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High level interface to PyTables for reading and writing pandas data structures
to disk
é    )Úannotations)ÚsuppressN)ÚdateÚtzinfo)Údedent)ÚTYPE_CHECKINGÚAnyÚCallableÚFinalÚLiteralÚcastÚoverload)ÚconfigÚ
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Ú	DataFrameÚDatetimeIndexÚIndexÚ
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isinstanceÚnpÚbytes_Údecode)Ús© rV   úD/var/www/html/env/lib/python3.10/site-packages/pandas/io/pytables.pyÚ_ensure_decodedŒ   s   
rX   Úencodingú
str | NoneÚreturnÚstrc                 C  s   | d u rt } | S ©N)Ú_default_encoding©rY   rV   rV   rW   Ú_ensure_encoding“   s   r`   c                 C  s   t | tƒr	t| ƒ} | S )zÓ
    Ensure that an index / column name is a str (python 3); otherwise they
    may be np.string dtype. Non-string dtypes are passed through unchanged.

    https://github.com/pandas-dev/pandas/issues/13492
    )rQ   r\   ©ÚnamerV   rV   rW   Ú_ensure_str›   s   
rc   Úscope_levelÚintc                   sV   |d ‰ t | ttfƒr‡ fdd„| D ƒ} n
t| ƒrt| ˆ d�} | du s't| ƒr)| S dS )zÔ
    Ensure that the where is a Term or a list of Term.

    This makes sure that we are capturing the scope of variables that are
    passed create the terms here with a frame_level=2 (we are 2 levels down)
    é   c                   s0   g | ]}|d urt |ƒrt|ˆ d d�n|‘qS )Nrf   ©rd   )r8   ÚTerm)Ú.0Úterm©ÚlevelrV   rW   Ú
<listcomp>µ   s
    þz _ensure_term.<locals>.<listcomp>rg   N)rQ   ÚlistÚtupler8   rh   Úlen)Úwhererd   rV   rk   rW   Ú_ensure_termª   s   	
þrr   z¨
where criteria is being ignored as this version [%s] is too old (or
not-defined), read the file in and write it out to a new file to upgrade (with
the copy_to method)
r
   Úincompatibility_doczu
the [%s] attribute of the existing index is [%s] which conflicts with the new
[%s], resetting the attribute to None
Úattribute_conflict_docz‘
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->%s,key->%s] [items->%s]
Úperformance_docÚfixedÚtable)Úfrv   Útrw   z;
: boolean
    drop ALL nan rows when appending to a table
Ú
dropna_docz~
: format
    default format writing format, if None, then
    put will default to 'fixed' and append will default to 'table'
Ú
format_doczio.hdfÚdropna_tableF)Ú	validatorÚdefault_format)rv   rw   Nc                  C  sN   t d u r%dd l} | a ttƒ� | jjdkaW d   ƒ t S 1 s w   Y  t S )Nr   Ústrict)Ú
_table_modÚtablesr   ÚAttributeErrorÚfileÚ_FILE_OPEN_POLICYÚ!_table_file_open_policy_is_strict)r�   rV   rV   rW   Ú_tablesî   s   

ÿ
ÿûr†   ÚaTr   Úpath_or_bufúFilePath | HDFStoreÚkeyÚvalueúDataFrame | SeriesÚmodeÚ	complevelú
int | NoneÚcomplibÚappendÚboolÚformatÚindexÚmin_itemsizeúint | dict[str, int] | NoneÚdropnaúbool | NoneÚdata_columnsú Literal[True] | list[str] | NoneÚerrorsÚNonec              
     sž   |r‡ ‡‡‡‡‡‡‡‡‡	f
dd„}n‡ ‡‡‡‡‡‡‡‡‡	f
dd„}t | ƒ} t| tƒrIt| |||d��}||ƒ W d  ƒ dS 1 sBw   Y  dS || ƒ dS )z+store this object, close it if we opened itc                   s   | j ˆˆ	ˆˆˆˆˆˆ ˆˆd�
S )N)r“   r”   r•   Únan_repr—   r™   r›   rY   )r‘   ©Ústore©
r™   r—   rY   r›   r“   r”   rŠ   r•   r�   r‹   rV   rW   Ú<lambda>  ó    özto_hdf.<locals>.<lambda>c                   s   | j ˆˆ	ˆˆˆˆˆ ˆˆˆd�
S )N)r“   r”   r•   r�   r™   r›   rY   r—   ©Úputrž   r    rV   rW   r¡   %  r¢   )r�   rŽ   r�   N)r=   rQ   r\   ÚHDFStore)rˆ   rŠ   r‹   r�   rŽ   r�   r‘   r“   r”   r•   r�   r—   r™   r›   rY   rx   rŸ   rV   r    rW   Úto_hdf  s    
ÿ
"ýr¦   Úrrq   ústr | list | NoneÚstartÚstopÚcolumnsúlist[str] | NoneÚiteratorÚ	chunksizec
                 K  s†  |dvrt d|› d�ƒ‚|durt|dd�}t| tƒr'| js"tdƒ‚| }d}n:t| ƒ} t| tƒs4td	ƒ‚zt	j
 | ¡}W n tt fyI   d}Y nw |sTtd
| › d�ƒ‚t| f||dœ|
¤Ž}d}z9|du r�| ¡ }t|ƒdkrtt dƒ‚|d }|dd… D ]}t||ƒs‰t dƒ‚q~|j}|j|||||||	|d�W S  t ttfyÂ   t| tƒsÁttƒ� | ¡  W d  ƒ ‚ 1 s¼w   Y  ‚ w )a"	  
    Read from the store, close it if we opened it.

    Retrieve pandas object stored in file, optionally based on where
    criteria.

    .. warning::

       Pandas uses PyTables for reading and writing HDF5 files, which allows
       serializing object-dtype data with pickle when using the "fixed" format.
       Loading pickled data received from untrusted sources can be unsafe.

       See: https://docs.python.org/3/library/pickle.html for more.

    Parameters
    ----------
    path_or_buf : str, path object, pandas.HDFStore
        Any valid string path is acceptable. Only supports the local file system,
        remote URLs and file-like objects are not supported.

        If you want to pass in a path object, pandas accepts any
        ``os.PathLike``.

        Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.

    key : object, optional
        The group identifier in the store. Can be omitted if the HDF file
        contains a single pandas object.
    mode : {'r', 'r+', 'a'}, default 'r'
        Mode to use when opening the file. Ignored if path_or_buf is a
        :class:`pandas.HDFStore`. Default is 'r'.
    errors : str, default 'strict'
        Specifies how encoding and decoding errors are to be handled.
        See the errors argument for :func:`open` for a full list
        of options.
    where : list, optional
        A list of Term (or convertible) objects.
    start : int, optional
        Row number to start selection.
    stop  : int, optional
        Row number to stop selection.
    columns : list, optional
        A list of columns names to return.
    iterator : bool, optional
        Return an iterator object.
    chunksize : int, optional
        Number of rows to include in an iteration when using an iterator.
    **kwargs
        Additional keyword arguments passed to HDFStore.

    Returns
    -------
    object
        The selected object. Return type depends on the object stored.

    See Also
    --------
    DataFrame.to_hdf : Write a HDF file from a DataFrame.
    HDFStore : Low-level access to HDF files.

    Examples
    --------
    >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])  # doctest: +SKIP
    >>> df.to_hdf('./store.h5', 'data')  # doctest: +SKIP
    >>> reread = pd.read_hdf('./store.h5')  # doctest: +SKIP
    )r§   úr+r‡   zmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nrf   rg   z&The HDFStore must be open for reading.Fz5Support for generic buffers has not been implemented.zFile z does not exist)r�   r›   Tr   z]Dataset(s) incompatible with Pandas data types, not table, or no datasets found in HDF5 file.z?key must be provided when HDF5 file contains multiple datasets.)rq   r©   rª   r«   r­   r®   Ú
auto_close)Ú
ValueErrorrr   rQ   r¥   Úis_openÚOSErrorr=   r\   ÚNotImplementedErrorÚosÚpathÚexistsÚ	TypeErrorÚFileNotFoundErrorÚgroupsrp   Ú_is_metadata_ofÚ_v_pathnameÚselectÚLookupErrorr   r‚   Úclose)rˆ   rŠ   r�   r›   rq   r©   rª   r«   r­   r®   ÚkwargsrŸ   r°   r·   rº   Úcandidate_only_groupÚgroup_to_checkrV   rV   rW   Úread_hdf<  sv   O
ÿ

ÿÿÿ
ÿÿø

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ÿýúrÃ   ÚgrouprF   Úparent_groupc                 C  sN   | j |j krdS | }|j dkr%|j}||kr|jdkrdS |j}|j dksdS )zDCheck if a given group is a metadata group for a given parent_group.Frf   ÚmetaT)Ú_v_depthÚ	_v_parentÚ_v_name)rÄ   rÅ   ÚcurrentÚparentrV   rV   rW   r»   ×  s   

ür»   c                   @  s¢  e Zd ZU dZded< ded< 				dŸd dd„Zd¡dd„Zedd„ ƒZed¡dd„ƒZ	d¢dd„Z
d£dd„Zd£dd„Zd¤d d!„Zd¥d"d#„Zd¦d%d&„Zd¡d'd(„Zd§d*d+„Zd¨d2d3„Zd©dªd7d8„Zd«d:d;„Zd¬d=d>„Zd­d®d?d@„Zd¯dAdB„Zed°dCdD„ƒZd±d²dFdG„Zd¢dHdI„Z							d³d´dMdN„Z			dµd¶dQdR„Z		d·d¸dTdU„Z								d¹dºdVdW„Z		X								Y	X	d»d¼dedf„Zdµd£dgdh„Z 			X	X											Yd½d¾dkdl„Z!			d¿dÀdodp„Z"			dµdÁdtdu„Z#dÂdwdx„Z$dÃdÄd|d}„Z%dÅdd€„Z&dÆd‚dƒ„Z'	„	X					XdÇdÈd‡dˆ„Z(d¡d‰dŠ„Z)d¯d‹dŒ„Z*dÉdŽd�„Z+			�	YdÊdËd“d”„Z,		X												Y	XdÌdÍd•d–„Z-dÎd™dš„Z.dÏd›dœ„Z/dÐd�dž„Z0dS )Ñr¥   aS	  
    Dict-like IO interface for storing pandas objects in PyTables.

    Either Fixed or Table format.

    .. warning::

       Pandas uses PyTables for reading and writing HDF5 files, which allows
       serializing object-dtype data with pickle when using the "fixed" format.
       Loading pickled data received from untrusted sources can be unsafe.

       See: https://docs.python.org/3/library/pickle.html for more.

    Parameters
    ----------
    path : str
        File path to HDF5 file.
    mode : {'a', 'w', 'r', 'r+'}, default 'a'

        ``'r'``
            Read-only; no data can be modified.
        ``'w'``
            Write; a new file is created (an existing file with the same
            name would be deleted).
        ``'a'``
            Append; an existing file is opened for reading and writing,
            and if the file does not exist it is created.
        ``'r+'``
            It is similar to ``'a'``, but the file must already exist.
    complevel : int, 0-9, default None
        Specifies a compression level for data.
        A value of 0 or None disables compression.
    complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
        Specifies the compression library to be used.
        These additional compressors for Blosc are supported
        (default if no compressor specified: 'blosc:blosclz'):
        {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
         'blosc:zlib', 'blosc:zstd'}.
        Specifying a compression library which is not available issues
        a ValueError.
    fletcher32 : bool, default False
        If applying compression use the fletcher32 checksum.
    **kwargs
        These parameters will be passed to the PyTables open_file method.

    Examples
    --------
    >>> bar = pd.DataFrame(np.random.randn(10, 4))
    >>> store = pd.HDFStore('test.h5')
    >>> store['foo'] = bar   # write to HDF5
    >>> bar = store['foo']   # retrieve
    >>> store.close()

    **Create or load HDF5 file in-memory**

    When passing the `driver` option to the PyTables open_file method through
    **kwargs, the HDF5 file is loaded or created in-memory and will only be
    written when closed:

    >>> bar = pd.DataFrame(np.random.randn(10, 4))
    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
    >>> store['foo'] = bar
    >>> store.close()   # only now, data is written to disk
    zFile | NoneÚ_handler\   Ú_moder‡   NFr�   rŽ   r�   Ú
fletcher32r’   r[   rœ   c                 K  s²   d|v rt dƒ‚tdƒ}|d ur ||jjvr t d|jj› d�ƒ‚|d u r,|d ur,|jj}t|ƒ| _|d u r7d}|| _d | _|rA|nd| _	|| _
|| _d | _| jd	d|i|¤Ž d S )
Nr“   z-format is not a defined argument for HDFStorer�   zcomplib only supports z compression.r‡   r   r�   rV   )r±   r   ÚfiltersÚall_complibsÚdefault_complibr=   Ú_pathrÍ   rÌ   Ú
_complevelÚ_complibÚ_fletcher32Ú_filtersÚopen)Úselfr¶   r�   rŽ   r�   rÎ   rÀ   r�   rV   rV   rW   Ú__init__*  s&   	ÿ
zHDFStore.__init__c                 C  ó   | j S r]   ©rÒ   ©rØ   rV   rV   rW   Ú
__fspath__K  s   zHDFStore.__fspath__c                 C  s   |   ¡  | jdusJ ‚| jjS )zreturn the root nodeN)Ú_check_if_openrÌ   ÚrootrÜ   rV   rV   rW   rß   N  s   zHDFStore.rootc                 C  rÚ   r]   rÛ   rÜ   rV   rV   rW   ÚfilenameU  ó   zHDFStore.filenamerŠ   c                 C  ó
   |   |¡S r]   )Úget©rØ   rŠ   rV   rV   rW   Ú__getitem__Y  ó   
zHDFStore.__getitem__c                 C  s   |   ||¡ d S r]   r£   )rØ   rŠ   r‹   rV   rV   rW   Ú__setitem__\  s   zHDFStore.__setitem__c                 C  râ   r]   )Úremoverä   rV   rV   rW   Ú__delitem___  ræ   zHDFStore.__delitem__rb   c              	   C  s@   z|   |¡W S  ttfy   Y nw tdt| ƒj› d|› d�ƒ‚)z$allow attribute access to get storesú'z' object has no attribute ')rã   ÚKeyErrorr   r‚   ÚtypeÚ__name__)rØ   rb   rV   rV   rW   Ú__getattr__b  s   ÿÿzHDFStore.__getattr__c                 C  s4   |   |¡}|dur|j}|||dd… fv rdS dS )zx
        check for existence of this key
        can match the exact pathname or the pathnm w/o the leading '/'
        Nrf   TF)Úget_noder¼   )rØ   rŠ   Únoderb   rV   rV   rW   Ú__contains__l  s   
zHDFStore.__contains__re   c                 C  ó   t |  ¡ ƒS r]   )rp   rº   rÜ   rV   rV   rW   Ú__len__x  ó   zHDFStore.__len__c                 C  s   t | jƒ}t| ƒ› d|› d�S )Nú
File path: Ú
)r?   rÒ   rì   )rØ   ÚpstrrV   rV   rW   Ú__repr__{  s   
zHDFStore.__repr__rL   c                 C  s   | S r]   rV   rÜ   rV   rV   rW   Ú	__enter__  ó   zHDFStore.__enter__Úexc_typeútype[BaseException] | NoneÚ	exc_valueúBaseException | NoneÚ	tracebackúTracebackType | Nonec                 C  ó   |   ¡  d S r]   )r¿   )rØ   rû   rý   rÿ   rV   rV   rW   Ú__exit__‚  s   zHDFStore.__exit__ÚpandasÚincludeú	list[str]c                 C  sZ   |dkrdd„ |   ¡ D ƒS |dkr%| jdusJ ‚dd„ | jjddd	�D ƒS td
|› d�ƒ‚)aƒ  
        Return a list of keys corresponding to objects stored in HDFStore.

        Parameters
        ----------

        include : str, default 'pandas'
                When kind equals 'pandas' return pandas objects.
                When kind equals 'native' return native HDF5 Table objects.

        Returns
        -------
        list
            List of ABSOLUTE path-names (e.g. have the leading '/').

        Raises
        ------
        raises ValueError if kind has an illegal value

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> store.get('data')  # doctest: +SKIP
        >>> print(store.keys())  # doctest: +SKIP
        ['/data1', '/data2']
        >>> store.close()  # doctest: +SKIP
        r  c                 S  ó   g | ]}|j ‘qS rV   ©r¼   ©ri   ÚnrV   rV   rW   rm   ©  ó    z!HDFStore.keys.<locals>.<listcomp>ÚnativeNc                 S  r  rV   r  r  rV   rV   rW   rm   ­  s    ÿú/ÚTable)Ú	classnamez8`include` should be either 'pandas' or 'native' but is 'rê   )rº   rÌ   Ú
walk_nodesr±   )rØ   r  rV   rV   rW   ÚkeysŠ  s   ÿ
ÿzHDFStore.keysúIterator[str]c                 C  rò   r]   )Úiterr  rÜ   rV   rV   rW   Ú__iter__´  rô   zHDFStore.__iter__úIterator[tuple[str, list]]c                 c  s    � |   ¡ D ]}|j|fV  qdS )z'
        iterate on key->group
        N)rº   r¼   )rØ   ÚgrV   rV   rW   Úitems·  s   €ÿzHDFStore.itemsc                 K  s¾   t ƒ }| j|kr)| jdv r|dv rn|dv r&| jr&td| j› d| j› d�ƒ‚|| _| jr0|  ¡  | jrE| jdkrEt ƒ j| j| j| j	d�| _
trP| jrPd	}t|ƒ‚|j| j| jfi |¤Ž| _d
S )a9  
        Open the file in the specified mode

        Parameters
        ----------
        mode : {'a', 'w', 'r', 'r+'}, default 'a'
            See HDFStore docstring or tables.open_file for info about modes
        **kwargs
            These parameters will be passed to the PyTables open_file method.
        )r‡   Úw)r§   r¯   )r  zRe-opening the file [z] with mode [z] will delete the current file!r   )rÎ   zGCannot open HDF5 file, which is already opened, even in read-only mode.N)r†   rÍ   r²   r   rÒ   r¿   rÓ   ÚFiltersrÔ   rÕ   rÖ   r…   r±   Ú	open_filerÌ   )rØ   r�   rÀ   r�   ÚmsgrV   rV   rW   r×   ¾  s*   
ÿÿ
ÿzHDFStore.openc                 C  s   | j dur
| j  ¡  d| _ dS )z0
        Close the PyTables file handle
        N)rÌ   r¿   rÜ   rV   rV   rW   r¿   ë  s   


zHDFStore.closec                 C  s   | j du rdS t| j jƒS )zF
        return a boolean indicating whether the file is open
        NF)rÌ   r’   ÚisopenrÜ   rV   rV   rW   r²   ó  s   
zHDFStore.is_openÚfsyncc                 C  s^   | j dur+| j  ¡  |r-ttƒ� t | j  ¡ ¡ W d  ƒ dS 1 s$w   Y  dS dS dS )aó  
        Force all buffered modifications to be written to disk.

        Parameters
        ----------
        fsync : bool (default False)
          call ``os.fsync()`` on the file handle to force writing to disk.

        Notes
        -----
        Without ``fsync=True``, flushing may not guarantee that the OS writes
        to disk. With fsync, the operation will block until the OS claims the
        file has been written; however, other caching layers may still
        interfere.
        N)rÌ   Úflushr   r³   rµ   r  Úfileno)rØ   r  rV   rV   rW   r  ü  s   


"ÿýzHDFStore.flushc                 C  sV   t ƒ � |  |¡}|du rtd|› d�ƒ‚|  |¡W  d  ƒ S 1 s$w   Y  dS )a  
        Retrieve pandas object stored in file.

        Parameters
        ----------
        key : str

        Returns
        -------
        object
            Same type as object stored in file.

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> store.get('data')  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        NúNo object named ú in the file)r   rï   rë   Ú_read_group©rØ   rŠ   rÄ   rV   rV   rW   rã     s   
$úzHDFStore.getr­   r®   r°   c	                   st   |   |¡}	|	du rtd|› d�ƒ‚t|dd�}|  |	¡‰ˆ ¡  ‡ ‡fdd„}
t| ˆ|
|ˆj|||||d�
}| ¡ S )	a6  
        Retrieve pandas object stored in file, optionally based on where criteria.

        .. warning::

           Pandas uses PyTables for reading and writing HDF5 files, which allows
           serializing object-dtype data with pickle when using the "fixed" format.
           Loading pickled data received from untrusted sources can be unsafe.

           See: https://docs.python.org/3/library/pickle.html for more.

        Parameters
        ----------
        key : str
            Object being retrieved from file.
        where : list or None
            List of Term (or convertible) objects, optional.
        start : int or None
            Row number to start selection.
        stop : int, default None
            Row number to stop selection.
        columns : list or None
            A list of columns that if not None, will limit the return columns.
        iterator : bool or False
            Returns an iterator.
        chunksize : int or None
            Number or rows to include in iteration, return an iterator.
        auto_close : bool or False
            Should automatically close the store when finished.

        Returns
        -------
        object
            Retrieved object from file.

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> store.get('data')  # doctest: +SKIP
        >>> print(store.keys())  # doctest: +SKIP
        ['/data1', '/data2']
        >>> store.select('/data1')  # doctest: +SKIP
           A  B
        0  1  2
        1  3  4
        >>> store.select('/data1', where='columns == A')  # doctest: +SKIP
           A
        0  1
        1  3
        >>> store.close()  # doctest: +SKIP
        Nr  r   rf   rg   c                   s   ˆj | ||ˆ d�S )N)r©   rª   rq   r«   ©Úread©Ú_startÚ_stopÚ_where©r«   rU   rV   rW   Úfuncy  s   zHDFStore.select.<locals>.func©rq   Únrowsr©   rª   r­   r®   r°   )rï   rë   rr   Ú_create_storerÚ
infer_axesÚTableIteratorr,  Ú
get_result)rØ   rŠ   rq   r©   rª   r«   r­   r®   r°   rÄ   r*  ÚitrV   r)  rW   r½   /  s(   
@
özHDFStore.selectr©   rª   c                 C  s8   t |dd�}|  |¡}t|tƒstdƒ‚|j|||d�S )a“  
        return the selection as an Index

        .. warning::

           Pandas uses PyTables for reading and writing HDF5 files, which allows
           serializing object-dtype data with pickle when using the "fixed" format.
           Loading pickled data received from untrusted sources can be unsafe.

           See: https://docs.python.org/3/library/pickle.html for more.


        Parameters
        ----------
        key : str
        where : list of Term (or convertible) objects, optional
        start : integer (defaults to None), row number to start selection
        stop  : integer (defaults to None), row number to stop selection
        rf   rg   z&can only read_coordinates with a table©rq   r©   rª   )rr   Ú
get_storerrQ   r  r¸   Úread_coordinates)rØ   rŠ   rq   r©   rª   ÚtblrV   rV   rW   Úselect_as_coordinatesŒ  s
   

zHDFStore.select_as_coordinatesÚcolumnc                 C  s,   |   |¡}t|tƒstdƒ‚|j|||d�S )a~  
        return a single column from the table. This is generally only useful to
        select an indexable

        .. warning::

           Pandas uses PyTables for reading and writing HDF5 files, which allows
           serializing object-dtype data with pickle when using the "fixed" format.
           Loading pickled data received from untrusted sources can be unsafe.

           See: https://docs.python.org/3/library/pickle.html for more.

        Parameters
        ----------
        key : str
        column : str
            The column of interest.
        start : int or None, default None
        stop : int or None, default None

        Raises
        ------
        raises KeyError if the column is not found (or key is not a valid
            store)
        raises ValueError if the column can not be extracted individually (it
            is part of a data block)

        z!can only read_column with a table)r7  r©   rª   )r3  rQ   r  r¸   Úread_column)rØ   rŠ   r7  r©   rª   r5  rV   rV   rW   Úselect_column¬  s   
#
zHDFStore.select_columnc
                   st  t |dd�}t|ttfƒrt|ƒdkr|d }t|tƒr)ˆj||ˆ|||||	d�S t|ttfƒs4tdƒ‚t|ƒs<tdƒ‚|du rD|d }‡fdd	„|D ƒ‰ˆ 	|¡}
d}t
 |
|fgtˆ|ƒ¡D ]-\}}|du rptd
|› d�ƒ‚|js|td|j› d�ƒ‚|du r„|j}q`|j|kr�tdƒ‚q`dd	„ ˆD ƒ}dd„ |D ƒ ¡ ‰ ‡ ‡‡fdd„}tˆ|
||||||||	d�
}|jdd�S )aÙ  
        Retrieve pandas objects from multiple tables.

        .. warning::

           Pandas uses PyTables for reading and writing HDF5 files, which allows
           serializing object-dtype data with pickle when using the "fixed" format.
           Loading pickled data received from untrusted sources can be unsafe.

           See: https://docs.python.org/3/library/pickle.html for more.

        Parameters
        ----------
        keys : a list of the tables
        selector : the table to apply the where criteria (defaults to keys[0]
            if not supplied)
        columns : the columns I want back
        start : integer (defaults to None), row number to start selection
        stop  : integer (defaults to None), row number to stop selection
        iterator : bool, return an iterator, default False
        chunksize : nrows to include in iteration, return an iterator
        auto_close : bool, default False
            Should automatically close the store when finished.

        Raises
        ------
        raises KeyError if keys or selector is not found or keys is empty
        raises TypeError if keys is not a list or tuple
        raises ValueError if the tables are not ALL THE SAME DIMENSIONS
        rf   rg   r   )rŠ   rq   r«   r©   rª   r­   r®   r°   zkeys must be a list/tuplez keys must have a non-zero lengthNc                   ó   g | ]}ˆ   |¡‘qS rV   )r3  ©ri   ÚkrÜ   rV   rW   rm     ó    z/HDFStore.select_as_multiple.<locals>.<listcomp>zInvalid table [ú]zobject [z>] is not a table, and cannot be used in all select as multiplez,all tables must have exactly the same nrows!c                 S  s   g | ]	}t |tƒr|‘qS rV   )rQ   r  ©ri   ÚxrV   rV   rW   rm   -  ó    c                 S  s   h | ]	}|j d  d  ’qS ©r   )Únon_index_axes©ri   ry   rV   rV   rW   Ú	<setcomp>0  rA  z.HDFStore.select_as_multiple.<locals>.<setcomp>c                   s*   ‡ ‡‡‡fdd„ˆD ƒ}t |ˆdd� ¡ S )Nc                   s   g | ]}|j ˆˆˆ ˆd �‘qS )©rq   r«   r©   rª   r#  rD  )r&  r'  r(  r«   rV   rW   rm   5  s    ÿÿz=HDFStore.select_as_multiple.<locals>.func.<locals>.<listcomp>F)ÚaxisÚverify_integrity)r2   Ú_consolidate)r&  r'  r(  Úobjs)rG  r«   Útblsr%  rW   r*  2  s   þz)HDFStore.select_as_multiple.<locals>.funcr+  T)Úcoordinates)rr   rQ   rn   ro   rp   r\   r½   r¸   r±   r3  Ú	itertoolsÚchainÚziprë   Úis_tableÚpathnamer,  Úpopr/  r0  )rØ   r  rq   Úselectorr«   r©   rª   r­   r®   r°   rU   r,  ry   r<  Ú_tblsr*  r1  rV   )rG  r«   rØ   rK  rW   Úselect_as_multipleÔ  sf   +
ø
 ÿ
ÿözHDFStore.select_as_multipleTr   r‹   rŒ   r”   r‘   r•   r–   r™   rš   r›   Útrack_timesr—   c                 C  sH   |du r
t dƒp	d}|  |¡}| j|||||||||	|
||||d� dS )a¬  
        Store object in HDFStore.

        Parameters
        ----------
        key : str
        value : {Series, DataFrame}
        format : 'fixed(f)|table(t)', default is 'fixed'
            Format to use when storing object in HDFStore. Value can be one of:

            ``'fixed'``
                Fixed format.  Fast writing/reading. Not-appendable, nor searchable.
            ``'table'``
                Table format.  Write as a PyTables Table structure which may perform
                worse but allow more flexible operations like searching / selecting
                subsets of the data.
        index : bool, default True
            Write DataFrame index as a column.
        append : bool, default False
            This will force Table format, append the input data to the existing.
        data_columns : list of columns or True, default None
            List of columns to create as data columns, or True to use all columns.
            See `here
            <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
        encoding : str, default None
            Provide an encoding for strings.
        track_times : bool, default True
            Parameter is propagated to 'create_table' method of 'PyTables'.
            If set to False it enables to have the same h5 files (same hashes)
            independent on creation time.
        dropna : bool, default False, optional
            Remove missing values.

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        Núio.hdf.default_formatrv   )r“   r”   r‘   r�   rŽ   r•   r�   r™   rY   r›   rV  r—   )r   Ú_validate_formatÚ_write_to_group)rØ   rŠ   r‹   r“   r”   r‘   r�   rŽ   r•   r�   r™   rY   r›   rV  r—   rV   rV   rW   r¤   M  s&   8

òzHDFStore.putc              
   C  sØ   t |dd�}z|  |¡}W n? ty   ‚  ty   ‚  tyL } z%|dur,tdƒ|‚|  |¡}|durB|jdd� W Y d}~dS W Y d}~nd}~ww t 	|||¡r]|j
jdd� dS |jsdtdƒ‚|j|||d�S )	a:  
        Remove pandas object partially by specifying the where condition

        Parameters
        ----------
        key : str
            Node to remove or delete rows from
        where : list of Term (or convertible) objects, optional
        start : integer (defaults to None), row number to start selection
        stop  : integer (defaults to None), row number to stop selection

        Returns
        -------
        number of rows removed (or None if not a Table)

        Raises
        ------
        raises KeyError if key is not a valid store

        rf   rg   Nz5trying to remove a node with a non-None where clause!T©Ú	recursivez7can only remove with where on objects written as tablesr2  )rr   r3  rë   ÚAssertionErrorÚ	Exceptionr±   rï   Ú	_f_removeÚcomÚall_nonerÄ   rP  Údelete)rØ   rŠ   rq   r©   rª   rU   Úerrrð   rV   rV   rW   rè   ™  s8   ÿþ
þ€õÿzHDFStore.removeúbool | list[str]r˜   c                 C  sl   |	durt dƒ‚|du rtdƒ}|du rtdƒpd}|  |¡}| j|||||||||
|||||||d� dS )a|  
        Append to Table in file.

        Node must already exist and be Table format.

        Parameters
        ----------
        key : str
        value : {Series, DataFrame}
        format : 'table' is the default
            Format to use when storing object in HDFStore.  Value can be one of:

            ``'table'``
                Table format. Write as a PyTables Table structure which may perform
                worse but allow more flexible operations like searching / selecting
                subsets of the data.
        index : bool, default True
            Write DataFrame index as a column.
        append       : bool, default True
            Append the input data to the existing.
        data_columns : list of columns, or True, default None
            List of columns to create as indexed data columns for on-disk
            queries, or True to use all columns. By default only the axes
            of the object are indexed. See `here
            <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
        min_itemsize : dict of columns that specify minimum str sizes
        nan_rep      : str to use as str nan representation
        chunksize    : size to chunk the writing
        expectedrows : expected TOTAL row size of this table
        encoding     : default None, provide an encoding for str
        dropna : bool, default False, optional
            Do not write an ALL nan row to the store settable
            by the option 'io.hdf.dropna_table'.

        Notes
        -----
        Does *not* check if data being appended overlaps with existing
        data in the table, so be careful

        Examples
        --------
        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df1, format='table')  # doctest: +SKIP
        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
        >>> store.append('data', df2)  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
           A  B
        0  1  2
        1  3  4
        0  5  6
        1  7  8
        Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerW  rw   )r“   Úaxesr”   r‘   r�   rŽ   r•   r�   r®   Úexpectedrowsr—   r™   rY   r›   )r¸   r   rX  rY  )rØ   rŠ   r‹   r“   rd  r”   r‘   r�   rŽ   r«   r•   r�   r®   re  r—   r™   rY   r›   rV   rV   rW   r‘   Ò  s6   Iÿ

ðzHDFStore.appendÚdÚdictc                   s¢  |durt dƒ‚t|tƒstdƒ‚||vrtdƒ‚ttttˆjƒƒtt	t
ˆƒ ƒ ƒƒ}d}	g }
| ¡ D ]\}‰ ˆ du rG|	durDtdƒ‚|}	q4|
 ˆ ¡ q4|	durkˆj| }| t|
ƒ¡}t| |¡ƒ}| |¡||	< |du rs|| }|r“‡fdd„| ¡ D ƒ}t|ƒ}|D ]}| |¡}q†ˆj| ‰| dd¡}| ¡ D ]1\}‰ ||kr§|nd}ˆjˆ |d	�}|dur¿‡ fd
d„| ¡ D ƒnd}| j||f||dœ|¤Ž q�dS )a  
        Append to multiple tables

        Parameters
        ----------
        d : a dict of table_name to table_columns, None is acceptable as the
            values of one node (this will get all the remaining columns)
        value : a pandas object
        selector : a string that designates the indexable table; all of its
            columns will be designed as data_columns, unless data_columns is
            passed, in which case these are used
        data_columns : list of columns to create as data columns, or True to
            use all columns
        dropna : if evaluates to True, drop rows from all tables if any single
                 row in each table has all NaN. Default False.

        Notes
        -----
        axes parameter is currently not accepted

        Nztaxes is currently not accepted as a parameter to append_to_multiple; you can create the tables independently insteadzQappend_to_multiple must have a dictionary specified as the way to split the valuez=append_to_multiple requires a selector that is in passed dictz<append_to_multiple can only have one value in d that is Nonec                 3  s"   � | ]}ˆ | j d d�jV  qdS )Úall)ÚhowN)r—   r”   )ri   Úcols)r‹   rV   rW   Ú	<genexpr>ƒ  s   €  z.HDFStore.append_to_multiple.<locals>.<genexpr>r•   ©rG  c                   s   i | ]\}}|ˆ v r||“qS rV   rV   ©ri   rŠ   r‹   )ÚvrV   rW   Ú
<dictcomp>“  s    z/HDFStore.append_to_multiple.<locals>.<dictcomp>)r™   r•   )r¸   rQ   rg  r±   Únextr  ÚsetÚrangeÚndimÚ	_AXES_MAPrì   r  Úextendrd  Ú
differencer,   ÚsortedÚget_indexerÚtakeÚvaluesÚintersectionÚlocrR  Úreindexr‘   )rØ   rf  r‹   rS  r™   rd  r—   rÀ   rG  Ú
remain_keyÚremain_valuesr<  ÚorderedÚorddÚidxsÚvalid_indexr”   r•   ÚdcÚvalÚfilteredrV   )rn  r‹   rW   Úappend_to_multiple8  s\   ÿ
ÿÿ&ÿ

ÿýõzHDFStore.append_to_multipleÚoptlevelÚkindrZ   c                 C  sB   t ƒ  |  |¡}|du rdS t|tƒstdƒ‚|j|||d� dS )aà  
        Create a pytables index on the table.

        Parameters
        ----------
        key : str
        columns : None, bool, or listlike[str]
            Indicate which columns to create an index on.

            * False : Do not create any indexes.
            * True : Create indexes on all columns.
            * None : Create indexes on all columns.
            * listlike : Create indexes on the given columns.

        optlevel : int or None, default None
            Optimization level, if None, pytables defaults to 6.
        kind : str or None, default None
            Kind of index, if None, pytables defaults to "medium".

        Raises
        ------
        TypeError: raises if the node is not a table
        Nz1cannot create table index on a Fixed format store)r«   rˆ  r‰  )r†   r3  rQ   r  r¸   Úcreate_index)rØ   rŠ   r«   rˆ  r‰  rU   rV   rV   rW   Úcreate_table_index™  s   

zHDFStore.create_table_indexrn   c                 C  s<   t ƒ  |  ¡  | jdusJ ‚tdusJ ‚dd„ | j ¡ D ƒS )a�  
        Return a list of all the top-level nodes.

        Each node returned is not a pandas storage object.

        Returns
        -------
        list
            List of objects.

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> print(store.groups())  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        [/data (Group) ''
          children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array),
          'block0_items' (Array)]]
        Nc                 S  sP   g | ]$}t |tjjƒs&t|jd dƒs$t|ddƒs$t |tjjƒr&|jdkr|‘qS )Úpandas_typeNrw   )	rQ   r€   ÚlinkÚLinkÚgetattrÚ_v_attrsrw   r  rÉ   )ri   r  rV   rV   rW   rm   Û  s    üú
ùø
ùz#HDFStore.groups.<locals>.<listcomp>)r†   rÞ   rÌ   r€   Úwalk_groupsrÜ   rV   rV   rW   rº   Á  s   þzHDFStore.groupsr  rq   ú*Iterator[tuple[str, list[str], list[str]]]c                 c  s¾   � t ƒ  |  ¡  | jdusJ ‚tdusJ ‚| j |¡D ]A}t|jddƒdur'qg }g }|j ¡ D ]!}t|jddƒ}|du rKt	|tj
jƒrJ| |j¡ q0| |j¡ q0|j d¡||fV  qdS )a€  
        Walk the pytables group hierarchy for pandas objects.

        This generator will yield the group path, subgroups and pandas object
        names for each group.

        Any non-pandas PyTables objects that are not a group will be ignored.

        The `where` group itself is listed first (preorder), then each of its
        child groups (following an alphanumerical order) is also traversed,
        following the same procedure.

        Parameters
        ----------
        where : str, default "/"
            Group where to start walking.

        Yields
        ------
        path : str
            Full path to a group (without trailing '/').
        groups : list
            Names (strings) of the groups contained in `path`.
        leaves : list
            Names (strings) of the pandas objects contained in `path`.

        Examples
        --------
        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df1, format='table')  # doctest: +SKIP
        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
        >>> store.append('data', df2)  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        >>> for group in store.walk():  # doctest: +SKIP
        ...     print(group)  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        NrŒ  r  )r†   rÞ   rÌ   r€   r‘  r�  r�  Ú_v_childrenrz  rQ   rÄ   ÚGroupr‘   rÉ   r¼   Úrstrip)rØ   rq   r  rº   ÚleavesÚchildrŒ  rV   rV   rW   Úwalkè  s&   €'€òzHDFStore.walkúNode | Nonec                 C  s~   |   ¡  | d¡sd| }| jdusJ ‚tdusJ ‚z
| j | j|¡}W n tjjy0   Y dS w t|tj	ƒs=J t
|ƒƒ‚|S )z9return the node with the key or None if it does not existr  N)rÞ   Ú
startswithrÌ   r€   rï   rß   Ú
exceptionsÚNoSuchNodeErrorrQ   rF   rì   )rØ   rŠ   rð   rV   rV   rW   rï   $  s   
ÿzHDFStore.get_nodeúGenericFixed | Tablec                 C  s8   |   |¡}|du rtd|› d�ƒ‚|  |¡}| ¡  |S )z<return the storer object for a key, raise if not in the fileNr  r   )rï   rë   r-  r.  )rØ   rŠ   rÄ   rU   rV   rV   rW   r3  4  s   

zHDFStore.get_storerr  ÚpropindexesÚ	overwritec	              	   C  sÎ   t |||||d�}	|du rt|  ¡ ƒ}t|ttfƒs|g}|D ]E}
|  |
¡}|durd|
|	v r5|r5|	 |
¡ |  |
¡}t|tƒr[d}|rKdd„ |j	D ƒ}|	j
|
||t|ddƒ|jd� q|	j|
||jd� q|	S )	a;  
        Copy the existing store to a new file, updating in place.

        Parameters
        ----------
        propindexes : bool, default True
            Restore indexes in copied file.
        keys : list, optional
            List of keys to include in the copy (defaults to all).
        overwrite : bool, default True
            Whether to overwrite (remove and replace) existing nodes in the new store.
        mode, complib, complevel, fletcher32 same as in HDFStore.__init__

        Returns
        -------
        open file handle of the new store
        )r�   r�   rŽ   rÎ   NFc                 S  ó   g | ]}|j r|j‘qS rV   )Ú
is_indexedrb   ©ri   r‡   rV   rV   rW   rm   l  ó    z!HDFStore.copy.<locals>.<listcomp>r™   )r”   r™   rY   r_   )r¥   rn   r  rQ   ro   r3  rè   r½   r  rd  r‘   r�  rY   r¤   )rØ   rƒ   r�   rž  r  r�   rŽ   rÎ   rŸ  Ú	new_storer<  rU   Údatar”   rV   rV   rW   Úcopy>  s8   
ÿ




û€zHDFStore.copyc           
      C  s  t | jƒ}t| ƒ› d|› d�}| jr~t|  ¡ ƒ}t|ƒrxg }g }|D ]K}z|  |¡}|durA| t |j	p5|ƒ¡ | t |p>dƒ¡ W q" t
yJ   ‚  tym } z| |¡ t |ƒ}	| d|	› d�¡ W Y d}~q"d}~ww |td||ƒ7 }|S |d7 }|S |d	7 }|S )
a  
        Print detailed information on the store.

        Returns
        -------
        str

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> print(store.info())  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        <class 'pandas.io.pytables.HDFStore'>
        File path: store.h5
        /data    frame    (shape->[2,2])
        rõ   rö   Nzinvalid_HDFStore nodez[invalid_HDFStore node: r>  é   ÚEmptyzFile is CLOSED)r?   rÒ   rì   r²   rw  r  rp   r3  r‘   rQ  r\  r]  r>   )
rØ   r¶   ÚoutputÚlkeysr  rz  r<  rU   ÚdetailÚdstrrV   rV   rW   Úinfoy  s8   

€
€ýüþzHDFStore.infoc                 C  s   | j st| j› d�ƒ‚d S )Nz file is not open!)r²   r   rÒ   rÜ   rV   rV   rW   rÞ   ®  s   ÿzHDFStore._check_if_openr“   c              
   C  s>   z	t | ¡  }W |S  ty } z	td|› d�ƒ|‚d}~ww )zvalidate / deprecate formatsz#invalid HDFStore format specified [r>  N)Ú_FORMAT_MAPÚlowerrë   r¸   )rØ   r“   rb  rV   rV   rW   rX  ²  s   ý€ÿzHDFStore._validate_formatrP   úDataFrame | Series | NonerY   c              
   C  s
  |durt |ttfƒstdƒ‚tt|jddƒƒ}tt|jddƒƒ}|du rZ|du rHtƒ  tdus2J ‚t|ddƒs?t |tj	j
ƒrDd}d}ntdƒ‚t |tƒrPd	}nd
}|dkrZ|d7 }d|vrŽttdœ}z|| }	W n ty… }
 ztd|› dt|ƒ› d|› �ƒ|
‚d}
~
ww |	| |||d�S |du rÑ|durÑ|dkr´t|ddƒ}|dur³|jdkr¬d}n%|jdkr³d}n|dkrÑt|ddƒ}|durÑ|jdkrÊd}n|jdkrÑd}ttttttdœ}z|| }	W n tyü }
 ztd|› dt|ƒ› d|› �ƒ|
‚d}
~
ww |	| |||d�S )z"return a suitable class to operateNz(value must be None, Series, or DataFramerŒ  Ú
table_typerw   Úframe_tableÚgeneric_tablezKcannot create a storer if the object is not existing nor a value are passedÚseriesÚframeÚ_table)r´  rµ  z=cannot properly create the storer for: [_STORER_MAP] [group->ú,value->z	,format->©rY   r›   Úseries_tabler”   rf   Úappendable_seriesÚappendable_multiseriesÚappendable_frameÚappendable_multiframe)r³  rº  r»  r¼  r½  Úwormz<cannot properly create the storer for: [_TABLE_MAP] [group->)rQ   r0   r*   r¸   rX   r�  r�  r†   r€   rw   r  ÚSeriesFixedÚ
FrameFixedrë   rì   ÚnlevelsÚGenericTableÚAppendableSeriesTableÚAppendableMultiSeriesTableÚAppendableFrameTableÚAppendableMultiFrameTableÚ	WORMTable)rØ   rÄ   r“   r‹   rY   r›   ÚptÚttÚ_STORER_MAPÚclsrb  r”   Ú
_TABLE_MAPrV   rV   rW   r-  ¼  s¢   ÿÿ

ÿÿÿÿý€ÿ

€

úÿÿÿÿý€ÿzHDFStore._create_storerc                 C  sÖ   t |dd ƒr|dks|rd S |  ||¡}| j|||||d�}|r9|jr-|jr1|dkr1|jr1tdƒ‚|js8| ¡  n| ¡  |jsF|rFtdƒ‚|j||||||	|
||||||d� t|t	ƒrg|ri|j
|d� d S d S d S )	NÚemptyrw   r¸  rv   zCan only append to Tablesz0Compression not supported on Fixed format stores)Úobjrd  r‘   r�   rŽ   rÎ   r•   r®   re  r—   r�   r™   rV  )r«   )r�  Ú_identify_groupr-  rP  Ú	is_existsr±   Úset_object_infoÚwriterQ   r  rŠ  )rØ   rŠ   r‹   r“   rd  r”   r‘   r�   rŽ   rÎ   r•   r®   re  r—   r�   r™   rY   r›   rV  rÄ   rU   rV   rV   rW   rY    s>   €
óÿzHDFStore._write_to_grouprÄ   rF   c                 C  s   |   |¡}| ¡  | ¡ S r]   )r-  r.  r$  )rØ   rÄ   rU   rV   rV   rW   r!  U  s   
zHDFStore._read_groupc                 C  sN   |   |¡}| jdusJ ‚|dur|s| jj|dd� d}|du r%|  |¡}|S )z@Identify HDF5 group based on key, delete/create group if needed.NTrZ  )rï   rÌ   Úremove_nodeÚ_create_nodes_and_group)rØ   rŠ   r‘   rÄ   rV   rV   rW   rÏ  Z  s   

zHDFStore._identify_groupc                 C  sv   | j dusJ ‚| d¡}d}|D ](}t|ƒsq|}| d¡s"|d7 }||7 }|  |¡}|du r6| j  ||¡}|}q|S )z,Create nodes from key and return group name.Nr  )rÌ   Úsplitrp   Úendswithrï   Úcreate_group)rØ   rŠ   Úpathsr¶   ÚpÚnew_pathrÄ   rV   rV   rW   rÔ  l  s   


z HDFStore._create_nodes_and_group)r‡   NNF)r�   r\   rŽ   r�   rÎ   r’   r[   rœ   ©r[   r\   ©rŠ   r\   )rŠ   r\   r[   rœ   )rb   r\   )rŠ   r\   r[   r’   ©r[   re   )r[   rL   )rû   rü   rý   rþ   rÿ   r   r[   rœ   )r  )r  r\   r[   r  )r[   r  )r[   r  )r‡   )r�   r\   r[   rœ   ©r[   rœ   ©r[   r’   ©F)r  r’   r[   rœ   )NNNNFNF)rŠ   r\   r­   r’   r®   r�   r°   r’   ©NNN©rŠ   r\   r©   r�   rª   r�   ©NN)rŠ   r\   r7  r\   r©   r�   rª   r�   )NNNNNFNF)r­   r’   r®   r�   r°   r’   )NTFNNNNNNr   TF)rŠ   r\   r‹   rŒ   r”   r’   r‘   r’   rŽ   r�   r•   r–   r™   rš   r›   r\   rV  r’   r—   r’   r[   rœ   )NNTTNNNNNNNNNNr   )rŠ   r\   r‹   rŒ   r”   rc  r‘   r’   rŽ   r�   r•   r–   r®   r�   r—   r˜   r™   rš   r›   r\   r[   rœ   )NNF)rf  rg  r—   r’   r[   rœ   )rŠ   r\   rˆ  r�   r‰  rZ   r[   rœ   )r[   rn   )r  )rq   r\   r[   r’  )rŠ   r\   r[   r™  )rŠ   r\   r[   r�  )r  TNNNFT)r�   r\   rž  r’   rŽ   r�   rÎ   r’   rŸ  r’   r[   r¥   )r“   r\   r[   r\   )NNrP   r   )r‹   r°  rY   r\   r›   r\   r[   r�  )NTFNNNNNNFNNNr   T)rŠ   r\   r‹   rŒ   r”   rc  r‘   r’   rŽ   r�   r•   r–   r®   r�   r—   r’   r›   r\   rV  r’   r[   rœ   )rÄ   rF   )rŠ   r\   r‘   r’   r[   rF   )rŠ   r\   r[   rF   )1rí   Ú
__module__Ú__qualname__Ú__doc__Ú__annotations__rÙ   rÝ   Úpropertyrß   rà   rå   rç   ré   rî   rñ   ró   rø   rù   r  r  r  r  r×   r¿   r²   r  rã   r½   r6  r9  rU  r¤   rè   r‘   r‡  r‹  rº   r˜  rï   r3  r¦  r­  rÞ   rX  r-  rY  r!  rÏ  rÔ  rV   rV   rV   rW   r¥   å  s
  
 Aú
!











*

-
 ÷`û$û+ö}ñL=îkùdû
('
<
÷
;
5
ú`í
>
r¥   c                   @  s`   e Zd ZU dZded< ded< ded< 							dddd„Zddd„Zddd„Zdddd„ZdS )r/  aa  
    Define the iteration interface on a table

    Parameters
    ----------
    store : HDFStore
    s     : the referred storer
    func  : the function to execute the query
    where : the where of the query
    nrows : the rows to iterate on
    start : the passed start value (default is None)
    stop  : the passed stop value (default is None)
    iterator : bool, default False
        Whether to use the default iterator.
    chunksize : the passed chunking value (default is 100000)
    auto_close : bool, default False
        Whether to automatically close the store at the end of iteration.
    r�   r®   r¥   rŸ   r�  rU   NFr­   r’   r°   r[   rœ   c                 C  sš   || _ || _|| _|| _| jjr'|d u rd}|d u rd}|d u r"|}t||ƒ}|| _|| _|| _d | _	|s9|	d urE|	d u r?d}	t
|	ƒ| _nd | _|
| _d S )Nr   é † )rŸ   rU   r*  rq   rP  Úminr,  r©   rª   rL  re   r®   r°   )rØ   rŸ   rU   r*  rq   r,  r©   rª   r­   r®   r°   rV   rV   rW   rÙ   š  s,   

zTableIterator.__init__rA   c                 c  s€   � | j }| jd u rtdƒ‚|| jk r:t|| j | jƒ}|  d d | j||… ¡}|}|d u s1t|ƒs2q|V  || jk s|  ¡  d S )Nz*Cannot iterate until get_result is called.)	r©   rL  r±   rª   rê  r®   r*  rp   r¿   )rØ   rÊ   rª   r‹   rV   rV   rW   r  Ä  s   €


ù	zTableIterator.__iter__c                 C  s   | j r
| j ¡  d S d S r]   )r°   rŸ   r¿   rÜ   rV   rV   rW   r¿   Ô  s   ÿzTableIterator.closerL  c                 C  sŠ   | j d urt| jtƒstdƒ‚| jj| jd�| _| S |r3t| jtƒs&tdƒ‚| jj| j| j| j	d�}n| j}|  
| j| j	|¡}|  ¡  |S )Nz0can only use an iterator or chunksize on a table)rq   z$can only read_coordinates on a tabler2  )r®   rQ   rU   r  r¸   r4  rq   rL  r©   rª   r*  r¿   )rØ   rL  rq   ÚresultsrV   rV   rW   r0  Ø  s   
ÿzTableIterator.get_result)NNFNF)rŸ   r¥   rU   r�  r­   r’   r®   r�   r°   r’   r[   rœ   ©r[   rA   rÞ  rà  )rL  r’   )	rí   rä  rå  ræ  rç  rÙ   r  r¿   r0  rV   rV   rV   rW   r/  ‚  s   
 	õ
*
r/  c                   @  s\  e Zd ZU dZdZded< dZded< g d¢Z													dMdNdd„Ze	dOdd„ƒZ
e	dPdd„ƒZdQdd„ZdPdd„ZdRdd„ZdSdd„Ze	dSd d!„ƒZdTd'd(„Zd)d*„ Ze	d+d,„ ƒZe	d-d.„ ƒZe	d/d0„ ƒZe	d1d2„ ƒZdUd4d5„ZdVdWd6d7„ZdWd8d9„ZdXd=d>„ZdVd?d@„ZdYdAdB„ZdWdCdD„ZdWdEdF„ZdWdGdH„ZdZdIdJ„Z dZdKdL„Z!dS )[ÚIndexCola  
    an index column description class

    Parameters
    ----------
    axis   : axis which I reference
    values : the ndarray like converted values
    kind   : a string description of this type
    typ    : the pytables type
    pos    : the position in the pytables

    Tr’   Úis_an_indexableÚis_data_indexable)ÚfreqÚtzÚ
index_nameNrb   r\   ÚcnamerZ   r[   rœ   c                 C  s    t |tƒs	tdƒ‚|| _|| _|| _|| _|p|| _|| _|| _	|| _
|	| _|
| _|| _|| _|| _|| _|d ur>|  |¡ t | jtƒsFJ ‚t | jtƒsNJ ‚d S )Nz`name` must be a str.)rQ   r\   r±   rz  r‰  Útyprb   ró  rG  Úposrð  rñ  rò  r€  rw   rÆ   ÚmetadataÚset_pos)rØ   rb   rz  r‰  rô  ró  rG  rõ  rð  rñ  rò  r€  rw   rÆ   rö  rV   rV   rW   rÙ     s(   


zIndexCol.__init__re   c                 C  ó   | j jS r]   )rô  ÚitemsizerÜ   rV   rV   rW   rù  /  s   zIndexCol.itemsizec                 C  ó   | j › d�S )NÚ_kindra   rÜ   rV   rV   rW   Ú	kind_attr4  ó   zIndexCol.kind_attrrõ  c                 C  s,   || _ |dur| jdur|| j_dS dS dS )z,set the position of this column in the TableN)rõ  rô  Ú_v_pos)rØ   rõ  rV   rV   rW   r÷  8  s   ÿzIndexCol.set_posc                 C  ó@   t tt| j| j| j| j| jfƒƒ}d dd„ t	g d¢|ƒD ƒ¡S )Nú,c                 S  ó   g | ]\}}|› d |› �‘qS ©z->rV   rm  rV   rV   rW   rm   C  ó    ÿÿz%IndexCol.__repr__.<locals>.<listcomp>)rb   ró  rG  rõ  r‰  )
ro   Úmapr?   rb   ró  rG  rõ  r‰  ÚjoinrO  ©rØ   ÚtemprV   rV   rW   rø   >  s   ÿþÿzIndexCol.__repr__ÚotherÚobjectc                   ó   t ‡ ‡fdd„dD ƒƒS )úcompare 2 col itemsc                 3  ó(   � | ]}t ˆ|d ƒt ˆ |d ƒkV  qd S r]   ©r�  r¢  ©r  rØ   rV   rW   rk  K  ó
   € ÿ
ÿz"IndexCol.__eq__.<locals>.<genexpr>)rb   ró  rG  rõ  ©rh  ©rØ   r  rV   r  rW   Ú__eq__I  ó   þzIndexCol.__eq__c                 C  s   |   |¡ S r]   )r  r  rV   rV   rW   Ú__ne__P  rô   zIndexCol.__ne__c                 C  s"   t | jdƒsdS t| jj| jƒjS )z%return whether I am an indexed columnrj  F)Úhasattrrw   r�  rj  ró  r¡  rÜ   rV   rV   rW   r¡  S  s   zIndexCol.is_indexedrz  ú
np.ndarrayrY   r›   ú3tuple[np.ndarray, np.ndarray] | tuple[Index, Index]c           
      C  s  t |tjƒsJ t|ƒƒ‚|jjdur|| j  ¡ }t| j	ƒ}t
||||ƒ}i }t| jƒ|d< | jdur:t| jƒ|d< t}t |jd¡sIt |jtƒrLt}n|jdkrYd|v rYdd„ }z
||fi |¤Ž}W n ty|   d|v rrd|d< ||fi |¤Ž}Y nw t|| jƒ}	|	|	fS )zV
        Convert the data from this selection to the appropriate pandas type.
        Nrb   rð  ÚMÚi8c                 [  s    t j| | dd ¡d� |d ¡S )Nrð  )rð  rb   )r.   Úfrom_ordinalsrã   Ú_rename)r@  ÚkwdsrV   rV   rW   r¡   {  s    ÿÿz"IndexCol.convert.<locals>.<lambda>)rQ   rR   Úndarrayrì   ÚdtypeÚfieldsró  r¦  rX   r‰  Ú_maybe_convertrò  rð  r,   r   Úis_np_dtyper&   r+   r±   Ú_set_tzrñ  )
rØ   rz  r�   rY   r›   Úval_kindrÀ   ÚfactoryÚnew_pd_indexÚfinal_pd_indexrV   rV   rW   Úconvert[  s2   

ÿûzIndexCol.convertc                 C  rÚ   )zreturn the values©rz  rÜ   rV   rV   rW   Ú	take_data�  rá   zIndexCol.take_datac                 C  rø  r]   )rw   r�  rÜ   rV   rV   rW   Úattrs‘  ó   zIndexCol.attrsc                 C  rø  r]   ©rw   ÚdescriptionrÜ   rV   rV   rW   r-  •  r+  zIndexCol.descriptionc                 C  s   t | j| jdƒS )z!return my current col descriptionN)r�  r-  ró  rÜ   rV   rV   rW   Úcol™  ó   zIndexCol.colc                 C  rÚ   ©zreturn my cython valuesr(  rÜ   rV   rV   rW   Úcvaluesž  ó   zIndexCol.cvaluesrA   c                 C  s
   t | jƒS r]   )r  rz  rÜ   rV   rV   rW   r  £  ræ   zIndexCol.__iter__c                 C  s\   t | jƒdkr(t|tƒr| | j¡}|dur*| jj|k r,tƒ j	|| j
d�| _dS dS dS dS )zŸ
        maybe set a string col itemsize:
            min_itemsize can be an integer or a dict with this columns name
            with an integer size
        ÚstringN)rù  rõ  )rX   r‰  rQ   rg  rã   rb   rô  rù  r†   Ú	StringColrõ  )rØ   r•   rV   rV   rW   Úmaybe_set_size¦  s   
ûzIndexCol.maybe_set_sizec                 C  ó   d S r]   rV   rÜ   rV   rV   rW   Úvalidate_names³  rú   zIndexCol.validate_namesÚhandlerÚAppendableTabler‘   c                 C  s:   |j | _ |  ¡  |  |¡ |  |¡ |  |¡ |  ¡  d S r]   )rw   Úvalidate_colÚvalidate_attrÚvalidate_metadataÚwrite_metadataÚset_attr)rØ   r8  r‘   rV   rV   rW   Úvalidate_and_set¶  s   


zIndexCol.validate_and_setc                 C  s^   t | jƒdkr-| j}|dur-|du r| j}|j|k r*td|› d| j› d|j› d�ƒ‚|jS dS )z:validate this column: return the compared against itemsizer3  Nz#Trying to store a string with len [z] in [z)] column but
this column has a limit of [zC]!
Consider using min_itemsize to preset the sizes on these columns)rX   r‰  r.  rù  r±   ró  )rØ   rù  ÚcrV   rV   rW   r:  ¾  s   
ÿþÿzIndexCol.validate_colc                 C  sJ   |rt | j| jd ƒ}|d ur!|| jkr#td|› d| j› d�ƒ‚d S d S d S )Nzincompatible kind in col [ú - r>  )r�  r*  rü  r‰  r¸   )rØ   r‘   Úexisting_kindrV   rV   rW   r;  Ñ  s   ÿýzIndexCol.validate_attrc                 C  sÆ   | j D ]]}t| |dƒ}| | ji ¡}| |¡}||v rT|durT||krT|dv rBt|||f }tj|tt	ƒ d� d||< t
| |dƒ qtd| j› d|› d|› d|› d�	ƒ‚|dus\|dur`|||< qdS )	z
        set/update the info for this indexable with the key/value
        if there is a conflict raise/warn as needed
        N)rð  rò  ©Ú
stacklevelzinvalid info for [z] for [z], existing_value [z] conflicts with new value [r>  )Ú_info_fieldsr�  Ú
setdefaultrb   rã   rt   ÚwarningsÚwarnr   r   Úsetattrr±   )rØ   r­  rŠ   r‹   ÚidxÚexisting_valueÚwsrV   rV   rW   Úupdate_infoÚ  s.   

ÿÿþÿ€èzIndexCol.update_infoc                 C  s(   |  | j¡}|dur| j |¡ dS dS )z!set my state from the passed infoN)rã   rb   Ú__dict__Úupdate)rØ   r­  rJ  rV   rV   rW   Úset_infoù  s   ÿzIndexCol.set_infoc                 C  s   t | j| j| jƒ dS )zset the kind for this columnN)rI  r*  rü  r‰  rÜ   rV   rV   rW   r>  ÿ  ó   zIndexCol.set_attrc                 C  sT   | j dkr"| j}| | j¡}|dur$|dur&t||ddd�s(tdƒ‚dS dS dS dS )z:validate that kind=category does not change the categoriesÚcategoryNT©Ú
strict_nanÚdtype_equalzEcannot append a categorical with different categories to the existing)rÆ   rö  Úread_metadataró  r)   r±   )rØ   r8  Únew_metadataÚcur_metadatarV   rV   rW   r<  	  s   
ÿÿÿözIndexCol.validate_metadatac                 C  s"   | j dur| | j| j ¡ dS dS )zset the meta dataN)rö  r=  ró  )rØ   r8  rV   rV   rW   r=  	  s   
ÿzIndexCol.write_metadata)NNNNNNNNNNNNN)rb   r\   ró  rZ   r[   rœ   rÝ  rÛ  )rõ  re   r[   rœ   ©r  r	  r[   r’   rß  )rz  r  rY   r\   r›   r\   r[   r  rì  r]   rÞ  )r8  r9  r‘   r’   r[   rœ   )r‘   r’   r[   rœ   )r8  r9  r[   rœ   )"rí   rä  rå  ræ  rî  rç  rï  rE  rÙ   rè  rù  rü  r÷  rø   r  r  r¡  r'  r)  r*  r-  r.  r1  r  r5  r7  r?  r:  r;  rM  rP  r>  r<  r=  rV   rV   rV   rW   rí  ò  sd   
 ñ+




2









	


rí  c                   @  s2   e Zd ZdZeddd„ƒZddd„Zddd„ZdS )ÚGenericIndexColz:an index which is not represented in the data of the tabler[   r’   c                 C  ó   dS ©NFrV   rÜ   rV   rV   rW   r¡  	  ó   zGenericIndexCol.is_indexedrz  r  rY   r\   r›   útuple[Index, Index]c                 C  s,   t |tjƒsJ t|ƒƒ‚tt|ƒƒ}||fS )zÛ
        Convert the data from this selection to the appropriate pandas type.

        Parameters
        ----------
        values : np.ndarray
        nan_rep : str
        encoding : str
        errors : str
        )rQ   rR   r  rì   r/   rp   )rØ   rz  r�   rY   r›   r”   rV   rV   rW   r'  !	  s   zGenericIndexCol.convertrœ   c                 C  r6  r]   rV   rÜ   rV   rV   rW   r>  3	  rú   zGenericIndexCol.set_attrNrß  )rz  r  rY   r\   r›   r\   r[   r^  rÞ  )rí   rä  rå  ræ  rè  r¡  r'  r>  rV   rV   rV   rW   rZ  	  s    
rZ  c                      s  e Zd ZdZdZdZddgZ												d>d?‡ fdd„Zed@dd„ƒZ	ed@dd„ƒZ
d@dd„ZdAdd„ZdBdd„Zdd „ ZedCd#d$„ƒZed%d&„ ƒZedDd)d*„ƒZedEd+d,„ƒZed-d.„ ƒZed/d0„ ƒZed1d2„ ƒZed3d4„ ƒZdFd5d6„ZdGd:d;„ZdFd<d=„Z‡  ZS )HÚDataCola3  
    a data holding column, by definition this is not indexable

    Parameters
    ----------
    data   : the actual data
    cname  : the column name in the table to hold the data (typically
                values)
    meta   : a string description of the metadata
    metadata : the actual metadata
    Frñ  r€  Nrb   r\   ró  rZ   r  úDtypeArg | Noner[   rœ   c                   s2   t ƒ j|||||||||	|
|d� || _|| _d S )N)rb   rz  r‰  rô  rõ  ró  rñ  r€  rw   rÆ   rö  )ÚsuperrÙ   r  r¥  )rØ   rb   rz  r‰  rô  ró  rõ  rñ  r€  rw   rÆ   rö  r  r¥  ©Ú	__class__rV   rW   rÙ   H	  s   õ
zDataCol.__init__c                 C  rú  )NÚ_dtypera   rÜ   rV   rV   rW   Ú
dtype_attrh	  rý  zDataCol.dtype_attrc                 C  rú  )NÚ_metara   rÜ   rV   rV   rW   Ú	meta_attrl	  rý  zDataCol.meta_attrc                 C  rÿ  )Nr   c                 S  r  r  rV   rm  rV   rV   rW   rm   w	  r  z$DataCol.__repr__.<locals>.<listcomp>)rb   ró  r  r‰  Úshape)
ro   r  r?   rb   ró  r  r‰  rh  r  rO  r  rV   rV   rW   rø   p	  s   ÿÿþÿzDataCol.__repr__r  r	  r’   c                   r
  )r  c                 3  r  r]   r  r¢  r  rV   rW   rk  	  r  z!DataCol.__eq__.<locals>.<genexpr>)rb   ró  r  rõ  r  r  rV   r  rW   r  }	  r  zDataCol.__eq__r¥  rH   c                 C  s@   |d usJ ‚| j d u sJ ‚t|ƒ\}}|| _|| _ t|ƒ| _d S r]   )r  Ú_get_data_and_dtype_namer¥  Ú_dtype_to_kindr‰  )rØ   r¥  Ú
dtype_namerV   rV   rW   Úset_data„	  s   zDataCol.set_datac                 C  rÚ   )zreturn the data©r¥  rÜ   rV   rV   rW   r)  Ž	  rá   zDataCol.take_datarz  rD   c                 C  sÖ   |j }|j}|j}|jdkrd|jf}t|tƒr&|j}| j||j j	d�}|S t
 |d¡s1t|tƒr8|  |¡}|S t
 |d¡rE|  |¡}|S t|ƒrUtƒ j||d d�}|S t|ƒra|  ||¡}|S | j||j	d�}|S )zW
        Get an appropriately typed and shaped pytables.Col object for values.
        rf   ©r‰  r  Úmr   ©rù  rh  )r  rù  rh  rs  ÚsizerQ   r4   ÚcodesÚget_atom_datarb   r   r!  r&   Úget_atom_datetime64Úget_atom_timedelta64r!   r†   Ú
ComplexColr#   Úget_atom_string)rË  rz  r  rù  rh  rr  ÚatomrV   rV   rW   Ú	_get_atom’	  s.   


õ

÷
ùûþzDataCol._get_atomc                 C  s   t ƒ j||d d�S )Nr   rp  ©r†   r4  ©rË  rh  rù  rV   rV   rW   rw  ²	  ó   zDataCol.get_atom_stringr‰  ú	type[Col]c                 C  sR   |  d¡r|dd… }d|› d�}n|  d¡rd}n	| ¡ }|› d�}ttƒ |ƒS )z0return the PyTables column class for this columnÚuinté   NÚUIntrD   ÚperiodÚInt64Col)rš  Ú
capitalizer�  r†   )rË  r‰  Úk4Úcol_nameÚkcaprV   rV   rW   Úget_atom_coltype¶	  s   


zDataCol.get_atom_coltypec                 C  s   | j |d�|d d�S )Nrn  r   ©rh  ©r‡  ©rË  rh  r‰  rV   rV   rW   rs  Å	  rQ  zDataCol.get_atom_datac                 C  ó   t ƒ j|d d�S ©Nr   rˆ  ©r†   r‚  ©rË  rh  rV   rV   rW   rt  É	  ó   zDataCol.get_atom_datetime64c                 C  r‹  rŒ  r�  rŽ  rV   rV   rW   ru  Í	  r�  zDataCol.get_atom_timedelta64c                 C  ó   t | jdd ƒS )Nrh  )r�  r¥  rÜ   rV   rV   rW   rh  Ñ	  ó   zDataCol.shapec                 C  rÚ   r0  rm  rÜ   rV   rV   rW   r1  Õ	  r2  zDataCol.cvaluesc                 C  sh   |r.t | j| jdƒ}|dur|t| jƒkrtdƒ‚t | j| jdƒ}|dur0|| jkr2tdƒ‚dS dS dS )zAvalidate that we have the same order as the existing & same dtypeNz4appended items do not match existing items in table!z@appended items dtype do not match existing items dtype in table!)r�  r*  rü  rn   rz  r±   re  r  )rØ   r‘   Úexisting_fieldsÚexisting_dtyperV   rV   rW   r;  Ú	  s   ÿùzDataCol.validate_attrr  rY   r›   c                 C  s  t |tjƒsJ t|ƒƒ‚|jjdur|| j }| jdusJ ‚| jdu r.t|ƒ\}}t	|ƒ}n|}| j}| j
}t |tjƒs>J ‚t| jƒ}| j}	| j}
| j}|dusRJ ‚t|ƒ}| d¡rct||dd�}n‹|dkrotj|dd�}n|dkr—ztjd	d
„ |D ƒtd�}W nl ty–   tjdd
„ |D ƒtd�}Y nXw |dkrÔ|	}| ¡ }|du r­tg tjd�}nt|ƒ}| ¡ rÊ||  }||dk  | t¡ ¡ j8  < tj|||
dd�}nz	|j|dd�}W n t yí   |jddd�}Y nw t|ƒdkrüt!||||d�}| j"|fS )aR  
        Convert the data from this selection to the appropriate pandas type.

        Parameters
        ----------
        values : np.ndarray
        nan_rep :
        encoding : str
        errors : str

        Returns
        -------
        index : listlike to become an Index
        data : ndarraylike to become a column
        NÚ
datetime64T©ÚcoerceÚtimedelta64úm8[ns]©r  r   c                 S  ó   g | ]}t  |¡‘qS rV   ©r   Úfromordinal©ri   rn  rV   rV   rW   rm   
  r=  z#DataCol.convert.<locals>.<listcomp>c                 S  rš  rV   ©r   Úfromtimestampr�  rV   rV   rW   rm   "
  r=  rR  éÿÿÿÿF)Ú
categoriesr€  Úvalidate©r¦  ÚOr3  ©r�   rY   r›   )#rQ   rR   r  rì   r  r  ró  rô  ri  rj  r‰  rX   rÆ   rö  r€  rñ  rš  r"  Úasarrayr	  r±   Úravelr,   Úfloat64r3   ÚanyÚastypere   ÚcumsumÚ_valuesr4   Ú
from_codesr¸   Ú_unconvert_string_arrayrz  )rØ   rz  r�   rY   r›   Ú	convertedrk  r‰  rÆ   rö  r€  rñ  r  r¡  rr  ÚmaskrV   rV   rW   r'  ç	  sj   





ÿ
ÿÿ
 ÿÿÿ
zDataCol.convertc                 C  sH   t | j| j| jƒ t | j| j| jƒ | jdusJ ‚t | j| j| jƒ dS )zset the data for this columnN)rI  r*  rü  rz  rg  rÆ   r  re  rÜ   rV   rV   rW   r>  K
  s   zDataCol.set_attr)NNNNNNNNNNNN)rb   r\   ró  rZ   r  r`  r[   rœ   rÛ  rY  )r¥  rH   r[   rœ   )rz  rH   r[   rD   )r‰  r\   r[   r}  ©r‰  r\   r[   rD   rÞ  )rz  r  rY   r\   r›   r\   )rí   rä  rå  ræ  rî  rï  rE  rÙ   rè  re  rg  rø   r  rl  r)  Úclassmethodry  rw  r‡  rs  rt  ru  rh  r1  r;  r'  r>  Ú__classcell__rV   rV   rb  rW   r_  7	  sZ    ò 










dr_  c                   @  sP   e Zd ZdZdZddd„Zedd„ ƒZeddd„ƒZedd„ ƒZ	edd„ ƒZ
dS )ÚDataIndexableColz+represent a data column that can be indexedTr[   rœ   c                 C  s   t t| jƒjƒstdƒ‚d S )Nú-cannot have non-object label DataIndexableCol)r#   r,   rz  r  r±   rÜ   rV   rV   rW   r7  X
  s   þzDataIndexableCol.validate_namesc                 C  s   t ƒ j|d�S )N)rù  rz  r{  rV   rV   rW   rw  ]
  r‘  z DataIndexableCol.get_atom_stringr‰  r\   rD   c                 C  s   | j |d�ƒ S )Nrn  r‰  rŠ  rV   rV   rW   rs  a
  r‘  zDataIndexableCol.get_atom_datac                 C  ó
   t ƒ  ¡ S r]   r�  rŽ  rV   rV   rW   rt  e
  ó   
z$DataIndexableCol.get_atom_datetime64c                 C  r¶  r]   r�  rŽ  rV   rV   rW   ru  i
  r·  z%DataIndexableCol.get_atom_timedelta64NrÞ  r±  )rí   rä  rå  ræ  rï  r7  r²  rw  rs  rt  ru  rV   rV   rV   rW   r´  S
  s    


r´  c                   @  s   e Zd ZdZdS )ÚGenericDataIndexableColz(represent a generic pytables data columnN)rí   rä  rå  ræ  rV   rV   rV   rW   r¸  n
  s    r¸  c                   @  s~  e Zd ZU dZded< dZded< ded< ded	< d
ed< dZded< 		dPdQdd„ZedRdd„ƒZ	edSdd„ƒZ
edd „ ƒZdTd!d"„ZdUd#d$„ZdVd%d&„Zed'd(„ ƒZed)d*„ ƒZed+d,„ ƒZed-d.„ ƒZedWd/d0„ƒZedRd1d2„ƒZed3d4„ ƒZdUd5d6„ZdUd7d8„Zed9d:„ ƒZedRd;d<„ƒZed=d>„ ƒZdXd@dA„ZdYdUdCdD„ZdRdEdF„Z	B	B	B	BdZd[dJdK„ZdUdLdM„Z	Bd\d]dNdO„Z dBS )^ÚFixedzø
    represent an object in my store
    facilitate read/write of various types of objects
    this is an abstract base class

    Parameters
    ----------
    parent : HDFStore
    group : Node
        The group node where the table resides.
    r\   Úpandas_kindrv   Úformat_typeútype[DataFrame | Series]Úobj_typere   rs  r¥   rË   Fr’   rP  rP   r   rÄ   rF   rY   rZ   r›   r[   rœ   c                 C  sZ   t |tƒsJ t|ƒƒ‚td usJ ‚t |tjƒsJ t|ƒƒ‚|| _|| _t|ƒ| _|| _	d S r]   )
rQ   r¥   rì   r€   rF   rË   rÄ   r`   rY   r›   )rØ   rË   rÄ   rY   r›   rV   rV   rW   rÙ   †
  s   

zFixed.__init__c                 C  s*   | j d dko| j d dko| j d dk S )Nr   rf   é
   é   )ÚversionrÜ   rV   rV   rW   Úis_old_version•
  s   *zFixed.is_old_versionútuple[int, int, int]c                 C  sf   t t| jjddƒƒ}ztdd„ | d¡D ƒƒ}t|ƒdkr$|d }W |S W |S  ty2   d}Y |S w )	zcompute and set our versionÚpandas_versionNc                 s  s   � | ]}t |ƒV  qd S r]   ©re   r?  rV   rV   rW   rk  ž
  s   € z Fixed.version.<locals>.<genexpr>Ú.r¿  rB  )r   r   r   )rX   r�  rÄ   r�  ro   rÕ  rp   r‚   )rØ   rÀ  rV   rV   rW   rÀ  ™
  s   
üþþzFixed.versionc                 C  s   t t| jjdd ƒƒS )NrŒ  )rX   r�  rÄ   r�  rÜ   rV   rV   rW   rŒ  ¥
  r|  zFixed.pandas_typec                 C  s^   |   ¡  | j}|dur,t|ttfƒr"d dd„ |D ƒ¡}d|› d�}| jd›d|› d	�S | jS )
ú(return a pretty representation of myselfNr   c                 S  ó   g | ]}t |ƒ‘qS rV   ©r?   r?  rV   rV   rW   rm   ¯
  ó    z"Fixed.__repr__.<locals>.<listcomp>ú[r>  ú12.12z	 (shape->ú))r.  rh  rQ   rn   ro   r  rŒ  )rØ   rU   ÚjshaperV   rV   rW   rø   ©
  s   zFixed.__repr__c                 C  s   t | jƒ| j_t tƒ| j_dS )zset my pandas type & versionN)r\   rº  r*  rŒ  Ú_versionrÃ  rÜ   rV   rV   rW   rÑ  ´
  s   zFixed.set_object_infoc                 C  s   t   | ¡}|S r]   r£  )rØ   Únew_selfrV   rV   rW   r¦  ¹
  s   
z
Fixed.copyc                 C  rÚ   r]   )r,  rÜ   rV   rV   rW   rh  ½
  rá   zFixed.shapec                 C  rø  r]   ©rÄ   r¼   rÜ   rV   rV   rW   rQ  Á
  r+  zFixed.pathnamec                 C  rø  r]   )rË   rÌ   rÜ   rV   rV   rW   rÌ   Å
  r+  zFixed._handlec                 C  rø  r]   )rË   rÖ   rÜ   rV   rV   rW   rÖ   É
  r+  zFixed._filtersc                 C  rø  r]   )rË   rÓ   rÜ   rV   rV   rW   rÓ   Í
  r+  zFixed._complevelc                 C  rø  r]   )rË   rÕ   rÜ   rV   rV   rW   rÕ   Ñ
  r+  zFixed._fletcher32c                 C  rø  r]   )rÄ   r�  rÜ   rV   rV   rW   r*  Õ
  r+  zFixed.attrsc                 C  r[  ©zset our object attributesNrV   rÜ   rV   rV   rW   Ú	set_attrsÙ
  ó    zFixed.set_attrsc                 C  r[  )zget our object attributesNrV   rÜ   rV   rV   rW   Ú	get_attrsÜ
  rÓ  zFixed.get_attrsc                 C  rÚ   )zreturn my storable©rÄ   rÜ   rV   rV   rW   Ústorableß
  r2  zFixed.storablec                 C  r[  r\  rV   rÜ   rV   rV   rW   rÐ  ä
  r]  zFixed.is_existsc                 C  r�  )Nr,  )r�  rÖ  rÜ   rV   rV   rW   r,  è
  r‘  zFixed.nrowsúLiteral[True] | Nonec                 C  s   |du rdS dS )z%validate against an existing storableNTrV   r  rV   rV   rW   r¢  ì
  s   zFixed.validateNc                 C  r[  )ú+are we trying to operate on an old version?NrV   )rØ   rq   rV   rV   rW   Úvalidate_versionò
  rÓ  zFixed.validate_versionc                 C  s   | j }|du r	dS |  ¡  dS )zr
        infer the axes of my storer
        return a boolean indicating if we have a valid storer or not
        NFT)rÖ  rÔ  )rØ   rU   rV   rV   rW   r.  õ
  s
   zFixed.infer_axesr©   r�   rª   c                 C  ó   t dƒ‚)Nz>cannot read on an abstract storer: subclasses should implement©r´   ©rØ   rq   r«   r©   rª   rV   rV   rW   r$     s   ÿz
Fixed.readc                 K  rÚ  )Nz?cannot write on an abstract storer: subclasses should implementrÛ  ©rØ   rÎ  rÀ   rV   rV   rW   rÒ    s   ÿzFixed.writec                 C  s,   t  |||¡r| jj| jdd� dS tdƒ‚)zs
        support fully deleting the node in its entirety (only) - where
        specification must be None
        TrZ  Nz#cannot delete on an abstract storer)r_  r`  rÌ   rÓ  rÄ   r¸   )rØ   rq   r©   rª   rV   rV   rW   ra    s   zFixed.delete)rP   r   )
rË   r¥   rÄ   rF   rY   rZ   r›   r\   r[   rœ   rß  )r[   rÂ  rÛ  rÞ  )r[   r¹  rÝ  )r[   r×  r]   ©NNNN©r©   r�   rª   r�   rá  )r©   r�   rª   r�   r[   rœ   )!rí   rä  rå  ræ  rç  r»  rP  rÙ   rè  rÁ  rÀ  rŒ  rø   rÑ  r¦  rh  rQ  rÌ   rÖ   rÓ   rÕ   r*  rÒ  rÔ  rÖ  rÐ  r,  r¢  rÙ  r.  r$  rÒ  ra  rV   rV   rV   rW   r¹  r
  sj   
 û














û
ÿr¹  c                   @  sî   e Zd ZU dZedediZdd„ e ¡ D ƒZg Z	de
d< d<d
d„Zdd„ Zdd„ Zd=dd„Zed>dd„ƒZd=dd„Zd=dd„Zd=dd„Zd?d@d!d"„Z	d?dAd$d%„ZdBd'd(„ZdCd*d+„Z	d?dDd,d-„Z	d?dEd0d1„ZdFd4d5„Z	dGdHd:d;„ZdS )IÚGenericFixedza generified fixed versionÚdatetimer�  c                 C  s   i | ]\}}||“qS rV   rV   )ri   r<  rn  rV   rV   rW   ro  "  r=  zGenericFixed.<dictcomp>r  Ú
attributesr[   r\   c                 C  s   | j  |d¡S )NÚ )Ú_index_type_maprã   )rØ   rË  rV   rV   rW   Ú_class_to_alias&  s   zGenericFixed._class_to_aliasc                 C  s   t |tƒr|S | j |t¡S r]   )rQ   rì   Ú_reverse_index_maprã   r,   )rØ   ÚaliasrV   rV   rW   Ú_alias_to_class)  s   
zGenericFixed._alias_to_classc                 C  s¸   |   tt|ddƒƒ¡}|tkrd	dd„}|}n|tkr#d	dd„}|}n|}i }d|v r7|d |d< |tu r7t}d|v rXt|d tƒrL|d  	d¡|d< n|d |d< |tu sXJ ‚||fS )
NÚindex_classrã  c                 S  s>   t j| j| j|d�}tj|d d�}|d ur| d¡ |¡}|S )N)r  rð  ra   ÚUTC)r5   Ú_simple_newrz  r  r+   Útz_localizeÚ
tz_convert)rz  rð  rñ  ÚdtaÚresultrV   rV   rW   rx   8  s   
ÿz*GenericFixed._get_index_factory.<locals>.fc                 S  s$   t |ƒ}tj| |d�}tj|d d�S )Nr™  ra   )r(   r6   rë  r.   )rz  rð  rñ  r  ÚparrrV   rV   rW   rx   E  s   rð  rñ  zutf-8rã  )
rè  rX   r�  r+   r.   r,   r1   rQ   ÚbytesrT   )rØ   r*  ré  rx   r$  rÀ   rV   rV   rW   Ú_get_index_factory/  s*   ÿ


zGenericFixed._get_index_factoryrœ   c                 C  s$   |durt dƒ‚|durt dƒ‚dS )zE
        raise if any keywords are passed which are not-None
        Nzqcannot pass a column specification when reading a Fixed format store. this store must be selected in its entiretyzucannot pass a where specification when reading from a Fixed format store. this store must be selected in its entirety)r¸   )rØ   r«   rq   rV   rV   rW   Úvalidate_read`  s   ÿÿÿzGenericFixed.validate_readr’   c                 C  r[  )NTrV   rÜ   rV   rV   rW   rÐ  o  r]  zGenericFixed.is_existsc                 C  s   | j | j_ | j| j_dS rÑ  )rY   r*  r›   rÜ   rV   rV   rW   rÒ  s  s   
zGenericFixed.set_attrsc              	   C  sR   t t| jddƒƒ| _tt| jddƒƒ| _| jD ]}t| |tt| j|dƒƒƒ qdS )úretrieve our attributesrY   Nr›   r   )r`   r�  r*  rY   rX   r›   râ  rI  )rØ   r	  rV   rV   rW   rÔ  x  s
   
ÿzGenericFixed.get_attrsc                 K  r  r]   )rÒ  rÝ  rV   rV   rW   rÒ    rô   zGenericFixed.writeNrŠ   r©   r�   rª   c                 C  sÒ   ddl }t| j|ƒ}|j}t|ddƒ}t||jƒr"|d ||… }n@tt|ddƒƒ}	t|ddƒ}
|
dur<tj|
|	d�}n|||… }|	rW|	 	d¡rWt|d	dƒ}t
||d
d�}n|	dkrbtj|dd�}|rg|jS |S )z2read an array for the specified node (off of groupr   NÚ
transposedFÚ
value_typerh  r™  r”  rñ  Tr•  r—  r˜  )r�   r�  rÄ   r�  rQ   ÚVLArrayrX   rR   rÍ  rš  r"  r¦  ÚT)rØ   rŠ   r©   rª   r�   rð   r*  rõ  Úretr  rh  rñ  rV   rV   rW   Ú
read_array‚  s&   zGenericFixed.read_arrayr,   c                 C  sd   t t| j|› d�ƒƒ}|dkr| j|||d�S |dkr+t| j|ƒ}| j|||d�}|S td|› �ƒ‚)NÚ_varietyÚmulti©r©   rª   Úregularzunrecognized index variety: )rX   r�  r*  Úread_multi_indexrÄ   Úread_index_noder¸   )rØ   rŠ   r©   rª   Úvarietyrð   r”   rV   rV   rW   Ú
read_index¤  s   zGenericFixed.read_indexr”   c                 C  sê   t |tƒrt| j|› d�dƒ |  ||¡ d S t| j|› d�dƒ td|| j| jƒ}|  ||j	¡ t
| j|ƒ}|j|j_|j|j_t |ttfƒrQ|  t|ƒ¡|j_t |tttfƒr^|j|j_t |tƒrq|jd urst|jƒ|j_d S d S d S )Nrû  rü  rþ  r”   )rQ   r-   rI  r*  Úwrite_multi_indexÚ_convert_indexrY   r›   Úwrite_arrayrz  r�  rÄ   r‰  r�  rb   r+   r.   rå  rì   ré  r1   rð  rñ  Ú_get_tz)rØ   rŠ   r”   r¯  rð   rV   rV   rW   Úwrite_index²  s    



ÿzGenericFixed.write_indexr-   c                 C  sÒ   t | j|› d�|jƒ tt|j|j|jƒƒD ]P\}\}}}t|j	t
ƒr'tdƒ‚|› d|› �}t||| j| jƒ}|  ||j¡ t| j|ƒ}	|j|	j_||	j_t |	j|› d|› �|ƒ |› d|› �}
|  |
|¡ qd S )NÚ_nlevelsz=Saving a MultiIndex with an extension dtype is not supported.Ú_levelÚ_nameÚ_label)rI  r*  rÁ  Ú	enumeraterO  Úlevelsrr  ÚnamesrQ   r  r'   r´   r  rY   r›   r  rz  r�  rÄ   r‰  r�  rb   )rØ   rŠ   r”   ÚiÚlevÚlevel_codesrb   Ú	level_keyÚ
conv_levelrð   Ú	label_keyrV   rV   rW   r  É  s$   ÿÿ
ìzGenericFixed.write_multi_indexc                 C  s¤   t | j|› d�ƒ}g }g }g }t|ƒD ]6}|› d|› �}	t | j|	ƒ}
| j|
||d�}| |¡ | |j¡ |› d|› �}| j|||d�}| |¡ qt|||dd�S )Nr  r	  rý  r  T)r  rr  r  rH  )	r�  r*  rr  rÄ   r   r‘   rb   rú  r-   )rØ   rŠ   r©   rª   rÁ  r  rr  r  r  r  rð   r  r  r  rV   rV   rW   rÿ  â  s    
ÿzGenericFixed.read_multi_indexrð   rF   c                 C  sØ   |||… }d|j v rt |j j¡dkrtj|j j|j jd�}t|j jƒ}d }d|j v r6t|j j	ƒ}t|ƒ}|j }|  
|¡\}}	|dv rW|t||| j| jd�fdti|	¤Ž}
n|t||| j| jd�fi |	¤Ž}
||
_	|
S )Nrh  r   r™  rb   )r   r	  r¸  r  )r�  rR   Úprodrh  rÍ  rö  rX   r‰  rc   rb   rò  Ú_unconvert_indexrY   r›   r	  )rØ   rð   r©   rª   r¥  r‰  rb   r*  r$  rÀ   r”   rV   rV   rW   r   ù  s:   
ÿÿüûÿÿüzGenericFixed.read_index_noder‹   rH   c                 C  sJ   t  d|j ¡}| j | j||¡ t| j|ƒ}t|jƒ|j	_
|j|j	_dS )zwrite a 0-len array©rf   N)rR   rÍ  rs  rÌ   Úcreate_arrayrÄ   r�  r\   r  r�  rö  rh  )rØ   rŠ   r‹   Úarrrð   rV   rV   rW   Úwrite_array_empty  s
   zGenericFixed.write_array_emptyrÎ  rG   r  úIndex | Nonec                 C  s\  t |dd�}|| jv r| j | j|¡ |jdk}d}t|jtƒr$tdƒ‚|s0t	|dƒr0|j
}d}d }| jd urSttƒ� tƒ j |j¡}W d   ƒ n1 sNw   Y  |d uru|sn| jj| j|||j| jd�}||d d …< n¶|  ||¡ n¯|jjtjkr­tj|dd�}	|r†n|	d	kr‹nt|	||f }
tj|
ttƒ d
� | j | j|tƒ  ¡ ¡}|  |¡ nwt !|jd¡rÌ| j "| j|| #d¡¡ t$|jƒt%| j|ƒj&_'nXt|jt(ƒrô| j "| j||j)¡ t%| j|ƒ}t*|j+ƒ|j&_+d|jj,› d�|j&_'n0t !|jd¡�r| j "| j|| #d¡¡ dt%| j|ƒj&_'n|�r|  ||¡ n	| j "| j||¡ |t%| j|ƒj&_-d S )NT)Úextract_numpyr   Fz]Cannot store a category dtype in a HDF5 dataset that uses format="fixed". Use format="table".rø  )rÏ   ©Úskipnar3  rC  r  r  údatetime64[r>  ro  r—  ).r9   rÄ   rÌ   rÓ  rq  rQ   r  r%   r´   r  rø  rÖ   r   r±   r†   ÚAtomÚ
from_dtypeÚcreate_carrayrh  r  rì   rR   Úobject_r   Úinfer_dtyperu   rG  rH  r   r   Úcreate_vlarrayÚ
ObjectAtomr‘   r!  r  Úviewr\   r�  r�  rö  r&   Úasi8r  rñ  Úunitrõ  )rØ   rŠ   rÎ  r  r‹   Úempty_arrayrõ  rx  ÚcaÚinferred_typerL  Úvlarrrð   rV   rV   rW   r  (  sh   

ÿ


þÿ
ÿzGenericFixed.write_arrayrÛ  rÞ  rß  rã  râ  )rŠ   r\   r©   r�   rª   r�   r[   r,   )rŠ   r\   r”   r,   r[   rœ   )rŠ   r\   r”   r-   r[   rœ   )rŠ   r\   r©   r�   rª   r�   r[   r-   )rð   rF   r©   r�   rª   r�   r[   r,   )rŠ   r\   r‹   rH   r[   rœ   r]   )rŠ   r\   rÎ  rG   r  r  r[   rœ   )rí   rä  rå  ræ  r+   r.   rä  r  ræ  râ  rç  rå  rè  rò  ró  rè  rÐ  rÒ  rÔ  rÒ  rú  r  r  r  rÿ  r   r  r  rV   rV   rV   rW   rà    s4   
 

1


#ÿ

ÿÿ
&
ÿrà  c                      sR   e Zd ZU dZdgZded< edd„ ƒZ				dddd„Zd‡ fdd„Z	‡  Z
S )r¿  r´  rb   r@   c              	   C  s*   zt | jjƒfW S  ttfy   Y d S w r]   )rp   rÄ   rz  r¸   r‚   rÜ   rV   rV   rW   rh  ‰  s
   ÿzSeriesFixed.shapeNr©   r�   rª   r[   r0   c                 C  s^   |   ||¡ | jd||d�}| jd||d�}t||| jdd�}tƒ r-t|dd�r-| d¡}|S )	Nr”   rý  rz  F)r”   rb   r¦  Tr  ústring[pyarrow_numpy])ró  r  rú  r0   rb   r   r   rª  )rØ   rq   r«   r©   rª   r”   rz  rï  rV   rV   rW   r$  �  s   
zSeriesFixed.readrœ   c                   s<   t ƒ j|fi |¤Ž |  d|j¡ |  d|¡ |j| j_d S )Nr”   rz  )ra  rÒ  r  r”   r  rb   r*  rÝ  rb  rV   rW   rÒ  Ÿ  s   zSeriesFixed.writerÞ  ©r©   r�   rª   r�   r[   r0   rÞ  )rí   rä  rå  rº  râ  rç  rè  rh  r$  rÒ  r³  rV   rV   rb  rW   r¿  ƒ  s   
 
ûr¿  c                      sR   e Zd ZU ddgZded< eddd„ƒZ				dddd„Zd‡ fdd„Z‡  Z	S )ÚBlockManagerFixedrs  Únblocksre   r[   úShape | Nonec                 C  sª   zJ| j }d}t| jƒD ]}t| jd|› d�ƒ}t|dd ƒ}|d ur'||d 7 }q| jj}t|dd ƒ}|d urAt|d|d … ƒ}ng }| |¡ |W S  tyT   Y d S w )Nr   ÚblockÚ_itemsrh  rf   )	rs  rr  r1  r�  rÄ   Úblock0_valuesrn   r‘   r‚   )rØ   rs  r  r  rð   rh  rV   rV   rW   rh  «  s&   €
ÿzBlockManagerFixed.shapeNr©   r�   rª   r*   c                 C  sD  |   ||¡ |  ¡  d¡}g }t| jƒD ]}||kr||fnd\}}	| jd|› �||	d�}
| |
¡ q|d }g }t| jƒD ]<}|  d|› d�¡}| jd|› d�||	d�}|| 	|¡ }t
|j||d d	d
�}tƒ rut|dd�ru| d¡}| |¡ q>t|ƒdkr˜t|ddd�}tƒ r�| ¡ }|j|d	d�}|S t
|d |d d�S )Nr   rã  rG  rý  r3  r4  r¬  rf   F©r«   r”   r¦  Tr  r.  )rG  r¦  )r«   r¦  ©r«   r”   )ró  r½  Ú_get_block_manager_axisrr  rs  r  r‘   r1  rú  rx  r*   rø  r   r   rª  rp   r2   r   r¦  r}  )rØ   rq   r«   r©   rª   Úselect_axisrd  r  r&  r'  Úaxr  ÚdfsÚ	blk_itemsrz  ÚdfÚoutrV   rV   rW   r$  Æ  s0   
zBlockManagerFixed.readrœ   c                   sè   t ƒ j|fi |¤Ž t|jtƒr| d¡}|j}| ¡ s | ¡ }|j| j	_t
|jƒD ]\}}|dkr9|js9tdƒ‚|  d|› �|¡ q*t|jƒ| j	_t
|jƒD ]"\}}|j |j¡}| jd|› d�|j|d� |  d|› d�|¡ qOd S )Nr3  r   z/Columns index has to be unique for fixed formatrG  r¬  )r  r4  )ra  rÒ  rQ   Ú_mgrr;   Ú_as_managerÚis_consolidatedÚconsolidaters  r*  r  rd  Ú	is_uniquer±   r  rp   Úblocksr1  r  ry  Úmgr_locsr  rz  )rØ   rÎ  rÀ   r¥  r  r:  Úblkr<  rb  rV   rW   rÒ  ï  s"   

üzBlockManagerFixed.write)r[   r2  rÞ  )r©   r�   rª   r�   r[   r*   rÞ  )
rí   rä  rå  râ  rç  rè  rh  r$  rÒ  r³  rV   rV   rb  rW   r0  ¦  s   
 û)r0  c                   @  s   e Zd ZdZeZdS )rÀ  rµ  N)rí   rä  rå  rº  r*   r½  rV   rV   rV   rW   rÀ  	  s    rÀ  c                      s(  e Zd ZU dZdZdZded< ded< dZded	< d
Zded< 								d‹dŒ‡ fd"d#„Z	e
d�d$d%„ƒZd�d&d'„ZdŽd)d*„Zd�d+d,„Ze
d�d.d/„ƒZd‘d3d4„Ze
d’d6d7„ƒZe
d�d8d9„ƒZe
d:d;„ ƒZe
d<d=„ ƒZe
d>d?„ ƒZe
d@dA„ ƒZe
d“dCdD„ƒZe
d’dEdF„ƒZe
d�dGdH„ƒZe
d”dJdK„ƒZd•dMdN„ZdOdP„ Zd–dRdS„Zd—dUdV„Zd˜dYdZ„Zd™d[d\„Z d�d]d^„Z!d�d_d`„Z"dšd�dadb„Z#d�dcdd„Z$e%dedf„ ƒZ&	d›dœdhdi„Z'	d�dždndo„Z(e)dŸdqdr„ƒZ*dsdt„ Z+	
			d d¡dwdx„Z,e-d¢d{d|„ƒZ.dšd£dd€„Z/d¤d„d…„Z0	d›d¥d†d‡„Z1			d›d¦d‰dŠ„Z2‡  Z3S )§r  aa  
    represent a table:
        facilitate read/write of various types of tables

    Attrs in Table Node
    -------------------
    These are attributes that are store in the main table node, they are
    necessary to recreate these tables when read back in.

    index_axes    : a list of tuples of the (original indexing axis and
        index column)
    non_index_axes: a list of tuples of the (original index axis and
        columns on a non-indexing axis)
    values_axes   : a list of the columns which comprise the data of this
        table
    data_columns  : a list of the columns that we are allowing indexing
        (these become single columns in values_axes)
    nan_rep       : the string to use for nan representations for string
        objects
    levels        : the names of levels
    metadata      : the names of the metadata columns
    Ú
wide_tablerw   r\   r»  r±  rf   zint | list[Hashable]r  Trn   rö  Nr   rË   r¥   rÄ   rF   rY   rZ   r›   Ú
index_axesúlist[IndexCol] | NonerC  ú list[tuple[AxisInt, Any]] | NoneÚvalues_axesúlist[DataCol] | Noner™   úlist | Noner­  údict | Noner[   rœ   c                   sP   t ƒ j||||d� |pg | _|pg | _|pg | _|pg | _|	p!i | _|
| _d S )Nr¸  )ra  rÙ   rH  rC  rK  r™   r­  r�   )rØ   rË   rÄ   rY   r›   rH  rC  rK  r™   r­  r�   rb  rV   rW   rÙ   .  s   





zTable.__init__c                 C  s   | j  d¡d S )NÚ_r   )r±  rÕ  rÜ   rV   rV   rW   Útable_type_shortC  ó   zTable.table_type_shortc                 C  s¦   |   ¡  t| jƒrd | j¡nd}d|› d�}d}| jr-d dd„ | jD ƒ¡}d|› d�}d d	d„ | jD ƒ¡}| jd
›|› d| j› d| j	› d| j
› d|› d|› d�S )rÆ  r   rã  z,dc->[r>  rÅ  c                 S  rÇ  rV   ©r\   r?  rV   rV   rW   rm   O  rÉ  z"Table.__repr__.<locals>.<listcomp>rÊ  c                 S  r  rV   ra   r¢  rV   rV   rW   rm   R  r
  rË  z (typ->z,nrows->z,ncols->z,indexers->[rÌ  )r.  rp   r™   r  rÁ  rÀ  rH  rŒ  rP  r,  Úncols)rØ   Újdcr„  ÚverÚjverÚjindex_axesrV   rV   rW   rø   G  s(   ÿÿþþþÿzTable.__repr__r@  c                 C  s"   | j D ]}||jkr|  S qdS )zreturn the axis for cN)rd  rb   )rØ   r@  r‡   rV   rV   rW   rå   Y  s
   

ÿzTable.__getitem__c              
   C  sº   |du rdS |j | j krtd|j › d| j › d�ƒ‚dD ]?}t| |dƒ}t||dƒ}||krZt|ƒD ]\}}|| }||krKtd|› d|› d|› d�ƒ‚q1td|› d|› d|› d�ƒ‚qdS )	z"validate against an existing tableNz'incompatible table_type with existing [rA  r>  )rH  rC  rK  zinvalid combination of [z] on appending data [z] vs current table [)r±  r¸   r�  r  r±   r]  )rØ   r  r@  ÚsvÚovr  ÚsaxÚoaxrV   rV   rW   r¢  `  s@   ÿÿÿÿÿÿÿÿÿòýzTable.validater’   c                 C  s   t | jtƒS )z@the levels attribute is 1 or a list in the case of a multi-index)rQ   r  rn   rÜ   rV   rV   rW   Úis_multi_index�  s   zTable.is_multi_indexrÎ  rŒ   ú tuple[DataFrame, list[Hashable]]c              
   C  sT   t  |jj¡}z| ¡ }W n ty } ztdƒ|‚d}~ww t|tƒs&J ‚||fS )ze
        validate that we can store the multi-index; reset and return the
        new object
        zBduplicate names/columns in the multi-index when storing as a tableN)r_  Úfill_missing_namesr”   r  Úreset_indexr±   rQ   r*   )rØ   rÎ  r  Ú	reset_objrb  rV   rV   rW   Úvalidate_multiindex†  s   ÿþ€ÿzTable.validate_multiindexre   c                 C  s   t  dd„ | jD ƒ¡S )z-based on our axes, compute the expected nrowsc                 S  s   g | ]}|j jd  ‘qS rB  )r1  rh  ©ri   r  rV   rV   rW   rm   š  r£  z(Table.nrows_expected.<locals>.<listcomp>)rR   r  rH  rÜ   rV   rV   rW   Únrows_expected—  s   zTable.nrows_expectedc                 C  s
   d| j v S )zhas this table been createdrw   rÕ  rÜ   rV   rV   rW   rÐ  œ  s   
zTable.is_existsc                 C  r�  ©Nrw   ©r�  rÄ   rÜ   rV   rV   rW   rÖ  ¡  r‘  zTable.storablec                 C  rÚ   )z,return the table group (this is my storable))rÖ  rÜ   rV   rV   rW   rw   ¥  r2  zTable.tablec                 C  rø  r]   )rw   r  rÜ   rV   rV   rW   r  ª  r+  zTable.dtypec                 C  rø  r]   r,  rÜ   rV   rV   rW   r-  ®  r+  zTable.descriptionúitertools.chain[IndexCol]c                 C  s   t  | j| j¡S r]   )rM  rN  rH  rK  rÜ   rV   rV   rW   rd  ²  rQ  z
Table.axesc                 C  s   t dd„ | jD ƒƒS )z.the number of total columns in the values axesc                 s  s   � | ]}t |jƒV  qd S r]   )rp   rz  r¢  rV   rV   rW   rk  ¹  s   € zTable.ncols.<locals>.<genexpr>)ÚsumrK  rÜ   rV   rV   rW   rS  ¶  s   zTable.ncolsc                 C  r[  r\  rV   rÜ   rV   rV   rW   Úis_transposed»  r]  zTable.is_transposedútuple[int, ...]c                 C  s(   t t dd„ | jD ƒdd„ | jD ƒ¡ƒS )z@return a tuple of my permutated axes, non_indexable at the frontc                 S  s   g | ]}t |d  ƒ‘qS rB  rÄ  r¢  rV   rV   rW   rm   Ä  r£  z*Table.data_orientation.<locals>.<listcomp>c                 S  s   g | ]}t |jƒ‘qS rV   )re   rG  r¢  rV   rV   rW   rm   Å  r=  )ro   rM  rN  rC  rH  rÜ   rV   rV   rW   Údata_orientation¿  s   þÿzTable.data_orientationúdict[str, Any]c                   sR   dddœ‰ dd„ ˆj D ƒ}‡ fdd„ˆjD ƒ}‡fdd„ˆjD ƒ}t|| | ƒS )z<return a dict of the kinds allowable columns for this objectr”   r«   ©r   rf   c                 S  s   g | ]}|j |f‘qS rV   ©ró  r¢  rV   rV   rW   rm   Ï  r=  z$Table.queryables.<locals>.<listcomp>c                   s   g | ]
\}}ˆ | d f‘qS r]   rV   )ri   rG  rz  )Ú
axis_namesrV   rW   rm   Ð  s    c                   s&   g | ]}|j tˆ jƒv r|j|f‘qS rV   )rb   rq  r™   ró  r�  rÜ   rV   rW   rm   Ñ  s     )rH  rC  rK  rg  )rØ   Úd1Úd2Úd3rV   )rn  rØ   rW   Ú
queryablesÉ  s   

ÿzTable.queryablesc                 C  ó   dd„ | j D ƒS )zreturn a list of my index colsc                 S  s   g | ]}|j |jf‘qS rV   )rG  ró  rb  rV   rV   rW   rm   Ú  r£  z$Table.index_cols.<locals>.<listcomp>©rH  rÜ   rV   rV   rW   Ú
index_cols×  r/  zTable.index_colsr  c                 C  rs  )zreturn a list of my values colsc                 S  r  rV   rm  rb  rV   rV   rW   rm   Þ  r
  z%Table.values_cols.<locals>.<listcomp>)rK  rÜ   rV   rV   rW   Úvalues_colsÜ  rQ  zTable.values_colsrŠ   c                 C  s   | j j}|› d|› d�S )z)return the metadata pathname for this keyz/meta/z/metarÐ  r"  rV   rV   rW   Ú_get_metadata_pathà  s   zTable._get_metadata_pathrz  r  c                 C  s0   | j j|  |¡t|dd�d| j| j| jd� dS )z£
        Write out a metadata array to the key as a fixed-format Series.

        Parameters
        ----------
        key : str
        values : ndarray
        Fr£  rw   )r“   rY   r›   r�   N)rË   r¤   rw  r0   rY   r›   r�   )rØ   rŠ   rz  rV   rV   rW   r=  å  s   	

úzTable.write_metadatac                 C  s0   t t | jddƒ|dƒdur| j |  |¡¡S dS )z'return the meta data array for this keyrÆ   N)r�  rÄ   rË   r½   rw  rä   rV   rV   rW   rV  ÷  s   zTable.read_metadatac                 C  sp   t | jƒ| j_|  ¡ | j_|  ¡ | j_| j| j_| j| j_| j| j_| j| j_| j	| j_	| j
| j_
| j| j_dS )zset our table type & indexablesN)r\   r±  r*  ru  rv  rC  r™   r�   rY   r›   r  r­  rÜ   rV   rV   rW   rÒ  ý  s   





zTable.set_attrsc                 C  s°   t | jddƒpg | _t | jddƒpg | _t | jddƒpi | _t | jddƒ| _tt | jddƒƒ| _tt | jddƒƒ| _	t | jd	dƒpBg | _
d
d„ | jD ƒ| _dd„ | jD ƒ| _dS )rô  rC  Nr™   r­  r�   rY   r›   r   r  c                 S  ó   g | ]}|j r|‘qS rV   ©rî  r¢  rV   rV   rW   rm     r=  z#Table.get_attrs.<locals>.<listcomp>c                 S  ó   g | ]}|j s|‘qS rV   ry  r¢  rV   rV   rW   rm     r=  )r�  r*  rC  r™   r­  r�   r`   rY   rX   r›   r  Ú
indexablesrH  rK  rÜ   rV   rV   rW   rÔ  
  s   zTable.get_attrsc                 C  sF   |dur| j r!td dd„ | jD ƒ¡ }tj|ttƒ d� dS dS dS )rØ  NrÅ  c                 S  rÇ  rV   rR  r?  rV   rV   rW   rm     rÉ  z*Table.validate_version.<locals>.<listcomp>rC  )rÁ  rs   r  rÀ  rG  rH  r   r   )rØ   rq   rL  rV   rV   rW   rÙ    s   
ýýzTable.validate_versionc                 C  sR   |du rdS t |tƒsdS |  ¡ }|D ]}|dkrq||vr&td|› d�ƒ‚qdS )zˆ
        validate the min_itemsize doesn't contain items that are not in the
        axes this needs data_columns to be defined
        Nrz  zmin_itemsize has the key [z%] which is not an axis or data_column)rQ   rg  rr  r±   )rØ   r•   Úqr<  rV   rV   rW   Úvalidate_min_itemsize!  s   

ÿÿüzTable.validate_min_itemsizec                   sÔ   g }ˆj ‰ˆjj‰tˆjjƒD ]5\}\}}tˆ|ƒ}ˆ |¡}|dur%dnd}|› d�}tˆ|dƒ}	t||||	|ˆj||d�}
| |
¡ qt	ˆj
ƒ‰t|ƒ‰ ‡ ‡‡‡‡fdd„‰| ‡fdd„tˆjjƒD ƒ¡ |S )	z/create/cache the indexables if they don't existNrR  rû  )rb   rG  rõ  r‰  rô  rw   rÆ   rö  c                   s¢   t |tƒsJ ‚t}|ˆv rt}tˆ|ƒ}t|ˆjƒ}tˆ|› d�d ƒ}tˆ|› d�d ƒ}t|ƒ}ˆ |¡}tˆ|› d�d ƒ}	|||||ˆ |  |ˆj	|	||d�
}
|
S )Nrû  rd  rf  )
rb   ró  rz  r‰  rõ  rô  rw   rÆ   rö  r  )
rQ   r\   r_  r´  r�  Ú_maybe_adjust_namerÀ  rj  rV  rw   )r  r@  Úklassrx  Úadj_namerz  r  r‰  ÚmdrÆ   rÎ  )Úbase_posr„  ÚdescrØ   Útable_attrsrV   rW   rx   Y  s0   

özTable.indexables.<locals>.fc                   s   g | ]	\}}ˆ ||ƒ‘qS rV   rV   )ri   r  r@  )rx   rV   rW   rm   ~  rA  z$Table.indexables.<locals>.<listcomp>)r-  rw   r*  r  ru  r�  rV  rí  r‘   rq  r™   rp   ru  rv  )rØ   Ú_indexablesr  rG  rb   rx  r�  rÆ   rü  r‰  Ú	index_colrV   )r‚  r„  rƒ  rx   rØ   r„  rW   r{  6  s2   


ø

 %zTable.indexablesr‰  c              	   C  sP  |   ¡ sdS |du rdS |du s|du rdd„ | jD ƒ}t|ttfƒs&|g}i }|dur0||d< |dur8||d< | j}|D ]h}t|j|dƒ}|durŽ|jrx|j	}|j
}	|j}
|durc|
|krc| ¡  n|
|d< |durt|	|krt| ¡  n|	|d< |js�|j d¡r…td	ƒ‚|jdi |¤Ž q=|| jd
 d v r¥td|› d|› d|› d�ƒ‚q=dS )aZ  
        Create a pytables index on the specified columns.

        Parameters
        ----------
        columns : None, bool, or listlike[str]
            Indicate which columns to create an index on.

            * False : Do not create any indexes.
            * True : Create indexes on all columns.
            * None : Create indexes on all columns.
            * listlike : Create indexes on the given columns.

        optlevel : int or None, default None
            Optimization level, if None, pytables defaults to 6.
        kind : str or None, default None
            Kind of index, if None, pytables defaults to "medium".

        Raises
        ------
        TypeError if trying to create an index on a complex-type column.

        Notes
        -----
        Cannot index Time64Col or ComplexCol.
        Pytables must be >= 3.0.
        NFTc                 S  r   rV   )rï  ró  r¢  rV   rV   rW   rm   §  r£  z&Table.create_index.<locals>.<listcomp>rˆ  r‰  ÚcomplexzíColumns containing complex values can be stored but cannot be indexed when using table format. Either use fixed format, set index=False, or do not include the columns containing complex values to data_columns when initializing the table.r   rf   zcolumn z/ is not a data_column.
In order to read column z: you must reload the dataframe 
into HDFStore and include z  with the data_columns argument.rV   )r.  rd  rQ   ro   rn   rw   r�  rj  r¡  r”   rˆ  r‰  Úremove_indexrì   rš  r¸   rŠ  rC  r‚   )rØ   r«   rˆ  r‰  Úkwrw   r@  rn  r”   Úcur_optlevelÚcur_kindrV   rV   rW   rŠ  ‚  sX   

ÿ€ÿþÿþâzTable.create_indexr©   r�   rª   ú9list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]c           	      C  sZ   t | |||d�}| ¡ }g }| jD ]}| | j¡ |j|| j| j| jd�}| 	|¡ q|S )a  
        Create the axes sniffed from the table.

        Parameters
        ----------
        where : ???
        start : int or None, default None
        stop : int or None, default None

        Returns
        -------
        List[Tuple[index_values, column_values]]
        r2  r¥  )
Ú	Selectionr½   rd  rP  r­  r'  r�   rY   r›   r‘   )	rØ   rq   r©   rª   Ú	selectionrz  rë  r‡   ÚresrV   rV   rW   Ú
_read_axesØ  s   
üzTable._read_axesrõ  c                 C  ó   |S )zreturn the data for this objrV   ©rË  rÎ  rõ  rV   rV   rW   Ú
get_objectú  s   zTable.get_objectc                   s²   t |ƒsg S |d \}‰ | j |i ¡}| d¡dkr&|r&td|› d|› �ƒ‚|du r/tˆ ƒ}n|du r5g }t|tƒrPt|ƒ‰t|ƒ}| ‡fdd	„| 	¡ D ƒ¡ ‡ fd
d	„|D ƒS )zd
        take the input data_columns and min_itemize and create a data
        columns spec
        r   rì   r-   z"cannot use a multi-index on axis [z] with data_columns TNc                   s    g | ]}|d kr|ˆ vr|‘qS r(  rV   r;  )Úexisting_data_columnsrV   rW   rm     s
    þz/Table.validate_data_columns.<locals>.<listcomp>c                   s   g | ]}|ˆ v r|‘qS rV   rV   )ri   r@  )Úaxis_labelsrV   rW   rm   #  r£  )
rp   r­  rã   r±   rn   rQ   rg  rq  ru  r  )rØ   r™   r•   rC  rG  r­  rV   )r•  r”  rW   Úvalidate_data_columnsÿ  s.   ÿÿ


þÿ	zTable.validate_data_columnsr*   r¢  c           /        s~  t ˆtƒs| jj}td|› dtˆƒ› d�ƒ‚ˆ du rdg‰ ‡fdd„ˆ D ƒ‰ |  ¡ r=d}d	d„ | jD ƒ‰ t| j	ƒ}| j
}nd
}| j}	| jdksIJ ‚tˆ ƒ| jd krVtdƒ‚g }
|du r^d}t‡ fdd„dD ƒƒ}ˆj| }t|ƒ}|r¡t|
ƒ}| j| d }tt |¡t |¡ddd�s¡tt t|ƒ¡t t|ƒ¡ddd�r¡|}|	 |i ¡}t|jƒ|d< t|ƒj|d< |
 ||f¡ ˆ d }ˆj| }ˆ |¡}t||| j| jƒ}||_| d¡ |  |	¡ | !|¡ |g}t|ƒ}|dksòJ ‚t|
ƒdksúJ ‚|
D ]}t"ˆ|d |d ƒ‰qü|jdk}|  #|||
¡}|  $ˆ|¡ %¡ }|  &|||
| j'|¡\}}g }t(t)||ƒƒD ]Õ\}\}}t*}d}|�rbt|ƒdk�rb|d |v �rbt+}|d }|du �sbt |t,ƒ�sbtdƒ‚|�rŒ|�rŒz| j'| }W n t-t.f�y‹ }  ztd|› d| j'› d�ƒ| ‚d} ~ ww d}|�p•d|› �}!t/|!|j0|||| j| j|d�}"t1|!| j2ƒ}#| 3|"¡}$t4|"j5j6ƒ}%d}&t7|"ddƒdu�rÆt8|"j9ƒ}&d }' }(})t |"j5t:ƒ�rà|"j;})d}'t <|"j=¡ >¡ }(t?|"ƒ\}*}+||#|!t|ƒ|$||%|&|)|'|(|+|*d�},|,  |	¡ | |,¡ |d7 }�q2dd„ |D ƒ}-t| ƒ| j@| j| j| j||
||-|	|d�
}.tA| dƒ�r-| jB|._B|. C|¡ |�r=|�r=|. D| ¡ |.S ) a0  
        Create and return the axes.

        Parameters
        ----------
        axes: list or None
            The names or numbers of the axes to create.
        obj : DataFrame
            The object to create axes on.
        validate: bool, default True
            Whether to validate the obj against an existing object already written.
        nan_rep :
            A value to use for string column nan_rep.
        data_columns : List[str], True, or None, default None
            Specify the columns that we want to create to allow indexing on.

            * True : Use all available columns.
            * None : Use no columns.
            * List[str] : Use the specified columns.

        min_itemsize: Dict[str, int] or None, default None
            The min itemsize for a column in bytes.
        z/cannot properly create the storer for: [group->r·  r>  Nr   c                   r:  rV   )Ú_get_axis_numberr¢  )rÎ  rV   rW   rm   Q  r=  z&Table._create_axes.<locals>.<listcomp>Tc                 S  r  rV   rl  r¢  rV   rV   rW   rm   V  r
  Fr¿  rf   z<currently only support ndim-1 indexers in an AppendableTableÚnanc                 3  s   � | ]	}|ˆ vr|V  qd S r]   rV   r?  )rd  rV   rW   rk  n  s   € z%Table._create_axes.<locals>.<genexpr>rl  rS  r  rì   rµ  zIncompatible appended table [z]with existing table [Úvalues_block_)Úexisting_colr•   r�   rY   r›   r«   rñ  rR  )rb   ró  rz  rô  rõ  r‰  rñ  r€  rÆ   rö  r  r¥  c                 S  r   rV   )rï  rb   )ri   r.  rV   rV   rW   rm   ü  r£  )
rË   rÄ   rY   r›   rH  rC  rK  r™   r­  r�   r  )ErQ   r*   rÄ   rÉ   r¸   rì   r.  rH  rn   r™   r�   r­  rs  rp   r±   rp  rd  rC  r)   rR   Úarrayrw  rF  r  rí   r‘   Ú_get_axis_namer  rY   r›   rG  r÷  rM  r5  Ú_reindex_axisr–  r“  rI  Ú_get_blocks_and_itemsrK  r  rO  r_  r´  r\   Ú
IndexErrorrë   Ú_maybe_convert_for_string_atomrz  r~  rÀ  ry  rj  r  rb   r�  r  rñ  r%   r€  r¦  r¡  r§  ri  rË   r  r  r}  r¢  )/rØ   rd  rÎ  r¢  r�   r™   r•   rÄ   Útable_existsÚnew_infoÚnew_non_index_axesrJ  r‡   Úappend_axisÚindexerÚ
exist_axisr­  Ú	axis_nameÚ	new_indexÚnew_index_axesÚjrõ  rµ  rD  r<  Úvaxesr  rF  Úb_itemsr  rb   rš  rb  Únew_nameÚdata_convertedr€  rô  r‰  rñ  rÆ   rö  r€  r¥  rk  r.  ÚdcsÚ	new_tablerV   )rd  rÎ  rW   Ú_create_axes%  s0  
 ÿÿ
ÿ
üü





ÿÿ"ÿÿý€ÿø


ô

ö

zTable._create_axesrµ  r¡  c                 C  s~  t | jtƒr|  d¡} dd„ }| j}tt|ƒ}t|jƒ}||ƒ}t|ƒri|d \}	}
t	|
ƒ 
t	|ƒ¡}| j||	d�j}tt|ƒ}t|jƒ}||ƒ}|D ]}| j|g|	d�j}tt|ƒ}| |j¡ | ||ƒ¡ qK|r»dd„ t||ƒD ƒ}g }g }|D ];}t|jƒ}z| |¡\}}| |¡ | |¡ W q{ ttfy¶ } zd d	d
„ |D ƒ¡}td|› d�ƒ|‚d }~ww |}|}||fS )Nr3  c                   s   ‡ fdd„ˆ j D ƒS )Nc                   s   g | ]	}ˆ j  |j¡‘qS rV   )r  ry  rE  )ri   rF  ©ÚmgrrV   rW   rm   $  rA  zFTable._get_blocks_and_items.<locals>.get_blk_items.<locals>.<listcomp>)rD  r²  rV   r²  rW   Úget_blk_items#  s   z2Table._get_blocks_and_items.<locals>.get_blk_itemsr   rl  c                 S  s"   i | ]\}}t | ¡ ƒ||f“qS rV   )ro   Útolist)ri   Úbr¬  rV   rV   rW   ro  B  s    ÿÿz/Table._get_blocks_and_items.<locals>.<dictcomp>r   c                 S  rÇ  rV   rÈ  )ri   ÚitemrV   rV   rW   rm   O  rÉ  z/Table._get_blocks_and_items.<locals>.<listcomp>z+cannot match existing table structure for [z] on appending data)rQ   r?  r;   r@  r   r<   rn   rD  rp   r,   rv  r}  ru  rO  ro   rz  rR  r‘   rŸ  rë   r  r±   )rµ  r¡  r£  rK  r™   r´  r³  rD  r<  rG  r•  Ú
new_labelsr@  Úby_itemsÚ
new_blocksÚnew_blk_itemsÚear  r¶  r¬  rb  ÚjitemsrV   rV   rW   rž    sV   





þ


ÿý€þzTable._get_blocks_and_itemsrŽ  r�  c                   sª   |durt |ƒ}|dur'ˆjr'tˆjt ƒsJ ‚ˆjD ]}||vr&| d|¡ qˆjD ]\}}tˆ |||ƒ‰ ‡ ‡fdd„}q*|jdurS|j ¡ D ]\}}	}
|||
|	ƒ‰ qGˆ S )zprocess axes filtersNr   c                   sÈ   ˆ j D ]X}ˆ  |¡}ˆ  |¡}|d usJ ‚| |kr3ˆjr$| tˆjƒ¡}|||ƒ}ˆ j|d�|   S | |v r[tt	ˆ | ƒj
ƒ}t|ƒ}tˆ tƒrLd| }|||ƒ}ˆ j|d�|   S qtd| › d�ƒ‚)Nrl  rf   zcannot find the field [z] for filtering!)Ú_AXIS_ORDERSr—  Ú	_get_axisr\  Úunionr,   r  r|  r:   r�  rz  rQ   r*   r±   )ÚfieldÚfiltÚopr§  Úaxis_numberÚaxis_valuesÚtakersrz  ©rÎ  rØ   rV   rW   Úprocess_filterj  s$   





öz*Table.process_axes.<locals>.process_filter)	rn   r\  rQ   r  ÚinsertrC  r�  Úfilterr“   )rØ   rÎ  rŽ  r«   r	  rG  ÚlabelsrÈ  rÁ  rÃ  rÂ  rV   rÇ  rW   Úprocess_axesY  s   
€
 zTable.process_axesrŽ   rÎ   re  c                 C  s„   |du r
t | jdƒ}d|dœ}dd„ | jD ƒ|d< |r6|du r$| jp#d}tƒ j|||p-| jd	�}||d
< |S | jdur@| j|d
< |S )z:create the description of the table from the axes & valuesNi'  rw   )rb   re  c                 S  s   i | ]}|j |j“qS rV   )ró  rô  r¢  rV   rV   rW   ro  Ÿ  r=  z,Table.create_description.<locals>.<dictcomp>r-  é	   )rŽ   r�   rÎ   rÏ   )Úmaxrc  rd  rÓ   r†   r  rÕ   rÖ   )rØ   r�   rŽ   rÎ   re  rf  rÏ   rV   rV   rW   Úcreate_description�  s"   	

ý
ý
zTable.create_descriptionc           
      C  s�   |   |¡ |  ¡ sdS t| |||d�}| ¡ }|jdurD|j ¡ D ]"\}}}| j|| ¡ | ¡ d d�}	|||	j	|| ¡   |ƒj
 }q!t|ƒS )zf
        select coordinates (row numbers) from a table; return the
        coordinates object
        Fr2  Nrf   rý  )rÙ  r.  r�  Úselect_coordsrÊ  r“   r8  rê  rÎ  Úilocrz  r,   )
rØ   rq   r©   rª   rŽ  ÚcoordsrÁ  rÃ  rÂ  r¥  rV   rV   rW   r4  ¯  s   

ÿ zTable.read_coordinatesr7  c                 C  s¸   |   ¡  |  ¡ s
dS |durtdƒ‚| jD ]>}||jkrS|js'td|› d�ƒ‚t| jj	|ƒ}| 
| j¡ |j|||… | j| j| jd�}tt|d |jƒ|dd�  S qtd|› d	�ƒ‚)
zj
        return a single column from the table, generally only indexables
        are interesting
        FNz4read_column does not currently accept a where clausezcolumn [z=] can not be extracted individually; it is not data indexabler¥  rf   )rb   r¦  z] not found in the table)rÙ  r.  r¸   rd  rb   rï  r±   r�  rw   rj  rP  r­  r'  r�   rY   r›   r0   r"  rñ  rë   )rØ   r7  rq   r©   rª   r‡   r@  Ú
col_valuesrV   rV   rW   r8  É  s,   


ÿ
üðzTable.read_column)Nr   NNNNNN)rË   r¥   rÄ   rF   rY   rZ   r›   r\   rH  rI  rC  rJ  rK  rL  r™   rM  r­  rN  r[   rœ   rÛ  )r@  r\   rÞ  rß  )rÎ  rŒ   r[   r]  rÝ  )r[   rf  )r[   ri  )r[   rk  )r[   r  )rŠ   r\   r[   r\   )rŠ   r\   rz  r  r[   rœ   rÜ  r]   rá  )r‰  rZ   r[   rœ   rã  )r©   r�   rª   r�   r[   rŒ  ©rõ  r’   )TNNN)rÎ  r*   r¢  r’   )rµ  r*   r¡  r’   )rŽ  r�  r[   r*   )rŽ   r�   rÎ   r’   re  r�   r[   rk  rß  )r7  r\   r©   r�   rª   r�   )4rí   rä  rå  ræ  rº  r»  rç  r  rP  rÙ   rè  rP  rø   rå   r¢  r\  ra  rc  rÐ  rÖ  rw   r  r-  rd  rS  rh  rj  rr  ru  rv  rw  r=  rV  rÒ  rÔ  rÙ  r}  r   r{  rŠ  r�  r²  r“  r–  r±  Ústaticmethodrž  rÌ  rÏ  r4  r8  r³  rV   rV   rb  rW   r    s    
 õ


!





	







LÿWÿ"*ù qC
7 ÿûr  c                   @  s2   e Zd ZdZdZ				dddd„Zddd„ZdS )rÇ  zË
    a write-once read-many table: this format DOES NOT ALLOW appending to a
    table. writing is a one-time operation the data are stored in a format
    that allows for searching the data on disk
    r¾  Nr©   r�   rª   c                 C  rÚ  )z[
        read the indices and the indexing array, calculate offset rows and return
        z!WORMTable needs to implement readrÛ  rÜ  rV   rV   rW   r$  þ  s   
zWORMTable.readr[   rœ   c                 K  rÚ  )zÞ
        write in a format that we can search later on (but cannot append
        to): write out the indices and the values using _write_array
        (e.g. a CArray) create an indexing table so that we can search
        z"WORMTable needs to implement writerÛ  rÝ  rV   rV   rW   rÒ  
  s   zWORMTable.writerÞ  rß  rÞ  )rí   rä  rå  ræ  r±  r$  rÒ  rV   rV   rV   rW   rÇ  õ  s    ûrÇ  c                   @  sZ   e Zd ZdZdZ												dd dd„Zd!d"dd„Zd#dd„Zd$d%dd„ZdS )&r9  ú(support the new appendable table formatsÚ
appendableNFTr‘   r’   r®   r�   r—   rV  r[   rœ   c                 C  s¶   |s| j r| j | jd¡ | j||||||d�}|jD ]}| ¡  q|j sA|j||||	d�}| ¡  ||d< |jj	|jfi |¤Ž |j
|j_
|jD ]}| ||¡ qI|j||
d� d S )Nrw   )rd  rÎ  r¢  r•   r�   r™   )r�   rŽ   rÎ   re  rV  )r—   )rÐ  rÌ   rÓ  rÄ   r±  rd  r7  rÏ  rÒ  Úcreate_tabler­  r*  r?  Ú
write_data)rØ   rÎ  rd  r‘   r�   rŽ   rÎ   r•   r®   re  r—   r�   r™   rV  rw   r‡   ÚoptionsrV   rV   rW   rÒ    s4   
ú
	
ü

zAppendableTable.writec                   sÀ  | j j}| j}g }|r*| jD ]}t|jƒjdd�}t|tj	ƒr)| 
|jddd�¡ qt|ƒrD|d }|dd… D ]}||@ }q8| ¡ }nd}dd	„ | jD ƒ}	t|	ƒ}
|
dksZJ |
ƒ‚d
d	„ | jD ƒ}dd	„ |D ƒ}g }t|ƒD ]\}}|f| j ||
|   j }| 
| |¡¡ qo|du r�d}tjt||ƒ| j d�}|| d }t|ƒD ]9}|| ‰t|d | |ƒ‰ ˆˆ krº dS | j|‡ ‡fdd	„|	D ƒ|durÐ|ˆˆ … nd‡ ‡fdd	„|D ƒd� q¤dS )z`
        we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
        r   rl  Úu1Fr£  rf   Nc                 S  r  rV   )r1  r¢  rV   rV   rW   rm   o  r
  z.AppendableTable.write_data.<locals>.<listcomp>c                 S  ó   g | ]}|  ¡ ‘qS rV   )r)  r¢  rV   rV   rW   rm   u  rÉ  c              	   S  s,   g | ]}|  t t |j¡|jd  ¡¡‘qS r  )Ú	transposerR   ÚrollÚarangers  r�  rV   rV   rW   rm   v  s   , ré  r™  c                   ó   g | ]}|ˆˆ … ‘qS rV   rV   r¢  ©Úend_iÚstart_irV   rW   rm   Š  r£  c                   rà  rV   rV   r�  rá  rV   rW   rm   Œ  r£  )Úindexesr°  rz  )r  r  rc  rK  r3   r¥  rh  rQ   rR   r  r‘   rª  rp   r§  rH  r  rh  ÚreshaperÍ  rê  rr  Úwrite_data_chunk)rØ   r®   r—   r  r,  Úmasksr‡   r°  ro  rä  Únindexesrz  Úbvaluesr  rn  Ú	new_shapeÚrowsÚchunksrV   rá  rW   rÙ  T  sP   
€

üúzAppendableTable.write_datarë  r  rä  úlist[np.ndarray]r°  únpt.NDArray[np.bool_] | Nonerz  c                 C  sè   |D ]}t  |j¡s dS q|d jd }|t|ƒkr#t j|| jd�}| jj}t|ƒ}t|ƒD ]
\}	}
|
|||	 < q/t|ƒD ]\}	}||||	|  < q>|dura| ¡ j	t
dd� }| ¡ sa|| }t|ƒrr| j |¡ | j ¡  dS dS )zê
        Parameters
        ----------
        rows : an empty memory space where we are putting the chunk
        indexes : an array of the indexes
        mask : an array of the masks
        values : an array of the values
        Nr   r™  Fr£  )rR   r  rh  rp   rÍ  r  r  r  r§  rª  r’   rh  rw   r‘   r  )rØ   rë  rä  r°  rz  rn  r,  r  rè  r  rJ  ro  rV   rV   rW   ræ  �  s*   ÿþz AppendableTable.write_data_chunkr©   rª   c                 C  sb  |d u st |ƒs4|d u r|d u r| j}| jj| jdd� |S |d u r%| j}| jj||d�}| j ¡  |S |  ¡ s:d S | j}t	| |||d�}| 
¡ }t|dd� ¡ }t |ƒ}	|	r¯| ¡ }
t|
|
dk jƒ}t |ƒskdg}|d |	krv| |	¡ |d dkr‚| dd¡ | ¡ }t|ƒD ]}| t||ƒ¡}|j||jd  ||jd  d d� |}qŠ| j ¡  |	S )	NTrZ  rý  Fr£  rf   r   r   )rp   r,  rÌ   rÓ  rÄ   rw   Úremove_rowsr  r.  r�  rÐ  r0   Úsort_valuesÚdiffrn   r”   r‘   rÉ  rR  Úreversedry  rr  )rØ   rq   r©   rª   r,  rw   rŽ  rz  Úsorted_seriesÚlnrñ  rº   Úpgr  rë  rV   rV   rW   ra  »  sF   ü

ÿ
zAppendableTable.delete)NFNNNNNNFNNT)
r‘   r’   r®   r�   r—   r’   rV  r’   r[   rœ   rà  )r®   r�   r—   r’   r[   rœ   )
rë  r  rä  rí  r°  rî  rz  rí  r[   rœ   rá  rß  )	rí   rä  rå  ræ  r±  rÒ  rÙ  ræ  ra  rV   rV   rV   rW   r9    s&    ò;
;,r9  c                   @  sZ   e Zd ZU dZdZdZdZeZde	d< e
dd	d
„ƒZeddd„ƒZ				dddd„ZdS )rÅ  rÖ  r²  r¼  r¿  r¼  r½  r[   r’   c                 C  s   | j d jdkS )Nr   rf   )rH  rG  rÜ   rV   rV   rW   rh  þ  rQ  z"AppendableFrameTable.is_transposedrõ  c                 C  s   |r|j }|S )zthese are written transposed)rø  r’  rV   rV   rW   r“    s   zAppendableFrameTable.get_objectNr©   r�   rª   c                   sR  ˆ   |¡ ˆ  ¡ sd S ˆ j|||d�}tˆ jƒr$ˆ j ˆ jd d i ¡ni }‡ fdd„tˆ jƒD ƒ}t|ƒdks:J ‚|d }|| d }	g }
tˆ jƒD ]¹\}}|ˆ j	vrUqK|| \}}| d¡dkrgt
|ƒ}nt |¡}| d¡}|d ur||j|d	d
� ˆ jrŽ|}|}t
|	t|	dd ƒd�}n|j}t
|	t|	dd ƒd�}|}|jdkr²t|tjƒr²| d|jd f¡}t|tjƒrÂt|j||dd�}nt|t
ƒrÏt|||d�}n	tj|g||d�}tƒ rá|jjdksñ|j|jk ¡ sñJ |j|jfƒ‚tƒ rÿt|d	d�rÿ| d¡}|
  |¡ qKt|
ƒdk�r|
d }nt!|
dd�}t"ˆ |||d�}ˆ j#|||d�}|S )Nr2  r   c                   s"   g | ]\}}|ˆ j d  u r|‘qS rB  rt  )ri   r  r:  rÜ   rV   rW   rm     s   " z-AppendableFrameTable.read.<locals>.<listcomp>rf   rì   r-   r  T©Úinplacerb   ra   Fr6  r7  r¤  r  r.  rl  )rŽ  r«   )$rÙ  r.  r�  rp   rC  r­  rã   r  rd  rK  r,   r-   Úfrom_tuplesÚ	set_namesrh  r�  rø  rs  rQ   rR   r  rå  rh  r*   Ú_from_arraysr   r  r‰  Údtypesrh  r   rª  r‘   r2   r�  rÌ  )rØ   rq   r«   r©   rª   rï  r­  ÚindsÚindr”   Úframesr  r‡   Ú
index_valsr1  rj  r  rz  Úindex_Úcols_r=  rŽ  rV   rÜ   rW   r$  	  sf   
ÿý




 þ

zAppendableFrameTable.readrß  rÔ  rÞ  rß  )rí   rä  rå  ræ  rº  r±  rs  r*   r½  rç  rè  rh  r²  r“  r$  rV   rV   rV   rW   rÅ  ö  s   
 ûrÅ  c                      sh   e Zd ZdZdZdZdZeZe	ddd„ƒZ
edd
d„ƒZdd‡ fdd„Z				dd‡ fdd„Z‡  ZS )rÃ  rÖ  r¹  rº  r¿  r[   r’   c                 C  r[  r\  rV   rÜ   rV   rV   rW   rh  f  r]  z#AppendableSeriesTable.is_transposedrõ  c                 C  r‘  r]   rV   r’  rV   rV   rW   r“  j  r]  z AppendableSeriesTable.get_objectNrœ   c                   s@   t |tƒs|jp	d}| |¡}tƒ jd||j ¡ dœ|¤Ž dS )ú+we are going to write this as a frame tablerz  ©rÎ  r™   NrV   )rQ   r*   rb   Úto_framera  rÒ  r«   rµ  )rØ   rÎ  r™   rÀ   rb   rb  rV   rW   rÒ  o  s   


"zAppendableSeriesTable.writer©   r�   rª   r0   c                   s�   | j }|d ur!|r!t| jtƒsJ ‚| jD ]}||vr | d|¡ qtƒ j||||d�}|r5|j| jdd� |jd d …df }|j	dkrFd |_	|S )Nr   rF  Trö  rz  )
r\  rQ   r  rn   rÉ  ra  r$  Ú	set_indexrÑ  rb   )rØ   rq   r«   r©   rª   r\  r	  rU   rb  rV   rW   r$  v  s   
€
zAppendableSeriesTable.readrß  rÔ  r]   rÞ  rÞ  r/  )rí   rä  rå  ræ  rº  r±  rs  r0   r½  rè  rh  r²  r“  rÒ  r$  r³  rV   rV   rb  rW   rÃ  ^  s     	ûrÃ  c                      s*   e Zd ZdZdZdZd‡ fdd„Z‡  ZS )	rÄ  rÖ  r¹  r»  r[   rœ   c                   sb   |j pd}|  |¡\}| _t| jtƒsJ ‚t| jƒ}| |¡ t|ƒ|_tƒ j	dd|i|¤Ž dS )r  rz  rÎ  NrV   )
rb   ra  r  rQ   rn   r‘   r,   r«   ra  rÒ  )rØ   rÎ  rÀ   rb   Únewobjrj  rb  rV   rW   rÒ  –  s   



z AppendableMultiSeriesTable.writerÞ  )rí   rä  rå  ræ  rº  r±  rÒ  r³  rV   rV   rb  rW   rÄ  �  s
    rÄ  c                   @  sd   e Zd ZU dZdZdZdZeZde	d< e
dd	d
„ƒZe
dd„ ƒZddd„Zedd„ ƒZddd„ZdS )rÂ  z:a table that read/writes the generic pytables table formatr²  r³  r¿  zlist[Hashable]r  r[   r\   c                 C  rÚ   r]   )rº  rÜ   rV   rV   rW   rŒ  ª  rá   zGenericTable.pandas_typec                 C  s   t | jdd ƒp	| jS rd  re  rÜ   rV   rV   rW   rÖ  ®  r|  zGenericTable.storablerœ   c                 C  sL   g | _ d| _g | _dd„ | jD ƒ| _dd„ | jD ƒ| _dd„ | jD ƒ| _dS )rô  Nc                 S  rx  rV   ry  r¢  rV   rV   rW   rm   ¸  r=  z*GenericTable.get_attrs.<locals>.<listcomp>c                 S  rz  rV   ry  r¢  rV   rV   rW   rm   ¹  r=  c                 S  r  rV   ra   r¢  rV   rV   rW   rm   º  r
  )rC  r�   r  r{  rH  rK  r™   rÜ   rV   rV   rW   rÔ  ²  s   zGenericTable.get_attrsc           
   
   C  s¨   | j }|  d¡}|durdnd}tdd| j||d�}|g}t|jƒD ]/\}}t|tƒs-J ‚t||ƒ}|  |¡}|dur=dnd}t	|||g|| j||d�}	| 
|	¡ q"|S )z0create the indexables from the table descriptionr”   NrR  r   )rb   rG  rw   rÆ   rö  )rb   rõ  rz  rô  rw   rÆ   rö  )r-  rV  rZ  rw   r  Ú_v_namesrQ   r\   r�  r¸  r‘   )
rØ   rf  r�  rÆ   r†  r…  r  r	  rx  r„  rV   rV   rW   r{  ¼  s.   
ÿ

ù	zGenericTable.indexablesc                 K  rÚ  )Nz cannot write on an generic tablerÛ  )rØ   rÀ   rV   rV   rW   rÒ  à  s   zGenericTable.writeNrÛ  rÞ  )rí   rä  rå  ræ  rº  r±  rs  r*   r½  rç  rè  rŒ  rÖ  rÔ  r   r{  rÒ  rV   rV   rV   rW   rÂ  ¡  s   
 



#rÂ  c                      s`   e Zd ZdZdZeZdZe 	d¡Z
eddd„ƒZdd‡ fdd„Z								dd‡ fdd„Z‡  ZS )rÆ  za frame with a multi-indexr½  r¿  z^level_\d+$r[   r\   c                 C  r[  )NÚappendable_multirV   rÜ   rV   rV   rW   rP  ì  r]  z*AppendableMultiFrameTable.table_type_shortNrœ   c                   s|   |d u rg }n	|du r|j  ¡ }|  |¡\}| _t| jtƒs J ‚| jD ]}||vr/| d|¡ q#tƒ jd||dœ|¤Ž d S )NTr   r  rV   )	r«   rµ  ra  r  rQ   rn   rÉ  ra  rÒ  )rØ   rÎ  r™   rÀ   r	  rb  rV   rW   rÒ  ñ  s   

€zAppendableMultiFrameTable.writer©   r�   rª   c                   sD   t ƒ j||||d�}| ˆ j¡}|j ‡ fdd„|jjD ƒ¡|_|S )NrF  c                   s    g | ]}ˆ j  |¡rd n|‘qS r]   )Ú
_re_levelsÚsearch)ri   rb   rÜ   rV   rW   rm   	  s     z2AppendableMultiFrameTable.read.<locals>.<listcomp>)ra  r$  r  r  r”   rù  r  )rØ   rq   r«   r©   rª   r=  rb  rÜ   rW   r$  ý  s   ÿzAppendableMultiFrameTable.readrÛ  r]   rÞ  rÞ  rß  )rí   rä  rå  ræ  r±  r*   r½  rs  ÚreÚcompiler	  rè  rP  rÒ  r$  r³  rV   rV   rb  rW   rÆ  ä  s    
ûrÆ  rÎ  r*   rG  rI   rË  r,   c                 C  s¢   |   |¡}t|ƒ}|d urt|ƒ}|d u s| |¡r!| |¡r!| S t| ¡ ƒ}|d ur6t| ¡ ƒj|dd�}| |¡sOtd d ƒg| j }|||< | jt|ƒ } | S )NF)Úsort)	r¿  r:   ÚequalsÚuniquer{  Úslicers  r|  ro   )rÎ  rG  rË  r  r:  ÚslicerrV   rV   rW   r�    s   

r�  rñ  r   ústr | tzinfoc                 C  s   t  | ¡}|S )z+for a tz-aware type, return an encoded zone)r   Úget_timezone)rñ  ÚzonerV   rV   rW   r  )  s   
r  rz  únp.ndarray | Indexr–  r+   c                 C  r6  r]   rV   ©rz  rñ  r–  rV   rV   rW   r"  /  s   r"  r  c                 C  r6  r]   rV   r  rV   rV   rW   r"  6  r]  ústr | tzinfo | Noneúnp.ndarray | DatetimeIndexc                 C  s”   t | tƒr| jdu s| j|ksJ ‚| jdur| S |dur?t | tƒr%| j}nd}|  ¡ } t|ƒ}t| |d�} |  d¡ |¡} | S |rHtj	| dd�} | S )a  
    coerce the values to a DatetimeIndex if tz is set
    preserve the input shape if possible

    Parameters
    ----------
    values : ndarray or Index
    tz : str or tzinfo
    coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
    Nra   rê  úM8[ns]r™  )
rQ   r+   rñ  rb   r§  rX   rì  rí  rR   r¦  )rz  rñ  r–  rb   rV   rV   rW   r"  ;  s    


ûrb   c              
   C  st  t | tƒsJ ‚|j}t|ƒ\}}t|ƒ}t |¡}t |j	d¡s*t
|j	ƒs*t|j	ƒr=t| |||t|dd ƒt|dd ƒ|d�S t |tƒrFtdƒ‚tj|dd�}	t |¡}
|	dkrotjd	d
„ |
D ƒtjd�}t| |dtƒ  ¡ |d�S |	dkrŠt|
||ƒ}|j	j}t| |dtƒ  |¡|d�S |	dv r—t| ||||d�S t |tjƒr¢|j	tks¤J ‚|dks¬J |ƒ‚tƒ  ¡ }t| ||||d�S )NÚiurð  rñ  )rz  r‰  rô  rð  rñ  rò  zMultiIndex not supported here!Fr  r   c                 S  rÜ  rV   )Ú	toordinalr�  rV   rV   rW   rm   ˆ  rÉ  z"_convert_index.<locals>.<listcomp>r™  )rò  r3  )ÚintegerÚfloating)rz  r‰  rô  rò  r	  )rQ   r\   rb   ri  rj  r´  ry  r   r!  r  r$   r    rí  r�  r-   r¸   r$  rR   r¦  Úint32r†   Ú	Time32ColÚ_convert_string_arrayrù  r4  r  r	  r&  )rb   r”   rY   r›   rò  r¯  rk  r‰  rx  r,  rz  rù  rV   rV   rW   r  b  s^   
ÿþý

ù


ÿ
û
ÿ
r  r‰  c                 C  sò   |  d¡r|dkrt| ƒ}|S t|  |¡ƒ}|S |dkr"t| ƒ}|S |dkrLztjdd„ | D ƒtd�}W |S  tyK   tjdd„ | D ƒtd�}Y |S w |dv rWt | ¡}|S |d	v ret| d ||d
�}|S |dkrrt | d ¡}|S td|› �ƒ‚)Nr”  r—  r   c                 S  rš  rV   r›  r�  rV   rV   rW   rm   ¯  r=  z$_unconvert_index.<locals>.<listcomp>r™  c                 S  rš  rV   rž  r�  rV   rV   rW   rm   ±  r=  )r  Úfloatr’   r3  r¥  r	  r   zunrecognized index type )	rš  r+   r'  r1   rR   r¦  r	  r±   r®  )r¥  r‰  rY   r›   r”   rV   rV   rW   r  ¢  s:   
îïñôô
	øÿüÿr  ré  rH   r  c                 C  s„  |j tkr|S ttj|ƒ}|j j}tj|dd�}	|	dkr tdƒ‚|	dkr(tdƒ‚|	dks2|dks2|S t	|ƒ}
| 
¡ }|||
< tj|dd�}	|	dkr|t|jd	 ƒD ]+}|| }tj|dd�}	|	dkr{t|ƒ|krk|| nd
|› �}td|› d|	› d�ƒ‚qPt|||ƒ |j¡}|j}t|tƒrœt| | ¡pš| d¡pšd	ƒ}t|p d	|ƒ}|d ur¶| |¡}|d ur¶||kr¶|}|jd|› �dd�}|S )NFr  r   z+[date] is not implemented as a table columnrá  z>too many timezones in this block, create separate data columnsr3  r	  r   zNo.zCannot serialize the column [z2]
because its data contents are not [string] but [z] object dtyperz  z|Sr£  )r  r	  r   rR   r  rb   r   r$  r¸   r3   r¦  rr  rh  rp   r   rå  rù  rQ   rg  re   rã   rÎ  r:  rª  )rb   ré  rš  r•   r�   rY   r›   r«   rk  r,  r°  r¥  r  r.  Úerror_column_labelr®  rù  ÚecirV   rV   rW   r   ¿  sP   

ÿþÿþ


r   r¥  c                 C  s`   t | ƒrt|  ¡ dd�j ||¡j | j¡} t|  ¡ ƒ}t	dt
 |¡ƒ}tj| d|› �d�} | S )a  
    Take a string-like that is object dtype and coerce to a fixed size string type.

    Parameters
    ----------
    data : np.ndarray[object]
    encoding : str
    errors : str
        Handler for encoding errors.

    Returns
    -------
    np.ndarray[fixed-length-string]
    Fr£  rf   ÚSr™  )rp   r0   r§  r\   Úencoder¬  rå  rh  r   rÎ  Ú
libwritersÚmax_len_string_arrayrR   r¦  )r¥  rY   r›   Úensuredrù  rV   rV   rW   r     s   

ýr   c                 C  sœ   | j }tj|  ¡ td�} t| ƒr=t t| ƒ¡}d|› �}t	| d t
ƒr1t| dd�jj||d�j} n| j|dd�jtdd�} |du rCd}t | |¡ |  |¡S )	a*  
    Inverse of _convert_string_array.

    Parameters
    ----------
    data : np.ndarray[fixed-length-string]
    nan_rep : the storage repr of NaN
    encoding : str
    errors : str
        Handler for encoding errors.

    Returns
    -------
    np.ndarray[object]
        Decoded data.
    r™  ÚUr   Fr£  )r›   Nr˜  )rh  rR   r¦  r§  r	  rp   r&  r'  r   rQ   rñ  r0   r\   rT   r¬  rª  Ú!string_array_replace_from_nan_reprå  )r¥  r�   rY   r›   rh  rù  r  rV   rV   rW   r®  '  s   

r®  r#  c                 C  s6   t |tƒsJ t|ƒƒ‚t|ƒrt|||ƒ}|| ƒ} | S r]   )rQ   r\   rì   Ú_need_convertÚ_get_converter)rz  r#  rY   r›   ÚconvrV   rV   rW   r   M  s
   r   c                   sH   ˆdkrdd„ S dˆv r‡fdd„S ˆdkr‡ ‡fdd„S t dˆ› �ƒ‚)Nr”  c                 S  s   t j| dd�S )Nr  r™  ©rR   r¦  ©r@  rV   rV   rW   r¡   W  ó    z _get_converter.<locals>.<lambda>c                   s   t j| ˆ d�S )Nr™  r.  r/  rn  rV   rW   r¡   Y  r0  r3  c                   s   t | d ˆ ˆd�S )Nr¥  )r®  r/  r¸  rV   rW   r¡   [  s    ÿzinvalid kind )r±   )r‰  rY   r›   rV   )rY   r›   r‰  rW   r,  U  s   r,  c                 C  s   | dv sd| v r
dS dS )N)r”  r3  r”  TFrV   rn  rV   rV   rW   r+  b  s   r+  rÀ  úSequence[int]c                 C  sl   t |tƒst|ƒdk rtdƒ‚|d dkr4|d dkr4|d dkr4t d| ¡}|r4| ¡ d }d|› �} | S )	zö
    Prior to 0.10.1, we named values blocks like: values_block_0 an the
    name values_0, adjust the given name if necessary.

    Parameters
    ----------
    name : str
    version : Tuple[int, int, int]

    Returns
    -------
    str
    é   z6Version is incorrect, expected sequence of 3 integers.r   rf   r¾  r¿  zvalues_block_(\d+)Úvalues_)rQ   r\   rp   r±   r  r
  rº   )rb   rÀ  ro  ÚgrprV   rV   rW   r~  h  s   $
r~  Ú	dtype_strc                 C  sÊ   t | ƒ} |  d¡rd}|S |  d¡rd}|S |  d¡rd}|S |  d¡r(d}|S |  d¡r1| }|S |  d¡r:d	}|S |  d
¡rCd
}|S |  d¡rLd}|S |  d¡rUd}|S | dkr]d}|S td| › d�ƒ‚)zA
    Find the "kind" string describing the given dtype name.
    )r3  rñ  r3  r!  r‡  )re   r~  r  r”  Ú	timedeltar—  r’   rR  r�  r	  zcannot interpret dtype of [r>  )rX   rš  r±   )r5  r‰  rV   rV   rW   rj  �  s@   

ê
ì
î
ð
ò
ô
ö	
øûþrj  c                 C  sv   t | tƒr| j} t | jtƒrd| jj› d�}n| jj}| jjdv r*t 	|  
d¡¡} nt | tƒr2| j} t 	| ¡} | |fS )zJ
    Convert the passed data into a storable form and a dtype string.
    r  r>  ÚmMr  )rQ   r4   rr  r  r&   r)  rb   r‰  rR   r¦  r'  r.   r(  )r¥  rk  rV   rV   rW   ri  ¢  s   


ri  c                   @  s:   e Zd ZdZ			ddd
d„Zdd„ Zdd„ Zdd„ ZdS )r�  zæ
    Carries out a selection operation on a tables.Table object.

    Parameters
    ----------
    table : a Table object
    where : list of Terms (or convertible to)
    start, stop: indices to start and/or stop selection

    Nrw   r  r©   r�   rª   r[   rœ   c                 C  sV  || _ || _|| _|| _d | _d | _d | _d | _t|ƒrŒt	t
ƒ�d tj|dd�}|dv r}t |¡}|jtjkrV| j| j}}|d u rDd}|d u rL| j j}t ||¡| | _n't|jjtjƒr}| jd urj|| jk  ¡ sv| jd urz|| jk ¡ rzt
dƒ‚|| _W d   ƒ n1 s‡w   Y  | jd u r§|  |¡| _| jd ur©| j ¡ \| _| _d S d S d S )NFr  )r  Úbooleanr   z3where must have index locations >= start and < stop)rw   rq   r©   rª   Ú	conditionrÊ  ÚtermsrL  r"   r   r±   r   r$  rR   r¦  r  Úbool_r,  rß  Ú
issubclassrì   r  r©  ÚgenerateÚevaluate)rØ   rw   rq   r©   rª   ÚinferredrV   rV   rW   rÙ   Ç  sF   

ÿ€î

ûzSelection.__init__c              
   C  sr   |du rdS | j  ¡ }z
t||| j jd�W S  ty8 } zd | ¡ ¡}td|› d|› d�ƒ}t|ƒ|‚d}~ww )z'where can be a : dict,list,tuple,stringN)rr  rY   r   z-                The passed where expression: a*  
                            contains an invalid variable reference
                            all of the variable references must be a reference to
                            an axis (e.g. 'index' or 'columns'), or a data_column
                            The currently defined references are: z
                )	rw   rr  r7   rY   Ú	NameErrorr  r  r   r±   )rØ   rq   r|  rb  Úqkeysr  rV   rV   rW   r=  ô  s"   
ÿûÿ
	€ózSelection.generatec                 C  sX   | j dur| jjj| j  ¡ | j| jd�S | jdur!| jj | j¡S | jjj| j| jd�S )ú(
        generate the selection
        Nrý  )	r9  rw   Ú
read_wherer“   r©   rª   rL  r4  r$  rÜ   rV   rV   rW   r½     s   
ÿ
zSelection.selectc                 C  s”   | j | j}}| jj}|du rd}n|dk r||7 }|du r!|}n|dk r)||7 }| jdur<| jjj| j ¡ ||dd�S | jdurD| jS t 	||¡S )rB  Nr   T)r©   rª   r  )
r©   rª   rw   r,  r9  Úget_where_listr“   rL  rR   rß  )rØ   r©   rª   r,  rV   rV   rW   rÐ    s"   
ÿ
zSelection.select_coordsrá  )rw   r  r©   r�   rª   r�   r[   rœ   )rí   rä  rå  ræ  rÙ   r=  r½   rÐ  rV   rV   rV   rW   r�  »  s    û-r�  )rY   rZ   r[   r\   )rd   re   )r‡   NNFNTNNNNr   rP   )rˆ   r‰   rŠ   r\   r‹   rŒ   r�   r\   rŽ   r�   r�   rZ   r‘   r’   r“   rZ   r”   r’   r•   r–   r—   r˜   r™   rš   r›   r\   rY   r\   r[   rœ   )	Nr§   r   NNNNFN)rˆ   r‰   r�   r\   r›   r\   rq   r¨   r©   r�   rª   r�   r«   r¬   r­   r’   r®   r�   )rÄ   rF   rÅ   rF   r[   r’   r]   )rÎ  r*   rG  rI   rË  r,   r[   r*   )rñ  r   r[   r  rà  )rz  r  rñ  r  r–  r’   r[   r+   )rz  r  rñ  rœ   r–  r’   r[   r  )rz  r  rñ  r  r–  r’   r[   r  )
rb   r\   r”   r,   rY   r\   r›   r\   r[   rí  )r‰  r\   rY   r\   r›   r\   r[   r  )rb   r\   ré  rH   r«   r  )r¥  r  rY   r\   r›   r\   r[   r  )rz  r  r#  r\   rY   r\   r›   r\   )r‰  r\   rY   r\   r›   r\   )r‰  r\   r[   r’   )rb   r\   rÀ  r1  r[   r\   )r5  r\   r[   r\   )r¥  rH   )´ræ  Ú
__future__r   Ú
contextlibr   r¦  rá  r   r   rM  rµ   r  Útextwrapr   Útypingr   r   r	   r
   r   r   r   rG  ÚnumpyrR   Úpandas._configr   r   r   r   Úpandas._libsr   r   r&  Úpandas._libs.libr   Úpandas._libs.tslibsr   Úpandas.compat._optionalr   Úpandas.compat.pickle_compatr   Úpandas.errorsr   r   r   r   r   Úpandas.util._decoratorsr   Úpandas.util._exceptionsr   Úpandas.core.dtypes.commonr   r    r!   r"   r#   r$   Úpandas.core.dtypes.dtypesr%   r&   r'   r(   Úpandas.core.dtypes.missingr)   r  r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   Úpandas.core.arraysr4   r5   r6   Úpandas.core.commonÚcoreÚcommonr_  Ú pandas.core.computation.pytablesr7   r8   Úpandas.core.constructionr9   Úpandas.core.indexes.apir:   Úpandas.core.internalsr;   r<   Úpandas.io.commonr=   Úpandas.io.formats.printingr>   r?   Úcollections.abcr@   rA   rB   ÚtypesrC   r�   rD   rE   rF   Úpandas._typingrG   rH   rI   rJ   rK   rL   rM   rN   rO   rÎ  r^   rX   r`   rc   rh   rr   rs   rç  rt   ru   r®  rt  rz   r{   Úconfig_prefixÚregister_optionÚis_boolÚis_one_of_factoryr€   r…   r†   r¦   rÃ   r»   r¥   r/  rí  rZ  r_  r´  r¸  r¹  rà  r¿  r0  rÀ  r  rÇ  r9  rÅ  rÃ  rÄ  rÂ  rÆ  r�  r  r"  r  r  r   r   r®  r   r,  r+  r~  rj  ri  r�  rV   rV   rV   rW   Ú<module>   s<   $	 0(


üþ
ñ:ö 
           (p  *   -  g#c       n dh1C,ÿ
ÿÿ
'
@

I

&




!