Edit File: npyio.cpython-37.pyc
B ��FdA' � @ s d dl mZmZmZ d dlZd dlZd dlZd dlZd dlZd dl Z d dl m Z m Z d dlZddlmZ ddlmZ d dlmZmZ ddlmZmZmZmZm Z m Z m Z m Z m Z m!Z!m"Z" d d l#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z* ej+d d kr�d dl,Z,nd dl-Z,d d l.m/Z/ e,j0Z0d d ddddddddddddddgZ1G d d � d e2�Z3d d � Z4G d d!� d!e2�Z5d=d$d�Z6d>d%d�Z7d&d� Z8d'd� Z9d?d(d)�Z:d*d+� Z;e<d,ddd dd-d fd.d �Z=d@d4d �Z>d5d� Z?e<d,dd d dddddddd6d-d"d7dd-d"d"dfd8d�Z@d9d� ZAd:d� ZBd;d� ZCd<d� ZDdS )A� )�division�absolute_import�print_functionN)� itemgetter�index� )�format)� DataSource)�packbits� unpackbits) � LineSplitter� NameValidator�StringConverter�ConverterError�ConverterLockError�ConversionWarning�_is_string_like�has_nested_fields� flatten_dtype� easy_dtype�_bytes_to_name)�asbytes�asstr�asbytes_nested�bytes� basestring�unicode�is_pathlib_path� )�map�savetxt�loadtxt� genfromtxt� ndfromtxt� mafromtxt� recfromtxt� recfromcsv�load�loads�save�savez�savez_compressedr r � fromregexr c @ s( e Zd ZdZdd� Zdd� Zdd� ZdS ) �BagObjau BagObj(obj) Convert attribute look-ups to getitems on the object passed in. Parameters ---------- obj : class instance Object on which attribute look-up is performed. Examples -------- >>> from numpy.lib.npyio import BagObj as BO >>> class BagDemo(object): ... def __getitem__(self, key): # An instance of BagObj(BagDemo) ... # will call this method when any ... # attribute look-up is required ... result = "Doesn't matter what you want, " ... return result + "you're gonna get this" ... >>> demo_obj = BagDemo() >>> bagobj = BO(demo_obj) >>> bagobj.hello_there "Doesn't matter what you want, you're gonna get this" >>> bagobj.I_can_be_anything "Doesn't matter what you want, you're gonna get this" c C s t �|�| _d S )N)�weakref�proxy�_obj)�self�obj� r3 �B/opt/alt/python37/lib64/python3.7/site-packages/numpy/lib/npyio.py�__init__F s zBagObj.__init__c C s2 yt �| d�| S tk r, t|��Y nX d S )Nr0 )�object�__getattribute__�KeyError�AttributeError)r1 �keyr3 r3 r4 r7 J s zBagObj.__getattribute__c C s t �| d��� S )z� Enables dir(bagobj) to list the files in an NpzFile. This also enables tab-completion in an interpreter or IPython. r0 )r6 r7 �keys)r1 r3 r3 r4 �__dir__P s zBagObj.__dir__N)�__name__� __module__� __qualname__�__doc__r5 r7 r< r3 r3 r3 r4 r- ( s r- c O s2 t | �rt| �} ddl}d|d< |j| f|�|�S )z� Create a ZipFile. Allows for Zip64, and the `file` argument can accept file, str, or pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile constructor. r NT� allowZip64)r �str�zipfile�ZipFile)�file�args�kwargsrC r3 r3 r4 �zipfile_factoryY s rH c @ sr e Zd ZdZd dd�Zdd� Zd d � Zd d � Zd d� Zdd� Z dd� Z dd� Z dd� Z dd� Z dd� Zdd � ZdS ) �NpzFilea NpzFile(fid) A dictionary-like object with lazy-loading of files in the zipped archive provided on construction. `NpzFile` is used to load files in the NumPy ``.npz`` data archive format. It assumes that files in the archive have a ``.npy`` extension, other files are ignored. The arrays and file strings are lazily loaded on either getitem access using ``obj['key']`` or attribute lookup using ``obj.f.key``. A list of all files (without ``.npy`` extensions) can be obtained with ``obj.files`` and the ZipFile object itself using ``obj.zip``. Attributes ---------- files : list of str List of all files in the archive with a ``.npy`` extension. zip : ZipFile instance The ZipFile object initialized with the zipped archive. f : BagObj instance An object on which attribute can be performed as an alternative to getitem access on the `NpzFile` instance itself. allow_pickle : bool, optional Allow loading pickled data. Default: True pickle_kwargs : dict, optional Additional keyword arguments to pass on to pickle.load. These are only useful when loading object arrays saved on Python 2 when using Python 3. Parameters ---------- fid : file or str The zipped archive to open. This is either a file-like object or a string containing the path to the archive. own_fid : bool, optional Whether NpzFile should close the file handle. Requires that `fid` is a file-like object. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) >>> np.savez(outfile, x=x, y=y) >>> outfile.seek(0) >>> npz = np.load(outfile) >>> isinstance(npz, np.lib.io.NpzFile) True >>> npz.files ['y', 'x'] >>> npz['x'] # getitem access array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> npz.f.x # attribute lookup array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) FTNc C s� t |�}|�� | _g | _|| _|| _x:| jD ]0}|�d�rP| j�|d d� � q,| j�|� q,W || _t | �| _ |r||| _ nd | _ d S )Nz.npy���) rH �namelist�_files�files� allow_pickle� pickle_kwargs�endswith�append�zipr- �f�fid)r1 rT �own_fidrN rO Z_zip�xr3 r3 r4 r5 � s zNpzFile.__init__c C s | S )Nr3 )r1 r3 r3 r4 � __enter__� s zNpzFile.__enter__c C s | � � d S )N)�close)r1 �exc_type� exc_value� tracebackr3 r3 r4 �__exit__� s zNpzFile.__exit__c C s> | j dk r| j �� d| _ | jdk r4| j�� d| _d| _dS )z" Close the file. N)rR rX rT rS )r1 r3 r3 r4 rX � s z NpzFile.closec C s | � � d S )N)rX )r1 r3 r3 r4 �__del__� s zNpzFile.__del__c C s� d}|| j krd}n|| jkr*d}|d7 }|r�| j�|�}|�ttj��}|�� |tjkr|| j�|�}tj || j | j d�S | j�|�S n t d| ��d S )Nr r z.npy)rN rO z %s is not a file in the archive) rL rM rR �open�read�lenr � MAGIC_PREFIXrX � read_arrayrN rO r8 )r1 r: �memberr �magicr3 r3 r4 � __getitem__� s" zNpzFile.__getitem__c C s t | j�S )N)�iterrM )r1 r3 r3 r4 �__iter__� s zNpzFile.__iter__c s � fdd�� j D �S )zV Return a list of tuples, with each tuple (filename, array in file). c s g | ]}|� | f�qS r3 r3 )�.0rS )r1 r3 r4 � <listcomp>� s z!NpzFile.items.<locals>.<listcomp>)rM )r1 r3 )r1 r4 �items� s z NpzFile.itemsc c s" x | j D ]}|| | fV qW dS )z8Generator that returns tuples (filename, array in file).N)rM )r1 rS r3 r3 r4 � iteritems� s zNpzFile.iteritemsc C s | j S )z6Return files in the archive with a ``.npy`` extension.)rM )r1 r3 r3 r4 r; � s z NpzFile.keysc C s | � � S )z1Return an iterator over the files in the archive.)rg )r1 r3 r3 r4 �iterkeys s zNpzFile.iterkeysc C s | j �|�S )N)rM � __contains__)r1 r: r3 r3 r4 rm s zNpzFile.__contains__)FTN)r= r> r? r@ r5 rW r\ rX r] re rg rj rk r; rl rm r3 r3 r3 r4 rI h s = rI T�ASCIIc C s< d}t | t�r t| d�}d}n t| �r6| �d�}d}n| }|dkrJtd��tjd dkrft||d�}ni }z�d }tt j �} |� | �} |� t | t| �� d � | �|�r�|} d}t|| ||d �S | t j kr�|r�t j| |d �S t j|||d �S n8|s�td��ytj|f|�S tdt| � ��Y nX W d|�r6|�� X dS )a� Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files. Parameters ---------- file : file-like object, string, or pathlib.Path The file to read. File-like objects must support the ``seek()`` and ``read()`` methods. Pickled files require that the file-like object support the ``readline()`` method as well. mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional If not None, then memory-map the file, using the given mode (see `numpy.memmap` for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory. allow_pickle : bool, optional Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail. Default: True fix_imports : bool, optional Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. If `fix_imports` is True, pickle will try to map the old Python 2 names to the new names used in Python 3. encoding : str, optional What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. Values other than 'latin1', 'ASCII', and 'bytes' are not allowed, as they can corrupt numerical data. Default: 'ASCII' Returns ------- result : array, tuple, dict, etc. Data stored in the file. For ``.npz`` files, the returned instance of NpzFile class must be closed to avoid leaking file descriptors. Raises ------ IOError If the input file does not exist or cannot be read. ValueError The file contains an object array, but allow_pickle=False given. See Also -------- save, savez, savez_compressed, loadtxt memmap : Create a memory-map to an array stored in a file on disk. lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file. Notes ----- - If the file contains pickle data, then whatever object is stored in the pickle is returned. - If the file is a ``.npy`` file, then a single array is returned. - If the file is a ``.npz`` file, then a dictionary-like object is returned, containing ``{filename: array}`` key-value pairs, one for each file in the archive. - If the file is a ``.npz`` file, the returned value supports the context manager protocol in a similar fashion to the open function:: with load('foo.npz') as data: a = data['a'] The underlying file descriptor is closed when exiting the 'with' block. Examples -------- Store data to disk, and load it again: >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]])) >>> np.load('/tmp/123.npy') array([[1, 2, 3], [4, 5, 6]]) Store compressed data to disk, and load it again: >>> a=np.array([[1, 2, 3], [4, 5, 6]]) >>> b=np.array([1, 2]) >>> np.savez('/tmp/123.npz', a=a, b=b) >>> data = np.load('/tmp/123.npz') >>> data['a'] array([[1, 2, 3], [4, 5, 6]]) >>> data['b'] array([1, 2]) >>> data.close() Mem-map the stored array, and then access the second row directly from disk: >>> X = np.load('/tmp/123.npy', mmap_mode='r') >>> X[1, :] memmap([4, 5, 6]) F�rbT)rn �latin1r z.encoding must be 'ASCII', 'latin1', or 'bytes'r r )�encoding� fix_importss PKr )rU rN rO )�mode)rN rO z>allow_pickle=False, but file does not contain non-pickled dataz'Failed to interpret file %s as a pickleN)� isinstancer r^ r � ValueError�sys� version_info�dictr` r ra r_ �seek�min� startswithrI Z open_memmaprb �pickler' �IOError�reprrX ) rE Z mmap_moderN rr rq rU rT rO Z _ZIP_PREFIX�Nrd �tmpr3 r3 r4 r' sJ f c C s� d}t | t�r0| �d�s | d } t| d�}d}n8t| �rd| j�d�sT| j| jd } | �d�}d}n| }tjd dkr�t |d�}nd}z t � |�}t j ||||d � W d|r�|�� X dS ) a` Save an array to a binary file in NumPy ``.npy`` format. Parameters ---------- file : file, str, or pathlib.Path File or filename to which the data is saved. If file is a file-object, then the filename is unchanged. If file is a string or Path, a ``.npy`` extension will be appended to the file name if it does not already have one. allow_pickle : bool, optional Allow saving object arrays using Python pickles. Reasons for disallowing pickles include security (loading pickled data can execute arbitrary code) and portability (pickled objects may not be loadable on different Python installations, for example if the stored objects require libraries that are not available, and not all pickled data is compatible between Python 2 and Python 3). Default: True fix_imports : bool, optional Only useful in forcing objects in object arrays on Python 3 to be pickled in a Python 2 compatible way. If `fix_imports` is True, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2. arr : array_like Array data to be saved. See Also -------- savez : Save several arrays into a ``.npz`` archive savetxt, load Notes ----- For a description of the ``.npy`` format, see the module docstring of `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> np.save(outfile, x) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file >>> np.load(outfile) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Fz.npy�wbTr r )rr N)rN rO )rt r rP r^ r �name�parentrv rw rx �np� asanyarrayr � write_arrayrX )rE ZarrrN rr rU rT rO r3 r3 r4 r) � s* 3 c O s t | ||d� dS )a� Save several arrays into a single file in uncompressed ``.npz`` format. If arguments are passed in with no keywords, the corresponding variable names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword arguments are given, the corresponding variable names, in the ``.npz`` file will match the keyword names. Parameters ---------- file : str or file Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the ``.npz`` extension will be appended to the file name if it is not already there. args : Arguments, optional Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside `savez`, the arrays will be saved with names "arr_0", "arr_1", and so on. These arguments can be any expression. kwds : Keyword arguments, optional Arrays to save to the file. Arrays will be saved in the file with the keyword names. Returns ------- None See Also -------- save : Save a single array to a binary file in NumPy format. savetxt : Save an array to a file as plain text. savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is not compressed and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) Using `savez` with \*args, the arrays are saved with default names. >>> np.savez(outfile, x, y) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file >>> npzfile = np.load(outfile) >>> npzfile.files ['arr_1', 'arr_0'] >>> npzfile['arr_0'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Using `savez` with \**kwds, the arrays are saved with the keyword names. >>> outfile = TemporaryFile() >>> np.savez(outfile, x=x, y=y) >>> outfile.seek(0) >>> npzfile = np.load(outfile) >>> npzfile.files ['y', 'x'] >>> npzfile['x'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) FN)�_savez)rE rF �kwdsr3 r3 r4 r* s Nc O s t | ||d� dS )a Save several arrays into a single file in compressed ``.npz`` format. If keyword arguments are given, then filenames are taken from the keywords. If arguments are passed in with no keywords, then stored file names are arr_0, arr_1, etc. Parameters ---------- file : str or file Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the ``.npz`` extension will be appended to the file name if it is not already there. args : Arguments, optional Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside `savez`, the arrays will be saved with names "arr_0", "arr_1", and so on. These arguments can be any expression. kwds : Keyword arguments, optional Arrays to save to the file. Arrays will be saved in the file with the keyword names. Returns ------- None See Also -------- numpy.save : Save a single array to a binary file in NumPy format. numpy.savetxt : Save an array to a file as plain text. numpy.savez : Save several arrays into an uncompressed ``.npz`` file format numpy.load : Load the files created by savez_compressed. Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is compressed with ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Examples -------- >>> test_array = np.random.rand(3, 2) >>> test_vector = np.random.rand(4) >>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector) >>> loaded = np.load('/tmp/123.npz') >>> print(np.array_equal(test_array, loaded['a'])) True >>> print(np.array_equal(test_vector, loaded['b'])) True TN)r� )rE rF r� r3 r3 r4 r+ T s =c C s� dd l }dd l}t| t�r.| �d�sR| d } n$t| �rR| j�d�sR| j| jd } |}x<t|�D ]0\} } d| } | |� � kr�t d| ��| || < q`W |r�|j } n|j } t | d| d�} t| �r�tj�| �nd\}}|j||dd �\}}t�|� z�x�|�� D ]�\} } | d }t|d �}zpy6tj|t�| �||d � |�� d }| j||d � W n4 tk �r� } ztd||f ��W d d }~X Y nX W d |�r�|�� X q�W W d t� |� X | �� d S )Nr z.npzzarr_%dz,Cannot use un-named variables and keyword %s�w)rs � compression)Nr� z -numpy.npy)�prefix�dir�suffixz.npyr� )rN rO )�arcnamezFailed to write to %s: %s) rC �tempfilert r rP r r� r� � enumerater; ru � ZIP_DEFLATED� ZIP_STOREDrH r �os�path�split�mkstemprX rj r^ r r� r� r� �writer} �remove)rE rF r� �compressrN rO rC r� Znamedict�i�valr: r� ZzipfZfile_dirZ file_prefix�fdZtmpfile�fnamerT �excr3 r3 r4 r� � sN & r� c C s� dd� }| j }t|tj�r"dd� S t|tj�r4tjS t|tj�rFtjS t|tj�rZdd� S t|tj�rltjS t|tj�r||S t|tj �r�dd� S t|tj �r�t S t S dS )z; Find the correct dtype converter. Adapted from matplotlib c S s& | � � d| kr t�t| ��S t| �S )Ns 0x)�lower�float�fromhexr )rV r3 r3 r4 � floatconv� s z_getconv.<locals>.floatconvc S s t t| ��S )N)�bool�int)rV r3 r3 r4 �<lambda>� � z_getconv.<locals>.<lambda>c S s t t| ��S )N)r� r� )rV r3 r3 r4 r� � r� c S s t t| ��S )N)�complexr )rV r3 r3 r4 r� � r� N) �type� issubclassr� Zbool_�uint64�int64�integer� longdoubleZfloatingr� Zbytes_r r )�dtyper� �typr3 r3 r4 �_getconv� s& r� �#Fc s� �dk rNt �ttf�r"t��g�ndd� �D ��dd� �D ��t�d�����|} �dk rbt���|dk r�y t|�} W n tk r� |g} Y nX xN| D ]F} y t | � W q� tk r� } zdt | � f| _ � W dd} ~ X Y q�X q�W | }d} y�t | �r�t | �} t| ��r�d } | �d ��r.d dl}t|�| ��}nP| �d ��rRd dl}t|�| ��}n,tjd d k�rrtt| d��}n tt| ��}nt| �}W n tk �r� td��Y nX g � �fdd���fdd�����fdd�}�z�t�|�}t|��xt |�D ]}t |� �q�W d}y"x |�s*t |�}||�}�qW W n0 t k �r^ d}g }t j d| d d� Y nX t!|�pj|�}�|�\}}t!|�dk�r�dd� |D �}n*�fdd�t |�D �}|dk�r�|t"fg}xT| �p�i �#� D ]B\}}|�r y|�$|�}W n tk �r �w�Y nX |||< �q�W x�t%t&�'|g|��D ]�\}}||��t!��d k�rN�q,|�rf�fd d�|D ��t!��|k�r�|| d }td | ��d d� t(|��D �}�||�}� �)|� �q,W W d| �r�|�*� X t�+� |�� � j,d k�r � j-dd � d k�r d!� _-|d"k�rtd#| ��� j,|k�r,t�.� �� � j,|k �rd|dk�rNt�/� �� n|d k�rdt�0� �j1� |�r�t!|�dk�r�� fd$d�|j2D �S � j1S n� S dS )%a� Load data from a text file. Each row in the text file must have the same number of values. Parameters ---------- fname : file, str, or pathlib.Path File, filename, or generator to read. If the filename extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note that generators should return byte strings for Python 3k. dtype : data-type, optional Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type. comments : str or sequence, optional The characters or list of characters used to indicate the start of a comment; default: '#'. delimiter : str, optional The string used to separate values. By default, this is any whitespace. converters : dict, optional A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: ``converters = {0: datestr2num}``. Converters can also be used to provide a default value for missing data (but see also `genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None. skiprows : int, optional Skip the first `skiprows` lines; default: 0. usecols : int or sequence, optional Which columns to read, with 0 being the first. For example, usecols = (1,4,5) will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read. .. versionadded:: 1.11.0 Also when a single column has to be read it is possible to use an integer instead of a tuple. E.g ``usecols = 3`` reads the fourth column the same way as `usecols = (3,)`` would. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)``. When used with a structured data-type, arrays are returned for each field. Default is False. ndmin : int, optional The returned array will have at least `ndmin` dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. .. versionadded:: 1.6.0 Returns ------- out : ndarray Data read from the text file. See Also -------- load, fromstring, fromregex genfromtxt : Load data with missing values handled as specified. scipy.io.loadmat : reads MATLAB data files Notes ----- This function aims to be a fast reader for simply formatted files. The `genfromtxt` function provides more sophisticated handling of, e.g., lines with missing values. .. versionadded:: 1.10.0 The strings produced by the Python float.hex method can be used as input for floats. Examples -------- >>> from io import StringIO # StringIO behaves like a file object >>> c = StringIO("0 1\n2 3") >>> np.loadtxt(c) array([[ 0., 1.], [ 2., 3.]]) >>> d = StringIO("M 21 72\nF 35 58") >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'), ... 'formats': ('S1', 'i4', 'f4')}) array([('M', 21, 72.0), ('F', 35, 58.0)], dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')]) >>> c = StringIO("1,0,2\n3,0,4") >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True) >>> x array([ 1., 3.]) >>> y array([ 2., 4.]) Nc S s g | ] }t |��qS r3 )r )rh �commentr3 r3 r4 ri W s zloadtxt.<locals>.<listcomp>c s s | ]}t �|�V qd S )N)�re�escape)rh r� r3 r3 r4 � <genexpr>Z s zloadtxt.<locals>.<genexpr>� |z\usecols must be an int or a sequence of ints but it contains at least one element of type %sFTz.gzr z.bz2� �Uz1fname must be a string, file handle, or generatorc s | j dkr�| j}t|�dkr(| jgdfS |d tfg}t|�dkrvx2| jddd� D ] }||d d || fg}qTW | jgtt�| j�� |fS nlg }g }xZ| j D ]P}| j| \}}� |�\}} |� |� |j dkr�|� | � q�|� t|�| f� q�W ||fS dS )z;Unpack a structured data-type, and produce re-packing info.Nr ���r ���) �names�shaper` �base�listr� r� �prod�fields�extend�ndimrQ ) �dtr� �packingZdim�types�field�tpr Zflat_dtZ flat_packing)�flatten_dtype_internalr3 r4 r� � s& z'loadtxt.<locals>.flatten_dtype_internalc sz |dkr| d S |t kr t | �S |tkr0t| �S d}g }x4|D ],\}}|�� | ||| � |�� ||7 }q>W t |�S dS )z6Pack items into nested lists based on re-packing info.Nr )�tupler� rQ )rj r� �start�ret�lengthZ subpacking)� pack_itemsr3 r4 r� � s zloadtxt.<locals>.pack_itemsc sF t | �} � dk r&�jt | �dd�d } | �d�} | r>| ���S g S dS )z�Chop off comments, strip, and split at delimiter. 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Nr )�maxsplitr s )r r� �strip)�line)�comments� delimiter�regex_commentsr3 r4 � split_line� s zloadtxt.<locals>.split_line� z loadtxt: Empty input file: "%s")� stacklevelr c S s g | ] }t |��qS r3 )r� )rh r� r3 r3 r4 ri � s c s g | ]}� �qS r3 r3 )rh r� )�defconvr3 r4 ri � s c s g | ] }� | �qS r3 r3 )rh r� )�valsr3 r4 ri � s z"Wrong number of columns at line %dc S s g | ]\}}||��qS r3 r3 )rh �convr� r3 r3 r4 ri s r )r r )r r� )r r r� z"Illegal value of ndmin keyword: %sc s g | ] }� | �qS r3 r3 )rh r� )�Xr3 r4 ri s )3rt r r r r� �compile�joinr� � TypeError�opindexr� rF r rB r rP �gziprf �GzipFile�bz2�BZ2Filerv rw r^ ru r� r� r� �range�next� StopIteration�warnings�warnr` r� rj r r� � itertools�chainrR rQ rX �arrayr� r� �squeezeZ atleast_1d� atleast_2d�Tr� ) r� r� r� r� � convertersZskiprows�usecols�unpackZndmin�user_convertersZusecols_as_listZcol_idx�eZfownr� �fhr� r� r� Z first_vals� first_liner Z dtype_typesr� r� r� Zline_numrj r3 )r� r� r� r� r� r� r� r� r4 r! � s� g �%.18e� � r� �# c C s t |t�rt|�}t|�}d}t| �r.t| �} t| �r�d}| �d�rZddl} | �| d�} q�t j d dkrtt| d�} q�t| d�} nt | d �r�| } nt d ���z`t �|�}|jd kr�|jjdkr�t �|�j}d } q�t|jj�} n |jd } t �|�} t|�ttfk�r6t|�| k�r td t|� ��t|�� t t|��} n�t |t��r�|� d �}t d| �}|d k�r�| �r|d||f g| }n |g| }|� |�} n4| �r�|d| k�r�|�n| �s�|| k�r�|�n|} nt d|f ��t|�dk�r |� dd| �}| � t!|| | �� | �rhx�|D ]L}g }x&|D ] }|�"|j#� |�"|j$� �q$W | � t!| t|� | �� �qW n\xZ|D ]R}y | � t!| t|� | �� W n, t%k �r� t%dt|j�| f ��Y nX �qnW t|�dk�r�|� dd| �}| � t!|| | �� W d|�r | �&� X dS )a� Save an array to a text file. Parameters ---------- fname : filename or file handle If the filename ends in ``.gz``, the file is automatically saved in compressed gzip format. `loadtxt` understands gzipped files transparently. X : array_like Data to be saved to a text file. fmt : str or sequence of strs, optional A single format (%10.5f), a sequence of formats, or a multi-format string, e.g. 'Iteration %d -- %10.5f', in which case `delimiter` is ignored. For complex `X`, the legal options for `fmt` are: a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted like `' (%s+%sj)' % (fmt, fmt)` b) a full string specifying every real and imaginary part, e.g. `' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns c) a list of specifiers, one per column - in this case, the real and imaginary part must have separate specifiers, e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns delimiter : str, optional String or character separating columns. newline : str, optional String or character separating lines. .. versionadded:: 1.5.0 header : str, optional String that will be written at the beginning of the file. .. versionadded:: 1.7.0 footer : str, optional String that will be written at the end of the file. .. versionadded:: 1.7.0 comments : str, optional String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# ', as expected by e.g. ``numpy.loadtxt``. .. versionadded:: 1.7.0 See Also -------- save : Save an array to a binary file in NumPy ``.npy`` format savez : Save several arrays into an uncompressed ``.npz`` archive savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- Further explanation of the `fmt` parameter (``%[flag]width[.precision]specifier``): flags: ``-`` : left justify ``+`` : Forces to precede result with + or -. ``0`` : Left pad the number with zeros instead of space (see width). width: Minimum number of characters to be printed. The value is not truncated if it has more characters. precision: - For integer specifiers (eg. ``d,i,o,x``), the minimum number of digits. - For ``e, E`` and ``f`` specifiers, the number of digits to print after the decimal point. - For ``g`` and ``G``, the maximum number of significant digits. - For ``s``, the maximum number of characters. specifiers: ``c`` : character ``d`` or ``i`` : signed decimal integer ``e`` or ``E`` : scientific notation with ``e`` or ``E``. ``f`` : decimal floating point ``g,G`` : use the shorter of ``e,E`` or ``f`` ``o`` : signed octal ``s`` : string of characters ``u`` : unsigned decimal integer ``x,X`` : unsigned hexadecimal integer This explanation of ``fmt`` is not complete, for an exhaustive specification see [1]_. References ---------- .. [1] `Format Specification Mini-Language <http://docs.python.org/library/string.html# format-specification-mini-language>`_, Python Documentation. Examples -------- >>> x = y = z = np.arange(0.0,5.0,1.0) >>> np.savetxt('test.out', x, delimiter=',') # X is an array >>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays >>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation FTz.gzr Nr� r r� r� z%fname must be a string or file handler zfmt has wrong shape. %s�%z'fmt has wrong number of %% formats: %sz (%s+%sj)r� zinvalid fmt: %rr� z?Mismatch between array dtype ('%s') and format specifier ('%s'))'rt r r r rB r rP r� r^ rv rw �hasattrru r� Zasarrayr� r� r� r� r� r` �descrr� Z iscomplexobjr� r� r� r9 r� r �count�replacer� r rQ �real�imagr� rX )r� r� �fmtr� �newline�headerZfooterr� �own_fhr� r� ZncolZ iscomplex_Xr Z n_fmt_chars�error�rowZrow2�numberr3 r3 r4 r ' s� s " c C s� d}t | d�s t| d�} d}z�t |d�s6t�t|��}t|tj�sLt�|�}|�| � � �}|r�t|d t �s�t�||j d �}tj ||d�}||_ntj ||d�}|S |r�| � � X dS ) a� Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from all matches of the regular expression in the file. Groups in the regular expression are converted to fields of the structured array. Parameters ---------- file : str or file File name or file object to read. regexp : str or regexp Regular expression used to parse the file. Groups in the regular expression correspond to fields in the dtype. dtype : dtype or list of dtypes Dtype for the structured array. Returns ------- output : ndarray The output array, containing the part of the content of `file` that was matched by `regexp`. `output` is always a structured array. Raises ------ TypeError When `dtype` is not a valid dtype for a structured array. See Also -------- fromstring, loadtxt Notes ----- Dtypes for structured arrays can be specified in several forms, but all forms specify at least the data type and field name. For details see `doc.structured_arrays`. Examples -------- >>> f = open('test.dat', 'w') >>> f.write("1312 foo\n1534 bar\n444 qux") >>> f.close() >>> regexp = r"(\d+)\s+(...)" # match [digits, whitespace, anything] >>> output = np.fromregex('test.dat', regexp, ... [('num', np.int64), ('key', 'S3')]) >>> output array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')], dtype=[('num', '<i8'), ('key', '|S3')]) >>> output['num'] array([1312, 1534, 444], dtype=int64) Fr_ ro T�matchr )r� N)r� r^ r� r� r rt r� r� �findallr_ r� r� r� rX )rE Zregexpr� r �seqZnewdtype�outputr3 r3 r4 r, � s$ 7 �_zf%ic I s< |dk r$|rt d��|dk r$t d��|dk r4t|�}t|t�rFt|�}t|tttf�r^t|�}|rrddlm}m } |pxi }t|t �s�t dt |� ��d}ydt | �r�t| �} t| t�r�tjd d kr�ttjj�| d ��}nttjj�| d ��}d }nt| �}W n& t k �r" t d t | � ��Y nX t|||d�j}t| | || d�} xt��D ]�t|� �qNW d} yNxH| �s�t|�} �d k�r�|| k�r�d� | � |�dd� �} || �} �qhW W n0 t k �r� d} g } t j d| d d� Y nX �d k�r | d �!� } | |k�r | d= | dk �rnydd� | � d�D �} W n@ t"k �rl y t| �} W n t k �rf | g} Y nX Y nX t#| �px| �} �d k�r�| dd� | D ���d} n2t$���r�| dd� �� d�D ���n��r�| ����dk �r�t%���| | || d���dk �rt���| �r�xJt&| �D ]>\�}!t$|!��r4��'|!�| �< n|!dk �r|!t#| � | �< �qW �dk �r�t#��| k�r��j(�t�)�fdd�| D ���t�j*��n*�dk �r�t#��| k�r�fdd�| D ��n �dk �r�dk �r�t�j*��|�p�d}"d d� t| �D �}t|"t ��r�x�|"�+� D ]�\}#}$t$|#��rNy��'|#�}#W n t k �rL �wY nX | �rzy| �'|#�}#W n t k �rx Y nX t|$ttf��r�d d� |$D �}$n t|$�g}$|#dk�r�x(|D ]}%|%�,|$� �q�W n||# �,|$� �qW n�t|"ttf��r(x�t-|"|�D ]&\}&}'t|&�}&|&|'k�r�|'�.|&� �q�W nRt|"t/��rZ|"� d �}(x:|D ]}'|'�,|(� �qDW n x |D ]}'|'�,t|"�g� �q`W |})|)dk�r�g })dg| }t|)t ��r$x�|)�+� D ]r\}#}$t$|#��r�y��'|#�}#W n t k �r� �w�Y nX | �ry| �'|#�}#W n t k �r Y nX |$||#< �q�W nHt|)ttf��rbt#|)�}*|*| k�rT|)|d|*�< n |)d| � }n |)g| }�dk�r�d d� t-||�D �}nRt0�d d �}+t#|+�dk�r�t-|+||�},d!d� |,D �}n t-||�},�fd"d�|,D �}g }-x�|�+� D ]�\}.� t$|.��r.y��'|.�}.|.�W n t k �r* �w�Y nX n6| �r`y| �'|.��W n t k �r\ �w�Y nX n|.�t#| ��rx| |. }/nd}/|� j1� d |/|� |� d#� |-�.�� f� �q�W |�1|-� g ��j.}0|�r�g }1|1j.}2g }3|3j.}4x�t&t2�3| g|��D ]�\�}5||5�� t#� �}6|6dk�r�q�| �rfy� fd$d�| D �� W n. t4k �rb |4�� d |6f� �w�Y nX n"|6| k�r�|4�� d |6f� �q�|0t� �� |�r�|2td%d� t-� |�D ��� t#��|k�r�P �q�W |�r�|�5� �dk� r�x�t&|�D ]�\�}7�fd&d��D �}8y|7�6|8� W n� t7k � r� d'� }9t8t9����}8xdt&|8�D ]X\}.}&y|7�:|&� W n> t;t fk � r� |9d(7 }9|9|.d � |&f; }9t;|9��Y nX � qHW Y nX �q�W t#|3�}:|:dk� rft#��|: | �d)| � |dk� rt#��fd*d�|3D ��};|3d|:|; � }3||;8 }� fd+d�|3D �}9t#|9�� rf|9�<dd,� d-� |9�}9|� rVt |9��nt j |9t=d d� |dk� r��d| � �|� r�|1d| � }1|� r�tt-�fd.d�t&|�D �� ��n tt-�fd/d�t&|�D �� ���}<�dk� r�d0d� |D �}=d1d� t&|=�D �}>x,|>D ]$�d2t>�fd3d4�|<D �� |=�< � q W �dk� r�t?d5d� |D ��}?t#|?�dk� rrt|?�d tj@ }@}An2�fd6d�t&|=�D �}@|� r·fd7d�t&|=�D �}An(tt-�|=��}@tt-�tj@gt#|=� ��}AtjA|<|@d8�}B|� r�tjA|1|Ad8�}C�n��� r�j*� r��_*t#|+�dk� r�d9d:d4� |+D �k� rNtB��� r>tCd;��ntjA|<�d8�}Bn"tjA|<d<d� |+D �d8����D��}B|� r�tjA|1t�)d=d� |+D ��d8�}D|��}A|D�D|A�}Cn�|� r^d }Eg �x|t&d>d� |D ��D ]f\�}F�|k� r |E|F�j kM }E|FtjEk� rd2t>�fd?d4�|<D �� }F��.d@|Ff� n��.d@�f� � q�W |E� s^t#��dk� rTt�)���n t�)|F��t�A|<��}B|� r��j*� r�dAd� �j*D �}Antj@}AtjA|1|Ad8�}C|Bj)j*�|�r��rxZt-�� p�d|�D ]F\}G� � fdBd�� jFD �}x&|D ] }H|C|G |B|G |HkO < � q�W � q�W |�r$|B�D|�}B|C|B_G|�r4|B�H� jIS |B�H� S )Ca� Load data from a text file, with missing values handled as specified. Each line past the first `skip_header` lines is split at the `delimiter` character, and characters following the `comments` character are discarded. Parameters ---------- fname : file, str, pathlib.Path, list of str, generator File, filename, list, or generator to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. Note that generators must return byte strings in Python 3k. The strings in a list or produced by a generator are treated as lines. dtype : dtype, optional Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually. comments : str, optional The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded delimiter : str, int, or sequence, optional The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field. skiprows : int, optional `skiprows` was removed in numpy 1.10. Please use `skip_header` instead. skip_header : int, optional The number of lines to skip at the beginning of the file. skip_footer : int, optional The number of lines to skip at the end of the file. converters : variable, optional The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: ``converters = {3: lambda s: float(s or 0)}``. missing : variable, optional `missing` was removed in numpy 1.10. Please use `missing_values` instead. missing_values : variable, optional The set of strings corresponding to missing data. filling_values : variable, optional The set of values to be used as default when the data are missing. usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first valid line after the first `skip_header` lines. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype fields will be used, if any. excludelist : sequence, optional A list of names to exclude. This list is appended to the default list ['return','file','print']. Excluded names are appended an underscore: for example, `file` would become `file_`. deletechars : str, optional A string combining invalid characters that must be deleted from the names. defaultfmt : str, optional A format used to define default field names, such as "f%i" or "f_%02i". autostrip : bool, optional Whether to automatically strip white spaces from the variables. replace_space : char, optional Character(s) used in replacement of white spaces in the variables names. By default, use a '_'. case_sensitive : {True, False, 'upper', 'lower'}, optional If True, field names are case sensitive. If False or 'upper', field names are converted to upper case. If 'lower', field names are converted to lower case. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)`` usemask : bool, optional If True, return a masked array. If False, return a regular array. loose : bool, optional If True, do not raise errors for invalid values. invalid_raise : bool, optional If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped. max_rows : int, optional The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file. .. versionadded:: 1.10.0 Returns ------- out : ndarray Data read from the text file. If `usemask` is True, this is a masked array. See Also -------- numpy.loadtxt : equivalent function when no data is missing. Notes ----- * When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields. * When the variables are named (either by a flexible dtype or with `names`, there must not be any header in the file (else a ValueError exception is raised). * Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces. References ---------- .. [1] NumPy User Guide, section `I/O with NumPy <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_. Examples --------- >>> from io import StringIO >>> import numpy as np Comma delimited file with mixed dtype >>> s = StringIO("1,1.3,abcde") >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ... ('mystring','S5')], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) Using dtype = None >>> s.seek(0) # needed for StringIO example only >>> data = np.genfromtxt(s, dtype=None, ... names = ['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) Specifying dtype and names >>> s.seek(0) >>> data = np.genfromtxt(s, dtype="i8,f8,S5", ... names=['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) An example with fixed-width columns >>> s = StringIO("11.3abcde") >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'], ... delimiter=[1,3,5]) >>> data array((1, 1.3, 'abcde'), dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')]) NzPThe keywords 'skip_footer' and 'max_rows' can not be specified at the same time.r z 'max_rows' must be at least 1.r )� MaskedArray�make_mask_descrzNThe input argument 'converter' should be a valid dictionary (got '%s' instead)Fr� ZrbUro TzRfname must be a string, filehandle, list of strings, or generator. 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