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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TracebackType)
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isinstanceÚnpÚbytes_Údecode)Ús© rU   úK/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/pandas/io/pytables.pyÚ_ensure_decoded†   s    
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str | NoneÚstr)ÚencodingÚreturnc                 C  s   | d krt } | S ©N)Ú_default_encoding©rZ   rU   rU   rV   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
    )rP   rY   ©ÚnamerU   rU   rV   Ú_ensure_str•   s    
rb   Úint©Úscope_levelc                   sV   |d ‰ t | ttfƒr*‡ fdd„| D ƒ} nt| ƒr>t| ˆ d�} | dksNt| ƒrR| 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 k	rt |ƒr(t|ˆ d d�n|‘qS )Nrf   rd   )rC   ÚTerm)Ú.0Úterm©ÚlevelrU   rV   Ú
<listcomp>¯   s   þz _ensure_term.<locals>.<listcomp>rd   N)rP   ÚlistÚtuplerC   rg   Úlen)Úwherere   rU   rj   rV   Ú_ensure_term¤   s    	
þrq   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fru   Útrv   z;
: boolean
    drop ALL nan rows when appending to a table
Ú
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: format
    default format writing format, if None, then
    put will default to 'fixed' and append will default to 'table'
Ú
format_doczio.hdfZdropna_tableF)Ú	validatorÚdefault_formatc               	   C  s8   t d kr4dd l} | a ttƒ� | jjdkaW 5 Q R X t S )Nr   Ústrict)Ú
_table_modÚtablesr   ÚAttributeErrorÚfileZ_FILE_OPEN_POLICYÚ!_table_file_open_policy_is_strict)r   rU   rU   rV   Ú_tablesè   s    

ÿrƒ   ÚaTr}   zFilePath | HDFStoreúDataFrame | Seriesú
int | NoneÚboolúint | dict[str, int] | Noneúbool | Noneú Literal[True] | list[str] | NoneÚNone)Úpath_or_bufÚkeyÚvalueÚmodeÚ	complevelÚcomplibÚappendÚformatÚindexÚmin_itemsizeÚdropnaÚdata_columnsÚerrorsrZ   r[   c              
     s†   |r$‡ ‡‡‡‡‡‡‡‡‡	f
dd„}n‡ ‡‡‡‡‡‡‡‡‡	f
dd„}t | ƒ} t| tƒrzt| |||d��}||ƒ W 5 Q R X n|| 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˜   rZ   )r’   ©Ústore©
r—   r–   rZ   r˜   r“   r”   r�   r•   r™   rŽ   rU   rV   Ú<lambda>  s   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˜   rZ   r–   ©Úputrš   rœ   rU   rV   r�     s   ö)r�   r�   r‘   N)rH   rP   rY   ÚHDFStore)rŒ   r�   rŽ   r�   r�   r‘   r’   r“   r”   r•   r™   r–   r—   r˜   rZ   rw   r›   rU   rœ   rV   Úto_hdfþ   s     
   ÿr¡   Úrzstr | list | Nonezlist[str] | None)	rŒ   r�   r˜   rp   ÚstartÚstopÚcolumnsÚiteratorÚ	chunksizec
                 K  sˆ  |dkrt d|› d�ƒ‚|dk	r,t|dd�}t| tƒrN| jsDtdƒ‚| }d}nvt| ƒ} t| tƒshtd	ƒ‚zt	j
 | ¡}W n tt fk
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—Ž}d}zx|dk�r"| ¡ }t|ƒdkrìt dƒ‚|d }|dd… D ]}t||ƒ�s t dƒ‚�q |j}|j|||||||	|d�W S  t ttfk
�r‚   t| tƒ�s|ttƒ� | ¡  W 5 Q R X ‚ Y nX dS )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   rd   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.)rp   r£   r¤   r¥   r¦   r§   Ú
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    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.
        As of v0.20.2 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_handlerY   Ú_moder„   NFr†   r‡   r‹   )r�   r�   Ú
fletcher32r[   c                 K  s²   d|krt dƒ‚tdƒ}|d k	r@||jjkr@t d|jj› d�ƒ‚|d krX|d k	rX|jj}t|ƒ| _|d krnd}|| _d | _|r‚|nd| _	|| _
|| _d | _| jf d|i|—Ž d S )	Nr“   z-format is not a defined argument for HDFStorer   zcomplib only supports z compression.r„   r   r�   )rª   r   ÚfiltersZall_complibsZdefault_complibrH   Ú_pathrÂ   rÁ   Ú
_complevelÚ_complibÚ_fletcher32Ú_filtersÚopen)Úselfr¯   r�   r�   r‘   rÃ   r¹   r   rU   rU   rV   Ú__init__$  s&    	ÿ
zHDFStore.__init__©r[   c                 C  s   | j S r\   ©rÅ   ©rË   rU   rU   rV   Ú
__fspath__E  s    zHDFStore.__fspath__c                 C  s   |   ¡  | jdk	st‚| jjS )zreturn the root nodeN)Ú_check_if_openrÁ   ÚAssertionErrorÚrootrÏ   rU   rU   rV   rÓ   H  s    zHDFStore.rootc                 C  s   | j S r\   rÎ   rÏ   rU   rU   rV   ÚfilenameO  s    zHDFStore.filename©r�   c                 C  s
   |   |¡S r\   )Úget©rË   r�   rU   rU   rV   Ú__getitem__S  s    zHDFStore.__getitem__©r�   r[   c                 C  s   |   ||¡ d S r\   rž   )rË   r�   rŽ   rU   rU   rV   Ú__setitem__V  s    zHDFStore.__setitem__c                 C  s
   |   |¡S r\   )Úremover×   rU   rU   rV   Ú__delitem__Y  s    zHDFStore.__delitem__r`   c              	   C  sF   z|   |¡W S  ttfk
r$   Y nX tdt| ƒj› d|› d�ƒ‚dS )z$allow attribute access to get storesú'z' object has no attribute 'N)rÖ   r·   r!   r€   ÚtypeÚ__name__)rË   ra   rU   rU   rV   Ú__getattr__\  s    ÿzHDFStore.__getattr__c                 C  s4   |   |¡}|dk	r0|j}|||dd… fkr0d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_noderµ   )rË   r�   Únodera   rU   rU   rV   Ú__contains__f  s    
zHDFStore.__contains__rc   c                 C  s   t |  ¡ ƒS r\   )ro   r³   rÏ   rU   rU   rV   Ú__len__r  s    zHDFStore.__len__c                 C  s   t | jƒ}t| ƒ› d|› d�S )Nú
File path: Ú
)rJ   rÅ   rÞ   )rË   ÚpstrrU   rU   rV   Ú__repr__u  s    
zHDFStore.__repr__c                 C  s   | S r\   rU   rÏ   rU   rU   rV   Ú	__enter__y  s    zHDFStore.__enter__ztype[BaseException] | NonezBaseException | NonezTracebackType | None)Úexc_typeÚ	exc_valueÚ	tracebackr[   c                 C  s   |   ¡  d S r\   )r¸   )rË   rê   rë   rì   rU   rU   rV   Ú__exit__|  s    zHDFStore.__exit__Úpandasú	list[str])Úincluder[   c                 C  s^   |dkrdd„ |   ¡ D ƒS |dkrJ| jdk	s0t‚dd„ | jjddd	�D ƒS td
|› d�ƒ‚dS )a#  
        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.

                .. versionadded:: 1.1.0

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

        Raises
        ------
        raises ValueError if kind has an illegal value
        rî   c                 S  s   g | ]
}|j ‘qS rU   ©rµ   ©rh   ÚnrU   rU   rV   rl   ›  s     z!HDFStore.keys.<locals>.<listcomp>ÚnativeNc                 S  s   g | ]
}|j ‘qS rU   rñ   rò   rU   rU   rV   rl   Ÿ  s    ú/ÚTable)Ú	classnamez8`include` should be either 'pandas' or 'native' but is 'rÝ   )r³   rÁ   rÒ   Z
walk_nodesrª   )rË   rð   rU   rU   rV   Úkeys„  s    ÿ
ÿzHDFStore.keyszIterator[str]c                 C  s   t |  ¡ ƒS r\   )Úiterrø   rÏ   rU   rU   rV   Ú__iter__¦  s    zHDFStore.__iter__zIterator[tuple[str, list]]c                 c  s   |   ¡ D ]}|j|fV  qdS )z'
        iterate on key->group
        N)r³   rµ   )rË   ÚgrU   rU   rV   Úitems©  s    zHDFStore.items)r�   r[   c                 K  sº   t ƒ }| j|krR| jdkr$|dkr$n(|dkrL| jrLtd| j› d| j› d�ƒ‚|| _| jr`|  ¡  | jrŠ| jdkrŠt ƒ j| j| j| j	d�| _
tr | jr d	}t|ƒ‚|j| j| jf|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¨   )rý   zRe-opening the file [z] with mode [z] will delete the current file!r   )rÃ   zGCannot open HDF5 file, which is already opened, even in read-only mode.N)rƒ   rÂ   r«   r$   rÅ   r¸   rÆ   ÚFiltersrÇ   rÈ   rÉ   r‚   rª   Ú	open_filerÁ   )rË   r�   r¹   r   ÚmsgrU   rU   rV   rÊ   °  s.    
ÿ  ÿ
ÿzHDFStore.openc                 C  s   | j dk	r| j  ¡  d| _ dS )z0
        Close the PyTables file handle
        N)rÁ   r¸   rÏ   rU   rU   rV   r¸   Ý  s    

zHDFStore.closec                 C  s   | j dkrdS t| j jƒS )zF
        return a boolean indicating whether the file is open
        NF)rÁ   r‡   ZisopenrÏ   rU   rU   rV   r«   å  s    
zHDFStore.is_open)Úfsyncr[   c              	   C  s@   | j dk	r<| j  ¡  |r<ttƒ� t | j  ¡ ¡ W 5 Q R X 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)rÁ   Úflushr   r¬   r®   r  Úfileno)rË   r  rU   rU   rV   r  î  s
    


zHDFStore.flushc              
   C  sJ   t ƒ �: |  |¡}|dkr*td|› d�ƒ‚|  |¡W  5 Q R £ S Q R X dS )zÑ
        Retrieve pandas object stored in file.

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

        Returns
        -------
        object
            Same type as object stored in file.
        NúNo object named ú in the file)r   rá   r·   Ú_read_group©rË   r�   r»   rU   rU   rV   rÖ     s
    
zHDFStore.get)r�   r¦   r©   c	                   st   |   |¡}	|	dkr"td|› d�ƒ‚t|dd�}|  |	¡‰ˆ ¡  ‡ ‡fdd„}
t| ˆ|
|ˆj|||||d�
}| ¡ S )	aÖ  
        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.
        Nr  r  rf   rd   c                   s   ˆj | ||ˆ d�S )N)r£   r¤   rp   r¥   ©Úread©Ú_startÚ_stopÚ_where©r¥   rT   rU   rV   ÚfuncQ  s    zHDFStore.select.<locals>.func©rp   Únrowsr£   r¤   r¦   r§   r©   )rá   r·   rq   Ú_create_storerÚ
infer_axesÚTableIteratorr  Ú
get_result)rË   r�   rp   r£   r¤   r¥   r¦   r§   r©   r»   r  ÚitrU   r  rV   r¶     s(    .

özHDFStore.select©r�   r£   r¤   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   rd   z&can only read_coordinates with a table©rp   r£   r¤   )rq   Ú
get_storerrP   rö   r±   Úread_coordinates)rË   r�   rp   r£   r¤   ÚtblrU   rU   rV   Úselect_as_coordinatesd  s
    

zHDFStore.select_as_coordinates)r�   Úcolumnr£   r¤   c                 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©r  r£   r¤   )r  rP   rö   r±   Úread_column)rË   r�   r  r£   r¤   r  rU   rU   rV   Úselect_column„  s    #

zHDFStore.select_column)r¦   r©   c
                   sv  t |dd�}t|ttfƒr.t|ƒdkr.|d }t|tƒrRˆj||ˆ|||||	d�S t|ttfƒshtdƒ‚t|ƒsxtdƒ‚|dkrˆ|d }‡fdd	„|D ƒ‰ˆ 	|¡}
d}t
 |
|fgtˆ|ƒ¡D ]\\}}|dkràtd
|› d�ƒ‚|jsøtd|j› d�ƒ‚|dk�r
|j}qÀ|j|krÀtdƒ‚qÀd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   rd   r   )r�   rp   r¥   r£   r¤   r¦   r§   r©   zkeys must be a list/tuplez keys must have a non-zero lengthNc                   s   g | ]}ˆ   |¡‘qS rU   )r  ©rh   ÚkrÏ   rU   rV   rl   ð  s     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 rU   )rP   rö   ©rh   ÚxrU   rU   rV   rl     s     
 c                 S  s   h | ]}|j d  d  ’qS ©r   )Únon_index_axes©rh   rx   rU   rU   rV   Ú	<setcomp>  s     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 )©rp   r¥   r£   r¤   r  r(  )r  r  r  r¥   rU   rV   rl     s   ÿz=HDFStore.select_as_multiple.<locals>.func.<locals>.<listcomp>F)ÚaxisÚverify_integrity)r=   Ú_consolidate)r  r  r  Zobjs)r+  r¥   Útblsr
  rV   r  
  s    þz)HDFStore.select_as_multiple.<locals>.funcr  T©Úcoordinates)rq   rP   rm   rn   ro   rY   r¶   r±   rª   r  Ú	itertoolsÚchainÚzipr·   Úis_tableÚpathnamer  Úpopr  r  )rË   rø   rp   Úselectorr¥   r£   r¤   r¦   r§   r©   rT   r  rx   r"  Z_tblsr  r  rU   )r+  r¥   rË   r.  rV   Úselect_as_multiple¬  sd    +
ø
 ÿ


özHDFStore.select_as_multipleTr}   r…   rˆ   rŠ   )r�   rŽ   r”   r’   r�   r•   r—   r˜   Útrack_timesr–   r[   c                 C  sH   |dkrt dƒpd}|  |¡}| j|||||||||	|
||||d� dS )aó  
        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.

            .. versionadded:: 1.1.0
        Núio.hdf.default_formatru   )r“   r”   r’   r‘   r�   r•   r™   r—   rZ   r˜   r9  r–   )r   Ú_validate_formatÚ_write_to_group)rË   r�   rŽ   r“   r”   r’   r‘   r�   r•   r™   r—   rZ   r˜   r9  r–   rU   rU   rV   rŸ   %  s&    4
òzHDFStore.putc              
   C  sà   t |dd�}z|  |¡}W n„ tk
r0   ‚ Y np tk
rD   ‚ Y n\ tk
rž } z>|dk	rftdƒ|‚|  |¡}|dk	rŽ|jdd� W Y ¢dS W 5 d}~X Y nX t 	|||¡r¾|j
jdd� n|jsÌtdƒ‚|j|||d�S 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   rd   Nz5trying to remove a node with a non-None where clause!T©Ú	recursivez7can only remove with where on objects written as tablesr  )rq   r  r·   rÒ   Ú	Exceptionrª   rá   Z	_f_removeÚcomÚall_noner»   r4  Údelete)rË   r�   rp   r£   r¤   rT   Úerrrâ   rU   rU   rV   rÛ   m  s2    ÿþ
ÿzHDFStore.removezbool | list[str]r‰   )
r�   rŽ   r”   r’   r�   r•   r–   r—   r˜   r[   c                 C  sl   |	dk	rt dƒ‚|dkr tdƒ}|dkr4tdƒp2d}|  |¡}| 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
        Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tabler:  rv   )r“   Úaxesr”   r’   r‘   r�   r•   r™   r§   Úexpectedrowsr–   r—   rZ   r˜   )r±   r   r;  r<  )rË   r�   rŽ   r“   rD  r”   r’   r‘   r�   r¥   r•   r™   r§   rE  r–   r—   rZ   r˜   rU   rU   rV   r’   ¦  s6    ;ÿ
ðzHDFStore.appendÚdict)Údr–   r[   c                   s¬  |dk	rt dƒ‚t|tƒs"tdƒ‚||kr2tdƒ‚tttˆjƒƒttt	ˆƒ ƒ ƒd }d}	g }
| 
¡ D ]0\}‰ ˆ dkrŽ|	dk	rˆtdƒ‚|}	qh|
 ˆ ¡ qh|	dk	rÖˆj| }| t|
ƒ¡}t| |¡ƒ}| |¡||	< |dkræ|| }|�r*‡fdd„| ¡ D ƒ}t|ƒ}|D ]}| |¡}�qˆj| ‰| d	d¡}| 
¡ D ]h\}‰ ||k�rT|nd}ˆjˆ |d
�}|dk	�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 dictr   z<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”   )rh   Úcols)rŽ   rU   rV   Ú	<genexpr>I  s     z.HDFStore.append_to_multiple.<locals>.<genexpr>r•   ©r+  c                   s   i | ]\}}|ˆ kr||“qS rU   rU   ©rh   r�   rŽ   )ÚvrU   rV   Ú
<dictcomp>Y  s       z/HDFStore.append_to_multiple.<locals>.<dictcomp>)r—   r•   )r±   rP   rF  rª   rm   ÚsetÚrangeÚndimÚ	_AXES_MAPrÞ   rü   ÚextendrD  Ú
differencer7   ÚsortedÚget_indexerÚtakeÚvaluesÚnextÚintersectionÚlocr6  Úreindexr’   )rË   rG  rŽ   r7  r—   rD  r–   r¹   r+  Z
remain_keyZremain_valuesr"  ÚorderedZorddZidxsZvalid_indexr”   r•   ÚdcÚvalÚfilteredrU   )rN  rŽ   rV   Úappend_to_multipleþ  sZ    ÿ
ÿÿ&ÿ

ÿýzHDFStore.append_to_multiplerX   )r�   ÚoptlevelÚkindr[   c                 C  sB   t ƒ  |  |¡}|dk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¥   rc  rd  )rƒ   r  rP   rö   r±   Úcreate_index)rË   r�   r¥   rc  rd  rT   rU   rU   rV   Úcreate_table_index_  s    

zHDFStore.create_table_indexrm   c                 C  s<   t ƒ  |  ¡  | jdk	st‚tdk	s(t‚dd„ | j ¡ D ƒS )zÂ
        Return a list of all the top-level nodes.

        Each node returned is not a pandas storage object.

        Returns
        -------
        list
            List of objects.
        Nc                 S  sP   g | ]H}t |tjjƒst|jd dƒsHt|ddƒsHt |tjjƒr|jdkr|‘qS )Úpandas_typeNrv   )	rP   r~   ÚlinkÚLinkÚgetattrÚ_v_attrsrv   rö   r¾   )rh   rû   rU   rU   rV   rl   –  s    
ùz#HDFStore.groups.<locals>.<listcomp>)rƒ   rÑ   rÁ   rÒ   r~   Úwalk_groupsrÏ   rU   rU   rV   r³   ‡  s    þzHDFStore.groupsrõ   z*Iterator[tuple[str, list[str], list[str]]])rp   r[   c                 c  s¼   t ƒ  |  ¡  | jdk	st‚tdk	s(t‚| j |¡D ]‚}t|jddƒdk	rLq4g }g }|j 	¡ D ]B}t|jddƒ}|dkr”t
|tjjƒr | |j¡ q^| |j¡ q^|j d¡||fV  q4dS )aS  
        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`.
        Nrg  rõ   )rƒ   rÑ   rÁ   rÒ   r~   rl  rj  rk  Z_v_childrenrY  rP   r»   ÚGroupr’   r¾   rµ   Úrstrip)rË   rp   rû   r³   ÚleavesÚchildrg  rU   rU   rV   Úwalk£  s     zHDFStore.walkzNode | Nonec                 C  s€   |   ¡  | d¡sd| }| jdk	s(t‚tdk	s4t‚z| j | j|¡}W n tjjk
rb   Y dS X t	|tj
ƒs|tt|ƒƒ‚|S )z9return the node with the key or None if it does not existrõ   N)rÑ   Ú
startswithrÁ   rÒ   r~   rá   rÓ   Ú
exceptionsZNoSuchNodeErrorrP   rM   rÞ   )rË   r�   râ   rU   rU   rV   rá   Ó  s    
zHDFStore.get_nodeúGenericFixed | Tablec                 C  s8   |   |¡}|dkr"td|› d�ƒ‚|  |¡}| ¡  |S )z<return the storer object for a key, raise if not in the fileNr  r  )rá   r·   r  r  )rË   r�   r»   rT   rU   rU   rV   r  ã  s    

zHDFStore.get_storerrý   )r�   Úpropindexesr�   rÃ   Ú	overwriter[   c	              	   C  sÎ   t |||||d�}	|dkr&t|  ¡ ƒ}t|ttfƒs:|g}|D ]Š}
|  |
¡}|dk	r>|
|	krj|rj|	 |
¡ |  |
¡}t|tƒr¶d}|r–d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‘   r�   rÃ   NFc                 S  s   g | ]}|j r|j‘qS rU   )Ú
is_indexedra   ©rh   r„   rU   rU   rV   rl     s      z!HDFStore.copy.<locals>.<listcomp>r—   )r”   r—   rZ   r^   )r    rm   rø   rP   rn   r  rÛ   r¶   rö   rD  r’   rj  rZ   rŸ   )rË   r�   r�   ru  rø   r‘   r�   rÃ   rv  Z	new_storer"  rT   Údatar”   rU   rU   rV   Úcopyí  s>        ÿ




ûzHDFStore.copyc           
      C  s
  t | jƒ}t| ƒ› d|› d�}| jrþt|  ¡ ƒ}t|ƒrôg }g }|D ]œ}z<|  |¡}|dk	r‚| t |j	pj|ƒ¡ | t |p|dƒ¡ W qD t
k
rš   ‚ Y qD tk
rÞ } z(| |¡ t |ƒ}	| d|	› d�¡ W 5 d}~X Y qDX qD|td||ƒ7 }n|d7 }n|d	7 }|S )
zg
        Print detailed information on the store.

        Returns
        -------
        str
        rå   ræ   Nzinvalid_HDFStore nodez[invalid_HDFStore node: r#  é   ÚEmptyzFile is CLOSED)rJ   rÅ   rÞ   r«   rV  rø   ro   r  r’   r5  rÒ   r?  rI   )
rË   r¯   ÚoutputZlkeysrø   rY  r"  rT   ÚdetailZdstrrU   rU   rV   Úinfo(  s.    


&
zHDFStore.infoc                 C  s   | j st| j› d�ƒ‚d S )Nz file is not open!)r«   r!   rÅ   rÏ   rU   rU   rV   rÑ   R  s    zHDFStore._check_if_open)r“   r[   c              
   C  sJ   zt | ¡  }W n4 tk
rD } ztd|› d�ƒ|‚W 5 d}~X Y nX |S )zvalidate / deprecate formatsz#invalid HDFStore format specified [r#  N)Ú_FORMAT_MAPÚlowerr·   r±   )rË   r“   rC  rU   rU   rV   r;  V  s
    $zHDFStore._validate_formatrO   zDataFrame | Series | None)rŽ   rZ   r˜   r[   c              
   C  s:  |dk	rt |ttfƒstdƒ‚tt|jddƒƒ}tt|jddƒƒ}|dkr¶|dkr’tƒ  tdk	sdt	‚t|ddƒs~t |tj
jƒrˆd}d}q¶tdƒ‚n$t |tƒr¢d	}nd
}|dkr¶|d7 }d|k�r,ttdœ}z|| }	W nD tk
�r }
 z$td|› dt|ƒ› d|› �ƒ|
‚W 5 d}
~
X Y nX |	| |||d�S |dk�rÆ|dk	�rÆ|dk�r„t|ddƒ}|dk	�rÆ|jdk�rrd}n|jdk�rÆd}nB|dk�rÆt|ddƒ}|dk	�rÆ|jdk�r¶d}n|jdk�rÆd}ttttttdœ}z|| }	W nD tk
�r( }
 z$td|› dt|ƒ› d|› �ƒ|
‚W 5 d}
~
X Y nX |	| |||d�S )z"return a suitable class to operateNz(value must be None, Series, or DataFramerg  Ú
table_typerv   Úframe_tableÚgeneric_tablezKcannot create a storer if the object is not existing nor a value are passedÚseriesÚframeÚ_table)r…  r†  z=cannot properly create the storer for: [_STORER_MAP] [group->ú,value->z	,format->©rZ   r˜   Úseries_tabler”   rf   Úappendable_seriesÚappendable_multiseriesÚappendable_frameÚappendable_multiframe)r„  r‹  rŒ  r�  rŽ  Úwormz<cannot properly create the storer for: [_TABLE_MAP] [group->)rP   r;   r5   r±   rW   rj  rk  rƒ   r~   rÒ   rv   rö   ÚSeriesFixedÚ
FrameFixedr·   rÞ   ÚnlevelsÚGenericTableÚAppendableSeriesTableÚAppendableMultiSeriesTableÚAppendableFrameTableÚAppendableMultiFrameTableÚ	WORMTable)rË   r»   r“   rŽ   rZ   r˜   ÚptÚttZ_STORER_MAPÚclsrC  r”   Z
_TABLE_MAPrU   rU   rV   r  `  s‚     ÿÿ


ÿý





úÿýzHDFStore._create_storer)
r�   rŽ   r”   r’   r�   r•   r–   r˜   r9  r[   c                 C  sÎ   t |dd ƒr|dks|rd S |  ||¡}| j|||||d�}|rr|jrZ|jrb|dkrb|jrbtdƒ‚|jsz| ¡  n| ¡  |jsŒ|rŒtdƒ‚|j||||||	|
||||||d� t|t	ƒrÊ|rÊ|j
|d� d S )	NÚemptyrv   r‰  ru   zCan only append to Tablesz0Compression not supported on Fixed format stores)ÚobjrD  r’   r‘   r�   rÃ   r•   r§   rE  r–   r™   r—   r9  )r¥   )rj  Ú_identify_groupr  r4  Ú	is_existsrª   Úset_object_infoÚwriterP   rö   re  )rË   r�   rŽ   r“   rD  r”   r’   r‘   r�   rÃ   r•   r§   rE  r–   r™   r—   rZ   r˜   r9  r»   rT   rU   rU   rV   r<  »  s:    

ózHDFStore._write_to_grouprM   ©r»   c                 C  s   |   |¡}| ¡  | ¡ S r\   )r  r  r	  )rË   r»   rT   rU   rU   rV   r  ù  s    
zHDFStore._read_group)r�   r’   r[   c                 C  sN   |   |¡}| jdk	st‚|dk	r8|s8| jj|dd� d}|dkrJ|  |¡}|S )z@Identify HDF5 group based on key, delete/create group if needed.NTr=  )rá   rÁ   rÒ   Úremove_nodeÚ_create_nodes_and_group)rË   r�   r’   r»   rU   rU   rV   rž  þ  s    

zHDFStore._identify_groupc                 C  sv   | j dk	st‚| d¡}d}|D ]P}t|ƒs.q |}| d¡sD|d7 }||7 }|  |¡}|dkrl| j  ||¡}|}q |S )z,Create nodes from key and return group name.Nrõ   )rÁ   rÒ   Úsplitro   Úendswithrá   Zcreate_group)rË   r�   Úpathsr¯   ÚpÚnew_pathr»   rU   rU   rV   r¤    s    


z HDFStore._create_nodes_and_group)r„   NNF)rî   )r„   )F)NNNNFNF)NNN)NN)NNNNNFNF)NTFNNNNNNr}   TF)NNN)NNTTNNNNNNNNNNr}   )NNF)NNN)rõ   )rý   TNNNFT)NNrO   r}   )NTFNNNNNNFNNNr}   T)1rß   Ú
__module__Ú__qualname__Ú__doc__Ú__annotations__rÌ   rÐ   ÚpropertyrÓ   rÔ   rØ   rÚ   rÜ   rà   rã   rä   rè   ré   rí   rø   rú   rü   rÊ   r¸   r«   r  rÖ   r¶   r  r   r8  rŸ   rÛ   r’   rb  rf  r³   rq  rá   r  rz  r  rÑ   r;  r  r<  r  rž  r¤  rU   rU   rU   rV   r    ß  s  
A    ú!

"-       ÷N   û$  û+        ö}            ñ$H=               î"]   ùd   û(0       ÷;*    ú`               í">r    c                   @  sp   e Zd ZU dZded< ded< ded< dddd
dd
ddœdd„Zddœdd„Zddœdd„Zdd
dœ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    r›   rt  rT   NFr‡   r‹   )r›   rT   r¦   r§   r©   r[   c                 C  sš   || _ || _|| _|| _| jjrN|d kr,d}|d kr8d}|d krD|}t||ƒ}|| _|| _|| _d | _	|sr|	d k	rŠ|	d kr~d}	t
|	ƒ| _nd | _|
| _d S )Nr   é † )r›   rT   r  rp   r4  Úminr  r£   r¤   r0  rc   r§   r©   )rË   r›   rT   r  rp   r  r£   r¤   r¦   r§   r©   rU   rU   rV   rÌ   >  s,    
zTableIterator.__init__r   rÍ   c                 c  sv   | j }| jd krtdƒ‚|| jk rjt|| j | jƒ}|  d d | j||… ¡}|}|d kst|ƒsbq|V  q|  ¡  d S )Nz*Cannot iterate until get_result is called.)	r£   r0  rª   r¤   r°  r§   r  ro   r¸   )rË   r¿   r¤   rŽ   rU   rU   rV   rú   h  s    

zTableIterator.__iter__c                 C  s   | j r| j ¡  d S r\   )r©   r›   r¸   rÏ   rU   rU   rV   r¸   x  s    zTableIterator.closer/  c                 C  sŠ   | j d k	r4t| jtƒstdƒ‚| jj| jd�| _| S |rft| jtƒsLtdƒ‚| jj| j| j| j	d�}n| j}|  
| j| j	|¡}|  ¡  |S )Nz0can only use an iterator or chunksize on a table)rp   z$can only read_coordinates on a tabler  )r§   rP   rT   rö   r±   r  rp   r0  r£   r¤   r  r¸   )rË   r0  rp   ÚresultsrU   rU   rV   r  |  s"    
  ÿzTableIterator.get_result)NNFNF)F)	rß   rª  r«  r¬  r­  rÌ   rú   r¸   r  rU   rU   rU   rV   r  &  s   
	     õ*r  c                   @  s¨  e Zd ZU dZdZded< dZded< dddgZdMd
dddœdd„Ze	ddœdd„ƒZ
e	d
dœdd„ƒZdddœdd„Zd
dœdd„Zdddœdd„Zddœdd „Ze	ddœd!d"„ƒZd#d
d
d$d%œd&d'„Zd(d)„ Ze	d*d+„ ƒZe	d,d-„ ƒZe	d.d/„ ƒZe	d0d1„ ƒZd2dœd3d4„ZdNddœd5d6„Zddœd7d8„Zd9ddd:œd;d<„ZdOd=d>„Zddd?œd@dA„ZddœdBdC„ZddœdDdE„ZddœdFdG„Zd9ddHœdIdJ„Z d9ddHœdKdL„Z!d	S )PÚ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_indexableÚfreqÚtzÚ
index_nameNrY   rX   r‹   )ra   Úcnamer[   c                 C  s    t |tƒstdƒ‚|| _|| _|| _|| _|p0|| _|| _|| _	|| _
|	| _|
| _|| _|| _|| _|| _|d k	r||  |¡ t | jtƒsŒt‚t | jtƒsœt‚d S )Nz`name` must be a str.)rP   rY   rª   rY  rd  Útypra   r¸  r+  Úposrµ  r¶  r·  r^  rv   r½   ÚmetadataÚset_posrÒ   )rË   ra   rY  rd  r¹  r¸  r+  rº  rµ  r¶  r·  r^  rv   r½   r»  rU   rU   rV   rÌ   ¨  s(    


zIndexCol.__init__rc   rÍ   c                 C  s   | j jS r\   )r¹  ÚitemsizerÏ   rU   rU   rV   r½  Ó  s    zIndexCol.itemsizec                 C  s   | j › d�S )NÚ_kindr`   rÏ   rU   rU   rV   Ú	kind_attrØ  s    zIndexCol.kind_attr)rº  r[   c                 C  s$   || _ |dk	r | jdk	r || j_dS )z,set the position of this column in the TableN)rº  r¹  Z_v_pos)rË   rº  rU   rU   rV   r¼  Ü  s    zIndexCol.set_posc              	   C  sF   t tt| j| j| j| j| jfƒƒ}d dd„ t	dddddg|ƒD ƒ¡S )	Nú,c                 S  s   g | ]\}}|› d |› �‘qS ©z->rU   rM  rU   rU   rV   rl   ç  s   ÿz%IndexCol.__repr__.<locals>.<listcomp>ra   r¸  r+  rº  rd  )
rn   ÚmaprJ   ra   r¸  r+  rº  rd  Újoinr3  ©rË   ÚtemprU   rU   rV   rè   â  s    ÿþÿzIndexCol.__repr__r	   ©Úotherr[   c                   s   t ‡ ‡fdd„dD ƒƒS )úcompare 2 col itemsc                 3  s&   | ]}t ˆ|d ƒt ˆ |d ƒkV  qd S r\   ©rj  rx  ©rÇ  rË   rU   rV   rK  ï  s   ÿz"IndexCol.__eq__.<locals>.<genexpr>)ra   r¸  r+  rº  ©rH  ©rË   rÇ  rU   rÊ  rV   Ú__eq__í  s    þzIndexCol.__eq__c                 C  s   |   |¡ S r\   )rÍ  rÌ  rU   rU   rV   Ú__ne__ô  s    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hasattrrv   rj  rJ  r¸  rw  rÏ   rU   rU   rV   rw  ÷  s    zIndexCol.is_indexedú
np.ndarrayz3tuple[np.ndarray, np.ndarray] | tuple[Index, Index]©rY  rZ   r˜   r[   c           
      C  s  t |tjƒstt|ƒƒ‚|jjdk	r2|| j  ¡ }t	| j
ƒ}t||||ƒ}i }t	| jƒ|d< | jdk	rtt	| jƒ|d< t}t|jƒsŒt|jƒr’t}n|jdkr¬d|kr¬dd„ }z||f|Ž}W n0 tk
rì   d|krÜd|d< ||f|Ž}Y nX t|| jƒ}	|	|	fS )zV
        Convert the data from this selection to the appropriate pandas type.
        Nra   rµ  Úi8c                 [  s   t f d| i|—ŽS )NZordinal)r9   )r%  ÚkwdsrU   rU   rV   r�     s   ÿÿz"IndexCol.convert.<locals>.<lambda>)rP   rQ   ÚndarrayrÒ   rÞ   ÚdtypeÚfieldsr¸  rz  rW   rd  Ú_maybe_convertr·  rµ  r7   r+   r,   r6   rª   Ú_set_tzr¶  )
rË   rY  r™   rZ   r˜   Úval_kindr¹   ÚfactoryZnew_pd_indexZfinal_pd_indexrU   rU   rV   Úconvertÿ  s,    

zIndexCol.convertc                 C  s   | j S )zreturn the values©rY  rÏ   rU   rU   rV   Ú	take_data.  s    zIndexCol.take_datac                 C  s   | j jS r\   )rv   rk  rÏ   rU   rU   rV   Úattrs2  s    zIndexCol.attrsc                 C  s   | j jS r\   ©rv   ÚdescriptionrÏ   rU   rU   rV   rà  6  s    zIndexCol.descriptionc                 C  s   t | j| jdƒS )z!return my current col descriptionN)rj  rà  r¸  rÏ   rU   rU   rV   Úcol:  s    zIndexCol.colc                 C  s   | j S ©zreturn my cython valuesrÜ  rÏ   rU   rU   rV   Úcvalues?  s    zIndexCol.cvaluesr   c                 C  s
   t | jƒS r\   )rù   rY  rÏ   rU   rU   rV   rú   D  s    zIndexCol.__iter__c                 C  sP   t | jƒdkrLt|tƒr$| | j¡}|dk	rL| jj|k rLtƒ j	|| j
d�| _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)r½  rº  )rW   rd  rP   rF  rÖ   ra   r¹  r½  rƒ   Ú	StringColrº  )rË   r•   rU   rU   rV   Úmaybe_set_sizeG  s
    
zIndexCol.maybe_set_sizec                 C  s   d S r\   rU   rÏ   rU   rU   rV   Úvalidate_namesT  s    zIndexCol.validate_namesÚAppendableTable)Úhandlerr’   r[   c                 C  s:   |j | _ |  ¡  |  |¡ |  |¡ |  |¡ |  ¡  d S r\   )rv   Úvalidate_colÚvalidate_attrÚvalidate_metadataÚwrite_metadataÚset_attr)rË   ré  r’   rU   rU   rV   Úvalidate_and_setW  s    


zIndexCol.validate_and_setc                 C  s^   t | jƒdkrZ| j}|dk	rZ|dkr*| j}|j|k rTtd|› d| j› d|j› d�ƒ‚|jS dS )z:validate this column: return the compared against itemsizerä  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)rW   rd  rá  r½  rª   r¸  )rË   r½  ÚcrU   rU   rV   rê  _  s    
ÿzIndexCol.validate_col)r’   r[   c                 C  sB   |r>t | j| jd ƒ}|d k	r>|| jkr>td|› d| j› d�ƒ‚d S )Nzincompatible kind in col [ú - r#  )rj  rÞ  r¿  rd  r±   )rË   r’   Zexisting_kindrU   rU   rV   rë  r  s    ÿzIndexCol.validate_attrc                 C  sÈ   | j D ]¼}t| |dƒ}| | ji ¡}| |¡}||krª|dk	rª||krª|dkr„t|||f }tj|tt	ƒ d� d||< t
| |dƒ qÂtd| j› d|› d|› d|› d�	ƒ‚q|dk	sº|dk	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)rµ  r·  ©Ú
stacklevelzinvalid info for [z] for [z], existing_value [z] conflicts with new value [r#  )Ú_info_fieldsrj  Ú
setdefaultra   rÖ   rs   ÚwarningsÚwarnr    r&   Úsetattrrª   )rË   r  r�   rŽ   ÚidxZexisting_valueÚwsrU   rU   rV   Úupdate_info{  s&    

  ÿÿzIndexCol.update_infoc                 C  s$   |  | j¡}|dk	r | j |¡ dS )z!set my state from the passed infoN)rÖ   ra   Ú__dict__Úupdate)rË   r  rù  rU   rU   rV   Úset_info›  s    zIndexCol.set_infoc                 C  s   t | j| j| jƒ dS )zset the kind for this columnN)rø  rÞ  r¿  rd  rÏ   rU   rU   rV   rî  ¡  s    zIndexCol.set_attr)ré  r[   c                 C  sB   | j dkr>| j}| | j¡}|dk	r>|dk	r>t||ƒs>tdƒ‚dS )z:validate that kind=category does not change the categoriesÚcategoryNzEcannot append a categorical with different categories to the existing)r½   r»  Úread_metadatar¸  r4   rª   )rË   ré  Znew_metadataZcur_metadatarU   rU   rV   rì  ¥  s    
ÿþýÿzIndexCol.validate_metadatac                 C  s   | j dk	r| | j| j ¡ dS )zset the meta dataN)r»  rí  r¸  )rË   ré  rU   rU   rV   rí  ´  s    
zIndexCol.write_metadata)NNNNNNNNNNNNN)N)N)"rß   rª  r«  r¬  r³  r­  r´  rô  rÌ   r®  r½  r¿  r¼  rè   rÍ  rÎ  rw  rÛ  rÝ  rÞ  rà  rá  rã  rú   ræ  rç  rï  rê  rë  rû  rþ  rî  rì  rí  rU   rU   rU   rV   r²  –  sb   

             ñ+/




	 r²  c                   @  sD   e Zd ZdZeddœdd„ƒZddddd	œd
d„Zddœdd„ZdS )ÚGenericIndexColz:an index which is not represented in the data of the tabler‡   rÍ   c                 C  s   dS ©NFrU   rÏ   rU   rU   rV   rw  ½  s    zGenericIndexCol.is_indexedrÐ  rY   ztuple[Index, Index]rÑ  c                 C  s,   t |tjƒst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
        )rP   rQ   rÔ  rÒ   rÞ   r:   ro   )rË   rY  r™   rZ   r˜   r”   rU   rU   rV   rÛ  Á  s    zGenericIndexCol.convertr‹   c                 C  s   d S r\   rU   rÏ   rU   rU   rV   rî  Ó  s    zGenericIndexCol.set_attrN)rß   rª  r«  r¬  r®  rw  rÛ  rî  rU   rU   rU   rV   r  º  s
   r  c                      s>  e Zd ZdZdZdZddgZd;dddd	d
œ‡ fdd„Zeddœdd„ƒZ	eddœdd„ƒZ
ddœdd„Zdddœdd„Zdd	dœdd„Zdd„ Zeddd œd!d"„ƒZed#d$„ ƒZedd%d&œd'd(„ƒZeddd&œd)d*„ƒZed+d,„ ƒZed-d.„ ƒZed/d0„ ƒZed1d2„ ƒZd	dœd3d4„Zd5ddd6œd7d8„Zd	dœd9d:„Z‡  ZS )<Ú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
    Fr¶  r^  NrY   rX   zDtypeArg | Noner‹   )ra   r¸  rÕ  r[   c                   s2   t ƒ j|||||||||	|
|d� || _|| _d S )N)ra   rY  rd  r¹  rº  r¸  r¶  r^  rv   r½   r»  )ÚsuperrÌ   rÕ  ry  )rË   ra   rY  rd  r¹  r¸  rº  r¶  r^  rv   r½   r»  rÕ  ry  ©Ú	__class__rU   rV   rÌ   è  s    õzDataCol.__init__rÍ   c                 C  s   | j › d�S )NÚ_dtyper`   rÏ   rU   rU   rV   Ú
dtype_attr	  s    zDataCol.dtype_attrc                 C  s   | j › d�S )NÚ_metar`   rÏ   rU   rU   rV   Ú	meta_attr	  s    zDataCol.meta_attrc              	   C  sF   t tt| j| j| j| j| jfƒƒ}d dd„ t	dddddg|ƒD ƒ¡S )	NrÀ  c                 S  s   g | ]\}}|› d |› �‘qS rÁ  rU   rM  rU   rU   rV   rl   	  s   ÿz$DataCol.__repr__.<locals>.<listcomp>ra   r¸  rÕ  rd  Úshape)
rn   rÂ  rJ   ra   r¸  rÕ  rd  r  rÃ  r3  rÄ  rU   rU   rV   rè   	  s     ÿÿþÿzDataCol.__repr__r	   r‡   rÆ  c                   s   t ‡ ‡fdd„dD ƒƒS )rÈ  c                 3  s&   | ]}t ˆ|d ƒt ˆ |d ƒkV  qd S r\   rÉ  rx  rÊ  rU   rV   rK  	  s   ÿz!DataCol.__eq__.<locals>.<genexpr>)ra   r¸  rÕ  rº  rË  rÌ  rU   rÊ  rV   rÍ  	  s    þzDataCol.__eq__r   )ry  r[   c                 C  s@   |d k	st ‚| jd kst ‚t|ƒ\}}|| _|| _t|ƒ| _d S r\   )rÒ   rÕ  Ú_get_data_and_dtype_namery  Ú_dtype_to_kindrd  )rË   ry  Ú
dtype_namerU   rU   rV   Úset_data$	  s    zDataCol.set_datac                 C  s   | j S )zreturn the data©ry  rÏ   rU   rU   rV   rÝ  .	  s    zDataCol.take_datarK   )rY  r[   c                 C  sÂ   |j }|j}|j}|jdkr&d|jf}t|tƒrJ|j}| j||j j	d�}ntt
|ƒsZt|ƒrf|  |¡}nXt|ƒrz|  |¡}nDt|ƒr˜tƒ j||d d�}n&t|ƒr®|  ||¡}n| j||j	d�}|S )zW
        Get an appropriately typed and shaped pytables.Col object for values.
        rf   ©rd  r   ©r½  r  )rÕ  r½  r  rR  ÚsizerP   r?   ÚcodesÚget_atom_datara   r+   r,   Úget_atom_datetime64r2   Úget_atom_timedelta64r*   rƒ   Z
ComplexColr1   Úget_atom_string)r›  rY  rÕ  r½  r  r  ÚatomrU   rU   rV   Ú	_get_atom2	  s$    


zDataCol._get_atomc                 C  s   t ƒ j||d d�S )Nr   r  ©rƒ   rå  ©r›  r  r½  rU   rU   rV   r  R	  s    zDataCol.get_atom_stringz	type[Col]©rd  r[   c                 C  sR   |  d¡r$|dd… }d|› d�}n"|  d¡r4d}n| ¡ }|› d�}ttƒ |ƒS )z0return the PyTables column class for this columnÚuinté   NZUIntrK   ÚperiodÚInt64Col)rr  Ú
capitalizerj  rƒ   )r›  rd  Zk4Zcol_nameZkcaprU   rU   rV   Úget_atom_coltypeV	  s    


zDataCol.get_atom_coltypec                 C  s   | j |d�|d d�S )Nr  r   ©r  ©r#  ©r›  r  rd  rU   rU   rV   r  e	  s    zDataCol.get_atom_datac                 C  s   t ƒ j|d d�S ©Nr   r$  ©rƒ   r!  ©r›  r  rU   rU   rV   r  i	  s    zDataCol.get_atom_datetime64c                 C  s   t ƒ j|d d�S r'  r(  r)  rU   rU   rV   r  m	  s    zDataCol.get_atom_timedelta64c                 C  s   t | jdd ƒS )Nr  )rj  ry  rÏ   rU   rU   rV   r  q	  s    zDataCol.shapec                 C  s   | j S râ  r  rÏ   rU   rU   rV   rã  u	  s    zDataCol.cvaluesc                 C  s`   |r\t | j| jdƒ}|dk	r2|t| jƒkr2tdƒ‚t | j| jdƒ}|dk	r\|| jkr\tdƒ‚dS )zAvalidate that we have the same order as the existing & same dtypeNz4appended items do not match existing items in table!z@appended items dtype do not match existing items dtype in table!)rj  rÞ  r¿  rm   rY  rª   r  rÕ  )rË   r’   Zexisting_fieldsZexisting_dtyperU   rU   rV   rë  z	  s    ÿzDataCol.validate_attrrÐ  )rY  rZ   r˜   c                 C  s  t |tjƒstt|ƒƒ‚|jjdk	r.|| j }| jdk	s<t‚| jdkr\t	|ƒ\}}t
|ƒ}n|}| j}| j}t |tjƒs|t‚t| jƒ}| j}	| j}
| j}|dk	s¤t‚t|ƒ}|dkrÆt||dd�}�n(|dkràtj|dd�}�n|dk�r8ztjd	d
„ |D ƒtd�}W n. tk
�r4   tjdd
„ |D ƒtd�}Y nX n¶|dk�r¶|	}| ¡ }|dk�rhtg tjd�}n<t|ƒ}| ¡ �r¤||  }||dk  | t¡ ¡ j8  < tj|||
d�}n8z|j|dd�}W n$ t k
�rì   |jddd�}Y nX t|ƒdk�rt!||||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]©rÕ  r   c                 S  s   g | ]}t  |¡‘qS rU   ©r   Úfromordinal©rh   rN  rU   rU   rV   rl   ¾	  s     z#DataCol.convert.<locals>.<listcomp>c                 S  s   g | ]}t  |¡‘qS rU   ©r   Úfromtimestampr2  rU   rU   rV   rl   Â	  s     rÿ  éÿÿÿÿ)Ú
categoriesr^  F©rz  ÚOrä  ©r™   rZ   r˜   )#rP   rQ   rÔ  rÒ   rÞ   rÕ  rÖ  r¸  r¹  r  r  rd  rW   r½   r»  r^  r¶  rØ  ÚasarrayÚobjectrª   Úravelr7   Zfloat64r>   ÚanyÚastyperc   ZcumsumÚ_valuesr?   Z
from_codesr±   Ú_unconvert_string_arrayrY  )rË   rY  r™   rZ   r˜   Ú	convertedr  rd  r½   r»  r^  r¶  rÕ  r6  r  ÚmaskrU   rU   rV   rÛ  ‡	  st    




 ÿ
 ÿ



   ÿ   ÿzDataCol.convertc                 C  sH   t | j| j| jƒ t | j| j| jƒ | jdk	s2t‚t | j| j| jƒ dS )zset the data for this columnN)	rø  rÞ  r¿  rY  r
  r½   rÕ  rÒ   r  rÏ   rU   rU   rV   rî  ë	  s    zDataCol.set_attr)NNNNNNNNNNNN)rß   rª  r«  r¬  r³  r´  rô  rÌ   r®  r  r
  rè   rÍ  r  rÝ  Úclassmethodr  r  r#  r  r  r  r  rã  rë  rÛ  rî  Ú__classcell__rU   rU   r  rV   r  ×  sX               ò 





dr  c                   @  sZ   e Zd ZdZdZddœdd„Zedd„ ƒZed	d
dœ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‹   rÍ   c                 C  s   t t| jƒƒstdƒ‚d S )Nú-cannot have non-object label DataIndexableCol)r0   r7   rY  rª   rÏ   rU   rU   rV   rç  ø	  s    zDataIndexableCol.validate_namesc                 C  s   t ƒ j|d�S )N)r½  r  r  rU   rU   rV   r  ý	  s    z DataIndexableCol.get_atom_stringrY   rK   r  c                 C  s   | j |d�ƒ S )Nr  r%  r&  rU   rU   rV   r  
  s    zDataIndexableCol.get_atom_datac                 C  s
   t ƒ  ¡ S r\   r(  r)  rU   rU   rV   r  
  s    z$DataIndexableCol.get_atom_datetime64c                 C  s
   t ƒ  ¡ S r\   r(  r)  rU   rU   rV   r  	
  s    z%DataIndexableCol.get_atom_timedelta64N)rß   rª  r«  r¬  r´  rç  rC  r  r  r  r  rU   rU   rU   rV   rE  ó	  s   

rE  c                   @  s   e Zd ZdZdS )ÚGenericDataIndexableColz(represent a generic pytables data columnN)rß   rª  r«  r¬  rU   rU   rU   rV   rG  
  s   rG  c                   @  s¼  e Zd ZU dZded< dZded< ded< ded	< d
ed< dZded< dNd
dddddœdd„Zeddœdd„ƒZ	eddœdd„ƒZ
edd„ ƒZddœdd „Zddœd!d"„Zd dœd#d$„Zed%d&„ ƒZed'd(„ ƒZed)d*„ ƒZed+d,„ ƒZeddœd-d.„ƒZeddœd/d0„ƒZed1d2„ ƒZddœd3d4„Zddœd5d6„Zed7d8„ ƒZeddœd9d:„ƒZed;d<„ ƒZd=dœd>d?„ZdOddœdAdB„ZddœdCdD„ZdPdEdEdFœdGdH„ZdIdJ„ ZdQdEdEddKœdLdM„Z d@S )RÚ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.
    rY   Úpandas_kindru   Úformat_typeútype[DataFrame | Series]Úobj_typerc   rR  r    rÀ   Fr‡   r4  rO   r}   rM   rX   r‹   )rÀ   r»   rZ   r˜   r[   c                 C  sZ   t |tƒstt|ƒƒ‚td k	s"t‚t |tjƒs:tt|ƒƒ‚|| _|| _t|ƒ| _	|| _
d S r\   )rP   r    rÒ   rÞ   r~   rM   rÀ   r»   r_   rZ   r˜   )rË   rÀ   r»   rZ   r˜   rU   rU   rV   rÌ   &
  s    
zFixed.__init__rÍ   c                 C  s*   | j d dko(| j d dko(| j d dk S )Nr   rf   é
   é   )ÚversionrÏ   rU   rU   rV   Úis_old_version5
  s    zFixed.is_old_versionztuple[int, int, int]c                 C  sb   t t| jjddƒƒ}z0tdd„ | d¡D ƒƒ}t|ƒdkrB|d }W n tk
r\   d}Y nX |S )	zcompute and set our versionÚpandas_versionNc                 s  s   | ]}t |ƒV  qd S r\   ©rc   r$  rU   rU   rV   rK  >
  s     z Fixed.version.<locals>.<genexpr>Ú.rN  r&  )r   r   r   )rW   rj  r»   rk  rn   r¥  ro   r€   )rË   rO  rU   rU   rV   rO  9
  s    
zFixed.versionc                 C  s   t t| jjdd ƒƒS )Nrg  )rW   rj  r»   rk  rÏ   rU   rU   rV   rg  E
  s    zFixed.pandas_typec                 C  s^   |   ¡  | j}|dk	rXt|ttfƒrDd dd„ |D ƒ¡}d|› d�}| jd›d|› d	�S | jS )
ú(return a pretty representation of myselfNrÀ  c                 S  s   g | ]}t |ƒ‘qS rU   ©rJ   r$  rU   rU   rV   rl   O
  s     z"Fixed.__repr__.<locals>.<listcomp>ú[r#  ú12.12z	 (shape->ú))r  r  rP   rm   rn   rÃ  rg  )rË   rT   ZjshaperU   rU   rV   rè   I
  s    zFixed.__repr__c                 C  s   t | jƒ| j_t tƒ| j_dS )zset my pandas type & versionN)rY   rI  rÞ  rg  Ú_versionrQ  rÏ   rU   rU   rV   r   T
  s    zFixed.set_object_infoc                 C  s   t   | ¡}|S r\   r7  )rË   Znew_selfrU   rU   rV   rz  Y
  s    
z
Fixed.copyc                 C  s   | j S r\   )r  rÏ   rU   rU   rV   r  ]
  s    zFixed.shapec                 C  s   | j jS r\   ©r»   rµ   rÏ   rU   rU   rV   r5  a
  s    zFixed.pathnamec                 C  s   | j jS r\   )rÀ   rÁ   rÏ   rU   rU   rV   rÁ   e
  s    zFixed._handlec                 C  s   | j jS r\   )rÀ   rÉ   rÏ   rU   rU   rV   rÉ   i
  s    zFixed._filtersc                 C  s   | j jS r\   )rÀ   rÆ   rÏ   rU   rU   rV   rÆ   m
  s    zFixed._complevelc                 C  s   | j jS r\   )rÀ   rÈ   rÏ   rU   rU   rV   rÈ   q
  s    zFixed._fletcher32c                 C  s   | j jS r\   )r»   rk  rÏ   rU   rU   rV   rÞ  u
  s    zFixed.attrsc                 C  s   dS ©zset our object attributesNrU   rÏ   rU   rU   rV   Ú	set_attrsy
  s    zFixed.set_attrsc                 C  s   dS )zget our object attributesNrU   rÏ   rU   rU   rV   Ú	get_attrs|
  s    zFixed.get_attrsc                 C  s   | j S )zreturn my storabler¢  rÏ   rU   rU   rV   Ústorable
  s    zFixed.storablec                 C  s   dS r  rU   rÏ   rU   rU   rV   rŸ  „
  s    zFixed.is_existsc                 C  s   t | jdd ƒS )Nr  )rj  r^  rÏ   rU   rU   rV   r  ˆ
  s    zFixed.nrowszLiteral[True] | Nonec                 C  s   |dkrdS dS )z%validate against an existing storableNTrU   rÌ  rU   rU   rV   ÚvalidateŒ
  s    zFixed.validateNc                 C  s   dS )ú+are we trying to operate on an old version?NrU   )rË   rp   rU   rU   rV   Úvalidate_version’
  s    zFixed.validate_versionc                 C  s   | j }|dk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)r^  r]  )rË   rT   rU   rU   rV   r  •
  s
    zFixed.infer_axesr†   ©r£   r¤   c                 C  s   t dƒ‚d S )Nz>cannot read on an abstract storer: subclasses should implement©r­   ©rË   rp   r¥   r£   r¤   rU   rU   rV   r	   
  s    ÿz
Fixed.readc                 K  s   t dƒ‚d S )Nz?cannot write on an abstract storer: subclasses should implementrc  ©rË   r¹   rU   rU   rV   r¡  «
  s    ÿzFixed.write©r£   r¤   r[   c                 C  s0   t  |||¡r$| jj| jdd� dS tdƒ‚dS )zs
        support fully deleting the node in its entirety (only) - where
        specification must be None
        Tr=  Nz#cannot delete on an abstract storer)r@  rA  rÁ   r£  r»   r±   )rË   rp   r£   r¤   rU   rU   rV   rB  °
  s    zFixed.delete)rO   r}   )N)NNNN)NNN)!rß   rª  r«  r¬  r­  rJ  r4  rÌ   r®  rP  rO  rg  rè   r   rz  r  r5  rÁ   rÉ   rÆ   rÈ   rÞ  r\  r]  r^  rŸ  r  r_  ra  r  r	  r¡  rB  rU   rU   rU   rV   rH  
  sl   
  û







    û     ÿrH  c                   @  sF  e Zd ZU dZedediZdd„ e ¡ D ƒZg Z	de
d< dd	œd
d„Zdd„ Zdd„ Zdd	œdd„Zedd	œdd„ƒZdd	œdd„Zdd	œdd„Zdd	œdd„Zd:ddddœdd „Zd;dddd!d"œd#d$„Zdd!dd%œd&d'„Zdd(dd%œd)d*„Zd<dddd(d"œd+d,„Zd=d-ddd!d.œd/d0„Zdd1dd2œd3d4„Zd>dd5d6dd7œd8d9„ZdS )?ÚGenericFixedza generified fixed versionÚdatetimer   c                 C  s   i | ]\}}||“qS rU   rU   )rh   r"  rN  rU   rU   rV   rO  Â
  s      zGenericFixed.<dictcomp>rï   Ú
attributesrY   rÍ   c                 C  s   | j  |d¡S )NÚ )Ú_index_type_maprÖ   )rË   r›  rU   rU   rV   Ú_class_to_aliasÆ
  s    zGenericFixed._class_to_aliasc                 C  s   t |tƒr|S | j |t¡S r\   )rP   rÞ   Ú_reverse_index_maprÖ   r7   )rË   ÚaliasrU   rU   rV   Ú_alias_to_classÉ
  s    
zGenericFixed._alias_to_classc                 C  s¸   |   tt|ddƒƒ¡}|tkr.d	dd„}|}n|tkrFd
dd„}|}n|}i }d|krn|d |d< |tkrnt}d|kr°t|d tƒr˜|d  	d¡|d< n|d |d< |tks°t
‚||fS )NÚindex_classrj  c                 S  s:   t j| j|d�}tj|d d�}|d k	r6| d¡ |¡}|S )N©rµ  r`   ÚUTC)r@   Ú_simple_newrY  r6   Útz_localizeÚ
tz_convert)rY  rµ  r¶  ZdtaÚresultrU   rU   rV   rw   Ø
  s
    z*GenericFixed._get_index_factory.<locals>.fc                 S  s   t j| |d�}tj|d d�S )Nrq  r`   )rA   rs  r9   )rY  rµ  r¶  ZparrrU   rU   rV   rw   ã
  s    rµ  r¶  zutf-8)NN)NN)ro  rW   rj  r6   r9   r7   r<   rP   ÚbytesrS   rÒ   )rË   rÞ  rp  rw   rÚ  r¹   rU   rU   rV   Ú_get_index_factoryÏ
  s*    ÿ

zGenericFixed._get_index_factoryr‹   c                 C  s$   |dk	rt dƒ‚|dk	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±   )rË   r¥   rp   rU   rU   rV   Úvalidate_readý
  s    ÿÿzGenericFixed.validate_readr‡   c                 C  s   dS )NTrU   rÏ   rU   rU   rV   rŸ    s    zGenericFixed.is_existsc                 C  s   | j | j_ | j| j_dS r[  )rZ   rÞ  r˜   rÏ   rU   rU   rV   r\    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 attributesrZ   Nr˜   r}   )r_   rj  rÞ  rZ   rW   r˜   ri  rø  )rË   ró   rU   rU   rV   r]    s    
zGenericFixed.get_attrsc                 K  s   |   ¡  d S r\   )r\  ©rË   r�  r¹   rU   rU   rV   r¡    s    zGenericFixed.writeNr†   r  c                 C  sÐ   ddl }t| j|ƒ}|j}t|ddƒ}t||jƒrD|d ||… }nztt|ddƒƒ}	t|ddƒ}
|
dk	rxtj|
|	d�}n|||… }|	dkr¨t|d	dƒ}t	||d
d�}n|	dkr¾tj
|dd�}|rÈ|jS |S dS )z2read an array for the specified node (off of groupr   NÚ
transposedFÚ
value_typer  r/  r*  r¶  Tr+  r-  r.  )r   rj  r»   rk  rP   ZVLArrayrW   rQ   rœ  rØ  r:  ÚT)rË   r�   r£   r¤   r   râ   rÞ  r|  ÚretrÕ  r  r¶  rU   rU   rV   Ú
read_array   s&    zGenericFixed.read_arrayr7   )r�   r£   r¤   r[   c                 C  sh   t t| j|› d�ƒƒ}|dkr.| j|||d�S |dkrVt| j|ƒ}| j|||d�}|S td|› �ƒ‚d S )NÚ_varietyÚmultirb  Úregularzunrecognized index variety: )rW   rj  rÞ  Úread_multi_indexr»   Úread_index_noder±   )rË   r�   r£   r¤   Zvarietyrâ   r”   rU   rU   rV   Ú
read_indexB  s    zGenericFixed.read_index)r�   r”   r[   c                 C  sà   t |tƒr,t| j|› d�dƒ |  ||¡ n°t| j|› d�dƒ td|| j| jƒ}|  ||j	¡ t
| j|ƒ}|j|j_|j|j_t |ttfƒr |  t|ƒ¡|j_t |tttfƒrº|j|j_t |tƒrÜ|jd k	rÜt|jƒ|j_d S )Nr�  r‚  rƒ  r”   )rP   r8   rø  rÞ  Úwrite_multi_indexÚ_convert_indexrZ   r˜   Úwrite_arrayrY  rj  r»   rd  rk  ra   r6   r9   rl  rÞ   rp  r<   rµ  r¶  Ú_get_tz)rË   r�   r”   rA  râ   rU   rU   rV   Úwrite_indexP  s    



zGenericFixed.write_indexr8   c                 C  sÎ   t | j|› d�|jƒ tt|j|j|jƒƒD ]œ\}\}}}t|ƒrJt	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)rø  rÞ  r’  Ú	enumerater3  Úlevelsr  Únamesr-   r­   rˆ  rZ   r˜   r‰  rY  rj  r»   rd  rk  ra   )rË   r�   r”   ÚiÚlevÚlevel_codesra   Ú	level_keyZ
conv_levelrâ   Ú	label_keyrU   rU   rV   r‡  g  s"    ÿÿ
zGenericFixed.write_multi_indexc                 C  s¤   t | j|› d�ƒ}g }g }g }t|ƒD ]l}|› d|› �}	t | j|	ƒ}
| j|
||d�}| |¡ | |j¡ |› d|› �}| j|||d�}| |¡ q&t|||dd�S )NrŒ  r�  rb  r�  T)r‘  r  r’  r,  )	rj  rÞ  rQ  r»   r…  r’   ra   r€  r8   )rË   r�   r£   r¤   r’  r‘  r  r’  r“  r–  râ   r”  r—  r•  rU   rU   rV   r„  €  s&    
   ÿzGenericFixed.read_multi_indexrM   )râ   r£   r¤   r[   c                 C  sÔ   |||… }d|j kr>t |j j¡dkr>tj|j j|j jd�}t|j jƒ}d }d|j krlt|j j	ƒ}t|ƒ}|j }|  
|¡\}}	|dkr®|t||| j| jd�fdti|	—Ž}
n|t||| j| jd�f|	Ž}
||
_	|
S )Nr  r   r/  ra   )r   r;  r‰  rÕ  )rk  rQ   Úprodr  rœ  r}  rW   rd  rb   ra   rx  Ú_unconvert_indexrZ   r˜   r;  )rË   râ   r£   r¤   ry  rd  ra   rÞ  rÚ  r¹   r”   rU   rU   rV   r…  —  sF    
   ÿÿüû   ÿÿüzGenericFixed.read_index_noder   )r�   rŽ   r[   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)rQ   rœ  rR  rÁ   Úcreate_arrayr»   rj  rY   rÕ  rk  r}  r  )rË   r�   rŽ   Zarrrâ   rU   rU   rV   Úwrite_array_empty½  s
    zGenericFixed.write_array_emptyr   zIndex | None)r�   r�  rü   r[   c              	   C  s4  t |dd�}|| jkr&| j | j|¡ |jdk}d}t|jƒrFtdƒ‚|s^t|dƒr^|j	}d}d }| j
d k	r�ttƒ� tƒ j |j¡}W 5 Q R X |d k	rÖ|sÆ| jj| j|||j| j
d�}||d d …< n|  ||¡ �nJ|jjtjk�rJtj|dd�}	|rún,|	d	k�rn t|	||f }
tj|
ttƒ d
� | j | j|tƒ  ¡ ¡}| |¡ nÖt |jƒ�r€| j !| j|| "d¡¡ dt#| j|ƒj$_%n t&|jƒ�rÄ| j !| j||j'¡ t#| j|ƒ}t(|j)ƒ|j$_)d|j$_%n\t*|jƒ�rú| j !| j|| "d¡¡ dt#| j|ƒj$_%n&|�r|  ||¡ n| j !| j||¡ |t#| j|ƒj$_+d S )NT)Zextract_numpyr   Fz]Cannot store a category dtype in a HDF5 dataset that uses format="fixed". Use format="table".r~  )rÄ   ©Zskipnarä  rò  rÒ  r*  r-  ),rD   r»   rÁ   r£  r  r)   rÕ  r­   rÏ  r~  rÉ   r   rª   rƒ   ZAtomZ
from_dtypeZcreate_carrayr  rœ  rÞ   rQ   Zobject_r   Úinfer_dtypert   rö  r÷  r#   r&   Zcreate_vlarrayÚ
ObjectAtomr’   r+   r›  Úviewrj  rk  r}  r,   Úasi8rŠ  r¶  r2   r|  )rË   r�   r�  rü   rŽ   Zempty_arrayr|  r  ÚcaÚinferred_typerú  Zvlarrrâ   rU   rU   rV   r‰  Æ  sr    


ÿ


    ÿ
  ÿ
zGenericFixed.write_array)NN)NN)NN)NN)N)rß   rª  r«  r¬  r6   r9   rk  rü   rm  ri  r­  rl  ro  rx  ry  r®  rŸ  r\  r]  r¡  r€  r†  r‹  r‡  r„  r…  rœ  r‰  rU   rU   rU   rV   rg  ¾
  s8   
.#   ÿ   ÿ   ÿ&
 ÿrg  c                      sV   e Zd ZU dZdgZded< edd„ ƒZddddd	œd
d„Zddœ‡ fdd„Z	‡  Z
S )r�  r…  ra   r   c              	   C  s0   zt | jjƒfW S  ttfk
r*   Y d S X d S r\   )ro   r»   rY  r±   r€   rÏ   rU   rU   rV   r  '  s    zSeriesFixed.shapeNr†   r;   rf  c                 C  s>   |   ||¡ | jd||d�}| jd||d�}t||| jdd�S )Nr”   rb  rY  F)r”   ra   rz  )ry  r†  r€  r;   ra   )rË   rp   r¥   r£   r¤   r”   rY  rU   rU   rV   r	  .  s    zSeriesFixed.readr‹   rÍ   c                   s8   t ƒ j|f|Ž |  d|j¡ |  d|¡ |j| j_d S )Nr”   rY  )r  r¡  r‹  r”   r‰  ra   rÞ  r{  r  rU   rV   r¡  ;  s    zSeriesFixed.write)NNNN)rß   rª  r«  rI  ri  r­  r®  r  r	  r¡  rD  rU   rU   r  rV   r�  !  s   

    ûr�  c                      sZ   e Zd ZU ddgZded< eddœdd„ƒZdd	d	d
dœdd„Zddœ‡ fdd„Z‡  Z	S )ÚBlockManagerFixedrR  Únblocksrc   zShape | NonerÍ   c                 C  s°   z”| j }d}t| jƒD ]8}t| jd|› d�ƒ}t|dd ƒ}|d k	r||d 7 }q| jj}t|dd ƒ}|d k	r‚t|d|d … ƒ}ng }| |¡ |W S  tk
rª   Y d S X d S )Nr   ÚblockÚ_itemsr  rf   )	rR  rQ  r¥  rj  r»   Zblock0_valuesrm   r’   r€   )rË   rR  rü   r“  râ   r  rU   rU   rV   r  G  s"    
zBlockManagerFixed.shapeNr†   r5   rf  c                 C  s  |   ||¡ |  ¡  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
�}| |¡ q|t|ƒdk�rt|ddd�}|j|d	d�}|S t
|d |d d�S )Nr   )NNr+  rb  r¦  r§  r?  rf   F©r¥   r”   rz  T)r+  rz  )r¥   rz  ©r¥   r”   )ry  rL  Z_get_block_manager_axisrQ  rR  r†  r’   r¥  r€  rW  r5   r~  ro   r=   r]  )rË   rp   r¥   r£   r¤   Zselect_axisrD  r“  r  r  Úaxrü   ÚdfsÚ	blk_itemsrY  ÚdfÚoutrU   rU   rV   r	  b  s(    zBlockManagerFixed.readr‹   c                   sä   t ƒ j|f|Ž t|jtƒr&| d¡}|j}| ¡ s<| ¡ }|j| j	_t
|jƒD ]0\}}|dkrn|jsntdƒ‚|  d|› �|¡ qPt|jƒ| j	_t
|jƒD ]D\}}|j |j¡}| jd|› d�|j|d� |  d|› d�|¡ qšd S )Nr¦  r   z/Columns index has to be unique for fixed formatr+  r?  )rü   r§  )r  r¡  rP   Ú_mgrrF   Ú_as_managerZis_consolidatedZconsolidaterR  rÞ  r�  rD  Z	is_uniquerª   r‹  ro   Úblocksr¥  rü   rX  Úmgr_locsr‰  rY  )rË   r�  r¹   ry  r“  rª  Úblkr¬  r  rU   rV   r¡  †  s     

zBlockManagerFixed.write)NNNN)
rß   rª  r«  ri  r­  r®  r  r	  r¡  rD  rU   rU   r  rV   r¤  B  s   
    û$r¤  c                   @  s   e Zd ZdZeZdS )r‘  r†  N)rß   rª  r«  rI  r5   rL  rU   rU   rU   rV   r‘     s   r‘  c                      s¬  e Zd ZU dZdZdZded< ded< dZded	< d
Zded< d€dddddddddddœ
‡ fdd„Z	e
ddœdd„ƒZddœdd„Zdd œd!d"„Zddœd#d$„Ze
d%dœd&d'„ƒZd(d)d*œd+d,„Ze
d-dœd.d/„ƒZe
d%dœd0d1„ƒZe
d2d3„ ƒZe
d4d5„ ƒZe
d6d7„ ƒZe
d8d9„ ƒZe
d:d;„ ƒZe
d-dœd<d=„ƒZe
d%dœd>d?„ƒZe
d@dœdAdB„ƒZdCdœdDdE„ZdFdG„ ZdHdœdIdJ„ZdddKœdLdM„ZddNddOœdPdQ„ZddRœdSdT„Z ddœdUdV„Z!ddœdWdX„Z"d�ddœdYdZ„Z#ddœd[d\„Z$e%d]d^„ ƒZ&d‚ddd_œd`da„Z'dƒdbdbdcddœdedf„Z(e)d%dgœdhdi„ƒZ*djdk„ Z+d„dld%dmœdndo„Z,e-dld%dpœdqdr„ƒZ.d…dsdldtœdudv„Z/dbd%dbdCdwœdxdy„Z0d†dbdbdzœd{d|„Z1d‡ddbdbd}œd~d„Z2‡  Z3S )ˆrö   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
    Z
wide_tablerv   rY   rJ  r‚  rf   zint | list[Hashable]r‘  Trm   r»  Nr}   r    rM   rX   zlist[IndexCol] | Nonez list[tuple[AxisInt, Any]] | Nonezlist[DataCol] | Nonezlist | Nonezdict | Noner‹   )
rÀ   r»   rZ   r˜   Ú
index_axesr'  Úvalues_axesr—   r  r[   c                   sP   t ƒ j||||d� |pg | _|p$g | _|p.g | _|p8g | _|	pBi | _|
| _d S )Nr‰  )r  rÌ   r´  r'  rµ  r—   r  r™   )rË   rÀ   r»   rZ   r˜   r´  r'  rµ  r—   r  r™   r  rU   rV   rÌ   Å  s    




zTable.__init__rÍ   c                 C  s   | j  d¡d S )NÚ_r   )r‚  r¥  rÏ   rU   rU   rV   Útable_type_shortÚ  s    zTable.table_type_shortc                 C  s¦   |   ¡  t| jƒrd | j¡nd}d|› d�}d}| jrZd dd„ | jD ƒ¡}d|› d�}d d	d„ | jD ƒ¡}| jd
›|› d| j› d| j	› d| j
› d|› d|› d�S )rT  rÀ  rj  z,dc->[r#  rS  c                 S  s   g | ]}t |ƒ‘qS rU   ©rY   r$  rU   rU   rV   rl   æ  s     z"Table.__repr__.<locals>.<listcomp>rV  c                 S  s   g | ]
}|j ‘qS rU   r`   rx  rU   rU   rV   rl   é  s     rW  z (typ->z,nrows->z,ncols->z,indexers->[rX  )r  ro   r—   rÃ  rP  rO  r´  rg  r·  r  Úncols)rË   Zjdcr_  ÚverZjverZjindex_axesrU   rU   rV   rè   Þ  s    4ÿzTable.__repr__)rð  c                 C  s"   | j D ]}||jkr|  S qdS )zreturn the axis for cN)rD  ra   )rË   rð  r„   rU   rU   rV   rØ   ð  s    


zTable.__getitem__c              
   C  sº   |dkrdS |j | j kr2td|j › d| j › d�ƒ‚dD ]~}t| |dƒ}t||dƒ}||kr6t|ƒD ]4\}}|| }||krbtd|› d|› d|› d�ƒ‚qbtd|› d|› d|› d�ƒ‚q6dS )	z"validate against an existing tableNz'incompatible table_type with existing [rñ  r#  )r´  r'  rµ  zinvalid combination of [z] on appending data [z] vs current table [)r‚  r±   rj  r�  rª   r?  )rË   rÇ  rð  ÚsvÚovr“  ÚsaxZoaxrU   rU   rV   r_  ÷  s&    ÿÿÿz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)rP   r‘  rm   rÏ   rU   rU   rV   Úis_multi_index  s    zTable.is_multi_indexr…   z tuple[DataFrame, list[Hashable]])r�  r[   c              
   C  s^   t  |jj¡}z| ¡ }W n, tk
rF } ztdƒ|‚W 5 d}~X Y nX t|tƒsVt‚||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@  Zfill_missing_namesr”   r’  Zreset_indexrª   rP   r5   rÒ   )rË   r�  r‘  Z	reset_objrC  rU   rU   rV   Úvalidate_multiindex  s    ÿþzTable.validate_multiindexrc   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 r&  )rã  r  ©rh   r“  rU   rU   rV   rl   1  s     z(Table.nrows_expected.<locals>.<listcomp>)rQ   r˜  r´  rÏ   rU   rU   rV   Únrows_expected.  s    zTable.nrows_expectedc                 C  s
   d| j kS )zhas this table been createdrv   r¢  rÏ   rU   rU   rV   rŸ  3  s    zTable.is_existsc                 C  s   t | jdd ƒS ©Nrv   ©rj  r»   rÏ   rU   rU   rV   r^  8  s    zTable.storablec                 C  s   | j S )z,return the table group (this is my storable))r^  rÏ   rU   rU   rV   rv   <  s    zTable.tablec                 C  s   | j jS r\   )rv   rÕ  rÏ   rU   rU   rV   rÕ  A  s    zTable.dtypec                 C  s   | j jS r\   rß  rÏ   rU   rU   rV   rà  E  s    zTable.descriptionc                 C  s   t  | j| j¡S r\   )r1  r2  r´  rµ  rÏ   rU   rU   rV   rD  I  s    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\   )ro   rY  rx  rU   rU   rV   rK  P  s     zTable.ncols.<locals>.<genexpr>)Úsumrµ  rÏ   rU   rU   rV   r¹  M  s    zTable.ncolsc                 C  s   dS r  rU   rÏ   rU   rU   rV   Úis_transposedR  s    zTable.is_transposedz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 r&  rR  rx  rU   rU   rV   rl   [  s     z*Table.data_orientation.<locals>.<listcomp>c                 S  s   g | ]}t |jƒ‘qS rU   )rc   r+  rx  rU   rU   rV   rl   \  s     )rn   r1  r2  r'  r´  rÏ   rU   rU   rV   Údata_orientationV  s    þÿzTable.data_orientationz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 rU   ©r¸  rx  rU   rU   rV   rl   f  s     z$Table.queryables.<locals>.<listcomp>c                   s   g | ]\}}ˆ | d f‘qS r\   rU   )rh   r+  rY  )Ú
axis_namesrU   rV   rl   g  s     c                   s&   g | ]}|j tˆ jƒkr|j|f‘qS rU   )ra   rP  r—   r¸  r2  rÏ   rU   rV   rl   h  s     )r´  r'  rµ  rF  )rË   Zd1Zd2Zd3rU   )rÉ  rË   rV   Ú
queryables`  s    

ÿzTable.queryablesc                 C  s   dd„ | j D ƒS )zreturn a list of my index colsc                 S  s   g | ]}|j |jf‘qS rU   )r+  r¸  rÀ  rU   rU   rV   rl   q  s     z$Table.index_cols.<locals>.<listcomp>©r´  rÏ   rU   rU   rV   Ú
index_colsn  s    zTable.index_colsrï   c                 C  s   dd„ | j D ƒS )zreturn a list of my values colsc                 S  s   g | ]
}|j ‘qS rU   rÈ  rÀ  rU   rU   rV   rl   u  s     z%Table.values_cols.<locals>.<listcomp>)rµ  rÏ   rU   rU   rV   Úvalues_colss  s    zTable.values_colsrÙ   c                 C  s   | j j}|› d|› d�S )z)return the metadata pathname for this keyz/meta/z/metarZ  r  rU   rU   rV   Ú_get_metadata_pathw  s    zTable._get_metadata_pathrÐ  )r�   rY  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
        Fr7  rv   )r“   rZ   r˜   r™   N)rÀ   rŸ   rÎ  r;   rZ   r˜   r™   )rË   r�   rY  rU   rU   rV   rí  |  s    	
úzTable.write_metadatarÕ   c                 C  s0   t t | jddƒ|dƒdk	r,| j |  |¡¡S dS )z'return the meta data array for this keyr½   N)rj  r»   rÀ   r¶   rÎ  r×   rU   rU   rV   r   Ž  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)rY   r‚  rÞ  rÌ  rÍ  r'  r—   r™   rZ   r˜   r‘  r  rÏ   rU   rU   rV   r\  ”  s    





zTable.set_attrsc                 C  s°   t | jddƒpg | _t | jddƒp$g | _t | jddƒp8i | _t | jddƒ| _tt | jddƒƒ| _tt | jddƒƒ| _	t | jd	dƒp„g | _
d
d„ | jD ƒ| _dd„ | jD ƒ| _dS )rz  r'  Nr—   r  r™   rZ   r˜   r}   r‘  c                 S  s   g | ]}|j r|‘qS rU   ©r³  rx  rU   rU   rV   rl   ª  s      z#Table.get_attrs.<locals>.<listcomp>c                 S  s   g | ]}|j s|‘qS rU   rÏ  rx  rU   rU   rV   rl   «  s      )rj  rÞ  r'  r—   r  r™   r_   rZ   rW   r˜   r‘  Ú
indexablesr´  rµ  rÏ   rU   rU   rV   r]  ¡  s    zTable.get_attrsc                 C  s>   |dk	r:| j r:td dd„ | jD ƒ¡ }tj|ttƒ d� dS )r`  NrS  c                 S  s   g | ]}t |ƒ‘qS rU   r¸  r$  rU   rU   rV   rl   ±  s     z*Table.validate_version.<locals>.<listcomp>rò  )rP  rr   rÃ  rO  rö  r÷  r"   r&   )rË   rp   rú  rU   rU   rV   ra  ­  s    ýzTable.validate_versionc                 C  sR   |dkrdS t |tƒsdS |  ¡ }|D ]&}|dkr4q&||k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
        NrY  zmin_itemsize has the key [z%] which is not an axis or data_column)rP   rF  rÊ  rª   )rË   r•   Úqr"  rU   rU   rV   Úvalidate_min_itemsize¸  s    

ÿzTable.validate_min_itemsizec                   sÔ   g }ˆj ‰ˆjj‰tˆjjƒD ]j\}\}}tˆ|ƒ}ˆ |¡}|dk	rJd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 existNrÿ  r¾  )ra   r+  rº  rd  r¹  rv   r½   r»  c                   s¢   t |tƒst‚t}|ˆk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 )Nr¾  r  r	  )
ra   r¸  rY  rd  rº  r¹  rv   r½   r»  rÕ  )rP   rY   rÒ   r  rE  rj  Ú_maybe_adjust_namerO  r  r   rv   )r“  rð  Úklassr  Úadj_namerY  rÕ  rd  Úmdr½   r�  )Úbase_posr_  ÚdescrË   Útable_attrsrU   rV   rw   ð  s0    

özTable.indexables.<locals>.fc                   s   g | ]\}}ˆ ||ƒ‘qS rU   rU   )rh   r“  rð  )rw   rU   rV   rl     s     z$Table.indexables.<locals>.<listcomp>)rà  rv   rÞ  r�  rÌ  rj  r   r²  r’   rP  r—   ro   rT  rÍ  )rË   Ú_indexablesr“  r+  ra   r  rÖ  r½   r¿  rd  Ú	index_colrU   )r×  r_  rØ  rw   rË   rÙ  rV   rÐ  Í  s2    


ø

% zTable.indexablesr  c              	   C  sR  |   ¡ sdS |dkrdS |dks(|dkr8dd„ | jD ƒ}t|ttfƒsL|g}i }|dk	r`||d< |dk	rp||d< | j}|D ]Ò}t|j|dƒ}|dk	�r|jrò|j	}|j
}	|j}
|dk	rÈ|
|krÈ| ¡  n|
|d< |dk	rê|	|krê| ¡  n|	|d< |j�sL|j d¡�rtd	ƒ‚|jf |Ž qz|| jd
 d krztd|› d|› d|› d�ƒ‚qzd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  s   g | ]}|j r|j‘qS rU   )r´  r¸  rx  rU   rU   rV   rl   >  s      z&Table.create_index.<locals>.<listcomp>rc  rd  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.)r  rD  rP   rn   rm   rv   rj  rJ  rw  r”   rc  rd  Zremove_indexrÞ   rr  r±   re  r'  r€   )rË   r¥   rc  rd  Úkwrv   rð  rN  r”   Zcur_optlevelZcur_kindrU   rU   rV   re    sJ    


ÿÿzTable.create_indexr†   z!list[tuple[ArrayLike, ArrayLike]]rf  c           	      C  sZ   t | |||d�}| ¡ }g }| jD ]2}| | 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]]
        r  r9  )
Ú	Selectionr¶   rD  rþ  r  rÛ  r™   rZ   r˜   r’   )	rË   rp   r£   r¤   Ú	selectionrY  r±  r„   ÚresrU   rU   rV   Ú
_read_axeso  s    
üzTable._read_axes©r|  c                 C  s   |S )zreturn the data for this objrU   ©r›  r�  r|  rU   rU   rV   Ú
get_object‘  s    zTable.get_objectc                   s²   t |ƒsg S |d \}‰ | j |i ¡}| d¡dkrL|rLtd|› d|› �ƒ‚|dkr^tˆ ƒ}n|dkrjg }t|tƒr t|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   rÞ   r8   z"cannot use a multi-index on axis [z] with data_columns TNc                   s    g | ]}|d kr|ˆ kr|‘qS rÜ  rU   r!  )Úexisting_data_columnsrU   rV   rl   ²  s    þz/Table.validate_data_columns.<locals>.<listcomp>c                   s   g | ]}|ˆ kr|‘qS rU   rU   )rh   rð  )Úaxis_labelsrU   rV   rl   º  s      )
ro   r  rÖ   rª   rm   rP   rF  rP  rT  rø   )rË   r—   r•   r'  r+  r  rU   )ræ  rå  rV   Úvalidate_data_columns–  s*    ÿ


þÿ	zTable.validate_data_columnsr5   )r�  r_  c           /        sŽ  t ˆtƒs,| jj}td|› dtˆƒ› d�ƒ‚ˆ dkr:dg‰ ‡fdd„ˆ D ƒ‰ |  ¡ rzd}d	d„ | jD ƒ‰ t| j	ƒ}| j
}nd
}| j}	| jdks’t‚tˆ ƒ| jd kr¬tdƒ‚g }
|dkr¼d}‡ fdd„dD ƒd }ˆj| }t|ƒ}|�r<t|
ƒ}| j| d }tt |¡t |¡ƒ�s<tt t|ƒ¡t t|ƒ¡ƒ�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�sàt‚t|
ƒdk�sòt‚|
D ]}t"ˆ|d |d ƒ‰�qö|jdk}|  #|||
¡}|  $ˆ|¡ %¡ }|  &|||
| j'|¡\}}g }t(t)||ƒƒD �]¸\}\}}t*}d}|�rÆt|ƒdk�rÆ|d |k�rÆt+}|d }|dk�sÆt |t,ƒ�sÆtdƒ‚|�r&|�r&z| j'| }W nB t-t.fk
�r" }  ztd|› d| j'› d�ƒ| ‚W 5 d} ~ X Y nX nd}|�p8d|› �}!t/|!|j0|||| j| j|d�}"t1|!| j2ƒ}#| 3|"¡}$t4|"j5j6ƒ}%d}&t7|"ddƒdk	�ršt8|"j9ƒ}&d }' }(})t:|"j5ƒ�rÐ|"j;})d}'tj|"j<d
d� =¡ }(t>|"ƒ\}*}+||#|!t|ƒ|$||%|&|)|'|(|+|*d�},|,  |	¡ | |,¡ |d7 }�qddd„ |D ƒ}-t| ƒ| j?| j| j| j||
||-|	|d�
}.t@| dƒ�rj| jA|._A|. B|¡ |�rŠ|�rŠ|. C| ¡ |.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->rˆ  r#  Nr   c                   s   g | ]}ˆ   |¡‘qS rU   )Ú_get_axis_numberrx  )r�  rU   rV   rl   è  s     z&Table._create_axes.<locals>.<listcomp>Tc                 S  s   g | ]
}|j ‘qS rU   rL  rx  rU   rU   rV   rl   í  s     FrN  rf   z<currently only support ndim-1 indexers in an AppendableTableÚnanc                   s   g | ]}|ˆ kr|‘qS rU   rU   r$  )rD  rU   rV   rl     s      rÇ  r’  rÞ   rF  zIncompatible appended table [z]with existing table [Zvalues_block_)Úexisting_colr•   r™   rZ   r˜   r¥   r¶  rÿ  r7  )ra   r¸  rY  r¹  rº  rd  r¶  r^  r½   r»  rÕ  ry  c                 S  s   g | ]}|j r|j‘qS rU   )r´  ra   )rh   rá  rU   rU   rV   rl   ‹  s      )
rÀ   r»   rZ   r˜   r´  r'  rµ  r—   r  r™   r‘  )DrP   r5   r»   r¾   r±   rÞ   r  r´  rm   r—   r™   r  rR  rÒ   ro   rª   rD  r'  r4   rQ   ÚarrayrV  rõ  r’  rß   r’   Z_get_axis_namerˆ  rZ   r˜   r+  r¼  rû  ræ  Ú_reindex_axisrç  rä  r-  Ú_get_blocks_and_itemsrµ  r�  r3  r  rE  rY   Ú
IndexErrorr·   Ú_maybe_convert_for_string_atomrY  rÓ  rO  r  r  rÕ  ra   rj  rŠ  r¶  r)   r^  r6  r<  r  rÀ   rÏ  r‘  rÒ  r_  )/rË   rD  r�  r_  r™   r—   r•   r»   Útable_existsZnew_infoÚnew_non_index_axesrù  r„   Zappend_axisZindexerZ
exist_axisr  Ú	axis_nameZ	new_indexZnew_index_axesÚjr|  r†  r±  r¬  Zvaxesr“  r³  Úb_itemsrÔ  ra   rê  rC  Únew_nameÚdata_convertedrÕ  r¹  rd  r¶  r½   r»  r^  ry  r  rá  ZdcsZ	new_tablerU   )rD  r�  rV   Ú_create_axes¼  s"    
ÿ
ÿ
 ÿ





  ÿ    ÿ"ÿýø


ô

ö

zTable._create_axes)r†  rð  c                 C  sŽ  t | jtƒr|  d¡} dd„ }| j}tt|ƒ}t|jƒ}||ƒ}t|ƒrÒ|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¡ q–|�r†dd„ t||ƒD ƒ}g }g }|D ]„}t|jƒ}z&| |¡\}}| |¡ | |¡ W qø ttfk
�rz } z*d d	d
„ |D ƒ¡}td|› d�ƒ|‚W 5 d }~X Y qøX qø|}|}||fS )Nr¦  c                   s   ‡ fdd„ˆ j D ƒS )Nc                   s   g | ]}ˆ j  |j¡‘qS rU   )rü   rX  r²  )rh   r³  ©ÚmgrrU   rV   rl   ³  s     zFTable._get_blocks_and_items.<locals>.get_blk_items.<locals>.<listcomp>)r±  rø  rU   rø  rV   Úget_blk_items²  s    z2Table._get_blocks_and_items.<locals>.get_blk_itemsr   rL  c                 S  s"   i | ]\}}t | ¡ ƒ||f“qS rU   )rn   Útolist)rh   Úbrô  rU   rU   rV   rO  Ñ  s   ÿ
 z/Table._get_blocks_and_items.<locals>.<dictcomp>rÀ  c                 S  s   g | ]}t |ƒ‘qS rU   rU  )rh   ÚitemrU   rU   rV   rl   Þ  s     z/Table._get_blocks_and_items.<locals>.<listcomp>z+cannot match existing table structure for [z] on appending data)rP   r¯  rF   r°  r   rG   rm   r±  ro   r7   rU  r]  rT  r3  rn   rY  r6  r’   rî  r·   rÃ  rª   )r†  rð  rñ  rµ  r—   rú  rù  r±  r¬  r+  ræ  Z
new_labelsrð  Zby_itemsZ
new_blocksZnew_blk_itemsZearü   rü  rô  rC  ZjitemsrU   rU   rV   rí  ¤  sR    





þ


ÿýzTable._get_blocks_and_itemsrÞ  )rß  r[   c                   sª   |dk	rt |ƒ}|dk	rNˆjrNtˆjt ƒs.t‚ˆjD ]}||kr4| d|¡ q4ˆjD ]$\}}tˆ |||ƒ‰ ‡ ‡fdd„}qT|jdk	r¦|j 	¡ D ]\}}	}
|||
|	ƒ‰ qŽˆ S )zprocess axes filtersNr   c                   sÌ   ˆ j D ]°}ˆ  |¡}ˆ  |¡}|d k	s*t‚| |krfˆjrH| tˆjƒ¡}|||ƒ}ˆ j|d�|   S | |krt	t
ˆ | ƒjƒ}t	|ƒ}tˆ tƒr˜d| }|||ƒ}ˆ j|d�|   S qtd| › d�ƒ‚d S )NrL  rf   zcannot find the field [z] for filtering!)Z_AXIS_ORDERSrè  Ú	_get_axisrÒ   r¾  Úunionr7   r‘  r\  rE   rj  rY  rP   r5   rª   )ÚfieldÚfiltÚoprò  Zaxis_numberZaxis_valuesZtakersrY  ©r�  rË   rU   rV   Úprocess_filterù  s"    





z*Table.process_axes.<locals>.process_filter)
rm   r¾  rP   r‘  rÒ   Úinsertr'  rì  Úfilterr“   )rË   r�  rß  r¥   ró   r+  Úlabelsr  r   r  r  rU   r  rV   Úprocess_axesè  s    
 
zTable.process_axes)r�   rÃ   rE  r[   c                 C  s‚   |dkrt | jdƒ}d|dœ}dd„ | jD ƒ|d< |rj|dkrH| jpFd}tƒ j|||pZ| jd	�}||d
< n| jdk	r~| j|d
< |S )z:create the description of the table from the axes & valuesNi'  rv   )ra   rE  c                 S  s   i | ]}|j |j“qS rU   )r¸  r¹  rx  rU   rU   rV   rO  .  s      z,Table.create_description.<locals>.<dictcomp>rà  é	   )r�   r‘   rÃ   rÄ   )ÚmaxrÁ  rD  rÆ   rƒ   rþ   rÈ   rÉ   )rË   r‘   r�   rÃ   rE  rG  rÄ   rU   rU   rV   Úcreate_description  s     	

ý


zTable.create_descriptionrb  c           
      C  s�   |   |¡ |  ¡ sdS t| |||d�}| ¡ }|jdk	rˆ|j ¡ D ]D\}}}| j|| ¡ | ¡ d d�}	|||	j	|| ¡   |ƒj
 }qBt|ƒS )zf
        select coordinates (row numbers) from a table; return the
        coordinates object
        Fr  Nrf   rb  )ra  r  rÞ  Úselect_coordsr  r“   r  r°  r
  ÚilocrY  r7   )
rË   rp   r£   r¤   rß  Zcoordsr   r  r  ry  rU   rU   rV   r  >  s    

  
ÿ zTable.read_coordinatesr  c                 C  s¼   |   ¡  |  ¡ sdS |dk	r$tdƒ‚| jD ]|}||jkr*|jsNt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‚dS )
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 indexabler9  rf   )ra   rz  z] not found in the table)ra  r  r±   rD  ra   r´  rª   rj  rv   rJ  rþ  r  rÛ  r™   rZ   r˜   r;   rØ  r¶  r·   )rË   r  rp   r£   r¤   r„   rð  Z
col_valuesrU   rU   rV   r  X  s*    


ÿ
ü zTable.read_column)Nr}   NNNNNN)N)NNN)NN)TNNN)N)NNN)NNN)4rß   rª  r«  r¬  rI  rJ  r­  r‘  r4  rÌ   r®  r·  rè   rØ   r_  r¾  r¿  rÁ  rŸ  r^  rv   rÕ  rà  rD  r¹  rÅ  rÆ  rÊ  rÌ  rÍ  rÎ  rí  r   r\  r]  ra  rÒ  r%   rÐ  re  rá  rC  rä  rç  r÷  Ústaticmethodrí  r  r  r  r  rD  rU   rU   r  rV   rö   ¥  s¨   
        õ&!




	
L     ÿW   ÿ"*    ù iC7      ÿ   ûrö   c                   @  s4   e Zd ZdZdZddddœdd„Zdd	œd
d„ZdS )r˜  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
    r�  Nr†   rb  c                 C  s   t dƒ‚dS )z[
        read the indices and the indexing array, calculate offset rows and return
        z!WORMTable needs to implement readNrc  rd  rU   rU   rV   r	  �  s    
zWORMTable.readr‹   rÍ   c                 K  s   t dƒ‚dS )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 writeNrc  re  rU   rU   rV   r¡  ™  s    zWORMTable.write)NNNN)rß   rª  r«  r¬  r‚  r	  r¡  rU   rU   rU   rV   r˜  „  s       ûr˜  c                   @  sf   e Zd ZdZdZddddddœd	d
„Zdddddœdd„Zddddddœdd„Zddddœdd„ZdS )rè  ú(support the new appendable table formatsZ
appendableNFTr‡   r‹   )r’   r–   r9  r[   c                 C  s²   |s| j r| j | jd¡ | j||||||d�}|jD ]}| ¡  q6|j s~|j||||	d�}| ¡  ||d< |jj	|jf|Ž |j
|j_
|jD ]}| ||¡ qŽ|j||
d� d S )Nrv   )rD  r�  r_  r•   r™   r—   )r‘   r�   rÃ   rE  r9  )r–   )rŸ  rÁ   r£  r»   r÷  rD  rç  r  r\  Zcreate_tabler  rÞ  rï  Ú
write_data)rË   r�  rD  r’   r‘   r�   rÃ   r•   r§   rE  r–   r™   r—   r9  rv   r„   ÚoptionsrU   rU   rV   r¡  ¨  s4    
ú	

ü

zAppendableTable.writer†   )r§   r–   r[   c                   sÈ  | j j}| j}g }|rT| jD ]6}t|jƒjdd�}t|tj	ƒr| 
|jddd�¡ qt|ƒrˆ|d }|dd… D ]}||@ }qp| ¡ }nd}dd	„ | jD ƒ}	t|	ƒ}
|
dks´t|
ƒ‚d
d	„ | jD ƒ}dd	„ |D ƒ}g }t|ƒD ]2\}}|f| j ||
|   j }| 
| |¡¡ qÞ|dk�r d}tjt||ƒ| j d�}|| d }t|ƒD ]x}|| ‰t|d | |ƒ‰ ˆˆ k�rx �qÄ| j|‡ ‡fdd	„|	D ƒ|dk	�r¦|ˆˆ … nd‡ ‡fdd	„|D ƒd� �qJdS )z`
        we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
        r   rL  Úu1Fr7  rf   Nc                 S  s   g | ]
}|j ‘qS rU   )rã  rx  rU   rU   rV   rl   þ  s     z.AppendableTable.write_data.<locals>.<listcomp>c                 S  s   g | ]}|  ¡ ‘qS rU   )rÝ  rx  rU   rU   rV   rl     s     c              	   S  s,   g | ]$}|  t t |j¡|jd  ¡¡‘qS rš  )Z	transposerQ   ZrollÚarangerR  r2  rU   rU   rV   rl     s     r¯  r/  c                   s   g | ]}|ˆˆ … ‘qS rU   rU   rx  ©Zend_iZstart_irU   rV   rl     s     c                   s   g | ]}|ˆˆ … ‘qS rU   rU   r2  r  rU   rV   rl     s     )ÚindexesrB  rY  )rÕ  r’  rÁ  rµ  r>   ry  rH  rP   rQ   rÔ  r’   r>  ro   r<  r´  rÒ   r�  r  Úreshaperœ  r°  rQ  Úwrite_data_chunk)rË   r§   r–   r’  r  Úmasksr„   rB  Úmr  ÚnindexesrY  Úbvaluesr“  rN  Z	new_shapeÚrowsÚchunksrU   r  rV   r  ã  sL    




üzAppendableTable.write_datarÐ  zlist[np.ndarray]znpt.NDArray[np.bool_] | None)r  r  rB  rY  r[   c                 C  sä   |D ]}t  |j¡s dS q|d jd }|t|ƒkrFt j|| jd�}| jj}t|ƒ}t|ƒD ]\}	}
|
|||	 < q^t|ƒD ]\}	}||||	|  < q||dk	rÂ| ¡ j	t
dd� }| ¡ sÂ|| }t|ƒrà| j |¡ | j ¡  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/  Fr7  )rQ   r˜  r  ro   rœ  rÕ  r’  r�  r<  r>  r‡   rH  rv   r’   r  )rË   r  r  rB  rY  rN  r  r’  r  r“  rù  r  rU   rU   rV   r    s&    z AppendableTable.write_data_chunkrb  c                 C  sf  |d kst |ƒsf|d kr:|d kr:| j}| jj| jdd� n(|d krH| j}| jj||d�}| j ¡  |S |  ¡ srd S | j}t	| |||d�}| 
¡ }t|dd� ¡ }t |ƒ}	|	�rb| ¡ }
t|
|
dk jƒ}t |ƒsÖdg}|d |	krì| |	¡ |d dk�r| dd¡ | ¡ }t|ƒD ]@}| t||ƒ¡}|j||jd  ||jd  d d� |}�q| j ¡  |	S )	NTr=  rb  Fr7  rf   r   r5  )ro   r  rÁ   r£  r»   rv   Zremove_rowsr  r  rÞ  r  r;   Zsort_valuesÚdiffrm   r”   r’   r  r6  ÚreversedrX  rQ  )rË   rp   r£   r¤   r  rv   rß  rY  Zsorted_seriesÚlnr  r³   Zpgrû   r  rU   rU   rV   rB  J  sF    

 ÿ
zAppendableTable.delete)NFNNNNNNFNNT)F)NNN)	rß   rª  r«  r¬  r‚  r¡  r  r  rB  rU   rU   rU   rV   rè  ¢  s$               ò;;,rè  c                   @  s`   e Zd ZU dZdZdZdZeZde	d< e
ddœd	d
„ƒZeddœdd„ƒZddddœdd„ZdS )r–  r  rƒ  r�  rN  rK  rL  r‡   rÍ   c                 C  s   | j d jdkS )Nr   rf   )r´  r+  rÏ   rU   rU   rV   rÅ  �  s    z"AppendableFrameTable.is_transposedrâ  c                 C  s   |r
|j }|S )zthese are written transposed)r~  rã  rU   rU   rV   rä  ‘  s    zAppendableFrameTable.get_objectNr†   rb  c                   s2  ˆ   |¡ ˆ  ¡ sd S ˆ j|||d�}tˆ jƒrHˆ j ˆ jd d i ¡ni }‡ fdd„tˆ jƒD ƒ}t|ƒdkstt	‚|d }|| d }	g }
tˆ jƒD �]P\}}|ˆ j
kr¬q–|| \}}| d¡dkrÐt|ƒ}n
t |¡}| d¡}|d k	rú|j|d	d
� ˆ j�r |}|}t|	t|	dd ƒd�}n|j}t|	t|	dd ƒd�}|}|jdk�rlt|tjƒ�rl| d|jd f¡}t|tjƒ�rŽt|j||dd�}n.t|tƒ�rªt|||d�}ntj|g||d�}|j|jk ¡ �sÞt	|j|jfƒ‚|
 |¡ q–t|
ƒdk�r|
d }nt|
dd�}tˆ |||d�}ˆ j |||d�}|S )Nr  r   c                   s"   g | ]\}}|ˆ j d  kr|‘qS r&  rË  )rh   r“  rª  rÏ   rU   rV   rl   ®  s      z-AppendableFrameTable.read.<locals>.<listcomp>rf   rÞ   r8   r’  T©Zinplacera   r`   Fr¨  r©  rL  )rß  r¥   )!ra  r  rá  ro   r'  r  rÖ   r�  rD  rÒ   rµ  r7   r8   Úfrom_tuplesÚ	set_namesrÅ  rj  r~  rR  rP   rQ   rÔ  r  r  r5   Z_from_arraysZdtypesrÕ  rH  r’   r=   rÞ  r  )rË   rp   r¥   r£   r¤   rv  r  ZindsÚindr”   Úframesr“  r„   Z
index_valsrã  rJ  r’  rY  Zindex_Zcols_r­  rß  rU   rÏ   rV   r	  ˜  sZ    
ÿý



"
zAppendableFrameTable.read)NNNN)rß   rª  r«  r¬  rI  r‚  rR  r5   rL  r­  r®  rÅ  rC  rä  r	  rU   rU   rU   rV   r–  …  s   
    ûr–  c                      sn   e Zd ZdZdZdZdZeZe	ddœdd„ƒZ
edd	œd
d„ƒZd‡ fdd„	Zdddddœ‡ fdd„Z‡  ZS )r”  r  rŠ  r‹  rN  r‡   rÍ   c                 C  s   dS r  rU   rÏ   rU   rU   rV   rÅ  ð  s    z#AppendableSeriesTable.is_transposedrâ  c                 C  s   |S r\   rU   rã  rU   rU   rV   rä  ô  s    z AppendableSeriesTable.get_objectNc                   s<   t |tƒs|jpd}| |¡}tƒ jf ||j ¡ dœ|—ŽS )ú+we are going to write this as a frame tablerY  ©r�  r—   )rP   r5   ra   Zto_framer  r¡  r¥   rû  )rË   r�  r—   r¹   ra   r  rU   rV   r¡  ø  s    


zAppendableSeriesTable.writer†   r;   rf  c                   s�   | j }|d k	rB|rBt| jtƒs"t‚| jD ]}||kr(| d|¡ q(tƒ j||||d�}|rj|j| jdd� |j	d d …df }|j
dkrŒd |_
|S )Nr   r*  Tr!  rY  )r¾  rP   r‘  rm   rÒ   r  r  r	  Ú	set_indexr  ra   )rË   rp   r¥   r£   r¤   r¾  ró   rT   r  rU   rV   r	  ÿ  s    

zAppendableSeriesTable.read)N)NNNN)rß   rª  r«  r¬  rI  r‚  rR  r;   rL  r®  rÅ  rC  rä  r¡  r	  rD  rU   rU   r  rV   r”  è  s   	    ûr”  c                      s(   e Zd ZdZdZdZ‡ fdd„Z‡  ZS )r•  r  rŠ  rŒ  c                   s^   |j pd}|  |¡\}| _t| jtƒs*t‚t| jƒ}| |¡ t|ƒ|_t	ƒ j
f d|i|—ŽS )r&  rY  r�  )ra   r¿  r‘  rP   rm   rÒ   r’   r7   r¥   r  r¡  )rË   r�  r¹   ra   ZnewobjrJ  r  rU   rV   r¡    s    



z AppendableMultiSeriesTable.write)rß   rª  r«  r¬  rI  r‚  r¡  rD  rU   rU   r  rV   r•    s   r•  c                   @  sj   e Zd ZU dZdZdZdZeZde	d< e
ddœd	d
„ƒZe
dd„ ƒZddœdd„Zedd„ ƒZdd„ ZdS )r“  z:a table that read/writes the generic pytables table formatrƒ  r„  rN  zlist[Hashable]r‘  rY   rÍ   c                 C  s   | j S r\   )rI  rÏ   rU   rU   rV   rg  2  s    zGenericTable.pandas_typec                 C  s   t | jdd ƒp| jS rÂ  rÃ  rÏ   rU   rU   rV   r^  6  s    zGenericTable.storabler‹   c                 C  sL   g | _ d| _g | _dd„ | jD ƒ| _dd„ | jD ƒ| _dd„ | jD ƒ| _dS )rz  Nc                 S  s   g | ]}|j r|‘qS rU   rÏ  rx  rU   rU   rV   rl   @  s      z*GenericTable.get_attrs.<locals>.<listcomp>c                 S  s   g | ]}|j s|‘qS rU   rÏ  rx  rU   rU   rV   rl   A  s      c                 S  s   g | ]
}|j ‘qS rU   r`   rx  rU   rU   rV   rl   B  s     )r'  r™   r‘  rÐ  r´  rµ  r—   rÏ   rU   rU   rV   r]  :  s    zGenericTable.get_attrsc           
   
   C  s¨   | j }|  d¡}|dk	rdnd}tdd| j||d�}|g}t|jƒD ]^\}}t|tƒsZt‚t	||ƒ}|  |¡}|dk	rzdnd}t
|||g|| j||d�}	| |	¡ qD|S )z0create the indexables from the table descriptionr”   Nrÿ  r   )ra   r+  rv   r½   r»  )ra   rº  rY  r¹  rv   r½   r»  )rà  r   r  rv   r�  Z_v_namesrP   rY   rÒ   rj  rG  r’   )
rË   rG  rÖ  r½   rÛ  rÚ  r“  ró   r  r_  rU   rU   rV   rÐ  D  s6    
    ÿ

ù	zGenericTable.indexablesc                 K  s   t dƒ‚d S )Nz cannot write on an generic tablerc  re  rU   rU   rV   r¡  g  s    zGenericTable.writeN)rß   rª  r«  r¬  rI  r‚  rR  r5   rL  r­  r®  rg  r^  r]  r%   rÐ  r¡  rU   rU   rU   rV   r“  )  s   



"r“  c                      s`   e Zd ZdZdZeZdZe 	d¡Z
eddœdd„ƒZd‡ fd
d„	Zddddœ‡ fdd„Z‡  ZS )r—  za frame with a multi-indexrŽ  rN  z^level_\d+$rY   rÍ   c                 C  s   dS )NZappendable_multirU   rÏ   rU   rU   rV   r·  s  s    z*AppendableMultiFrameTable.table_type_shortNc                   sx   |d krg }n|dkr |j  ¡ }|  |¡\}| _t| jtƒs@t‚| jD ]}||krF| d|¡ qFtƒ j	f ||dœ|—ŽS )NTr   r'  )
r¥   rû  r¿  r‘  rP   rm   rÒ   r  r  r¡  )rË   r�  r—   r¹   ró   r  rU   rV   r¡  w  s    

zAppendableMultiFrameTable.writer†   rb  c                   sD   t ƒ j||||d�}| ˆ j¡}|j ‡ fdd„|jjD ƒ¡|_|S )Nr*  c                   s    g | ]}ˆ j  |¡rd n|‘qS r\   )Ú
_re_levelsÚsearch)rh   ra   rÏ   rU   rV   rl   �  s     z2AppendableMultiFrameTable.read.<locals>.<listcomp>)r  r	  r(  r‘  r”   r#  r’  )rË   rp   r¥   r£   r¤   r­  r  rÏ   rV   r	  ƒ  s    ÿzAppendableMultiFrameTable.read)N)NNNN)rß   rª  r«  r¬  r‚  r5   rL  rR  ÚreÚcompiler)  r®  r·  r¡  r	  rD  rU   rU   r  rV   r—  k  s   
    ûr—  r5   r   r7   )r�  r+  r  r[   c                 C  s¢   |   |¡}t|ƒ}|d k	r"t|ƒ}|d ks4| |¡rB| |¡rB| S t| ¡ ƒ}|d k	rlt| ¡ ƒj|dd�}| |¡sžtd d ƒg| j }|||< | jt|ƒ } | S )NF)Úsort)	rþ  rE   ÚequalsÚuniquer[  ÚslicerR  r\  rn   )r�  r+  r  rÇ  rª  ZslicerrU   rU   rV   rì  •  s    

rì  r   zstr | tzinfo)r¶  r[   c                 C  s   t  | ¡}|S )z+for a tz-aware type, return an encoded zone)r   Zget_timezone)r¶  ÚzonerU   rU   rV   rŠ  ¯  s    
rŠ  znp.ndarray | Indexr6   )rY  r¶  r,  r[   c                 C  s   d S r\   rU   ©rY  r¶  r,  rU   rU   rV   rØ  µ  s    rØ  rÐ  c                 C  s   d S r\   rU   r2  rU   rU   rV   rØ  ¼  s    zstr | tzinfo | Noneznp.ndarray | DatetimeIndexc                 C  sŠ   t | tƒr"| jdks"| j|ks"t‚|dk	rtt | tƒrB| j}| j} nd}|  ¡ } t|ƒ}t| |d�} |  d¡ 	|¡} n|r†t
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
    Nr`   rr  úM8[ns]r/  )rP   r6   r¶  rÒ   ra   r¡  r<  rW   rt  ru  rQ   r:  )rY  r¶  r,  ra   rU   rU   rV   rØ  Á  s    

)ra   r”   rZ   r˜   r[   c              
   C  sŠ  t | tƒst‚|j}t|ƒ\}}t|ƒ}t |¡}t |jt	jƒrHt
|ƒs\t|jƒs\t|jƒr‚t| |||t|dd ƒt|dd ƒ|d�S t |tƒr”tdƒ‚tj|dd�}	t	 |¡}
|	dkræt	jdd	„ |
D ƒt	jd
�}t| |dtƒ  ¡ |d�S |	dk�rt|
||ƒ}|jj}t| |dtƒ  |¡|d�S |	dk�r:t| ||||d�S t |t	jƒ�rT|jtk�sXt‚|dk�sjt|ƒ‚tƒ  ¡ }t| ||||d�S d S )Nrµ  r¶  )rY  rd  r¹  rµ  r¶  r·  zMultiIndex not supported here!Fr�  r   c                 S  s   g | ]}|  ¡ ‘qS rU   )Ú	toordinalr2  rU   rU   rV   rl     s     z"_convert_index.<locals>.<listcomp>r/  )r·  rä  )ÚintegerZfloating)rY  rd  r¹  r·  r;  )rP   rY   rÒ   ra   r  r  rE  r  rÕ  rQ   r.   r3   r(   r²  rj  r8   r±   r   rž  r:  Zint32rƒ   Z	Time32ColÚ_convert_string_arrayr½  rå  rÔ  r;  rŸ  )ra   r”   rZ   r˜   r·  rA  r  rd  r  r£  rY  r½  rU   rU   rV   rˆ  ç  sr    
ÿÿþý

ù


    ÿ

û
    ÿ
rˆ  )rd  rZ   r˜   r[   c                 C  sÐ   |dkrt | ƒ}nº|dkr$t| ƒ}n¨|dkrxztjdd„ | D ƒtd�}W qÌ tk
rt   tjdd„ | D ƒtd�}Y qÌX nT|dkrŒt | ¡}n@|d	kr¦t| d ||d
�}n&|dkr¾t | d ¡}ntd|› �ƒ‚|S )Nr*  r-  r   c                 S  s   g | ]}t  |¡‘qS rU   r0  r2  rU   rU   rV   rl   0  s     z$_unconvert_index.<locals>.<listcomp>r/  c                 S  s   g | ]}t  |¡‘qS rU   r3  r2  rU   rU   rV   rl   2  s     )r5  Úfloatr‡   rä  r9  r;  r   zunrecognized index type )r6   r<   rQ   r:  r;  rª   r@  )ry  rd  rZ   r˜   r”   rU   rU   rV   r™  '  s,    

    ÿr™  r   rï   )ra   r  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rPtdƒ‚|	dksd|dksd|S t	|ƒ}
| 
¡ }|||
< tj|dd�}	|	dkrøt|jd	 ƒD ]V}|| }tj|dd�}	|	dkr t|ƒ|krÖ|| nd
|› �}td|› d|	› d�ƒ‚q t|||ƒ |j¡}|j}t|tƒ�r>t| | ¡�p:| d¡�p:d	ƒ}t|�pHd	|ƒ}|d k	�rz| |¡}|d k	�rz||k�rz|}|jd|› �dd�}|S )NFr�  r   z+[date] is not implemented as a table columnrh  z>too many timezones in this block, create separate data columnsrä  r;  r   zNo.zCannot serialize the column [z2]
because its data contents are not [string] but [z] object dtyperY  z|Sr7  )rÕ  r;  r   rQ   rÔ  ra   r   rž  r±   r>   rz  rQ  r  ro   r6  r  r½  rP   rF  rc   rÖ   r
  rê  r>  )ra   r  rê  r•   r™   rZ   r˜   r¥   r  r£  rB  ry  r“  rá  Zerror_column_labelrö  r½  ZecirU   rU   rV   rï  @  sJ    

ÿÿ 

rï  )ry  rZ   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]
    Fr7  rf   ÚSr/  )ro   r;   r<  rY   Úencoder?  r  r  r'   r
  Ú
libwritersÚmax_len_string_arrayrQ   r:  )ry  rZ   r˜   Úensuredr½  rU   rU   rV   r6  ‰  s     ÿþÿr6  c                 C  sœ   | j }tj|  ¡ td�} t| ƒrzt t| ƒ¡}d|› �}t	| d t
ƒrbt| dd�jj||d�j} n| j|dd�jtdd�} |dkr†d}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   Fr7  )r˜   Nré  )r  rQ   r:  r<  r;  ro   r:  r;  r'   rP   rw  r;   rY   rS   r?  r>  Z!string_array_replace_from_nan_repr  )ry  r™   rZ   r˜   r  r½  rÕ  rU   rU   rV   r@  ¨  s    
r@  )rY  rÙ  rZ   r˜   c                 C  s6   t |tƒstt|ƒƒ‚t|ƒr2t|||ƒ}|| ƒ} | S r\   )rP   rY   rÒ   rÞ   Ú_need_convertÚ_get_converter)rY  rÙ  rZ   r˜   ÚconvrU   rU   rV   r×  Î  s
    r×  ©rd  rZ   r˜   c                   s8   | dkrdd„ S | dkr&‡ ‡fdd„S t d| › �ƒ‚d S )Nr*  c                 S  s   t j| dd�S )Nr3  r/  )rQ   r:  ©r%  rU   rU   rV   r�   Ø  ó    z _get_converter.<locals>.<lambda>rä  c                   s   t | d ˆ ˆd�S )Nr9  )r@  rB  r‰  rU   rV   r�   Ú  s
      ÿzinvalid kind )rª   rA  rU   r‰  rV   r?  Ö  s
    r?  r  c                 C  s   | dkrdS dS )N)r*  rä  TFrU   r  rU   rU   rV   r>  á  s    r>  zSequence[int])ra   rO  r[   c                 C  sl   t |tƒst|ƒdk rtdƒ‚|d dkrh|d dkrh|d dkrht d| ¡}|rh| ¡ 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   rM  rN  zvalues_block_(\d+)Zvalues_)rP   rY   ro   rª   r+  r*  r³   )ra   rO  r  ÚgrprU   rU   rV   rÓ  ç  s    $
rÓ  )Ú	dtype_strr[   c                 C  sÎ   t | ƒ} |  d¡s|  d¡r"d}n¨|  d¡r2d}n˜|  d¡rBd}nˆ|  d¡sV|  d¡r\d}nn|  d¡rld}n^|  d	¡r|d
}nN|  d¡rŒd}n>|  d¡rœd}n.|  d¡r¬d}n| dkrºd}ntd| › d�ƒ‚|S )zA
    Find the "kind" string describing the given dtype name.
    rä  rw  r7  rÜ  rc   r  r5  r*  Ú	timedeltar-  r‡   rÿ  r   r;  zcannot interpret dtype of [r#  )rW   rr  rª   )rF  rd  rU   rU   rV   r     s.    






r  r  c                 C  sb   t | tƒr| j} | jj d¡d }| jjdkr@t |  	d¡¡} nt | t
ƒrP| j} t | ¡} | |fS )zJ
    Convert the passed data into a storable form and a dtype string.
    rV  r   )r  ÚMrÒ  )rP   r?   r  rÕ  ra   r¥  rd  rQ   r:  r   r9   r¡  )ry  r  rU   rU   rV   r  !  s    


r  c                   @  s>   e Zd ZdZddddddœdd„Zd	d
„ Zdd„ Zdd„ ZdS )rÞ  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

    Nrö   r†   r‹   )rv   r£   r¤   r[   c              	   C  s@  || _ || _|| _|| _d | _d | _d | _d | _t|ƒ�rt	t
ƒ�¾ tj|dd�}|dkrüt |¡}|jtjkr®| j| j }}|d krŠd}|d krš| j j}t ||¡| | _nNt|jjtjƒrü| jd k	rÖ|| jk  ¡ sî| jd k	rö|| jk ¡ röt
dƒ‚|| _W 5 Q R X | jd k�r<|  |¡| _| jd k	�r<| j ¡ \| _| _d S )NFr�  )r5  Úbooleanr   z3where must have index locations >= start and < stop)rv   rp   r£   r¤   Ú	conditionr  Ztermsr0  r/   r   rª   r   rž  rQ   r:  rÕ  Zbool_r  r  Ú
issubclassrÞ   r5  r=  ÚgenerateÚevaluate)rË   rv   rp   r£   r¤   ÚinferredrU   rU   rV   rÌ   C  sD    


ÿÿÿzSelection.__init__c              
   C  s€   |dkrdS | j  ¡ }zt||| j jd�W S  tk
rz } z2d | ¡ ¡}td|› d|› d�ƒ}t|ƒ|‚W 5 d}~X Y nX dS )z'where can be a : dict,list,tuple,stringN)rÊ  rZ   rÀ  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
                )	rv   rÊ  rB   rZ   Ú	NameErrorrÃ  rø   r   rª   )rË   rp   rÑ  rC  Zqkeysr   rU   rU   rV   rL  p  s    
ÿûÿ	zSelection.generatec                 C  sX   | j dk	r(| jjj| j  ¡ | j| jd�S | jdk	rB| jj | j¡S | jjj| j| jd�S )ú(
        generate the selection
        Nrb  )	rJ  rv   Z
read_wherer“   r£   r¤   r0  r  r	  rÏ   rU   rU   rV   r¶   ‡  s    
  ÿ
zSelection.selectc                 C  s”   | j | j }}| jj}|dkr$d}n|dk r4||7 }|dkrB|}n|dk rR||7 }| jdk	rx| jjj| j ¡ ||dd�S | jdk	rˆ| jS t 	||¡S )rP  Nr   T)r£   r¤   r-  )
r£   r¤   rv   r  rJ  Zget_where_listr“   r0  rQ   r  )rË   r£   r¤   r  rU   rU   rV   r  “  s(    
   ÿ
zSelection.select_coords)NNN)rß   rª  r«  r¬  rÌ   rL  r¶   r  rU   rU   rU   rV   rÞ  7  s      û-rÞ  )r„   NNFNTNNNNr}   rO   )	Nr¢   r}   NNNNFN)N)F)F)F)°r¬  Ú
__future__r   Ú
contextlibr   rz  rh  r   r   r1  r®   r+  Útextwrapr   Útypesr   Útypingr   r	   r
   r   r   r   r   r   r   r   rö  ÚnumpyrQ   Zpandas._configr   r   Zpandas._libsr   r   r:  Zpandas._libs.tslibsr   Zpandas._typingr   r   r   r   r   r   r   Zpandas.compat._optionalr   Zpandas.compat.pickle_compatr   Zpandas.errorsr    r!   r"   r#   r$   Zpandas.util._decoratorsr%   Zpandas.util._exceptionsr&   Zpandas.core.dtypes.commonr'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   Zpandas.core.dtypes.missingr4   rî   r5   r6   r7   r8   r9   r:   r;   r<   r=   r>   Zpandas.core.arraysr?   r@   rA   Zpandas.core.commonÚcoreÚcommonr@  Z pandas.core.computation.pytablesrB   rC   Zpandas.core.constructionrD   Zpandas.core.indexes.apirE   Zpandas.core.internalsrF   rG   Zpandas.io.commonrH   Zpandas.io.formats.printingrI   rJ   r   rK   rL   rM   rN   rY  r]   rW   r_   rb   rg   rq   rr   r­  rs   rt   r€  rS  ry   rz   Zconfig_prefixZregister_optionZis_boolZis_one_of_factoryr~   r‚   rƒ   r¡   rº   r´   r    r  r²  r  r  rE  rG  rH  rg  r�  r¤  r‘  rö   r˜  rè  r–  r”  r•  r“  r—  rì  rŠ  rØ  rˆ  r™  rï  r6  r@  r×  r?  r>  rÓ  r  r  rÞ  rU   rU   rU   rV   Ú<module>   s0  0$	<0
ü            ñ,:         ö            Qp  &   -  e!^       f dc0B+ ÿ ÿ ÿ&@I&!