.
        The result can be evaluated.

    References
    ----------
    `np.sqrt <https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> data = np.array([0, np.pi/6, np.pi/4, np.pi/2, np.pi])
    >>> nd_array = blosc2.asarray(data)
    >>> result_ = blosc2.sqrt(nd_array)
    >>> result = result_[:]
    >>> print("Original numbers:", data)
    Original numbers: [ 0  1  4  9 16 25]
    >>> print("Square roots:", result)
    Square roots: [0. 1. 2. 3. 4. 5.]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "sqrt", None))


def sinh(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Hyperbolic sine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the hyperbolic sine of the input array.
        The result can be evaluated.

    References
    ----------
    `np.sinh <https://numpy.org/doc/stable/reference/generated/numpy.sinh.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> numbers = np.array([-2, -1, 0, 1, 2])
    >>> ndarray = blosc2.asarray(numbers)
    >>> result_lazy = blosc2.sinh(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original numbers:", numbers)
    Original numbers: [-2 -1  0  1  2]
    >>> print("Hyperbolic sine:", result)
    Hyperbolic sine: [-3.62686041 -1.17520119  0.          1.17520119  3.62686041]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "sinh", None))


def cosh(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the hyperbolic cosine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the hyperbolic cosine of the input array.
        The result can be evaluated.

    References
    ----------
    `np.cosh <https://numpy.org/doc/stable/reference/generated/numpy.cosh.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> numbers = np.array([-2, -1, 0, 1, 2])
    >>> ndarray = blosc2.asarray(numbers)
    >>> result_lazy = blosc2.cosh(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original numbers:", numbers)
    Original numbers: [-2 -1  0  1  2]
    >>> print("Hyperbolic cosine:", result)
    Hyperbolic cosine: [3.76219569 1.54308063 1.         1.54308063 3.76219569]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "cosh", None))


def tanh(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the hyperbolic tangent, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the hyperbolic tangent of the input array.
        The result can be evaluated.

    References
    ----------
    `np.tanh <https://numpy.org/doc/stable/reference/generated/numpy.tanh.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> numbers = np.array([-2, -1, 0, 1, 2])
    >>> ndarray = blosc2.asarray(numbers)
    >>> result_lazy = blosc2.tanh(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original numbers:", numbers)
    Original numbers: [-2 -1  0  1  2]
    >>> print("Hyperbolic tangent:", result)
    Hyperbolic tangent: [-0.96402758 -0.76159416  0.          0.76159416  0.96402758]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "tanh", None))


def arcsin(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the inverse sine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the inverse sine of the input array.
        The result can be evaluated.

    References
    ----------
    `np.arcsin <https://numpy.org/doc/stable/reference/generated/numpy.arcsin.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> numbers = np.array([-1, -0.5, 0, 0.5, 1])
    >>> ndarray = blosc2.asarray(numbers)
    >>> result_lazy = blosc2.arcsin(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original numbers:", numbers)
    Original numbers: [-1.  -0.5  0.   0.5  1. ]
    >>> print("Arcsin:", result)
    Arcsin: [-1.57079633 -0.52359878  0.          0.52359878  1.57079633]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "arcsin", None))


asin = arcsin  # alias


def arccos(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the inverse cosine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the inverse cosine of the input array.
        The result can be evaluated.

    References
    ----------
    `np.arccos <https://numpy.org/doc/stable/reference/generated/numpy.arccos.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> numbers = np.array([-1, -0.5, 0, 0.5, 1])
    >>> ndarray = blosc2.asarray(numbers)
    >>> result_lazy = blosc2.arccos(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original numbers:", numbers)
    Original numbers: [-1.  -0.5  0.   0.5  1. ]
    >>> print("Arccos:", result)
    Arccos: [3.14159265 2.0943951  1.57079633 1.04719755 0.        ]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "arccos", None))


acos = arccos  # alias


def arctan(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the inverse tangent, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the inverse tangent of the input array.
        The result can be evaluated.

    References
    ----------
    `np.arctan <https://numpy.org/doc/stable/reference/generated/numpy.arctan.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> numbers = np.array([-1, -0.5, 0, 0.5, 1])
    >>> ndarray = blosc2.asarray(numbers)
    >>> result_lazy = blosc2.arctan(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original numbers:", numbers)
    Original numbers: [-1.  -0.5  0.   0.5  1. ]
    >>> print("Arctan:", result)
    Arctan: [-0.78539816 -0.46364761  0.          0.46364761  0.78539816]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "arctan", None))


atan = arctan  # alias


def arctan2(ndarr1: blosc2.Array, ndarr2: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the element-wise arc tangent of ``ndarr1 / ndarr2`` choosing the quadrant correctly.

    Parameters
    ----------
    ndarr1: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array`
        The first input array.
    ndarr2: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array`
        The second input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the element-wise arc tangent of ``ndarr1 / ndarr2``.
        The result can be evaluated.

    References
    ----------
    `np.arctan2 <https://numpy.org/doc/stable/reference/generated/numpy.arctan2.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> y = np.array([0, 1, 0, -1, 1])
    >>> x = np.array([1, 1, -1, -1, 0])
    >>> ndarray_y = blosc2.asarray(y)
    >>> ndarray_x = blosc2.asarray(x)
    >>> result_lazy = blosc2.arctan2(ndarray_y, ndarray_x)
    >>> result = result_lazy[:]
    >>> print("y:", y)
    y: [ 0  1  0 -1  1]
    >>> print("x:", x)
    x: [ 1  1 -1 -1  0]
    >>> print("Arctan2(y, x):", result)
    Arctan2(y, x): [ 0.          0.78539816  3.14159265 -2.35619449  1.57079633]
    """
    return blosc2.LazyExpr(new_op=(ndarr1, "arctan2", ndarr2))


atan2 = arctan2  # alias


def arcsinh(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the inverse hyperbolic sine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the inverse hyperbolic sine of the input array.
        The result can be evaluated.

    References
    ----------
    `np.arcsinh <https://numpy.org/doc/stable/reference/generated/numpy.arcsinh.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([-2, -1, 0, 1, 2])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.arcsinh(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [-2 -1  0  1  2]
    >>> print("Arcsinh:", result)
    Arcsinh: [-1.44363548 -0.88137359  0.          0.88137359  1.44363548]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "arcsinh", None))


asinh = arcsinh  # alias


def arccosh(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the inverse hyperbolic cosine, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the inverse hyperbolic cosine of the input array.
        The result can be evaluated.

    References
    ----------
    `np.arccosh <https://numpy.org/doc/stable/reference/generated/numpy.arccosh.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([1, 2, 3, 4, 5])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.arccosh(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [1 2 3 4 5]
    >>> print("Arccosh:", result)
    Arccosh: [0.         1.3169579  1.76274717 2.06343707 2.29243167]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "arccosh", None))


acosh = arccosh  # alias


def arctanh(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the inverse hyperbolic tangent, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the inverse hyperbolic tangent of the input array.
        The result can be evaluated.

    References
    ----------
    `np.arctanh <https://numpy.org/doc/stable/reference/generated/numpy.arctanh.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([-0.9, -0.5, 0, 0.5, 0.9])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.arctanh(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [-0.9 -0.5  0.   0.5  0.9]
    >>> print("Arctanh:", result)
    Arctanh: [-1.47221949 -0.54930614  0.          0.54930614  1.47221949]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "arctanh", None))


atanh = arctanh  # alias


def exp(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Calculate the exponential of all elements in the input array.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the exponential of the input array.
        The result can be evaluated.

    References
    ----------
    `np.exp <https://numpy.org/doc/stable/reference/generated/numpy.exp.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([0, 1, 2, 3, 4])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.exp(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [0 1 2 3 4]
    >>> print("Exponential:", result)
    Exponential: [ 1.          2.71828183  7.3890561  20.08553692 54.59815003]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "exp", None))


def expm1(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Calculate ``exp(ndarr) - 1`` for all elements in the array.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing ``exp(ndarr) - 1`` of the input array.
        The result can be evaluated.

    References
    ----------
    `np.expm1 <https://numpy.org/doc/stable/reference/generated/numpy.expm1.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([-1, -0.5, 0, 0.5, 1])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.expm1(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [-1.  -0.5  0.   0.5  1. ]
    >>> print("Expm1:", result)
    Expm1: [-0.63212056 -0.39346934  0.          0.64872127  1.71828183]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "expm1", None))


def log(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Compute the natural logarithm, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the natural logarithm of the input array

    References
    ----------
    `np.log <https://numpy.org/doc/stable/reference/generated/numpy.log.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([1, 2, 3, 4, 5])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.log(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [1 2 3 4 5]
    >>> print("Logarithm (base e):", result)
    Logarithm (base e): [0.         0.69314718 1.09861229 1.38629436 1.60943791]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "log", None))


def log10(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the base 10 logarithm of the input array, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the base 10 logarithm of the input array.

    References
    ----------
    `np.log10 <https://numpy.org/doc/stable/reference/generated/numpy.log10.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([1, 10, 100, 1000, 10000])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.log10(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [    1    10   100  1000 10000]
    >>> print("Logarithm (base 10):", result)
    Logarithm (base 10): [0. 1. 2. 3. 4.]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "log10", None))


def log1p(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the natural logarithm of one plus the input array, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the natural logarithm of one plus the input array.

    References
    ----------
    `np.log1p <https://numpy.org/doc/stable/reference/generated/numpy.log1p.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([-0.9, -0.5, 0, 0.5, 0.9])
    >>> ndarray = blosc2.asarray(values)
    >>> result_lazy = blosc2.log1p(ndarray)
    >>> result = result_lazy[:]
    >>> print("Original values:", values)
    Original values: [-0.9 -0.5  0.   0.5  0.9]
    >>> print("Log1p (log(1 + x)):", result)
    Log1p (log(1 + x)): [-2.30258509 -0.69314718  0.          0.40546511  0.64185389]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "log1p", None))


def log2(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the base 2 logarithm of the input array, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the base 2 logarithm of the input array.

    References
    ----------
    `np.log2 <https://numpy.org/doc/stable/reference/generated/numpy.log2.html>`_

    """
    return blosc2.LazyExpr(new_op=(ndarr, "log2", None))


def conj(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the complex conjugate, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the complex conjugate of the input array.

    References
    ----------
    `np.conj <https://numpy.org/doc/stable/reference/generated/numpy.conj.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([1+2j, 3-4j, -5+6j, 7-8j])
    >>> ndarray = blosc2.asarray(values)
    >>> result_ = blosc2.conj(ndarray)
    >>> result = result_[:]
    >>> print("Original values:", values)
    Original values: [ 1.+2.j  3.-4.j -5.+6.j  7.-8.j]
    >>> print("Complex conjugates:", result)
    Complex conjugates: [ 1.-2.j  3.+4.j -5.-6.j  7.+8.j]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "conj", None))


def real(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the real part of the complex array, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the real part of the input array.

    References
    ----------
    `np.real <https://numpy.org/doc/stable/reference/generated/numpy.real.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> complex_values = np.array([1+2j, 3-4j, -5+6j, 7-8j])
    >>> ndarray = blosc2.asarray(complex_values)
    >>> result_ = blosc2.real(ndarray)
    >>> result = result_[:]
    >>> print("Original complex values:", complex_values)
    Original values: [ 1.+2.j  3.-4.j -5.+6.j  7.-8.j]
    >>> print("Real parts:", result)
    Real parts: [ 1.  3. -5.  7.]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "real", None))


def imag(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return the imaginary part of the complex array, element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression representing the imaginary part of the input array.

    References
    ----------
    `np.imag <https://numpy.org/doc/stable/reference/generated/numpy.imag.html>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> complex_values = np.array([2+3j, -1+4j, 0-2j, 5+6j])
    >>> ndarray = blosc2.asarray(complex_values)
    >>> result_ = blosc2.imag(ndarray)
    >>> result = result_[:]
    >>> print("Original complex values:", complex_values)
    Original complex values: [ 2.+3.j -1.+4.j  0.-2.j  5.+6.j]
    >>> print("Imaginary parts:", result)
    Imaginary parts: [ 3.  4. -2.  6.]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "imag", None))


@incomplete_lazyfunc
def contains(ndarr: blosc2.Array, value: str | bytes | blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Check if the array contains a specified value.

    Parameters
    ----------
    ndarr: :ref:`Array`
        The input array.
    value: str or bytes or :ref:`Array`
        The value to be checked.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression that can be evaluated to check if the value
        is contained in the array.

    Examples
    --------
    >>> import blosc2
    >>> text_values = blosc2.array([b"apple", b"xxbananaxxx", b"cherry", b"date"])
    >>> value_to_check = b"banana"
    >>> expr = blosc2.contains(text_values, value_to_check)
    >>> result = expr.compute()
    >>> print("Contains 'banana':", result[:])
    Contains 'banana': [False  True False False]
    """
    # def chunkwise_contains(inputs, output, offset):
    #     x1, x2 = inputs
    #     # output[...] = np.isin(x1, x2, assume_unique=assume_unique, invert=invert, kind=kind)
    #     output[...] = np.char.find(x1, x2) != -1

    if not isinstance(value, str | bytes | NDArray):
        raise TypeError("value should be a string, bytes or a NDArray!")

    return blosc2.LazyExpr(new_op=(ndarr, "contains", value))


def abs(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Calculate the absolute value element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression that can be evaluated to get the absolute values.

    References
    ----------
    `np.abs <https://numpy.org/doc/stable/reference/generated/numpy.absolute.html#numpy.absolute>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> values = np.array([-5, -3, 0, 2, 4])
    >>> ndarray = blosc2.asarray(values)
    >>> result_ = blosc2.abs(ndarray)
    >>> result = result_[:]
    >>> print("Original values:", values)
    Original values: [-5 -3  0  2  4]
    >>> print("Absolute values:", result)
    Absolute values: [5. 3. 0. 2. 4.]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "abs", None))


def isnan(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return True/False for not-a-number values element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression that can be evaluated to get the True/False array of results.

    References
    ----------
    `np.isnan <https://numpy.org/doc/stable/reference/generated/numpy.isnan.html#numpy.isnan>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> ndarray = blosc2.array([-5, -3, np.nan, 2, 4])
    >>> result_ = blosc2.isnan(ndarray)
    >>> result = result_[:]
    >>> print("isnan:", result)
    isnan: [False, False, True, False, False]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "isnan", None))


def isfinite(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return True/False for finite values element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression that can be evaluated to get the True/False array of results.

    References
    ----------
    `np.isfinite <https://numpy.org/doc/stable/reference/generated/numpy.isfinite.html#numpy.isfinite>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> ndarray = blosc2.array([-5, -3, np.inf, 2, 4])
    >>> result_ = blosc2.isfinite(ndarray)
    >>> result = result_[:]
    >>> print("isfinite:", result)
    isfinite: [True, True, False, True, True]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "isfinite", None))


def isinf(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
    """
    Return True/False for infinite values element-wise.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    Returns
    -------
    out: :ref:`LazyExpr`
        A lazy expression that can be evaluated to get the True/False array of results.

    References
    ----------
    `np.isinf <https://numpy.org/doc/stable/reference/generated/numpy.isinf.html#numpy.isinf>`_

    Examples
    --------
    >>> import numpy as np
    >>> import blosc2
    >>> ndarray = blosc2.array([-5, -3, np.inf, 2, 4])
    >>> result_ = blosc2.isinf(ndarray)
    >>> result = result_[:]
    >>> print("isinf:", result)
    isinf: [False, False, True, False, False]
    """
    return blosc2.LazyExpr(new_op=(ndarr, "isinf", None))


# def nonzero(ndarr: blosc2.Array, /) -> blosc2.LazyExpr:
#     """
#     Return indices of nonzero values.

#     Parameters
#     ----------
#     ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
#         The input array.

#     Returns
#     -------
#     out: :ref:`LazyExpr`
#         A lazy expression that can be evaluated to get the array of results.

#     References
#     ----------
#     `np.nonzero <https://numpy.org/doc/stable/reference/generated/numpy.nonzero.html#numpy.nonzero>`_
#     """
#     # FIXME: This is not correct
#     return ndarr.__ne__(0)


def count_nonzero(ndarr: blosc2.Array, axis: int | Sequence[int] | None = None) -> int:
    """
    Return number of nonzero values along axes.

    Parameters
    ----------
    ndarr: :ref:`NDArray` or :ref:`NDField` or :ref:`C2Array` or :ref:`LazyExpr`
        The input array.

    axis: int | Sequence[int] | None
        Axes along which to count nonzero entries. If None, sum over whole array. Default: None.

    Returns
    -------
    out: int
        Number of nonzero elements.

    References
    ----------
    `np.count_nonzero <https://numpy.org/doc/stable/reference/generated/numpy.count_nonzero.html#numpy.count_nonzero>`_
    """
    # TODO: Optimise this
    return sum(ndarr.__ne__(0), axis=axis)


def equal(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the truth value of x1_i == x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.equal <https://numpy.org/doc/stable/reference/generated/numpy.equal.html#numpy.equal>`_
    """
    return x1.__eq__(x2)


def not_equal(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the truth value of x1_i != x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.not_equal <https://numpy.org/doc/stable/reference/generated/numpy.not_equal.html#numpy.not_equal>`_
    """
    return x1.__ne__(x2)


def less_equal(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the truth value of x1_i <= x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.less_equal <https://numpy.org/doc/stable/reference/generated/numpy.less_equal.html#numpy.less_equal>`_
    """
    return x1.__le__(x2)


def less(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the truth value of x1_i < x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.less <https://numpy.org/doc/stable/reference/generated/numpy.less.html#numpy.less>`_
    """
    return x1.__lt__(x2)


def greater_equal(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the truth value of x1_i >= x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.greater_equal <https://numpy.org/doc/stable/reference/generated/numpy.greater_equal.html#numpy.greater_equal>`_
    """
    return x1.__ge__(x2)


def greater(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the truth value of x1_i > x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.greater <https://numpy.org/doc/stable/reference/generated/numpy.greater.html#numpy.greater>`_
    """
    return x1.__gt__(x2)


def multiply(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i * x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.multiply <https://numpy.org/doc/stable/reference/generated/numpy.multiply.html#numpy.multiply>`_
    """
    return x1 * x2


def divide(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i / x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.divide <https://numpy.org/doc/stable/reference/generated/numpy.divide.html#numpy.divide>`_
    """
    return x1 / x2


def nextafter(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Returns the next representable floating-point value for each element x1_i of the input
    array x1 in the direction of the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. Real-valued floating point dtype.

    x2:blosc2.Array
        Second input array. Must be compatible with x1 and have same data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.nextafter <https://numpy.org/doc/stable/reference/generated/numpy.nextafter.html#numpy.nextafter>`_
    """
    return blosc2.LazyExpr(new_op=(x1, "nextafter", x2))


def hypot(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the square root of the sum of squares for each element x1_i of the input array
    x1 with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. Real-valued floating point dtype.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. Real-valued floating point dtype.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.hypot <https://numpy.org/doc/stable/reference/generated/numpy.hypot.html#numpy.hypot>`_
    """
    return blosc2.LazyExpr(new_op=(x1, "hypot", x2))


def copysign(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Composes a floating-point value with the magnitude of x1_i and the sign of x2_i
    for each element of the input array x1.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. Real-valued floating point dtype.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. Real-valued floating point dtype.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.copysign <https://numpy.org/doc/stable/reference/generated/numpy.copysign.html#numpy.copysign>`_
    """
    return blosc2.LazyExpr(new_op=(x1, "copysign", x2))


def maximum(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the maximum value for each element x1_i of the input array x1 relative to the
    respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. Real-valued dtype.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. Real-valued dtype.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.maximum <https://numpy.org/doc/stable/reference/generated/numpy.maximum.html#numpy.maximum>`_
    """
    return blosc2.LazyExpr(new_op=(x1, "maximum", x2))


def minimum(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the minimum value for each element x1_i of the input array x1 relative to the
    respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. Real-valued dtype.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. Real-valued dtype.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.minimum <https://numpy.org/doc/stable/reference/generated/numpy.minimum.html#numpy.minimum>`_
    """
    return blosc2.LazyExpr(new_op=(x1, "minimum", x2))


def reciprocal(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Computes the value of 1/x1_i for each element x1_i of the input array x1.

    Parameters
    ----------
    x: blosc2.Array
        First input array, floating-point data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.reciprocal <https://numpy.org/doc/stable/reference/generated/numpy.reciprocal.html#numpy.reciprocal>`_
    """
    return 1.0 / x


def floor(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Rounds each element x_i of the input array x to the greatest (i.e., closest to +infinity)
    integer-valued number that is not greater than x_i.

    Parameters
    ----------
    x: blosc2.Array
        First input array. May have any real-valued data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.floor <https://numpy.org/doc/stable/reference/generated/numpy.floor.html#numpy.floor>`_
    """
    return blosc2.LazyExpr(new_op=(x, "floor", None))


def ceil(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Rounds each element x_i of the input array x to the smallest (i.e., closest to -infinity)
    integer-valued number that is not smaller than x_i.

    Parameters
    ----------
    x: blosc2.Array
        First input array. May have any real-valued data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.ceil <https://numpy.org/doc/stable/reference/generated/numpy.ceil.html#numpy.ceil>`_
    """
    return blosc2.LazyExpr(new_op=(x, "ceil", None))


def trunc(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Rounds each element x_i of the input array x to the closest to 0
    integer-valued number.

    Parameters
    ----------
    x: blosc2.Array
        First input array. May have any real-valued data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.trunc <https://numpy.org/doc/stable/reference/generated/numpy.trunc.html#numpy.trunc>`_
    """
    return blosc2.LazyExpr(new_op=(x, "trunc", None))


def signbit(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Determines whether the sign bit is set for each element x_i of the input array x.

    The sign bit of a real-valued floating-point number x_i is set whenever x_i is either -0,
    less than zero, or a signed NaN (i.e., a NaN value whose sign bit is 1).

    Parameters
    ----------
    x: blosc2.Array
        First input array. May have any real-valued floating-point data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.signbit <https://numpy.org/doc/stable/reference/generated/numpy.signbit.html#numpy.signbit>`_
    """
    return blosc2.LazyExpr(new_op=(x, "signbit", None))


def sign(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Returns an indication of the sign of a number for each element x_i of the input array x.

    Parameters
    ----------
    x: blosc2.Array
        First input array. May have any numeric data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results (-1, 0 or 1).

    References
    ----------
    `np.sign <https://numpy.org/doc/stable/reference/generated/numpy.sign.html#numpy.sign>`_
    """
    return blosc2.LazyExpr(new_op=(x, "sign", None))


def round(x: blosc2.Array) -> blosc2.LazyExpr:
    """
    Rounds each element x_i of the input array x to the nearest integer-valued number.

    Parameters
    ----------
    x: blosc2.Array
        First input array. May have any numeric data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results (-1, 0 or 1).

    References
    ----------
    `np.round <https://numpy.org/doc/stable/reference/generated/numpy.round.html#numpy.round>`_
    """
    return blosc2.LazyExpr(new_op=(x, "round", None))


def floor_divide(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i // x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any real-valued data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any real-valued data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.floor_divide <https://numpy.org/doc/stable/reference/generated/numpy.floor_divide.html#numpy.floor_divide>`_
    """
    return x1 // x2


def add(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i + x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.add <https://numpy.org/doc/stable/reference/generated/numpy.add.html#numpy.add>`_
    """
    return x1 + x2


def subtract(
    x1: blosc2.Array,
    x2: blosc2.Array,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i - x2_i for each element x1_i of the input array x1
    with the respective element x2_i of the input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.subtract <https://numpy.org/doc/stable/reference/generated/numpy.subtract.html#numpy.subtract>`_
    """
    return x1 - x2


def square(x1: blosc2.Array) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i**2 for each element x1_i of the input array x1.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.square <https://numpy.org/doc/stable/reference/generated/numpy.square.html#numpy.square>`_
    """
    return x1 * x1


def pow(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i**x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.pow <https://numpy.org/doc/stable/reference/generated/numpy.pow.html#numpy.pow>`_
    """
    return x1**x2


def logical_xor(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i ^ x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, boolean.

    x2:blosc2.Array
        Second input array. Must be compatible with x1, boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.logical_xor <https://numpy.org/doc/stable/reference/generated/numpy.logical_xor.html#numpy.logical_xor>`_
    """
    if blosc2.result_type(x1, x2) != blosc2.bool_:
        raise TypeError("Both operands must be boolean types for logical ops.")
    return x1 ^ x2


def logical_and(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i & x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, boolean.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. Boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.logical_and <https://numpy.org/doc/stable/reference/generated/numpy.logical_and.html#numpy.logical_and>`_
    """
    if blosc2.result_type(x1, x2) != blosc2.bool_:
        raise TypeError("Both operands must be boolean types for logical ops.")
    return x1 & x2


def logical_or(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i | x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, boolean.

    x2: blosc2.Array
        Second input array. Must be compatible with x1, boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.logical_or <https://numpy.org/doc/stable/reference/generated/numpy.logical_or.html#numpy.logical_or>`_
    """
    if blosc2.result_type(x1, x2) != blosc2.bool_:
        raise TypeError("Both operands must be boolean types for logical ops.")
    return x1 | x2


def logical_not(
    x1: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of ~x1_i for each element x1_i of the input array x1.

    Parameters
    ----------
    x1: blosc2.Array
        Input array, boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.logical_not <https://numpy.org/doc/stable/reference/generated/numpy.logical_not.html#numpy.logical_not>`_
    """
    if blosc2.result_type(x1) != blosc2.bool_:
        raise TypeError("Operand must be boolean type for logical ops.")
    return ~x1


def bitwise_xor(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i ^ x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, integer or boolean.

    x2:blosc2.Array
        Second input array. Must be compatible with x1, integer or boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.bitwise_xor <https://numpy.org/doc/stable/reference/generated/numpy.bitwise_xor.html#numpy.bitwise_xor>`_
    """
    return x1 ^ x2


def bitwise_and(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i & x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, integer or boolean.

    x2:blosc2.Array
        Second input array. Must be compatible with x1. Integer or boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.bitwise_and <https://numpy.org/doc/stable/reference/generated/numpy.bitwise_and.html#numpy.bitwise_and>`_
    """
    return x1 & x2


def bitwise_or(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of x1_i | x2_i for each element x1_i of the input array x1 and x2_i
    of x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, integer or boolean.

    x2: blosc2.Array
        Second input array. Must be compatible with x1, integer or boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.bitwise_or <https://numpy.org/doc/stable/reference/generated/numpy.bitwise_or.html#numpy.bitwise_or>`_
    """
    return x1 | x2


def bitwise_invert(
    x1: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the value of ~x1_i for each element x1_i of the input array x1.

    Parameters
    ----------
    x1: blosc2.Array
        Input array, integer or boolean.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.bitwise_invert <https://numpy.org/doc/stable/reference/generated/numpy.bitwise_invert.html#numpy.bitwise_invert>`_
    """
    return ~x1


def bitwise_right_shift(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Shifts the bits of each element x1_i of the input array x1 to the right according to
    the respective element x2_i of the input array x2.

    Note: This operation is an arithmetic shift (i.e., sign-propagating) and thus equivalent to
    floor division by a power of two.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, integer.

    x2: blosc2.Array
        Second input array. Must be compatible with x1, integer.
    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.bitwise_right_shift <https://numpy.org/doc/stable/reference/generated/numpy.bitwise_right_shift.html#numpy.bitwise_right_shift>`_
    """
    return x1.__rshift__(x2)


def bitwise_left_shift(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Shifts the bits of each element x1_i of the input array x1 to the left by appending x2_i
    (i.e., the respective element in the input array x2) zeros to the right of x1_i.

    Note: this operation is equivalent to multiplying x1 by 2**x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array, integer.

    x2: blosc2.Array
        Second input array. Must be compatible with x1, integer.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.bitwise_left_shift <https://numpy.org/doc/stable/reference/generated/numpy.bitwise_left_shift.html#numpy.bitwise_left_shift>`_
    """
    return x1.__lshift__(x2)


def positive(
    x1: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the numerical positive of each element x_i (i.e., out_i = +x_i) of the input array x.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.positive <https://numpy.org/doc/stable/reference/generated/numpy.positive.html#numpy.positive>`_
    """
    return blosc2.LazyExpr(new_op=(0, "+", x1))


def negative(
    x1: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Computes the numerical negative of each element x_i (i.e., out_i = -x_i) of the input array x.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.negative <https://numpy.org/doc/stable/reference/generated/numpy.negative.html#numpy.negative>`_
    """
    return blosc2.LazyExpr(new_op=(0, "-", x1))


def remainder(
    x1: blosc2.Array | int | float | complex,
    x2: blosc2.Array | int | float | complex,
) -> blosc2.LazyExpr:
    """
    Returns the remainder of division for each element x1_i of the input array x1 and the
    respective element x2_i of the input array x2.

    Note: This function is equivalent to the Python modulus operator x1_i % x2_i.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any data type.

    x2: blosc2.Array
        Second input array. Must be compatible with x1. May have any data type.

    Returns
    -------
    out: LazyExpr
        A LazyArray containing the element-wise results.

    References
    ----------
    `np.remainder <https://numpy.org/doc/stable/reference/generated/numpy.remainder.html#numpy.remainder>`_
    """
    return blosc2.LazyExpr(new_op=(x1, "%", x2))


@incomplete_lazyfunc
def clip(
    x: blosc2.Array,
    min: int | float | blosc2.Array | None = None,
    max: int | float | blosc2.Array | None = None,
    **kwargs: Any,
) -> NDArray:
    """
    Clamps each element x_i of the input array x to the range [min, max].

    Parameters
    ----------
    x: blosc2.Array
        Input array. Should have a real-valued data type.

    min: int | float | blosc2.Array | None
        Lower-bound of the range to which to clamp. If None, no lower bound must be applied.
        Default: None.

    max: int | float | blosc2.Array | None
        Upper-bound of the range to which to clamp. If None, no upper bound must be applied.
        Default: None.

    kwargs: Any
        kwargs accepted by the :func:`empty` constructor

    Returns
    -------
    out: NDArray
        An array containing element-wise results.

    """

    def chunkwise_clip(inputs, output, offset):
        x, min, max = inputs
        output[...] = np.clip(x, min, max)

    dtype = blosc2.result_type(x)
    shape = () if np.isscalar(x) else None
    return blosc2.lazyudf(chunkwise_clip, (x, min, max), dtype=dtype, shape=shape, **kwargs)


@incomplete_lazyfunc
def logaddexp(x1: int | float | blosc2.Array, x2: int | float | blosc2.Array, **kwargs: Any) -> NDArray:
    """
    Calculates the logarithm of the sum of exponentiations log(exp(x1) + exp(x2)) for
    each element x1_i of the input array x1 with the respective element x2_i of the
    input array x2.

    Parameters
    ----------
    x1: blosc2.Array
        First input array. May have any real-valued floating-point data type.

    x2: blosc2.Array
        Second input array. Must be compatible with x1. May have any
        real-valued floating-point data type.

    kwargs: Any
        kwargs accepted by the :func:`empty` constructor

    Returns
    -------
    out: NDArray
        An array containing element-wise results.

    """

    def chunkwise_logaddexp(inputs, output, offset):
        x1, x2 = inputs
        output[...] = np.logaddexp(x1, x2)

    dtype = blosc2.result_type(x1, x2)
    if dtype == blosc2.bool_:
        raise TypeError("logaddexp doesn't accept boolean arguments.")

    if np.issubdtype(dtype, np.integer):
        dtype = blosc2.float32
    shape = () if np.isscalar(x1) and np.isscalar(x2) else None
    return blosc2.lazyudf(chunkwise_logaddexp, (x1, x2), dtype=dtype, shape=shape, **kwargs)


# implemented in python-blosc2
local_ufunc_map = {
    np.logaddexp: logaddexp,
    np.logical_not: logical_not,
    np.logical_and: logical_and,
    np.logical_or: logical_or,
    np.logical_xor: logical_xor,
    np.matmul: matmul,
}


class Operand:
    """Base class for all operands in expressions."""

    _device = "cpu"

    def __array_namespace__(self, api_version: str | None = None) -> Any:
        """Return an object with all the functions and attributes of the module."""
        return blosc2

    # Provide minimal __array_interface__ to allow NumPy to work with this object
    @property
    def __array_interface__(self):
        return {
            "shape": self.shape,
            "typestr": self.dtype.str,
            "data": self[()],
            "version": 3,
        }

    @property
    @abstractmethod
    def dtype(self) -> np.dtype:
        """
        Get the data type of the :class:`Operand`.

        Returns
        -------
        out: np.dtype
            The data type of the :class:`Operand`.
        """
        pass

    @property
    @abstractmethod
    def shape(self) -> tuple[int]:
        """
        Get the shape of the :class:`Operand`.

        Returns
        -------
        out: tuple
                The shape of the :class:`Operand`.
        """
        pass

    @property
    @abstractmethod
    def ndim(self) -> int:
        """
        Get the number of dimensions of the :class:`Operand`.

        Returns
        -------
        out: int
            The number of dimensions of the :class:`Operand`.
        """
        pass

    @property
    @abstractmethod
    def info(self) -> InfoReporter:
        """
        Get information about the :class:`Operand`.

        Returns
        -------
        out: InfoReporter
            A printable class with information about the :class:`Operand`.
        """
        pass

    @property
    def device(self):
        "Hardware device the array data resides on. Always equal to 'cpu'."
        return self._device

    def to_device(self: NDArray, device: str):
        """
        Copy the array from the device on which it currently resides to the specified device.

        Parameters
        ----------
        self: NDArray
            Array instance.

        device: str
            Device to move array object to. Returns error except when device=='cpu'.

        Returns
        -------
        out: NDArray
            If device='cpu', the same array; else raises an Error.
        """
        if device != "cpu":
            raise ValueError(f"Unsupported device: {device}. Only 'cpu' is accepted.")
        return self

    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
        # Handle operations at the array level
        if method != "__call__":
            return NotImplemented

        if ufunc in local_ufunc_map:
            return local_ufunc_map[ufunc](*inputs)

        if ufunc in ufunc_map:
            value = inputs[0] if inputs[1] is self else inputs[1]
            _check_allowed_dtypes(value)
            return blosc2.LazyExpr(new_op=(inputs[0], ufunc_map[ufunc], inputs[1]))

        if ufunc in ufunc_map_1param:
            value = inputs[0]
            _check_allowed_dtypes(value)
            return blosc2.LazyExpr(new_op=(value, ufunc_map_1param[ufunc], None))

        return NotImplemented  # if not implemented in numexpr will default to NumPy

    def __add__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "+", value))

    def __radd__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__add__(value)

    def __iadd__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__add__(value)

    @is_documented_by(negative)
    def __neg__(self) -> blosc2.LazyExpr:
        return negative(self)

    @is_documented_by(positive)
    def __pos__(self) -> blosc2.LazyExpr:
        return positive(self)

    @is_documented_by(remainder)
    def __mod__(self, other) -> blosc2.LazyExpr:
        return remainder(self, other)

    @is_documented_by(remainder)
    def __imod__(self, other) -> blosc2.LazyExpr:
        return self.__mod__(other)

    @is_documented_by(remainder)
    def __rmod__(self, other) -> blosc2.LazyExpr:
        return remainder(other, self)

    def __sub__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "-", value))

    def __isub__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "-", value))

    def __rsub__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(value, "-", self))

    @is_documented_by(multiply)
    def __mul__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "*", value))

    def __imul__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__mul__(value)

    def __rmul__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__mul__(value)

    def __truediv__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "/", value))

    def __itruediv__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__truediv__(value)

    def __rtruediv__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(value, "/", self))

    @is_documented_by(floor_divide)
    def __floordiv__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "//", value))

    def __ifloordiv__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return self.__floordiv__(value)

    def __rfloordiv__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(value, "//", self))

    def __lt__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "<", value))

    def __le__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "<=", value))

    def __gt__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, ">", value))

    def __ge__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, ">=", value))

    def __eq__(self, value: int | float | blosc2.Array, /):
        _check_allowed_dtypes(value)
        if blosc2._disable_overloaded_equal:
            return self is value
        return blosc2.LazyExpr(new_op=(self, "==", value))

    def __ne__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "!=", value))

    def __pow__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "**", value))

    def __ipow__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "**", value))

    def __rpow__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(value, "**", self))

    @is_documented_by(abs)
    def __abs__(self) -> blosc2.LazyExpr:
        return abs(self)

    @is_documented_by(bitwise_and)
    def __and__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        value = _normalize_expr_operand(value)
        _check_allowed_dtypes(value)
        return blosc2.LazyExpr(new_op=(self, "&", value))

    def __iand__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__and__(value)

    def __rand__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr:
        return self.__and__(value)

    @is_documented_by(bitwise_xor)
    def __xor__(self, other) -> blosc2.LazyExpr:
        other = _normalize_expr_operand(other)
        _check_allowed_dtypes(other)
        return blosc2.LazyExpr(new_op=(self, "^", other))

    def __ixor__(self, other) -> blosc2.LazyExpr:
        return self.__xor__(other)

    def __rxor__(self, other) -> blosc2.LazyExpr:
        return self.__xor__(other)

    @is_documented_by(bitwise_or)
    def __or__(self, other) -> blosc2.LazyExpr:
        other = _normalize_expr_operand(other)
        _check_allowed_dtypes(other)
        return blosc2.LazyExpr(new_op=(self, "|", other))

    def __ior__(self, other) -> blosc2.LazyExpr:
        return self.__or__(other)

    def __ror__(self, other) -> blosc2.LazyExpr:
        return self.__or__(other)

    @is_documented_by(bitwise_invert)
    def __invert__(self) -> blosc2.LazyExpr:
        return blosc2.LazyExpr(new_op=(self, "~", None))

    @is_documented_by(bitwise_right_shift)
    def __rshift__(self, other) -> blosc2.LazyExpr:
        return blosc2.LazyExpr(new_op=(self, ">>", other))

    def __irshift__(self, other) -> blosc2.LazyExpr:
        return self.__rshift__(other)

    def __rrshift__(self, other) -> blosc2.LazyExpr:
        return blosc2.LazyExpr(new_op=(other, ">>", self))

    @is_documented_by(bitwise_left_shift)
    def __lshift__(self, other) -> blosc2.LazyExpr:
        return blosc2.LazyExpr(new_op=(self, "<<", other))

    def __ilshift__(self, other) -> blosc2.LazyExpr:
        return self.__lshift__(other)

    def __rlshift__(self, other) -> blosc2.LazyExpr:
        return blosc2.LazyExpr(new_op=(other, "<<", self))

    def __bool__(self) -> bool:
        if math.prod(self.shape) != 1:
            raise ValueError(f"The truth value of an array of shape {self.shape} is ambiguous.")
        return bool(self[()])

    def __float__(self) -> float:
        if math.prod(self.shape) != 1:
            raise ValueError(f"Cannot convert array of shape {self.shape} to float.")
        return float(self[()])

    def __int__(self) -> bool:
        if math.prod(self.shape) != 1:
            raise ValueError(f"Cannot convert array of shape {self.shape} to int.")
        return int(self[()])

    def __index__(self) -> bool:
        if not np.issubdtype(self.dtype, np.integer):
            raise ValueError(
                f"Cannot convert array of dtype {self.dtype} to index array (must have dtype int)."
            )
        return self.__int__()

    def __complex__(self) -> complex:
        if math.prod(self.shape) != 1:
            raise ValueError(f"Cannot convert array of shape {self.shape} to complex float.")
        return complex(self[()])

    def item(self) -> float | bool | complex | int:
        """
        Copy an element of an array to a standard Python scalar and return it.
        """
        return self[()].item()

    def where(self, value1=None, value2=None):
        """
        Select ``value1`` or ``value2`` values based on ``True``/``False`` for ``self``.

        Parameters
        ----------
        value1: array_like, optional
            The value to select when element of ``self`` is True.
        value2: array_like, optional
            The value to select when element of ``self`` is False.

        Returns
        -------
        out: LazyExpr
            A new expression with the where condition applied.
        """
        expr = blosc2.LazyExpr._new_expr("o0", {"o0": self}, guess=False)
        return expr.where(value1, value2)

    @is_documented_by(sum)
    def sum(self, axis=None, dtype=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.sum(axis=axis, dtype=dtype, keepdims=keepdims, **kwargs)

    @is_documented_by(cumulative_sum)
    def cumulative_sum(self, axis=None, dtype=None, include_initial=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.cumulative_sum(axis=axis, dtype=dtype, include_initial=include_initial, **kwargs)

    @is_documented_by(cumulative_prod)
    def cumulative_prod(self, axis=None, dtype=None, include_initial=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.cumulative_prod(axis=axis, dtype=dtype, include_initial=include_initial, **kwargs)

    @is_documented_by(mean)
    def mean(self, axis=None, dtype=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.mean(axis=axis, dtype=dtype, keepdims=keepdims, **kwargs)

    @is_documented_by(std)
    def std(self, axis=None, dtype=None, ddof=0, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.std(axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, **kwargs)

    @is_documented_by(var)
    def var(self, axis=None, dtype=None, ddof=0, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.var(axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, **kwargs)

    @is_documented_by(prod)
    def prod(self, axis=None, dtype=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.prod(axis=axis, dtype=dtype, keepdims=keepdims, **kwargs)

    @is_documented_by(min)
    def min(self, axis=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.min(axis=axis, keepdims=keepdims, **kwargs)

    @is_documented_by(max)
    def max(self, axis=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.max(axis=axis, keepdims=keepdims, **kwargs)

    @is_documented_by(argmax)
    def argmax(self, axis=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.argmax(axis=axis, keepdims=keepdims, **kwargs)

    @is_documented_by(argmin)
    def argmin(self, axis=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.argmin(axis=axis, keepdims=keepdims, **kwargs)

    @is_documented_by(any)
    def any(self, axis=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.any(axis=axis, keepdims=keepdims, **kwargs)

    @is_documented_by(all)
    def all(self, axis=None, keepdims=False, **kwargs):
        expr = blosc2.LazyExpr(new_op=(self, None, None))
        return expr.all(axis=axis, keepdims=keepdims, **kwargs)


class LimitedSizeDict(OrderedDict):
    def __init__(self, max_entries, *args, **kwargs):
        self.max_entries = max_entries
        super().__init__(*args, **kwargs)

    def __setitem__(self, key, value):
        if len(self) >= self.max_entries:
            self.popitem(last=False)
        super().__setitem__(key, value)


def detect_aligned_chunks(
    key: Sequence[slice], shape: Sequence[int], chunks: Sequence[int], consecutive: bool = False
) -> list[int]:
    """
    Detect whether a multidimensional slice is aligned with chunk boundaries.

    Parameters
    ----------
    key : Sequence of slice
        The multidimensional slice to check.
    shape : Sequence of int
        Shape of the NDArray.
    chunks : Sequence of int
        Chunk shape of the NDArray.
    consecutive : bool, default=False
        If True, check if the chunks are consecutive in storage order.
        If False, only check for chunk boundary alignment.

    Returns
    -------
    list[int]
        List of chunk indices (in C-order) that the slice overlaps with.
        If the slice isn't aligned with chunk boundaries, returns an empty list.
        If consecutive=True and chunks aren't consecutive, returns an empty list.
    """
    if len(key) != len(shape):
        return []

    # Check that slice boundaries are exact multiple of chunk boundaries
    for i, s in enumerate(key):
        if s.start is not None and s.start % chunks[i] != 0:
            return []
        if s.stop is not None and s.stop % chunks[i] != 0:
            return []

    # Parse the slice boundaries
    start_indices = []
    end_indices = []
    n_chunks = []

    for i, s in enumerate(key):
        start = s.start if s.start is not None else 0
        stop = s.stop if s.stop is not None else shape[i]
        chunk_size = chunks[i]
        start_idx = start // chunk_size
        end_idx = stop // chunk_size
        start_indices.append(start_idx)
        end_indices.append(end_idx)
        # Total chunk count along this dim: ceil, not floor -- a trailing
        # partial chunk (shape[i] not a multiple of chunk_size) still counts
        # as one chunk. Floor division here undercounts it, which corrupts
        # the flat chunk-index math below for any aligned slice with a
        # nonzero start in an earlier dimension (silently returns a
        # different chunk's data instead of the requested one).
        n_chunks.append(math.ceil(shape[i] / chunk_size))

    # Get all chunk combinations in the slice
    indices = [range(start, end) for start, end in zip(start_indices, end_indices, strict=False)]
    result = []

    for combination in product(*indices):
        flat_index = 0
        multiplier = 1
        for idx, n in zip(reversed(range(len(n_chunks))), reversed(n_chunks), strict=False):
            flat_index += combination[idx] * multiplier
            multiplier *= n
        result.append(flat_index)

    # Check if chunks are consecutive if requested
    if consecutive and result:
        sorted_result = sorted(result)
        if sorted_result[-1] - sorted_result[0] + 1 != len(sorted_result):
            return []

        # The array of indices must be consecutive
        for i in range(len(sorted_result) - 1):
            if sorted_result[i + 1] - sorted_result[i] != 1:
                return []

    return sorted(result)


class NDOuterIterator:
    def __init__(self, ndarray: NDArray | NDField, cache_size=1):
        self.ndarray = ndarray
        self.outer_dim_size = ndarray.shape[0]
        self.inner_shape = ndarray.shape[1:]
        self.current_index = 0
        # Cache for 1D arrays; for higher dimensions, the implementation should be more involved
        self.chunk_size = ndarray.chunks[0] if len(ndarray.shape) == 1 else None
        self.cache = {} if len(ndarray.shape) == 1 else None
        self.cache_size = cache_size

    def __iter__(self):
        return self

    def __next__(self):
        if self.current_index >= self.outer_dim_size:
            raise StopIteration

        outer_index = self.current_index
        self.current_index += 1

        if self.cache is not None:
            chunk_index = outer_index // self.chunk_size
            local_index = outer_index % self.chunk_size

            if chunk_index not in self.cache:
                if len(self.cache) >= self.cache_size:
                    self.cache.pop(next(iter(self.cache)))
                self.cache[chunk_index] = self.ndarray[
                    chunk_index * self.chunk_size : (chunk_index + 1) * self.chunk_size
                ]

            return self.cache[chunk_index][local_index]
        else:
            return self.ndarray[outer_index]


class NDArray(blosc2_ext.NDArray, Operand):
    """Compressed, chunked N-dimensional array with NumPy-like indexing."""

    def __init__(self, **kwargs):
        schunk_kwargs = {"_schunk": kwargs["_schunk"], "_is_view": True}
        mode = kwargs.pop("mode", None)
        if mode is not None:
            schunk_kwargs["mode"] = mode
        self._schunk = SChunk(**schunk_kwargs)  # SChunk Python instance
        self._keep_last_read = False
        # Where to store the last read data
        self._last_read = {}
        base = kwargs.pop("_base", None)
        super().__init__(kwargs["_array"], base=base)
        # Accessor to fields
        field_names = tuple(self.dtype.fields) if self.dtype.fields else ()
        self._fields = FieldsAccessor(self, field_names)

    def __enter__(self) -> NDArray:
        """Enter a context manager and return this array."""
        return self

    def __exit__(self, exc_type, exc_val, exc_tb) -> bool:
        """Exit a context manager.

        For regular :func:`blosc2.open` handles this is a logical no-op kept for
        API symmetry with higher-level persistent containers.
        """
        return False

    @property
    def cparams(self) -> blosc2.CParams:
        """The compression parameters used by the array."""
        return self.schunk.cparams

    @property
    def dparams(self) -> blosc2.DParams:
        """The decompression parameters used by the array."""
        return self.schunk.dparams

    @property
    def nbytes(self) -> int:
        """The number of bytes used by the array."""
        return self.schunk.nbytes

    @property
    def cbytes(self) -> int:
        """The number of compressed bytes used by the array."""
        return self.schunk.cbytes

    @property
    def cratio(self) -> float:
        """The compression ratio of the array."""
        return self.schunk.cratio

    # TODO: Uncomment when blosc2.Storage is available
    # @property
    # def storage(self) -> blosc2.Storage:
    #     """The storage of the array."""
    #     return self.schunk.storage

    @property
    def urlpath(self) -> str:
        """The URL path of the array."""
        return self.schunk.urlpath

    @property
    def meta(self) -> dict:
        """The metadata of the array."""
        return self.schunk.meta

    @property
    def vlmeta(self) -> dict:
        """The variable-length metadata of the array."""
        return self.schunk.vlmeta

    @contextmanager
    def holding_lock(self) -> Iterator[NDArray]:
        """Hold the exclusive frame lock across several operations.

        Delegates to :meth:`SChunk.holding_lock() <blosc2.SChunk.holding_lock>`
        on the underlying schunk for the locking itself; see there for
        details. On top of that, this also refreshes this handle's cached
        shape right after the lock is acquired.

        That refresh matters because acquiring the lock does not by itself
        update anything cached on this handle -- :attr:`shape` is a plain
        in-memory value, unchanged since this handle's last operation.
        Without it, code that reads :attr:`shape` first thing inside the
        block to decide a :meth:`resize` target can act on a stale, too-small
        shape; since ``resize()`` treats its target as absolute, resizing to
        a target computed that way can shrink an array another handle already
        grew further, silently deleting its data. Refreshing first closes
        that gap, so any :attr:`shape` read inside the block is guaranteed
        current.

        Examples
        --------
        >>> with arr.holding_lock():  # doctest: +SKIP
        ...     arr[0] = arr[0] + 1
        """
        with self.schunk.holding_lock():
            self.refresh()
            yield self

    @property
    def fields(self) -> Mapping[str, NDField]:
        """
        Read-only mapping with the fields of the structured array.

        Returns
        -------
        fields: Mapping
            A read-only mapping with the fields of the structured array.

        See Also
        --------
        :ref:`NDField`

        Examples
        --------
        >>> import blosc2
        >>> import numpy as np
        >>> shape = (10,)
        >>> dtype = np.dtype([('a', np.int32), ('b', np.float64)])
        >>> # Create a structured array
        >>> sa = blosc2.zeros(shape, dtype=dtype)
        >>> # Check that fields are equal
        >>> assert sa.fields['a'] == sa.fields['b']
        >>> # Assign through the field view
        >>> sa.fields['a'][:] = 1
        """
        return self._fields

    @property
    def keep_last_read(self) -> bool:
        """Indicates whether the last read data should be kept in memory."""
        return self._keep_last_read

    @keep_last_read.setter
    def keep_last_read(self, value: bool) -> None:
        """Set whether the last read data should be kept in memory.

        This always clears the last read data (if any).
        """
        if not isinstance(value, bool):
            raise TypeError("keep_last_read should be a boolean")
        # Reset last read data
        self._last_read.clear()
        self._keep_last_read = value

    @property
    def info(self) -> InfoReporter:
        """
        Print information about this array.

        Examples
        --------
        >>> import blosc2
        >>> array = blosc2.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
        >>> print(array.info)
        type    : NDArray
        shape   : (10,)
        chunks  : (10,)
        blocks  : (10,)
        dtype   : int64
        nbytes  : 80 (80 B)
        cbytes  : 98 (98 B)
        cratio  : 0.82x
        cparams : CParams(codec=<Codec.ZSTD: 5>, codec_meta=0, clevel=5, use_dict=False, typesize=8,
                : nthreads=8, blocksize=80, splitmode=<SplitMode.AUTO_SPLIT: 3>,
                : filters=[<Filter.NOFILTER: 0>, <Filter.NOFILTER: 0>, <Filter.NOFILTER: 0>,
                : <Filter.NOFILTER: 0>, <Filter.NOFILTER: 0>, <Filter.SHUFFLE: 1>], filters_meta=[0, 0,
                : 0, 0, 0, 0], tuner=<Tuner.STUNE: 0>)
        dparams : DParams(nthreads=8)
        <BLANKLINE>
        """
        return InfoReporter(self)

    @property
    def info_items(self) -> list:
        """A list of tuples with the information about this array.
        Each tuple contains the name of the attribute and its value.
        """
        items = []
        items += [("type", f"{self.__class__.__name__}")]
        items += [("shape", self.shape)]
        items += [("chunks", self.chunks)]
        items += [("blocks", self.blocks)]
        items += [("dtype", self.dtype)]
        items += [("nbytes", format_nbytes_info(self.nbytes))]
        items += [("cbytes", format_nbytes_info(self.cbytes))]
        items += [("cratio", f"{self.cratio:.2f}x")]
        items += [("cparams", self.cparams)]
        items += [("dparams", self.dparams)]
        return items

    @property
    def schunk(self) -> blosc2.SChunk:
        """
        The :ref:`SChunk <SChunk>` reference of the :ref:`NDArray`.
        All the attributes from the :ref:`SChunk <SChunk>` can be accessed through
        this instance as `self.schunk`.

        See Also
        --------
        :ref:`SChunk Attributes <SChunkAttributes>`
        """
        return self._schunk

    @property
    def shape(self) -> tuple[int]:
        """Returns the data shape of this container.

        If the shape is a multiple of each dimension of :attr:`chunks`,
        it will be the same as :attr:`ext_shape`.

        See Also
        --------
        :attr:`ext_shape`
        """
        return super().shape

    @property
    def ext_shape(self) -> tuple[int]:
        """The padded data shape.

        The padded data is filled with zeros to make the real data fit into blocks and chunks, but it
        will never be retrieved as actual data (so the user can ignore this).
        In case :attr:`shape` is multiple in each dimension of :attr:`chunks` it will be the same
        as :attr:`shape`.

        See Also
        --------
        :attr:`shape`
        :attr:`chunks`
        """
        return super().ext_shape

    @property
    def chunks(self) -> tuple[int]:
        """Returns the data chunk shape of this container.

        If the chunk shape is a multiple of each dimension of :attr:`blocks`,
        it will be the same as :attr:`ext_chunks`.

        See Also
        --------
        :attr:`ext_chunks`
        """
        return super().chunks

    @property
    def ext_chunks(self) -> tuple[int]:
        """
        Returns the padded chunk shape which defines the chunksize in the associated schunk.

        This will be the chunk shape used to store each chunk, filling the extra positions
        with zeros (padding). If the :attr:`chunks` is a multiple of
        each dimension of :attr:`blocks` it will be the same as :attr:`chunks`.

        See Also
        --------
        :attr:`chunks`
        """
        return super().ext_chunks

    @property
    def blocks(self) -> tuple[int]:
        """The block shape of this container."""
        return super().blocks

    @property
    def ndim(self) -> int:
        """The number of dimensions of this container."""
        return super().ndim

    @property
    def size(self) -> int:
        """The size (in elements) for this container."""
        return super().size

    @property
    def chunksize(self) -> int:
        """Returns the data chunk size (in bytes) for this container.

        This will not be the same as
        :attr:`SChunk.chunksize <blosc2.schunk.SChunk.chunksize>`
        in case :attr:`chunks` is not multiple in
        each dimension of :attr:`blocks` (or equivalently, if :attr:`chunks` is
        not the same as :attr:`ext_chunks`).

        See Also
        --------
        :attr:`chunks`
        :attr:`ext_chunks`
        """
        return super().chunksize

    @property
    def dtype(self) -> np.dtype:
        """
        Data-type of the array's elements.
        """
        return super().dtype

    @property
    def blocksize(self) -> int:
        """The block size (in bytes) for this container.

        This is a shortcut to
        :attr:`SChunk.blocksize <blosc2.schunk.SChunk.blocksize>` and can be accessed
        through the :attr:`schunk` attribute as well.

        See Also
        --------
        :attr:`schunk`

        Examples
        --------
        >>> import blosc2
        >>> ndarray = blosc2.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
        >>> print("Block size:", ndarray.blocksize)
        Block size: 80
        """
        return self._schunk.blocksize

    @property
    def oindex(self) -> OIndex:
        """Shortcut for orthogonal (outer) indexing, see :func:`get_oselection_numpy`"""
        return OIndex(self)

    # @property
    # def vindex(self) -> VIndex:
    #     """Shortcut for vectorised indexing. Not yet supported."""
    #     return VIndex(self)

    @property
    def T(self):
        """Return the transpose of a 2-dimensional array."""
        if self.ndim != 2:
            raise ValueError("This property only works for 2-dimensional arrays.")
        return blosc2.linalg.permute_dims(self)

    @property
    def mT(self):
        """Transpose of a matrix (or a stack of matrices)."""
        if self.ndim < 2:
            raise ValueError("This property only works for N-dimensional arrays with N>=2.")
        axes = np.arange(self.ndim)
        axes[-1] = self.ndim - 2
        axes[-2] = self.ndim - 1
        return blosc2.linalg.permute_dims(self, axes=axes)

    def get_fselection_numpy(self, key: list | np.ndarray) -> np.ndarray:
        """
        Select a slice from the array using a fancy index.
        Closely matches NumPy fancy indexing behaviour, except in
        some edge cases which are not supported by ndindex.
        Array indices separated by slice object - e.g. arr[0, :10, [0,1]] - are NOT supported.
        See https://www.blosc.org/posts/blosc2-fancy-indexing for more details.

        Parameters
        ----------
        key: list or np.ndarray

        Returns
        -------
        out: np.ndarray

        """
        # TODO: Make this faster and avoid running out of memory - avoid broadcasting keys

        ## Can't do this because ndindex doesn't support all the same indexing cases as Numpy
        # if math.prod(self.shape) * self.dtype.itemsize < blosc2.MAX_FAST_PATH_SIZE:
        #     return self[:][key]  # load into memory for smallish arrays
        shape = self.shape
        chunks = self.chunks

        # TODO: try to optimise and avoid this expand which seems to copy - maybe np.broadcast
        _slice = ndindex.ndindex(key).expand(shape)  # handles negative indices -> positive internally
        out_shape = _slice.newshape(shape)
        _slice = _slice.raw
        # now all indices are slices or arrays of integers (or booleans)
        # # moreover, all arrays are consecutive (otherwise an error is raised)

        if np.all([isinstance(s, slice | np.ndarray) for s in _slice]) and np.all(
            [s.dtype is not bool for s in _slice if isinstance(s, np.ndarray)]
        ):
            chunks = np.array(chunks)
            #       |------|
            # ------| arrs |------
            arridxs = [i for i, s in enumerate(_slice) if isinstance(s, np.ndarray)]
            begin, end = arridxs[0], arridxs[-1] + 1

            start, stop, step, _ = get_ndarray_start_stop(begin, _slice[:begin], self.shape[:begin])
            prior_tuple = tuple(
                slice(s, st, stp) for s, st, stp in zip(start, stop, step, strict=True)
            )  # convert to start and stop +ve
            start, stop, step, _ = get_ndarray_start_stop(
                len(self.shape[end:]), _slice[end:], self.shape[end:]
            )
            post_tuple = tuple(
                slice(s, st, stp) for s, st, stp in zip(start, stop, step, strict=True)
            )  # convert to start and stop +ve

            flat_shape = tuple(
                (i.stop - i.start - i.step // builtins.abs(i.step)) // i.step + 1 for i in prior_tuple
            )
            idx_dim = np.prod(_slice[begin].shape, dtype=np.int32)

            # TODO: find a nicer way to do the copy maybe
            arr = np.empty((idx_dim, end - begin), dtype=_slice[begin].dtype)
            for i, s in enumerate(_slice[begin:end]):
                arr[:, i] = s.reshape(-1)  # have to do a copy

            flat_shape += (idx_dim,)
            flat_shape += tuple(
                (i.stop - i.start - i.step // builtins.abs(i.step)) // i.step + 1 for i in post_tuple
            )
            # out_shape could have new dims if indexing arrays are not all 1D
            # (we have just flattened them so need to handle accordingly)
            divider = chunks[begin:end]
            chunked_arr = arr // divider
            if arr.shape[-1] == 1:  # 1D chunks, can avoid loading whole chunks
                idx_order = np.argsort(arr.squeeze(axis=1), axis=-1)  # sort by real index
                chunk_nitems = np.bincount(chunked_arr.reshape(-1), minlength=self.schunk.nchunks)
                unique_chunks = np.nonzero(chunk_nitems)[0][:, None]  # add dummy axis
                chunk_nitems = chunk_nitems[unique_chunks]
            else:
                chunked_arr = np.ascontiguousarray(
                    chunked_arr
                )  # ensure C-order memory to allow structured dtype view
                # TODO: check that avoids sort and copy (alternative: maybe do a bincount with structured data types?)
                _, row_ids, idx_inv, chunk_nitems = np.unique(
                    chunked_arr.view([("", chunked_arr.dtype)] * chunked_arr.shape[1]),
                    return_counts=True,
                    return_index=True,
                    return_inverse=True,
                )
                # In some versions of Numpy, output of np.unique has dummy dimension
                idx_inv = idx_inv if len(idx_inv.shape) == 1 else idx_inv.squeeze(-1)
                unique_chunks = chunked_arr[row_ids]
                # sort by chunks (can't sort by index since larger index could belong to lower chunk)
                # e.g. chunks of (100, 10) means (50, 15) has chunk idx (0,1) but (60,5) has (0, 0)
                idx_order = np.argsort(idx_inv)
            sorted_idxs = arr[idx_order]
            out = np.empty(flat_shape, dtype=self.dtype)
            shape = np.array(shape)

            chunk_nitems_cumsum = np.cumsum(chunk_nitems)
            cprior_slices = [
                slice_to_chunktuple(s, c) for s, c in zip(prior_tuple, chunks[:begin], strict=True)
            ]
            cpost_slices = [slice_to_chunktuple(s, c) for s, c in zip(post_tuple, chunks[end:], strict=True)]
            # TODO: rewrite to allow interleaved slices/array indexes
            for chunk_i, chunk_idx in enumerate(unique_chunks):
                start = 0 if chunk_i == 0 else chunk_nitems_cumsum[chunk_i - 1]
                stop = chunk_nitems_cumsum[chunk_i]
                selection = sorted_idxs[start:stop]
                out_mid_selection = (idx_order[start:stop],)
                if (
                    arr.shape[-1] == 1
                ):  # can avoid loading in whole chunk if 1D for array indexed chunks, a bit faster
                    chunk_begin = selection[0]
                    chunk_end = selection[-1] + 1
                else:
                    chunk_begin = chunk_idx * chunks[begin:end]
                    chunk_end = np.minimum((chunk_idx + 1) * chunks[begin:end], shape[begin:end])
                loc_mid_selection = tuple(a for a in (selection - chunk_begin).T)

                # loop over chunks coming from slices before and after array indices
                for cprior_tuple in product(*cprior_slices):
                    out_prior_selection, prior_selection, loc_prior_selection = get_selection(
                        cprior_tuple, prior_tuple, chunks[:begin]
                    )
                    for cpost_tuple in product(*cpost_slices):
                        out_post_selection, post_selection, loc_post_selection = get_selection(
                            cpost_tuple, post_tuple, chunks[end:]
                        )
                        locbegin, locend = get_local_slice(
                            prior_selection, post_selection, (chunk_begin, chunk_end)
                        )
                        to_be_loaded = np.empty(locend - locbegin, dtype=self.dtype)
                        # basically load whole chunk, except for slice part at beginning and end
                        super().get_slice_numpy(to_be_loaded, (locbegin, locend))
                        loc_idx = loc_prior_selection + loc_mid_selection + loc_post_selection
                        out_idx = out_prior_selection + out_mid_selection + out_post_selection
                        out[out_idx] = to_be_loaded[loc_idx]
            return out.reshape(out_shape)  # should have filled in correct order, just need to reshape

        # Default when there are booleans
        # TODO: for boolean indexing could be optimised by avoiding
        # calculating out_shape prior to loop and keeping track on-the-fly (like in LazyExpr machinery)
        out = np.empty(out_shape, dtype=self.dtype)
        return self._get_set_findex_default(_slice, out)

    def _get_set_findex_default(self, _slice, out=None, value=None):
        _get = out is not None
        out = self if out is None else out  # default return for setitem with no intersecting chunks
        if 0 in self.shape:
            return out
        chunk_size = ndindex.ChunkSize(self.chunks)  # only works with nonzero chunks
        # repeated indices are grouped together
        intersecting_chunks = chunk_size.as_subchunks(
            _slice, self.shape
        )  # if _slice is (), returns all chunks
        for c in intersecting_chunks:
            sub_idx = _slice.as_subindex(c).raw
            sel_idx = c.as_subindex(_slice)
            start, stop, step, _ = get_ndarray_start_stop(self.ndim, c.raw, self.shape)
            chunk = np.empty(tuple(sp - st for st, sp in zip(start, stop, strict=True)), dtype=self.dtype)
            super().get_slice_numpy(chunk, (start, stop))
            if _get:
                new_shape = sel_idx.newshape(out.shape)
                out[sel_idx.raw] = chunk[sub_idx].reshape(new_shape)
            else:
                chunk[sub_idx] = value if np.isscalar(value) else value[sel_idx.raw]
                out = super().set_slice((start, stop), chunk)
        return out

    def get_oselection_numpy(self, key: list | np.ndarray) -> np.ndarray:
        """
        Select independently from self along axes specified in key. Key must be same length as self shape.
        See Zarr https://zarr.readthedocs.io/en/stable/user-guide/arrays.html#orthogonal-indexing.
        """
        shape = tuple(len(k) for k in key) + self.shape[len(key) :]
        # Create the array to store the result
        arr = np.empty(shape, dtype=self.dtype)
        return super().get_oindex_numpy(arr, key)

    def set_oselection_numpy(self, key: list | np.ndarray, arr: NDArray) -> np.ndarray:
        """
        Select independently from self along axes specified in key and set to entries in arr.
        Key must be same length as self shape.
        See Zarr https://zarr.readthedocs.io/en/stable/user-guide/arrays.html#orthogonal-indexing.
        """
        return super().set_oindex_numpy(key, arr)

    def _try_subsample_gather(self, start, stop, step, shape):
        """Fast path for a coarse strided read via the sparse-gather primitive.

        Returns a NumPy array of the (post-squeeze) *shape* when the selection
        is a coarse subsample worth routing through ``b2nd_get_sparse_cbuffer``
        (decompressing only the blocks that actually hold a selected element),
        or ``None`` to fall back to the dense per-chunk path.

        Engages only when every step is positive, the result fits
        ``_SUBSAMPLE_GATHER_MAX_COORDS``, and at least one axis strides by at
        least its block extent (so whole blocks are skipped — the condition
        under which gather decompresses strictly fewer blocks than the dense
        bounding-box read).
        """
        ndim = self.ndim
        if builtins.any(s <= 0 for s in step):  # negative steps keep the dense path
            return None

        blocks = self.blocks
        if not builtins.any(step[d] > 1 and step[d] >= blocks[d] for d in range(ndim)):
            return None

        # Per-axis sample positions; matches the (sp-st-sign)//stp+1 count.
        positions = [np.arange(start[d], stop[d], step[d]) for d in range(ndim)]
        nelems = 1
        for p in positions:
            nelems *= p.size
        if not 0 < nelems <= _SUBSAMPLE_GATHER_MAX_COORDS:
            return None

        # Flat C-order linear indices over the full real-axis grid.
        flat = np.zeros((), dtype=np.int64)
        cstride = 1
        for d in range(ndim - 1, -1, -1):
            contrib = (positions[d].astype(np.int64) * cstride).reshape((-1,) + (1,) * (ndim - 1 - d))
            flat = flat + contrib
            cstride *= self.shape[d]
        flat = np.ascontiguousarray(flat).reshape(-1)

        return self._take_sparse_normalized(flat).reshape(shape)

    def _get_set_nonunit_steps(self, _slice, out=None, value=None):
        start, stop, step, mask = _slice
        _get = out is not None
        if _get:
            # Coarse strided reads can skip whole blocks: route them through the
            # sparse-gather primitive instead of decompressing full chunks.
            gathered = self._try_subsample_gather(start, stop, step, out.shape)
            if gathered is not None:
                return gathered
        out = self if out is None else out  # default return for setitem with no intersecting chunks
        if 0 in self.shape:
            return out

        chunks = self.chunks
        _slice = tuple(slice(s, st, stp) for s, st, stp in zip(start, stop, step, strict=True))
        intersecting_chunks = [
            slice_to_chunktuple(s, c) for s, c in zip(_slice, chunks, strict=True)
        ]  # internally handles negative steps
        for c in product(*intersecting_chunks):
            sel_idx, glob_selection, sub_idx = get_selection(c, _slice, chunks)
            sel_idx = tuple(s for s, m in zip(sel_idx, mask, strict=True) if not m)
            sub_idx = tuple(s if not m else s.start for s, m in zip(sub_idx, mask, strict=True))
            locstart, locstop = get_local_slice(
                glob_selection,
                (),
                ((), ()),  # switches start and stop for negative steps
            )
            chunk = np.empty(
                tuple(sp - st for st, sp in zip(locstart, locstop, strict=True)), dtype=self.dtype
            )
            # basically load whole chunk, except for slice part at beginning and end
            super().get_slice_numpy(chunk, (locstart, locstop))  # copy relevant slice of chunk
            if _get:
                out[sel_idx] = chunk[sub_idx]  # update relevant parts of chunk
            else:
                chunk[sub_idx] = (
                    value if np.isscalar(value) else value[sel_idx]
                )  # update relevant parts of chunk
                out = super().set_slice((locstart, locstop), chunk)  # load updated partial chunk into array
        return out

    @staticmethod
    def _normalize_take_indices(indices, size: int) -> np.ndarray:
        if isinstance(indices, NDArray):
            indices = indices[()]
        indices = np.asarray(indices)
        if indices.size == 0:
            return np.ascontiguousarray(indices, dtype=np.int64)
        if not np.issubdtype(indices.dtype, np.integer):
            raise TypeError("take indices must be an integer array")
        normalized = np.ascontiguousarray(indices, dtype=np.int64)
        negative = normalized < 0
        if np.any(negative):
            normalized = normalized.copy()
            normalized[negative] += size
        if np.any((normalized < 0) | (normalized >= size)):
            raise IndexError("take index out of bounds")
        return normalized

    @staticmethod
    def _normalize_take_axis(axis: int, ndim: int) -> int:
        if not isinstance(axis, (int, np.integer)):
            raise TypeError("axis must be an integer or None")
        axis = int(axis)
        if axis < 0:
            axis += ndim
        if not 0 <= axis < ndim:
            raise ValueError(f"axis {axis} is out of bounds for array of dimension {ndim}")
        return axis

    def _take_sparse_normalized(self, indices: np.ndarray, out: np.ndarray | None = None) -> np.ndarray:
        out = np.empty(indices.shape, dtype=self.dtype) if out is None else out
        return super().get_sparse_numpy(out, indices)

    def _take_numpy(self, indices, /, *, axis: int | None = None) -> np.ndarray:
        """Return a NumPy buffer for :meth:`take` and internal gather paths."""
        if axis is None:
            normalized = self._normalize_take_indices(indices, self.size)
            flat = normalized.reshape(-1)
            return self._take_sparse_normalized(flat).reshape(normalized.shape)

        axis = self._normalize_take_axis(axis, self.ndim)
        normalized = self._normalize_take_indices(indices, self.shape[axis])
        flat = normalized.reshape(-1)
        result_shape = self.shape[:axis] + normalized.shape + self.shape[axis + 1 :]
        if flat.size == 0:
            return np.empty(result_shape, dtype=self.dtype)
        if self.ndim == 1:
            return self._take_sparse_normalized(flat).reshape(result_shape)

        # For ndim > 1 axis-based take, use orthogonal selection which
        # decompresses each chunk once and copies contiguous row/slab
        # slices.  Per-element sparse gather is the wrong tool here
        # because it would iterate over every individual element
        # coordinate (n_indices × product of other dims).
        selection = [np.arange(dim, dtype=np.int64) for dim in self.shape]
        selection[axis] = flat
        orthogonal_shape = self.shape[:axis] + (flat.size,) + self.shape[axis + 1 :]
        out = np.empty(orthogonal_shape, dtype=self.dtype)
        self.get_oindex_numpy(out, selection)
        return out.reshape(result_shape)

    def take(self, indices, /, *, axis: int | None = None) -> NDArray:
        """Return elements selected by integer indices.

        This follows the Array API ``take`` shape rules: when ``axis`` is
        ``None`` the array is conceptually flattened and the result has the
        same shape as ``indices``; otherwise the indexed axis is replaced by
        ``indices.shape``.
        """
        return blosc2.asarray(self._take_numpy(indices, axis=axis))

    def _try_sparse_fancy_index(self, key) -> np.ndarray | None:
        """Try to handle integer-array fancy indexing via the sparse gather path.

        If *key* is a single integer array (list or ndarray, any dimensionality)
        route it through ``_take_numpy`` which uses ``b2nd_get_sparse_cbuffer``.
        Return the result ndarray on success, or ``None`` to signal that the
        caller should fall back to the regular fancy-indexing machinery.
        """
        if isinstance(key, (slice, tuple)):
            return None
        if not isinstance(key, (list, np.ndarray)):
            return None
        key_arr = np.asarray(key)
        if not (np.issubdtype(key_arr.dtype, np.integer) and key_arr.ndim >= 1):
            return None
        # 1-D: axis=None (flat); ndim>1: axis=0 (row selection)
        return self._take_numpy(key_arr, axis=None if self.ndim == 1 else 0)

    def _getitem_bool_mask(self, key):
        """Handle boolean array key with optional sparse-gather fast path.

        Returns the result array or ``None`` if *key* is not a matching
        boolean mask (caller should continue with regular indexing).
        """
        if not (hasattr(key, "dtype") and np.issubdtype(key.dtype, np.bool_) and key.shape == self.shape):
            return None
        # For sparse boolean masks, converting to flat indices and using the
        # sparse-gather path is faster than decompressing every data chunk.
        try:
            idx = _bool_mask_to_flat_indices(key, self.schunk.nchunks)
        except _BoolMaskDense:
            pass
        else:
            return blosc2.take(self, idx, axis=None)[:]
        # Fall through to the LazyExpr path for dense masks
        expr = blosc2.LazyExpr._new_expr("key", {"key": key}, guess=False).where(self)
        return expr[:]

    def __getitem__(
        self,
        key: None
        | int
        | slice
        | Sequence[slice | int | np.bool_ | np.ndarray[int | np.bool_] | None]
        | NDArray[int | np.bool_]
        | blosc2.LazyExpr
        | str,
    ) -> np.ndarray | blosc2.LazyExpr:
        """
        Retrieve a (multidimensional) slice as specified by the key.

        Note that this __getitem__ closely matches NumPy fancy indexing behaviour, except in
        some edge cases which are not supported by ndindex.
        Array indices separated by slice object - e.g. arr[0, :10, [0,1]] - are NOT supported.
        See https://www.blosc.org/posts/blosc2-fancy-indexing for more details.

        Parameters
        ----------
        key: int, slice, sequence of (slices, int), array of bools, LazyExpr or str
            The slice(s) to be retrieved. Slice steps (including negative steps)
            are honored. If a LazyExpr is provided, the expression is expected to be of
            boolean type, and the result will be another LazyExpr returning the values
            of this array where the expression is True.
            When key is a (nd-)array of bools, the result will be the values of ``self``
            where the bool values are True (similar to NumPy).
            If key is an N-dim array of integers, the result will be the values of
            this array at the specified indices with the shape of the index.
            If the key is a string, and it is a field name of self, a :ref:`NDField`
            accessor will be returned; if not, it will be attempted to convert to a
            :ref:`LazyExpr`, and will search for its operands in the fields of ``self``.

        Returns
        -------
        out: np.ndarray | blosc2.LazyExpr
            The requested data as a NumPy array or a :ref:`LazyExpr`.

        Examples
        --------
        >>> import blosc2
        >>> shape = [25, 10]
        >>> # Create an array
        >>> a = blosc2.full(shape, 3.3333)
        >>> # Get slice as a NumPy array
        >>> a[:5, :5]
        array([[3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333]])
        """
        # Follow shape changes made by another handle (SWMR) before validating the key
        self.refresh()
        # The more general case (this is quite slow)
        # If the key is a LazyExpr, decorate with ``where`` and return it
        if isinstance(key, blosc2.LazyExpr):
            return key.where(self)
        if isinstance(key, str):
            if self.dtype.fields is None:
                raise ValueError("The array is not structured (its dtype does not have fields)")
            if key in self.fields:
                # A shortcut to access fields
                return self.fields[key]
            # Assume that the key is a boolean expression
            expr = blosc2.LazyExpr._new_expr(key, self.fields, guess=False)
            return expr.where(self)

        key = key[()] if isinstance(key, NDArray) else key  # key not iterable
        key = tuple(k[()] if isinstance(k, NDArray) else k for k in key) if isinstance(key, tuple) else key

        # Check boolean array key early to avoid expensive process_key / nonzero
        result = self._getitem_bool_mask(key)
        if result is not None:
            return result

        # Integer array fancy indexing -> route through the efficient sparse
        # gather (b2nd_get_sparse_cbuffer) for all dimensionalities.
        result = self._try_sparse_fancy_index(key)
        if result is not None:
            return result

        # decompress NDArrays
        key_, mask = process_key(key, self.shape)  # internally handles key an integer
        key = key[()] if hasattr(key, "shape") and key.shape == () else key  # convert to scalar

        # fancy indexing
        if isinstance(key_, list | np.ndarray) or builtins.any(
            isinstance(k, list | np.ndarray) for k in key_
        ):
            # check scalar booleans, which add 1 dim to beginning
            if np.issubdtype(type(key), bool) and np.isscalar(key):
                if key:
                    _slice = ndindex.ndindex(()).expand(self.shape)  # just get whole array
                    out_shape = _slice.newshape(self.shape)
                    out = np.empty(out_shape, dtype=self.dtype)
                    return np.expand_dims(self._get_set_findex_default(_slice, out=out), 0)
                else:  # do nothing
                    return np.empty((0,) + self.shape, dtype=self.dtype)
            return self.get_fselection_numpy(key)  # fancy index default, can be quite slow

        start, stop, step, none_mask = get_ndarray_start_stop(self.ndim, key_, self.shape)
        shape = np.array(
            [(sp - st - np.sign(stp)) // stp + 1 for st, sp, stp in zip(start, stop, step, strict=True)]
        )
        if mask is not None:  # there are some dummy dims from ints
            # only get mask for not Nones in key to have nm_ same length as shape
            nm_ = [not m for m, n in zip(mask, none_mask, strict=True) if not n]
            # have to make none_mask refer to sliced dims (which will be less if ints present)
            none_mask = [n for m, n in zip(mask, none_mask, strict=True) if not m]
            shape = tuple(shape[nm_])

        # Create the array to store the result
        nparr = np.empty(shape, dtype=self.dtype)
        if step != (1,) * self.ndim:
            nparr = self._get_set_nonunit_steps((start, stop, step, [not i for i in nm_]), out=nparr)
        else:
            nparr = super().get_slice_numpy(nparr, (start, stop))

        if np.any(none_mask):
            nparr = np.expand_dims(nparr, axis=[i for i, n in enumerate(none_mask) if n])

        if self._keep_last_read:
            self._last_read.clear()
            inmutable_key = make_key_hashable(key)
            self._last_read[inmutable_key] = nparr

        return nparr

    def __setitem__(
        self,
        key: int | slice | Sequence[slice | int | np.bool_ | np.ndarray[int | np.bool_] | None] | None,
        value: object,
    ):
        """Set a slice of the array.

        Parameters
        ----------
        key: int, slice or sequence of slices
            The index or indices specifying the slice(s) to be updated. Note that the step parameter
            is not yet supported.
        value: Py_Object Supporting the Buffer Protocol
            An object supporting the
            `Buffer Protocol <https://docs.python.org/3/c-api/buffer.html>`_
            which will be used to overwrite the specified slice(s).

        Examples
        --------
        >>> import blosc2
        >>> # Create an array
        >>> a = blosc2.full([8, 8], 3.3333)
        >>> # Set a slice to 0
        >>> a[:5, :5] = 0
        >>> a[:]
        array([[0.    , 0.    , 0.    , 0.    , 0.    , 3.3333, 3.3333, 3.3333],
               [0.    , 0.    , 0.    , 0.    , 0.    , 3.3333, 3.3333, 3.3333],
               [0.    , 0.    , 0.    , 0.    , 0.    , 3.3333, 3.3333, 3.3333],
               [0.    , 0.    , 0.    , 0.    , 0.    , 3.3333, 3.3333, 3.3333],
               [0.    , 0.    , 0.    , 0.    , 0.    , 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
               [3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333]])
        """
        blosc2_ext.check_access_mode(self.schunk.urlpath, self.schunk.mode)
        # Follow shape changes made by another handle (SWMR) before validating the key
        self.refresh()

        # key not iterable
        key = key[()] if isinstance(key, NDArray) else key
        key = tuple(k[()] if isinstance(k, NDArray) else k for k in key) if isinstance(key, tuple) else key

        key_, mask = process_key(key, self.shape)  # internally handles key an integer
        if hasattr(value, "shape") and value.shape == ():
            value = value.item()
        value = (
            value if np.isscalar(value) else blosc2.as_simpleproxy(value)
        )  # convert to SimpleProxy for e.g. JAX, Tensorflow, PyTorch

        if builtins.any(isinstance(k, list | np.ndarray) for k in key_):  # fancy indexing
            _slice = ndindex.ndindex(key_).expand(
                self.shape
            )  # handles negative indices -> positive internally
            # check scalar booleans, which add 1 dim to beginning but which cause problems for ndindex.as_subindex
            if (
                key.shape == () and hasattr(key, "dtype") and np.issubdtype(key.dtype, np.bool_)
            ):  # check ORIGINAL key after decompression
                if key:
                    _slice = ndindex.ndindex(()).expand(self.shape)  # just get whole array
                else:  # do nothing
                    return self
            result = self._get_set_findex_default(_slice, value=value)
            from . import indexing

            indexing.mark_indexes_stale(self)
            return result

        start, stop, step, none_mask = get_ndarray_start_stop(self.ndim, key_, self.shape)

        if step != (1,) * self.ndim:  # handle non-unit or negative steps
            if np.any(none_mask):
                raise ValueError("Cannot mix non-unit steps and None indexing for __setitem__.")
            result = self._get_set_nonunit_steps((start, stop, step, mask), value=value)
            from . import indexing

            indexing.mark_indexes_stale(self)
            return result

        shape = [sp - st for sp, st in zip(stop, start, strict=False)]
        if isinstance(value, blosc2.Operand):  # handles SimpleProxy, NDArray, LazyExpr etc.
            value = value[()]  # convert to numpy
        if np.isscalar(value) or value.shape == ():
            value = np.full(shape, value, dtype=self.dtype)
        if value.dtype != self.dtype:  # handles decompressed NDArray too
            try:
                value = value.astype(self.dtype)
            except ComplexWarning:
                # numexpr type inference can lead to unnecessary type promotions
                # when using complex functions (e.g. conj) with real arrays
                value = value.real.astype(self.dtype)

        result = super().set_slice((start, stop), value)
        from . import indexing

        indexing.mark_indexes_stale(self)
        return result

    def __iter__(self):
        """Iterate over the (outer) elements of the array.

        Returns
        -------
        out: iterator
        """
        return NDOuterIterator(self)

    def __len__(self) -> int:
        """Returns the length of the first dimension of the array.
        This is equivalent to ``self.shape[0]``.
        """
        if self.shape == ():
            raise TypeError("len() of unsized object")
        return self.shape[0]

    def get_chunk(self, nchunk: int) -> bytes:
        """Shortcut to :meth:`SChunk.get_chunk <blosc2.schunk.SChunk.get_chunk>`. This can be accessed
        through the :attr:`schunk` attribute as well.

        Parameters
        ----------
        nchunk: int
            The index of the chunk to retrieve.

        Returns
        -------
        chunk: bytes
            The chunk data at the specified index.

        See Also
        --------
        :attr:`schunk`
            The attribute that provides access to the underlying `SChunk` object.

        Examples
        --------
        >>> import blosc2
        >>> import numpy as np
        >>> # Create an SChunk with some data
        >>> ndarray = blosc2.arange(10)
        >>> chunk = ndarray.get_chunk(0)
        >>> # Decompress the chunk to convert it into a numpy array
        >>> decompressed_chunk = blosc2.decompress(chunk)
        >>> np_array_chunk = np.frombuffer(decompressed_chunk, dtype=np.int64)
        >>> # Verify the content of the chunk
        >>> if isinstance(np_array_chunk, np.ndarray):
        >>>         print(np_array_chunk)
        >>>         print(np_array_chunk.shape) # Assuming chunk is a list or numpy array
        [ 0  1  2  3  4  5  6  7  8  9]
        (10,)
        """
        return self.schunk.get_chunk(nchunk)

    def reshape(self, shape: tuple[int], **kwargs: Any) -> NDArray:
        """Return a new array with the specified shape.

        See full documentation in :func:`reshape`.

        See Also
        --------
        :func:`reshape`
        """
        return reshape(self, shape, **kwargs)

    def iterchunks_info(
        self,
    ) -> Iterator[
        NamedTuple(
            "info",
            nchunk=int,
            coords=tuple,
            cratio=float,
            special=blosc2.SpecialValue,
            repeated_value=bytes | None,
            lazychunk=bytes,
        )
    ]:
        """
        Iterate over :paramref:`self` chunks of the array, providing information on index
        and special values.

        Yields
        ------
        info: namedtuple
            A namedtuple with the following fields:

                nchunk: int
                    The index of the chunk.
                coords: tuple
                    The coordinates of the chunk, in chunk units.
                cratio: float
                    The compression ratio of the chunk.
                special: :class:`SpecialValue`
                    The special value enum of the chunk; if 0, the chunk is not special.
                repeated_value: :attr:`self.dtype` or None
                    The repeated value for the chunk; if not SpecialValue.VALUE, it is None.
                lazychunk: bytes
                    A buffer containing the complete lazy chunk.

        Examples
        --------
        >>> import blosc2
        >>> a = blosc2.full(shape=(1000, ) * 3, fill_value=9, chunks=(500, ) * 3, dtype="f4")
        >>> for info in a.iterchunks_info():
        ...     print(info.coords)
        (0, 0, 0)
        (0, 0, 1)
        (0, 1, 0)
        (0, 1, 1)
        (1, 0, 0)
        (1, 0, 1)
        (1, 1, 0)
        (1, 1, 1)
        """
        ChunkInfoNDArray = namedtuple(
            "ChunkInfoNDArray", ["nchunk", "coords", "cratio", "special", "repeated_value", "lazychunk"]
        )
        chunks_idx = np.array(self.ext_shape) // np.array(self.chunks)
        for cinfo in self.schunk.iterchunks_info():
            nchunk, cratio, special, repeated_value, lazychunk = cinfo
            coords = tuple(np.unravel_index(cinfo.nchunk, chunks_idx))
            if cinfo.special == SpecialValue.VALUE:
                repeated_value = np.frombuffer(cinfo.repeated_value, dtype=self.dtype)[0]
            yield ChunkInfoNDArray(nchunk, coords, cratio, special, repeated_value, lazychunk)

    def tobytes(self) -> bytes:
        """Returns a buffer containing the data of the entire array.

        Returns
        -------
        out: bytes
            The buffer with the data of the whole array.

        Examples
        --------
        >>> import blosc2
        >>> import numpy as np
        >>> dtype = np.dtype("i4")
        >>> shape = [23, 11]
        >>> a = np.arange(0, int(np.prod(shape)), dtype=dtype).reshape(shape)
        >>> # Create an array
        >>> b = blosc2.asarray(a)
        >>> b.tobytes() == bytes(a[...])
        True
        """
        return super().tobytes()

    def to_cframe(self) -> bytes:
        """Get a bytes object containing the serialized :ref:`NDArray` instance.

        Returns
        -------
        out: bytes
            The buffer containing the serialized :ref:`NDArray` instance.

        See Also
        --------
        :func:`~blosc2.ndarray_from_cframe`
            This function can be used to reconstruct a NDArray from the serialized bytes.

        Examples
        --------
        >>> import blosc2
        >>> a = blosc2.full(shape=(1000, 1000), fill_value=9, dtype='i4')
        >>> # Get the bytes object containing the serialized instance
        >>> cframe_bytes = a.to_cframe()
        >>> blosc_array = blosc2.ndarray_from_cframe(cframe_bytes)
        >>> print("Shape of the NDArray:", blosc_array.shape)
        >>> print("Data type of the NDArray:", blosc_array.dtype)
        Shape of the NDArray: (1000, 1000)
        Data type of the NDArray: int32
        """
        return super().to_cframe()

    def copy(self, dtype: np.dtype | str = None, **kwargs: Any) -> NDArray:
        """Create a copy of an array with different parameters.

        Parameters
        ----------
        dtype: np.dtype or list str
            The new array dtype. Default is `self.dtype`.

        Other Parameters
        ----------------
        kwargs: dict, optional
            Additional keyword arguments supported by the :func:`empty` constructor.
            If not specified, the defaults will be taken from the original
            array (except for the urlpath).

        Returns
        -------
        out: :ref:`NDArray`
            A :ref:`NDArray` with a copy of the data.

        See Also
        --------
        :func:`copy`

        Examples
        --------
        >>> import blosc2
        >>> import numpy as np
        >>> shape = (10, 10)
        >>> blocks = (10, 10)
        >>> dtype = np.bool_
        >>> # Create a NDArray with default chunks
        >>> a = blosc2.zeros(shape, blocks=blocks, dtype=dtype)
        >>> # Get a copy with default chunks and blocks
        >>> b = a.copy(chunks=None, blocks=None)
        >>> np.array_equal(b[...], a[...])
        True
        """
        if dtype is None:
            dtype = self.dtype

        # Add the default parameters
        kwargs["cparams"] = kwargs.get("cparams", self.cparams)
        kwargs["dparams"] = kwargs.get("dparams", self.dparams)
        if "meta" in kwargs:
            # Do not allow to pass meta to copy
            raise ValueError("meta should not be passed to copy")

        kwargs = _check_ndarray_kwargs(**kwargs)
        return super().copy(dtype, **kwargs)

    def save(self, urlpath: str, contiguous=True, **kwargs: Any) -> None:
        """Save the array to a file.

        This is a convenience function that calls the :func:`copy` method with the
        `urlpath` parameter and the additional keyword arguments provided.

        See :func:`save` for more information.

        Parameters
        ----------
        urlpath: str
            The path where the array will be saved.
        contiguous: bool, optional
            Whether to save the array contiguously.

        Other Parameters
        ----------------
        kwargs: dict, optional
            Additional keyword arguments supported by the :func:`save` method.

        Returns
        -------
        out: None

        Examples
        --------
        >>> import blosc2
        >>> import numpy as np
        >>> shape = (10, 10)
        >>> blocks = (10, 10)
        >>> dtype = np.bool_
        >>> # Create a NDArray with default chunks
        >>> a = blosc2.zeros(shape, blocks=blocks, dtype=dtype)
        >>> # Save the array to a file
        >>> a.save("array.b2frame")
        """
        blosc2_ext.check_access_mode(urlpath, "w")
        # Add urlpath to kwargs
        kwargs["urlpath"] = urlpath
        # Add the contiguous parameter
        kwargs["contiguous"] = contiguous

        super().copy(self.dtype, cparams=asdict(self.cparams), **kwargs)

    def create_index(
        self,
        field: str | None = None,
        *,
        expression: str | None = None,
        operands: dict | None = None,
        kind: blosc2.IndexKind = blosc2.IndexKind.BUCKET,
        optlevel: int = 5,
        persistent: bool | None = None,
        build: str = "auto",
        name: str | None = None,
        tmpdir: str | None = None,
        **kwargs: Any,
    ) -> dict:
        """Create an index for a 1-D array, structured field, or expression.

        Parameters
        ----------
        field : str or None, optional
            Field to index for structured dtypes. If a positional string does
            not match a field name, it is treated as an expression.
        expression : str or None, optional
            Deterministic scalar expression to materialize and index.
            Expressions are matched by normalized identity.
        operands : dict or None, optional
            Operand mapping used for expression normalization and evaluation.
            When omitted, structured arrays default to ``self.fields`` and
            plain arrays use ``{"value": self}``.
        kind : IndexKind, optional
            Index tier to build. Use ``SUMMARY`` for the lightest pruning,
            ``BUCKET`` for bucketed chunk-local pruning, ``PARTIAL`` for exact
            filtering with local ordering, ``OPSI`` for a tunable iterative
            ordering index, and ``FULL`` when ordered access via
            ``sort(order=...)``, ``argsort(order=...)``, or
            ``iter_sorted(...)`` should reuse the index directly. ``OPSI`` is a
            separate exact-filtering index kind; it incrementally improves
            physical ordering but does not try to produce a completely sorted
            full/CSI payload.
        optlevel : int, optional
            Optimization level for index payload construction. For
            ``kind=OPSI``, this controls the default number of iterative OPSI
            cycles: ``optlevel`` cycles for levels below 8, and
            ``2 * optlevel`` cycles for levels 8 and above. More cycles can
            improve cold-query locality and compression, but increase build
            time.
        persistent : bool or None, optional
            Whether index sidecars should be persisted. If ``None``, this
            follows whether the base array is persistent.
        build : {"auto", "memory", "ooc"}, optional
            Builder policy. ``"memory"`` forces in-memory construction.
            ``"ooc"`` forces out-of-core construction for supported kinds.
            ``"auto"`` uses the default out-of-core-capable builders.
        name : str or None, optional
            Optional logical label stored in the descriptor. Index identity is
            still driven by the target, so creating another index on the same
            target replaces the previous one.
        tmpdir : str or None, optional
            Directory to use for temporary files during out-of-core
            ``kind=FULL`` builds. If ``None``, persistent arrays use the same
            directory as the array being indexed so temporaries stay on the
            same filesystem. In-memory arrays fall back to the system
            temporary directory.
        kwargs : dict, optional
            Keyword arguments forwarded to the index builder. Supported options
            include ``cparams``, ``opsi_max_cycles`` for ``kind=OPSI``, and,
            for ``kind=FULL``, ``method``. Pass ``method="global-sort"`` to
            select the full-index builder explicitly. OPSI is selected with
            ``kind=IndexKind.OPSI`` rather than as a full-index method. Pass
            ``opsi_max_cycles`` to override the optlevel-derived cycle count.
            Pass ``cparams`` to control the compression settings used for index
            sidecars, including ``codec``, ``clevel``, and ``nthreads``. If
            provided, ``cparams["nthreads"]`` becomes the default build-thread
            count for intra-chunk sorting unless ``BLOSC2_INDEX_BUILD_THREADS``
            overrides it.

        Notes
        -----
        The current indexing model supports one active index target per field
        or normalized expression. Append operations keep compatible indexes
        current, while general mutation and resize operations mark indexes as
        stale until rebuild.

        Chunk-local index creation uses parallel intra-chunk sorting by default.
        Set ``BLOSC2_INDEX_BUILD_THREADS=1`` to disable parallel sorting. If
        ``cparams`` is provided in ``kwargs``, its ``nthreads`` value becomes
        the default build-thread count unless
        ``BLOSC2_INDEX_BUILD_THREADS`` overrides it.
        """
        from . import indexing

        return indexing.create_index(
            self,
            field=field,
            expression=expression,
            operands=operands,
            kind=kind,
            optlevel=optlevel,
            persistent=persistent,
            build=build,
            name=name,
            tmpdir=tmpdir,
            **kwargs,
        )

    def drop_index(self, field: str | None = None, name: str | None = None) -> None:
        """Drop an index by field or optional descriptor label."""
        from . import indexing

        indexing.drop_index(self, field=field, name=name)

    def rebuild_index(self, field: str | None = None, name: str | None = None) -> dict:
        """Rebuild an index by field or optional descriptor label."""
        from . import indexing

        return indexing.rebuild_index(self, field=field, name=name)

    def compact_index(self, field: str | None = None, name: str | None = None) -> dict:
        """Compact a ``full`` index by merging its compact base and append runs.

        Parameters
        ----------
        field : str or None, optional
            Structured field identifying the target ``full`` index. Use
            ``None`` to compact the value index for a plain 1-D array.
        name : str or None, optional
            Optional logical index label. When omitted and the array has a
            single index, that index is selected automatically.

        Returns
        -------
        out : dict
            The updated index descriptor after compaction.

        Notes
        -----
        This is currently implemented only for ``kind=IndexKind.FULL`` indexes. It is
        a structural maintenance operation: the compact base sidecars and any
        pending append runs are merged into one compact full value sidecar
        and one compact full position sidecar. For persistent indexes, the
        compact lookup metadata is rebuilt as part of the process and
        ``full["runs"]`` becomes empty afterwards.

        Compaction does not change query results. It is useful after many
        append operations, where full-index maintenance stays cheap on append by
        recording ordered runs but later queries may still have extra work until
        the index is consolidated explicitly.

        Examples
        --------
        >>> import blosc2
        >>> import numpy as np
        >>> dtype = np.dtype([("id", np.int64), ("payload", np.int32)])
        >>> data = np.array([(3, 9), (1, 8), (2, 7), (1, 6)], dtype=dtype)
        >>> arr = blosc2.asarray(data, chunks=(2,), blocks=(2,))
        >>> _ = arr.create_index(field="id", kind=blosc2.IndexKind.FULL)
        >>> _ = arr.append(np.array([(0, 100), (3, 101)], dtype=dtype))
        >>> len(arr.indexes[0]["full"]["runs"])
        1
        >>> compacted = arr.compact_index("id")
        >>> compacted["full"]["runs"]
        []
        """
        from . import indexing

        return indexing.compact_index(self, field=field, name=name)

    def index(self, field: str | None = None, name: str | None = None) -> blosc2.indexing.Index:
        """Return a live view over one index.

        Parameters
        ----------
        field : str or None, optional
            Structured field identifying the target index. Use ``None`` for the
            value index on a plain 1-D array.
        name : str or None, optional
            Optional logical index label. When omitted and the array has a
            single index, that index is selected automatically.
        """
        from . import indexing

        return indexing.get_index(self, field=field, name=name)

    @property
    def indexes(self) -> list[blosc2.indexing.Index]:
        from . import indexing

        return indexing.get_indexes(self)

    def refresh(self) -> bool:
        """Re-sync the cached shape and metadata of a disk-based array.

        In a single-writer, multiple-readers (SWMR) workflow, a reader handle
        follows shape changes made through another handle (e.g. a
        :func:`resize` in another process) automatically on data access.
        Call this to observe shape changes without reading data, e.g. when
        polling :attr:`shape` while waiting for a writer to grow the array.

        Returns
        -------
        out: bool
            True if the cached state was re-synced with the on-disk state,
            False if it was already current.  Always False for in-memory
            arrays.

        Notes
        -----
        Only shape changes are followed: if another handle changed the
        number of dimensions, chunks or blocks, an exception is raised.
        """
        return super().refresh()

    def resize(self, newshape: tuple | list) -> None:
        """Change the shape of the array by growing or shrinking one or more dimensions.

        Parameters
        ----------
        newshape : tuple or list
            The new shape of the array. It should have the same number of dimensions
            as :paramref:`self`, the current shape.

        Returns
        -------
        out: None

        Notes
        -----
        The array values in the newly added positions are not initialized.
        The user is responsible for initializing them.

        Examples
        --------
        >>> import blosc2
        >>> import math
        >>> shape = [23, 11]
        >>> b = blosc2.linspace(1, 3, num=math.prod(shape), shape=shape)
        >>> newshape = [50, 10]
        >>> # Extend first dimension, shrink second dimension
        >>> b.resize(newshape)
        >>> b.shape
        (50, 10)
        """
        if 0 in self.chunks or 0 in self.blocks:
            raise ValueError(
                "Cannot resize array. Perhaps you want to specify chunks/blocks on array creation. For 1D arrays, a good chunks value is (cache_size/typesize,)!"
            )
        blosc2_ext.check_access_mode(self.schunk.urlpath, self.schunk.mode)
        super().resize(newshape)
        from . import indexing

        indexing.mark_indexes_stale(self)

    def append(self, values: object) -> int:
        """Append values to a 1-D array and keep indexes current when possible.

        Parameters
        ----------
        values : object
            Values to append. Scalars append one element; array-like inputs must be
            compatible with ``self.dtype`` and flatten to one dimension.

        Returns
        -------
        out : int
            The new length of the array.

        Notes
        -----
        Appending to indexed arrays updates the index sidecars as part of the
        append path. For ``full`` indexes this extends the ordered payload
        incrementally; for ``bucket`` and ``partial`` only the affected tail
        segments and block payloads are recomputed. General slice updates and
        resizes outside ``append()`` still mark indexes as stale.
        """
        if self.ndim != 1:
            raise ValueError("append() is only supported for 1-D arrays")
        if 0 in self.chunks or 0 in self.blocks:
            raise ValueError("Cannot append to arrays with zero-sized chunks or blocks")

        blosc2_ext.check_access_mode(self.schunk.urlpath, self.schunk.mode)

        appended = np.asarray(values, dtype=self.dtype)
        if appended.ndim == 0:
            appended = appended.reshape(1)
        elif appended.ndim != 1:
            appended = appended.reshape(-1)
        if appended.dtype != self.dtype:
            appended = appended.astype(self.dtype, copy=False)
        if len(appended) == 0:
            return int(self.shape[0])

        self.refresh()
        old_size = int(self.shape[0])
        super().resize((old_size + len(appended),))
        super().set_slice(([old_size], [old_size + len(appended)]), appended)

        from . import indexing

        indexing.append_to_indexes(self, old_size, appended)
        return int(self.shape[0])

    def slice(self, key: int | slice | Sequence[slice], **kwargs: Any) -> NDArray:
        """Get a (multidimensional) slice as a new :ref:`NDArray`.

        Parameters
        ----------
        key: int, slice or sequence of slices
            The index for the slices to be retrieved. Note that the step parameter is
            not yet supported in slices.

        Other Parameters
        ----------------
        kwargs: dict, optional
            Additional keyword arguments supported by the :func:`empty` constructor.

        Returns
        -------
        out: :ref:`NDArray`
            An array containing the requested data. The dtype will match that of `self`.

        Examples
        --------
        >>> import blosc2
        >>> shape = [23, 11]
        >>> b = blosc2.arange(253, shape=shape)
        >>> slices = (slice(3, 7), slice(1, 11))
        >>> # Get a slice as a new NDArray
        >>> c = b.slice(slices)
        >>> print(c.shape)
        (4, 10)
        >>> print(type(c))
        <class 'blosc2.ndarray.NDArray'>

        Notes
        -----
        There is a fast path for slices that are aligned with underlying chunks.
        Aligned means that the slices are made entirely with complete chunks.
        """
        if "cparams" not in kwargs:
            kwargs["cparams"] = {
                "codec": self.cparams.codec,
                "clevel": self.cparams.clevel,
                "filters": self.cparams.filters,
            }
        kwargs = _check_ndarray_kwargs(**kwargs)  # sets cparams to defaults
        key, mask = process_key(key, self.shape)
        start, stop, step, _ = get_ndarray_start_stop(self.ndim, key, self.shape)

        # Fast path for slices made with aligned chunks
        if step == (1,) * self.ndim:
            aligned_chunks = detect_aligned_chunks(key, self.shape, self.chunks, consecutive=False)
            if aligned_chunks:
                # print("Aligned chunks detected", aligned_chunks)
                # Create a new ndarray for the key slice
                new_shape = [
                    sp - st for sp, st in zip([k.stop for k in key], [k.start for k in key], strict=False)
                ]
                newarr = blosc2.empty(
                    shape=new_shape,
                    dtype=self.dtype,
                    chunks=self.chunks,
                    blocks=self.blocks,
                    **kwargs,
                )
                # Get the chunks from the original array and update the new array
                # No need for chunks to decompress and compress again
                for order, nchunk in enumerate(aligned_chunks):
                    chunk = self.schunk.get_chunk(nchunk)
                    newarr.schunk.update_chunk(order, chunk)
                return newarr.squeeze(axis=np.where(mask)[0])  # remove any dummy dims introduced

        key = (start, stop)
        ndslice = super().get_slice(key, mask, **kwargs)

        # This is memory intensive, but we have not a better way to do it yet
        # TODO: perhaps add a step param in the get_slice method in the future?
        if step != (1,) * self.ndim:
            nparr = ndslice[...]
            if len(step) == 1:
                nparr = nparr[:: step[0]]
            else:
                slice_ = tuple(slice(None, None, st) for st in step)
                nparr = nparr[slice_]
            return asarray(nparr, **kwargs)

        return ndslice

    def squeeze(self, axis: int | Sequence[int]) -> NDArray:
        """Remove single-dimensional entries from the shape of the array.

        This method modifies the array in-place. If mask is None removes any dimensions with size 1.
        If axis is provided, it should be an int or tuple of ints and the corresponding
        dimensions (of size 1) will be removed.

        Returns
        -------
        out: NDArray

        Examples
        --------
        >>> import blosc2
        >>> shape = [1, 23, 1, 11, 1]
        >>> # Create an array
        >>> a = blosc2.full(shape, 2**30)
        >>> a.shape
        (1, 23, 1, 11, 1)
        >>> # Squeeze the array
        >>> a.squeeze()
        >>> a.shape
        (23, 11)
        """
        return blosc2.squeeze(self, axis=axis)

    def argsort(self, order: str | list[str] | None = None, **kwargs: Any) -> NDArray:
        """
        Return the permutation that sorts the array.

        This follows :func:`numpy.argsort` semantics more closely than the
        older indexing-specific ``indices()`` helper: plain 1-D arrays are
        supported, and ``order=None`` means "use the array's natural order"
        rather than "leave unsorted".

        See full documentation in :func:`argsort`.
        """
        return argsort(self, order, **kwargs)

    def iter_sorted(
        self,
        order: str | list[str] | None = None,
        *,
        start: int | None = None,
        stop: int | None = None,
        step: int | None = None,
        batch_size: int | None = None,
    ) -> Iterator[np.generic | np.void]:
        """Iterate array values following a matching sorted index order.

        Parameters
        ----------
        order : str, list of str, optional
            Sort order to iterate. The first field must have an associated
            ``full`` index. Traversal is ascending and stable; if only the
            primary key is indexed, secondary keys refine ties after the primary
            indexed order.
        start, stop, step : int or None, optional
            Optional slice applied to the ordered sequence before iteration.
        batch_size : int or None, optional
            Internal prefetch size used when reading ordered rows. Larger values
            reduce read overhead at the cost of more temporary memory.
        """
        return iter_sorted(self, order, start=start, stop=stop, step=step, batch_size=batch_size)

    def sort(self, order: str | list[str] | None = None, **kwargs: Any) -> NDArray:
        """
        Return a sorted array following the specified order, or the order of the fields.

        This is only valid for 1-dim structured arrays.

        When the primary order key has a matching ``full`` index, the ordered
        rows are gathered directly from that index. Secondary keys refine ties
        after the primary indexed order and the traversal is ascending and
        stable.

        See full documentation in :func:`sort`.
        """
        return sort(self, order, **kwargs)

    def as_ffi_ptr(self):
        """Returns the pointer to the raw FFI blosc2::b2nd_array_t object.

        This function is useful for passing the array to C functions.
        """
        return super().as_ffi_ptr()

    def __matmul__(self, other):
        return blosc2.linalg.matmul(self, other)


def squeeze(x: Array, axis: int | Sequence[int]) -> NDArray:
    """
    Remove single-dimensional entries from the shape of the array.

    This method modifies the array in-place.

    Parameters
    ----------
    x: Array
        input array.
    axis: int | Sequence[int]
        Axis (or axes) to squeeze.

    Returns
    -------
    out: Array
        An output array having the same data type and elements as x.

    Examples
    --------
    >>> import blosc2
    >>> shape = [1, 23, 1, 11, 1]
    >>> # Create an array
    >>> b = blosc2.full(shape, 2**30)
    >>> b.shape
    (1, 23, 1, 11, 1)
    >>> # Squeeze the array
    >>> blosc2.squeeze(b)
    >>> b.shape
    (23, 11)
    """
    axis = [axis] if isinstance(axis, int) else axis
    mask = [False for i in range(x.ndim)]
    for a in axis:
        if a < 0:
            a += x.ndim  # Adjust axis to be within the array's dimensions
        if mask[a]:
            raise ValueError("Axis values must be unique.")
        mask[a] = True
    return blosc2_ext.squeeze(x, axis_mask=mask)


def array_from_ffi_ptr(array_ptr) -> NDArray:
    """
    Create an NDArray from a raw FFI pointer.

    This function is useful for passing arrays across FFI boundaries.
    This function move the ownership of the underlying `b2nd_array_t*` object to the new NDArray, and it will be freed
    when the object is destroyed.
    """
    return blosc2_ext.array_from_ffi_ptr(array_ptr)


def where(
    condition: blosc2.LazyExpr | NDArray,
    x: blosc2.Array | int | float | complex | bool | str | bytes | None = None,
    y: blosc2.Array | int | float | complex | bool | str | bytes | None = None,
) -> blosc2.LazyExpr:
    """
    Return elements chosen from `x` or `y` depending on `condition`.

    Parameters
    ----------
    condition: :ref:`LazyExpr`
        Where True, yield `x`, otherwise yield `y`.
    x: :ref:`NDArray` or :ref:`NDField` or np.ndarray or scalar or bytes
        Values from which to choose when `condition` is True.
    y: :ref:`NDArray` or :ref:`NDField` or np.ndarray or scalar or bytes
        Values from which to choose when `condition` is False.

    References
    ----------
    `np.where <https://numpy.org/doc/stable/reference/generated/numpy.where.html>`_
    """
    return condition.where(x, y)


@incomplete_lazyfunc
def startswith(
    a: str | blosc2.Array, prefix: str | blosc2.Array
) -> NDArray:  # start: int = 0, end: int | None = None, **kwargs)
    """
    Copy-pasted from numpy documentation: https://numpy.org/doc/stable/reference/generated/numpy.char.startswith.html
    Returns a boolean array which is True where the string element in a starts with prefix, otherwise False.

    Parameters
    ----------
    a : blosc2.Array
        Input array of bytes_ or str_ dtype

    prefix : blosc2.Array
        Prefix array of bytes_ or str_ dtype

    start: int | blosc2.Array
        With start, test beginning at that position.

    end: int | blosc2.Array
        With end, stop comparing at that position.

    kwargs: Any
        kwargs accepted by the :func:`empty` constructor

    Returns
    -------
    out: blosc2.Array, bool
        Has the same shape as element.

    """

    # def chunkwise_startswith(inputs, output, offset):
    #     x1, x2 = inputs
    #     # output[...] = np.char.startswith(x1, x2, start=start, end=end)
    #     output[...] = np.char.startswith(x1, x2)

    return blosc2.LazyExpr(new_op=(a, "startswith", prefix))


@incomplete_lazyfunc
def endswith(
    a: str | blosc2.Array, suffix: str | blosc2.Array
) -> NDArray:  # start: int = 0, end: int | None = None, **kwargs) -> NDArray:
    """
    Copy-pasted from numpy documentation: https://numpy.org/doc/stable/reference/generated/numpy.char.endswith.html
    Returns a boolean array which is True where the string element in a ends with suffix, otherwise False.

    Parameters
    ----------
    a : blosc2.Array
        Input array of bytes_ or str_ dtype

    suffix : blosc2.Array
        suffix array of bytes_ or str_ dtype

    start: int | blosc2.Array
        With start, test beginning at that position.

    end: int | blosc2.Array
        With end, stop comparing at that position.

    kwargs: Any
        kwargs accepted by the :func:`empty` constructor

    Returns
    -------
    out: blosc2.Array, bool
        Has the same shape as element.

    """
    # def chunkwise_endswith(inputs, output, offset):
    #     x1, x2 = inputs
    #     # output[...] = np.char.endswith(x1, x2, start=start, end=end)
    #     output[...] = np.char.endswith(x1, x2)

    return blosc2.LazyExpr(new_op=(a, "endswith", suffix))


@incomplete_lazyfunc
def lower(a: str | blosc2.Array) -> NDArray:
    """
    Copy-pasted from numpy documentation: https://numpy.org/doc/stable/reference/generated/numpy.char.lower.html
    Return an array with the elements converted to lowercase.
    Call str.lower element-wise.
    For 8-bit strings, this method is locale-dependent.

    Parameters
    ----------
    a : blosc2.Array
        Input array of bytes_ or str_ dtype
    kwargs: Any
        kwargs accepted by the :func:`empty` constructor

    Returns
    -------
    out: blosc2.Array, of bytes_ or str_ dtype
        Has the same shape as element.

    """
    return blosc2.LazyExpr(new_op=(a, "lower", None))


@incomplete_lazyfunc
def upper(a: str | blosc2.Array) -> NDArray:
    """
    Copy-pasted from numpy documentation: https://numpy.org/doc/stable/reference/generated/numpy.char.upper.html
    Return an array with the elements converted to uppercase.
    Call str.lower element-wise.
    For 8-bit strings, this method is locale-dependent.

    Parameters
    ----------
    a : blosc2.Array
        Input array of bytes_ or str_ dtype
    kwargs: Any
        kwargs accepted by the :func:`empty` constructor

    Returns
    -------
    out: blosc2.Array, of bytes_ or str_ dtype
        Has the same shape as element.

    """
    return blosc2.LazyExpr(new_op=(a, "upper", None))


def lazywhere(value1=None, value2=None):
    """Decorator to apply a where condition to a LazyExpr."""

    def inner_decorator(func):
        def wrapper(*args, **kwargs):
            return func(*args, **kwargs).where(value1, value2)

        return wrapper

    return inner_decorator


def _check_shape(shape):
    if isinstance(shape, int | np.integer):
        shape = (shape,)
    elif not isinstance(shape, tuple | list):
        raise TypeError("shape should be a tuple or a list!")
    if len(shape) > blosc2.MAX_DIM:
        raise ValueError(f"shape length {len(shape)} is too large (>{blosc2.MAX_DIM})!")
    return shape


def _check_dtype(dtype):
    dtype = np.dtype(dtype)
    if dtype.itemsize > blosc2.MAX_TYPESIZE:
        raise ValueError(f"dtype itemsize {dtype.itemsize} is too large (>{blosc2.MAX_TYPESIZE})!")
    if dtype == np.str_:  # itemsize is 0
        dtype = np.dtype("<U1")  # default to 1 char strings
    return dtype


def empty(shape: int | tuple | list, dtype: np.dtype | str | None = np.float64, **kwargs: Any) -> NDArray:
    """Create an empty array.

    Parameters
    ----------
    shape: int, tuple or list
        The shape for the final array.
    dtype: np.dtype or list str
        The data type of the array elements in NumPy format. Default is `np.uint8`.
        This will override the `typesize`
        in the compression parameters if they are provided.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments supported:
            chunks: tuple or list
                The chunk shape. If None (default), Blosc2 will compute
                an efficient chunk shape.
            blocks: tuple or list
                The block shape. If None (default), Blosc2 will compute
                an efficient block shape. This will override the `blocksize`
                in the cparams if they are provided.
            storage: :class:`blosc2.Storage` or dict
                All the storage parameters that you want to use as
                a :class:`blosc2.Storage` or dict instance.
            cparams: :class:`blosc2.CParams` or dict
                All the compression parameters that you want to use as
                a :class:`blosc2.CParams` or dict instance.
            dparams: :class:`blosc2.DParams` or dict
                All the decompression parameters that you want to use as
                a :class:`blosc2.DParams` or dict instance.
            others: Any
                If `storage` is not passed, all the parameters of a :class:`blosc2.Storage`
                can be passed as keyword arguments.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> shape = [20, 20]
    >>> dtype = np.int32
    >>> # Create empty array with default chunks and blocks
    >>> array = blosc2.empty(shape, dtype=dtype)
    >>> array.shape
    (20, 20)
    >>> array.dtype
    dtype('int32')
    """
    dtype = _check_dtype(dtype)
    shape = _check_shape(shape)
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    chunks, blocks = compute_chunks_blocks(shape, chunks, blocks, dtype, **kwargs)
    return blosc2_ext.empty(shape, chunks, blocks, dtype, **kwargs)


def uninit(shape: int | tuple | list, dtype: np.dtype | str = np.float64, **kwargs: Any) -> NDArray:
    """Create an array with uninitialized values.

    The parameters and keyword arguments are the same as for the
    :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> shape = [8, 8]
    >>> chunks = [6, 5]
    >>> # Create uninitialized array
    >>> array = blosc2.uninit(shape, dtype='f8', chunks=chunks)
    >>> array.shape
    (8, 8)
    >>> array.chunks
    (6, 5)
    >>> array.dtype
    dtype('float64')
    """
    dtype = _check_dtype(dtype)
    shape = _check_shape(shape)
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    chunks, blocks = compute_chunks_blocks(shape, chunks, blocks, dtype, **kwargs)
    return blosc2_ext.uninit(shape, chunks, blocks, dtype, **kwargs)


def nans(shape: int | tuple | list, dtype: np.dtype | str = np.float64, **kwargs: Any) -> NDArray:
    """Create an array with NaNs values.

    The parameters and keyword arguments are the same as for the
    :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray <NDArray>`
        A :ref:`NDArray <NDArray>` is returned.

    Examples
    --------
    >>> import blosc2
    >>> shape = [8, 8]
    >>> chunks = [6, 5]
    >>> # Create an array of NaNs
    >>> array = blosc2.nans(shape, dtype='f8', chunks=chunks)
    >>> array.shape
    (8, 8)
    >>> array.chunks
    (6, 5)
    >>> array.dtype
    dtype('float64')
    """
    dtype = _check_dtype(dtype)
    shape = _check_shape(shape)
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    chunks, blocks = compute_chunks_blocks(shape, chunks, blocks, dtype, **kwargs)
    return blosc2_ext.nans(shape, chunks, blocks, dtype, **kwargs)


def zeros(shape: int | tuple | list, dtype: np.dtype | str = np.float64, **kwargs: Any) -> NDArray:
    """Create an array with zero as the default value
    for uninitialized portions of the array.

    The parameters and keyword arguments are the same as for the
    :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> shape = [8, 8]
    >>> chunks = [6, 5]
    >>> blocks = [5, 5]
    >>> dtype = np.float64
    >>> # Create zeros array
    >>> array = blosc2.zeros(shape, dtype=dtype, chunks=chunks, blocks=blocks)
    >>> array.shape
    (8, 8)
    >>> array.chunks
    (6, 5)
    >>> array.blocks
    (5, 5)
    >>> array.dtype
    dtype('float64')
    """
    dtype = _check_dtype(dtype)
    shape = _check_shape(shape)
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    chunks, blocks = compute_chunks_blocks(shape, chunks, blocks, dtype, **kwargs)
    return blosc2_ext.zeros(shape, chunks, blocks, dtype, **kwargs)


def full(
    shape: int | tuple | list,
    fill_value: bytes | int | float | bool,
    dtype: np.dtype | str = None,
    **kwargs: Any,
) -> NDArray:
    """Create an array, with :paramref:`fill_value` being used as the default value
    for uninitialized portions of the array.

    Parameters
    ----------
    shape: int, tuple or list
        The shape of the final array.
    fill_value: bytes, int, float or bool
        Default value to use for uninitialized portions of the array.
        Its size will override the `typesize`
        in the cparams if they are passed.
    dtype: np.dtype or list str
        The ndarray dtype in NumPy format. By default, this will
        be taken from the :paramref:`fill_value`.
        This will override the `typesize`
        in the cparams if they are passed.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> shape = [25, 10]
    >>> # Create array filled with True
    >>> array = blosc2.full(shape, True)
    >>> array.shape
    (25, 10)
    >>> array.dtype
    dtype('bool')
    """
    if isinstance(fill_value, bytes):
        dtype = np.dtype(f"S{len(fill_value)}")
    if dtype is None:
        dtype = np.array(fill_value).dtype
    else:
        dtype = np.dtype(dtype)
    dtype = _check_dtype(dtype)
    shape = _check_shape(shape)
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    chunks, blocks = compute_chunks_blocks(shape, chunks, blocks, dtype, **kwargs)
    return blosc2_ext.full(shape, chunks, blocks, fill_value, dtype, **kwargs)


def ones(shape: int | tuple | list, dtype: np.dtype | str = None, **kwargs: Any) -> NDArray:
    """Create an array with one as values.

    The parameters and keyword arguments are the same as for the
    :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
            A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> shape = [8, 8]
    >>> chunks = [6, 5]
    >>> blocks = [5, 5]
    >>> dtype = np.float64
    >>> # Create ones array
    >>> array = blosc2.ones(shape, dtype=dtype, chunks=chunks, blocks=blocks)
    >>> array.shape
    (8, 8)
    >>> array.chunks
    (6, 5)
    >>> array.blocks
    (5, 5)
    >>> array.dtype
    dtype('float64')
    """
    if dtype is None:
        dtype = blosc2.DEFAULT_FLOAT
    return full(shape, 1, dtype, **kwargs)


def arange(
    start: int | float,
    stop: int | float | None = None,
    step: int | float | None = 1,
    dtype: np.dtype | str = None,
    shape: int | tuple | list | None = None,
    c_order: bool = True,
    **kwargs: Any,
) -> NDArray:
    """
    Return evenly spaced values within a given interval.
    Due to rounding errors for chunkwise filling, may differ
    from numpy.arange in edge cases.

    Parameters
    ----------
    start: int, float
        The starting value of the sequence.
    stop: int, float
        The end value of the sequence.
    step: int, float or None
        Spacing between values.
    dtype: np.dtype or list str
        The data type of the array elements in NumPy format. Default is
        None.  If dtype is None, inferred from start, stop and step.
        Output type is integer unless one or more have type float.
        This will override the `typesize` in the compression parameters if
        they are provided.
    shape: int, tuple or list
        The shape of the final array. If None, the shape will be computed.
    c_order: bool
        Whether to store the array in C order (row-major) or insertion order.
        Insertion order means that values will be stored in the array
        following the order of chunks in the array; this is more memory
        efficient, as it does not require an intermediate copy of the array.
        Default is C order.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> # Create an array with values from 0 to 10
    >>> array = blosc2.arange(0, 10, 1)
    >>> print(array)
    [0 1 2 3 4 5 6 7 8 9]
    """

    def _arange_num_elements(start, stop, step):
        return builtins.max(math.ceil((stop - start) / step), 0)

    def arange_fill(inputs, output, offset):
        lout = len(output)
        start, _, step = inputs
        start += offset[0] * step
        stop = start + lout * step
        if _arange_num_elements(start, stop, step) == lout:  # USE ARANGE IF POSSIBLE (2X FASTER)
            output[:] = np.arange(start, stop, step, dtype=output.dtype)
        else:  # use linspace to have finer control over exclusion of endpoint for float types
            output[:] = np.linspace(start, stop, lout, endpoint=False, dtype=output.dtype)

    @blosc2.dsl_kernel
    def ramp_arange(start, step):
        return start + _flat_idx * step  # noqa: F821  # DSL index/shape symbols resolved by miniexpr

    if step is None:  # not array-api compliant but for backwards compatibility
        step = 1
    if stop is None:
        stop = start
        start = 0
    NUM = _arange_num_elements(start, stop, step)
    if shape is None:
        shape = (builtins.max(NUM, 0),)
    else:
        # Check that the shape is consistent with the start, stop and step values
        if math.prod(shape) != NUM:
            raise ValueError("The shape is not consistent with the start, stop and step values")
    if dtype is None:
        dtype = (
            blosc2.DEFAULT_FLOAT
            if np.any([np.issubdtype(type(d), float) for d in (start, stop, step)])
            else blosc2.DEFAULT_INT
        )
    dtype = _check_dtype(dtype)

    if is_inside_new_expr() or NUM == 0:
        # We already have the dtype and shape, so return immediately
        return blosc2.zeros(shape, dtype=dtype, **kwargs)

    # Windows and wasm32 does not support complex numbers in DSL
    if blosc2.isdtype(dtype, "complex floating"):
        lshape = (math.prod(shape),)
        lazyarr = blosc2.lazyudf(arange_fill, (start, stop, step), dtype=dtype, shape=lshape)

        if len(shape) == 1:
            # C order is guaranteed, and no reshape is needed
            return lazyarr.compute(**kwargs)

        return reshape(lazyarr, shape, c_order=c_order, **kwargs)
    else:
        lazyarr = blosc2.lazyudf(ramp_arange, (start, step), dtype=dtype, shape=shape)
        return lazyarr.compute(**kwargs)


# Define a numpy linspace-like function
def linspace(
    start: int | float | complex,
    stop: int | float | complex,
    num: int | None = None,
    dtype=None,
    endpoint: bool = True,
    shape=None,
    c_order: bool = True,
    **kwargs: Any,
) -> NDArray:
    """Return evenly spaced numbers over a specified interval.

    This is similar to `numpy.linspace` but it returns a `NDArray`
    instead of a numpy array.  Also, it supports a `shape` parameter
    to return a ndim array.

    Parameters
    ----------
    start: int, float, complex
        The starting value of the sequence.
    stop: int, float, complex
        The end value of the sequence.
    num: int | None
        Number of samples to generate. Default None.
    dtype: np.dtype or list str
        The data type of the array elements in NumPy format. If None, inferred from
        start, stop, step. Default is None.
    endpoint: bool
        If True, `stop` is the last sample. Otherwise, it is not included.
    shape: int, tuple or list
        The shape of the final array. If None, the shape will be guessed from `num`.
    c_order: bool
        Whether to store the array in C order (row-major) or insertion order.
        Insertion order means that values will be stored in the array
        following the order of chunks in the array; this is more memory
        efficient, as it does not require an intermediate copy of the array.
        Default is True.
    **kwargs: Any
        Keyword arguments accepted by the :func:`empty` constructor.


    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.
    """

    def linspace_fill(inputs, output, offset):
        lout = len(output)
        start, stop, num, endpoint = inputs
        # if num = 1 do nothing
        step = (stop - start) / (num - 1) if endpoint and num > 1 else (stop - start) / num
        # Compute proper start and stop values for the current chunk
        # except for 0th iter, have already included start_ in prev iter
        start_ = start + offset[0] * step
        stop_ = start_ + lout * step
        if offset[0] + lout == num:  # reached end, include stop if necessary
            output[:] = np.linspace(start_, stop, lout, endpoint=endpoint, dtype=output.dtype)
        else:
            output[:] = np.linspace(start_, stop_, lout, endpoint=False, dtype=output.dtype)

    @blosc2.dsl_kernel
    def ramp_linspace(start, step):
        return float(start) + _flat_idx * float(step)  # noqa: F821  # DSL index/shape symbols resolved by miniexpr

    if shape is None:
        if num is None:
            raise ValueError("Either `shape` or `num` must be specified.")
        # num is not None
        shape = (num,)
    else:
        num = math.prod(shape) if num is None else num

    # check compatibility of shape and num
    if math.prod(shape) != num or num < 0:
        raise ValueError(
            f"Shape is not consistent with the specified num value {num}." + "num must be nonnegative."
            if num < 0
            else ""
        )

    if dtype is None:
        dtype = (
            blosc2.DEFAULT_COMPLEX
            if np.any([np.issubdtype(type(d), complex) for d in (start, stop)])
            else blosc2.DEFAULT_FLOAT
        )

    dtype = _check_dtype(dtype)

    if is_inside_new_expr() or num == 0:
        # We already have the dtype and shape, so return immediately
        return blosc2.zeros(shape, dtype=dtype, **kwargs)  # will return empty array for num == 0

    # Windows and wasm32 does not support complex numbers in DSL
    if blosc2.isdtype(dtype, "complex floating"):
        inputs = (start, stop, num, endpoint)
        lazyarr = blosc2.lazyudf(linspace_fill, inputs, dtype=dtype, shape=(num,))
        if len(shape) == 1:
            # C order is guaranteed, and no reshape is needed
            return lazyarr.compute(**kwargs)

        return reshape(lazyarr, shape, c_order=c_order, **kwargs)
    else:
        nitems = num - 1 if endpoint else num
        step = (float(stop) - float(start)) / float(nitems) if nitems > 0 else 0.0
        inputs = (start, step)
        lazyarr = blosc2.lazyudf(ramp_linspace, inputs, dtype=dtype, shape=shape)
        return lazyarr.compute(**kwargs)


def eye(N, M=None, k=0, dtype=np.float64, **kwargs: Any) -> NDArray:
    """Return a 2-D array with ones on the diagonal and zeros elsewhere.

    Parameters
    ----------
    N: int
        Number of rows in the output.
    M: int, optional
        Number of columns in the output. If None, defaults to `N`.
    k: int, optional
        Index of the diagonal: 0 (the default) refers to the main diagonal,
        a positive value refers to an upper diagonal, and a negative value
        to a lower diagonal.
    dtype: np.dtype or list str
        The data type of the array elements in NumPy format. Default is `np.float64`.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> array = blosc2.eye(2, 3, dtype=np.int32)
    >>> print(array[:])
    [[1 0 0]
     [0 1 0]]
    """

    def fill_eye(inputs, output: np.array, offset: tuple):
        out_k = offset[0] - offset[1] + inputs[0]
        output[:] = np.eye(*output.shape, out_k, dtype=output.dtype)

    if M is None:
        M = N
    shape = (N, M)
    dtype = _check_dtype(dtype)

    if is_inside_new_expr():
        # We already have the dtype and shape, so return immediately
        return blosc2.zeros(shape, dtype=dtype)

    lazyarr = blosc2.lazyudf(fill_eye, (k,), dtype=dtype, shape=shape)
    return lazyarr.compute(**kwargs)


def fromiter(iterable, shape, dtype, c_order=True, **kwargs) -> NDArray:
    """Create a new array from an iterable object.

    The iterable is consumed exactly once and in a defined order.  Values are
    never re-read, so plain generators and other one-pass sources are
    fully supported.

    Parameters
    ----------
    iterable: iterable
        An iterable object providing data for the array.  It must yield at
        least ``math.prod(shape)`` values; any surplus values are ignored.
        If the iterable is exhausted before the array is full, a
        ``ValueError`` is raised.

        As a special fast path, if *iterable* is a :class:`numpy.ndarray` the
        element-by-element Python iteration is skipped entirely: the array is
        cast to *dtype* and written directly to the destination.
    shape: int, tuple or list
        The shape of the final array.
    dtype: np.dtype or list str
        The data type of the array elements in NumPy format.
    c_order: bool
        Controls the order in which values are consumed from *iterable*.

        * ``True`` (default) – values are consumed in standard C / row-major
          order, matching ``numpy.fromiter(iterable, dtype).reshape(shape)``.
          A temporary in-memory buffer equal to the full array size is used.
        * ``False`` – values are consumed in *chunk-insertion order*: the
          first ``chunk_size`` values fill the first chunk, the next
          ``chunk_size`` values fill the second chunk, and so on.  A page
          buffer (default 1 M elements) amortises the Python iterator
          call overhead across chunks, so the number of :func:`numpy.fromiter`
          calls is O(total / page) rather than O(n_chunks).

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> # Create an array from an iterable
    >>> array = blosc2.fromiter(range(10), shape=(10,), dtype=np.int64)
    >>> print(array[:])
    [0 1 2 3 4 5 6 7 8 9]
    """
    # --- Optimisation C: numpy fast path ---
    # If the caller already holds the data in a numpy array we can bypass all
    # Python-level element iteration and write straight to the destination.
    dtype = _check_dtype(dtype)
    shape = tuple(shape) if not isinstance(shape, tuple) else shape

    if isinstance(iterable, np.ndarray):
        dst = empty(shape, dtype=dtype, **kwargs)
        if math.prod(shape) > 0:
            dst[:] = np.asarray(iterable, dtype=dtype).reshape(shape)
        return dst

    if is_inside_new_expr():
        # We already have the dtype and shape, so return immediately
        return blosc2.zeros(shape, dtype=dtype)

    # Ensure we hold a true one-shot iterator so that re-iterable objects
    # (e.g. range, list) are consumed sequentially across all chunks.
    iterable = iter(iterable)

    total_size = math.prod(shape)
    dst = empty(shape, dtype=dtype, **kwargs)

    if total_size == 0:
        return dst

    if c_order or len(shape) == 1:
        # --- Phase 3B: chunk-row buffering ---
        # Process one "chunk row" at a time (all chunks sharing the same
        # first-dimension chunk coordinate).  This bounds peak memory to
        # O(chunks[0] * prod(shape[1:])) instead of O(total_size), while
        # still using only one np.fromiter call per chunk row.
        #
        # For a (5 000, 5 000) array with chunks=(500, 500) this is
        # 10 × 20 MB reads instead of one 200 MB allocation.
        row_h = dst.chunks[0]  # elements in dim-0 per chunk row
        tail_nelems = math.prod(shape[1:])  # elements per row in the remaining dims

        for row_start in range(0, shape[0], row_h):
            row_end = builtins.min(row_start + row_h, shape[0])
            n = (row_end - row_start) * tail_nelems
            buf = np.fromiter(islice(iterable, n), dtype=dtype, count=n)
            dst[row_start:row_end] = buf.reshape((row_end - row_start,) + shape[1:])
    else:
        # --- Optimisation A: page-buffered chunk-insertion order ---
        # Instead of calling np.fromiter once per chunk (O(n_chunks) calls),
        # we pre-read a page of _PAGE_NELEMS elements at a time.  This reduces
        # call overhead from O(n_chunks) to O(total / _PAGE_NELEMS), which is
        # decisive when chunks are small (e.g. (10, 10) → 10 000 chunks vs
        # ~1 page for a 1 000 × 1 000 array).
        _PAGE_BYTES = 8 << 20  # 8 MB target page size
        _PAGE_NELEMS = builtins.max(1, _PAGE_BYTES // dtype.itemsize)

        page = np.empty(0, dtype=dtype)
        page_start = 0  # index of the first unread element in `page`

        for chunk_info in dst.iterchunks_info():
            dst_slice = tuple(
                slice(c * s, builtins.min((c + 1) * s, sh))
                for c, s, sh in zip(chunk_info.coords, dst.chunks, dst.shape, strict=False)
            )
            chunk_shape = tuple(s.stop - s.start for s in dst_slice)
            count = math.prod(chunk_shape)

            available = len(page) - page_start
            if available < count:
                # Compact the leftover tail, then read a new page.
                leftover = page[page_start:] if available > 0 else None
                to_read = builtins.max(_PAGE_NELEMS, count)
                new_data = np.fromiter(islice(iterable, to_read), dtype=dtype)
                if available + len(new_data) < count:
                    raise ValueError(
                        f"iterator exhausted: chunk at coords {chunk_info.coords} "
                        f"requires {count} elements but only {available + len(new_data)} remain"
                    )
                page = np.concatenate([leftover, new_data]) if leftover is not None else new_data
                page_start = 0

            dst[dst_slice] = page[page_start : page_start + count].reshape(chunk_shape)
            page_start += count

    return dst


def frombuffer(
    buffer: bytes, shape: int | tuple | list, dtype: np.dtype | str = np.uint8, **kwargs: Any
) -> NDArray:
    """Create an array out of a buffer.

    Parameters
    ----------
    buffer: bytes
        The buffer of the data to populate the container.
    shape: int, tuple or list
        The shape for the final container.
    dtype: np.dtype or list str
        The ndarray dtype in NumPy format. Default is `np.uint8`.
        This will override the `typesize`
        in the cparams if they are passed.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A :ref:`NDArray` is returned.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> shape = [25, 10]
    >>> chunks = (49, 49)
    >>> dtype = np.dtype("|S8")
    >>> typesize = dtype.itemsize
    >>> # Create a buffer
    >>> buffer = bytes(np.random.normal(0, 1, np.prod(shape)) * typesize)
    >>> # Create a NDArray from a buffer with default blocks
    >>> a = blosc2.frombuffer(buffer, shape, chunks=chunks, dtype=dtype)
    """
    shape = _check_shape(shape)
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    chunks, blocks = compute_chunks_blocks(shape, chunks, blocks, dtype, **kwargs)
    return blosc2_ext.from_buffer(buffer, shape, chunks, blocks, dtype, **kwargs)


def copy(array: NDArray, dtype: np.dtype | str = None, **kwargs: Any) -> NDArray:
    """
    This is equivalent to :meth:`NDArray.copy`

    Examples
    --------
    >>> import blosc2
    >>> original_array = blosc2.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]])
    >>> # Create a copy of the array without changing dtype
    >>> copied_array = blosc2.copy(original_array)
    >>> print("Copied array (default dtype):")
    >>> print(copied_array)
    Copied array (default dtype):
    [[1.1 2.2 3.3]
    [4.4 5.5 6.6]]
    """
    return array.copy(dtype, **kwargs)


def concat(arrays: list[NDArray], /, axis=0, **kwargs: Any) -> NDArray:
    """Concatenate a list of arrays along a specified axis.

    Parameters
    ----------
    arrays: list of :ref:`NDArray`
        A list containing two or more NDArray instances to be concatenated.
    axis: int, optional
        The axis along which the arrays will be concatenated. Default is 0.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A new NDArray containing the concatenated data.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> arr1 = blosc2.arange(0, 5, dtype=np.int32)
    >>> arr2 = blosc2.arange(5, 10, dtype=np.int32)
    >>> result = blosc2.concat([arr1, arr2])
    >>> print(result[:])
    [0 1 2 3 4 5 6 7 8 9]
    """
    if len(arrays) < 2:
        return arrays[0]
    arr1 = arrays[0]
    if not isinstance(arr1, blosc2.NDArray):
        raise TypeError("All inputs must be instances of blosc2.NDArray")
    # Do a first pass for checking array compatibility
    if axis < 0:
        axis += arr1.ndim
    if axis >= arr1.ndim:
        raise ValueError(f"Axis {axis} is out of bounds for array of dimension {arr1.ndim}.")
    for arr2 in arrays[1:]:
        if not isinstance(arr2, blosc2.NDArray):
            raise TypeError("All inputs must be instances of blosc2.NDArray")
        if arr1.ndim != arr2.ndim:
            raise ValueError("Both arrays must have the same number of dimensions for concatenation.")
        if arr1.dtype != arr2.dtype:
            raise ValueError("Both arrays must have the same dtype for concatenation.")
        # Check that the shapes match, except for the concatenation axis
        if arr1.shape[:axis] != arr2.shape[:axis] or arr1.shape[axis + 1 :] != arr2.shape[axis + 1 :]:
            raise ValueError(
                f"Shapes of the arrays do not match along the concatenation axis {axis}: "
                f"{arr1.shape} vs {arr2.shape}"
            )

    kwargs = _check_ndarray_kwargs(**kwargs)
    # Proceed with the actual concatenation
    copy = True
    # When provided urlpath coincides with an array
    mode = kwargs.pop("mode", "a")  # default mode for blosc2 is "a"
    for arr2 in arrays[1:]:
        arr1 = blosc2_ext.concat(arr1, arr2, axis, copy=copy, mode=mode, **kwargs)
        # Have now overwritten existing file (if mode ='w'), need to change mode
        # for concatenating to the same file
        mode = "r" if mode == "r" else "a"
        # arr1 is now the result of the concatenation, so we can now just enlarge it
        copy = False

    return arr1


def expand_dims(array: NDArray, axis=0) -> NDArray:
    """
    Expand the shape of an array by adding new axes at the specified positions.

    Parameters
    ----------
    array: :ref:`NDArray`
        The array to be expanded.
    axis: int or list of int, optional
        Position in the expanded axes where the new axis (or axes) is placed. Default is 0.

    Returns
    -------
    out: :ref:`NDArray`
        A new NDArray with the expanded shape.
    """
    array = blosc2.asarray(array)
    if not isinstance(array, blosc2.NDArray):
        raise TypeError("Argument array must be instance of blosc2.NDArray")
    axis = [axis] if isinstance(axis, int) else axis
    final_dims = array.ndim + len(axis)
    mask = [False for i in range(final_dims)]
    for a in axis:
        if a < 0:
            a += final_dims  # Adjust axis to be within the new stacked array's dimensions
        if mask[a]:
            raise ValueError("Axis values must be unique.")
        mask[a] = True
    return blosc2_ext.expand_dims(array, axis_mask=mask, final_dims=final_dims)


def stack(arrays: list[NDArray], axis=0, **kwargs: Any) -> NDArray:
    """Stack multiple arrays, creating a new axis.

    Parameters
    ----------
    arrays: list of :ref:`NDArray`
        A list containing two or more NDArray instances to be stacked.
    axis: int, optional
        The new axis along which the arrays will be stacked. Default is 0.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A new NDArray containing the stacked data.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> arr1 = blosc2.arange(0, 6, dtype=np.int32, shape=(2,3))
    >>> arr2 = blosc2.arange(6, 12, dtype=np.int32, shape=(2,3))
    >>> result = blosc2.stack([arr1, arr2])
    >>> print(result.shape)
    (2, 2, 3)
    """
    if axis < 0:
        axis += arrays[0].ndim + 1  # Adjust axis to be within the new stacked array's dimensions
    newarrays = []
    for arr in arrays:
        newarrays += [blosc2.expand_dims(arr, axis=axis)]
    return blosc2.concat(newarrays, axis, **kwargs)


def save(array: NDArray, urlpath: str, contiguous=True, **kwargs: Any) -> None:
    """Save an array to a file.

    Parameters
    ----------
    array: :ref:`NDArray`
        The array to be saved.
    urlpath: str
        The path to the file where the array will be saved.
    contiguous: bool, optional
        Whether to store the array contiguously.

    Other Parameters
    ----------------
    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`save` method.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> # Create an array
    >>> array = blosc2.arange(0, 100, dtype=np.int64, shape=(10, 10))
    >>> # Save the array to a file
    >>> blosc2.save(array, "array.b2", mode="w")
    """
    array.save(urlpath, contiguous, **kwargs)


def _ndarray_asarray_requires_copy(
    array: NDArray, dtype: np.dtype, chunks, blocks, user_kwargs: dict[str, Any]
) -> bool:
    if np.dtype(dtype) != np.dtype(array.dtype):
        return True
    if "chunks" in user_kwargs and tuple(chunks) != tuple(array.chunks):
        return True
    if "blocks" in user_kwargs and tuple(blocks) != tuple(array.blocks):
        return True

    copy_keys = {
        "cparams",
        "dparams",
        "meta",
        "urlpath",
        "contiguous",
        "mode",
        "mmap_mode",
        "initial_mapping_size",
        "locking",
        "storage",
        "out",
        "_chunksize_reduc_factor",
    }
    return builtins.any(key in user_kwargs for key in copy_keys)


def asarray(array: Sequence | blosc2.Array, copy: bool | None = None, **kwargs: Any) -> NDArray:
    """Convert the `array` to an `NDArray`.

    Parameters
    ----------
    array: array_like
        An array supporting numpy array interface.

    copy: bool | None, optional
        Whether to copy the input. If True, the function copies.
        If False, raise a ValueError if copy is necessary. If None and
        input is NDArray, return the original array when no dtype,
        partition, or storage-related changes are requested.
        Default: None.

    kwargs: dict, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        A new :ref:`NDArray` made of :paramref:`array`, or the original
        array when a copy is not required.

    Notes
    -----
    This will create the NDArray chunk-by-chunk directly from the input array,
    without the need to create a contiguous NumPy array internally.  This can
    be used for ingesting e.g. disk or network based arrays very effectively
    and without consuming lots of memory.

    Examples
    --------
    >>> import blosc2
    >>> import numpy as np
    >>> nda = blosc2.arange(250, dtype=np.int64, shape=(25, 10))
    >>> # NDArray inputs are returned as-is unless a copy is requested
    >>> blosc2.asarray(nda) is nda
    True
    """
    user_kwargs = kwargs.copy()
    # Convert scalars to numpy array
    casting = kwargs.pop("casting", "unsafe")
    if casting != "unsafe":
        raise ValueError("Only unsafe casting is supported at the moment.")
    if not hasattr(array, "shape"):
        array = np.asarray(array)  # defaults if dtype=None
    dtype_ = blosc2.proxy.convert_dtype(array.dtype)
    dtype = blosc2.proxy.convert_dtype(kwargs.pop("dtype", dtype_))  # check if dtype provided
    kwargs = _check_ndarray_kwargs(**kwargs)
    chunks = kwargs.pop("chunks", None)
    blocks = kwargs.pop("blocks", None)
    # Use the chunks and blocks from the array if they are not passed
    if chunks is None and hasattr(array, "chunks"):
        chunks = array.chunks
    # Zarr adds a .blocks property that maps to a zarr.indexing.BlockIndex object
    # Let's avoid this
    if blocks is None and hasattr(array, "blocks") and isinstance(array.blocks, tuple | list):
        blocks = array.blocks

    requires_copy = isinstance(array, NDArray) and _ndarray_asarray_requires_copy(
        array, dtype, chunks, blocks, user_kwargs
    )
    if copy is None:
        copy = not isinstance(array, NDArray) or requires_copy
    elif copy is False and requires_copy:
        raise ValueError(
            "Cannot satisfy dtype, partition, or storage changes with copy=False for NDArray input."
        )
    if copy:
        chunks, blocks = compute_chunks_blocks(array.shape, chunks, blocks, dtype, **kwargs)
        # Fast path for small arrays. This is not too expensive in terms of memory consumption.
        shape = array.shape
        small_size = 2**24  # 16 MB
        array_nbytes = math.prod(shape) * dtype_.itemsize
        if array_nbytes < small_size:
            if not isinstance(array, np.ndarray) and hasattr(array, "chunks"):
                # A getitem operation should be enough to get a numpy array
                array = array[()]

            array = np.require(array, dtype=dtype, requirements="C")  # require contiguous array

            return blosc2_ext.asarray(array, chunks, blocks, **kwargs)

        # Create the empty array
        ndarr = empty(shape, dtype, chunks=chunks, blocks=blocks, **kwargs)
        behaved = are_partitions_behaved(shape, chunks, blocks)

        # Get the coordinates of the chunks
        chunks_idx, nchunks = get_chunks_idx(shape, chunks)

        # Iterate over the chunks and update the empty array
        for nchunk in range(nchunks):
            # Compute current slice coordinates
            coords = tuple(np.unravel_index(nchunk, chunks_idx))
            slice_ = tuple(
                slice(c * s, builtins.min((c + 1) * s, shape[i]))
                for i, (c, s) in enumerate(zip(coords, chunks, strict=True))
            )
            # Ensure the array slice is contiguous and of correct dtype
            array_slice = np.require(array[slice_], dtype=dtype, requirements="C")
            if behaved:
                # The whole chunk is to be updated, so this fastpath is safe
                ndarr.schunk.update_data(nchunk, array_slice, copy=False)
            else:
                ndarr[slice_] = array_slice
    else:
        if not isinstance(array, NDArray):
            raise ValueError("Must always do a copy for asarray unless NDArray provided.")
        # TODO: make a direct view possible
        return array

    return ndarr


def array(array: Sequence | blosc2.Array, copy: bool | None = True, **kwargs: Any) -> NDArray:
    """Create an :class:`NDArray` from an array-like object.

    This is the NumPy-like constructor counterpart to :func:`asarray`.  It uses
    the same keyword arguments as :func:`asarray`, but defaults to ``copy=True``
    so an :class:`NDArray` input is copied unless explicitly requested
    otherwise.

    Parameters
    ----------
    array: array_like
        Input data.
    copy: bool | None, optional
        Whether to copy the input.  Defaults to ``True``.  If ``False``, raise a
        :class:`ValueError` when a copy is required.  If ``None``, use
        :func:`asarray` semantics.
    kwargs: dict, optional
        Keyword arguments supported by :func:`asarray`.

    Returns
    -------
    out: :ref:`NDArray`
        A new :ref:`NDArray`, unless ``copy`` is ``False`` or ``None`` and the
        input can be returned without copying.

    Examples
    --------
    >>> import blosc2
    >>> a = blosc2.array([1, 2, 3])
    >>> a[:]
    array([1, 2, 3])
    >>> blosc2.array(a) is a
    False
    >>> blosc2.array(a, copy=False) is a
    True
    """
    return asarray(array, copy=copy, **kwargs)


def astype(
    array: Sequence | blosc2.Array,
    dtype,
    casting: str = "unsafe",
    copy: bool = True,
    **kwargs: Any,
) -> NDArray:
    """
    Copy of the array, cast to a specified type. Does not support copy = False.

    Parameters
    ----------
    array: Sequence | blosc2.Array
        The array to be cast to a different type.
    dtype: DType-like
        The desired data type to cast to.
    casting: str = 'unsafe'
        Controls what kind of data casting may occur. Defaults to 'unsafe' for backwards compatibility.
        * 'no' means the data types should not be cast at all.
        * 'equiv' means only byte-order changes are allowed.
        * 'safe' means only casts which can preserve values are allowed.
        * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed.
        * 'unsafe' means any data conversions may be done.
    copy: bool = True
        Must always be True as copy is made by default. Will be changed in a future version

    Returns
    -------
    out: NDArray
        New array with specified data type.
    """
    return asarray(array, dtype=dtype, casting=casting, copy=copy, **kwargs)


def _check_ndarray_kwargs(**kwargs):  # noqa: C901
    storage = kwargs.get("storage")
    if storage is not None:
        for key in kwargs:
            if key in list(blosc2.Storage.__annotations__):
                raise AttributeError(
                    "Cannot pass both `storage` and other kwargs already included in Storage"
                )
        if isinstance(storage, blosc2.Storage):
            kwargs = {**kwargs, **asdict(storage)}
        else:
            kwargs = {**kwargs, **storage}
    else:
        # Add the default storage values as long as they are not already passed
        storage_dflts = asdict(blosc2.Storage(urlpath=kwargs.get("urlpath")))  # urlpath can affect defaults
        # If a key appears in both operands, the one from the right-hand operand wins
        kwargs = storage_dflts | kwargs

    supported_keys = [
        "chunks",
        "blocks",
        "cparams",
        "dparams",
        "meta",
        "urlpath",
        "contiguous",
        "mode",
        "mmap_mode",
        "initial_mapping_size",
        "locking",
        "storage",
        "out",
        "_chunksize_reduc_factor",
    ]
    _ = kwargs.pop("device", None)  # pop device (not used, but needs to be discarded)
    for key in kwargs:
        if key not in supported_keys:
            raise KeyError(
                f"Only {supported_keys} are supported as keyword arguments, and you passed '{key}'"
            )

    if "cparams" in kwargs:
        cparams = kwargs["cparams"]
        if cparams is None:
            kwargs["cparams"] = blosc2.CParams()
        elif isinstance(cparams, blosc2.CParams):
            kwargs["cparams"] = asdict(kwargs["cparams"])
        else:
            if "chunks" in kwargs["cparams"]:
                raise ValueError("You cannot pass chunks in cparams, use `chunks` argument instead")
            if "blocks" in kwargs["cparams"]:
                raise ValueError("You cannot pass chunks in cparams, use `blocks` argument instead")
            kwargs["cparams"] = cparams.copy()
    if "dparams" in kwargs and isinstance(kwargs["dparams"], blosc2.DParams):
        kwargs["dparams"] = asdict(kwargs["dparams"])
    if blosc2.IS_WASM:
        cparams = kwargs.get("cparams")
        if isinstance(cparams, dict) and cparams.get("nthreads", 1) != 1:
            cparams = cparams.copy()
            cparams["nthreads"] = 1
            kwargs["cparams"] = cparams
        dparams = kwargs.get("dparams")
        if isinstance(dparams, dict) and dparams.get("nthreads", 1) != 1:
            dparams = dparams.copy()
            dparams["nthreads"] = 1
            kwargs["dparams"] = dparams

    return kwargs


def get_slice_nchunks(
    schunk: blosc2.SChunk, key: tuple[(int, int)] | int | slice | Sequence[slice]
) -> np.ndarray:
    """
    Get the unidimensional chunk indexes needed to obtain a
    slice of a :ref:`SChunk <SChunk>` or a :ref:`NDArray`.

    Parameters
    ----------
    schunk: :ref:`SChunk <SChunk>` or :ref:`NDArray`
        The super-chunk or ndarray container.
    key: tuple(int, int), int, slice or sequence of slices
        For a SChunk: a tuple with the start and stop of the slice, an integer,
        or a single slice. For a ndarray, sequences of slices (one per dimension) are accepted.

    Returns
    -------
    out: np.ndarray
        An array with the unidimensional chunk indexes.
    """
    if isinstance(schunk, NDArray):
        array = schunk
        key, _ = process_key(key, array.shape)
        start, stop, step, _ = get_ndarray_start_stop(array.ndim, key, array.shape)
        if step != (1,) * array.ndim:
            raise IndexError("Step parameter is not supported yet")
        key = (start, stop)
        return blosc2_ext.array_get_slice_nchunks(array, key)
    else:
        if isinstance(key, int):
            key = (key, key + 1)
        elif isinstance(key, slice):
            if key.step not in (1, None):
                raise IndexError("Only step=1 is supported")
            key = (key.start, key.stop)
        return blosc2_ext.schunk_get_slice_nchunks(schunk, key)


def argsort(array: blosc2.Array, order: str | list[str] | None = None, **kwargs: Any) -> NDArray:
    """
    Return the indices that would sort the array.

    This mirrors :func:`numpy.argsort` for 1-D arrays. Plain arrays sort by
    their values. Structured arrays sort by ``order`` when provided, or by
    their dtype field order when ``order=None``. Expression orders such as
    ``"abs(x)"`` are also supported when a matching ``full`` expression index
    exists.

    Parameters
    ----------
    array: :class:`blosc2.Array`
        The 1-D array to be ordered.
    order: str, list of str, optional
        Primary and optional secondary order keys for structured arrays. When
        omitted, NumPy's default record order is used for structured dtypes and
        the array values themselves are used for plain dtypes.
    kwargs: Any, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        The ordered logical positions as ``int64``.

    Notes
    -----
    When the primary order key has a matching ``full`` field or expression
    index, the permutation is returned directly from that index in ascending
    stable order. Secondary keys refine ties after the primary indexed order.
    Without a matching ``full`` index, :func:`argsort` falls back to
    materializing the input values and delegating ordering to
    :func:`numpy.argsort`.

    The result is always a new array materialization. For persistent inputs,
    the returned permutation is in memory by default; pass storage kwargs such
    as ``urlpath`` (and typically ``mode="w"``) if the permutation should also
    be persisted on disk.
    """
    if isinstance(array, blosc2.NDArray):
        from . import indexing

        ordered = indexing.ordered_indices(array, order=order)
        if ordered is not None:
            return blosc2.asarray(ordered, **kwargs)
        if indexing.is_expression_order(array, order):
            raise ValueError("expression order requires a matching full expression index")

    values = array[:]
    positions = np.argsort(values, order=order, kind="stable")
    return blosc2.asarray(positions.astype(np.int64, copy=False), **kwargs)


def sort(array: blosc2.Array, order: str | list[str] | None = None, **kwargs: Any) -> NDArray:
    """
    Return a sorted array following the specified order.

    This is only valid for 1-dim structured arrays.

    Parameters
    ----------
    array: :class:`blosc2.Array`
        The (structured) array to be sorted.
    order: str, list of str, optional
        Specifies which fields to compare first, second, etc. A single
        field can be specified as a string. The primary order key may also be
        an indexed expression such as ``"abs(x)"`` when a matching ``full``
        expression index exists. Not all fields need to be specified, only the
        ones by which the array is to be sorted.
    kwargs: Any, optional
        Keyword arguments that are supported by the :func:`empty` constructor.

    Returns
    -------
    out: :ref:`NDArray`
        The sorted array.

    Notes
    -----
    If the primary order key has a matching ``full`` field or expression index,
    rows are gathered directly in ascending stable index order. Secondary keys
    refine ties after the primary indexed order. Field-based orders without a
    matching full index fall back to a scan-plus-sort path.

    Sorting never mutates the input array in place. The result is always a new
    array materialization. For persistent inputs, the sorted rows are returned
    as a new in-memory :ref:`NDArray` by default; pass storage kwargs such as
    ``urlpath`` (and typically ``mode="w"``) if the sorted output should also
    be persisted on disk.
    """
    if not order:
        return array

    if isinstance(array, blosc2.NDArray):
        from . import indexing

        ordered = indexing.read_sorted(array, order=order)
        if ordered is not None:
            return blosc2.asarray(ordered, **kwargs)
        if indexing.is_expression_order(array, order):
            raise ValueError("expression order requires a matching full expression index")

    # Create a lazy array to access the sort machinery there
    # This is a bit of a hack, but it is the simplest way to do it
    # (the sorting mechanism in LazyExpr should be improved to avoid this)
    lbool = blosc2.lazyexpr(blosc2.ones(array.shape, dtype=np.bool_))
    larr = array[lbool]
    return larr.sort(order).compute(**kwargs)


def iter_sorted(
    array: blosc2.Array,
    order: str | list[str] | None = None,
    *,
    start: int | None = None,
    stop: int | None = None,
    step: int | None = None,
    batch_size: int | None = None,
) -> Iterator[np.generic | np.void]:
    """
    Iterate array values following a matching sorted index order.

    Parameters
    ----------
    array : :class:`blosc2.Array`
        The array to iterate.
    order : str, list of str, optional
        Specifies which fields define the ordered traversal. The first field
        must have an associated ``full`` index.
    start, stop, step : int or None, optional
        Optional slice applied to the ordered sequence before iteration.
    batch_size : int or None, optional
        Internal prefetch size used during iteration.

    Notes
    -----
    This requires a matching ``full`` index on the primary order key. The
    iteration order is ascending and stable. Secondary keys refine ties after
    the primary indexed order.
    """
    if not isinstance(array, blosc2.NDArray):
        raise TypeError("iter_sorted() is only supported on NDArray")

    from . import indexing

    return indexing.iter_sorted(array, order=order, start=start, stop=stop, step=step, batch_size=batch_size)


# Class for dealing with fields in an NDArray
# This will allow to access fields by name in the dtype of the NDArray
class NDField(Operand):
    """View of one field from an :class:`NDArray` with a structured dtype."""

    def __init__(self, ndarr: NDArray, field: str):
        """
        Create a new NDField.

        Parameters
        ----------
        ndarr: :ref:`NDArray`
            The NDArray to which assign the field.
        field: str
            The field's name.

        Returns
        -------
        out: :ref:`NDField`
            The corresponding :ref:`NDField`.
        """
        if not isinstance(ndarr, NDArray):
            raise TypeError("ndarr should be a NDArray!")
        if not isinstance(field, str):
            raise TypeError("field should be a string!")
        if ndarr.dtype.fields is None:
            raise TypeError("NDArray does not have a structured dtype!")
        if field not in ndarr.dtype.fields:
            raise TypeError(f"Field {field} not found in the dtype of the NDArray")
        # Store immutable properties
        self.ndarr = ndarr
        self.chunks = ndarr.chunks
        self.blocks = ndarr.blocks
        self.field = field
        self._dtype = ndarr.dtype.fields[field][0]
        self.offset = ndarr.dtype.fields[field][1]

    def __repr__(self):
        """
        Get a string as a representation.

        Returns
        -------
        out: str
        """
        return f"NDField({self.ndarr}, {self.field})"

    @property
    def shape(self) -> tuple[int]:
        """The shape of the associated :ref:`NDArray`."""
        return self.ndarr.shape

    @property
    def dtype(self) -> np.dtype:
        """The dtype of the field of associated :ref:`NDArray`."""
        return self._dtype

    @property
    def schunk(self) -> blosc2.SChunk:
        """The associated :ref:`SChunk <SChunk>`."""
        return self.ndarr.schunk

    def __getitem__(self, key: int | slice | Sequence[slice]) -> np.ndarray:
        """
        Get a slice of :paramref:`self`.

        Parameters
        ----------
        key: int or slice or Sequence[slice]
            The slice to be retrieved.

        Returns
        -------
        out: NumPy.ndarray
            A NumPy array with the data slice.

        """
        # If key is a LazyExpr, decorate it with ``where`` and return it
        if isinstance(key, blosc2.LazyExpr):
            return key.where(self)

        if isinstance(key, str):
            # Try to compute the key as a boolean expression
            # Operands will be a dict with all the fields in the NDArray
            operands = {field: NDField(self.ndarr, field) for field in self.ndarr.dtype.names}
            expr = blosc2.lazyexpr(key, operands)
            if expr.dtype != np.bool_:
                raise TypeError("The expression should return a boolean array")
            return expr.where(self)
            # raise TypeError("This array is a NDField; use a structured NDArray for bool expressions")

        # Check if the key is in the last read cache
        inmutable_key = make_key_hashable(key)
        if inmutable_key in self.ndarr._last_read:
            return self.ndarr._last_read[inmutable_key][self.field]

        # Do the actual read in the parent NDArray
        nparr = self.ndarr[key]
        # And return the field
        return nparr[self.field]

    def __setitem__(self, key: int | slice | Sequence[slice], value: blosc2.Array) -> None:
        """
        Set a slice of :paramref:`self` to a value.

        Parameters
        ----------
        key: int or slice or Sequence[slice]
            The slice to be set.
        value: blosc2.Array
            The value to be set.
        """
        if isinstance(key, str):
            raise TypeError("This array is a NDField; use a structured NDArray for bool expressions")
        if not isinstance(value, np.ndarray):
            value = value[:]
        # Get the values in the parent NDArray
        nparr = self.ndarr[key]
        # Set the field
        nparr[self.field] = value
        # Save the values in the parent NDArray
        self.ndarr[key] = nparr

    def __iter__(self):
        """
        Iterate over the elements in the field.

        Returns
        -------
        out: iterator
        """
        return NDOuterIterator(self)

    def __len__(self) -> int:
        """
        Returns the length of the first dimension of the field.
        """
        return self.shape[0]


class OIndex:
    def __init__(self, array: NDArray):
        self.array = array

    def __getitem__(self, selection) -> np.ndarray:
        return self.array.get_oselection_numpy(selection)

    def __setitem__(self, selection, input) -> np.ndarray:
        return self.array.set_oselection_numpy(selection, input)


# class VIndex:
#     def __init__(self, array: NDArray):
#         self.array = array

#     # TODO: all this
#     def __getitem__(self, selection) -> np.ndarray:
#         return NotImplementedError

#     def __setitem__(self, selection, input) -> np.ndarray:
#         return NotImplementedError


def empty_like(x: blosc2.Array, dtype=None, **kwargs) -> NDArray:
    """
    Returns an uninitialized array with the same shape as an input array x.

    Parameters
    ----------
    x : blosc2.Array
        Input array from which to derive the output array shape.

    dtype (Optional):
        Output array data type. If dtype is None, the output array data type
        is inferred from x. Default: None.

    kwargs: Any, optional
        Keyword arguments that are supported by the :func:`empty` constructor.
        These arguments will be set in the resulting :ref:`NDArray`.

    Returns
    -------
    out : NDArray
        An array having the same shape as x and containing uninitialized data.
    """
    if dtype is None:
        dtype = x.dtype
    return blosc2.empty(shape=x.shape, dtype=dtype, **kwargs)


def ones_like(x: blosc2.Array, dtype=None, **kwargs) -> NDArray:
    """
    Returns an array of ones with the same shape as an input array x.

    Parameters
    ----------
    x : blosc2.Array
        Input array from which to derive the output array shape.

    dtype (Optional):
        Output array data type. If dtype is None, the output array data type
        is inferred from x. Default: None.

    kwargs: Any, optional
        Keyword arguments that are supported by the :func:`empty` constructor.
        These arguments will be set in the resulting :ref:`NDArray`.

    Returns
    -------
    out : NDArray
        An array having the same shape as x and containing ones.
    """
    if dtype is None:
        dtype = x.dtype
    return blosc2.ones(shape=x.shape, dtype=dtype, **kwargs)


def zeros_like(x: blosc2.Array, dtype=None, **kwargs) -> NDArray:
    """
    Returns an array of zeros with the same shape as an input array x.

    Parameters
    ----------
    x : blosc2.Array
        Input array from which to derive the output array shape.

    dtype (Optional):
        Output array data type. If dtype is None, the output array data type
        is inferred from x. Default: None.

    kwargs: Any, optional
        Keyword arguments that are supported by the :func:`empty` constructor.
        These arguments will be set in the resulting :ref:`NDArray`.

    Returns
    -------
    out : NDArray
        An array having the same shape as x and containing zeros.
    """
    if dtype is None:
        dtype = x.dtype
    return blosc2.zeros(shape=x.shape, dtype=dtype, **kwargs)


def full_like(x: blosc2.Array, fill_value: bool | int | float | complex, dtype=None, **kwargs) -> NDArray:
    """
    Returns an array filled with a value with the same shape as an input array x.

    Parameters
    ----------
    x : blosc2.Array
        Input array from which to derive the output array shape.

    fill_value: bool | int | float | complex
        The fill value.

    dtype (Optional):
        Output array data type. If dtype is None, the output array data type
        is inferred from x. Default: None.

    kwargs: Any, optional
        Keyword arguments that are supported by the :func:`empty` constructor.
        These arguments will be set in the resulting :ref:`NDArray`.

    Returns
    -------
    out : NDArray
        An array having the same shape as x and containing the fill value.
    """
    if dtype is None:
        dtype = x.dtype
    return blosc2.full(shape=x.shape, fill_value=fill_value, dtype=dtype, **kwargs)


def take(x: blosc2.Array, indices: blosc2.Array, axis: int | None = None):
    """Return elements selected by integer indices.

    For array inputs, this follows the Array API ``take`` shape rules: when
    ``axis`` is ``None``, *x* is conceptually flattened and the output shape is
    ``indices.shape``; otherwise the indexed axis is replaced by
    ``indices.shape``.  For :class:`CTable` and :class:`Column` inputs, indices
    select logical rows/values and ``axis`` is not supported.

    Parameters
    ----------
    x: blosc2.Array, CTable, Column, or array-like
        Input object.  ``NDArray`` inputs return an ``NDArray``;
        ``CTable`` inputs return a ``CTable``; ``Column`` inputs return a
        ``Column``.  Other array-like inputs are converted to a Blosc2
        ``NDArray`` result.

    indices: array-like
        Integer indices.  Negative indices are normalized relative to the
        selected axis (or to the flattened array when ``axis`` is ``None``).
        For array inputs, indices may have any shape.

    axis: int | None
        Axis over which to select values for array inputs.  If ``None``, the
        input array is flattened before selection.  Must be ``None`` for
        ``CTable`` and ``Column`` inputs.

    Returns
    -------
    out: NDArray | CTable | Column
        Selected values, preserving the container type for ``NDArray``,
        ``CTable`` and ``Column`` inputs.
    """
    if isinstance(x, NDArray):
        return x.take(indices, axis=axis)
    if isinstance(x, (blosc2.CTable, blosc2.Column)):
        if axis is not None:
            raise ValueError("axis is not supported for CTable or Column")
        return x.take(indices)
    return blosc2.asarray(np.take(np.asarray(x), np.asarray(indices), axis=axis))


def take_along_axis(x: blosc2.Array, indices: blosc2.Array, axis: int = -1) -> NDArray:
    """
    Returns elements of an array along an axis.

    Parameters
    ----------
    x: blosc2.Array
        Input array. Should have one or more dimensions (axes).

    indices: array-like
        Array indices. The array must have same number of dimensions as x and
        have an integer data type.

    axis: int
        Axis over which to select values. Default: -1.

    Returns
    -------
    out: NDArray
        Selected indices of x.
    """
    if not isinstance(axis, int | np.integer):
        raise ValueError("Axis must be integer.")
    if indices.ndim != x.ndim:
        raise ValueError("Indices must have same dimensions as x.")
    if axis < 0:
        axis += x.ndim
    if indices.shape[axis] == 0:
        return blosc2.empty(x.shape[:axis] + (0,) + x.shape[axis + 1 :], dtype=x.dtype)
    ones = (1,) * x.ndim
    # TODO: Implement fancy indexing in .slice so that this is more efficient and possibly use oindex(?)
    key = tuple(
        indices if i == axis else np.arange(x.shape[i]).reshape(ones[:i] + (-1,) + ones[i + 1 :])
        for i in range(x.ndim)
    )
    return blosc2.asarray(x[key])


def broadcast_to(arr: blosc2.Array, shape: tuple[int, ...]) -> NDArray:
    """
    Broadcast an array to a new shape.
    Warning: Computes a lazyexpr, so probably a bit suboptimal

    Parameters
    ----------
    arr: blosc2.Array
        The array to broadcast.

    shape: tuple
        The shape of the desired array.

    Returns
    -------
    broadcast: NDArray
    A new array with the given shape.
    """
    return (arr + blosc2.zeros(shape, dtype=arr.dtype)).compute()


def meshgrid(*arrays: blosc2.Array, indexing: str = "xy") -> Sequence[NDArray]:
    """
    Returns coordinate matrices from coordinate vectors.

    Parameters
    ----------
    *arrays: blosc2.Array
        An arbitrary number of one-dimensional arrays representing grid coordinates. Each array should have the same numeric data type.

    indexing: str
        Cartesian 'xy' or matrix 'ij' indexing of output. If provided zero or one one-dimensional vector(s) the indexing keyword is ignored.
        Default: 'xy'.

    Returns
    -------
    out: (List[NDArray])
        List of N arrays, where N is the number of provided one-dimensional input arrays, with same dtype.
        For N one-dimensional arrays having lengths Ni = len(xi),

        * if matrix indexing ij, then each returned array has shape (N1, N2, N3, ..., Nn).
        * if Cartesian indexing xy, then each returned array has shape (N2, N1, N3, ..., Nn).
    """
    out = ()
    shape = np.ones(len(arrays))
    first_arr = arrays[0]
    myarrs = ()
    if indexing == "xy" and len(shape) > 1:
        # switch 0th and 1st shapes around
        def mygen(i):
            if i not in (0, 1):
                return (j for j in range(len(arrays)) if j != i)
            else:
                return (j for j in range(len(arrays)) if j != builtins.abs(i - 1))
    else:
        mygen = lambda i: (j for j in range(len(arrays)) if j != i)  # noqa : E731

    for i, a in enumerate(arrays):
        if len(a.shape) != 1 or a.dtype != first_arr.dtype:
            raise ValueError("All arrays must be 1D and of same dtype.")
        shape[i] = a.shape[0]
        myarrs += (blosc2.expand_dims(a, tuple(mygen(i))),)  # cheap, creates a view

    # handle Cartesian indexing
    shape = tuple(shape)
    if indexing == "xy" and len(shape) > 1:
        shape = (shape[1], shape[0]) + shape[2:]

    # do broadcast
    for a in myarrs:
        out += (broadcast_to(a, shape),)
    return out


# ---------------------------------------------------------------------------
# Sparse boolean-mask helper (used by NDArray.__getitem__)
# ---------------------------------------------------------------------------


class _BoolMaskDense(Exception):
    """Raised when a boolean mask is too dense for the sparse-gather fast path."""


def _bool_mask_to_flat_indices(bool_arr, nchunks_data):
    """Convert a sparse boolean mask to flat indices, or raise _BoolMaskDense.

    For numpy masks, uses ``np.count_nonzero`` / ``np.flatnonzero``.
    For blosc2 NDArray masks, iterates chunks incrementally and bails out
    early when the mask is too dense.
    """
    # Threshold: if True values exceed the number of data chunks times a
    # generous factor, the LazyExpr full-scan path is likely faster.
    threshold = builtins.max(nchunks_data * 500, 50_000)

    if isinstance(bool_arr, np.ndarray):
        n_true = np.count_nonzero(bool_arr)
        if n_true >= threshold:
            raise _BoolMaskDense
        return np.flatnonzero(bool_arr)

    # blosc2 NDArray: iterate chunks incrementally
    total_true = 0
    flat_parts = []
    offset = 0
    for nchunk in range(bool_arr.schunk.nchunks):
        raw = bool_arr.schunk.decompress_chunk(nchunk)
        chunk = np.frombuffer(raw, dtype=np.bool_)
        n_true = np.count_nonzero(chunk)
        total_true += n_true
        if total_true >= threshold:
            raise _BoolMaskDense
        if n_true > 0:
            flat_parts.append(np.flatnonzero(chunk) + offset)
        offset += len(chunk)
    if not flat_parts:
        return np.array([], dtype=np.int64)
    return np.concatenate(flat_parts)
