135 lines
3.8 KiB
Python
135 lines
3.8 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 .overrides import (
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array_function_dispatch,
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set_array_function_like_doc,
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set_module,
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)
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from .multiarray import array, asanyarray
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__all__ = ["require"]
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POSSIBLE_FLAGS = {
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'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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}
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@set_array_function_like_doc
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@set_module('numpy')
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def require(a, dtype=None, requirements=None, *, like=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 sequence 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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${ARRAY_FUNCTION_LIKE}
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.. versionadded:: 1.20.0
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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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>>> 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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"""
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if like is not None:
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return _require_with_like(
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like,
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a,
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dtype=dtype,
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requirements=requirements,
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)
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if not requirements:
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return asanyarray(a, dtype=dtype)
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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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return arr.copy(order)
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return arr
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_require_with_like = array_function_dispatch()(require)
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