Edit File: records.cpython-37.pyc
B ��Fd�r � @ s d Z ddlmZmZmZ ddlZddlZddlmZ ddlm Z ddl m Z mZmZ dd d gZe jZd d d d d d ddd d d dddd�Ze jZdd� ZG dd � d �ZG dd� de j�ZG dd � d e�Zd$dd�Zd%dd�Zd&dd �Z d d � Z d'd d �Z d(d"d#�Z dS ))a� Record Arrays ============= Record arrays expose the fields of structured arrays as properties. Most commonly, ndarrays contain elements of a single type, e.g. floats, integers, bools etc. However, it is possible for elements to be combinations of these using structured types, such as:: >>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', int), ('y', float)]) >>> a array([(1, 2.0), (1, 2.0)], dtype=[('x', '<i4'), ('y', '<f8')]) Here, each element consists of two fields: x (and int), and y (a float). This is known as a structured array. The different fields are analogous to columns in a spread-sheet. The different fields can be accessed as one would a dictionary:: >>> a['x'] array([1, 1]) >>> a['y'] array([ 2., 2.]) Record arrays allow us to access fields as properties:: >>> ar = np.rec.array(a) >>> ar.x array([1, 1]) >>> ar.y array([ 2., 2.]) � )�division�absolute_import�print_functionN� )�numeric)� numerictypes)� isfileobj�bytes�long�record�recarray� format_parser�>�<�=�s�|)�b�l�n�B�L�N�Sr r r r r �I�ic C sP g }xFt t| ��D ]6}| | | |d d� kr| | |kr|�| | � qW |S )z@Find duplication in a list, return a list of duplicated elementsr N)�range�len�append)�list�dupr � r! �E/opt/alt/python37/lib64/python3.7/site-packages/numpy/core/records.py�find_duplicateJ s r# c @ s4 e Zd ZdZd dd�Zddd�Zd d � Zd d � ZdS )r a� Class to convert formats, names, titles description to a dtype. After constructing the format_parser object, the dtype attribute is the converted data-type: ``dtype = format_parser(formats, names, titles).dtype`` Attributes ---------- dtype : dtype The converted data-type. Parameters ---------- formats : str or list of str The format description, either specified as a string with comma-separated format descriptions in the form ``'f8, i4, a5'``, or a list of format description strings in the form ``['f8', 'i4', 'a5']``. names : str or list/tuple of str The field names, either specified as a comma-separated string in the form ``'col1, col2, col3'``, or as a list or tuple of strings in the form ``['col1', 'col2', 'col3']``. An empty list can be used, in that case default field names ('f0', 'f1', ...) are used. titles : sequence Sequence of title strings. An empty list can be used to leave titles out. aligned : bool, optional If True, align the fields by padding as the C-compiler would. Default is False. byteorder : str, optional If specified, all the fields will be changed to the provided byte-order. Otherwise, the default byte-order is used. For all available string specifiers, see `dtype.newbyteorder`. See Also -------- dtype, typename, sctype2char Examples -------- >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... ['T1', 'T2', 'T3']).dtype dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), '|S5')]) `names` and/or `titles` can be empty lists. If `titles` is an empty list, titles will simply not appear. If `names` is empty, default field names will be used. >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... []).dtype dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '|S5')]) >>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', '|S5')]) FNc C s. | � ||� | �||� | �|� | j| _d S )N)� _parseFormats�_setfieldnames� _createdescr�_descr�dtype)�self�formats�names�titles�aligned� byteorderr! r! r" �__init__� s zformat_parser.__init__r c s� |dkrt d��t|t�r:t|�dk r0|�d� d�|�}t�||�}|j� � dkrlt�d|fg|�}|j� |j }� fdd�|D �| _ � fd d�|D �| _ t|�| _ dS ) z Parse the field formats NzNeed formats argument� � �,�f1c s g | ]}� | d �qS )r r! )�.0�key)�fieldsr! r" � <listcomp>� s z/format_parser._parseFormats.<locals>.<listcomp>c s g | ]}� | d �qS )r r! )r4 r5 )r6 r! r" r7 � s ) � ValueError� isinstancer r r �join�sbr( r6 r+ � _f_formats�_offsets�_nfields)r) r* r- r( �keysr! )r6 r" r$ � s zformat_parser._parseFormatsc C s� |rXt |�ttgkrn&t|t�r,|�d�}ntdt|� ��dd� |d| j� D �| _ ng | _ | j dd� t t | j �| j�D �7 _ t | j �}|r�t d| ��|r�dd� |d| j� D �| _n g | _g }| jt |�kr�| jdg| jt |� 7 _dS ) zQconvert input field names into a list and assign to the _names attribute r2 zillegal input names %sc S s g | ] }|� � �qS r! )�strip)r4 r r! r! r" r7 � s z0format_parser._setfieldnames.<locals>.<listcomp>Nc S s g | ] }d | �qS )zf%dr! )r4 r r! r! r" r7 � s zDuplicate field names: %sc S s g | ] }|� � �qS r! )r@ )r4 r r! r! r" r7 � s )�typer �tupler9 �str�split� NameError�reprr> �_namesr r r# r8 �_titles)r) r+ r, Z_dupr! r! r" r% � s& z format_parser._setfieldnamesc C sD t �| j| j| j| jd��}|d k r:t|d }|�|�}|| _d S )N)r+ r* �offsetsr, r ) r; r( rG r< r= rH �_byteorderconvZ newbyteorderr' )r) r. �descrr! r! r" r& � s zformat_parser._createdescr)FN)r )�__name__� __module__� __qualname__�__doc__r/ r$ r% r& r! r! r! r" r S s : $c @ sH e Zd ZdZd Z dZdd� Zdd� Zdd� Zd d � Zd d � Zd d� Z dS )r zEA data-type scalar that allows field access as attribute lookup. �numpyc C s | � � S )N)�__str__)r) r! r! r" �__repr__� s zrecord.__repr__c C s t | �� �S )N)rC �item)r) r! r! r" rQ � s zrecord.__str__c C s� |dkrt j�| |�S yt j�| |�S tk r8 Y nX t j�| d�j}|�|d �}|r�| j|d d� � }y |j}W n tk r� |S X |jr�|�| j |jjf�S |S td| ��d S )N)�setfield�getfieldr( r( r0 z%'record' object has no attribute '%s') �nt�void�__getattribute__�AttributeErrorr6 �getrU r( �view� __class__)r) �attr� fielddict�res�obj�dtr! r! r" rX � s& zrecord.__getattribute__c C sx |dkrt d| ��tj�| d�j}|�|d �}|rL| j|f|d d� �� S t| |d �rhtj�| ||�S t d| ��d S )N)rT rU r( zCannot set '%s' attributer( r0 z%'record' object has no attribute '%s') rY rV rW rX r6 rZ rT �getattr� __setattr__)r) r] �valr^ r_ r! r! r" rc � s zrecord.__setattr__c C s> t j�| |�}t|t j�r6|jjr6|�| j|jjf�S |S d S )N)rV rW � __getitem__r9 r( r6 r[ r\ )r) �indxr` r! r! r" re s zrecord.__getitem__c C sV | j j}tdd� |D ��}g }d| }x$|D ] }|�||t| |�f � q,W d�|�S )zPretty-print all fields.c s s | ]}t |�V qd S )N)r )r4 �namer! r! r" � <genexpr> s z record.pprint.<locals>.<genexpr>z %% %ds: %%s� )r( r+ �maxr rb r: )r) r+ �maxlenZrows�fmtrg r! r! r" �pprint s z record.pprintN) rL rM rN rO rR rQ rX rc re rm r! r! r! r" r � s c s\ e Zd ZdZd Z dZddd�Zd d � Zd d � Zd d� Z� fdd�Zdd� Z ddd�Z � Z S )r a2 Construct an ndarray that allows field access using attributes. Arrays may have a data-types containing fields, analogous to columns in a spread sheet. An example is ``[(x, int), (y, float)]``, where each entry in the array is a pair of ``(int, float)``. Normally, these attributes are accessed using dictionary lookups such as ``arr['x']`` and ``arr['y']``. Record arrays allow the fields to be accessed as members of the array, using ``arr.x`` and ``arr.y``. Parameters ---------- shape : tuple Shape of output array. dtype : data-type, optional The desired data-type. By default, the data-type is determined from `formats`, `names`, `titles`, `aligned` and `byteorder`. formats : list of data-types, optional A list containing the data-types for the different columns, e.g. ``['i4', 'f8', 'i4']``. `formats` does *not* support the new convention of using types directly, i.e. ``(int, float, int)``. Note that `formats` must be a list, not a tuple. Given that `formats` is somewhat limited, we recommend specifying `dtype` instead. names : tuple of str, optional The name of each column, e.g. ``('x', 'y', 'z')``. buf : buffer, optional By default, a new array is created of the given shape and data-type. If `buf` is specified and is an object exposing the buffer interface, the array will use the memory from the existing buffer. In this case, the `offset` and `strides` keywords are available. Other Parameters ---------------- titles : tuple of str, optional Aliases for column names. For example, if `names` were ``('x', 'y', 'z')`` and `titles` is ``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then ``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``. byteorder : {'<', '>', '='}, optional Byte-order for all fields. aligned : bool, optional Align the fields in memory as the C-compiler would. strides : tuple of ints, optional Buffer (`buf`) is interpreted according to these strides (strides define how many bytes each array element, row, column, etc. occupy in memory). offset : int, optional Start reading buffer (`buf`) from this offset onwards. order : {'C', 'F'}, optional Row-major (C-style) or column-major (Fortran-style) order. Returns ------- rec : recarray Empty array of the given shape and type. See Also -------- rec.fromrecords : Construct a record array from data. record : fundamental data-type for `recarray`. format_parser : determine a data-type from formats, names, titles. Notes ----- This constructor can be compared to ``empty``: it creates a new record array but does not fill it with data. To create a record array from data, use one of the following methods: 1. Create a standard ndarray and convert it to a record array, using ``arr.view(np.recarray)`` 2. Use the `buf` keyword. 3. Use `np.rec.fromrecords`. Examples -------- Create an array with two fields, ``x`` and ``y``: >>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)]) >>> x array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i4')]) >>> x['x'] array([ 1., 3.]) View the array as a record array: >>> x = x.view(np.recarray) >>> x.x array([ 1., 3.]) >>> x.y array([2, 4]) Create a new, empty record array: >>> np.recarray((2,), ... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP rec.array([(-1073741821, 1.2249118382103472e-301, 24547520), (3471280, 1.2134086255804012e-316, 0)], dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')]) rP Nr F�Cc C sf |d k rt �|�} nt|||| | �j} |d krFtj| |t| f| d�} n tj| |t| f|||| d�} | S )N)�order)�buffer�offset�stridesro )r; r( r r' �ndarray�__new__r )�subtype�shaper( �bufrq rr r* r+ r, r. r- ro rK r) r! r! r" rt � s zrecarray.__new__c C s | j jtk r | j jr | j | _ d S )N)r( rA r r6 )r) r` r! r! r" �__array_finalize__� s zrecarray.__array_finalize__c C s� y t �| |�S tk r Y nX t�| d�j}y|| d d� }W n$ ttfk rh td| ��Y nX | j|� }|jjr�t |jj t j �r�|j | jj |jfd�S |S |� t�S d S )Nr( r0 z recarray has no attribute %s)r( )�objectrX rY rs r6 � TypeError�KeyErrorrU r( � issubclassrA rV rW r[ )r) r] r^ r_ r` r! r! r" rX � s zrecarray.__getattribute__c C s |dkr*t |jtj�r*|jr*t�t|f�}|| jk}yt � | ||�}W nB t � | d�jp^i }||kr�t �� d d� \}}||��Y n@X t � | d�jp�i }||kr�|S |r�yt �| |� W n |S y|| d d� }W n& ttfk �r td| ��Y nX | j|f|�� S )Nr( r0 z record array has no attribute %s)r| rA rV rW r6 r; r( r �__dict__ry rc rs rX �sys�exc_info� __delattr__rz r{ rY rT ) r) r] rd Znewattr�retr^ �exctype�valuer_ r! r! r" rc � s. zrecarray.__setattr__c sp t t| ��|�}t|t�rh|jjrZ|�t| ��}t |jjt j �rV|j| jj|jfd�S |S |jtd�S n|S d S )N)r( )rA ) �superr re r9 rs r( r6 r[ rA r| rV rW )r) rf r` )r\ r! r" re � s zrecarray.__getitem__c C s� | j }| j jtks"t| j jtj�sF|jtkr<t� tj|f�}d}d}nd}d}| jdksb| jdkrttj | d|d�}nd t | j�f }d d t |� }||||f S ) Nz rec.array(zrec.array(%s, %sdtype=%s)zarray(z*array(%s, %sdtype=%s).view(numpy.recarray)r )r z, )� separator�prefixz [], shape=%sri � ) r( rA r r| rV rW r; �sizerv Z array2stringrF r )r) Z repr_dtyper� rl �lst�lfr! r! r" rR s zrecarray.__repr__c C sz t |t�r t�| d�j}|| }t�| d�j}|| d d� }|d krf| j|� }|jjr\|S |�t�S | j |f|�� S d S )Nr( r0 ) r9 �intrs rX r+ r6 rU r( r[ rT )r) r] rd r+ r^ r_ r` r! r! r" �field" s zrecarray.field) NNr NNNNNFrn )N) rL rM rN rO rt rx rX rc re rR r� � __classcell__r! r! )r\ r" r * s h " Fc C s| dd� | D �} |dks |dkr(| d j }t|t�r8|f}|dkr�|dkr�g }x,| D ]$}t|t�shtd��|�|jj� qRW d�|�}|dk r�t �|�} | j } n t |||||�} | j } | j } t| �t| �kr�td��| d j } t| �} | dkr�|d| � }xJt| �D ]>\}}| | j} |j d|j| � }||k�rtd| ���qW t|| �}x&tt| ��D ]}| | || | < �q^W |S ) aA create a record array from a (flat) list of arrays >>> x1=np.array([1,2,3,4]) >>> x2=np.array(['a','dd','xyz','12']) >>> x3=np.array([1.1,2,3,4]) >>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c') >>> print(r[1]) (2, 'dd', 2.0) >>> x1[1]=34 >>> r.a array([1, 2, 3, 4]) c S s g | ]}t �|��qS r! )r; Zasarray)r4 �xr! r! r" r7 C s z fromarrays.<locals>.<listcomp>Nr z*item in the array list must be an ndarray.r2 z>mismatch between the number of fields and the number of arraysz array-shape mismatch in array %d)rv r9 r� rs r8 r r( rC r: r; r+ r rG r' r � enumerate�ndimr r )Z arrayListr( rv r* r+ r, r- r. r` rK rG �parsedZd0�nn�kZ testshape�_arrayr r! r! r" � fromarrays4 sB r� c s< |dkrP|dkrPt j| td�� � fdd�t� jd �D �}t|||||||d�S |dk rht �t|f�} nt|||||�j } yt j| | d�} W n� t k �r |dks�|dkr�t | �}t |t tf�r�|f}t |�dkr�td ��t|| �} x"t| j�D ]} t| | �| | < q�W | S X |dk �r.| j|k�r.|| _| �t�} | S ) ad create a recarray from a list of records in text form The data in the same field can be heterogeneous, they will be promoted to the highest data type. This method is intended for creating smaller record arrays. If used to create large array without formats defined r=fromrecords([(2,3.,'abc')]*100000) it can be slow. If formats is None, then this will auto-detect formats. Use list of tuples rather than list of lists for faster processing. >>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)], ... names='col1,col2,col3') >>> print(r[0]) (456, 'dbe', 1.2) >>> r.col1 array([456, 2]) >>> r.col2 array(['dbe', 'de'], dtype='|S3') >>> import pickle >>> print(pickle.loads(pickle.dumps(r))) [(456, 'dbe', 1.2) (2, 'de', 1.3)] N)r( c s" g | ]}t �� d |f �� ��qS ).)r; �array�tolist)r4 r )r` r! r" r7 � s z fromrecords.<locals>.<listcomp>���)r* rv r+ r, r- r. r r z Can only deal with 1-d array.)r; r� ry r rv r� r( r r r' rz r r9 r� r r8 r r� rB r[ )ZrecListr( rv r* r+ r, r- r. ZarrlistrK �retvalr� r� r_ r! )r` r" � fromrecordsu s2 r� c C s� |dkr|dkrt d��|dk r,t�|�} nt|||||�j} | j} |dks\|dks\|dkrlt| �| | }t|| | |d�} | S )zM create a (read-only) record array from binary data contained in a stringNz Must have dtype= or formats=r r� )rw rq )r8 r; r( r r' �itemsizer r ) Z datastringr( rv rq r* r+ r, r- r. rK r� r� r! r! r" � fromstring� s r� c C sP y | � � }W n& tk r2 tj�| j�| �� S X t�|�}|j| �� }|S )N) �filenorY �os�path�getsizerg �tell�fstat�st_size)�fd�fn�str� r! r! r" �get_remaining_size� s r� c C s0 |dks|dkrd}nt |ttf�r*|f}d} t | t�rFd} t| d�} |dkrZ| �|d� t| �} |dk rvt�|�} nt |||||�j } | j } t� |�� � } | | }|dk r�t|�}| | ||�d�< t|�}t� |�� � } | | }|| kr�td��t|| �}| �|j�}||k�r td��| �r,| �� |S ) am Create an array from binary file data If file is a string then that file is opened, else it is assumed to be a file object. The file object must support random access (i.e. it must have tell and seek methods). >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,a5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.newbyteorder('<') >>> a.tofile(fd) >>> >>> fd.seek(0) >>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10, ... byteorder='<') >>> print(r[5]) (0.5, 10, 'abcde') >>> r.shape (10,) Nr )r� r �rbr� z:Not enough bytes left in file for specified shape and typez%Didn't read as many bytes as expected)r9 r� r rC �open�seekr� r; r( r r' r� r� �prodr �indexrB r8 r �readinto�data�IOError�close)r� r( rv rq r* r+ r, r- r. rg r� rK r� Z shapeprodZ shapesize�nbytesr� Z nbytesreadr! r! r" �fromfile� sB r� Tc C s t | td�tf�st| �r2|dkr2|dkr2td��i } |dk rJt�|�}n,|dk rft||||| �j}n||||| d�} | dkr�|dkr�td��t ||| ||d�S t | t �r�t | |f||d�| ��S t | t t f��rt | d t t f�� r�t| f||d�| ��S t| f||d�| ��S �nt | t ��rZ|dk �rD| j|k�rD| �|�} n| } | �rV| �� } | S t| ��rtt| |||d �S t | t��r�|dk �r�| j|k�r�| �|�} n| } | �r�| �� } | �t �S t| d d�} | dk�s�t | t��s�td ��t�| �} |dk �r| j|k�r| �|�} | �t �S dS ) z=Construct a record array from a wide-variety of objects. NzIMust define formats (or dtype) if object is None, string, or an open file)r* r+ r, r- r. z"Must define a shape if obj is None)rw rq rr )rv rq r )r( rv )r( rv rq Z__array_interface__zUnknown input type)r9 rA rC r r8 r; r( r r' r r r� r rB r� r� r[ �copyr� rs rb �dictr� )r` r( rv rq rr r* r+ r, r- r. r� �kwds�newZ interfacer! r! r" r� s^ r� )NNNNNFN)NNNNNFN)NNr NNNFN)NNr NNNFN) NNr NNNNFNT) rO � __future__r r r r~ r� r1 r r; r rV � numpy.compatr r r �__all__rs rJ ZtypeDictZnumfmtr# r rW r r r� r� r� r� r� r� r! r! r! r" �<module>$ sR S @ > D