bgчdZddlZddlZddlmZmZddlmZmZm Z gZ hdZ dZ e e dzZdZd Zd Zd d d dZdZdZdZdZdZdZdZdZdZd(dZdZdZefdZefdZ dZ!efdZ"d)d Z#d*ed"d#Z$ d+ed"d%Z%d,d'Z&dS)-a Binary serialization NPY format ========== 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 they have been given without much documentation. - Allows memory-mapping of the data. See `open_memmap`. - 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 of ``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible by 64 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." Format Version 3.0 ------------------ This version replaces the ASCII string (which in practice was latin1) with a utf8-encoded string, so supports structured types with any unicode field names. Notes ----- The ``.npy`` format, including motivation for creating it and a comparison of alternatives, is described in the :doc:`"npy-format" NEP `, however details have evolved with time and this document is more current. N) safe_eval drop_metadata) isfileobj os_fspathpickle>descrshape fortran_ordersNUMPY@i)zwe only support format version (1,0), (2,0), and (3,0), not %s) ValueError)versionmsgs G/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/format.py_check_versionrs0444Nz)***54c|dks|dkrtd|dks|dkrtdtt||gzS)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. rz&major version must be 0 <= major < 256z&minor version must be 0 <= minor < 256)r MAGIC_PREFIXbytes)majorminors rmagicr#s[  qyyECKKABBB qyyECKKABBB %// //rct|td}|ddtkr#d}t|t|ddfz|dd\}}||fS)z Read the magic string to get the version of the file format. Parameters ---------- fp : filelike object Returns ------- major : int minor : int z magic stringNz4the magic string is not correct; expected %r, got %r) _read_bytes MAGIC_LENrr)fp magic_strrr!r"s r read_magicr*shB >::I"~%%D in==>>>RSS>LE5 %<rct|}||urtjdtd|j|jS|jS)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. z`metadata on a dtype is not saved to an npy/npz. Use another format (such as pickle) to store it.r  stacklevel)rwarningswarn UserWarningnamesrstr)dtype new_dtypes rdtype_to_descrr5s\2e$$I I!a 1 1 1 1 { {yrct|trtj|St|tr1t |d}tj||dfSg}g}g}g}d}|D]}t |dkr|\}} t | }n)|\}} } tjt | | f}|dko|jtjuo|j du} | srt|tr|nd|f\} }| | | || || |||j z }tj|||||dS)a. Returns a dtype based off the given description. This is essentially the reverse of `dtype_to_descr()`. It will remove the valueless padding fields created by, i.e. simple fields like dtype('float32'), and then convert the description to its corresponding dtype. Parameters ---------- descr : object The object retrieved by dtype.descr. Can be passed to `numpy.dtype()` in order to replicate the input dtype. Returns ------- dtype : dtype The dtype constructed by the description. rrr N)r1formatstitlesoffsetsitemsize) isinstancer2numpyr3tupledescr_to_dtypelentypevoidr1appendr;) rdtr9r1r8r:offsetfieldname descr_strr is_padtitles rr?r?s*%+{5!!! E5 ! !+ E!H % %{Ba>*** F EGG F u::??#OD) **BB%* "D)UnY77?@@B"*KEJ!6K28t;K #",T5"9"9K$$d|KE4 MM% LL    NN2    NN6 " " ""+ ;7f#*@@ A AArcd|ji}|jjrd|d<n|jjrd|d<nd|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. r Fr Tr)r flags c_contiguous f_contiguousr5r3)arrayds rheader_data_from_array_1_0rQRsh %+A {#"/  !#!/ #/ ,,AgJ Hrcddl}|Jt|\}}||}t|dz}tt ||z|ztzz } t|||||zz}n4#|j $r'd ||}t|dwxYw||zd|zzdzS)zO Takes a stringified header, and attaches the prefix and padding to it rNrz'Header length {} too big for version={}  ) struct_header_size_infoencoder@ ARRAY_ALIGNr'calcsizer#packerrorformatr) headerrrUfmtencodinghlenpadlen header_prefixrs r _wrap_headerrcnsMMM    %g.MC ]]8 $ $F v;;?D Y)=)==D S TF(w&++c4&=*I*II <(((7>>tWMMoo4'( 6 !DK /% 77s 1#B1Cc t|dS#t$rYnwxYw t|d}tjdtd|S#t $rYnwxYwt|d}tjdtd|S)zT Like `_wrap_header`, but chooses an appropriate version given the contents rrz>Stored array in format 2.0. It can only beread by NumPy >= 1.9r r,rz@Stored array in format 3.0. It can only be read by NumPy >= 1.17)rcrr.r/r0UnicodeEncodeError)r]rets r_wrap_header_guess_versionrgs FF+++      66**  -.9a I I I I      && ) )F M*+61FFFF Ms A AAc dg}t|D].\}}|d|dt|d/|dd|}|d}|dt |d kr4t t t||d rd nd z nd zz }|t|}nt||}| |d 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. Providing an explicit version will raise a ValueError if the format does not allow saving this data. Default: None {'z': z, }r7r  rr N) sorteditemsrCreprjoinr@GROWTH_AXIS_MAX_DIGITSrgrcwrite)r(rPrr]keyvaluer s r_write_array_headerrvs!UFQWWYY''99 U ccc4;;;;78888 MM# WWV__F gJE c u::>>-s4 Ao&-bbA.4400 ""F+F33fg..HHVrc(t||ddS)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. rNrvr(rPs rwrite_array_header_1_0rzsAv&&&&&rc(t||ddS)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. rNrxrys rwrite_array_header_2_0r|sAv&&&&&rc&t|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. max_header_size : int, optional Maximum allowed size of the header. Large headers may not be safe to load securely and thus require explicitly passing a larger value. See :py:func:`ast.literal_eval()` for details. Raises ------ ValueError If the data is invalid. rrmax_header_size_read_array_headerr(rs rread_array_header_1_0rs'B   A A AArc&t|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. max_header_size : int, optional Maximum allowed size of the header. Large headers may not be safe to load securely and thus require explicitly passing a larger value. See :py:func:`ast.literal_eval()` for details. 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. rr~rrs rread_array_header_2_0rs'F   A A AArcddl}ddlm}g}d}|||jD]F}|d}|d}|r||jkr|dkr&||||jk}G||S)a6Clean 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 : string Npy file header. Returns ------- header : str Cleaned up header. rN)StringIOFrL) tokenizeiorgenerate_tokensreadlineNAMErCNUMBER untokenize)srrtokenslast_token_was_numbertoken token_type token_strings r_filter_headerr(s"OOO F!))((1++*>?? @ @1X Qx ! !hm++##  MM% !+x!>   v & &&rcddl}t|}|"td||\}}t |||d}|||d}t ||d} | |} t| |kr tdt| d t| } n#t$r} |dkrst| } t| } tjd td n\#t$r*} d } t| | | d} ~ wwxYwd } t| | | Yd} ~ nd} ~ wwxYwt!| t"s$d } t| | t$| krEt)| }d} t| |t!| dt*rt-d| dDs*d} t| | dt!| dt.s*d} t| | d t1| d}n=#t2$r0} d} t| | d| d} ~ wwxYw| d| d|fS)z# see read_array_header_1_0 rNzInvalid version {!r}zarray header lengthz array headerzHeader info length (z) is large and may not be safe to load securely. To allow loading, adjust `max_header_size` or fully trust the `.npy` file using `allow_pickle=True`. For safety against large resource use or crashes, sandboxing may be necessary.rzReading `.npy` or `.npz` file required additional header parsing as it was created on Python 2. Save the file again to speed up loading and avoid this warning.r,zCannot parse header: {!r}z Header is not a dictionary: {!r}z.Header does not contain the correct keys: {!r}r c3@K|]}t|tVdSN)r<int).0xs r z%_read_array_header..s,;;1Jq#&&;;;;;;rzshape is not valid: {!r}r z'fortran_order is not a valid bool: {!r}rz+descr is not a valid dtype descriptor: {!r})rUrVgetrr\r&rYunpackdecoder@r SyntaxErrorrr.r/r0r<dict EXPECTED_KEYSkeysrnr>allboolr? TypeError)r(rrrUhinfo hlength_typer_ hlength_str header_lengthr]rPee2rrr3s rrrKsp MMM  ! !' * *E }/66w??@@@"L(b&//,"?"?AVWWKMM, <D))*** qz5 ) )1;;' ;;;;;1(AgJ//000 a($ / /97Ao$6778888qz** 888;AgJ//00a78 W:q)5 00sT"C22 F$<FD>!F> E2%E--E22(FF$,L L< +L77L<Tct|t|t|||jdkrd}nt d|jzd}|jjr,|std|i}tj ||fddi|dS|j j r~|j j srt|r|j|dSt!j|gd|d D]*}||d +dSt|r||dSt!j|gd|d D]*}||d +dS) a' Write an array to an NPY file, including a header. If the array is neither C-contiguous nor Fortran-contiguous AND the file_like object is not a real file object, this function will have to copy data in memory. Parameters ---------- fp : file_like object An open, writable file object, or similar object with a ``.write()`` method. array : ndarray The array to write to disk. version : (int, int) or None, optional The version number of the format. None means use the oldest supported version that is able to store the data. Default: None allow_pickle : bool, optional Whether to allow writing pickled data. Default: True pickle_kwargs : dict, optional Additional keyword arguments to pass to pickle.dump, excluding 'protocol'. These are only useful when pickling objects in object arrays on Python 3 to Python 2 compatible format. Raises ------ ValueError If the array cannot be persisted. This includes the case of allow_pickle=False and array being an object array. Various other errors If the array contains Python objects as part of its dtype, the process of pickling them may raise various errors if the objects are not picklable. rirz5Object arrays cannot be saved when allow_pickle=FalseNprotocolr) external_loopbuffered zerosize_okF)rL buffersizeorderC)rrvrQr;maxr3 hasobjectrrdumprLrNrMrTtofiler=nditerrstobytes)r(rOr allow_pickle pickle_kwargsrchunks r write_arrayrsH76u==wGGG ~ 5>91==  {- 3233 3  M E2;;;];;;;;  !-%+*B- R== - GNN2     !M!M!M)666 - -s++,,,, - - R== - LL     !M!M!M)666 - -s++,,,, - -rFrc|rd}t|}t|t|||\}}}t|dkrd}n+tj|tj}|jrP|std|i} tj |fi|} n,#t$r} td| d | d} ~ wwxYwt|rt j||| } nt j||} |jdkrt"t%t"|jz} t'd|| D]\} t%| || z } t)| |jz}t+||d }t j||| | | | | z<]|r%|ddd | _| } 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 writing pickled data. Default: False .. versionchanged:: 1.16.3 Made default False in response to CVE-2019-6446. 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. max_header_size : int, optional Maximum allowed size of the header. Large headers may not be safe to load securely and thus require explicitly passing a larger value. See :py:func:`ast.literal_eval()` for details. This option is ignored when `allow_pickle` is passed. In that case the file is by definition trusted and the limit is unnecessary. 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. lrrr)r3z6Object arrays cannot be loaded when allow_pickle=FalseNz#Unpickling a python object failed: z8 You may need to pass the encoding= option to numpy.load)r3countz array datarm)r*rrr@r=multiplyreduceint64rrrload UnicodeErrorrfromfilendarrayr; BUFFER_SIZEminrangerr& frombufferr transpose)r(rrrrr r r3rrOerrmax_read_counti read_count read_sizedatas r read_arrayrs=N  nnG7"4 #:#:#:E=% 5zzQ%%e5;%?? /  3233 3  M BK44m44EE B B B,25## 899>A B B R== ON2U%@@@EEM%u555E~!!!,K0P0P!Pq%88OOA!$^UQY!?!?J #J$? @ @I&r9lCCD,1,rswbbF 44444444 433  C   !      +++ 000,(%%%N6A6A6Ap   888800!!!!F ' ' ' ' ' '/?"A"A"A"AH/?$A$A$A$AN ' ' 'F5EM1M1M1M1^E-E-E-E-Pg/gggggT8<-1h 0hhhhhVr