325 lines
9.7 KiB
Python
325 lines
9.7 KiB
Python
"""
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Functions in the ``as*array`` family that promote array-likes into arrays.
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`require` fits this category despite its name not matching this pattern.
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"""
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from __future__ import division, absolute_import, print_function
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from .overrides import set_module
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from .multiarray import array
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__all__ = [
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"asarray", "asanyarray", "ascontiguousarray", "asfortranarray", "require",
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]
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@set_module('numpy')
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def asarray(a, dtype=None, order=None):
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"""Convert the input to an array.
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Parameters
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----------
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a : array_like
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Input data, in any form that can be converted to an array. This
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includes lists, lists of tuples, tuples, tuples of tuples, tuples
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of lists and ndarrays.
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dtype : data-type, optional
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By default, the data-type is inferred from the input data.
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order : {'C', 'F'}, optional
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Whether to use row-major (C-style) or
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column-major (Fortran-style) memory representation.
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Defaults to 'C'.
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Returns
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-------
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out : ndarray
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Array interpretation of `a`. No copy is performed if the input
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is already an ndarray with matching dtype and order. If `a` is a
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subclass of ndarray, a base class ndarray is returned.
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See Also
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--------
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asanyarray : Similar function which passes through subclasses.
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ascontiguousarray : Convert input to a contiguous array.
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asfarray : Convert input to a floating point ndarray.
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asfortranarray : Convert input to an ndarray with column-major
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memory order.
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asarray_chkfinite : Similar function which checks input for NaNs and Infs.
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fromiter : Create an array from an iterator.
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fromfunction : Construct an array by executing a function on grid
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positions.
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Examples
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--------
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Convert a list into an array:
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>>> a = [1, 2]
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>>> np.asarray(a)
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array([1, 2])
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Existing arrays are not copied:
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>>> a = np.array([1, 2])
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>>> np.asarray(a) is a
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True
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If `dtype` is set, array is copied only if dtype does not match:
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>>> a = np.array([1, 2], dtype=np.float32)
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>>> np.asarray(a, dtype=np.float32) is a
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True
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>>> np.asarray(a, dtype=np.float64) is a
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False
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Contrary to `asanyarray`, ndarray subclasses are not passed through:
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>>> issubclass(np.recarray, np.ndarray)
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True
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>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
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>>> np.asarray(a) is a
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False
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>>> np.asanyarray(a) is a
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True
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"""
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return array(a, dtype, copy=False, order=order)
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@set_module('numpy')
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def asanyarray(a, dtype=None, order=None):
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"""Convert the input to an ndarray, but pass ndarray subclasses through.
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Parameters
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----------
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a : array_like
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Input data, in any form that can be converted to an array. This
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includes scalars, lists, lists of tuples, tuples, tuples of tuples,
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tuples of lists, and ndarrays.
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dtype : data-type, optional
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By default, the data-type is inferred from the input data.
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order : {'C', 'F'}, optional
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Whether to use row-major (C-style) or column-major
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(Fortran-style) memory representation. Defaults to 'C'.
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Returns
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-------
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out : ndarray or an ndarray subclass
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Array interpretation of `a`. If `a` is an ndarray or a subclass
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of ndarray, it is returned as-is and no copy is performed.
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See Also
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--------
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asarray : Similar function which always returns ndarrays.
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ascontiguousarray : Convert input to a contiguous array.
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asfarray : Convert input to a floating point ndarray.
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asfortranarray : Convert input to an ndarray with column-major
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memory order.
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asarray_chkfinite : Similar function which checks input for NaNs and
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Infs.
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fromiter : Create an array from an iterator.
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fromfunction : Construct an array by executing a function on grid
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positions.
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Examples
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--------
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Convert a list into an array:
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>>> a = [1, 2]
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>>> np.asanyarray(a)
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array([1, 2])
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Instances of `ndarray` subclasses are passed through as-is:
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>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
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>>> np.asanyarray(a) is a
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True
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"""
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return array(a, dtype, copy=False, order=order, subok=True)
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@set_module('numpy')
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def ascontiguousarray(a, dtype=None):
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"""
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Return a contiguous array (ndim >= 1) in memory (C order).
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Parameters
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----------
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a : array_like
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Input array.
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dtype : str or dtype object, optional
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Data-type of returned array.
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Returns
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-------
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out : ndarray
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Contiguous array of same shape and content as `a`, with type `dtype`
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if specified.
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See Also
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--------
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asfortranarray : Convert input to an ndarray with column-major
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memory order.
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require : Return an ndarray that satisfies requirements.
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ndarray.flags : Information about the memory layout of the array.
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Examples
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--------
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>>> x = np.arange(6).reshape(2,3)
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>>> np.ascontiguousarray(x, dtype=np.float32)
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array([[0., 1., 2.],
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[3., 4., 5.]], dtype=float32)
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>>> x.flags['C_CONTIGUOUS']
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True
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Note: This function returns an array with at least one-dimension (1-d)
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so it will not preserve 0-d arrays.
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"""
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return array(a, dtype, copy=False, order='C', ndmin=1)
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@set_module('numpy')
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def asfortranarray(a, dtype=None):
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"""
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Return an array (ndim >= 1) laid out in Fortran order in memory.
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Parameters
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----------
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a : array_like
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Input array.
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dtype : str or dtype object, optional
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By default, the data-type is inferred from the input data.
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Returns
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-------
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out : ndarray
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The input `a` in Fortran, or column-major, order.
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See Also
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--------
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ascontiguousarray : Convert input to a contiguous (C order) array.
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asanyarray : Convert input to an ndarray with either row or
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column-major memory order.
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require : Return an ndarray that satisfies requirements.
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ndarray.flags : Information about the memory layout of the array.
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Examples
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--------
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>>> x = np.arange(6).reshape(2,3)
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>>> y = np.asfortranarray(x)
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>>> x.flags['F_CONTIGUOUS']
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False
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>>> y.flags['F_CONTIGUOUS']
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True
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Note: This function returns an array with at least one-dimension (1-d)
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so it will not preserve 0-d arrays.
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"""
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return array(a, dtype, copy=False, order='F', ndmin=1)
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@set_module('numpy')
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def require(a, dtype=None, requirements=None):
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"""
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Return an ndarray of the provided type that satisfies requirements.
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This function is useful to be sure that an array with the correct flags
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is returned for passing to compiled code (perhaps through ctypes).
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Parameters
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----------
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a : array_like
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The object to be converted to a type-and-requirement-satisfying array.
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dtype : data-type
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The required data-type. If None preserve the current dtype. If your
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application requires the data to be in native byteorder, include
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a byteorder specification as a part of the dtype specification.
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requirements : str or list of str
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The requirements list can be any of the following
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* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
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* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
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* 'ALIGNED' ('A') - ensure a data-type aligned array
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* 'WRITEABLE' ('W') - ensure a writable array
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* 'OWNDATA' ('O') - ensure an array that owns its own data
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* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
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Returns
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-------
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out : ndarray
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Array with specified requirements and type if given.
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See Also
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--------
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asarray : Convert input to an ndarray.
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asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
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ascontiguousarray : Convert input to a contiguous array.
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asfortranarray : Convert input to an ndarray with column-major
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memory order.
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ndarray.flags : Information about the memory layout of the array.
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Notes
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-----
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The returned array will be guaranteed to have the listed requirements
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by making a copy if needed.
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Examples
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--------
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>>> x = np.arange(6).reshape(2,3)
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>>> x.flags
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C_CONTIGUOUS : True
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F_CONTIGUOUS : False
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OWNDATA : False
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WRITEABLE : True
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ALIGNED : True
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WRITEBACKIFCOPY : False
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UPDATEIFCOPY : False
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>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
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>>> y.flags
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C_CONTIGUOUS : False
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F_CONTIGUOUS : True
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OWNDATA : True
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WRITEABLE : True
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ALIGNED : True
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WRITEBACKIFCOPY : False
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UPDATEIFCOPY : False
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"""
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possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
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'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
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'A': 'A', 'ALIGNED': 'A',
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'W': 'W', 'WRITEABLE': 'W',
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'O': 'O', 'OWNDATA': 'O',
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'E': 'E', 'ENSUREARRAY': 'E'}
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if not requirements:
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return asanyarray(a, dtype=dtype)
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else:
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requirements = {possible_flags[x.upper()] for x in requirements}
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if 'E' in requirements:
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requirements.remove('E')
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subok = False
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else:
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subok = True
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order = 'A'
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if requirements >= {'C', 'F'}:
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raise ValueError('Cannot specify both "C" and "F" order')
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elif 'F' in requirements:
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order = 'F'
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requirements.remove('F')
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elif 'C' in requirements:
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order = 'C'
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requirements.remove('C')
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arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
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for prop in requirements:
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if not arr.flags[prop]:
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arr = arr.copy(order)
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break
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return arr
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