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Edit File: _asarray.cpython-38.pyc
U �p�]�& � @ s� d Z ddlmZmZmZ ddlmZ ddlmZ dddd d gZ ed �dd d��Z ed �ddd��Z ed �ddd��Z ed �ddd ��Z ed �ddd ��Zd S )z� Functions in the ``as*array`` family that promote array-likes into arrays. `require` fits this category despite its name not matching this pattern. � )�division�absolute_import�print_function� )� set_module��array�asarray� asanyarray�ascontiguousarray�asfortranarray�requireZnumpyNc C s t | |d|d�S )a 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 F)�copy�orderr ��a�dtyper � r �5/usr/lib/python3/dist-packages/numpy/core/_asarray.pyr s Ec C s t | |d|dd�S )aj 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 FT)r r �subokr r r r r r X s 2c C s t | |dddd�S )a� 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. F�Cr �r r Zndminr �r r r r r r � s &c C s t | |dddd�S )a� 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. F�Fr r r r r r r r � s &c s� ddddddddddddddd�� |s2t | |d�S � fd d �|D �}d|kr\|�d� d }nd }d}|ddhkrztd ��n.d|kr�d}|�d� nd|kr�d}|�d� t| ||d |d�}|D ] }|j| s�|�|�} q�q�|S )a� 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 r r �A�W�O�E)r Z C_CONTIGUOUSZ CONTIGUOUSr Z F_CONTIGUOUSZFORTRANr ZALIGNEDr Z WRITEABLEr ZOWNDATAr Z ENSUREARRAY)r c s h | ]}� |� � �qS r )�upper)�.0�x�Zpossible_flagsr r � <setcomp>, s zrequire.<locals>.<setcomp>FTz%Cannot specify both "C" and "F" order)r r r r )r �remove� ValueErrorr �flagsr )r r Z requirementsr r ZarrZpropr r! r r � sF D � )NN)NN)N)N)NN)�__doc__Z __future__r r r Z overridesr Z multiarrayr �__all__r r r r r r r r r �<module> s& � G 4 ( (