Merge branch 'main' into matrix_rank-doc-fix

1 files changed, 2 insertions, 273 deletions

diff --git a/numpy/core/_asarray.py b/numpy/core/_asarray.py
index a406308f3..ecb4e7c39 100644
--- a/numpy/core/_asarray.py
+++ b/numpy/core/_asarray.py
@@ -8,283 +8,12 @@ from .overrides import (
     set_array_function_like_doc,
     set_module,
 )
-from .multiarray import array
+from .multiarray import array, asanyarray
 
 
-__all__ = [
-    "asarray", "asanyarray", "ascontiguousarray", "asfortranarray", "require",
-]
+__all__ = ["require"]
 
 
-def _asarray_dispatcher(a, dtype=None, order=None, *, like=None):
-    return (like,)
-
-
-@set_array_function_like_doc
-@set_module('numpy')
-def asarray(a, dtype=None, order=None, *, like=None):
-    """Convert the input to an array.
-
-    Parameters
-    ----------
-    a : array_like
-        Input data, in any form that can be converted to an array.  This
-        includes lists, lists of tuples, tuples, tuples of tuples, tuples
-        of lists and ndarrays.
-    dtype : data-type, optional
-        By default, the data-type is inferred from the input data.
-    order : {'C', 'F', 'A', 'K'}, optional
-        Memory layout.  'A' and 'K' depend on the order of input array a.
-        'C' row-major (C-style), 
-        'F' column-major (Fortran-style) memory representation.
-        'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
-        'K' (keep) preserve input order
-        Defaults to 'C'.
-    ${ARRAY_FUNCTION_LIKE}
-
-        .. versionadded:: 1.20.0
-
-    Returns
-    -------
-    out : ndarray
-        Array interpretation of `a`.  No copy is performed if the input
-        is already an ndarray with matching dtype and order.  If `a` is a
-        subclass of ndarray, a base class ndarray is returned.
-
-    See Also
-    --------
-    asanyarray : Similar function which passes through subclasses.
-    ascontiguousarray : Convert input to a contiguous array.
-    asfarray : Convert input to a floating point ndarray.
-    asfortranarray : Convert input to an ndarray with column-major
-                     memory order.
-    asarray_chkfinite : Similar function which checks input for NaNs and Infs.
-    fromiter : Create an array from an iterator.
-    fromfunction : Construct an array by executing a function on grid
-                   positions.
-
-    Examples
-    --------
-    Convert a list into an array:
-
-    >>> a = [1, 2]
-    >>> np.asarray(a)
-    array([1, 2])
-
-    Existing arrays are not copied:
-
-    >>> a = np.array([1, 2])
-    >>> np.asarray(a) is a
-    True
-
-    If `dtype` is set, array is copied only if dtype does not match:
-
-    >>> a = np.array([1, 2], dtype=np.float32)
-    >>> np.asarray(a, dtype=np.float32) is a
-    True
-    >>> np.asarray(a, dtype=np.float64) is a
-    False
-
-    Contrary to `asanyarray`, ndarray subclasses are not passed through:
-
-    >>> issubclass(np.recarray, np.ndarray)
-    True
-    >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
-    >>> np.asarray(a) is a
-    False
-    >>> np.asanyarray(a) is a
-    True
-
-    """
-    if like is not None:
-        return _asarray_with_like(a, dtype=dtype, order=order, like=like)
-
-    return array(a, dtype, copy=False, order=order)
-
-
-_asarray_with_like = array_function_dispatch(
-    _asarray_dispatcher
-)(asarray)
-
-
-@set_array_function_like_doc
-@set_module('numpy')
-def asanyarray(a, dtype=None, order=None, *, like=None):
-    """Convert the input to an ndarray, but pass ndarray subclasses through.
-
-    Parameters
-    ----------
-    a : array_like
-        Input data, in any form that can be converted to an array.  This
-        includes scalars, lists, lists of tuples, tuples, tuples of tuples,
-        tuples of lists, and ndarrays.
-    dtype : data-type, optional
-        By default, the data-type is inferred from the input data.
-    order : {'C', 'F', 'A', 'K'}, optional
-        Memory layout.  'A' and 'K' depend on the order of input array a.
-        'C' row-major (C-style), 
-        'F' column-major (Fortran-style) memory representation.
-        'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
-        'K' (keep) preserve input order
-        Defaults to 'C'.
-    ${ARRAY_FUNCTION_LIKE}
-
-        .. versionadded:: 1.20.0
-
-    Returns
-    -------
-    out : ndarray or an ndarray subclass
-        Array interpretation of `a`.  If `a` is an ndarray or a subclass
-        of ndarray, it is returned as-is and no copy is performed.
-
-    See Also
-    --------
-    asarray : Similar function which always returns ndarrays.
-    ascontiguousarray : Convert input to a contiguous array.
-    asfarray : Convert input to a floating point ndarray.
-    asfortranarray : Convert input to an ndarray with column-major
-                     memory order.
-    asarray_chkfinite : Similar function which checks input for NaNs and
-                        Infs.
-    fromiter : Create an array from an iterator.
-    fromfunction : Construct an array by executing a function on grid
-                   positions.
-
-    Examples
-    --------
-    Convert a list into an array:
-
-    >>> a = [1, 2]
-    >>> np.asanyarray(a)
-    array([1, 2])
-
-    Instances of `ndarray` subclasses are passed through as-is:
-
-    >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
-    >>> np.asanyarray(a) is a
-    True
-
-    """
-    if like is not None:
-        return _asanyarray_with_like(a, dtype=dtype, order=order, like=like)
-
-    return array(a, dtype, copy=False, order=order, subok=True)
-
-
-_asanyarray_with_like = array_function_dispatch(
-    _asarray_dispatcher
-)(asanyarray)
-
-
-def _asarray_contiguous_fortran_dispatcher(a, dtype=None, *, like=None):
-    return (like,)
-
-
-@set_array_function_like_doc
-@set_module('numpy')
-def ascontiguousarray(a, dtype=None, *, like=None):
-    """
-    Return a contiguous array (ndim >= 1) in memory (C order).
-
-    Parameters
-    ----------
-    a : array_like
-        Input array.
-    dtype : str or dtype object, optional
-        Data-type of returned array.
-    ${ARRAY_FUNCTION_LIKE}
-
-        .. versionadded:: 1.20.0
-
-    Returns
-    -------
-    out : ndarray
-        Contiguous array of same shape and content as `a`, with type `dtype`
-        if specified.
-
-    See Also
-    --------
-    asfortranarray : Convert input to an ndarray with column-major
-                     memory order.
-    require : Return an ndarray that satisfies requirements.
-    ndarray.flags : Information about the memory layout of the array.
-
-    Examples
-    --------
-    >>> x = np.arange(6).reshape(2,3)
-    >>> np.ascontiguousarray(x, dtype=np.float32)
-    array([[0., 1., 2.],
-           [3., 4., 5.]], dtype=float32)
-    >>> x.flags['C_CONTIGUOUS']
-    True
-
-    Note: This function returns an array with at least one-dimension (1-d) 
-    so it will not preserve 0-d arrays.  
-
-    """
-    if like is not None:
-        return _ascontiguousarray_with_like(a, dtype=dtype, like=like)
-
-    return array(a, dtype, copy=False, order='C', ndmin=1)
-
-
-_ascontiguousarray_with_like = array_function_dispatch(
-    _asarray_contiguous_fortran_dispatcher
-)(ascontiguousarray)
-
-
-@set_array_function_like_doc
-@set_module('numpy')
-def asfortranarray(a, dtype=None, *, like=None):
-    """
-    Return an array (ndim >= 1) laid out in Fortran order in memory.
-
-    Parameters
-    ----------
-    a : array_like
-        Input array.
-    dtype : str or dtype object, optional
-        By default, the data-type is inferred from the input data.
-    ${ARRAY_FUNCTION_LIKE}
-
-        .. versionadded:: 1.20.0
-
-    Returns
-    -------
-    out : ndarray
-        The input `a` in Fortran, or column-major, order.
-
-    See Also
-    --------
-    ascontiguousarray : Convert input to a contiguous (C order) array.
-    asanyarray : Convert input to an ndarray with either row or
-        column-major memory order.
-    require : Return an ndarray that satisfies requirements.
-    ndarray.flags : Information about the memory layout of the array.
-
-    Examples
-    --------
-    >>> x = np.arange(6).reshape(2,3)
-    >>> y = np.asfortranarray(x)
-    >>> x.flags['F_CONTIGUOUS']
-    False
-    >>> y.flags['F_CONTIGUOUS']
-    True
-
-    Note: This function returns an array with at least one-dimension (1-d) 
-    so it will not preserve 0-d arrays.  
-
-    """
-    if like is not None:
-        return _asfortranarray_with_like(a, dtype=dtype, like=like)
-
-    return array(a, dtype, copy=False, order='F', ndmin=1)
-
-
-_asfortranarray_with_like = array_function_dispatch(
-    _asarray_contiguous_fortran_dispatcher
-)(asfortranarray)
-
 
 def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
     return (like,)