MAINT: Move asarray helpers into their own module

This is a direct move, with some tweaks to imports.

This breaks a cyclic imports between `core.numeric` and `core.fromnumeric`.

This doesn't affect the value of `np.core.numeric.__all__` which keeps code doing `from numpy.core.numeric import *` working.

1 files changed, 6 insertions, 309 deletions

diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index b4255e733..c15b021c2 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -39,6 +39,7 @@ from .umath import (multiply, invert, sin, UFUNC_BUFSIZE_DEFAULT,
 from . import numerictypes
 from .numerictypes import longlong, intc, int_, float_, complex_, bool_
 from ._exceptions import TooHardError, AxisError
+from ._asarray import asarray, asanyarray
 
 bitwise_not = invert
 ufunc = type(sin)
@@ -68,10 +69,9 @@ __all__ = [
     'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer', 'where',
     'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort',
     'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type',
-    'result_type', 'asarray', 'asanyarray', 'ascontiguousarray',
-    'asfortranarray', 'isfortran', 'empty_like', 'zeros_like', 'ones_like',
+    'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like',
     'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll',
-    'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian', 'require',
+    'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian',
     'fromiter', 'array_equal', 'array_equiv', 'indices', 'fromfunction',
     'isclose', 'load', 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones',
     'identity', 'allclose', 'compare_chararrays', 'putmask', 'seterr',
@@ -460,312 +460,6 @@ def count_nonzero(a, axis=None):
 
 
 @set_module('numpy')
-def asarray(a, dtype=None, order=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'}, optional
-        Whether to use row-major (C-style) or
-        column-major (Fortran-style) memory representation.
-        Defaults to 'C'.
-
-    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
-
-    """
-    return array(a, dtype, copy=False, order=order)
-
-
-@set_module('numpy')
-def asanyarray(a, dtype=None, order=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'}, optional
-        Whether to use row-major (C-style) or column-major
-        (Fortran-style) memory representation.  Defaults to 'C'.
-
-    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
-
-    """
-    return array(a, dtype, copy=False, order=order, subok=True)
-
-
-@set_module('numpy')
-def ascontiguousarray(a, dtype=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.
-
-    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.  
-
-    """
-    return array(a, dtype, copy=False, order='C', ndmin=1)
-
-
-@set_module('numpy')
-def asfortranarray(a, dtype=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.
-
-    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.  
-
-    """
-    return array(a, dtype, copy=False, order='F', ndmin=1)
-
-
-@set_module('numpy')
-def require(a, dtype=None, requirements=None):
-    """
-    Return an ndarray of the provided type that satisfies requirements.
-
-    This function is useful to be sure that an array with the correct flags
-    is returned for passing to compiled code (perhaps through ctypes).
-
-    Parameters
-    ----------
-    a : array_like
-       The object to be converted to a type-and-requirement-satisfying array.
-    dtype : data-type
-       The required data-type. If None preserve the current dtype. If your
-       application requires the data to be in native byteorder, include
-       a byteorder specification as a part of the dtype specification.
-    requirements : str or list of str
-       The requirements list can be any of the following
-
-       * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
-       * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
-       * 'ALIGNED' ('A')      - ensure a data-type aligned array
-       * 'WRITEABLE' ('W')    - ensure a writable array
-       * 'OWNDATA' ('O')      - ensure an array that owns its own data
-       * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
-
-    See Also
-    --------
-    asarray : Convert input to an ndarray.
-    asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
-    ascontiguousarray : Convert input to a contiguous array.
-    asfortranarray : Convert input to an ndarray with column-major
-                     memory order.
-    ndarray.flags : Information about the memory layout of the array.
-
-    Notes
-    -----
-    The returned array will be guaranteed to have the listed requirements
-    by making a copy if needed.
-
-    Examples
-    --------
-    >>> x = np.arange(6).reshape(2,3)
-    >>> x.flags
-      C_CONTIGUOUS : True
-      F_CONTIGUOUS : False
-      OWNDATA : False
-      WRITEABLE : True
-      ALIGNED : True
-      WRITEBACKIFCOPY : False
-      UPDATEIFCOPY : False
-
-    >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
-    >>> y.flags
-      C_CONTIGUOUS : False
-      F_CONTIGUOUS : True
-      OWNDATA : True
-      WRITEABLE : True
-      ALIGNED : True
-      WRITEBACKIFCOPY : False
-      UPDATEIFCOPY : False
-
-    """
-    possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
-                      'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
-                      'A': 'A', 'ALIGNED': 'A',
-                      'W': 'W', 'WRITEABLE': 'W',
-                      'O': 'O', 'OWNDATA': 'O',
-                      'E': 'E', 'ENSUREARRAY': 'E'}
-    if not requirements:
-        return asanyarray(a, dtype=dtype)
-    else:
-        requirements = {possible_flags[x.upper()] for x in requirements}
-
-    if 'E' in requirements:
-        requirements.remove('E')
-        subok = False
-    else:
-        subok = True
-
-    order = 'A'
-    if requirements >= {'C', 'F'}:
-        raise ValueError('Cannot specify both "C" and "F" order')
-    elif 'F' in requirements:
-        order = 'F'
-        requirements.remove('F')
-    elif 'C' in requirements:
-        order = 'C'
-        requirements.remove('C')
-
-    arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
-
-    for prop in requirements:
-        if not arr.flags[prop]:
-            arr = arr.copy(order)
-            break
-    return arr
-
-
-@set_module('numpy')
 def isfortran(a):
     """
     Returns True if the array is Fortran contiguous but *not* C contiguous.
@@ -3096,7 +2790,10 @@ from . import fromnumeric
 from .fromnumeric import *
 from . import arrayprint
 from .arrayprint import *
+from . import _asarray
+from ._asarray import *
 extend_all(fromnumeric)
 extend_all(umath)
 extend_all(numerictypes)
 extend_all(arrayprint)
+extend_all(_asarray)