o
    gW                 	   @  s  U d Z ddlmZ ddlmZ ddlZddlmZmZ ddl	Z	ddl
Z
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l"m#Z# ddl$m%Z% ddl&m'Z' ddl(m)Z) ddl*m+Z+m,Z,m-Z-m.Z.m/Z/ ddl0m1Z1 ddl2m3Z3 ddl4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z: ddl;m<Z<m=Z=m>Z>m?Z? ddl@mAZA ddlBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZL ddlMmNZNmOZOmPZP ddlQmR  mSZT ddlUmVZVmWZW ddlXmYZY ddlZm[Z[ ddl\m]Z]m^Z^ ddl_m`Z` ddlambZbmcZc er;ddldmeZemfZfmgZg ddlhmiZi ddljmkZkmlZlmmZm ddlnmoZompZpmqZqmrZrmsZsmtZtmuZumvZv dd l\mwZw d!Zxd"Zyd#d$ Zzdd)d*Z{d+d, Z|eVZ}dd/d0Z~d1Zd2ed3< d4Zd2ed5< d6Zd2ed7< d8d8d9d9d:ZeCdgiZd;Zd2ed<< d=Zd2ed>< ed?  ejd@dAeejdB ejdCdeeg dDdB W d   n	1 sw   Y  dadAadEdF Z	G			A		H					I	"ddd_d`Z		a	I					A	dddjdkZddodpZG dqdr drZG dsdt dtZG dudv dvZG dwdx dxeZG dydz dzeZG d{d| d|eZG d}d~ d~eZG dd dZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZ	ddddZdddZe	AddddZeddddZ	AddddZdddZdddZdddZdddZdddZdddÄZdddńZdddǄZddd˄Zddd΄ZdddЄZG dd҄ d҃ZdS )zY
High level interface to PyTables for reading and writing pandas data structures
to disk
    )annotations)suppressN)datetzinfo)dedent)TYPE_CHECKINGAnyCallableFinalLiteralcastoverload)config
get_optionusing_copy_on_writeusing_pyarrow_string_dtype)libwriters)is_string_array)	timezones)import_optional_dependency)patch_pickle)AttributeConflictWarningClosedFileErrorIncompatibilityWarningPerformanceWarningPossibleDataLossError)cache_readonly)find_stack_level)ensure_objectis_bool_dtypeis_complex_dtypeis_list_likeis_string_dtypeneeds_i8_conversion)CategoricalDtypeDatetimeTZDtypeExtensionDtypePeriodDtype)array_equivalent)
	DataFrameDatetimeIndexIndex
MultiIndexPeriodIndex
RangeIndexSeriesTimedeltaIndexconcatisna)CategoricalDatetimeArrayPeriodArray)PyTablesExprmaybe_expression)extract_array)ensure_index)ArrayManagerBlockManager)stringify_path)adjoinpprint_thing)HashableIteratorSequence)TracebackType)ColFileNode)AnyArrayLike	ArrayLikeAxisIntDtypeArgFilePathSelfShapenpt)Blockz0.15.2UTF-8c                 C  s   t | tjr| d} | S )z(if we have bytes, decode them to unicoderP   )
isinstancenpbytes_decode)s rV   T/var/www/html/ecg_monitoring/venv/lib/python3.10/site-packages/pandas/io/pytables.py_ensure_decoded   s   
rX   encoding
str | Nonereturnstrc                 C  s   | d u rt } | S N)_default_encodingrY   rV   rV   rW   _ensure_encoding   s   r`   c                 C  s   t | tr	t| } | S )z
    Ensure that an index / column name is a str (python 3); otherwise they
    may be np.string dtype. Non-string dtypes are passed through unchanged.

    https://github.com/pandas-dev/pandas/issues/13492
    )rQ   r\   namerV   rV   rW   _ensure_str   s   
rc   scope_levelintc                   sV   |d  t | ttfr fdd| D } n
t| rt|  d} | du s't| r)| S dS )z
    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)
       c                   s0   g | ]}|d urt |rt| d dn|qS )Nrf   rd   )r8   Term).0termlevelrV   rW   
<listcomp>   s
    z _ensure_term.<locals>.<listcomp>rg   N)rQ   listtupler8   rh   len)whererd   rV   rk   rW   _ensure_term   s   	
rr   z
where criteria is being ignored as this version [%s] is too old (or
not-defined), read the file in and write it out to a new file to upgrade (with
the copy_to method)
r
   incompatibility_doczu
the [%s] attribute of the existing index is [%s] which conflicts with the new
[%s], resetting the attribute to None
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]
performance_docfixedtable)frv   trw   z;
: boolean
    drop ALL nan rows when appending to a table

dropna_docz~
: format
    default format writing format, if None, then
    put will default to 'fixed' and append will default to 'table'

format_doczio.hdfdropna_tableF)	validatordefault_format)rv   rw   Nc                  C  sN   t d u r%dd l} | a tt | jjdkaW d    t S 1 s w   Y  t S )Nr   strict)
_table_modtablesr   AttributeErrorfile_FILE_OPEN_POLICY!_table_file_open_policy_is_strict)r   rV   rV   rW   _tables   s   


r   aTr   path_or_bufFilePath | HDFStorekeyvalueDataFrame | Seriesmode	complevel
int | Nonecomplibappendboolformatindexmin_itemsizeint | dict[str, int] | Nonedropnabool | Nonedata_columns Literal[True] | list[str] | NoneerrorsNonec              
     s   |r 	f
dd}n 	f
dd}t | } t| trIt| |||d}|| W d   dS 1 sBw   Y  dS ||  dS )z+store this object, close it if we opened itc                   s   | j 	 d
S )N)r   r   r   nan_repr   r   r   rY   )r   store
r   r   rY   r   r   r   r   r   r   r   rV   rW   <lambda>      zto_hdf.<locals>.<lambda>c                   s   | j 	 d
S )N)r   r   r   r   r   r   rY   r   putr   r   rV   rW   r   %  r   )r   r   r   N)r=   rQ   r\   HDFStore)r   r   r   r   r   r   r   r   r   r   r   r   r   r   rY   rx   r   rV   r   rW   to_hdf  s    

"r   rrq   str | list | Nonestartstopcolumnslist[str] | Noneiterator	chunksizec
                 K  s  |dvrt d| d|durt|dd}t| tr'| js"td| }d}n:t| } t| ts4td	zt	j
| }W n tt fyI   d}Y nw |sTtd
|  dt| f||d|
}d}z9|du r| }t|dkrtt d|d }|dd D ]}t||st dq~|j}|j|||||||	|dW S  t ttfy   t| tstt |  W d    1 sw   Y   w )a"	  
    Read from the store, close it if we opened it.

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

    .. warning::

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

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

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

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

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

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

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

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

    Examples
    --------
    >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])  # doctest: +SKIP
    >>> df.to_hdf('./store.h5', 'data')  # doctest: +SKIP
    >>> reread = pd.read_hdf('./store.h5')  # doctest: +SKIP
    )r   r+r   zmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nrf   rg   z&The HDFStore must be open for reading.Fz5Support for generic buffers has not been implemented.zFile z does not exist)r   r   Tr   z]Dataset(s) incompatible with Pandas data types, not table, or no datasets found in HDF5 file.z?key must be provided when HDF5 file contains multiple datasets.)rq   r   r   r   r   r   
auto_close)
ValueErrorrr   rQ   r   is_openOSErrorr=   r\   NotImplementedErrorospathexists	TypeErrorFileNotFoundErrorgroupsrp   _is_metadata_of_v_pathnameselectLookupErrorr   r   close)r   r   r   r   rq   r   r   r   r   r   kwargsr   r   r   r   candidate_only_groupgroup_to_checkrV   rV   rW   read_hdf<  sv   O








r   grouprF   parent_groupc                 C  sN   | j |j krdS | }|j dkr%|j}||kr|jdkrdS |j}|j dksdS )zDCheck if a given group is a metadata group for a given parent_group.Frf   metaT)_v_depth	_v_parent_v_name)r   r   currentparentrV   rV   rW   r     s   

r   c                   @  s  e Zd ZU dZded< ded< 				ddddZdddZedd ZedddZ	dddZ
dddZdddZdd d!Zdd"d#Zdd%d&Zdd'd(Zdd*d+Zdd2d3Zddd7d8Zdd:d;Zdd=d>Zddd?d@ZddAdBZeddCdDZdddFdGZddHdIZ							dddMdNZ			dddQdRZ		dddTdUZ								dddVdWZ		X								Y	X	dddedfZdddgdhZ 			X	X											YdddkdlZ!			dddodpZ"			dddtduZ#ddwdxZ$ddd|d}Z%dddZ&dddZ'		X					XddddZ(dddZ)dddZ*dddZ+				YddddZ,		X												Y	XddddZ-dddZ.dddZ/dddZ0dS )r   aS	  
    Dict-like IO interface for storing pandas objects in PyTables.

    Either Fixed or Table format.

    .. warning::

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

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

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

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

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

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

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

    >>> bar = pd.DataFrame(np.random.randn(10, 4))
    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
    >>> store['foo'] = bar
    >>> store.close()   # only now, data is written to disk
    zFile | None_handler\   _moder   NFr   r   r   
fletcher32r   r[   r   c                 K  s   d|v rt dtd}|d ur ||jjvr t d|jj d|d u r,|d ur,|jj}t|| _|d u r7d}|| _d | _|rA|nd| _	|| _
|| _d | _| jd	d|i| d S )
Nr   z-format is not a defined argument for HDFStorer   zcomplib only supports z compression.r   r   r   rV   )r   r   filtersall_complibsdefault_complibr=   _pathr   r   
_complevel_complib_fletcher32_filtersopen)selfr   r   r   r   r   r   r   rV   rV   rW   __init__*  s&   	
zHDFStore.__init__c                 C     | j S r]   r   r   rV   rV   rW   
__fspath__K  s   zHDFStore.__fspath__c                 C  s   |    | jdusJ | jjS )zreturn the root nodeN)_check_if_openr   rootr   rV   rV   rW   r   N  s   zHDFStore.rootc                 C  r   r]   r   r   rV   rV   rW   filenameU     zHDFStore.filenamer   c                 C  
   |  |S r]   )getr   r   rV   rV   rW   __getitem__Y     
zHDFStore.__getitem__c                 C  s   |  || d S r]   r   )r   r   r   rV   rV   rW   __setitem__\  s   zHDFStore.__setitem__c                 C  r   r]   )remover   rV   rV   rW   __delitem___  r   zHDFStore.__delitem__rb   c              	   C  s@   z|  |W S  ttfy   Y nw tdt| j d| d)z$allow attribute access to get stores'z' object has no attribute ')r   KeyErrorr   r   type__name__)r   rb   rV   rV   rW   __getattr__b  s   zHDFStore.__getattr__c                 C  s4   |  |}|dur|j}|||dd fv rdS dS )zx
        check for existence of this key
        can match the exact pathname or the pathnm w/o the leading '/'
        Nrf   TF)get_noder   )r   r   noderb   rV   rV   rW   __contains__l  s   
zHDFStore.__contains__re   c                 C     t |  S r]   )rp   r   r   rV   rV   rW   __len__x     zHDFStore.__len__c                 C  s   t | j}t|  d| dS )N
File path: 
)r?   r   r   )r   pstrrV   rV   rW   __repr__{  s   
zHDFStore.__repr__rL   c                 C  s   | S r]   rV   r   rV   rV   rW   	__enter__     zHDFStore.__enter__exc_typetype[BaseException] | None	exc_valueBaseException | None	tracebackTracebackType | Nonec                 C     |    d S r]   )r   )r   r   r   r   rV   rV   rW   __exit__  s   zHDFStore.__exit__pandasinclude	list[str]c                 C  sZ   |dkrdd |   D S |dkr%| jdusJ dd | jjddd	D S td
| d)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.

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

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

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> store.get('data')  # doctest: +SKIP
        >>> print(store.keys())  # doctest: +SKIP
        ['/data1', '/data2']
        >>> store.close()  # doctest: +SKIP
        r  c                 S     g | ]}|j qS rV   r   ri   nrV   rV   rW   rm         z!HDFStore.keys.<locals>.<listcomp>nativeNc                 S  r  rV   r  r  rV   rV   rW   rm     s    /Table)	classnamez8`include` should be either 'pandas' or 'native' but is 'r   )r   r   
walk_nodesr   )r   r  rV   rV   rW   keys  s   
zHDFStore.keysIterator[str]c                 C  r   r]   )iterr  r   rV   rV   rW   __iter__  r   zHDFStore.__iter__Iterator[tuple[str, list]]c                 c  s     |   D ]}|j|fV  qdS )z'
        iterate on key->group
        N)r   r   )r   grV   rV   rW   items  s   zHDFStore.itemsc                 K  s   t  }| j|kr)| jdv r|dv rn|dv r&| jr&td| j d| j d|| _| jr0|   | jrE| jdkrEt  j| j| j| j	d| _
trP| jrPd	}t||j| j| jfi || _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   w)r   r   )r  zRe-opening the file [z] with mode [z] will delete the current file!r   )r   zGCannot open HDF5 file, which is already opened, even in read-only mode.N)r   r   r   r   r   r   r   Filtersr   r   r   r   r   	open_filer   )r   r   r   r   msgrV   rV   rW   r     s*   

zHDFStore.openc                 C  s   | j dur
| j   d| _ dS )z0
        Close the PyTables file handle
        N)r   r   r   rV   rV   rW   r     s   


zHDFStore.closec                 C  s   | j du rdS t| j jS )zF
        return a boolean indicating whether the file is open
        NF)r   r   isopenr   rV   rV   rW   r     s   
zHDFStore.is_openfsyncc                 C  s^   | j dur+| j   |r-tt t| j   W d   dS 1 s$w   Y  dS dS dS )a  
        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)r   flushr   r   r   r  fileno)r   r  rV   rV   rW   r    s   


"zHDFStore.flushc                 C  sV   t   | |}|du rtd| d| |W  d   S 1 s$w   Y  dS )a  
        Retrieve pandas object stored in file.

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

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

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> store.get('data')  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        NNo object named  in the file)r   r   r   _read_groupr   r   r   rV   rV   rW   r     s   
$zHDFStore.getr   r   r   c	                   st   |  |}	|	du rtd| dt|dd}| |	   fdd}
t| |
|j|||||d
}| S )	a6  
        Retrieve pandas object stored in file, optionally based on where criteria.

        .. warning::

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

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

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

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

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> store.get('data')  # doctest: +SKIP
        >>> print(store.keys())  # doctest: +SKIP
        ['/data1', '/data2']
        >>> store.select('/data1')  # doctest: +SKIP
           A  B
        0  1  2
        1  3  4
        >>> store.select('/data1', where='columns == A')  # doctest: +SKIP
           A
        0  1
        1  3
        >>> store.close()  # doctest: +SKIP
        Nr  r   rf   rg   c                   s   j | || dS )N)r   r   rq   r   read_start_stop_wherer   rU   rV   rW   funcy  s   zHDFStore.select.<locals>.funcrq   nrowsr   r   r   r   r   )r   r   rr   _create_storer
infer_axesTableIteratorr,  
get_result)r   r   rq   r   r   r   r   r   r   r   r*  itrV   r)  rW   r   /  s(   
@
zHDFStore.selectr   r   c                 C  s8   t |dd}| |}t|tstd|j|||dS )a  
        return the selection as an Index

        .. warning::

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

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


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

        .. warning::

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

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

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

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

        z!can only read_column with a table)r7  r   r   )r3  rQ   r  r   read_column)r   r   r7  r   r   r5  rV   rV   rW   select_column  s   
#
zHDFStore.select_columnc
                   st  t |dd}t|ttfrt|dkr|d }t|tr)j|||||||	dS t|ttfs4tdt|s<td|du rD|d }fdd	|D 	|}
d}t
|
|fgt|D ]-\}}|du rptd
| d|js|td|j d|du r|j}q`|j|krtdq`dd	 D }dd |D    fdd}t|
||||||||	d
}|jddS )a  
        Retrieve pandas objects from multiple tables.

        .. warning::

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

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

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

        Raises
        ------
        raises KeyError if keys or selector is not found or keys is empty
        raises TypeError if keys is not a list or tuple
        raises ValueError if the tables are not ALL THE SAME DIMENSIONS
        rf   rg   r   )r   rq   r   r   r   r   r   r   zkeys must be a list/tuplez keys must have a non-zero lengthNc                      g | ]}  |qS rV   )r3  ri   kr   rV   rW   rm         z/HDFStore.select_as_multiple.<locals>.<listcomp>zInvalid table []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r|qS rV   )rQ   r  ri   xrV   rV   rW   rm   -      c                 S  s   h | ]	}|j d  d  qS r   )non_index_axesri   ry   rV   rV   rW   	<setcomp>0  rA  z.HDFStore.select_as_multiple.<locals>.<setcomp>c                   s*    fddD }t |dd S )Nc                   s   g | ]}|j  d qS )rq   r   r   r   r#  rD  )r&  r'  r(  r   rV   rW   rm   5  s    z=HDFStore.select_as_multiple.<locals>.func.<locals>.<listcomp>F)axisverify_integrity)r2   _consolidate)r&  r'  r(  objs)rG  r   tblsr%  rW   r*  2  s   z)HDFStore.select_as_multiple.<locals>.funcr+  T)coordinates)rr   rQ   rn   ro   rp   r\   r   r   r   r3  	itertoolschainzipr   is_tablepathnamer,  popr/  r0  )r   r  rq   selectorr   r   r   r   r   r   rU   r,  ry   r<  _tblsr*  r1  rV   )rG  r   r   rK  rW   select_as_multiple  sf   +

 
zHDFStore.select_as_multipleTr   r   r   r   r   r   r   r   r   r   track_timesr   c                 C  sH   |du r
t dp	d}| |}| j|||||||||	|
||||d dS )a  
        Store object in HDFStore.

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

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

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        Nio.hdf.default_formatrv   )r   r   r   r   r   r   r   r   rY   r   rV  r   )r   _validate_format_write_to_group)r   r   r   r   r   r   r   r   r   r   r   rY   r   rV  r   rV   rV   rW   r   M  s&   8

zHDFStore.putc              
   C  s   t |dd}z| |}W n? ty     ty     tyL } z%|dur,td|| |}|durB|jdd W Y d}~dS W Y d}~nd}~ww t	|||r]|j
jdd dS |jsdtd|j|||dS )	a:  
        Remove pandas object partially by specifying the where condition

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

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

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

        rf   rg   Nz5trying to remove a node with a non-None where clause!T	recursivez7can only remove with where on objects written as tablesr2  )rr   r3  r   AssertionError	Exceptionr   r   	_f_removecomall_noner   rP  delete)r   r   rq   r   r   rU   errr   rV   rV   rW   r     s8   
zHDFStore.removebool | list[str]r   c                 C  sl   |	durt d|du rtd}|du rtdpd}| |}| j|||||||||
|||||||d dS )a|  
        Append to Table in file.

        Node must already exist and be Table format.

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

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

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

        Examples
        --------
        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df1, format='table')  # doctest: +SKIP
        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
        >>> store.append('data', df2)  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
           A  B
        0  1  2
        1  3  4
        0  5  6
        1  7  8
        Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerW  rw   )r   axesr   r   r   r   r   r   r   expectedrowsr   r   rY   r   )r   r   rX  rY  )r   r   r   r   rd  r   r   r   r   r   r   r   r   re  r   r   rY   r   rV   rV   rW   r     s6   I

zHDFStore.appendddictc                   s  |durt dt|tstd||vrtdttttjtt	t
  }d}	g }
| D ]\}  du rG|	durDtd|}	q4|
  q4|	durkj| }|t|
}t||}||||	< |du rs|| }|rfdd| D }t|}|D ]}||}qj| |dd}| D ]1\} ||kr|nd}j |d	}|dur fd
d| D nd}| j||f||d| qdS )a  
        Append to multiple tables

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

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

        Nztaxes is currently not accepted as a parameter to append_to_multiple; you can create the tables independently insteadzQappend_to_multiple must have a dictionary specified as the way to split the valuez=append_to_multiple requires a selector that is in passed dictz<append_to_multiple can only have one value in d that is Nonec                 3  s"    | ]} | j d djV  qdS )all)howN)r   r   )ri   cols)r   rV   rW   	<genexpr>  s     z.HDFStore.append_to_multiple.<locals>.<genexpr>r   rG  c                   s   i | ]\}}| v r||qS rV   rV   ri   r   r   )vrV   rW   
<dictcomp>  s    z/HDFStore.append_to_multiple.<locals>.<dictcomp>)r   r   )r   rQ   rg  r   nextr  setrangendim	_AXES_MAPr   r  extendrd  
differencer,   sortedget_indexertakevaluesintersectionlocrR  reindexr   )r   rf  r   rS  r   rd  r   r   rG  
remain_keyremain_valuesr<  orderedorddidxsvalid_indexr   r   dcvalfilteredrV   )rn  r   rW   append_to_multiple8  s\   
&

zHDFStore.append_to_multipleoptlevelkindrZ   c                 C  sB   t   | |}|du rdS t|tstd|j|||d dS )a  
        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   r  r  )r   r3  rQ   r  r   create_index)r   r   r   r  r  rU   rV   rV   rW   create_table_index  s   

zHDFStore.create_table_indexrn   c                 C  s<   t   |   | jdusJ tdusJ dd | j D S )a  
        Return a list of all the top-level nodes.

        Each node returned is not a pandas storage object.

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

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> print(store.groups())  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        [/data (Group) ''
          children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array),
          'block0_items' (Array)]]
        Nc                 S  sP   g | ]$}t |tjjs&t|jd ds$t|dds$t |tjjr&|jdkr|qS )pandas_typeNrw   )	rQ   r   linkLinkgetattr_v_attrsrw   r  r   )ri   r  rV   rV   rW   rm     s    

z#HDFStore.groups.<locals>.<listcomp>)r   r   r   r   walk_groupsr   rV   rV   rW   r     s   zHDFStore.groupsr  rq   *Iterator[tuple[str, list[str], list[str]]]c                 c  s    t   |   | jdusJ tdusJ | j|D ]A}t|jdddur'qg }g }|j D ]!}t|jdd}|du rKt	|tj
jrJ||j q0||j q0|jd||fV  qdS )a  
        Walk the pytables group hierarchy for pandas objects.

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

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

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

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

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

        Examples
        --------
        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df1, format='table')  # doctest: +SKIP
        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
        >>> store.append('data', df2)  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        >>> for group in store.walk():  # doctest: +SKIP
        ...     print(group)  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        Nr  r  )r   r   r   r   r  r  r  _v_childrenrz  rQ   r   Groupr   r   r   rstrip)r   rq   r  r   leaveschildr  rV   rV   rW   walk  s&   'zHDFStore.walkNode | Nonec                 C  s~   |    |dsd| }| jdusJ tdusJ z
| j| j|}W n tjjy0   Y dS w t|tj	s=J t
||S )z9return the node with the key or None if it does not existr  N)r   
startswithr   r   r   r   
exceptionsNoSuchNodeErrorrQ   rF   r   )r   r   r   rV   rV   rW   r   $  s   
zHDFStore.get_nodeGenericFixed | Tablec                 C  s8   |  |}|du rtd| d| |}|  |S )z<return the storer object for a key, raise if not in the fileNr  r   )r   r   r-  r.  )r   r   r   rU   rV   rV   rW   r3  4  s   

zHDFStore.get_storerr  propindexes	overwritec	              	   C  s   t |||||d}	|du rt|  }t|ttfs|g}|D ]E}
| |
}|durd|
|	v r5|r5|	|
 | |
}t|tr[d}|rKdd |j	D }|	j
|
||t|dd|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   r   NFc                 S     g | ]}|j r|jqS rV   )
is_indexedrb   ri   r   rV   rV   rW   rm   l      z!HDFStore.copy.<locals>.<listcomp>r   )r   r   rY   r_   )r   rn   r  rQ   ro   r3  r   r   r  rd  r   r  rY   r   )r   r   r   r  r  r   r   r   r  	new_storer<  rU   datar   rV   rV   rW   copy>  s8   





zHDFStore.copyc           
      C  s  t | j}t|  d| d}| jr~t|  }t|rxg }g }|D ]K}z| |}|durA|t |j	p5| |t |p>d W q" t
yJ     tym } z|| t |}	|d|	 d W Y d}~q"d}~ww |td||7 }|S |d7 }|S |d	7 }|S )
a  
        Print detailed information on the store.

        Returns
        -------
        str

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
        >>> store.put('data', df)  # doctest: +SKIP
        >>> print(store.info())  # doctest: +SKIP
        >>> store.close()  # doctest: +SKIP
        <class 'pandas.io.pytables.HDFStore'>
        File path: store.h5
        /data    frame    (shape->[2,2])
        r   r   Nzinvalid_HDFStore nodez[invalid_HDFStore node: r>     EmptyzFile is CLOSED)r?   r   r   r   rw  r  rp   r3  r   rQ  r\  r]  r>   )
r   r   outputlkeysr  rz  r<  rU   detaildstrrV   rV   rW   infoy  s8   


zHDFStore.infoc                 C  s   | j st| j dd S )Nz file is not open!)r   r   r   r   rV   rV   rW   r     s   zHDFStore._check_if_openr   c              
   C  s>   z	t |  }W |S  ty } z	td| d|d}~ww )zvalidate / deprecate formatsz#invalid HDFStore format specified [r>  N)_FORMAT_MAPlowerr   r   )r   r   rb  rV   rV   rW   rX    s   zHDFStore._validate_formatrP   DataFrame | Series | NonerY   c              
   C  s
  |durt |ttfstdtt|jdd}tt|jdd}|du rZ|du rHt  tdus2J t|dds?t |tj	j
rDd}d}ntdt |trPd	}nd
}|dkrZ|d7 }d|vrttd}z|| }	W n ty }
 ztd| dt| d| |
d}
~
ww |	| |||dS |du r|dur|dkrt|dd}|dur|jdkrd}n%|jdkrd}n|dkrt|dd}|dur|jdkrd}n|jdkrd}ttttttd}z|| }	W n ty }
 ztd| dt| d| |
d}
~
ww |	| |||dS )z"return a suitable class to operateNz(value must be None, Series, or DataFramer  
table_typerw   frame_tablegeneric_tablezKcannot create a storer if the object is not existing nor a value are passedseriesframe_table)r  r  z=cannot properly create the storer for: [_STORER_MAP] [group->,value->z	,format->rY   r   series_tabler   rf   appendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)r  r  r  r  r  wormz<cannot properly create the storer for: [_TABLE_MAP] [group->)rQ   r0   r*   r   rX   r  r  r   r   rw   r  SeriesFixed
FrameFixedr   r   nlevelsGenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable	WORMTable)r   r   r   r   rY   r   pttt_STORER_MAPclsrb  r   
_TABLE_MAPrV   rV   rW   r-    s   





zHDFStore._create_storerc                 C  s   t |dd r|dks|rd S | ||}| j|||||d}|r9|jr-|jr1|dkr1|jr1td|js8|  n|  |jsF|rFtd|j||||||	|
||||||d t|t	rg|ri|j
|d d S d S d S )	Nemptyrw   r  rv   zCan only append to Tablesz0Compression not supported on Fixed format stores)objrd  r   r   r   r   r   r   re  r   r   r   rV  )r   )r  _identify_groupr-  rP  	is_existsr   set_object_infowriterQ   r  r  )r   r   r   r   rd  r   r   r   r   r   r   r   re  r   r   r   rY   r   rV  r   rU   rV   rV   rW   rY    s>   
zHDFStore._write_to_groupr   rF   c                 C  s   |  |}|  | S r]   )r-  r.  r$  )r   r   rU   rV   rV   rW   r!  U  s   
zHDFStore._read_groupc                 C  sN   |  |}| jdusJ |dur|s| jj|dd d}|du r%| |}|S )z@Identify HDF5 group based on key, delete/create group if needed.NTrZ  )r   r   remove_node_create_nodes_and_group)r   r   r   r   rV   rV   rW   r  Z  s   

zHDFStore._identify_groupc                 C  sv   | j dusJ |d}d}|D ](}t|sq|}|ds"|d7 }||7 }| |}|du r6| j ||}|}q|S )z,Create nodes from key and return group name.Nr  )r   splitrp   endswithr   create_group)r   r   pathsr   pnew_pathr   rV   rV   rW   r  l  s   


z HDFStore._create_nodes_and_group)r   NNF)r   r\   r   r   r   r   r[   r   r[   r\   r   r\   )r   r\   r[   r   )rb   r\   )r   r\   r[   r   r[   re   )r[   rL   )r   r   r   r   r   r   r[   r   )r  )r  r\   r[   r  )r[   r  )r[   r  )r   )r   r\   r[   r   r[   r   r[   r   F)r  r   r[   r   )NNNNFNF)r   r\   r   r   r   r   r   r   NNNr   r\   r   r   r   r   NN)r   r\   r7  r\   r   r   r   r   )NNNNNFNF)r   r   r   r   r   r   )NTFNNNNNNr   TF)r   r\   r   r   r   r   r   r   r   r   r   r   r   r   r   r\   rV  r   r   r   r[   r   )NNTTNNNNNNNNNNr   )r   r\   r   r   r   rc  r   r   r   r   r   r   r   r   r   r   r   r   r   r\   r[   r   )NNF)rf  rg  r   r   r[   r   )r   r\   r  r   r  rZ   r[   r   )r[   rn   )r  )rq   r\   r[   r  )r   r\   r[   r  )r   r\   r[   r  )r  TNNNFT)r   r\   r  r   r   r   r   r   r  r   r[   r   )r   r\   r[   r\   )NNrP   r   )r   r  rY   r\   r   r\   r[   r  )NTFNNNNNNFNNNr   T)r   r\   r   r   r   rc  r   r   r   r   r   r   r   r   r   r   r   r\   rV  r   r[   r   )r   rF   )r   r\   r   r   r[   rF   )r   r\   r[   rF   )1r   
__module____qualname____doc____annotations__r   r   propertyr   r   r   r   r   r   r   r   r   r   r  r  r  r  r   r   r   r  r   r   r6  r9  rU  r   r   r   r  r  r   r  r   r3  r  r  r   rX  r-  rY  r!  r  r  rV   rV   rV   rW   r     s
  
 A
!







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

    Parameters
    ----------
    store : HDFStore
    s     : the referred storer
    func  : the function to execute the query
    where : the where of the query
    nrows : the rows to iterate on
    start : the passed start value (default is None)
    stop  : the passed stop value (default is None)
    iterator : bool, default False
        Whether to use the default iterator.
    chunksize : the passed chunking value (default is 100000)
    auto_close : bool, default False
        Whether to automatically close the store at the end of iteration.
    r   r   r   r   r  rU   NFr   r   r   r[   r   c                 C  s   || _ || _|| _|| _| jjr'|d u rd}|d u rd}|d u r"|}t||}|| _|| _|| _d | _	|s9|	d urE|	d u r?d}	t
|	| _nd | _|
| _d S )Nr   順 )r   rU   r*  rq   rP  minr,  r   r   rL  re   r   r   )r   r   rU   r*  rq   r,  r   r   r   r   r   rV   rV   rW   r     s,   

zTableIterator.__init__rA   c                 c  s    | j }| jd u rtd|| jk r:t|| j | j}| d d | j|| }|}|d u s1t|s2q|V  || jk s|   d S )Nz*Cannot iterate until get_result is called.)	r   rL  r   r   r  r   r*  rp   r   )r   r   r   r   rV   rV   rW   r    s   


	zTableIterator.__iter__c                 C  s   | j r
| j  d S d S r]   )r   r   r   r   rV   rV   rW   r     s   zTableIterator.closerL  c                 C  s   | j d urt| jtstd| jj| jd| _| S |r3t| jts&td| jj| j| j| j	d}n| j}| 
| j| j	|}|   |S )Nz0can only use an iterator or chunksize on a table)rq   z$can only read_coordinates on a tabler2  )r   rQ   rU   r  r   r4  rq   rL  r   r   r*  r   )r   rL  rq   resultsrV   rV   rW   r0    s   
zTableIterator.get_result)NNFNF)r   r   rU   r  r   r   r   r   r   r   r[   r   r[   rA   r  r  )rL  r   )	r   r  r  r  r  r   r  r   r0  rV   rV   rV   rW   r/    s   
 	
*
r/  c                   @  s\  e Zd ZU dZdZded< dZded< g dZ													dMdNddZe	dOddZ
e	dPddZdQddZdPddZdRddZdSddZe	dSd d!ZdTd'd(Zd)d* Ze	d+d, Ze	d-d. Ze	d/d0 Ze	d1d2 ZdUd4d5ZdVdWd6d7ZdWd8d9ZdXd=d>ZdVd?d@ZdYdAdBZdWdCdDZdWdEdFZdWdGdHZdZdIdJZ dZdKdLZ!dS )[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   is_an_indexableis_data_indexable)freqtz
index_nameNrb   r\   cnamerZ   r[   r   c                 C  s   t |ts	td|| _|| _|| _|| _|p|| _|| _|| _	|| _
|	| _|
| _|| _|| _|| _|| _|d ur>| | t | jtsFJ t | jtsNJ d S )Nz`name` must be a str.)rQ   r\   r   rz  r  typrb   r  rG  posr  r  r  r  rw   r   metadataset_pos)r   rb   rz  r  r  r  rG  r  r  r  r  r  rw   r   r  rV   rV   rW   r     s(   


zIndexCol.__init__re   c                 C     | j jS r]   )r  itemsizer   rV   rV   rW   r  /  s   zIndexCol.itemsizec                 C     | j  dS )N_kindra   r   rV   rV   rW   	kind_attr4     zIndexCol.kind_attrr  c                 C  s,   || _ |dur| jdur|| j_dS dS dS )z,set the position of this column in the TableN)r  r  _v_pos)r   r  rV   rV   rW   r  8  s   zIndexCol.set_posc                 C  @   t tt| j| j| j| j| jf}ddd t	g d|D S )N,c                 S     g | ]\}}| d | qS z->rV   rm  rV   rV   rW   rm   C      z%IndexCol.__repr__.<locals>.<listcomp>)rb   r  rG  r  r  )
ro   mapr?   rb   r  rG  r  r  joinrO  r   temprV   rV   rW   r   >  s   zIndexCol.__repr__otherobjectc                      t  fdddD S )compare 2 col itemsc                 3  (    | ]}t |d t  |d kV  qd S r]   r  r  r  r   rV   rW   rk  K  
    
z"IndexCol.__eq__.<locals>.<genexpr>)rb   r  rG  r  rh  r   r  rV   r  rW   __eq__I     zIndexCol.__eq__c                 C  s   |  | S r]   )r  r  rV   rV   rW   __ne__P  r   zIndexCol.__ne__c                 C  s"   t | jdsdS t| jj| jjS )z%return whether I am an indexed columnrj  F)hasattrrw   r  rj  r  r  r   rV   rV   rW   r  S  s   zIndexCol.is_indexedrz  
np.ndarrayrY   r   3tuple[np.ndarray, np.ndarray] | tuple[Index, Index]c           
      C  s  t |tjsJ t||jjdur|| j  }t| j	}t
||||}i }t| j|d< | jdur:t| j|d< t}t|jdsIt |jtrLt}n|jdkrYd|v rYdd }z
||fi |}W n ty|   d|v rrd|d< ||fi |}Y nw t|| j}	|	|	fS )zV
        Convert the data from this selection to the appropriate pandas type.
        Nrb   r  Mi8c                 [  s    t j| |dd d|d S )Nr  )r  rb   )r.   from_ordinalsr   _rename)r@  kwdsrV   rV   rW   r   {  s    z"IndexCol.convert.<locals>.<lambda>)rQ   rR   ndarrayr   dtypefieldsr  r  rX   r  _maybe_convertr  r  r,   r   is_np_dtyper&   r+   r   _set_tzr  )
r   rz  r   rY   r   val_kindr   factorynew_pd_indexfinal_pd_indexrV   rV   rW   convert[  s2   

zIndexCol.convertc                 C  r   )zreturn the valuesrz  r   rV   rV   rW   	take_data  r   zIndexCol.take_datac                 C  r  r]   )rw   r  r   rV   rV   rW   attrs     zIndexCol.attrsc                 C  r  r]   rw   descriptionr   rV   rV   rW   r-    r+  zIndexCol.descriptionc                 C  s   t | j| jdS )z!return my current col descriptionN)r  r-  r  r   rV   rV   rW   col     zIndexCol.colc                 C  r   zreturn my cython valuesr(  r   rV   rV   rW   cvalues     zIndexCol.cvaluesrA   c                 C  s
   t | jS r]   )r  rz  r   rV   rV   rW   r    r   zIndexCol.__iter__c                 C  s\   t | jdkr(t|tr|| j}|dur*| jj|k r,t j	|| j
d| _dS dS dS dS )z
        maybe set a string col itemsize:
            min_itemsize can be an integer or a dict with this columns name
            with an integer size
        stringN)r  r  )rX   r  rQ   rg  r   rb   r  r  r   	StringColr  )r   r   rV   rV   rW   maybe_set_size  s   
zIndexCol.maybe_set_sizec                 C     d S r]   rV   r   rV   rV   rW   validate_names  r   zIndexCol.validate_nameshandlerAppendableTabler   c                 C  s:   |j | _ |   | | | | | | |   d S r]   )rw   validate_colvalidate_attrvalidate_metadatawrite_metadataset_attr)r   r8  r   rV   rV   rW   validate_and_set  s   


zIndexCol.validate_and_setc                 C  s^   t | jdkr-| j}|dur-|du r| j}|j|k r*td| d| j d|j d|jS dS )z:validate this column: return the compared against itemsizer3  Nz#Trying to store a string with len [z] in [z)] column but
this column has a limit of [zC]!
Consider using min_itemsize to preset the sizes on these columns)rX   r  r.  r  r   r  )r   r  crV   rV   rW   r:    s   
zIndexCol.validate_colc                 C  sJ   |rt | j| jd }|d ur!|| jkr#td| d| j dd S d S d S )Nzincompatible kind in col [ - r>  )r  r*  r  r  r   )r   r   existing_kindrV   rV   rW   r;    s   zIndexCol.validate_attrc                 C  s   | j D ]]}t| |d}|| ji }||}||v rT|durT||krT|dv rBt|||f }tj|tt	 d d||< t
| |d qtd| j d| d| d| d	|dus\|dur`|||< qdS )	z
        set/update the info for this indexable with the key/value
        if there is a conflict raise/warn as needed
        N)r  r  
stacklevelzinvalid info for [z] for [z], existing_value [z] conflicts with new value [r>  )_info_fieldsr  
setdefaultrb   r   rt   warningswarnr   r   setattrr   )r   r  r   r   idxexisting_valuewsrV   rV   rW   update_info  s.   

zIndexCol.update_infoc                 C  s(   | | j}|dur| j| dS dS )z!set my state from the passed infoN)r   rb   __dict__update)r   r  rJ  rV   rV   rW   set_info  s   zIndexCol.set_infoc                 C  s   t | j| j| j dS )zset the kind for this columnN)rI  r*  r  r  r   rV   rV   rW   r>       zIndexCol.set_attrc                 C  sT   | j dkr"| j}|| j}|dur$|dur&t||ddds(tddS dS dS dS )z:validate that kind=category does not change the categoriescategoryNT
strict_nandtype_equalzEcannot append a categorical with different categories to the existing)r   r  read_metadatar  r)   r   )r   r8  new_metadatacur_metadatarV   rV   rW   r<  	  s   
zIndexCol.validate_metadatac                 C  s"   | j dur|| j| j  dS dS )zset the meta dataN)r  r=  r  )r   r8  rV   rV   rW   r=  	  s   
zIndexCol.write_metadata)NNNNNNNNNNNNN)rb   r\   r  rZ   r[   r   r  r  )r  re   r[   r   r  r	  r[   r   r  )rz  r  rY   r\   r   r\   r[   r  r  r]   r  )r8  r9  r   r   r[   r   )r   r   r[   r   )r8  r9  r[   r   )"r   r  r  r  r  r  r  rE  r   r  r  r  r  r   r  r  r  r'  r)  r*  r-  r.  r1  r  r5  r7  r?  r:  r;  rM  rP  r>  r<  r=  rV   rV   rV   rW   r    sd   
 +




2









	


r  c                   @  s2   e Zd ZdZedddZdddZdddZdS )GenericIndexColz:an index which is not represented in the data of the tabler[   r   c                 C     dS NFrV   r   rV   rV   rW   r  	     zGenericIndexCol.is_indexedrz  r  rY   r\   r   tuple[Index, Index]c                 C  s,   t |tjsJ t|tt|}||fS )z
        Convert the data from this selection to the appropriate pandas type.

        Parameters
        ----------
        values : np.ndarray
        nan_rep : str
        encoding : str
        errors : str
        )rQ   rR   r  r   r/   rp   )r   rz  r   rY   r   r   rV   rV   rW   r'  !	  s   zGenericIndexCol.convertr   c                 C  r6  r]   rV   r   rV   rV   rW   r>  3	  r   zGenericIndexCol.set_attrNr  )rz  r  rY   r\   r   r\   r[   r^  r  )r   r  r  r  r  r  r'  r>  rV   rV   rV   rW   rZ  	  s    
rZ  c                      s  e Zd ZdZdZdZddgZ												d>d? fddZed@ddZ	ed@ddZ
d@ddZdAddZdBddZdd  ZedCd#d$Zed%d& ZedDd)d*ZedEd+d,Zed-d. Zed/d0 Zed1d2 Zed3d4 ZdFd5d6ZdGd:d;ZdFd<d=Z  ZS )H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  Nrb   r\   r  rZ   r  DtypeArg | Noner[   r   c                   s2   t  j|||||||||	|
|d || _|| _d S )N)rb   rz  r  r  r  r  r  r  rw   r   r  )superr   r  r  )r   rb   rz  r  r  r  r  r  r  rw   r   r  r  r  	__class__rV   rW   r   H	  s   
zDataCol.__init__c                 C  r  )N_dtypera   r   rV   rV   rW   
dtype_attrh	  r  zDataCol.dtype_attrc                 C  r  )N_metara   r   rV   rV   rW   	meta_attrl	  r  zDataCol.meta_attrc                 C  r  )Nr   c                 S  r  r  rV   rm  rV   rV   rW   rm   w	  r  z$DataCol.__repr__.<locals>.<listcomp>)rb   r  r  r  shape)
ro   r  r?   rb   r  r  r  rh  r  rO  r  rV   rV   rW   r   p	  s   zDataCol.__repr__r  r	  r   c                   r
  )r  c                 3  r  r]   r  r  r  rV   rW   rk  	  r  z!DataCol.__eq__.<locals>.<genexpr>)rb   r  r  r  r  r  rV   r  rW   r  }	  r  zDataCol.__eq__r  rH   c                 C  s@   |d usJ | j d u sJ t|\}}|| _|| _ t|| _d S r]   )r  _get_data_and_dtype_namer  _dtype_to_kindr  )r   r  
dtype_namerV   rV   rW   set_data	  s   zDataCol.set_datac                 C  r   )zreturn the datar  r   rV   rV   rW   r)  	  r   zDataCol.take_datarz  rD   c                 C  s   |j }|j}|j}|jdkrd|jf}t|tr&|j}| j||j j	d}|S t
|ds1t|tr8| |}|S t
|drE| |}|S t|rUt j||d d}|S t|ra| ||}|S | j||j	d}|S )zW
        Get an appropriately typed and shaped pytables.Col object for values.
        rf   r  r  mr   r  rh  )r  r  rh  rs  sizerQ   r4   codesget_atom_datarb   r   r!  r&   get_atom_datetime64get_atom_timedelta64r!   r   
ComplexColr#   get_atom_string)r  rz  r  r  rh  rr  atomrV   rV   rW   	_get_atom	  s.   





zDataCol._get_atomc                 C  s   t  j||d dS )Nr   rp  r   r4  r  rh  r  rV   rV   rW   rw  	     zDataCol.get_atom_stringr  	type[Col]c                 C  sR   | dr|dd }d| d}n| drd}n	| }| d}tt |S )z0return the PyTables column class for this columnuint   NUIntrD   periodInt64Col)r  
capitalizer  r   )r  r  k4col_namekcaprV   rV   rW   get_atom_coltype	  s   


zDataCol.get_atom_coltypec                 C  s   | j |d|d dS )Nrn  r   rh  r  r  rh  r  rV   rV   rW   rs  	  rQ  zDataCol.get_atom_datac                 C     t  j|d dS Nr   r  r   r  r  rh  rV   rV   rW   rt  	     zDataCol.get_atom_datetime64c                 C  r  r  r  r  rV   rV   rW   ru  	  r  zDataCol.get_atom_timedelta64c                 C     t | jdd S )Nrh  )r  r  r   rV   rV   rW   rh  	     zDataCol.shapec                 C  r   r0  rm  r   rV   rV   rW   r1  	  r2  zDataCol.cvaluesc                 C  sh   |r.t | j| jd}|dur|t| jkrtdt | j| jd}|dur0|| jkr2tddS dS dS )zAvalidate that we have the same order as the existing & same dtypeNz4appended items do not match existing items in table!z@appended items dtype do not match existing items dtype in table!)r  r*  r  rn   rz  r   re  r  )r   r   existing_fieldsexisting_dtyperV   rV   rW   r;  	  s   zDataCol.validate_attrr  rY   r   c                 C  s  t |tjsJ t||jjdur|| j }| jdusJ | jdu r.t|\}}t	|}n|}| j}| j
}t |tjs>J t| j}| j}	| j}
| j}|dusRJ t|}|drct||dd}n|dkrotj|dd}n|dkrztjd	d
 |D td}W nl ty   tjdd
 |D td}Y nXw |dkr|	}| }|du rtg tjd}nt|}| r||  }||dk  |t j8  < tj|||
dd}nz	|j|dd}W n t y   |jddd}Y nw t|dkrt!||||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
        N
datetime64Tcoercetimedelta64m8[ns]r  r   c                 S     g | ]}t |qS rV   r   fromordinalri   rn  rV   rV   rW   rm   
  r=  z#DataCol.convert.<locals>.<listcomp>c                 S  r  rV   r   fromtimestampr  rV   rV   rW   rm   "
  r=  rR  F)
categoriesr  validater  Or3  r   rY   r   )#rQ   rR   r  r   r  r  r  r  ri  rj  r  rX   r   r  r  r  r  r"  asarrayr	  r   ravelr,   float64r3   anyastypere   cumsum_valuesr4   
from_codesr   _unconvert_string_arrayrz  )r   rz  r   rY   r   	convertedrk  r  r   r  r  r  r  r  rr  maskrV   rV   rW   r'  	  sj   







 
zDataCol.convertc                 C  sH   t | j| j| j t | j| j| j | jdusJ t | j| j| j dS )zset the data for this columnN)rI  r*  r  rz  rg  r   r  re  r   rV   rV   rW   r>  K
  s   zDataCol.set_attr)NNNNNNNNNNNN)rb   r\   r  rZ   r  r`  r[   r   r  rY  )r  rH   r[   r   )rz  rH   r[   rD   )r  r\   r[   r}  r  r\   r[   rD   r  )rz  r  rY   r\   r   r\   )r   r  r  r  r  r  rE  r   r  re  rg  r   r  rl  r)  classmethodry  rw  r  rs  rt  ru  rh  r1  r;  r'  r>  __classcell__rV   rV   rb  rW   r_  7	  sZ     










dr_  c                   @  sP   e Zd ZdZdZdddZedd ZedddZedd Z	edd Z
dS )DataIndexableColz+represent a data column that can be indexedTr[   r   c                 C  s   t t| jjstdd S )N-cannot have non-object label DataIndexableCol)r#   r,   rz  r  r   r   rV   rV   rW   r7  X
  s   zDataIndexableCol.validate_namesc                 C  s   t  j|dS )N)r  rz  r{  rV   rV   rW   rw  ]
  r  z DataIndexableCol.get_atom_stringr  r\   rD   c                 C  s   | j |d S )Nrn  r  r  rV   rV   rW   rs  a
  r  zDataIndexableCol.get_atom_datac                 C  
   t   S r]   r  r  rV   rV   rW   rt  e
     
z$DataIndexableCol.get_atom_datetime64c                 C  r  r]   r  r  rV   rV   rW   ru  i
  r  z%DataIndexableCol.get_atom_timedelta64Nr  r  )r   r  r  r  r  r7  r  rw  rs  rt  ru  rV   rV   rV   rW   r  S
  s    


r  c                   @  s   e Zd ZdZdS )GenericDataIndexableColz(represent a generic pytables data columnN)r   r  r  r  rV   rV   rV   rW   r  n
  s    r  c                   @  s~  e Zd ZU dZded< dZded< ded< ded	< d
ed< dZded< 		dPdQddZedRddZ	edSddZ
edd  ZdTd!d"ZdUd#d$ZdVd%d&Zed'd( Zed)d* Zed+d, Zed-d. ZedWd/d0ZedRd1d2Zed3d4 ZdUd5d6ZdUd7d8Zed9d: ZedRd;d<Zed=d> ZdXd@dAZdYdUdCdDZdRdEdFZ	B	B	B	BdZd[dJdKZdUdLdMZ	Bd\d]dNdOZ dBS )^Fixedz
    represent an object in my store
    facilitate read/write of various types of objects
    this is an abstract base class

    Parameters
    ----------
    parent : HDFStore
    group : Node
        The group node where the table resides.
    r\   pandas_kindrv   format_typetype[DataFrame | Series]obj_typere   rs  r   r   Fr   rP  rP   r   r   rF   rY   rZ   r   r[   r   c                 C  sZ   t |tsJ t|td usJ t |tjsJ t||| _|| _t|| _|| _	d S r]   )
rQ   r   r   r   rF   r   r   r`   rY   r   )r   r   r   rY   r   rV   rV   rW   r   
  s   

zFixed.__init__c                 C  s*   | j d dko| j d dko| j d dk S )Nr   rf   
      )versionr   rV   rV   rW   is_old_version
  s   *zFixed.is_old_versiontuple[int, int, int]c                 C  sf   t t| jjdd}ztdd |dD }t|dkr$|d }W |S W |S  ty2   d}Y |S w )	zcompute and set our versionpandas_versionNc                 s  s    | ]}t |V  qd S r]   re   r?  rV   rV   rW   rk  
  s    z Fixed.version.<locals>.<genexpr>.r  rB  )r   r   r   )rX   r  r   r  ro   r  rp   r   )r   r  rV   rV   rW   r  
  s   
zFixed.versionc                 C  s   t t| jjdd S )Nr  )rX   r  r   r  r   rV   rV   rW   r  
  r|  zFixed.pandas_typec                 C  s^   |    | j}|dur,t|ttfr"ddd |D }d| d}| jdd| d	S | jS )
(return a pretty representation of myselfNr   c                 S     g | ]}t |qS rV   r?   r?  rV   rV   rW   rm   
      z"Fixed.__repr__.<locals>.<listcomp>[r>  12.12z	 (shape->))r.  rh  rQ   rn   ro   r  r  )r   rU   jshaperV   rV   rW   r   
  s   zFixed.__repr__c                 C  s   t | j| j_t t| j_dS )zset my pandas type & versionN)r\   r  r*  r  _versionr  r   rV   rV   rW   r  
  s   zFixed.set_object_infoc                 C  s   t  | }|S r]   r  )r   new_selfrV   rV   rW   r  
  s   
z
Fixed.copyc                 C  r   r]   )r,  r   rV   rV   rW   rh  
  r   zFixed.shapec                 C  r  r]   r   r   r   rV   rV   rW   rQ  
  r+  zFixed.pathnamec                 C  r  r]   )r   r   r   rV   rV   rW   r   
  r+  zFixed._handlec                 C  r  r]   )r   r   r   rV   rV   rW   r   
  r+  zFixed._filtersc                 C  r  r]   )r   r   r   rV   rV   rW   r   
  r+  zFixed._complevelc                 C  r  r]   )r   r   r   rV   rV   rW   r   
  r+  zFixed._fletcher32c                 C  r  r]   )r   r  r   rV   rV   rW   r*  
  r+  zFixed.attrsc                 C  r[  zset our object attributesNrV   r   rV   rV   rW   	set_attrs
      zFixed.set_attrsc                 C  r[  )zget our object attributesNrV   r   rV   rV   rW   	get_attrs
  r  zFixed.get_attrsc                 C  r   )zreturn my storabler   r   rV   rV   rW   storable
  r2  zFixed.storablec                 C  r[  r\  rV   r   rV   rV   rW   r  
  r]  zFixed.is_existsc                 C  r  )Nr,  )r  r  r   rV   rV   rW   r,  
  r  zFixed.nrowsLiteral[True] | Nonec                 C  s   |du rdS dS )z%validate against an existing storableNTrV   r  rV   rV   rW   r  
  s   zFixed.validateNc                 C  r[  )+are we trying to operate on an old version?NrV   )r   rq   rV   rV   rW   validate_version
  r  zFixed.validate_versionc                 C  s   | j }|du r	dS |   dS )zr
        infer the axes of my storer
        return a boolean indicating if we have a valid storer or not
        NFT)r  r  )r   rU   rV   rV   rW   r.  
  s
   zFixed.infer_axesr   r   r   c                 C     t d)Nz>cannot read on an abstract storer: subclasses should implementr   r   rq   r   r   r   rV   rV   rW   r$     s   z
Fixed.readc                 K  r  )Nz?cannot write on an abstract storer: subclasses should implementr  r   r  r   rV   rV   rW   r    s   zFixed.writec                 C  s,   t |||r| jj| jdd dS td)zs
        support fully deleting the node in its entirety (only) - where
        specification must be None
        TrZ  Nz#cannot delete on an abstract storer)r_  r`  r   r  r   r   )r   rq   r   r   rV   rV   rW   ra    s   zFixed.delete)rP   r   )
r   r   r   rF   rY   rZ   r   r\   r[   r   r  )r[   r  r  r  )r[   r  r  )r[   r  r]   NNNNr   r   r   r   r  )r   r   r   r   r[   r   )!r   r  r  r  r  r  rP  r   r  r  r  r  r   r  r  rh  rQ  r   r   r   r   r*  r  r  r  r  r,  r  r  r.  r$  r  ra  rV   rV   rV   rW   r  r
  sj   
 















r  c                   @  s   e Zd ZU dZedediZdd e D Zg Z	de
d< d<d
dZdd Zdd Zd=ddZed>ddZd=ddZd=ddZd=ddZd?d@d!d"Z	d?dAd$d%ZdBd'd(ZdCd*d+Z	d?dDd,d-Z	d?dEd0d1ZdFd4d5Z	dGdHd:d;ZdS )IGenericFixedza generified fixed versiondatetimer  c                 C  s   i | ]\}}||qS rV   rV   )ri   r<  rn  rV   rV   rW   ro  "  r=  zGenericFixed.<dictcomp>r  
attributesr[   r\   c                 C  s   | j |dS )N )_index_type_mapr   )r   r  rV   rV   rW   _class_to_alias&  s   zGenericFixed._class_to_aliasc                 C  s   t |tr|S | j|tS r]   )rQ   r   _reverse_index_mapr   r,   )r   aliasrV   rV   rW   _alias_to_class)  s   
zGenericFixed._alias_to_classc                 C  s   |  tt|dd}|tkrd	dd}|}n|tkr#d	dd}|}n|}i }d|v r7|d |d< |tu r7t}d|v rXt|d trL|d 	d|d< n|d |d< |tu sXJ ||fS )
Nindex_classr  c                 S  s>   t j| j| j|d}tj|d d}|d ur|d|}|S )N)r  r  ra   UTC)r5   _simple_newrz  r  r+   tz_localize
tz_convert)rz  r  r  dtaresultrV   rV   rW   rx   8  s   
z*GenericFixed._get_index_factory.<locals>.fc                 S  s$   t |}tj| |d}tj|d dS )Nr  ra   )r(   r6   r  r.   )rz  r  r  r  parrrV   rV   rW   rx   E  s   r  r  zutf-8r  )
r  rX   r  r+   r.   r,   r1   rQ   bytesrT   )r   r*  r  rx   r$  r   rV   rV   rW   _get_index_factory/  s*   


zGenericFixed._get_index_factoryr   c                 C  s$   |durt d|durt d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   )r   r   rq   rV   rV   rW   validate_read`  s   zGenericFixed.validate_readr   c                 C  r[  )NTrV   r   rV   rV   rW   r  o  r]  zGenericFixed.is_existsc                 C  s   | j | j_ | j| j_dS r  )rY   r*  r   r   rV   rV   rW   r  s  s   
zGenericFixed.set_attrsc              	   C  sR   t t| jdd| _tt| jdd| _| jD ]}t| |tt| j|d qdS )retrieve our attributesrY   Nr   r   )r`   r  r*  rY   rX   r   r  rI  )r   r	  rV   rV   rW   r  x  s
   
zGenericFixed.get_attrsc                 K  r  r]   )r  r  rV   rV   rW   r    r   zGenericFixed.writeNr   r   r   r   c                 C  s   ddl }t| j|}|j}t|dd}t||jr"|d || }n@tt|dd}	t|dd}
|
dur<tj|
|	d}n||| }|	rW|		drWt|d	d}t
||d
d}n|	dkrbtj|dd}|rg|jS |S )z2read an array for the specified node (off of groupr   N
transposedF
value_typerh  r  r  r  Tr  r  r  )r   r  r   r  rQ   VLArrayrX   rR   r  r  r"  r  T)r   r   r   r   r   r   r*  r  retr  rh  r  rV   rV   rW   
read_array  s&   zGenericFixed.read_arrayr,   c                 C  sd   t t| j| d}|dkr| j|||dS |dkr+t| j|}| j|||d}|S td| )N_varietymultir   r   regularzunrecognized index variety: )rX   r  r*  read_multi_indexr   read_index_noder   )r   r   r   r   varietyr   r   rV   rV   rW   
read_index  s   zGenericFixed.read_indexr   c                 C  s   t |trt| j| dd | || d S t| j| dd td|| j| j}| ||j	 t
| j|}|j|j_|j|j_t |ttfrQ| t||j_t |tttfr^|j|j_t |trq|jd urst|j|j_d S d S d S )Nr  r  r  r   )rQ   r-   rI  r*  write_multi_index_convert_indexrY   r   write_arrayrz  r  r   r  r  rb   r+   r.   r  r   r  r1   r  r  _get_tz)r   r   r   r  r   rV   rV   rW   write_index  s    



zGenericFixed.write_indexr-   c                 C  s   t | j| d|j tt|j|j|jD ]P\}\}}}t|j	t
r'td| d| }t||| j| j}| ||j t| j|}	|j|	j_||	j_t |	j| d| | | d| }
| |
| qd S )N_nlevelsz=Saving a MultiIndex with an extension dtype is not supported._level_name_label)rI  r*  r  	enumeraterO  levelsrr  namesrQ   r  r'   r   r  rY   r   r  rz  r  r   r  r  rb   )r   r   r   ilevlevel_codesrb   	level_key
conv_levelr   	label_keyrV   rV   rW   r    s$   
zGenericFixed.write_multi_indexc                 C  s   t | j| d}g }g }g }t|D ]6}| d| }	t | j|	}
| j|
||d}|| ||j | d| }| j|||d}|| qt|||ddS )Nr  r	  r  r  T)r  rr  r  rH  )	r  r*  rr  r   r   r   rb   r  r-   )r   r   r   r   r  r  rr  r  r  r  r   r  r  r  rV   rV   rW   r    s    
zGenericFixed.read_multi_indexr   rF   c                 C  s   ||| }d|j v rt|j jdkrtj|j j|j jd}t|j j}d }d|j v r6t|j j	}t|}|j }| 
|\}}	|dv rW|t||| j| jdfdti|	}
n|t||| j| jdfi |	}
||
_	|
S )Nrh  r   r  rb   )r   r	  r  r  )r  rR   prodrh  r  r  rX   r  rc   rb   r  _unconvert_indexrY   r   r	  )r   r   r   r   r  r  rb   r*  r$  r   r   rV   rV   rW   r     s:   
zGenericFixed.read_index_noder   rH   c                 C  sJ   t d|j }| j| j|| t| j|}t|j|j	_
|j|j	_dS )zwrite a 0-len arrayrf   N)rR   r  rs  r   create_arrayr   r  r\   r  r  r  rh  )r   r   r   arrr   rV   rV   rW   write_array_empty  s
   zGenericFixed.write_array_emptyr  rG   r  Index | Nonec                 C  s\  t |dd}|| jv r| j| j| |jdk}d}t|jtr$td|s0t	|dr0|j
}d}d }| jd urStt t j|j}W d    n1 sNw   Y  |d uru|sn| jj| j|||j| jd}||d d < n| || n|jjtjkrtj|dd}	|rn|	d	krnt|	||f }
tj|
tt d
 | j| j|t  }| | nwt!|jdr| j"| j||#d t$|jt%| j|j&_'nXt|jt(r| j"| j||j) t%| j|}t*|j+|j&_+d|jj, d|j&_'n0t!|jdr| j"| j||#d dt%| j|j&_'n|r| || n	| j"| j|| |t%| j|j&_-d S )NT)extract_numpyr   Fz]Cannot store a category dtype in a HDF5 dataset that uses format="fixed". Use format="table".r  )r   skipnar3  rC  r  r  datetime64[r>  ro  r  ).r9   r   r   r  rq  rQ   r  r%   r   r  r  r   r   r   r   Atom
from_dtypecreate_carrayrh  r  r   rR   object_r   infer_dtyperu   rG  rH  r   r   create_vlarray
ObjectAtomr   r!  r  viewr\   r  r  r  r&   asi8r  r  unitr  )r   r   r  r  r   empty_arrayr  rx  cainferred_typerL  vlarrr   rV   rV   rW   r  (  sh   





zGenericFixed.write_arrayr  r  r  r  r  )r   r\   r   r   r   r   r[   r,   )r   r\   r   r,   r[   r   )r   r\   r   r-   r[   r   )r   r\   r   r   r   r   r[   r-   )r   rF   r   r   r   r   r[   r,   )r   r\   r   rH   r[   r   r]   )r   r\   r  rG   r  r  r[   r   )r   r  r  r  r+   r.   r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r   r  r  rV   rV   rV   rW   r    s4   
 

1


#


&
r  c                      sR   e Zd ZU dZdgZded< edd Z				ddddZd fddZ	  Z
S )r  r  rb   r@   c              	   C  s*   zt | jjfW S  ttfy   Y d S w r]   )rp   r   rz  r   r   r   rV   rV   rW   rh    s
   zSeriesFixed.shapeNr   r   r   r[   r0   c                 C  s^   |  || | jd||d}| jd||d}t||| jdd}t r-t|ddr-|d}|S )	Nr   r  rz  F)r   rb   r  Tr  string[pyarrow_numpy])r  r  r  r0   rb   r   r   r  )r   rq   r   r   r   r   rz  r  rV   rV   rW   r$    s   
zSeriesFixed.readr   c                   s<   t  j|fi | | d|j | d| |j| j_d S )Nr   rz  )ra  r  r  r   r  rb   r*  r  rb  rV   rW   r    s   zSeriesFixed.writer  r   r   r   r   r[   r0   r  )r   r  r  r  r  r  r  rh  r$  r  r  rV   rV   rb  rW   r    s   
 
r  c                      sR   e Zd ZU ddgZded< edddZ				ddddZd fddZ  Z	S )BlockManagerFixedrs  nblocksre   r[   Shape | Nonec                 C  s   zJ| j }d}t| jD ]}t| jd| d}t|dd }|d ur'||d 7 }q| jj}t|dd }|d urAt|d|d  }ng }|| |W S  tyT   Y d S w )Nr   block_itemsrh  rf   )	rs  rr  r1  r  r   block0_valuesrn   r   r   )r   rs  r  r  r   rh  rV   rV   rW   rh    s&   
zBlockManagerFixed.shapeNr   r   r   r*   c                 C  sD  |  || |  d}g }t| jD ]}||kr||fnd\}}	| jd| ||	d}
||
 q|d }g }t| jD ]<}| d| d}| jd| d||	d}||	| }t
|j||d d	d
}t rut|ddru|d}|| q>t|dkrt|ddd}t r| }|j|d	d}|S t
|d |d dS )Nr   r  rG  r  r3  r4  r  rf   Fr   r   r  Tr  r.  )rG  r  )r   r  r   r   )r  r  _get_block_manager_axisrr  rs  r  r   r1  r  rx  r*   r  r   r   r  rp   r2   r   r  r}  )r   rq   r   r   r   select_axisrd  r  r&  r'  axr  dfs	blk_itemsrz  dfoutrV   rV   rW   r$    s0   
zBlockManagerFixed.readr   c                   s   t  j|fi | t|jtr|d}|j}| s | }|j| j	_t
|jD ]\}}|dkr9|js9td| d| | q*t|j| j	_t
|jD ]"\}}|j|j}| jd| d|j|d | d| d| qOd S )Nr3  r   z/Columns index has to be unique for fixed formatrG  r  )r  r4  )ra  r  rQ   _mgrr;   _as_manageris_consolidatedconsolidaters  r*  r  rd  	is_uniquer   r  rp   blocksr1  r  ry  mgr_locsr  rz  )r   r  r   r  r  r:  blkr<  rb  rV   rW   r    s"   

zBlockManagerFixed.write)r[   r2  r  )r   r   r   r   r[   r*   r  )
r   r  r  r  r  r  rh  r$  r  r  rV   rV   rb  rW   r0    s   
 )r0  c                   @  s   e Zd ZdZeZdS )r  r  N)r   r  r  r  r*   r  rV   rV   rV   rW   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< 								dd fd"d#Z	e
dd$d%Zdd&d'Zdd)d*Zdd+d,Ze
dd.d/Zdd3d4Ze
dd6d7Ze
dd8d9Ze
d:d; Ze
d<d= Ze
d>d? Ze
d@dA Ze
ddCdDZe
ddEdFZe
ddGdHZe
ddJdKZddMdNZdOdP ZddRdSZddUdVZddYdZZdd[d\Z dd]d^Z!dd_d`Z"dddadbZ#ddcddZ$e%dedf Z&	dddhdiZ'	dddndoZ(e)ddqdrZ*dsdt Z+	
			dddwdxZ,e-dd{d|Z.ddddZ/dddZ0	ddddZ1			ddddZ2  Z3S )r  aa  
    represent a table:
        facilitate read/write of various types of tables

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

    index_axes    : a list of tuples of the (original indexing axis and
        index column)
    non_index_axes: a list of tuples of the (original index axis and
        columns on a non-indexing axis)
    values_axes   : a list of the columns which comprise the data of this
        table
    data_columns  : a list of the columns that we are allowing indexing
        (these become single columns in values_axes)
    nan_rep       : the string to use for nan representations for string
        objects
    levels        : the names of levels
    metadata      : the names of the metadata columns
    
wide_tablerw   r\   r  r  rf   zint | list[Hashable]r  Trn   r  Nr   r   r   r   rF   rY   rZ   r   
index_axeslist[IndexCol] | NonerC   list[tuple[AxisInt, Any]] | Nonevalues_axeslist[DataCol] | Noner   list | Noner  dict | Noner[   r   c                   sP   t  j||||d |pg | _|pg | _|pg | _|pg | _|	p!i | _|
| _d S )Nr  )ra  r   rH  rC  rK  r   r  r   )r   r   r   rY   r   rH  rC  rK  r   r  r   rb  rV   rW   r   .  s   





zTable.__init__c                 C  s   | j dd S )N_r   )r  r  r   rV   rV   rW   table_type_shortC     zTable.table_type_shortc                 C  s   |    t| jrd| jnd}d| d}d}| jr-ddd | jD }d| d}dd	d | jD }| jd
| d| j d| j	 d| j
 d| d| dS )r  r   r  z,dc->[r>  r  c                 S  r  rV   r\   r?  rV   rV   rW   rm   O  r  z"Table.__repr__.<locals>.<listcomp>r  c                 S  r  rV   ra   r  rV   rV   rW   rm   R  r
  r  z (typ->z,nrows->z,ncols->z,indexers->[r  )r.  rp   r   r  r  r  rH  r  rP  r,  ncols)r   jdcr  verjverjindex_axesrV   rV   rW   r   G  s(   zTable.__repr__r@  c                 C  s"   | j D ]}||jkr|  S qdS )zreturn the axis for cN)rd  rb   )r   r@  r   rV   rV   rW   r   Y  s
   

zTable.__getitem__c              
   C  s   |du rdS |j | j krtd|j  d| j  ddD ]?}t| |d}t||d}||krZt|D ]\}}|| }||krKtd| d| d| dq1td| d| d| dqdS )	z"validate against an existing tableNz'incompatible table_type with existing [rA  r>  )rH  rC  rK  zinvalid combination of [z] on appending data [z] vs current table [)r  r   r  r  r   r]  )r   r  r@  svovr  saxoaxrV   rV   rW   r  `  s@   zTable.validater   c                 C  s   t | jtS )z@the levels attribute is 1 or a list in the case of a multi-index)rQ   r  rn   r   rV   rV   rW   is_multi_index  s   zTable.is_multi_indexr  r    tuple[DataFrame, list[Hashable]]c              
   C  sT   t |jj}z| }W n ty } ztd|d}~ww t|ts&J ||fS )ze
        validate that we can store the multi-index; reset and return the
        new object
        zBduplicate names/columns in the multi-index when storing as a tableN)r_  fill_missing_namesr   r  reset_indexr   rQ   r*   )r   r  r  	reset_objrb  rV   rV   rW   validate_multiindex  s   zTable.validate_multiindexre   c                 C  s   t dd | jD S )z-based on our axes, compute the expected nrowsc                 S  s   g | ]}|j jd  qS rB  )r1  rh  ri   r  rV   rV   rW   rm     r  z(Table.nrows_expected.<locals>.<listcomp>)rR   r  rH  r   rV   rV   rW   nrows_expected  s   zTable.nrows_expectedc                 C  s
   d| j v S )zhas this table been createdrw   r  r   rV   rV   rW   r    s   
zTable.is_existsc                 C  r  Nrw   r  r   r   rV   rV   rW   r    r  zTable.storablec                 C  r   )z,return the table group (this is my storable))r  r   rV   rV   rW   rw     r2  zTable.tablec                 C  r  r]   )rw   r  r   rV   rV   rW   r    r+  zTable.dtypec                 C  r  r]   r,  r   rV   rV   rW   r-    r+  zTable.descriptionitertools.chain[IndexCol]c                 C  s   t | j| jS r]   )rM  rN  rH  rK  r   rV   rV   rW   rd    rQ  z
Table.axesc                 C  s   t dd | jD S )z.the number of total columns in the values axesc                 s  s    | ]}t |jV  qd S r]   )rp   rz  r  rV   rV   rW   rk    s    zTable.ncols.<locals>.<genexpr>)sumrK  r   rV   rV   rW   rS    s   zTable.ncolsc                 C  r[  r\  rV   r   rV   rV   rW   is_transposed  r]  zTable.is_transposedtuple[int, ...]c                 C  s(   t tdd | jD dd | jD S )z@return a tuple of my permutated axes, non_indexable at the frontc                 S  s   g | ]}t |d  qS rB  r  r  rV   rV   rW   rm     r  z*Table.data_orientation.<locals>.<listcomp>c                 S  s   g | ]}t |jqS rV   )re   rG  r  rV   rV   rW   rm     r=  )ro   rM  rN  rC  rH  r   rV   rV   rW   data_orientation  s   zTable.data_orientationdict[str, Any]c                   sR   ddd dd j D } fddjD }fddjD }t|| | S )z<return a dict of the kinds allowable columns for this objectr   r   r   rf   c                 S  s   g | ]}|j |fqS rV   r  r  rV   rV   rW   rm     r=  z$Table.queryables.<locals>.<listcomp>c                   s   g | ]
\}} | d fqS r]   rV   )ri   rG  rz  )
axis_namesrV   rW   rm     s    c                   s&   g | ]}|j t jv r|j|fqS rV   )rb   rq  r   r  r  r   rV   rW   rm     s     )rH  rC  rK  rg  )r   d1d2d3rV   )rn  r   rW   
queryables  s   

zTable.queryablesc                 C     dd | j D S )zreturn a list of my index colsc                 S  s   g | ]}|j |jfqS rV   )rG  r  rb  rV   rV   rW   rm     r  z$Table.index_cols.<locals>.<listcomp>rH  r   rV   rV   rW   
index_cols  r/  zTable.index_colsr  c                 C  rs  )zreturn a list of my values colsc                 S  r  rV   rm  rb  rV   rV   rW   rm     r
  z%Table.values_cols.<locals>.<listcomp>)rK  r   rV   rV   rW   values_cols  rQ  zTable.values_colsr   c                 C  s   | j j}| d| dS )z)return the metadata pathname for this keyz/meta/z/metar  r"  rV   rV   rW   _get_metadata_path  s   zTable._get_metadata_pathrz  r  c                 C  s0   | j j| |t|ddd| j| j| jd dS )z
        Write out a metadata array to the key as a fixed-format Series.

        Parameters
        ----------
        key : str
        values : ndarray
        Fr  rw   )r   rY   r   r   N)r   r   rw  r0   rY   r   r   )r   r   rz  rV   rV   rW   r=    s   	

zTable.write_metadatac                 C  s0   t t | jdd|ddur| j| |S dS )z'return the meta data array for this keyr   N)r  r   r   r   rw  r   rV   rV   rW   rV    s   zTable.read_metadatac                 C  sp   t | j| j_|  | j_|  | j_| j| j_| j| j_| j| j_| j| j_| j	| j_	| j
| j_
| j| j_dS )zset our table type & indexablesN)r\   r  r*  ru  rv  rC  r   r   rY   r   r  r  r   rV   rV   rW   r    s   





zTable.set_attrsc                 C  s   t | jddpg | _t | jddpg | _t | jddpi | _t | jdd| _tt | jdd| _tt | jdd| _	t | jd	dpBg | _
d
d | jD | _dd | jD | _dS )r  rC  Nr   r  r   rY   r   r   r  c                 S     g | ]}|j r|qS rV   r  r  rV   rV   rW   rm     r=  z#Table.get_attrs.<locals>.<listcomp>c                 S     g | ]}|j s|qS rV   ry  r  rV   rV   rW   rm     r=  )r  r*  rC  r   r  r   r`   rY   rX   r   r  
indexablesrH  rK  r   rV   rV   rW   r  
  s   zTable.get_attrsc                 C  sF   |dur| j r!tddd | jD  }tj|tt d dS dS dS )r  Nr  c                 S  r  rV   rR  r?  rV   rV   rW   rm     r  z*Table.validate_version.<locals>.<listcomp>rC  )r  rs   r  r  rG  rH  r   r   )r   rq   rL  rV   rV   rW   r    s   
zTable.validate_versionc                 C  sR   |du rdS t |tsdS |  }|D ]}|dkrq||vr&td| dqdS )z
        validate the min_itemsize doesn't contain items that are not in the
        axes this needs data_columns to be defined
        Nrz  zmin_itemsize has the key [z%] which is not an axis or data_column)rQ   rg  rr  r   )r   r   qr<  rV   rV   rW   validate_min_itemsize!  s   

zTable.validate_min_itemsizec                   s   g }j jjtjjD ]5\}\}}t|}|}|dur%dnd}| d}t|d}	t||||	|j||d}
||
 qt	j
t|  fdd|fddtjjD  |S )	z/create/cache the indexables if they don't existNrR  r  )rb   rG  r  r  r  rw   r   r  c                   s   t |tsJ t}|v rt}t|}t|j}t| dd }t| dd }t|}|}t| dd }	||||| |  |j	|	||d
}
|
S )Nr  rd  rf  )
rb   r  rz  r  r  r  rw   r   r  r  )
rQ   r\   r_  r  r  _maybe_adjust_namer  rj  rV  rw   )r  r@  klassrx  adj_namerz  r  r  mdr   r  )base_posr  descr   table_attrsrV   rW   rx   Y  s0   

zTable.indexables.<locals>.fc                   s   g | ]	\}} ||qS rV   rV   )ri   r  r@  )rx   rV   rW   rm   ~  rA  z$Table.indexables.<locals>.<listcomp>)r-  rw   r*  r  ru  r  rV  r  r   rq  r   rp   ru  rv  )r   _indexablesr  rG  rb   rx  r  r   r  r  	index_colrV   )r  r  r  rx   r   r  rW   r{  6  s2   




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

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

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

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

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

        Notes
        -----
        Cannot index Time64Col or ComplexCol.
        Pytables must be >= 3.0.
        NFTc                 S  r  rV   )r  r  r  rV   rV   rW   rm     r  z&Table.create_index.<locals>.<listcomp>r  r  complexzColumns containing complex values can be stored but cannot be indexed when using table format. Either use fixed format, set index=False, or do not include the columns containing complex values to data_columns when initializing the table.r   rf   zcolumn z/ is not a data_column.
In order to read column z: you must reload the dataframe 
into HDFStore and include z  with the data_columns argument.rV   )r.  rd  rQ   ro   rn   rw   r  rj  r  r   r  r  remove_indexr   r  r   r  rC  r   )r   r   r  r  kwrw   r@  rn  r   cur_optlevelcur_kindrV   rV   rW   r    sX   

zTable.create_indexr   r   r   9list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]c           	      C  sZ   t | |||d}| }g }| jD ]}|| j |j|| j| j| jd}|	| q|S )a  
        Create the axes sniffed from the table.

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

        Returns
        -------
        List[Tuple[index_values, column_values]]
        r2  r  )
	Selectionr   rd  rP  r  r'  r   rY   r   r   )	r   rq   r   r   	selectionrz  r  r   resrV   rV   rW   
_read_axes  s   
zTable._read_axesr  c                 C     |S )zreturn the data for this objrV   r  r  r  rV   rV   rW   
get_object  s   zTable.get_objectc                   s   t |sg S |d \} | j|i }|ddkr&|r&td| d| |du r/t }n|du r5g }t|trPt|t|}|fdd	|	 D   fd
d	|D S )zd
        take the input data_columns and min_itemize and create a data
        columns spec
        r   r   r-   z"cannot use a multi-index on axis [z] with data_columns TNc                   s    g | ]}|d kr| vr|qS r(  rV   r;  )existing_data_columnsrV   rW   rm     s
    z/Table.validate_data_columns.<locals>.<listcomp>c                   s   g | ]}| v r|qS rV   rV   )ri   r@  )axis_labelsrV   rW   rm   #  r  )
rp   r  r   r   rn   rQ   rg  rq  ru  r  )r   r   r   rC  rG  r  rV   )r  r  rW   validate_data_columns  s.   


	zTable.validate_data_columnsr*   r  c           /        s~  t ts| jj}td| dt d du rdg fdd D  |  r=d}d	d | jD  t| j	}| j
}nd
}| j}	| jdksIJ t | jd krVtdg }
|du r^d}t fdddD }j| }t|}|rt|
}| j| d }tt|t|dddsttt|tt|dddr|}|	|i }t|j|d< t|j|d< |
||f  d }j| }|}t||| j| j}||_|d | |	 |!| |g}t|}|dksJ t|
dksJ |
D ]}t"|d |d q|jdk}| #|||
}| $|% }| &|||
| j'|\}}g }t(t)||D ]\}\}}t*}d}|rbt|dkrb|d |v rbt+}|d }|du sbt |t,sbtd|r|rz| j'| }W n t-t.fy }  ztd| d| j' d| d} ~ ww d}|pd| }!t/|!|j0|||| j| j|d}"t1|!| j2}#|3|"}$t4|"j5j6}%d}&t7|"dddurt8|"j9}&d }' }(})t |"j5t:r|"j;})d}'t<|"j=> }(t?|"\}*}+||#|!t||$||%|&|)|'|(|+|*d},|, |	 ||, |d7 }q2dd |D }-t| | j@| j| j| j||
||-|	|d
}.tA| dr-| jB|._B|.C| |r=|r=|.D|  |.S ) a0  
        Create and return the axes.

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

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

        min_itemsize: Dict[str, int] or None, default None
            The min itemsize for a column in bytes.
        z/cannot properly create the storer for: [group->r  r>  Nr   c                   r:  rV   )_get_axis_numberr  )r  rV   rW   rm   Q  r=  z&Table._create_axes.<locals>.<listcomp>Tc                 S  r  rV   rl  r  rV   rV   rW   rm   V  r
  Fr  rf   z<currently only support ndim-1 indexers in an AppendableTablenanc                 3  s    | ]	}| vr|V  qd S r]   rV   r?  )rd  rV   rW   rk  n  s    z%Table._create_axes.<locals>.<genexpr>rl  rS  r  r   r  zIncompatible appended table [z]with existing table [values_block_)existing_colr   r   rY   r   r   r  rR  )rb   r  rz  r  r  r  r  r  r   r  r  r  c                 S  r  rV   )r  rb   )ri   r.  rV   rV   rW   rm     r  )
r   r   rY   r   rH  rC  rK  r   r  r   r  )ErQ   r*   r   r   r   r   r.  rH  rn   r   r   r  rs  rp   r   rp  rd  rC  r)   rR   arrayrw  rF  r  r   r   _get_axis_namer  rY   r   rG  r  rM  r5  _reindex_axisr  r  rI  _get_blocks_and_itemsrK  r  rO  r_  r  r\   
IndexErrorr   _maybe_convert_for_string_atomrz  r~  r  ry  rj  r  rb   r  r  r  r%   r  r  r  r  ri  r   r  r  r}  r  )/r   rd  r  r  r   r   r   r   table_existsnew_infonew_non_index_axesrJ  r   append_axisindexer
exist_axisr  	axis_name	new_indexnew_index_axesjr  r  rD  r<  vaxesr  rF  b_itemsr  rb   r  rb  new_namedata_convertedr  r  r  r  r   r  r  r  rk  r.  dcs	new_tablerV   )rd  r  rW   _create_axes%  s0  
 







"






zTable._create_axesr  r  c                 C  s~  t | jtr| d} dd }| j}tt|}t|j}||}t|ri|d \}	}
t	|

t	|}| j||	dj}tt|}t|j}||}|D ]}| j|g|	dj}tt|}||j ||| qK|rdd t||D }g }g }|D ];}t|j}z||\}}|| || W q{ ttfy } zdd	d
 |D }td| d|d }~ww |}|}||fS )Nr3  c                   s    fdd j D S )Nc                   s   g | ]	} j |jqS rV   )r  ry  rE  )ri   rF  mgrrV   rW   rm   $  rA  zFTable._get_blocks_and_items.<locals>.get_blk_items.<locals>.<listcomp>)rD  r  rV   r  rW   get_blk_items#  s   z2Table._get_blocks_and_items.<locals>.get_blk_itemsr   rl  c                 S  s"   i | ]\}}t | ||fqS rV   )ro   tolist)ri   br  rV   rV   rW   ro  B  s    z/Table._get_blocks_and_items.<locals>.<dictcomp>r   c                 S  r  rV   r  )ri   itemrV   rV   rW   rm   O  r  z/Table._get_blocks_and_items.<locals>.<listcomp>z+cannot match existing table structure for [z] on appending data)rQ   r?  r;   r@  r   r<   rn   rD  rp   r,   rv  r}  ru  rO  ro   rz  rR  r   r  r   r  r   )r  r  r  rK  r   r  r  rD  r<  rG  r  
new_labelsr@  by_items
new_blocksnew_blk_itemsear  r  r  rb  jitemsrV   rV   rW   r    sV   








zTable._get_blocks_and_itemsr  r  c                   s   |durt |}|dur'jr'tjt sJ jD ]}||vr&|d| qjD ]\}}t |||  fdd}q*|jdurS|j D ]\}}	}
|||
|	 qG S )zprocess axes filtersNr   c                   s    j D ]X} |} |}|d usJ | |kr3jr$|tj}|||} j|d|   S | |v r[tt	 | j
}t|}t trLd| }|||} j|d|   S qtd|  d)Nrl  rf   zcannot find the field [z] for filtering!)_AXIS_ORDERSr  	_get_axisr\  unionr,   r  r|  r:   r  rz  rQ   r*   r   )fieldfiltopr  axis_numberaxis_valuestakersrz  r  r   rV   rW   process_filterj  s$   





z*Table.process_axes.<locals>.process_filter)	rn   r\  rQ   r  insertrC  r  filterr   )r   r  r  r   r	  rG  labelsr  r  r  r  rV   r  rW   process_axesY  s   

 zTable.process_axesr   r   re  c                 C  s   |du r
t | jd}d|d}dd | jD |d< |r6|du r$| jp#d}t j|||p-| jd	}||d
< |S | jdur@| j|d
< |S )z:create the description of the table from the axes & valuesNi'  rw   )rb   re  c                 S  s   i | ]}|j |jqS rV   )r  r  r  rV   rV   rW   ro    r=  z,Table.create_description.<locals>.<dictcomp>r-  	   )r   r   r   r   )maxrc  rd  r   r   r  r   r   )r   r   r   r   re  rf  r   rV   rV   rW   create_description  s"   	



zTable.create_descriptionc           
      C  s   |  | |  sdS t| |||d}| }|jdurD|j D ]"\}}}| j|| | d d}	|||	j	||   |j
 }q!t|S )zf
        select coordinates (row numbers) from a table; return the
        coordinates object
        Fr2  Nrf   r  )r  r.  r  select_coordsr  r   r8  r  r  ilocrz  r,   )
r   rq   r   r   r  coordsr  r  r  r  rV   rV   rW   r4    s   

 zTable.read_coordinatesr7  c                 C  s   |    |  s
dS |durtd| jD ]>}||jkrS|js'td| dt| jj	|}|
| j |j||| | j| j| jd}tt|d |j|dd  S qtd| d	)
zj
        return a single column from the table, generally only indexables
        are interesting
        FNz4read_column does not currently accept a where clausezcolumn [z=] can not be extracted individually; it is not data indexabler  rf   )rb   r  z] not found in the table)r  r.  r   rd  rb   r  r   r  rw   rj  rP  r  r'  r   rY   r   r0   r"  r  r   )r   r7  rq   r   r   r   r@  
col_valuesrV   rV   rW   r8    s,   



zTable.read_column)Nr   NNNNNN)r   r   r   rF   rY   rZ   r   r\   rH  rI  rC  rJ  rK  rL  r   rM  r  rN  r[   r   r  )r@  r\   r  r  )r  r   r[   r]  r  )r[   rf  )r[   ri  )r[   rk  )r[   r  )r   r\   r[   r\   )r   r\   rz  r  r[   r   r  r]   r  )r  rZ   r[   r   r  )r   r   r   r   r[   r  r  r   )TNNN)r  r*   r  r   )r  r*   r  r   )r  r  r[   r*   )r   r   r   r   re  r   r[   rk  r  )r7  r\   r   r   r   r   )4r   r  r  r  r  r  r  r  rP  r   r  rP  r   r   r  r\  ra  rc  r  r  rw   r  r-  rd  rS  rh  rj  rr  ru  rv  rw  r=  rV  r  r  r  r}  r   r{  r  r  r  r  r  r  staticmethodr  r  r  r4  r8  r  rV   rV   rb  rW   r    s   
 


!





	







LW"* qC
7 r  c                   @  s2   e Zd ZdZdZ				ddddZdddZdS )r  z
    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   r   r   c                 C  r  )z[
        read the indices and the indexing array, calculate offset rows and return
        z!WORMTable needs to implement readr  r  rV   rV   rW   r$    s   
zWORMTable.readr[   r   c                 K  r  )z
        write in a format that we can search later on (but cannot append
        to): write out the indices and the values using _write_array
        (e.g. a CArray) create an indexing table so that we can search
        z"WORMTable needs to implement writer  r  rV   rV   rW   r  
  s   zWORMTable.writer  r  r  )r   r  r  r  r  r$  r  rV   rV   rV   rW   r    s    r  c                   @  sZ   e Zd ZdZdZ												dd ddZd!d"ddZd#ddZd$d%ddZdS )&r9  (support the new appendable table formats
appendableNFTr   r   r   r   r   rV  r[   r   c                 C  s   |s| j r| j| jd | j||||||d}|jD ]}|  q|j sA|j||||	d}|  ||d< |jj	|jfi | |j
|j_
|jD ]}||| qI|j||
d d S )Nrw   )rd  r  r  r   r   r   )r   r   r   re  rV  )r   )r  r   r  r   r  rd  r7  r  r  create_tabler  r*  r?  
write_data)r   r  rd  r   r   r   r   r   r   re  r   r   r   rV  rw   r   optionsrV   rV   rW   r    s4   

	


zAppendableTable.writec                   s  | j j}| j}g }|r*| jD ]}t|jjdd}t|tj	r)|
|jddd qt|rD|d }|dd D ]}||@ }q8| }nd}dd	 | jD }	t|	}
|
dksZJ |
d
d	 | jD }dd	 |D }g }t|D ]\}}|f| j ||
|   j }|
|| qo|du rd}tjt||| j d}|| d }t|D ]9}|| t|d | |  kr dS | j| fdd	|	D |dur|  nd fdd	|D d qdS )z`
        we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
        r   rl  u1Fr  rf   Nc                 S  r  rV   )r1  r  rV   rV   rW   rm   o  r
  z.AppendableTable.write_data.<locals>.<listcomp>c                 S     g | ]}|  qS rV   )r)  r  rV   rV   rW   rm   u  r  c              	   S  s,   g | ]}| tt|j|jd  qS r  )	transposerR   rollarangers  r  rV   rV   rW   rm   v  s   , r  r  c                      g | ]}|  qS rV   rV   r  end_istart_irV   rW   rm     r  c                   r  rV   rV   r  r  rV   rW   rm     r  )indexesr  rz  )r  r  rc  rK  r3   r  rh  rQ   rR   r  r   r  rp   r  rH  r  rh  reshaper  r  rr  write_data_chunk)r   r   r   r  r,  masksr   r  ro  r  nindexesrz  bvaluesr  rn  	new_shaperowschunksrV   r  rW   r  T  sP   


zAppendableTable.write_datar  r  r  list[np.ndarray]r  npt.NDArray[np.bool_] | Nonerz  c                 C  s   |D ]}t |js dS q|d jd }|t|kr#t j|| jd}| jj}t|}t|D ]
\}	}
|
|||	 < q/t|D ]\}	}||||	|  < q>|dura| j	t
dd }| sa|| }t|rr| j| | j  dS dS )z
        Parameters
        ----------
        rows : an empty memory space where we are putting the chunk
        indexes : an array of the indexes
        mask : an array of the masks
        values : an array of the values
        Nr   r  Fr  )rR   r  rh  rp   r  r  r  r  r  r  r   rh  rw   r   r  )r   r  r  r  rz  rn  r,  r  r  r  rJ  ro  rV   rV   rW   r    s*   z AppendableTable.write_data_chunkr   r   c                 C  sb  |d u st |s4|d u r|d u r| j}| jj| jdd |S |d u r%| j}| jj||d}| j  |S |  s:d S | j}t	| |||d}|
 }t|dd }t |}	|	r| }
t|
|
dk j}t |skdg}|d |	krv||	 |d dkr|dd | }t|D ]}|t||}|j||jd  ||jd  d d |}q| j  |	S )	NTrZ  r  Fr  rf   r   r  )rp   r,  r   r  r   rw   remove_rowsr  r.  r  r  r0   sort_valuesdiffrn   r   r   r  rR  reversedry  rr  )r   rq   r   r   r,  rw   r  rz  sorted_serieslnr  r   pgr  r  rV   rV   rW   ra    sF   


zAppendableTable.delete)NFNNNNNNFNNT)
r   r   r   r   r   r   rV  r   r[   r   r  )r   r   r   r   r[   r   )
r  r  r  r  r  r  rz  r  r[   r   r  r  )	r   r  r  r  r  r  r  r  ra  rV   rV   rV   rW   r9    s&    ;
;,r9  c                   @  sZ   e Zd ZU dZdZdZdZeZde	d< e
dd	d
ZedddZ				ddddZdS )r  r  r  r  r  r  r  r[   r   c                 C  s   | j d jdkS )Nr   rf   )rH  rG  r   rV   rV   rW   rh    rQ  z"AppendableFrameTable.is_transposedr  c                 C  s   |r|j }|S )zthese are written transposed)r  r  rV   rV   rW   r    s   zAppendableFrameTable.get_objectNr   r   r   c                   sR    |   sd S  j|||d}t jr$ j jd d i ni } fddt jD }t|dks:J |d }|| d }	g }
t jD ]\}}| j	vrUqK|| \}}|ddkrgt
|}nt|}|d}|d ur||j|d	d
  jr|}|}t
|	t|	dd d}n|j}t
|	t|	dd d}|}|jdkrt|tjr|d|jd f}t|tjrt|j||dd}nt|t
rt|||d}n	tj|g||d}t r|jjdks|j|jk sJ |j|jft rt|d	dr|d}|
 | qKt|
dkr|
d }nt!|
dd}t" |||d} j#|||d}|S )Nr2  r   c                   s"   g | ]\}}| j d  u r|qS rB  rt  )ri   r  r:  r   rV   rW   rm     s   " z-AppendableFrameTable.read.<locals>.<listcomp>rf   r   r-   r  Tinplacerb   ra   Fr6  r7  r  r  r.  rl  )r  r   )$r  r.  r  rp   rC  r  r   r  rd  rK  r,   r-   from_tuples	set_namesrh  r  r  rs  rQ   rR   r  r  rh  r*   _from_arraysr   r  r  dtypesrh  r   r  r   r2   r  r  )r   rq   r   r   r   r  r  indsindr   framesr  r   
index_valsr1  rj  r  rz  index_cols_r=  r  rV   r   rW   r$  	  sf   





 

zAppendableFrameTable.readr  r  r  r  )r   r  r  r  r  r  rs  r*   r  r  r  rh  r  r  r$  rV   rV   rV   rW   r    s   
 r  c                      sh   e Zd ZdZdZdZdZeZe	dddZ
edd
dZdd fddZ				dd fddZ  ZS )r  r  r  r  r  r[   r   c                 C  r[  r\  rV   r   rV   rV   rW   rh  f  r]  z#AppendableSeriesTable.is_transposedr  c                 C  r  r]   rV   r  rV   rV   rW   r  j  r]  z AppendableSeriesTable.get_objectNr   c                   s@   t |ts|jp	d}||}t jd||j d| dS )+we are going to write this as a frame tablerz  r  r   NrV   )rQ   r*   rb   to_framera  r  r   r  )r   r  r   r   rb   rb  rV   rW   r  o  s   


"zAppendableSeriesTable.writer   r   r   r0   c                   s   | j }|d ur!|r!t| jtsJ | jD ]}||vr |d| qt j||||d}|r5|j| jdd |jd d df }|j	dkrFd |_	|S )Nr   rF  Tr  rz  )
r\  rQ   r  rn   r  ra  r$  	set_indexr  rb   )r   rq   r   r   r   r\  r	  rU   rb  rV   rW   r$  v  s   

zAppendableSeriesTable.readr  r  r]   r  r  r/  )r   r  r  r  r  r  rs  r0   r  r  rh  r  r  r  r$  r  rV   rV   rb  rW   r  ^  s     	r  c                      s*   e Zd ZdZdZdZd fddZ  ZS )	r  r  r  r  r[   r   c                   sb   |j pd}| |\}| _t| jtsJ t| j}|| t||_t j	dd|i| dS )r  rz  r  NrV   )
rb   ra  r  rQ   rn   r   r,   r   ra  r  )r   r  r   rb   newobjrj  rb  rV   rW   r    s   



z AppendableMultiSeriesTable.writer  )r   r  r  r  r  r  r  r  rV   rV   rb  rW   r    s
    r  c                   @  sd   e Zd ZU dZdZdZdZeZde	d< e
dd	d
Ze
dd ZdddZedd ZdddZdS )r  z:a table that read/writes the generic pytables table formatr  r  r  zlist[Hashable]r  r[   r\   c                 C  r   r]   )r  r   rV   rV   rW   r    r   zGenericTable.pandas_typec                 C  s   t | jdd p	| jS rd  re  r   rV   rV   rW   r    r|  zGenericTable.storabler   c                 C  sL   g | _ d| _g | _dd | jD | _dd | jD | _dd | jD | _dS )r  Nc                 S  rx  rV   ry  r  rV   rV   rW   rm     r=  z*GenericTable.get_attrs.<locals>.<listcomp>c                 S  rz  rV   ry  r  rV   rV   rW   rm     r=  c                 S  r  rV   ra   r  rV   rV   rW   rm     r
  )rC  r   r  r{  rH  rK  r   r   rV   rV   rW   r    s   zGenericTable.get_attrsc           
   
   C  s   | j }| d}|durdnd}tdd| j||d}|g}t|jD ]/\}}t|ts-J t||}| |}|dur=dnd}t	|||g|| j||d}	|
|	 q"|S )z0create the indexables from the table descriptionr   NrR  r   )rb   rG  rw   r   r  )rb   r  rz  r  rw   r   r  )r-  rV  rZ  rw   r  _v_namesrQ   r\   r  r  r   )
r   rf  r  r   r  r  r  r	  rx  r  rV   rV   rW   r{    s.   


	zGenericTable.indexablesc                 K  r  )Nz cannot write on an generic tabler  )r   r   rV   rV   rW   r    s   zGenericTable.writeNr  r  )r   r  r  r  r  r  rs  r*   r  r  r  r  r  r  r   r{  r  rV   rV   rV   rW   r    s   
 



#r  c                      s`   e Zd ZdZdZeZdZe	dZ
edddZdd fddZ								dd fddZ  ZS )r  za frame with a multi-indexr  r  z^level_\d+$r[   r\   c                 C  r[  )Nappendable_multirV   r   rV   rV   rW   rP    r]  z*AppendableMultiFrameTable.table_type_shortNr   c                   s|   |d u rg }n	|du r|j  }| |\}| _t| jts J | jD ]}||vr/|d| q#t jd||d| d S )NTr   r  rV   )	r   r  ra  r  rQ   rn   r  ra  r  )r   r  r   r   r	  rb  rV   rW   r    s   

zAppendableMultiFrameTable.writer   r   r   c                   sD   t  j||||d}| j}|j fdd|jjD |_|S )NrF  c                   s    g | ]} j |rd n|qS r]   )
_re_levelssearch)ri   rb   r   rV   rW   rm   	  s     z2AppendableMultiFrameTable.read.<locals>.<listcomp>)ra  r$  r  r  r   r  r  )r   rq   r   r   r   r=  rb  r   rW   r$    s   zAppendableMultiFrameTable.readr  r]   r  r  r  )r   r  r  r  r  r*   r  rs  recompiler	  r  rP  r  r$  r  rV   rV   rb  rW   r    s    
r  r  r*   rG  rI   r  r,   c                 C  s   |  |}t|}|d urt|}|d u s||r!||r!| S t| }|d ur6t| j|dd}||sOtd d g| j }|||< | jt| } | S )NF)sort)	r  r:   equalsuniquer{  slicers  r|  ro   )r  rG  r  r  r:  slicerrV   rV   rW   r    s   

r  r  r   str | tzinfoc                 C  s   t | }|S )z+for a tz-aware type, return an encoded zone)r   get_timezone)r  zonerV   rV   rW   r  )  s   
r  rz  np.ndarray | Indexr  r+   c                 C  r6  r]   rV   rz  r  r  rV   rV   rW   r"  /  s   r"  r  c                 C  r6  r]   rV   r  rV   rV   rW   r"  6  r]  str | tzinfo | Nonenp.ndarray | DatetimeIndexc                 C  s   t | tr| jdu s| j|ksJ | jdur| S |dur?t | tr%| j}nd}|  } t|}t| |d} | d|} | S |rHtj	| dd} | S )a  
    coerce the values to a DatetimeIndex if tz is set
    preserve the input shape if possible

    Parameters
    ----------
    values : ndarray or Index
    tz : str or tzinfo
    coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
    Nra   r  M8[ns]r  )
rQ   r+   r  rb   r  rX   r  r  rR   r  )rz  r  r  rb   rV   rV   rW   r"  ;  s    


rb   c              
   C  st  t | tsJ |j}t|\}}t|}t|}t|j	ds*t
|j	s*t|j	r=t| |||t|dd t|dd |dS t |trFtdtj|dd}	t|}
|	dkrotjd	d
 |
D tjd}t| |dt  |dS |	dkrt|
||}|j	j}t| |dt ||dS |	dv rt| ||||dS t |tjr|j	tksJ |dksJ |t  }t| ||||dS )Niur  r  )rz  r  r  r  r  r  zMultiIndex not supported here!Fr  r   c                 S  r  rV   )	toordinalr  rV   rV   rW   rm     r  z"_convert_index.<locals>.<listcomp>r  )r  r3  )integerfloating)rz  r  r  r  r	  )rQ   r\   rb   ri  rj  r  ry  r   r!  r  r$   r    r  r  r-   r   r$  rR   r  int32r   	Time32Col_convert_string_arrayr  r4  r  r	  r&  )rb   r   rY   r   r  r  rk  r  rx  r,  rz  r  rV   rV   rW   r  b  s^   








r  r  c                 C  s   | dr|dkrt| }|S t| |}|S |dkr"t| }|S |dkrLztjdd | D td}W |S  tyK   tjdd | D td}Y |S w |dv rWt| }|S |d	v ret| d ||d
}|S |dkrrt| d }|S td| )Nr  r  r   c                 S  r  rV   r  r  rV   rV   rW   rm     r=  z$_unconvert_index.<locals>.<listcomp>r  c                 S  r  rV   r  r  rV   rV   rW   rm     r=  )r  floatr   r3  r  r	  r   zunrecognized index type )	r  r+   r'  r1   rR   r  r	  r   r  )r  r  rY   r   r   rV   rV   rW   r    s:   

	r  r  rH   r  c                 C  s  |j tkr|S ttj|}|j j}tj|dd}	|	dkr td|	dkr(td|	dks2|dks2|S t	|}
|
 }|||
< tj|dd}	|	dkr|t|jd	 D ]+}|| }tj|dd}	|	dkr{t||krk|| nd
| }td| d|	 dqPt||||j}|j}t|trt|| p|dpd	}t|pd	|}|d ur||}|d ur||kr|}|jd| dd}|S )NFr  r   z+[date] is not implemented as a table columnr  z>too many timezones in this block, create separate data columnsr3  r	  r   zNo.zCannot serialize the column [z2]
because its data contents are not [string] but [z] object dtyperz  z|Sr  )r  r	  r   rR   r  rb   r   r$  r   r3   r  rr  rh  rp   r   r  r  rQ   rg  re   r   r  r:  r  )rb   r  r  r   r   rY   r   r   rk  r,  r  r  r  r.  error_column_labelr  r  ecirV   rV   rW   r    sP   

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

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

    Returns
    -------
    np.ndarray[fixed-length-string]
    Fr  rf   Sr  )rp   r0   r  r\   encoder  r  rh  r   r  
libwritersmax_len_string_arrayrR   r  )r  rY   r   ensuredr  rV   rV   rW   r     s   

r   c                 C  s   | j }tj|  td} t| r=tt| }d| }t	| d t
r1t| ddjj||dj} n| j|ddjtdd} |du rCd}t| | | |S )	a*  
    Inverse of _convert_string_array.

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

    Returns
    -------
    np.ndarray[object]
        Decoded data.
    r  Ur   Fr  )r   Nr  )rh  rR   r  r  r	  rp   r&  r'  r   rQ   r  r0   r\   rT   r  r  !string_array_replace_from_nan_repr  )r  r   rY   r   rh  r  r  rV   rV   rW   r  '  s   

r  r#  c                 C  s6   t |tsJ t|t|rt|||}|| } | S r]   )rQ   r\   r   _need_convert_get_converter)rz  r#  rY   r   convrV   rV   rW   r   M  s
   r   c                   sH   dkrdd S dv rfddS dkr fddS t d )Nr  c                 S  s   t j| ddS )Nr  r  rR   r  r@  rV   rV   rW   r   W      z _get_converter.<locals>.<lambda>c                   s   t j|  dS )Nr  r.  r/  rn  rV   rW   r   Y  r0  r3  c                   s   t | d  dS )Nr  )r  r/  r  rV   rW   r   [  s    zinvalid kind )r   )r  rY   r   rV   )rY   r   r  rW   r,  U  s   r,  c                 C  s   | dv sd| v r
dS dS )N)r  r3  r  TFrV   rn  rV   rV   rW   r+  b  s   r+  r  Sequence[int]c                 C  sl   t |tst|dk rtd|d dkr4|d dkr4|d dkr4td| }|r4| d }d| } | S )	z
    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
       z6Version is incorrect, expected sequence of 3 integers.r   rf   r  r  zvalues_block_(\d+)values_)rQ   r\   rp   r   r  r
  r   )rb   r  ro  grprV   rV   rW   r~  h  s   $
r~  	dtype_strc                 C  s   t | } | drd}|S | drd}|S | drd}|S | dr(d}|S | dr1| }|S | dr:d	}|S | d
rCd
}|S | drLd}|S | drUd}|S | dkr]d}|S td|  d)zA
    Find the "kind" string describing the given dtype name.
    )r3  r  r3  r!  r  )re   r~  r  r  	timedeltar  r   rR  r  r	  zcannot interpret dtype of [r>  )rX   r  r   )r5  r  rV   rV   rW   rj    s@   







	
rj  c                 C  sv   t | tr| j} t | jtrd| jj d}n| jj}| jjdv r*t	| 
d} nt | tr2| j} t	| } | |fS )zJ
    Convert the passed data into a storable form and a dtype string.
    r  r>  mMr  )rQ   r4   rr  r  r&   r)  rb   r  rR   r  r'  r.   r(  )r  rk  rV   rV   rW   ri    s   


ri  c                   @  s:   e Zd ZdZ			ddd
dZdd Zdd Zdd ZdS )r  z
    Carries out a selection operation on a tables.Table object.

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

    Nrw   r  r   r   r   r[   r   c                 C  sV  || _ || _|| _|| _d | _d | _d | _d | _t|rt	t
d tj|dd}|dv r}t|}|jtjkrV| j| j}}|d u rDd}|d u rL| j j}t||| | _n't|jjtjr}| jd urj|| jk  sv| jd urz|| jk rzt
d|| _W d    n1 sw   Y  | jd u r| || _| jd ur| j \| _| _d S d S d S )NFr  )r  booleanr   z3where must have index locations >= start and < stop)rw   rq   r   r   	conditionr  termsrL  r"   r   r   r   r$  rR   r  r  bool_r,  r  
issubclassr   r  r  generateevaluate)r   rw   rq   r   r   inferredrV   rV   rW   r     sF   



zSelection.__init__c              
   C  sr   |du rdS | j  }z
t||| j jdW S  ty8 } zd| }td| d| d}t||d}~ww )z'where can be a : dict,list,tuple,stringN)rr  rY   r   z-                The passed where expression: a*  
                            contains an invalid variable reference
                            all of the variable references must be a reference to
                            an axis (e.g. 'index' or 'columns'), or a data_column
                            The currently defined references are: z
                )	rw   rr  r7   rY   	NameErrorr  r  r   r   )r   rq   r|  rb  qkeysr  rV   rV   rW   r=    s"   

	zSelection.generatec                 C  sX   | j dur| jjj| j  | j| jdS | jdur!| jj| jS | jjj| j| jdS )(
        generate the selection
        Nr  )	r9  rw   
read_wherer   r   r   rL  r4  r$  r   rV   rV   rW   r     s   

zSelection.selectc                 C  s   | j | j}}| jj}|du rd}n|dk r||7 }|du r!|}n|dk r)||7 }| jdur<| jjj| j ||ddS | jdurD| jS t	||S )rB  Nr   T)r   r   r  )
r   r   rw   r,  r9  get_where_listr   rL  rR   r  )r   r   r   r,  rV   rV   rW   r    s"   

zSelection.select_coordsr  )rw   r  r   r   r   r   r[   r   )r   r  r  r  r   r=  r   r  rV   rV   rV   rW   r    s    -r  )rY   rZ   r[   r\   )rd   re   )r   NNFNTNNNNr   rP   )r   r   r   r\   r   r   r   r\   r   r   r   rZ   r   r   r   rZ   r   r   r   r   r   r   r   r   r   r\   rY   r\   r[   r   )	Nr   r   NNNNFN)r   r   r   r\   r   r\   rq   r   r   r   r   r   r   r   r   r   r   r   )r   rF   r   rF   r[   r   r]   )r  r*   rG  rI   r  r,   r[   r*   )r  r   r[   r  r  )rz  r  r  r  r  r   r[   r+   )rz  r  r  r   r  r   r[   r  )rz  r  r  r  r  r   r[   r  )
rb   r\   r   r,   rY   r\   r   r\   r[   r  )r  r\   rY   r\   r   r\   r[   r  )rb   r\   r  rH   r   r  )r  r  rY   r\   r   r\   r[   r  )rz  r  r#  r\   rY   r\   r   r\   )r  r\   rY   r\   r   r\   )r  r\   r[   r   )rb   r\   r  r1  r[   r\   )r5  r\   r[   r\   )r  rH   )r  
__future__r   
contextlibr   r  r  r   r   rM  r   r  textwrapr   typingr   r   r	   r
   r   r   r   rG  numpyrR   pandas._configr   r   r   r   pandas._libsr   r   r&  pandas._libs.libr   pandas._libs.tslibsr   pandas.compat._optionalr   pandas.compat.pickle_compatr   pandas.errorsr   r   r   r   r   pandas.util._decoratorsr   pandas.util._exceptionsr   pandas.core.dtypes.commonr   r    r!   r"   r#   r$   pandas.core.dtypes.dtypesr%   r&   r'   r(   pandas.core.dtypes.missingr)   r  r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   pandas.core.arraysr4   r5   r6   pandas.core.commoncorecommonr_   pandas.core.computation.pytablesr7   r8   pandas.core.constructionr9   pandas.core.indexes.apir:   pandas.core.internalsr;   r<   pandas.io.commonr=   pandas.io.formats.printingr>   r?   collections.abcr@   rA   rB   typesrC   r   rD   rE   rF   pandas._typingrG   rH   rI   rJ   rK   rL   rM   rN   rO   r  r^   rX   r`   rc   rh   rr   rs   r  rt   ru   r  rt  rz   r{   config_prefixregister_optionis_boolis_one_of_factoryr   r   r   r   r   r   r   r/  r  rZ  r_  r  r  r  r  r  r0  r  r  r  r9  r  r  r  r  r  r  r  r"  r  r  r  r   r  r   r,  r+  r~  rj  ri  r  rV   rV   rV   rW   <module>   s<   $	 0(



: 
           (p  *   -  g#c       n dh1C,

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

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