o
    &zh                 	   @  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
ddlZddlmZ ddlmZmZmZmZmZmZmZ ddlZddlZddlmZmZmZmZ dd	lmZm Z! dd
l"m#Z# ddl$m%Z% ddl&m'Z' ddl(m)Z) ddl*m+Z+ ddl,m-Z-m.Z.m/Z/m0Z0m1Z1 ddl2m3Z3 ddl4m5Z5 ddl6m7Z7m8Z8m9Z9m:Z:m;Z;m<Z< ddl=m>Z>m?Z?m@Z@mAZA ddlBmCZC ddlDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZO ddlPmQZQmRZRmSZS ddlTmUZU ddlVmW  mXZY ddlZm[Z[m\Z\ ddl]m^Z_m`Z` ddlambZb ddlcmdZdmeZe ddlfmgZg ddlhmiZimjZj erKddlkmlZlmmZmmnZn ddlompZp dd lqmrZrmsZsmtZt dd!lumvZvmwZwmxZxmyZymzZzm{Z{m|Z|m}Z} dd"lcm~Z~ d#Zd$Zd%d& Zdd+d,Zd-d. Ze[Zdd1d2Zd3Zd4ed5< d6Zd4ed7< d8Zd4ed9< d:d:d;d;d<ZeEdgiZd=Zd4ed>< d?Zd4ed@< edA  ejdBdCeejdD ejdEdeeg dFdD W d   n	1 sw   Y  dadCadGdH Z	I			C		J					K	$dddadbZ		c	K					C	dddldmZddqdrZG dsdt dtZG dudv dvZG dwdx dxZG dydz dzeZG d{d| d|eZG d}d~ d~eZG dd deZG dd 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	CddddZeddddZ	CddddZdddZdddZdddZdddZdddZdddńZdddǄZdddɄZddd̈́ZdddЄZddd҄ZG ddԄ dԃ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_string_dtype)libwriters)is_string_array)	timezones)HAS_PYARROW)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StringDtypeTimedeltaIndexconcatisna)CategoricalDatetimeArrayPeriodArray)BaseStringArray)PyTablesExprmaybe_expression)array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 unicoderT   )
isinstancenpbytes_decode)s rZ   M/var/www/html/kangema/venv/lib/python3.10/site-packages/pandas/io/pytables.py_ensure_decoded   s   
r\   encoding
str | Nonereturnstrc                 C  s   | d u rt } | S N)_default_encodingr]   rZ   rZ   r[   _ensure_encoding   s   rd   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
    )rU   r`   namerZ   rZ   r[   _ensure_str   s   
rg   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 )Nrj   rh   )r;   Term).0termlevelrZ   r[   
<listcomp>   s
    z _ensure_term.<locals>.<listcomp>rk   N)rU   listtupler;   rl   len)whererh   rZ   ro   r[   _ensure_term   s   	
rv   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)frz   tr{   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)rz   r{   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   rZ   rZ   r[   _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   r]   )r   store
r   r   r]   r   r   r   r   r   r   r   rZ   r[   <lambda>      zto_hdf.<locals>.<lambda>c                   s   | j 	 d
S )N)r   r   r   r   r   r   r]   r   putr   r   rZ   r[   r   +  r   )r   r   r   N)rA   rU   r`   HDFStore)r   r   r   r   r   r   r   r   r   r   r   r   r   r   r]   r|   r   rZ   r   r[   to_hdf
  s    

"r   rru   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.

    Notes
    -----
    When ``errors="surrogatepass"``, ``pd.options.future.infer_string`` is true,
    and PyArrow is installed, if a UTF-16 surrogate is encountered when decoding
    to UTF-8, the resulting dtype will be
    ``pd.StringDtype(storage="python", na_value=np.nan)``.

    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.Nrj   rk   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.)ru   r   r   r   r   r   
auto_close)
ValueErrorrv   rU   r   is_openOSErrorrA   r`   NotImplementedErrorospathexists	TypeErrorFileNotFoundErrorgroupsrt   _is_metadata_of_v_pathnameselectLookupErrorr   r   close)r   r   r   r   ru   r   r   r   r   r   kwargsr   r   r   r   candidate_only_groupgroup_to_checkrZ   rZ   r[   read_hdfB  sv   V








r   grouprJ   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.Frj   metaT)_v_depth	_v_parent_v_name)r   r   currentparentrZ   rZ   r[   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   rZ   )r   r   filtersall_complibsdefault_complibrA   _pathr   r   
_complevel_complib_fletcher32_filtersopen)selfr   r   r   r   r   r   r   rZ   rZ   r[   __init__7  s&   	
zHDFStore.__init__c                 C     | j S ra   r   r   rZ   rZ   r[   
__fspath__X  s   zHDFStore.__fspath__c                 C  s   |    | jdusJ | jjS )zreturn the root nodeN)_check_if_openr   rootr   rZ   rZ   r[   r   [  s   zHDFStore.rootc                 C  r   ra   r   r   rZ   rZ   r[   filenameb     zHDFStore.filenamer   c                 C  
   |  |S ra   )getr   r   rZ   rZ   r[   __getitem__f     
zHDFStore.__getitem__c                 C  s   |  || d S ra   r   )r   r   r   rZ   rZ   r[   __setitem__i  s   zHDFStore.__setitem__c                 C  r   ra   )remover   rZ   rZ   r[   __delitem__l  r   zHDFStore.__delitem__rf   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   rf   rZ   rZ   r[   __getattr__o  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 '/'
        Nrj   TF)get_noder   )r   r   noderf   rZ   rZ   r[   __contains__y  s   
zHDFStore.__contains__ri   c                 C     t |  S ra   )rt   r   r   rZ   rZ   r[   __len__     zHDFStore.__len__c                 C  s   t | j}t|  d| dS )N
File path: 
)rC   r   r   )r   pstrrZ   rZ   r[   __repr__  s   
zHDFStore.__repr__rP   c                 C  s   | S ra   rZ   r   rZ   rZ   r[   	__enter__     zHDFStore.__enter__exc_typetype[BaseException] | None	exc_valueBaseException | None	tracebackTracebackType | Nonec                 C     |    d S ra   )r   )r   r   r  r  rZ   rZ   r[   __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 rZ   r   rm   nrZ   rZ   r[   rq         z!HDFStore.keys.<locals>.<listcomp>nativeNc                 S  r
  rZ   r  r  rZ   rZ   r[   rq     s    /Table)	classnamez8`include` should be either 'pandas' or 'native' but is 'r   )r   r   
walk_nodesr   )r   r  rZ   rZ   r[   keys  s   
zHDFStore.keysIterator[str]c                 C  r   ra   )iterr  r   rZ   rZ   r[   __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   grZ   rZ   r[   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   msgrZ   rZ   r[   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   rZ   rZ   r[   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   rZ   rZ   r[   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   rZ   rZ   r[   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   rZ   rZ   r[   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$  rj   rk   c                   s   j | || dS )N)r   r   ru   r   read_start_stop_wherer   rY   rZ   r[   func  s   zHDFStore.select.<locals>.funcru   nrowsr   r   r   r   r   )r   r   rv   _create_storer
infer_axesTableIteratorr0  
get_result)r   r   ru   r   r   r   r   r   r   r   r.  itrZ   r-  r[   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
        rj   rk   z&can only read_coordinates with a tableru   r   r   )rv   
get_storerrU   r  r   read_coordinates)r   r   ru   r   r   tblrZ   rZ   r[   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)r;  r   r   )r7  rU   r  r   read_column)r   r   r;  r   r   r9  rZ   rZ   r[   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
        rj   rk   r   )r   ru   r   r   r   r   r   r   zkeys must be a list/tuplez keys must have a non-zero lengthNc                      g | ]}  |qS rZ   )r7  rm   kr   rZ   r[   rq   %      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 rZ   )rU   r  rm   xrZ   rZ   r[   rq   :      c                 S  s   h | ]	}|j d  d  qS r   )non_index_axesrm   r}   rZ   rZ   r[   	<setcomp>=  rE  z.HDFStore.select_as_multiple.<locals>.<setcomp>c                   s*    fddD }t |dd S )Nc                   s   g | ]}|j  d qS )ru   r   r   r   r'  rH  )r*  r+  r,  r   rZ   r[   rq   B  s    z=HDFStore.select_as_multiple.<locals>.func.<locals>.<listcomp>F)axisverify_integrity)r4   _consolidate)r*  r+  r,  objs)rK  r   tblsr)  r[   r.  ?  s   z)HDFStore.select_as_multiple.<locals>.funcr/  T)coordinates)rv   rU   rr   rs   rt   r`   r   r   r   r7  	itertoolschainzipr   is_tablepathnamer0  popr3  r4  )r   r  ru   selectorr   r   r   r   r   r   rY   r0  r}   r@  _tblsr.  r5  rZ   )rK  r   r   rO  r[   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_formatrz   )r   r   r   r   r   r   r   r   r]   r   rZ  r   )r   _validate_format_write_to_group)r   r   r   r   r   r   r   r   r   r   r   r]   r   rZ  r   rZ   rZ   r[   r   Z  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

        rj   rk   Nz5trying to remove a node with a non-None where clause!T	recursivez7can only remove with where on objects written as tablesr6  )rv   r7  r   AssertionError	Exceptionr   r   	_f_removecomall_noner   rT  delete)r   r   ru   r   r   rY   errr   rZ   rZ   r[   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_tabler[  r{   )r   axesr   r   r   r   r   r   r   expectedrowsr   r   r]   r   )r   r   r\  r]  )r   r   r   r   rh  r   r   r   r   r   r   r   r   ri  r   r   r]   r   rZ   rZ   r[   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   )rm   cols)r   rZ   r[   	<genexpr>  s     z.HDFStore.append_to_multiple.<locals>.<genexpr>r   rK  c                   s   i | ]\}}| v r||qS rZ   rZ   rm   r   r   )vrZ   r[   
<dictcomp>  s    z/HDFStore.append_to_multiple.<locals>.<dictcomp>)r   r   )r   rU   rk  r   nextr  setrangendim	_AXES_MAPr   r  extendrh  
differencer-   sortedget_indexertakevaluesintersectionlocrV  reindexr   )r   rj  r   rW  r   rh  r   r   rK  
remain_keyremain_valuesr@  orderedorddidxsvalid_indexr   r   dcvalfilteredrZ   )rr  r   r[   append_to_multipleE  s\   
&

zHDFStore.append_to_multipleoptlevelkindr^   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   r7  rU   r  r   create_index)r   r   r   r  r  rY   rZ   rZ   r[   create_table_index  s   

zHDFStore.create_table_indexrr   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_typeNr{   )	rU   r   linkLinkgetattr_v_attrsr{   r  r   )rm   r  rZ   rZ   r[   rq     s    

z#HDFStore.groups.<locals>.<listcomp>)r   r   r   r   walk_groupsr   rZ   rZ   r[   r     s   zHDFStore.groupsr  ru   *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_childrenr~  rU   r   Groupr   r   r   rstrip)r   ru   r  r   leaveschildr  rZ   rZ   r[   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NoSuchNodeErrorrU   rJ   r   )r   r   r   rZ   rZ   r[   r   1  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   r1  r2  )r   r   r   rY   rZ   rZ   r[   r7  A  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 rZ   )
is_indexedrf   rm   r   rZ   rZ   r[   rq   y      z!HDFStore.copy.<locals>.<listcomp>r   )r   r   r]   rc   )r   rr   r  rU   rs   r7  r   r   r  rh  r   r  r]   r   )r   r   r   r  r  r   r   r   r  	new_storer@  rY   datar   rZ   rZ   r[   copyK  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: rB     EmptyzFile is CLOSED)rC   r   r   r   r{  r  rt   r7  r   rU  r`  ra  rB   )
r   r   outputlkeysr  r~  r@  rY   detaildstrrZ   rZ   r[   info  s8   


zHDFStore.infoc                 C  s   | j st| j dd S )Nz file is not open!)r   r   r   r   rZ   rZ   r[   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 [rB  N)_FORMAT_MAPlowerr   r   )r   r   rf  rZ   rZ   r[   r\    s   zHDFStore._validate_formatrT   DataFrame | Series | Noner]   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_typer{   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->r]   r   series_tabler   rj   appendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)r  r  r  r  r  wormz<cannot properly create the storer for: [_TABLE_MAP] [group->)rU   r1   r+   r   r\   r  r  r   r   r{   r  SeriesFixed
FrameFixedr   r   nlevelsGenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable	WORMTable)r   r   r   r   r]   r   pttt_STORER_MAPclsrf  r   
_TABLE_MAPrZ   rZ   r[   r1    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emptyr{   r  rz   zCan only append to Tablesz0Compression not supported on Fixed format stores)objrh  r   r   r   r   r   r   ri  r   r   r   rZ  )r   )r  _identify_groupr1  rT  	is_existsr   set_object_infowriterU   r  r  )r   r   r   r   rh  r   r   r   r   r   r   r   ri  r   r   r   r]   r   rZ  r   rY   rZ   rZ   r[   r]  $  s>   
zHDFStore._write_to_groupr   rJ   c                 C  s   |  |}|  | S ra   )r1  r2  r(  )r   r   rY   rZ   rZ   r[   r%  b  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.NTr^  )r   r   remove_node_create_nodes_and_group)r   r   r   r   rZ   rZ   r[   r  g  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   splitrt   endswithr   create_group)r   r   pathsr   pnew_pathr   rZ   rZ   r[   r  y  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   )rf   r`   )r   r`   r_   r   r_   ri   )r_   rP   )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`   r;  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`   rZ  r   r   r   r_   r   )NNTTNNNNNNNNNNr   )r   r`   r   r   r   rg  r   r   r   r   r   r   r   r   r   r   r   r   r   r`   r_   r   )NNF)rj  rk  r   r   r_   r   )r   r`   r  r   r  r^   r_   r   )r_   rr   )r  )ru   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`   )NNrT   r   )r   r  r]   r`   r   r`   r_   r  )NTFNNNNNNFNNNr   T)r   r`   r   r   r   rg  r   r   r   r   r   r   r   r   r   r   r   r`   rZ  r   r_   r   )r   rJ   )r   r`   r   r   r_   rJ   )r   r`   r_   rJ   )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   r:  r=  rY  r   r   r   r  r  r   r  r   r7  r  r  r   r\  r1  r]  r%  r  r  rZ   rZ   rZ   r[   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 )r3  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  rY   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   rY   r.  ru   rT  minr0  r   r   rP  ri   r   r   )r   r   rY   r.  ru   r0  r   r   r   r   r   rZ   rZ   r[   r     s,   

zTableIterator.__init__rE   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   rP  r   r   r  r   r.  rt   r   )r   r   r   r   rZ   rZ   r[   r    s   


	zTableIterator.__iter__c                 C  s   | j r
| j  d S d S ra   )r   r   r   r   rZ   rZ   r[   r     s   zTableIterator.closerP  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)ru   z$can only read_coordinates on a tabler6  )r   rU   rY   r  r   r8  ru   rP  r   r   r.  r   )r   rP  ru   resultsrZ   rZ   r[   r4    s   
zTableIterator.get_result)NNFNF)r   r   rY   r  r   r   r   r   r   r   r_   r   r_   rE   r  r  )rP  r   )	r   r  r  r  r  r   r  r   r4  rZ   rZ   rZ   r[   r3    s   
 	
*
r3  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_nameNrf   r`   cnamer^   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.)rU   r`   r   r~  r  typrf   r  rK  posr  r  r  r  r{   r   metadataset_pos)r   rf   r~  r  r  r  rK  r  r  r  r  r  r{   r   r  rZ   rZ   r[   r     s(   


zIndexCol.__init__ri   c                 C     | j jS ra   )r  itemsizer   rZ   rZ   r[   r  <  s   zIndexCol.itemsizec                 C     | j  dS )N_kindre   r   rZ   rZ   r[   	kind_attrA     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  rZ   rZ   r[   r  E  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->rZ   rq  rZ   rZ   r[   rq   P      z%IndexCol.__repr__.<locals>.<listcomp>)rf   r  rK  r  r  )
rs   maprC   rf   r  rK  r  r  joinrS  r   temprZ   rZ   r[   r   K  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 ra   r  r  r  r   rZ   r[   ro  X  
    
z"IndexCol.__eq__.<locals>.<genexpr>)rf   r  rK  r  rl  r   r  rZ   r  r[   __eq__V     zIndexCol.__eq__c                 C  s   |  | S ra   )r  r  rZ   rZ   r[   __ne__]  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 columnrn  F)hasattrr{   r  rn  r  r  r   rZ   rZ   r[   r  `  s   zIndexCol.is_indexedr~  
np.ndarrayr]   r   3tuple[np.ndarray, np.ndarray] | tuple[Index, Index]c              
   C  st  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 nL ty }	 z(|dkrtd	rt|	d
rtr||fdtdtjdi|}n W Y d}	~	nd}	~	w ty   d|v rd|d< ||fi |}Y nw t|| j}
|
|
fS )zV
        Convert the data from this selection to the appropriate pandas type.
        Nrf   r  Mi8c                 [  s    t j| |dd d|d S )Nr  )r  rf   )r/   from_ordinalsr   _rename)rD  kwdsrZ   rZ   r[   r     s    z"IndexCol.convert.<locals>.<lambda>surrogatepassfuture.infer_stringsurrogates not alloweddtypepythonstoragena_value)rU   rV   ndarrayr   r$  fieldsr  r  r\   r  _maybe_convertr  r  r-   r   is_np_dtyper'   r,   UnicodeEncodeErrorr   r`   r  r   r2   nanr   _set_tzr  )r   r~  r   r]   r   val_kindr   factorynew_pd_indexrf  final_pd_indexrZ   rZ   r[   converth  sV   

zIndexCol.convertc                 C  r   )zreturn the valuesr~  r   rZ   rZ   r[   	take_data  r   zIndexCol.take_datac                 C  r  ra   )r{   r  r   rZ   rZ   r[   attrs     zIndexCol.attrsc                 C  r  ra   r{   descriptionr   rZ   rZ   r[   r:    r8  zIndexCol.descriptionc                 C  s   t | j| jdS )z!return my current col descriptionN)r  r:  r  r   rZ   rZ   r[   col     zIndexCol.colc                 C  r   zreturn my cython valuesr5  r   rZ   rZ   r[   cvalues     zIndexCol.cvaluesrE   c                 C  s
   t | jS ra   )r  r~  r   rZ   rZ   r[   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  )r\   r  rU   rk  r   rf   r  r  r   	StringColr  )r   r   rZ   rZ   r[   maybe_set_size  s   
zIndexCol.maybe_set_sizec                 C     d S ra   rZ   r   rZ   rZ   r[   validate_names  r   zIndexCol.validate_nameshandlerAppendableTabler   c                 C  s:   |j | _ |   | | | | | | |   d S ra   )r{   validate_colvalidate_attrvalidate_metadatawrite_metadataset_attr)r   rE  r   rZ   rZ   r[   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 itemsizer@  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)r\   r  r;  r  r   r  )r   r  crZ   rZ   r[   rG    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 [ - rB  )r  r7  r   r  r   )r   r   existing_kindrZ   rZ   r[   rH    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 [rB  )_info_fieldsr  
setdefaultrf   r   rx   warningswarnr   r   setattrr   )r   r  r   r   idxexisting_valuewsrZ   rZ   r[   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   rf   __dict__update)r   r  rW  rZ   rZ   r[   set_info	  s   zIndexCol.set_infoc                 C  s   t | j| j| j dS )zset the kind for this columnN)rV  r7  r   r  r   rZ   rZ   r[   rK  	     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   rE  new_metadatacur_metadatarZ   rZ   r[   rI  	  s   
zIndexCol.validate_metadatac                 C  s"   | j dur|| j| j  dS dS )zset the meta dataN)r  rJ  r  )r   rE  rZ   rZ   r[   rJ  /	  s   
zIndexCol.write_metadata)NNNNNNNNNNNNN)rf   r`   r  r^   r_   r   r  r  )r  ri   r_   r   r  r  r_   r   r  )r~  r  r]   r`   r   r`   r_   r  r  ra   r  )rE  rF  r   r   r_   r   )r   r   r_   r   )rE  rF  r_   r   )"r   r  r  r  r  r  r  rR  r   r  r  r   r  r   r  r  r  r4  r6  r7  r:  r;  r>  r  rB  rD  rL  rG  rH  rZ  r]  rK  rI  rJ  rZ   rZ   rZ   r[   r    sd   
 +




@









	


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 NFrZ   r   rZ   rZ   r[   r  8	     zGenericIndexCol.is_indexedr~  r  r]   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
        )rU   rV   r)  r   r0   rt   )r   r~  r   r]   r   r   rZ   rZ   r[   r4  <	  s   zGenericIndexCol.convertr   c                 C  rC  ra   rZ   r   rZ   rZ   r[   rK  N	  r   zGenericIndexCol.set_attrNr  )r~  r  r]   r`   r   r`   r_   rk  r  )r   r  r  r  r  r  r4  rK  rZ   rZ   rZ   r[   rg  5	  s    
rg  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  Nrf   r`   r  r^   r$  DtypeArg | Noner_   r   c                   s2   t  j|||||||||	|
|d || _|| _d S )N)rf   r~  r  r  r  r  r  r  r{   r   r  )superr   r$  r  )r   rf   r~  r  r  r  r  r  r  r{   r   r  r$  r  	__class__rZ   r[   r   c	  s   
zDataCol.__init__c                 C  r  )N_dtypere   r   rZ   rZ   r[   
dtype_attr	  r  zDataCol.dtype_attrc                 C  r  )N_metare   r   rZ   rZ   r[   	meta_attr	  r  zDataCol.meta_attrc                 C  r  )Nr  c                 S  r  r  rZ   rq  rZ   rZ   r[   rq   	  r  z$DataCol.__repr__.<locals>.<listcomp>)rf   r  r$  r  shape)
rs   r  rC   rf   r  r$  r  ru  r	  rS  r
  rZ   rZ   r[   r   	  s   zDataCol.__repr__r  r  r   c                   r  )r  c                 3  r  ra   r  r  r  rZ   r[   ro  	  r  z!DataCol.__eq__.<locals>.<genexpr>)rf   r  r$  r  r  r  rZ   r  r[   r  	  r  zDataCol.__eq__r  rL   c                 C  s@   |d usJ | j d u sJ t|\}}|| _|| _ t|| _d S ra   )r$  _get_data_and_dtype_namer  _dtype_to_kindr  )r   r  
dtype_namerZ   rZ   r[   set_data	  s   zDataCol.set_datac                 C  r   )zreturn the datar  r   rZ   rZ   r[   r6  	  r   zDataCol.take_datar~  rH   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.
        rj   r  r  mr   r  ru  )r$  r  ru  rw  sizerU   r6   codesget_atom_datarf   r   r,  r'   get_atom_datetime64get_atom_timedelta64r"   r   
ComplexColr$   get_atom_string)r  r~  r$  r  ru  r  atomrZ   rZ   r[   	_get_atom	  s.   





zDataCol._get_atomc                 C  s   t  j||d dS )Nr   r}  r   rA  r  ru  r  rZ   rZ   r[   r  	     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UIntrH   periodInt64Col)r  
capitalizer  r   )r  r  k4col_namekcaprZ   rZ   r[   get_atom_coltype	  s   


zDataCol.get_atom_coltypec                 C  s   | j |d|d dS )Nr{  r   ru  r  r  ru  r  rZ   rZ   r[   r  	  r^  zDataCol.get_atom_datac                 C     t  j|d dS Nr   r  r   r  r  ru  rZ   rZ   r[   r  	     zDataCol.get_atom_datetime64c                 C  r  r  r  r  rZ   rZ   r[   r  	  r  zDataCol.get_atom_timedelta64c                 C     t | jdd S )Nru  )r  r  r   rZ   rZ   r[   ru  	     zDataCol.shapec                 C  r   r=  rz  r   rZ   rZ   r[   r>  	  r?  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  r7  r   rr   r~  r   rr  r$  )r   r   existing_fieldsexisting_dtyperZ   rZ   r[   rH  	  s   zDataCol.validate_attrr  r]   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$  r   c                 S     g | ]}t |qS rZ   r   fromordinalrm   rr  rZ   rZ   r[   rq   9
  rA  z#DataCol.convert.<locals>.<listcomp>c                 S  r  rZ   r   fromtimestampr  rZ   rZ   r[   rq   =
  rA  r_  F)
categoriesr  validater  Or@  r   r]   r   )#rU   rV   r)  r   r$  r*  r  r  rv  rw  r  r\   r   r  r  r  r  r/  asarrayr  r   ravelr-   float64r5   anyastyperi   cumsum_valuesr6   
from_codesr   _unconvert_string_arrayr~  )r   r~  r   r]   r   	convertedrx  r  r   r  r  r  r$  r  r  maskrZ   rZ   r[   r4  
  sj   







 
zDataCol.convertc                 C  sH   t | j| j| j t | j| j| j | jdusJ t | j| j| j dS )zset the data for this columnN)rV  r7  r   r~  rt  r   r$  rr  r   rZ   rZ   r[   rK  f
  s   zDataCol.set_attr)NNNNNNNNNNNN)rf   r`   r  r^   r$  rm  r_   r   r  rf  )r  rL   r_   r   )r~  rL   r_   rH   )r  r`   r_   r  r  r`   r_   rH   r  )r~  r  r]   r`   r   r`   )r   r  r  r  r  r  rR  r   r  rr  rt  r   r  ry  r6  classmethodr  r  r  r  r  r  ru  r>  rH  r4  rK  __classcell__rZ   rZ   ro  r[   rl  R	  sZ     










drl  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$   r-   r~  r$  r   r   rZ   rZ   r[   rD  s
  s   zDataIndexableCol.validate_namesc                 C  s   t  j|dS )N)r  r  r  rZ   rZ   r[   r  x
  r  z DataIndexableCol.get_atom_stringr  r`   rH   c                 C  s   | j |d S )Nr{  r  r  rZ   rZ   r[   r  |
  r  zDataIndexableCol.get_atom_datac                 C  
   t   S ra   r  r  rZ   rZ   r[   r  
     
z$DataIndexableCol.get_atom_datetime64c                 C  r  ra   r  r  rZ   rZ   r[   r  
  r  z%DataIndexableCol.get_atom_timedelta64Nr  r  )r   r  r  r  r  rD  r  r  r  r  r  rZ   rZ   rZ   r[   r  n
  s    


r  c                   @  s   e Zd ZdZdS )GenericDataIndexableColz(represent a generic pytables data columnN)r   r  r  r  rZ   rZ   rZ   r[   r  
  s    r  c                   @  s~  e Zd ZU dZded< dZded< ded< ded	< d
ed< dZded< 		dPdQddZedRddZ	edSddZ
edd  ZdTd!d"ZdUd#d$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_kindrz   format_typetype[DataFrame | Series]obj_typeri   rw  r   r   Fr   rT  rT   r   r   rJ   r]   r^   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 ra   )
rU   r   r   r   rJ   r   r   rd   r]   r   )r   r   r   r]   r   rZ   rZ   r[   r   
  s   

zFixed.__init__c                 C  s*   | j d dko| j d dko| j d dk S )Nr   rj   
      )versionr   rZ   rZ   r[   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 ra   ri   rC  rZ   rZ   r[   ro  
  s    z Fixed.version.<locals>.<genexpr>.r  rF  )r   r   r   )r\   r  r   r  rs   r  rt   r   )r   r  rZ   rZ   r[   r  
  s   
zFixed.versionc                 C  s   t t| jjdd S )Nr  )r\   r  r   r  r   rZ   rZ   r[   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 rZ   rC   rC  rZ   rZ   r[   rq   
      z"Fixed.__repr__.<locals>.<listcomp>[rB  12.12z	 (shape->))r2  ru  rU   rr   rs   r	  r  )r   rY   jshaperZ   rZ   r[   r   
  s   zFixed.__repr__c                 C  s   t | j| j_t t| j_dS )zset my pandas type & versionN)r`   r  r7  r  _versionr  r   rZ   rZ   r[   r  
  s   zFixed.set_object_infoc                 C  s   t  | }|S ra   r  )r   new_selfrZ   rZ   r[   r  
  s   
z
Fixed.copyc                 C  r   ra   )r0  r   rZ   rZ   r[   ru  
  r   zFixed.shapec                 C  r  ra   r   r   r   rZ   rZ   r[   rU  
  r8  zFixed.pathnamec                 C  r  ra   )r   r   r   rZ   rZ   r[   r   
  r8  zFixed._handlec                 C  r  ra   )r   r   r   rZ   rZ   r[   r   
  r8  zFixed._filtersc                 C  r  ra   )r   r   r   rZ   rZ   r[   r   
  r8  zFixed._complevelc                 C  r  ra   )r   r   r   rZ   rZ   r[   r   
  r8  zFixed._fletcher32c                 C  r  ra   )r   r  r   rZ   rZ   r[   r7  
  r8  zFixed.attrsc                 C  rh  zset our object attributesNrZ   r   rZ   rZ   r[   	set_attrs
      zFixed.set_attrsc                 C  rh  )zget our object attributesNrZ   r   rZ   rZ   r[   	get_attrs
  r  zFixed.get_attrsc                 C  r   )zreturn my storabler   r   rZ   rZ   r[   storable
  r?  zFixed.storablec                 C  rh  ri  rZ   r   rZ   rZ   r[   r  
  rj  zFixed.is_existsc                 C  r  )Nr0  )r  r  r   rZ   rZ   r[   r0    r  zFixed.nrowsLiteral[True] | Nonec                 C  s   |du rdS dS )z%validate against an existing storableNTrZ   r  rZ   rZ   r[   r    s   zFixed.validateNc                 C  rh  )+are we trying to operate on an old version?NrZ   )r   ru   rZ   rZ   r[   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   rY   rZ   rZ   r[   r2    s
   zFixed.infer_axesr   r   r   c                 C     t d)Nz>cannot read on an abstract storer: subclasses should implementr   r   ru   r   r   r   rZ   rZ   r[   r(    s   z
Fixed.readc                 K  r  )Nz?cannot write on an abstract storer: subclasses should implementr  r   r  r   rZ   rZ   r[   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
        Tr^  Nz#cannot delete on an abstract storer)rc  rd  r   r  r   r   )r   ru   r   r   rZ   rZ   r[   re  +  s   zFixed.delete)rT   r   )
r   r   r   rJ   r]   r^   r   r`   r_   r   r  )r_   r  r  r  )r_   r  r  )r_   r  ra   NNNNr   r   r   r   r  )r   r   r   r   r_   r   )!r   r  r  r  r  r  rT  r   r  r  r  r  r   r  r  ru  rU  r   r   r   r   r7  r  r  r  r  r0  r  r  r2  r(  r  re  rZ   rZ   rZ   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$d%ZdBd'd(ZdCd*d+Z	d?dDd,d-Z	d?dEd0d1ZdFd4d5Z	dGdHd:d;ZdS )IGenericFixedza generified fixed versiondatetimer  c                 C  s   i | ]\}}||qS rZ   rZ   )rm   r@  rr  rZ   rZ   r[   rs  =  rA  zGenericFixed.<dictcomp>r	  
attributesr_   r`   c                 C  s   | j |dS )N )_index_type_mapr   )r   r  rZ   rZ   r[   _class_to_aliasA  s   zGenericFixed._class_to_aliasc                 C  s   t |tr|S | j|tS ra   )rU   r   _reverse_index_mapr   r-   )r   aliasrZ   rZ   r[   _alias_to_classD  s   
zGenericFixed._alias_to_classc                 C  s   |  tt|dd}|tkrd	dd}|}n|tkr#d	dd}|}n|}i }d|v r7|d |d< |tu r7t}d|v rXt|d trL|d 	d|d< n|d |d< |tu sXJ ||fS )
Nindex_classr  c                 S  s>   t j| j| j|d}tj|d d}|d ur|d|}|S )N)r$  r  re   UTC)r7   _simple_newr~  r$  r,   tz_localize
tz_convert)r~  r  r  dtaresultrZ   rZ   r[   r|   S  s   
z*GenericFixed._get_index_factory.<locals>.fc                 S  s$   t |}tj| |d}tj|d dS )Nr  re   )r)   r8   r  r/   )r~  r  r  r$  parrrZ   rZ   r[   r|   `  s   r  r  zutf-8r  )
r  r\   r  r,   r/   r-   r3   rU   bytesrX   )r   r7  r  r|   r1  r   rZ   rZ   r[   _get_index_factoryJ  s*   


zGenericFixed._get_index_factoryr   c                 C  s$   |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   ru   rZ   rZ   r[   validate_read{  s   zGenericFixed.validate_readr   c                 C  rh  )NTrZ   r   rZ   rZ   r[   r    rj  zGenericFixed.is_existsc                 C  s   | j | j_ | j| j_dS r  )r]   r7  r   r   rZ   rZ   r[   r    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 attributesr]   Nr   r   )rd   r  r7  r]   r\   r   r  rV  )r   r  rZ   rZ   r[   r    s
   
zGenericFixed.get_attrsc                 K  r  ra   )r  r  rZ   rZ   r[   r    r   zGenericFixed.writeNr   r   r   r   c                 C  s   ddl }t| j|}|j}t|dd}t||jr2|d || }t|dd}	|	dur1t||	d}n@tt|dd}	t|dd}
|
durLtj	|
|	d}n||| }|	rg|	
drgt|d	d}t||d
d}n|	dkrrtj|dd}|rw|jS |S )z2read an array for the specified node (off of groupr   N
transposedF
value_typer  ru  r  r  Tr  r  r  )r   r  r   r  rU   VLArraypd_arrayr\   rV   r  r  r/  r  T)r   r   r   r   r   r   r7  r  retr$  ru  r  rZ   rZ   r[   
read_array  s.   zGenericFixed.read_arrayr-   c                 C  sd   t t| j| d}|dkr| j|||dS |dkr+t| j|}| j|||d}|S td| )N_varietymultir   r   regularzunrecognized index variety: )r\   r  r7  read_multi_indexr   read_index_noder   )r   r   r   r   varietyr   r   rZ   rZ   r[   
read_index  s   zGenericFixed.read_indexr   c                 C  s   t |trt| j| dd | || d S t| j| dd td|| j| j}| ||j	 t
| j|}|j|j_|j|j_t |ttfrQ| t||j_t |tttfr^|j|j_t |trq|jd urst|j|j_d S d S d S )Nr	  r
  r  r   )rU   r.   rV  r7  write_multi_index_convert_indexr]   r   write_arrayr~  r  r   r  r  rf   r,   r/   r  r   r  r3   r  r  _get_tz)r   r   r   r  r   rZ   rZ   r[   write_index  s    



zGenericFixed.write_indexr.   c                 C  s   t | j| d|j tt|j|j|jD ]P\}\}}}t|j	t
r'td| d| }t||| j| j}| ||j t| j|}	|j|	j_||	j_t |	j| d| | | d| }
| |
| qd S )N_nlevelsz=Saving a MultiIndex with an extension dtype is not supported._level_name_label)rV  r7  r  	enumeraterS  levelsr  namesrU   r$  r(   r   r  r]   r   r  r~  r  r   r  r  rf   )r   r   r   ilevlevel_codesrf   	level_key
conv_levelr   	label_keyrZ   rZ   r[   r    s$   
zGenericFixed.write_multi_indexc                 C  s   t | j| d}g }g }g }t|D ]6}| d| }	t | j|	}
| j|
||d}|| ||j | d| }| j|||d}|| qt|||ddS )Nr  r  r  r  T)r  r  r  rL  )	r  r7  rv  r   r  r   rf   r  r.   )r   r   r   r   r  r  r  r  r  r   r   r  r"  r  rZ   rZ   r[   r     s    
zGenericFixed.read_multi_indexr   rJ   c              
   C  sX  ||| }d|j v rt|j jdkrtj|j j|j jd}t|j j}d }d|j v r6t|j j	}t|}|j }| 
|\}}	|dv rW|t||| j| jdfdti|	}
nPz|t||| j| jdfi |	}
W n= ty } z1| jdkrtd	rt|d
rtr|t||| j| jdfdtdtjdi|	}
n W Y d }~nd }~ww ||
_	|
S )Nru  r   r  rf   )r   r  r  r$  r!  r"  r#  r%  r&  )r  rV   prodru  r  r  r\   r  rg   rf   r  _unconvert_indexr]   r   r  r-  r   r`   r  r   r2   r.  )r   r   r   r   r  r  rf   r7  r1  r   r   rf  rZ   rZ   r[   r    sf   


zGenericFixed.read_index_noder   rL   c                 C  sJ   t d|j }| j| j|| t| j|}t|j|j	_
|j|j	_dS )zwrite a 0-len arrayrj   N)rV   r  rw  r   create_arrayr   r  r`   r$  r  r  ru  )r   r   r   arrr   rZ   rZ   r[   write_array_emptyN  s
   zGenericFixed.write_array_emptyr  rK   r  Index | Nonec                 C  s  t |dd}|| jv r| j| j| |jdk}d}t|jtr$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  }| | nt!|jdr| j"| j||#d t$|jt%| j|j&_'nt|jt(r| j"| j||j) t%| j|}t*|j+|j&_+d|jj, d|j&_'nWt!|jdr| j"| j||#d dt%| j|j&_'n:t|t-r8| j| j|t  }| |.  t%| j|}t$|j|j&_'n|rB| || 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   skipnar@  rP  r  r  datetime64[rB  r|  r  )0r=   r   r   r  r~  rU   r$  r&   r   r  r  r   r   r   r   Atom
from_dtypecreate_carrayru  r(  r   rV   object_r   infer_dtypery   rT  rU  r   r   create_vlarray
ObjectAtomr   r,  r&  viewr`   r  r  r  r'   asi8r  r  unitr9   to_numpyr  )r   r   r  r  r   empty_arrayr  r  cainferred_typerY  vlarrr   rZ   rZ   r[   r  W  sr   





zGenericFixed.write_arrayr  r  r  r  r  )r   r`   r   r   r   r   r_   r-   )r   r`   r   r-   r_   r   )r   r`   r   r.   r_   r   )r   r`   r   r   r   r   r_   r.   )r   rJ   r   r   r   r   r_   r-   )r   r`   r   rL   r_   r   ra   )r   r`   r  rK   r  r)  r_   r   )r   r  r  r  r,   r/   r  r  r  r  r  r  r  r  r   r  r  r  r  r  r  r  r  r  r  r  r(  r  rZ   rZ   rZ   r[   r  9  s4   
 

1


&


7
r  c                      sR   e Zd ZU dZdgZded< edd Z				ddddZd fddZ	  Z
S )r  r  rf   rD   c              	   C  s*   zt | jjfW S  ttfy   Y d S w ra   )rt   r   r~  r   r   r   rZ   rZ   r[   ru    s
   zSeriesFixed.shapeNr   r   r   r_   r1   c           	      C  s   |  || | jd||d}| jd||d}zt||| jdd}W |S  tyX } z*| jdkrLtdrLt|	drLt
rLt||| jdtd	tjd
d}n W Y d }~|S d }~ww )Nr   r  r~  F)r   rf   r  r!  r"  r#  r%  r&  )r   rf   r  r$  )r   r  r  r1   rf   r-  r   r   r`   r  r   r2   rV   r.  )	r   ru   r   r   r   r   r~  r  rf  rZ   rZ   r[   r(    s4   

	zSeriesFixed.readr   c                   s<   t  j|fi | | d|j | d| |j| j_d S )Nr   r~  )rn  r  r  r   r  rf   r7  r  ro  rZ   r[   r    s   zSeriesFixed.writer  r   r   r   r   r_   r1   r  )r   r  r  r  r  r  r  ru  r(  r  r  rZ   rZ   ro  r[   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 )BlockManagerFixedrw  nblocksri   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_itemsru  rj   )	rw  rv  r?  r  r   block0_valuesrr   r   r   )r   rw  r  r  r   ru  rZ   rZ   r[   ru    s&   
zBlockManagerFixed.shapeNr   r   r   r+   c                 C  sX  |  || |  d}g }t| jD ]}||kr||fnd\}}	| jd| ||	d}
||
 q|d }g }t| jD ]F}| d| d}| jd| d||	d}||	| }t
|j||d d	d
}t rt|tjrt|ddr|ttjd}|| q>t|dkrt|ddd}t r| }|j|d	d}|S t
|d |d dS )Nr   r  rK  r  rA  rB  r  rj   Fr   r   r  Tr+  )r(  )rK  r  )r   r  r   r   )r   r  _get_block_manager_axisrv  rw  r  r   r?  r  r|  r+   r  r   rU   rV   r)  r   r  r2   r.  rt   r4   r   r  r  )r   ru   r   r   r   select_axisrh  r  r*  r+  axr  dfs	blk_itemsr~  dfoutrZ   rZ   r[   r(  	  s:   

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 )NrA  r   z/Columns index has to be unique for fixed formatrK  r  )r  rB  )rn  r  rU   _mgrr?   _as_manageris_consolidatedconsolidaterw  r7  r  rh  	is_uniquer   r  rt   blocksr?  r  r}  mgr_locsr  r~  )r   r  r   r  r  rH  blkrJ  ro  rZ   r[   r  6  s"   

zBlockManagerFixed.write)r_   r@  r  )r   r   r   r   r_   r+   r  )
r   r  r  r  r  r  ru  r(  r  r  rZ   rZ   ro  r[   r>    s   
 -r>  c                   @  s   e Zd ZdZeZdS )r  r  N)r   r  r  r  r+   r  rZ   rZ   rZ   r[   r  P  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_tabler{   r`   r  r  rj   zint | list[Hashable]r  Trr   r  Nr   r   r   r   rJ   r]   r^   r   
index_axeslist[IndexCol] | NonerG   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  )rn  r   rV  rG  rY  r   r  r   )r   r   r   r]   r   rV  rG  rY  r   r  r   ro  rZ   r[   r   u  s   





zTable.__init__c                 C  s   | j dd S )N_r   )r  r  r   rZ   rZ   r[   table_type_short     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->[rB  r  c                 S  r  rZ   r`   rC  rZ   rZ   r[   rq     r  z"Table.__repr__.<locals>.<listcomp>r  c                 S  r
  rZ   re   r  rZ   rZ   r[   rq     r  r  z (typ->z,nrows->z,ncols->z,indexers->[r  )r2  rt   r   r	  r  r  rV  r  r^  r0  ncols)r   jdcr  verjverjindex_axesrZ   rZ   r[   r     s(   zTable.__repr__rM  c                 C  s"   | j D ]}||jkr|  S qdS )zreturn the axis for cN)rh  rf   )r   rM  r   rZ   rZ   r[   r     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rwt|D ]7\}}|| }||krh|dkrZ|j|jkrZtd|jd  d	|j d
|j dtd| d| d| dq1td| d| d| dqdS )z"validate against an existing tableNz'incompatible table_type with existing [rN  rB  )rV  rG  rY  rY  Cannot serialize the column [r   z%] because its data contents are not [z] but [] object dtypezinvalid combination of [z] on appending data [z] vs current table [)r  r   r  r  r  r   r~  ra  )r   r  rM  svovr  saxoaxrZ   rZ   r[   r    sP   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)rU   r  rr   r   rZ   rZ   r[   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)rc  fill_missing_namesr   r  reset_indexr   rU   r+   )r   r  r  	reset_objrf  rZ   rZ   r[   validate_multiindex  s   zTable.validate_multiindexri   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 rF  )r>  ru  rm   r  rZ   rZ   r[   rq     r  z(Table.nrows_expected.<locals>.<listcomp>)rV   r#  rV  r   rZ   rZ   r[   nrows_expected  s   zTable.nrows_expectedc                 C  s
   d| j v S )zhas this table been createdr{   r  r   rZ   rZ   r[   r    s   
zTable.is_existsc                 C  r  Nr{   r  r   r   rZ   rZ   r[   r    r  zTable.storablec                 C  r   )z,return the table group (this is my storable))r  r   rZ   rZ   r[   r{     r?  zTable.tablec                 C  r  ra   )r{   r$  r   rZ   rZ   r[   r$    r8  zTable.dtypec                 C  r  ra   r9  r   rZ   rZ   r[   r:    r8  zTable.descriptionitertools.chain[IndexCol]c                 C  s   t | j| jS ra   )rQ  rR  rV  rY  r   rZ   rZ   r[   rh    r_  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 ra   )rt   r~  r  rZ   rZ   r[   ro    s    zTable.ncols.<locals>.<genexpr>)sumrY  r   rZ   rZ   r[   ra    s   zTable.ncolsc                 C  rh  ri  rZ   r   rZ   rZ   r[   is_transposed  rj  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 rF  r  r  rZ   rZ   r[   rq     r  z*Table.data_orientation.<locals>.<listcomp>c                 S  s   g | ]}t |jqS rZ   )ri   rK  r  rZ   rZ   r[   rq     rA  )rs   rQ  rR  rG  rV  r   rZ   rZ   r[   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   rj   c                 S  s   g | ]}|j |fqS rZ   r  r  rZ   rZ   r[   rq     rA  z$Table.queryables.<locals>.<listcomp>c                   s   g | ]
\}} | d fqS ra   rZ   )rm   rK  r~  )
axis_namesrZ   r[   rq     s    c                   s&   g | ]}|j t jv r|j|fqS rZ   )rf   ru  r   r  r  r   rZ   r[   rq     s     )rV  rG  rY  rk  )r   d1d2d3rZ   )r~  r   r[   
queryables  s   

zTable.queryablesc                 C     dd | j D S )zreturn a list of my index colsc                 S  s   g | ]}|j |jfqS rZ   )rK  r  rr  rZ   rZ   r[   rq   '  r  z$Table.index_cols.<locals>.<listcomp>rV  r   rZ   rZ   r[   
index_cols$  r<  zTable.index_colsr	  c                 C  r  )zreturn a list of my values colsc                 S  r
  rZ   r}  rr  rZ   rZ   r[   rq   +  r  z%Table.values_cols.<locals>.<listcomp>)rY  r   rZ   rZ   r[   values_cols)  r_  zTable.values_colsr   c                 C  s   | j j}| d| dS )z)return the metadata pathname for this keyz/meta/z/metar  r&  rZ   rZ   r[   _get_metadata_path-  s   zTable._get_metadata_pathr~  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  r{   )r   r]   r   r   N)r   r   r  r1   r]   r   r   )r   r   r~  rZ   rZ   r[   rJ  2  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   r  r   rZ   rZ   r[   rc  D  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  r7  r  r  rG  r   r   r]   r   r  r  r   rZ   rZ   r[   r  J  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  rG  Nr   r  r   r]   r   r   r  c                 S     g | ]}|j r|qS rZ   r  r  rZ   rZ   r[   rq   `  rA  z#Table.get_attrs.<locals>.<listcomp>c                 S     g | ]}|j s|qS rZ   r  r  rZ   rZ   r[   rq   a  rA  )r  r7  rG  r   r  r   rd   r]   r\   r   r  
indexablesrV  rY  r   rZ   rZ   r[   r  W  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  rZ   r`  rC  rZ   rZ   r[   rq   g  r  z*Table.validate_version.<locals>.<listcomp>rP  )r  rw   r	  r  rT  rU  r   r   )r   ru   rY  rZ   rZ   r[   r  c  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
        Nr~  zmin_itemsize has the key [z%] which is not an axis or data_column)rU   rk  r  r   )r   r   qr@  rZ   rZ   r[   validate_min_itemsizen  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 existNr_  r  )rf   rK  r  r  r  r{   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  rq  rs  )
rf   r  r~  r  r  r  r{   r   r  r$  )
rU   r`   rl  r  r  _maybe_adjust_namer  rw  rc  r{   )r  rM  klassr  adj_namer~  r$  r  mdr   r  )base_posr  descr   table_attrsrZ   r[   r|     s0   

zTable.indexables.<locals>.fc                   s   g | ]	\}} ||qS rZ   rZ   )rm   r  rM  )r|   rZ   r[   rq     rE  z$Table.indexables.<locals>.<listcomp>)r:  r{   r7  r  r  r  rc  r  r   ru  r   rt   ry  r  )r   _indexablesr  rK  rf   r  r  r   r   r  	index_colrZ   )r  r  r  r|   r   r  r[   r    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  rZ   )r  r  r  rZ   rZ   r[   rq     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   rj   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.rZ   )r2  rh  rU   rs   rr   r{   r  rn  r  r   r  r  remove_indexr   r  r   r  rG  r   )r   r   r  r  kwr{   rM  rr  r   cur_optlevelcur_kindrZ   rZ   r[   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]]
        r6  r  )
	Selectionr   rh  r]  r  r4  r   r]   r   r   )	r   ru   r   r   	selectionr~  r  r   resrZ   rZ   r[   
_read_axes%  s   
zTable._read_axesr  c                 C     |S )zreturn the data for this objrZ   r  r  r  rZ   rZ   r[   
get_objectG  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 r5  rZ   r?  )existing_data_columnsrZ   r[   rq   h  s
    z/Table.validate_data_columns.<locals>.<listcomp>c                   s   g | ]}| v r|qS rZ   rZ   )rm   rM  )axis_labelsrZ   r[   rq   p  r  )
rt   r  r   r   rr   rU   rk  ru  ry  r  )r   r   r   rG  rK  r  rZ   )r  r  r[   validate_data_columnsL  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=> }(nt |j5t?rt,|j5}'t@|"\}*}+||#|!t||$||%|&|)|'|(|+|*d},|, |	 ||, |d7 }q2dd |D }-t| | jA| j| j| j||
||-|	|d
}.tB| dr:| jC|._C|.D| |rJ|rJ|.E|  |.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  rB  Nr   c                   r>  rZ   )_get_axis_numberr  )r  rZ   r[   rq     rA  z&Table._create_axes.<locals>.<listcomp>Tc                 S  r
  rZ   rp  r  rZ   rZ   r[   rq     r  Fr  rj   z<currently only support ndim-1 indexers in an AppendableTabler.  c                 3  s    | ]	}| vr|V  qd S ra   rZ   rC  )rh  rZ   r[   ro    s    z%Table._create_axes.<locals>.<genexpr>r|  r`  r  r   r  zIncompatible appended table [z]with existing table [values_block_)existing_colr   r   r]   r   r   r  r_  )rf   r  r~  r  r  r  r  r  r   r  r$  r  c                 S  r  rZ   )r  rf   )rm   r;  rZ   rZ   r[   rq   K  r  )
r   r   r]   r   rV  rG  rY  r   r  r   r  )FrU   r+   r   r   r   r   r2  rV  rr   r   r   r  rw  rt   r   rt  rh  rG  r*   rV   r<   r{  rS  r  r   r   _get_axis_namer  r]   r   rK  r  rZ  rB  _reindex_axisr  r  rM  _get_blocks_and_itemsrY  r  rS  rl  r  r`   
IndexErrorr   _maybe_convert_for_string_atomr~  r  r  r  rw  r$  rf   r  r  r  r&   r  r  r  r  r2   rv  r   r  r  r  r  )/r   rh  r  r  r   r   r   r   table_existsnew_infonew_non_index_axesrW  r   append_axisindexer
exist_axisr  	axis_name	new_indexnew_index_axesjr  r  rR  rJ  vaxesr  rT  b_itemsr  rf   r  rf  new_namedata_convertedr  r  r  r  r   r  r  r  rx  r;  dcs	new_tablerZ   )rh  r  r[   _create_axesr  s4  
 







"







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 )NrA  c                   s    fdd j D S )Nc                   s   g | ]	} j |jqS rZ   )r  r}  rS  )rm   rT  mgrrZ   r[   rq   s  rE  zFTable._get_blocks_and_items.<locals>.get_blk_items.<locals>.<listcomp>)rR  r  rZ   r  r[   get_blk_itemsr  s   z2Table._get_blocks_and_items.<locals>.get_blk_itemsr   rp  c                 S  s"   i | ]\}}t | ||fqS rZ   )rs   tolist)rm   br  rZ   rZ   r[   rs    s    z/Table._get_blocks_and_items.<locals>.<dictcomp>r  c                 S  r  rZ   r  )rm   itemrZ   rZ   r[   rq     r  z/Table._get_blocks_and_items.<locals>.<listcomp>z+cannot match existing table structure for [z] on appending data)rU   rM  r?   rN  r   r@   rr   rR  rt   r-   rz  r  ry  rS  rs   r~  rV  r   r  r   r	  r   )r  r  r  rY  r   r  r  rR  rJ  rK  r  
new_labelsrM  by_items
new_blocksnew_blk_itemsear  r  r  rf  jitemsrZ   rZ   r[   r  d  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$|tj}|||} j|d|   S | |v r[tt	 | j
}t|}t trLd| }|||} j|d|   S qtd|  d)Nrp  rj   zcannot find the field [z] for filtering!)_AXIS_ORDERSr  	_get_axisrl  unionr-   r  r  r>   r  r~  rU   r+   r   )fieldfiltopr  axis_numberaxis_valuestakersr~  r  r   rZ   r[   process_filter  s$   





z*Table.process_axes.<locals>.process_filter)	rr   rl  rU   r  insertrG  r  filterr   )r   r  r  r   r  rK  labelsr  r  r  r  rZ   r  r[   process_axes  s   

 zTable.process_axesr   r   ri  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'  r{   )rf   ri  c                 S  s   i | ]}|j |jqS rZ   )r  r  r  rZ   rZ   r[   rs    rA  z,Table.create_description.<locals>.<dictcomp>r:  	   )r   r   r   r   )maxrs  rh  r   r   r  r   r   )r   r   r   r   ri  rj  r   rZ   rZ   r[   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
        Fr6  Nrj   r  )r  r2  r  select_coordsr  r   r<  r  r  ilocr~  r-   )
r   ru   r   r   r  coordsr  r  r  r  rZ   rZ   r[   r8    s   

 zTable.read_coordinatesr;  c           
      C  s   |    |  s
dS |durtd| jD ]L}||jkra|js'td| dt| jj	|}|
| j |j||| | j| j| jd}t|d |j}t| jj| dd}	t||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  rj   rs  )rf   r  r$  z] not found in the table)r  r2  r   rh  rf   r  r   r  r{   rn  r]  r  r4  r   r]   r   r/  r  r7  r1   r   )
r   r;  ru   r   r   r   rM  
col_valuescvsr$  rZ   rZ   r[   r<    s0   



zTable.read_column)Nr   NNNNNN)r   r   r   rJ   r]   r^   r   r`   rV  rW  rG  rX  rY  rZ  r   r[  r  r\  r_   r   r  )rM  r`   r  r  )r  r   r_   rm  r  )r_   rv  )r_   ry  )r_   r{  )r_   r	  )r   r`   r_   r`   )r   r`   r~  r  r_   r   r  ra   r  )r  r^   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   ri  r   r_   r{  r  )r;  r`   r   r   r   r   )4r   r  r  r  r  r  r  r  rT  r   r  r^  r   r   r  rl  rq  rs  r  r  r{   r$  r:  rh  ra  rx  rz  r  r  r  r  rJ  rc  r  r  r  r  r   r  r  r  r  r  r  r  staticmethodr  r  r  r8  r<  r  rZ   rZ   ro  r[   r  U  s   
 


'





	







LW"* s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  rZ   rZ   r[   r(  O  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  rZ   rZ   r[   r  [  s   zWORMTable.writer  r  r  )r   r  r  r  r  r(  r  rZ   rZ   rZ   r[   r  F  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 )&rF  (support the new appendable table formats
appendableNFTr   r   r   r   r   rZ  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 )Nr{   )rh  r  r  r   r   r   )r   r   r   ri  rZ  )r   )r  r   r  r   r  rh  rD  r  r  create_tabler  r7  rL  
write_data)r   r  rh  r   r   r   r   r   r   ri  r   r   r   rZ  r{   r   optionsrZ   rZ   r[   r  j  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   rp  u1Fr  rj   Nc                 S  r
  rZ   )r>  r  rZ   rZ   r[   rq     r  z.AppendableTable.write_data.<locals>.<listcomp>c                 S     g | ]}|  qS rZ   )r6  r  rZ   rZ   r[   rq     r  c              	   S  s,   g | ]}| tt|j|jd  qS r%  )	transposerV   rollarangerw  r  rZ   rZ   r[   rq     s   , r  r  c                      g | ]}|  qS rZ   rZ   r  end_istart_irZ   r[   rq     r  c                   r  rZ   rZ   r  r  rZ   r[   rq     r  )indexesr  r~  )r$  r  rs  rY  r5   r  rl  rU   rV   r)  r   r  rt   r  rV  r  ru  reshaper  r  rv  write_data_chunk)r   r   r   r  r0  masksr   r  r|  r  nindexesr~  bvaluesr  rr  	new_shaperowschunksrZ   r  r[   r    sP   


zAppendableTable.write_datar  r  r  list[np.ndarray]r  npt.NDArray[np.bool_] | Noner~  c                 C  s   |D ]}t |js dS q|d jd }|t|kr#t j|| jd}| jj}t|}t|D ]
\}	}
|
|||	 < q/t|D ]\}	}||||	|  < q>|dura| j	t
dd }| sa|| }t|rr| j| | j  dS dS )z
        Parameters
        ----------
        rows : an empty memory space where we are putting the chunk
        indexes : an array of the indexes
        mask : an array of the masks
        values : an array of the values
        Nr   r  Fr  )rV   r#  ru  rt   r  r$  r  r  r  r  r   rl  r{   r   r!  )r   r  r  r  r~  rr  r0  r  r  r  rW  r|  rZ   rZ   r[   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 )	NTr^  r  Fr  rj   r   r  )rt   r0  r   r  r   r{   remove_rowsr!  r2  r  r  r1   sort_valuesdiffrr   r   r   r  rV  reversedr}  rv  )r   ru   r   r   r0  r{   r  r~  sorted_serieslnr   r   pgr  r  rZ   rZ   r[   re    sF   


zAppendableTable.delete)NFNNNNNNFNNT)
r   r   r   r   r   r   rZ  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  re  rZ   rZ   rZ   r[   rF  d  s&    ;
;,rF  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   rj   )rV  rK  r   rZ   rZ   r[   rx  O  r_  z"AppendableFrameTable.is_transposedr  c                 C  s   |r|j }|S )zthese are written transposed)r  r  rZ   rZ   r[   r  S  s   zAppendableFrameTable.get_objectNr   r   r   c                   s    |   sd S  j|||d}t jr$ j jd d i ni } fddt jD }t|dks:J |d }|| d }	g }
t jD ]\}}| j	vrVqK|| \}}|ddkrht
|}nt|}|d}|d ur}|j|d	d
  jr|}|}t
|	t|	dd d}n|j}t
|	t|	dd d}|}|jdkrt|tjr|d|jd f}t|tjrzt|j||dd}W nL ty } z) jdkrtdrt|drtrt|j||dtdtjdd}n W Y d }~nd }~ww t|t
rt|||d}n	tj |g||d}t! r|j"j#dks-|j$|j"k% s-J |j$|j"f|D ]}t j&j'| dd }|dv rJ|| (|||< q/|
)| qKt|
dkr^|
d }nt*|
dd}t+ |||d} j,|||d}|S )Nr6  r   c                   s"   g | ]\}}| j d  u r|qS rF  r  )rm   r  rH  r   rZ   r[   rq   p  s   " z-AppendableFrameTable.read.<locals>.<listcomp>rj   r   r.   r  Tinplacerf   re   FrD  r!  r"  r#  r%  r&  )r   r   r  r$  rE  r  rs  )r`   r@  rp  )r  r   )-r  r2  r  rt   rG  r  r   r  rh  rY  r-   r.   from_tuples	set_namesrx  r  r  rw  rU   rV   r)  r  ru  r+   r-  r   r   r`   r  r   r2   r.  _from_arraysr   r$  r  dtypesrl  r{   r7  r  r   r4   r  r  )r   ru   r   r   r   r  r  indsindr   framesr  r   
index_valsr>  rn  r  r~  index_cols_rK  rf  r;  r$  r  rZ   r   r[   r(  Z  s   





"

zAppendableFrameTable.readr  r  r  r  )r   r  r  r  r  r  rw  r+   r  r  r  rx  r  r  r(  rZ   rZ   rZ   r[   r  G  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  rh  ri  rZ   r   rZ   rZ   r[   rx    rj  z#AppendableSeriesTable.is_transposedr  c                 C  r  ra   rZ   r  rZ   rZ   r[   r    rj  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 tabler~  r  r   NrZ   )rU   r+   rf   to_framern  r  r   r  )r   r  r   r   rf   ro  rZ   r[   r    s   


"zAppendableSeriesTable.writer   r   r   r1   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   rJ  Tr  r~  )
rl  rU   r  rr   r  rn  r(  	set_indexr  rf   )r   ru   r   r   r   rl  r  rY   ro  rZ   r[   r(    s   

zAppendableSeriesTable.readr  r  ra   r  r  r=  )r   r  r  r  r  r  rw  r1   r  r  rx  r  r  r  r(  r  rZ   rZ   ro  r[   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  r~  r  NrZ   )
rf   rq  r  rU   rr   r   r-   r   rn  r  )r   r  r   rf   newobjrn  ro  rZ   r[   r    s   



z AppendableMultiSeriesTable.writer  )r   r  r  r  r  r  r  r  rZ   rZ   ro  r[   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   ra   )r  r   rZ   rZ   r[   r    r   zGenericTable.pandas_typec                 C  s   t | jdd p	| jS rt  ru  r   rZ   rZ   r[   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  r  rZ   r  r  rZ   rZ   r[   rq     rA  z*GenericTable.get_attrs.<locals>.<listcomp>c                 S  r  rZ   r  r  rZ   rZ   r[   rq     rA  c                 S  r
  rZ   re   r  rZ   rZ   r[   rq     r  )rG  r   r  r  rV  rY  r   r   rZ   rZ   r[   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   Nr_  r   )rf   rK  r{   r   r  )rf   r  r~  r  r{   r   r  )r:  rc  rg  r{   r  _v_namesrU   r`   r  r  r   )
r   rj  r  r   r  r  r  r  r  r  rZ   rZ   r[   r    s.   


	zGenericTable.indexablesc                 K  r  )Nz cannot write on an generic tabler  )r   r   rZ   rZ   r[   r  C  s   zGenericTable.writeNr  r  )r   r  r  r  r  r  rw  r+   r  r  r  r  r  r  r   r  r  rZ   rZ   rZ   r[   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  rh  )Nappendable_multirZ   r   rZ   rZ   r[   r^  O  rj  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  rZ   )	r   r  rq  r  rU   rr   r  rn  r  )r   r  r   r   r  ro  rZ   r[   r  T  s   

zAppendableMultiFrameTable.writer   r   r   c                   sD   t  j||||d}| j}|j fdd|jjD |_|S )NrJ  c                   s    g | ]} j |rd n|qS ra   )
_re_levelssearch)rm   rf   r   rZ   r[   rq   l  s     z2AppendableMultiFrameTable.read.<locals>.<listcomp>)rn  r(  r  r  r   r  r  )r   ru   r   r   r   rK  ro  r   r[   r(  `  s   zAppendableMultiFrameTable.readr  ra   r  r  r  )r   r  r  r  r  r+   r  rw  recompiler  r  r^  r  r(  r  rZ   rZ   ro  r[   r  G  s    
r  r  r+   rK  rM   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  slicerw  r  rs   )r  rK  r  r  rH  slicerrZ   rZ   r[   r  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  zonerZ   rZ   r[   r    s   
r  r~  np.ndarray | Indexr  r,   c                 C  rC  ra   rZ   r~  r  r  rZ   rZ   r[   r/    s   r/  r  c                 C  rC  ra   rZ   r%  rZ   rZ   r[   r/    rj  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
    Nre   r  M8[ns]r  )
rU   r,   r  rf   r  r\   r  r  rV   r  )r~  r  r  rf   rZ   rZ   r[   r/    s    


rf   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  )r~  r  r  r  r  r  zMultiIndex not supported here!Fr+  r   c                 S  r  rZ   )	toordinalr  rZ   rZ   r[   rq     r  z"_convert_index.<locals>.<listcomp>r  )r  r@  )integerfloating)r~  r  r  r  r  )rU   r`   rf   rv  rw  r  r  r   r,  r$  r%   r!   r  r  r.   r   r2  rV   r  int32r   	Time32Col_convert_string_arrayr  rA  r)  r  r4  )rf   r   r]   r   r  r  rx  r  r  r;  r~  r  rZ   rZ   r[   r    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  rZ   r  r  rZ   rZ   r[   rq     rA  z$_unconvert_index.<locals>.<listcomp>r  c                 S  r  rZ   r  r  rZ   rZ   r[   rq     rA  )r+  floatr   r@  r  r  r   zunrecognized index type )	r  r,   r5  r3   rV   r  r  r   r  )r  r  r]   r   r   rZ   rZ   r[   r$    s:   

	r$  r  rL   r	  c                 C  s  t |jtr
| }|jtkr|S ttj|}|jj}t	j
|dd}	|	dkr*td|	dkr2td|	dks<|dks<|S t|}
| }|||
< |rY|
 rYt||jkrYtd	t	j
|dd}	|	dkrt|jd
 D ]+}|| }t	j
|dd}	|	dkrt||kr|| nd| }td| d|	 dqkt||||j}|j}t |trt|| p|dpd
}t|pd
|}|d ur||}|d ur||kr|}|jd| dd}|S )NFr+  r   z+[date] is not implemented as a table columnr  z>too many timezones in this block, create separate data columnsr@  r  z8NaN representation is too large for existing column sizer   zNo.rf  z2]
because its data contents are not [string] but [rg  r~  z|Sr  )rU   r$  r2   r8  r  r   rV   r)  rf   r   r2  r   r5   r  r  rt   r  r   rv  ru  r/  r  rk  ri   r   r  rG  r  )rf   r  r  r   r   r]   r   r   rx  r;  r  r  r  r;  error_column_labelr  r  ecirZ   rZ   r[   r  "  sX   




r  r  c                 C  sb   t | rt|  d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  )r  r$  rj   Sr  )rt   r1   r  r`   encoder  r  ru  r    r  
libwritersmax_len_string_arrayrV   r  )r  r]   r   ensuredr  rZ   rZ   r[   r/  q  s   

r/  c                 C  s   | j }tj|  td} t| rEtt| }d| }t	| d t
r9t| ddjj||dd}| } d| j_n| j|ddjtdd} |d	u rKd
}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  )r   r$  TNr.  )ru  rV   r  r  r  rt   r5  r6  r    rU   r  r1   r`   rX   r8  flags	writeabler  !string_array_replace_from_nan_repr  )r  r   r]   r   ru  r  r$  serrZ   rZ   r[   r    s    


r  r0  c                 C  s6   t |tsJ t|t|rt|||}|| } | S ra   )rU   r`   r   _need_convert_get_converter)r~  r0  r]   r   convrZ   rZ   r[   r+    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  rV   r  rD  rZ   rZ   r[   r         z _get_converter.<locals>.<lambda>c                   s   t j|  dS )Nr  r@  rA  r{  rZ   r[   r     rB  r@  c                   s   t | d  dS )Nr  )r  rA  r  rZ   r[   r     s    zinvalid kind )r   )r  r]   r   rZ   )r]   r   r  r[   r>    s   r>  c                 C  s   | dv sd| v r
dS dS )N)r  r@  r  TFrZ   r{  rZ   rZ   r[   r=    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   rj   r  r  zvalues_block_(\d+)values_)rU   r`   rt   r   r  r  r   )rf   r  r|  grprZ   rZ   r[   r    s   $
r  	dtype_strc                 C  s   t | } | drd}|S | drd}|S | drd}|S | dr(d}|S | dr1| }|S | dr:d	}|S | d
rCd
}|S | drLd}|S | drUd}|S | dkr]d}|S | dkred}|S td|  d)zA
    Find the "kind" string describing the given dtype name.
    )r@  r  r@  r0  r  )ri   r  r+  r  	timedeltar  r   r_  r  r  r`   zcannot interpret dtype of [rB  )r\   r  r   )rG  r  rZ   rZ   r[   rw    sF   








rw  c                 C  sv   t | tr| j} t | jtrd| jj d}n| jj}| jjdv r*t	| 
d} nt | tr2| j} t	| } | |fS )zJ
    Convert the passed data into a storable form and a dtype string.
    r-  rB  mMr  )rU   r6   r  r$  r'   r7  rf   r  rV   r  r5  r/   r6  )r  rx  rZ   rZ   r[   rv    s   


rv  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

    Nr{   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)r{   ru   r   r   	conditionr  termsrP  r#   r   r   r   r2  rV   r  r$  bool_r0  r  
issubclassr   r+  r  generateevaluate)r   r{   ru   r   r   inferredrZ   rZ   r[   r   6  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)r  r]   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
                )	r{   r  r:   r]   	NameErrorr	  r  r   r   )r   ru   r  rf  qkeysr  rZ   rZ   r[   rO  c  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  )	rK  r{   
read_wherer   r   r   rP  r8  r(  r   rZ   rZ   r[   r   z  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 )rT  Nr   T)r   r   r  )
r   r   r{   r0  rK  get_where_listr   rP  rV   r  )r   r   r   r0  rZ   rZ   r[   r    s"   

zSelection.select_coordsr  )r{   r  r   r   r   r   r_   r   )r   r  r  r  r   rO  r   r  rZ   rZ   rZ   r[   r  *  s    -r  )r]   r^   r_   r`   )rh   ri   )r   NNFNTNNNNr   rT   )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   )	Nr   r   NNNNFN)r   r   r   r`   r   r`   ru   r   r   r   r   r   r   r   r   r   r   r   )r   rJ   r   rJ   r_   r   ra   )r  r+   rK  rM   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  )r  r`   r]   r`   r   r`   r_   r$  )rf   r`   r  rL   r   r	  )r  r  r]   r`   r   r`   r_   r  )r~  r  r0  r`   r]   r`   r   r`   )r  r`   r]   r`   r   r`   )r  r`   r_   r   )rf   r`   r  rC  r_   r`   )rG  r`   r_   r`   )r  rL   )r  
__future__r   
contextlibr   r  r  r   r   rQ  r   r  textwrapr   typingr   r   r	   r
   r   r   r   rT  numpyrV   pandas._configr   r   r   r   pandas._libsr   r   r5  pandas._libs.libr   pandas._libs.tslibsr   pandas.compatr   pandas.compat._optionalr   pandas.compat.pickle_compatr   pandas.errorsr   r   r   r   r   pandas.util._decoratorsr   pandas.util._exceptionsr   pandas.core.dtypes.commonr    r!   r"   r#   r$   r%   pandas.core.dtypes.dtypesr&   r'   r(   r)   pandas.core.dtypes.missingr*   r  r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r5   pandas.core.arraysr6   r7   r8   pandas.core.arrays.string_r9   pandas.core.commoncorecommonrc   pandas.core.computation.pytablesr:   r;   pandas.core.constructionr<   r  r=   pandas.core.indexes.apir>   pandas.core.internalsr?   r@   pandas.io.commonrA   pandas.io.formats.printingrB   rC   collections.abcrD   rE   rF   typesrG   r   rH   rI   rJ   pandas._typingrK   rL   rM   rN   rO   rP   rQ   rR   rS   r  rb   r\   rd   rg   rl   rv   rw   r  rx   ry   r  rx  r~   r   config_prefixregister_optionis_boolis_one_of_factoryr   r   r   r   r   r   r   r3  r  rg  rl  r  r  r  r  r  r>  r  r  r  rF  r  r  r  r  r  r  r  r/  r  r$  r  r/  r  r+  r>  r=  r  rw  rv  r  rZ   rZ   rZ   r[   <module>   sB   $	 4(



: 
#           (p  8   -   2g       x dz1C,

'
@

O

*




#