Файловый менеджер - Редактировать - /home/digitalm/venv/lib/python3.7/site-packages/numpy/core/__pycache__/records.cpython-37.pyc
Назад
B �5�gP� � @ s0 d Z ddlZddlZddlmZ ddlmZ ddlmZ ddlm Z ddl m Z dd lmZ dd lmZ d d d dddddgZe jZddddddddddddddd�Ze jZdd� Zed�G d d � d ��ZG d d � d e j�ZG d d � d e�Zd d � Z d*d"d�Z d+d#d�Z d,d$d�Z d%d&� Z d-d'd�Z!d.d)d�Z"dS )/a� Record Arrays ============= Record arrays expose the fields of structured arrays as properties. Most commonly, ndarrays contain elements of a single type, e.g. floats, integers, bools etc. However, it is possible for elements to be combinations of these using structured types, such as:: >>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', np.int64), ('y', np.float64)]) >>> a array([(1, 2.), (1, 2.)], dtype=[('x', '<i8'), ('y', '<f8')]) Here, each element consists of two fields: x (and int), and y (a float). This is known as a structured array. The different fields are analogous to columns in a spread-sheet. The different fields can be accessed as one would a dictionary:: >>> a['x'] array([1, 1]) >>> a['y'] array([2., 2.]) Record arrays allow us to access fields as properties:: >>> ar = np.rec.array(a) >>> ar.x array([1, 1]) >>> ar.y array([2., 2.]) � N)�Counter)� nullcontext� )�numeric)� numerictypes)� os_fspath)� set_module)�get_printoptions�record�recarray� format_parser� fromarrays� fromrecords� fromstring�fromfile�array�>�<�=�s�|)�b�l�n�B�L�N�Sr r r r r �I�ic C s dd� t | ��� D �S )z@Find duplication in a list, return a list of duplicated elementsc S s g | ]\}}|d kr|�qS )r � )�.0�item�countsr r �H/home/digitalm-up/venv/lib/python3.7/site-packages/numpy/core/records.py� <listcomp>R s z"find_duplicate.<locals>.<listcomp>)r �items)�listr r r$ �find_duplicateO s r( �numpyc @ s4 e Zd ZdZd dd�Zd dd�Zdd � 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& | � ||� | �||� | �|� d S )N)� _parseFormats�_setfieldnames� _createdtype)�self�formats�names�titles�aligned� byteorderr r r$ �__init__� s zformat_parser.__init__c s� |dkrt d��t|t�r6t�dd� t|�D �|�}n t�||�}|j� � dkrht�d|fg|�}|j� |j}� fdd�|D �| _� fdd�|D �| _ t |�| _ dS )z Parse the field formats NzNeed formats argumentc S s g | ]\}}d � |�|f�qS )zf{})�format)r! r Zformat_r r r$ r% � s z/format_parser._parseFormats.<locals>.<listcomp>�f1c s g | ]}� | d �qS )r r )r! �key)�fieldsr r$ r% � s c s g | ]}� | d �qS )r r )r! r6 )r7 r r$ r% � s ) � ValueError� isinstancer' �sb�dtype� enumerater7 r/ � _f_formats�_offsets�len�_nfields)r- r. r1 r; �keysr )r7 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 �,zillegal input names %sc S s g | ] }|� � �qS r )�strip)r! r r r r$ r% � s z0format_parser._setfieldnames.<locals>.<listcomp>Nc S s g | ] }d | �qS )zf%dr )r! r r r r$ r% � s zDuplicate field names: %sc S s g | ] }|� � �qS r )rC )r! r r r r$ r% � s )�typer' �tupler9 �str�split� NameError�reprr@ �_names�ranger? r( r8 �_titles)r- r/ r0 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. �offsetsr0 r )r: r; rJ r= r> rL �_byteorderconvZ newbyteorder)r- r2 r; r r r$ r, � s zformat_parser._createdtype)FN)F)�__name__� __module__� __qualname__�__doc__r3 r* r+ r, r r r r$ r X s : $c sT e Zd ZdZd Z dZ� fdd�Z� fdd�Zdd� Zd d � Zd d � Zd d� Z � Z S )r zEA data-type scalar that allows field access as attribute lookup. r) c s t � d dkr| �� S t� �� S )N�legacyz1.13)r �__str__�super�__repr__)r- )� __class__r r$ rV � s zrecord.__repr__c s$ t � d dkrt| �� �S t� �� S )NrS z1.13)r rF r" rU rT )r- )rW r r$ rT � 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 |jd k r�|� | j |jf�S |S td| ��d S )N)�setfield�getfieldr; r; � z%'record' object has no attribute '%s') �nt�void�__getattribute__�AttributeErrorr7 �getrY r; r/ �viewrW )r- �attr� fielddict�res�obj�dtr r r$ r] � 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)rX rY r; zCannot set '%s' attributer; rZ z%'record' object has no attribute '%s') r^ r[ r\ r] r7 r_ rX �getattr� __setattr__)r- ra �valrb rc r r r$ rg s zrecord.__setattr__c C s@ t j�| |�}t|t j�r8|jjd k r8|�| j|jf�S |S d S )N)r[ r\ � __getitem__r9 r; r/ r` rW )r- �indxrd r r r$ ri s zrecord.__getitem__c s@ �j j}tdd� |D ��}d| � � �fdd�|D �}d�|�S )zPretty-print all fields.c s s | ]}t |�V qd S )N)r? )r! �namer r r$ � <genexpr>( s z record.pprint.<locals>.<genexpr>z %% %ds: %%sc s g | ]}� |t �|�f �qS r )rf )r! rk )�fmtr- r r$ r% * s z!record.pprint.<locals>.<listcomp>� )r; r/ �max�join)r- r/ �maxlen�rowsr )rm r- r$ �pprint$ s z record.pprint) rO rP rQ rR rV rT r] rg ri rs � __classcell__r r )rW 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 a- 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 -------- core.records.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', '<f8'), ('y', '<i8')]) >>> x array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')]) >>> 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')]) r) 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�stridesrv )r: r; r �ndarray�__new__r )�subtype�shaper; �bufrx ry r. r/ r0 r2 r1 rv �descrr- r r r$ r{ � s zrecarray.__new__c C s$ | j jtk r | j jd k r | j | _ d S )N)r; rD r r/ )r- rd 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 n4 ttfk rx } ztd| �|�W d d }~X Y nX | j|� }|jj d k r�t |jj t j �r�|j| jj |jfd�S |S |�t�S d S )Nr; rZ z recarray has no attribute %s)r; )�objectr] r^ rz r7 � TypeError�KeyErrorrY r; r/ � issubclassrD r[ r\ r` )r- ra rb rc �erd r r r$ r] � s zrecarray.__getattribute__c C s |dkr.t |jtj�r.|jd k r.t�t|f�}|| jk}yt � | ||�}W n0 t k rz t � | d�jpji }||krv� Y nJX t � | d�jp�i }||kr�|S |r�yt �| |� W n t k r� |S X y|| d d� }W n6 ttfk �r } ztd| �|�W d d }~X Y nX | j|f|�� S )Nr; rZ z record array has no attribute %s)r� rD r[ r\ r/ r: r; r �__dict__r� rg � Exceptionrz r] r7 � __delattr__r� r� r^ rX )r- ra rh Znewattr�retrb rc r� r r r$ rg � s0 zrecarray.__setattr__c sp t � �|�}t|t�rh|jjd k rZ|�t| ��}t|jjt j �rV|j| jj|jfd�S |S |jtd�S n|S d S )N)r; )rD ) rU ri r9 rz r; r/ r` rD r� r[ r\ )r- rj rd )rW r r$ ri � s zrecarray.__getitem__c C s� | j }| j jtks"t| j jtj�sF|jtkr<t� tj|f�}d}d}nd}d}| jdksb| jdkrvtj | d|dd �}nd t | j�f }d d t |� }t � d dkr�d | }||||f S )Nz rec.array(zrec.array(%s,%sdtype=%s)zarray(z)array(%s,%sdtype=%s).view(numpy.recarray)r )r z, rB )� separator�prefix�suffixz [], shape=%srn � rS z1.13) r; rD r r� r[ r\ r: �sizer} Z array2stringrI r? r )r- Z repr_dtyper� rm �lst�lfr r r$ rV s zrecarray.__repr__c C s~ t |t�r t�| d�j}|| }t�| d�j}|| d d� }|d krj| j|� }|jjd k r`|S |�t�S | j |f|�� S d S )Nr; rZ ) r9 �intrz r] r/ r7 rY r; r` rX )r- ra rh r/ rb rc rd r r r$ �field/ s zrecarray.field) NNr NNNNNFru )N) rO rP rQ rR r{ r� r] rg ri rV r� rt r r )rW r$ r 5 s g "! !c C s$ | dkr t jdtdd� d S | S d S )Nr z�Passing `shape=0` to have the shape be inferred is deprecated, and in future will be equivalent to `shape=(0,)`. To infer the shape and suppress this warning, pass `shape=None` instead.� )� stacklevel)�warnings�warn� FutureWarning)r} r r r$ �_deprecate_shape_0_as_NoneA s r� Fc C sB dd� | D �} t |�}|dkr*| d j}nt|t�r:|f}|dkrX|dkrXdd� | D �}|dk rlt�|�}nt|||||�j}|j} t|�t| �kr�t d��|d j} t| �} | dkr�|d| � }xFt | �D ]:\} } || j } | jd| j | � }||kr�t d| ��q�W t ||�}x&t t| ��D ]}| | || | < �q$W |S )a� Create a record array from a (flat) list of arrays Parameters ---------- arrayList : list or tuple List of array-like objects (such as lists, tuples, and ndarrays). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional Shape of the resulting array. If not provided, inferred from ``arrayList[0]``. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. Returns ------- np.recarray Record array consisting of given arrayList columns. Examples -------- >>> x1=np.array([1,2,3,4]) >>> x2=np.array(['a','dd','xyz','12']) >>> x3=np.array([1.1,2,3,4]) >>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c') >>> print(r[1]) (2, 'dd', 2.0) # may vary >>> x1[1]=34 >>> r.a array([1, 2, 3, 4]) >>> x1 = np.array([1, 2, 3, 4]) >>> x2 = np.array(['a', 'dd', 'xyz', '12']) >>> x3 = np.array([1.1, 2, 3,4]) >>> r = np.core.records.fromarrays( ... [x1, x2, x3], ... dtype=np.dtype([('a', np.int32), ('b', 'S3'), ('c', np.float32)])) >>> r rec.array([(1, b'a', 1.1), (2, b'dd', 2. ), (3, b'xyz', 3. ), (4, b'12', 4. )], dtype=[('a', '<i4'), ('b', 'S3'), ('c', '<f4')]) c S s g | ]}t �|��qS r )r: Zasarray)r! �xr r r$ r% } s z fromarrays.<locals>.<listcomp>Nr c S s g | ] }|j �qS r )r; )r! rd r r r$ r% � s z>mismatch between the number of fields and the number of arraysz array-shape mismatch in array %d)r� r} r9 r� r: r; r r/ r? r8 r< �ndimr rK )Z arrayListr; r} r. r/ r0 r1 r2 r rJ Zd0�nn�krd Z testshape�_arrayr r r r$ r M s6 0 c sL |dkrP|dkrPt j| td�� � fdd�t� jd �D �}t|||||||d�S |dk rht �t|f�} nt|||||�j} yt j| | d�} W n� t t fk �r t |�}|dkr�t | �}t |t�r�|f}t |�dkr�t d��t|| �} x"t| j�D ]} t| | �| | < q�W tjd td d � | S X |dk �r>| j|k�r>|| _| �t�} | S ) a Create a recarray from a list of records in text form. Parameters ---------- recList : sequence data in the same field may be heterogeneous - they will be promoted to the highest data type. dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. If both `formats` and `dtype` are None, then this will auto-detect formats. Use list of tuples rather than list of lists for faster processing. Returns ------- np.recarray record array consisting of given recList rows. Examples -------- >>> 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='<U3') >>> import pickle >>> pickle.loads(pickle.dumps(r)) rec.array([(456, 'dbe', 1.2), ( 2, 'de', 1.3)], dtype=[('col1', '<i8'), ('col2', '<U3'), ('col3', '<f8')]) N)r; c s" g | ]}t �� d |f �� ��qS ).)r: r �tolist)r! r )rd r r$ r% � s z fromrecords.<locals>.<listcomp>���)r. r} r/ r0 r1 r2 r z Can only deal with 1-d array.zxfromrecords expected a list of tuples, may have received a list of lists instead. In the future that will raise an errorrZ )r� )r: r r� rK r} r r; r r r� r8 r� r? r9 r� r r� rE r� r� r� r` )ZrecListr; r} r. r/ r0 r1 r2 Zarrlistr �retvalr� r� rc r )rd r$ r � s: + c C sx |dkr|dkrt d��|dk r,t�|�} nt|||||�j} | j} t|�}|dkrdt| �| | }t|| | |d�} | S )a� Create a record array from binary data Note that despite the name of this function it does not accept `str` instances. Parameters ---------- datastring : bytes-like Buffer of binary data dtype : data-type, optional Valid dtype for all arrays shape : int or tuple of ints, optional Shape of each array. offset : int, optional Position in the buffer to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. Returns ------- np.recarray Record array view into the data in datastring. This will be readonly if `datastring` is readonly. See Also -------- numpy.frombuffer Examples -------- >>> a = b'\x01\x02\x03abc' >>> np.core.records.fromstring(a, dtype='u1,u1,u1,S3') rec.array([(1, 2, 3, b'abc')], dtype=[('f0', 'u1'), ('f1', 'u1'), ('f2', 'u1'), ('f3', 'S3')]) >>> grades_dtype = [('Name', (np.str_, 10)), ('Marks', np.float64), ... ('GradeLevel', np.int32)] >>> grades_array = np.array([('Sam', 33.3, 3), ('Mike', 44.4, 5), ... ('Aadi', 66.6, 6)], dtype=grades_dtype) >>> np.core.records.fromstring(grades_array.tobytes(), dtype=grades_dtype) rec.array([('Sam', 33.3, 3), ('Mike', 44.4, 5), ('Aadi', 66.6, 6)], dtype=[('Name', '<U10'), ('Marks', '<f8'), ('GradeLevel', '<i4')]) >>> s = '\x01\x02\x03abc' >>> np.core.records.fromstring(s, dtype='u1,u1,u1,S3') Traceback (most recent call last) ... TypeError: a bytes-like object is required, not 'str' Nz2fromstring() needs a 'dtype' or 'formats' argument)Nr� )r~ rx )r� r: r; r �itemsizer� r? r ) Z datastringr; r} rx r. r/ r0 r1 r2 r r� r� r r r$ r � s 7 c C s4 | � � }z| �dd� | � � | S | �|d� X d S )Nr rZ )�tell�seek)�fd�posr r r$ �get_remaining_sizeH s r� c C s\ |dkr|dkrt d��t|�}|dkr.d}nt|t�r>|f}t| d�rRt| �} ntt| �d�} | ��} |dkrz| �|d� t | �} |dk r�t � |�} nt |||||�j } | j } t �|�jtjd�} | | }|dk �r t|�}| | ||�d �< t|�}t �|�jtjd�} | | }|| k�r&td ��t|| �}| �|j�}||k�rNtd ��W dQ R X |S ) a� Create an array from binary file data Parameters ---------- fd : str or file type If file is a string or a path-like object 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). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. offset : int, optional Position in the file to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation Returns ------- np.recarray record array consisting of data enclosed in file. Examples -------- >>> 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,) Nz0fromfile() needs a 'dtype' or 'formats' argument)r� �readinto�rbr r )r; r� z:Not enough bytes left in file for specified shape and typez%Didn't read as many bytes as expected)r� r� r9 r� �hasattrr �openr r� r� r: r; r r� r �prodr[ Zintpr' �indexrE r8 r r� �data�IOError)r� r; r} rx r. r/ r0 r1 r2 �ctxr� r r� Z shapeprodZ shapesize�nbytesr� Z nbytesreadr r r$ r P sD - Tc C s$ t | td�tf�s t| d�r4|dkr4|dkr4td��i } |dk rLt�|�}n,|dk rht||||| �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��r\|dk �rF| j|k�rF| �|�} n| } | �rX| �� } | S t| d��rxt| |||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 ) a� Construct a record array from a wide-variety of objects. A general-purpose record array constructor that dispatches to the appropriate `recarray` creation function based on the inputs (see Notes). Parameters ---------- obj : any Input object. See Notes for details on how various input types are treated. dtype : data-type, optional Valid dtype for array. shape : int or tuple of ints, optional Shape of each array. offset : int, optional Position in the file or buffer to start reading from. 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). formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. copy : bool, optional Whether to copy the input object (True), or to use a reference instead. This option only applies when the input is an ndarray or recarray. Defaults to True. Returns ------- np.recarray Record array created from the specified object. Notes ----- If `obj` is ``None``, then call the `~numpy.recarray` constructor. If `obj` is a string, then call the `fromstring` constructor. If `obj` is a list or a tuple, then if the first object is an `~numpy.ndarray`, call `fromarrays`, otherwise call `fromrecords`. If `obj` is a `~numpy.recarray`, then make a copy of the data in the recarray (if ``copy=True``) and use the new formats, names, and titles. If `obj` is a file, then call `fromfile`. Finally, if obj is an `ndarray`, then return ``obj.view(recarray)``, making a copy of the data if ``copy=True``. Examples -------- >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> np.core.records.array(a) rec.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=int32) >>> b = [(1, 1), (2, 4), (3, 9)] >>> c = np.core.records.array(b, formats = ['i2', 'f2'], names = ('x', 'y')) >>> c rec.array([(1, 1.0), (2, 4.0), (3, 9.0)], dtype=[('x', '<i2'), ('y', '<f2')]) >>> c.x rec.array([1, 2, 3], dtype=int16) >>> c.y rec.array([ 1.0, 4.0, 9.0], dtype=float16) >>> r = np.rec.array(['abc','def'], names=['col1','col2']) >>> print(r.col1) abc >>> r.col1 array('abc', dtype='<U3') >>> r.col2 array('def', dtype='<U3') Nr� zIMust define formats (or dtype) if object is None, string, or an open file)r. r/ r0 r1 r2 z"Must define a shape if obj is None)r~ rx ry )r} rx r )r; r} )r; r} rx Z__array_interface__zUnknown input type)r9 rD rF r� r8 r: r; r r �bytesr r' rE r r r` �copyr rz rf �dictr )rd r; r} rx ry r. r/ r0 r1 r2 r� �kwds�newZ interfacer r r$ r � s^ T )NNNNNFN)NNNNNFN)NNr NNNFN)NNr NNNFN) NNr NNNNFNT)#rR �osr� � collectionsr � contextlibr � r r: r r[ Z numpy.compatr Znumpy.core.overridesr Z arrayprintr �__all__rz rN Z sctypeDictZnumfmtr( r r\ r r r� r r r r� r r r r r r$ �<module># s\ U \ S I b
| ver. 1.4 |
Github
|
.
| PHP 8.3.23 | Генерация страницы: 0.05 |
proxy
|
phpinfo
|
Настройка