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/usr/local/lib64/python3.6/site-packages/numpy/core/

/usr/local/lib64/python3.6/site-packages/numpy/core

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include/	-	0755	rm
lib/	-	0755	rm
tests/	-	0755	rm
__pycache__/	-	0755	rm
arrayprint.py	59114	0644	edit dl rm
cversions.py	347	0644	edit dl rm
defchararray.py	70041	0644	edit dl rm
einsumfunc.py	50689	0644	edit dl rm
fromnumeric.py	119216	0644	edit dl rm
function_base.py	18034	0644	edit dl rm
generate_numpy_api.py	7022	0644	edit dl rm
getlimits.py	19076	0644	edit dl rm
machar.py	10786	0644	edit dl rm
memmap.py	11562	0644	edit dl rm
multiarray.py	54410	0644	edit dl rm
numeric.py	74615	0644	edit dl rm
numerictypes.py	16563	0644	edit dl rm
overrides.py	7481	0644	edit dl rm
records.py	35083	0644	edit dl rm
setup.py	42409	0644	edit dl rm
setup_common.py	18410	0644	edit dl rm
shape_base.py	29014	0644	edit dl rm
umath.py	2040	0644	edit dl rm
umath_tests.py	389	0644	edit dl rm
_add_newdocs.py	202741	0644	edit dl rm
_asarray.py	9874	0644	edit dl rm
_dtype.py	9820	0644	edit dl rm
_dtype_ctypes.py	3673	0644	edit dl rm
_exceptions.py	6145	0644	edit dl rm
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_multiarray_tests.cpython-36m-x86_64-linux-gnu.so	167112	0755	edit dl rm
_multiarray_umath.cpython-36m-x86_64-linux-gnu.so	4169248	0755	edit dl rm
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_struct_ufunc_tests.cpython-36m-x86_64-linux-gnu.so	13272	0755	edit dl rm
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_ufunc_config.py	13821	0644	edit dl rm
_umath_tests.cpython-36m-x86_64-linux-gnu.so	27768	0755	edit dl rm
__init__.py	4335	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/numpy/core/_asarray.py (9874B)

"""
Functions in the ``as*array`` family that promote array-likes into arrays.

`require` fits this category despite its name not matching this pattern.
"""
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

Returns
    -------
    out : ndarray
        Array with specified requirements and type if given.

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

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