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, 319 insertions, 0 deletions

diff --git a/numpy/core/_asarray.py b/numpy/core/_asarray.py
new file mode 100644
index 000000000..fbb7ffa9d
--- /dev/null
+++ b/numpy/core/_asarray.py
@@ -0,0 +1,319 @@
+"""
+Functions in the ``as*array`` family that promote array-likes into arrays.
+
+`require` fits this category despite its name not matching this pattern.
+"""
+from __future__ import division, absolute_import, print_function
+
+from .overrides import set_module
+from .multiarray import array
+
+
+__all__ = [
+    "asarray", "asanyarray", "ascontiguousarray", "asfortranarray", "require",
+]
+
+@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