Submit
Path:
~
/
/
proc
/
thread-self
/
root
/
opt
/
alt
/
python35
/
lib64
/
python3.5
/
site-packages
/
numpy
/
lib
/
__pycache__
/
File Content:
format.cpython-35.pyc
W��fJn � @ s� d Z d d l m Z m Z m Z d d l Z d d l Z d d l Z d d l Z d d l m Z d d l m Z m Z m Z m Z m Z e j d d k r� d d l Z n d d l Z e d � Z e e � d Z d. Z d d � Z d d � Z d d � Z d d � Z d d � Z d d d � Z d d � Z d d � Z d d � Z d d � Z! d d � Z" d d! � Z# d d" d d# d$ � Z$ d" d d% d& � Z% d' d d d( d d) d* � Z& d+ d, d- � Z' d S)/a� Define a simple format for saving numpy arrays to disk with the full information about them. The ``.npy`` format is the standard binary file format in NumPy for persisting a *single* arbitrary NumPy array on disk. The format stores all of the shape and dtype information necessary to reconstruct the array correctly even on another machine with a different architecture. The format is designed to be as simple as possible while achieving its limited goals. The ``.npz`` format is the standard format for persisting *multiple* NumPy arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy`` files, one for each array. Capabilities ------------ - Can represent all NumPy arrays including nested record arrays and object arrays. - Represents the data in its native binary form. - Supports Fortran-contiguous arrays directly. - Stores all of the necessary information to reconstruct the array including shape and dtype on a machine of a different architecture. Both little-endian and big-endian arrays are supported, and a file with little-endian numbers will yield a little-endian array on any machine reading the file. The types are described in terms of their actual sizes. For example, if a machine with a 64-bit C "long int" writes out an array with "long ints", a reading machine with 32-bit C "long ints" will yield an array with 64-bit integers. - Is straightforward to reverse engineer. Datasets often live longer than the programs that created them. A competent developer should be able to create a solution in their preferred programming language to read most ``.npy`` files that he has been given without much documentation. - Allows memory-mapping of the data. See `open_memmep`. - Can be read from a filelike stream object instead of an actual file. - Stores object arrays, i.e. arrays containing elements that are arbitrary Python objects. Files with object arrays are not to be mmapable, but can be read and written to disk. Limitations ----------- - Arbitrary subclasses of numpy.ndarray are not completely preserved. Subclasses will be accepted for writing, but only the array data will be written out. A regular numpy.ndarray object will be created upon reading the file. .. warning:: Due to limitations in the interpretation of structured dtypes, dtypes with fields with empty names will have the names replaced by 'f0', 'f1', etc. Such arrays will not round-trip through the format entirely accurately. The data is intact; only the field names will differ. We are working on a fix for this. This fix will not require a change in the file format. The arrays with such structures can still be saved and restored, and the correct dtype may be restored by using the ``loadedarray.view(correct_dtype)`` method. File extensions --------------- We recommend using the ``.npy`` and ``.npz`` extensions for files saved in this format. This is by no means a requirement; applications may wish to use these file formats but use an extension specific to the application. In the absence of an obvious alternative, however, we suggest using ``.npy`` and ``.npz``. Version numbering ----------------- The version numbering of these formats is independent of NumPy version numbering. If the format is upgraded, the code in `numpy.io` will still be able to read and write Version 1.0 files. Format Version 1.0 ------------------ The first 6 bytes are a magic string: exactly ``\x93NUMPY``. The next 1 byte is an unsigned byte: the major version number of the file format, e.g. ``\x01``. The next 1 byte is an unsigned byte: the minor version number of the file format, e.g. ``\x00``. Note: the version of the file format is not tied to the version of the numpy package. The next 2 bytes form a little-endian unsigned short int: the length of the header data HEADER_LEN. The next HEADER_LEN bytes form the header data describing the array's format. It is an ASCII string which contains a Python literal expression of a dictionary. It is terminated by a newline (``\n``) and padded with spaces (``\x20``) to make the total length of ``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment purposes. The dictionary contains three keys: "descr" : dtype.descr An object that can be passed as an argument to the `numpy.dtype` constructor to create the array's dtype. "fortran_order" : bool Whether the array data is Fortran-contiguous or not. Since Fortran-contiguous arrays are a common form of non-C-contiguity, we allow them to be written directly to disk for efficiency. "shape" : tuple of int The shape of the array. For repeatability and readability, the dictionary keys are sorted in alphabetic order. This is for convenience only. A writer SHOULD implement this if possible. A reader MUST NOT depend on this. Following the header comes the array data. If the dtype contains Python objects (i.e. ``dtype.hasobject is True``), then the data is a Python pickle of the array. Otherwise the data is the contiguous (either C- or Fortran-, depending on ``fortran_order``) bytes of the array. Consumers can figure out the number of bytes by multiplying the number of elements given by the shape (noting that ``shape=()`` means there is 1 element) by ``dtype.itemsize``. Format Version 2.0 ------------------ The version 1.0 format only allowed the array header to have a total size of 65535 bytes. This can be exceeded by structured arrays with a large number of columns. The version 2.0 format extends the header size to 4 GiB. `numpy.save` will automatically save in 2.0 format if the data requires it, else it will always use the more compatible 1.0 format. The description of the fourth element of the header therefore has become: "The next 4 bytes form a little-endian unsigned int: the length of the header data HEADER_LEN." Notes ----- The ``.npy`` format, including reasons for creating it and a comparison of alternatives, is described fully in the "npy-format" NEP. � )�division�absolute_import�print_functionN)� safe_eval)�asbytes�asstr� isfileobj�long� basestring� u NUMPY� � c C s) | d k r% d } t | | f � � d S)N� r r z7we only support format version (1,0) and (2, 0), not %s�r r �r r )r r N)� ValueError)�version�msg� r � /format.py�_check_version� s r c C s� | d k s | d k r$ t d � � | d k s< | d k rH t d � � t j d d k rs t t | � t | � St t | | g � Sd S)a Return the magic string for the given file format version. Parameters ---------- major : int in [0, 255] minor : int in [0, 255] Returns ------- magic : str Raises ------ ValueError if the version cannot be formatted. r � z&major version must be 0 <= major < 256z&minor version must be 0 <= minor < 256r N)r �sys� version_info� MAGIC_PREFIX�chr�bytes)�major�minorr r r �magic� s r c C s� t | t d � } | d d � t k rN d } t | t | d d � f � � t j d d k r� t t | d d � � \ } } n | d d � \ } } | | f S) z� Read the magic string to get the version of the file format. Parameters ---------- fp : filelike object Returns ------- major : int minor : int z magic stringNr z4the magic string is not correct; expected %r, got %rr r ���r r r )� _read_bytes� MAGIC_LENr r r r �map�ord)�fpZ magic_strr r r r r r � read_magic� s "r& c C s! | j d k r | j S| j Sd S)a� Get a serializable descriptor from the dtype. The .descr attribute of a dtype object cannot be round-tripped through the dtype() constructor. Simple types, like dtype('float32'), have a descr which looks like a record array with one field with '' as a name. The dtype() constructor interprets this as a request to give a default name. Instead, we construct descriptor that can be passed to dtype(). Parameters ---------- dtype : dtype The dtype of the array that will be written to disk. Returns ------- descr : object An object that can be passed to `numpy.dtype()` in order to replicate the input dtype. N)�names�descr�str)�dtyper r r �dtype_to_descr� s r+ c C sb d | j i } | j j r( d | d <n# | j j rA d | d <n d | d <t | j � | d <| S)a Get the dictionary of header metadata from a numpy.ndarray. Parameters ---------- array : numpy.ndarray Returns ------- d : dict This has the appropriate entries for writing its string representation to the header of the file. �shapeF� fortran_orderTr( )r, �flags� c_contiguous� f_contiguousr+ r* )�array�dr r r �header_data_from_array_1_0� s r3 c C s� d d l } d g } x= t | j � � D]) \ } } | j d | t | � f � q( W| j d � d j | � } t d t | � d } d | d } | d | d } t t | � � } t | � } | d k r| d k rd } t d d � | j d | � } nb | d k rG| d k rGd } t d d � | j d | � } n" d } | | | f ;} t | � � | j | � | j | � | S)a' Write the header for an array and returns the version used Parameters ---------- fp : filelike object d : dict This has the appropriate entries for writing its string representation to the header of the file. version: tuple or None None means use oldest that works explicit version will raise a ValueError if the format does not allow saving this data. Default: None Returns ------- version : tuple of int the file version which needs to be used to store the data r N�{z '%s': %s, �}� r r � � � � z<H� z<Iz'Header length %s too big for version=%si �r r )Nr< )r r l �r r )Nr= )r r )�struct�sorted�items�append�repr�joinr" �lenr �_filter_headerr �packr �write) r% r2 r r> �header�key�valueZcurrent_header_lenZtopadZhlenZ header_prefixr r r r �_write_array_header s. ! "" rK c C s t | | d � d S)z� Write the header for an array using the 1.0 format. Parameters ---------- fp : filelike object d : dict This has the appropriate entries for writing its string representation to the header of the file. r r N)r r )rK )r% r2 r r r �write_array_header_1_0L s rL c C s t | | d � d S)aQ Write the header for an array using the 2.0 format. The 2.0 format allows storing very large structured arrays. .. versionadded:: 1.9.0 Parameters ---------- fp : filelike object d : dict This has the appropriate entries for writing its string representation to the header of the file. r r N)r r )rK )r% r2 r r r �write_array_header_2_0Y s rM c C s t | d d �S)a� Read an array header from a filelike object using the 1.0 file format version. This will leave the file object located just after the header. Parameters ---------- fp : filelike object A file object or something with a `.read()` method like a file. Returns ------- shape : tuple of int The shape of the array. fortran_order : bool The array data will be written out directly if it is either C-contiguous or Fortran-contiguous. Otherwise, it will be made contiguous before writing it out. dtype : dtype The dtype of the file's data. Raises ------ ValueError If the data is invalid. r r r )r r )�_read_array_header)r% r r r �read_array_header_1_0h s rO c C s t | d d �S)a� Read an array header from a filelike object using the 2.0 file format version. This will leave the file object located just after the header. .. versionadded:: 1.9.0 Parameters ---------- fp : filelike object A file object or something with a `.read()` method like a file. Returns ------- shape : tuple of int The shape of the array. fortran_order : bool The array data will be written out directly if it is either C-contiguous or Fortran-contiguous. Otherwise, it will be made contiguous before writing it out. dtype : dtype The dtype of the file's data. Raises ------ ValueError If the data is invalid. r r r )r r )rN )r% r r r �read_array_header_2_0� s rP c C s� d d l } t j d d k r2 d d l m } n d d l m } g } d } x} | j | t | � � j � D]] } | d } | d } | r� | | j k r� | d k r� qm n | j | � | | j k } qm W| j | � S)a; Clean up 'L' in npz header ints. Cleans up the 'L' in strings representing integers. Needed to allow npz headers produced in Python2 to be read in Python3. Parameters ---------- s : byte string Npy file header. Returns ------- header : str Cleaned up header. r Nr )�StringIOFr �L) �tokenizer r �iorQ �generate_tokensr �read�NAMErA �NUMBER� untokenize)�srS rQ �tokensZlast_token_was_number�token� token_typeZ token_stringr r r rE � s % rE c C sf d d l } | d k rU t | d d � } | j d | � d } t | | d � } nY | d k r� t | d d � } | j d | � d } t | | d � } n t d | � � t | � } y t | � } WnA t k r } z! d } t | | | f � � WYd d } ~ Xn Xt | t � s3d } t | | � � t | j � � } | d d d g k rsd } t | | f � � t | d t � s�t j d d � | d D� � r�d } t | | d f � � t | d t � s�d } t | | d f � � y t j | d � } WnB t k rP} z" d } t | | d f � � WYd d } ~ Xn X| d | d | f S)z# see read_array_header_1_0 r Nr r zarray header lengthz<Hz array header� z<IzInvalid version %rz%Cannot parse header: %r Exception: %rz Header is not a dictionary: %rr( r- r, z,Header does not contain the correct keys: %rc S s% g | ] } t | t t f � � q Sr )� isinstance�intr )�.0�xr r r � <listcomp>� s z&_read_array_header.<locals>.<listcomp>zshape is not valid: %rz%fortran_order is not a valid bool: %rz)descr is not a valid dtype descriptor: %r)r r )r r )r> r! �unpackr rE r � SyntaxErrorr_ �dictr? �keys�tuple�numpy�all�boolr* � TypeError) r% r r> Z hlength_strZ header_lengthrH r2 �er rg r* r r r rN � sH )!*rN Tc C s� t | � t | t | � | � } | d k rJ | d k rJ t j d t � t d | j d � } | j j r� | s~ t d � � | d k r� i } t j | | d d | �n� | j j r2| j j r2t | � r� | j j | � q�x� t j | d d d d g d | d d �D] } | j | j d � � qWng t | � rN| j | � nK xH t j | d d d d g d | d d �D] } | j | j d � � qyWd S)a' Write an array to an NPY file, including a header. If the array is neither C-contiguous nor Fortran-contiguous AND the file_like object is not a real file object, this function will have to copy data in memory. Parameters ---------- fp : file_like object An open, writable file object, or similar object with a ``.write()`` method. array : ndarray The array to write to disk. version : (int, int) or None, optional The version number of the format. None means use the oldest supported version that is able to store the data. Default: None allow_pickle : bool, optional Whether to allow writing pickled data. Default: True pickle_kwargs : dict, optional Additional keyword arguments to pass to pickle.dump, excluding 'protocol'. These are only useful when pickling objects in object arrays on Python 3 to Python 2 compatible format. Raises ------ ValueError If the array cannot be persisted. This includes the case of allow_pickle=False and array being an object array. Various other errors If the array contains Python objects as part of its dtype, the process of pickling them may raise various errors if the objects are not picklable. r r z>Stored array in format 2.0. It can only beread by NumPy >= 1.9r7 i r z5Object arrays cannot be saved when allow_pickle=FalseN�protocolr. Z external_loopZbufferedZ zerosize_ok� buffersize�order�F�C)r r )r r i i )r rK r3 �warnings�warn� UserWarning�max�itemsizer* � hasobjectr �pickle�dumpr. r0 r/ r �T�tofileri ZnditerrG �tobytes)r% r1 r � allow_pickle� pickle_kwargs�used_verro �chunkr r r � write_array s4 $ r� c C s� t | � } t | � t | | � \ } } } t | � d k rI d } n t j j | � } | j r� | sv t d � � | d k r� i } y t j | | � } Wq�t k r� } z. t j d d k r� t d | f � � � WYd d } ~ Xq�Xnt | � r t j | d | d | �} n� t t t | j � } t j | d | �} x{ t d | | � D]g } t | | | � } t | | j � } t | | d � } t j | d | d | �| | | | � <qZW| r�| d d d � | _ | j � } n | | _ | S) a� Read an array from an NPY file. Parameters ---------- fp : file_like object If this is not a real file object, then this may take extra memory and time. allow_pickle : bool, optional Whether to allow reading pickled data. Default: True pickle_kwargs : dict Additional keyword arguments to pass to pickle.load. These are only useful when loading object arrays saved on Python 2 when using Python 3. Returns ------- array : ndarray The array from the data on disk. Raises ------ ValueError If the data is invalid, or allow_pickle=False and the file contains an object array. r r z6Object arrays cannot be loaded when allow_pickle=FalseNr z]Unpickling a python object failed: %r You may need to pass the encoding= option to numpy.loadr* �countz array data���)r& r rN rD ri Zmultiply�reducerx r ry �load� UnicodeErrorr r r �fromfile� BUFFER_SIZE�minrw �empty�ranger` r! Z frombufferr, Z transpose)r% r~ r r r, r- r* r� r1 �errZmax_read_count�iZ read_countZ read_size�datar r r � read_arrayP sB r� zr+Fc C s� t | t � s t d � � d | k r� t | � t j | � } | j r[ d } t | � � t d t | � d | d | � } t | | d � } zJ t | | | � } | d k r� | d k r� t j d t � | j � } Wd | j � Xnv t | d � } zY t | � } t | � t | | � \ } } } | j rEd } t | � � | j � } Wd | j � X| rod } n d } | d k r�d } t j | d | d | d | d | d | �} | S)a� Open a .npy file as a memory-mapped array. This may be used to read an existing file or create a new one. Parameters ---------- filename : str The name of the file on disk. This may *not* be a file-like object. mode : str, optional The mode in which to open the file; the default is 'r+'. In addition to the standard file modes, 'c' is also accepted to mean "copy on write." See `memmap` for the available mode strings. dtype : data-type, optional The data type of the array if we are creating a new file in "write" mode, if not, `dtype` is ignored. The default value is None, which results in a data-type of `float64`. shape : tuple of int The shape of the array if we are creating a new file in "write" mode, in which case this parameter is required. Otherwise, this parameter is ignored and is thus optional. fortran_order : bool, optional Whether the array should be Fortran-contiguous (True) or C-contiguous (False, the default) if we are creating a new file in "write" mode. version : tuple of int (major, minor) or None If the mode is a "write" mode, then this is the version of the file format used to create the file. None means use the oldest supported version that is able to store the data. Default: None Returns ------- marray : memmap The memory-mapped array. Raises ------ ValueError If the data or the mode is invalid. IOError If the file is not found or cannot be opened correctly. See Also -------- memmap zDFilename must be a string. Memmap cannot use existing file handles.�wz6Array can't be memory-mapped: Python objects in dtype.r( r- r, �br r z>Stored array in format 2.0. It can only beread by NumPy >= 1.9N�rbrq rr zw+zr+r* rp �mode�offset)r r )r r )r_ r r r ri r* rx rf r+ �openrK rs rt ru �tell�closer& rN Zmemmap) �filenamer� r* r, r- r r r2 r% r� r� rp Zmarrayr r r � open_memmap� sL 2 r� zran out of datac C s� t � } xh yL | j | t | � � } | | 7} t | � d k sV t | � | k rW PWq t j k ro Yq Xq Wt | � | k r� d } t | | | t | � f � � n | Sd S)a+ Read from file-like object until size bytes are read. Raises ValueError if not EOF is encountered before size bytes are read. Non-blocking objects only supported if they derive from io objects. Required as e.g. ZipExtFile in python 2.6 can return less data than requested. r z)EOF: reading %s, expected %d bytes got %dN)r rV rD rT �BlockingIOErrorr )r% �sizeZerror_templater� �rr r r r r! s $ "r! i )(�__doc__Z __future__r r r ri r rT rs Znumpy.lib.utilsr Z numpy.compatr r r r r r ry �cPickler rD r" r� r r r& r+ r3 rK rL rM rO rP rE rN r� r� r� r! r r r r �<module>� s< ( 2 " & 6KU n
Edit
Rename
Chmod
Delete
FILE
FOLDER
Name
Size
Permission
Action
__init__.cpython-35.opt-1.pyc
1131 bytes
0644
__init__.cpython-35.pyc
1131 bytes
0644
_datasource.cpython-35.opt-1.pyc
19733 bytes
0644
_datasource.cpython-35.pyc
19733 bytes
0644
_iotools.cpython-35.opt-1.pyc
28014 bytes
0644
_iotools.cpython-35.pyc
28014 bytes
0644
_version.cpython-35.opt-1.pyc
5182 bytes
0644
_version.cpython-35.pyc
5182 bytes
0644
arraypad.cpython-35.opt-1.pyc
44366 bytes
0644
arraypad.cpython-35.pyc
44366 bytes
0644
arraysetops.cpython-35.opt-1.pyc
12927 bytes
0644
arraysetops.cpython-35.pyc
12927 bytes
0644
arrayterator.cpython-35.opt-1.pyc
7528 bytes
0644
arrayterator.cpython-35.pyc
7528 bytes
0644
financial.cpython-35.opt-1.pyc
23604 bytes
0644
financial.cpython-35.pyc
23604 bytes
0644
format.cpython-35.opt-1.pyc
23998 bytes
0644
format.cpython-35.pyc
23998 bytes
0644
function_base.cpython-35.opt-1.pyc
131773 bytes
0644
function_base.cpython-35.pyc
131773 bytes
0644
index_tricks.cpython-35.opt-1.pyc
25147 bytes
0644
index_tricks.cpython-35.pyc
25147 bytes
0644
info.cpython-35.opt-1.pyc
6658 bytes
0644
info.cpython-35.pyc
6658 bytes
0644
nanfunctions.cpython-35.opt-1.pyc
43318 bytes
0644
nanfunctions.cpython-35.pyc
43318 bytes
0644
npyio.cpython-35.opt-1.pyc
58048 bytes
0644
npyio.cpython-35.pyc
58048 bytes
0644
polynomial.cpython-35.opt-1.pyc
37107 bytes
0644
polynomial.cpython-35.pyc
37107 bytes
0644
recfunctions.cpython-35.opt-1.pyc
30167 bytes
0644
recfunctions.cpython-35.pyc
30167 bytes
0644
scimath.cpython-35.opt-1.pyc
15056 bytes
0644
scimath.cpython-35.pyc
15056 bytes
0644
setup.cpython-35.opt-1.pyc
551 bytes
0644
setup.cpython-35.pyc
551 bytes
0644
shape_base.cpython-35.opt-1.pyc
25290 bytes
0644
shape_base.cpython-35.pyc
25290 bytes
0644
stride_tricks.cpython-35.opt-1.pyc
6324 bytes
0644
stride_tricks.cpython-35.pyc
6324 bytes
0644
twodim_base.cpython-35.opt-1.pyc
28183 bytes
0644
twodim_base.cpython-35.pyc
28183 bytes
0644
type_check.cpython-35.opt-1.pyc
15932 bytes
0644
type_check.cpython-35.pyc
15932 bytes
0644
ufunclike.cpython-35.opt-1.pyc
5237 bytes
0644
ufunclike.cpython-35.pyc
5237 bytes
0644
user_array.cpython-35.opt-1.pyc
12625 bytes
0644
user_array.cpython-35.pyc
12625 bytes
0644
utils.cpython-35.opt-1.pyc
28570 bytes
0644
utils.cpython-35.pyc
28570 bytes
0644
N4ST4R_ID | Naxtarrr
