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Code Editor : ctypeslib.cpython-310.pyc
o ��c�D � @ sb d Z g d�ZddlZddlmZmZmZmZm Z ddl m Z m Z zddl Z W n ey1 dZ Y nw e du rIdd� ZeZeZeZddlmZ eZnddlm mZ e�� Z[e j Zd d � Zd d � Z g d �Z dd� Z G dd� de�Z G dd� de �Z!i Z"d(dd�Z#e dur�dd� Z$dd� Z%e%� Z&dd� Z'd d � Z(d d � Z)d d!� Z*d"d#� Z+d)d$d%�Zd&d'� ZdS dS )*a7 ============================ ``ctypes`` Utility Functions ============================ See Also -------- load_library : Load a C library. ndpointer : Array restype/argtype with verification. as_ctypes : Create a ctypes array from an ndarray. as_array : Create an ndarray from a ctypes array. References ---------- .. [1] "SciPy Cookbook: ctypes", https://scipy-cookbook.readthedocs.io/items/Ctypes.html Examples -------- Load the C library: >>> _lib = np.ctypeslib.load_library('libmystuff', '.') #doctest: +SKIP Our result type, an ndarray that must be of type double, be 1-dimensional and is C-contiguous in memory: >>> array_1d_double = np.ctypeslib.ndpointer( ... dtype=np.double, ... ndim=1, flags='CONTIGUOUS') #doctest: +SKIP Our C-function typically takes an array and updates its values in-place. For example:: void foo_func(double* x, int length) { int i; for (i = 0; i < length; i++) { x[i] = i*i; } } We wrap it using: >>> _lib.foo_func.restype = None #doctest: +SKIP >>> _lib.foo_func.argtypes = [array_1d_double, c_int] #doctest: +SKIP Then, we're ready to call ``foo_func``: >>> out = np.empty(15, dtype=np.double) >>> _lib.foo_func(out, len(out)) #doctest: +SKIP )� load_library� ndpointer�c_intp� as_ctypes�as_array�as_ctypes_type� N)�integer�ndarray�dtype�asarray� frombuffer)� _flagdict�flagsobjc O s t d��)z� Dummy object that raises an ImportError if ctypes is not available. Raises ------ ImportError If ctypes is not available. zctypes is not available.)� ImportError)�args�kwds� r �1/usr/lib/python3/dist-packages/numpy/ctypeslib.py�_dummyC s r )�intpc C s< t jdk rddl}|jddd� tj�| �d }|s_ddlm} |� }| | g}|d d �}||ks9|� d| | � zddl }d |� d �|� d �f } |� d| | � W n t t fy^ Y nw | g}tj�|�}tj�|�sutj�|�} n|} |D ] } tj�| | �} tj�| �r�zt j| W S ty� � w qytd��)av It is possible to load a library using >>> lib = ctypes.cdll[<full_path_name>] # doctest: +SKIP But there are cross-platform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the load_library function as a convenience. Parameters ---------- libname : str Name of the library, which can have 'lib' as a prefix, but without an extension. loader_path : str Where the library can be found. Returns ------- ctypes.cdll[libpath] : library object A ctypes library object Raises ------ OSError If there is no library with the expected extension, or the library is defective and cannot be loaded. z1.0.1r NzAAll features of ctypes interface may not work with ctypes < 1.0.1� )� stacklevel� )�get_shared_lib_extensionT)� is_python_extz .%s-%s.so�SOABI� MULTIARCHz no file with expected extension)�ctypes� __version__�warnings�warn�os�path�splitext�numpy.distutils.misc_utilr �insert� sysconfig�get_config_var�KeyErrorr �abspath�isdir�dirname�join�exists�cdll�OSError) �libname� loader_pathr �extr �so_ext� libname_ext�so_ext2r& �so_ext3�libdir�ln�libpathr r r r Z sJ � �� ��r c C s d}| D ]}|t | 7 }q|S �Nr )r )�flaglist�num�valr r r �_num_fromflags� s r> )� C_CONTIGUOUS� F_CONTIGUOUS�ALIGNED� WRITEABLE�OWNDATA� UPDATEIFCOPY�WRITEBACKIFCOPYc C s, g }t D ]}t| }| |@ r|�|� q|S �N)� _flagnamesr �append)r< �res�key�valuer r r �_flags_fromnum� s �rL c @ s e Zd Zedd� �ZdS )�_ndptrc C s� t |t�s td��| jd ur|j| jkrtd| j ��| jd ur-|j| jkr-td| j ��| jd urA|j| jkrAtdt | j� ��| j d urY|j j | j @ | j krYtdt | j � ��|jS )Nzargument must be an ndarrayz array must have data type %sz array must have %d dimension(s)zarray must have shape %szarray must have flags %s)� isinstancer � TypeError�_dtype_r �_ndim_�ndim�_shape_�shape�str�_flags_�flagsr< rL r )�cls�objr r r � from_param� s" �z_ndptr.from_paramN)�__name__� __module__� __qualname__� classmethodrZ r r r r rM � s rM c @ s$ e Zd ZdZdd� Zedd� �ZdS )�_concrete_ndptrz� Like _ndptr, but with `_shape_` and `_dtype_` specified. Notably, this means the pointer has enough information to reconstruct the array, which is not generally true. c C s | j S )z� This method is called when this class is used as the .restype attribute for a shared-library function, to automatically wrap the pointer into an array. )�contents)�selfr r r �_check_retval_� s z _concrete_ndptr._check_retval_c C sD t | j| jf�}tj|j }t�| t�|��j}t ||d�j dd�S )z� Get an ndarray viewing the data pointed to by this pointer. This mirrors the `contents` attribute of a normal ctypes pointer �r r )�axis) �_dtyperP rS r �c_char�itemsize�cast�POINTERr` r �squeeze)ra � full_dtype� full_ctype�bufferr r r r` � s z_concrete_ndptr.contentsN)r[ r\ r] �__doc__rb �propertyr` r r r r r_ � s r_ c C s� | durt | �} d}|durVt|t�r|�d�}nt|ttf�r'|}t|�}n t|t�r3|j}t|�}|du rVz dd� |D �}W n t yQ } zt d�|�d}~ww t |�}|durmzt |�}W n t yl |f}Y nw | |||f}zt | W S ty� Y nw | du r�d}n| jdur�tt| ��}n| j}|dur�|d| 7 }|dur�|dd �d d � |D �� 7 }|dur�|dd�|� 7 }| dur�|dur�t}nt}td | |f| |||d ��} | t |< | S )ac Array-checking restype/argtypes. An ndpointer instance is used to describe an ndarray in restypes and argtypes specifications. This approach is more flexible than using, for example, ``POINTER(c_double)``, since several restrictions can be specified, which are verified upon calling the ctypes function. These include data type, number of dimensions, shape and flags. If a given array does not satisfy the specified restrictions, a ``TypeError`` is raised. Parameters ---------- dtype : data-type, optional Array data-type. ndim : int, optional Number of array dimensions. shape : tuple of ints, optional Array shape. flags : str or tuple of str Array flags; may be one or more of: - C_CONTIGUOUS / C / CONTIGUOUS - F_CONTIGUOUS / F / FORTRAN - OWNDATA / O - WRITEABLE / W - ALIGNED / A - WRITEBACKIFCOPY / X - UPDATEIFCOPY / U Returns ------- klass : ndpointer type object A type object, which is an ``_ndtpr`` instance containing dtype, ndim, shape and flags information. Raises ------ TypeError If a given array does not satisfy the specified restrictions. Examples -------- >>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64, ... ndim=1, ... flags='C_CONTIGUOUS')] ... #doctest: +SKIP >>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64)) ... #doctest: +SKIP N�,c S s g | ]}|� � �� �qS r )�strip�upper��.0�xr r r � <listcomp>0 s z ndpointer.<locals>.<listcomp>zinvalid flags specification�anyz_%dd�_ru c s s � | ]}t |�V qd S rF )rU rs r r r � <genexpr>N s � z ndpointer.<locals>.<genexpr>z ndpointer_%s)rP rS rQ rV )re rN rU �split�intr rL r r< � ExceptionrO r> �tuple�_pointer_type_cacher( �names�idr, r_ rM �type) r rR rT rW r< �e� cache_key�name�base�klassr r r r � sn 6 �� � � ��r c C s& |ddd� D ] }|| } d| _ q| S )z7 Create an ndarray of the given element type and shape N���)r\ )� element_typerT �dimr r r �_ctype_ndarraya s r� c C sJ t } | j| j| j| j| j| j| j| j| j | j | j | j | j g }dd� |D �S )zX Return a dictionary mapping native endian scalar dtype to ctypes types c S s i | ]}t |�|�qS r )re )rt �ctyper r r � <dictcomp>u s z(_get_scalar_type_map.<locals>.<dictcomp>)r �c_byte�c_short�c_int�c_long� c_longlong�c_ubyte�c_ushort�c_uint�c_ulong� c_ulonglong�c_float�c_double�c_bool)�ct� simple_typesr r r �_get_scalar_type_mapj s �r� c C sx | � d�� d�}| � d�}zt| }W n ty' } ztd�| ��d �d }~ww |jdkr2|j}|S |jdkr:|j}|S )N�S�=z Converting {!r} to a ctypes type�>�<)� newbyteorder�_scalar_type_mapr( �NotImplementedError�format� byteorder� __ctype_be__� __ctype_le__)r �dtype_with_endian� dtype_nativer� r� r r r �_ctype_from_dtype_scalar{ s$ ���� �r� c C s | j \}}t|�}t||�S rF )�subdtype�_ctype_from_dtyper� )r � element_dtyperT r� r r r �_ctype_from_dtype_subarray� s r� c C s� g }| j D ]}| j| d d� \}}|�||t|�f� qt|dd� d�}t|�dkrltdd� |D ��rld}g }|D ]\}}}|�||f� t|t� |��}q:| j |kr_|�d tj | j f� t d tj ft|dd d ��S d}g }|D ]-\}}}|| } | dk r�td ��| dkr�|�d tj | f� |�||f� |t� |� }qr| j | } | dkr�|�d tj | f� t d tjft|dd d ��S )Nr c S s | d S r: r )�fr r r �<lambda>� s z._ctype_from_dtype_structured.<locals>.<lambda>)rJ r c s s � | ] \}}}|d kV qdS )r Nr )rt �offsetr� r� r r r ry � s � z/_ctype_from_dtype_structured.<locals>.<genexpr>r � �union)�_fields_�_pack_r\ zOverlapping fields�struct)r �fieldsrH r� �sorted�len�all�maxr �sizeofrg rf r� �Union�dictr� � Structure) r � field_datar� � field_dtyper� �sizer� r� � last_offset�paddingr r r � _ctype_from_dtype_structured� sH � �r� c C s, | j d ur t| �S | jd urt| �S t| �S rF )r� r� r� r� r� rc r r r r� � s r� c C s t t| ��S )a� Convert a dtype into a ctypes type. Parameters ---------- dtype : dtype The dtype to convert Returns ------- ctype A ctype scalar, union, array, or struct Raises ------ NotImplementedError If the conversion is not possible Notes ----- This function does not losslessly round-trip in either direction. ``np.dtype(as_ctypes_type(dt))`` will: - insert padding fields - reorder fields to be sorted by offset - discard field titles ``as_ctypes_type(np.dtype(ctype))`` will: - discard the class names of `ctypes.Structure`\ s and `ctypes.Union`\ s - convert single-element `ctypes.Union`\ s into single-element `ctypes.Structure`\ s - insert padding fields )r� re rc r r r r � s &r c C sD t | tj�r |du rtd��t�t| j|��}t�| |�j} t | �S )a" Create a numpy array from a ctypes array or POINTER. The numpy array shares the memory with the ctypes object. The shape parameter must be given if converting from a ctypes POINTER. The shape parameter is ignored if converting from a ctypes array Nz=as_array() requires a shape argument when called on a pointer) rN r �_PointerrO ri r� �_type_rh r` r )rY rT � p_arr_typer r r r � s �r c C sp | j }|d r td��|d dkrtd��|d \}}|r!td��t|d �}t||d �}|�|�}| |_|S ) z�Create and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted.�stridesz strided arrays not supported�version� z,only __array_interface__ version 3 supported�datazreadonly arrays unsupported�typestrrT )�__array_interface__rO r r� � from_address�__keep)rY �ai�addr�readonly� ctype_scalar� result_type�resultr r r r s r )NNNNrF ),rn �__all__r! �numpyr r r re r r �numpy.core.multiarrayr r r r r r r r r r �object� _ndptr_base�numpy.core._internal�core� _internal�nic�_getintp_ctype�c_void_pr> rG rL rM r_ r~ r r� r� r� r� r� r� r� r r r r r �<module> sT 3 � K v 6 ) ��
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