o
    d                 	   @  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 ddlmZmZmZ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  dd
l!m"Z"m#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/m0Z0 ddl1m2Z2 ddl3m4Z4m5Z5m6Z6m7Z7m8Z8 ddl9m:Z: ddl;m<Z< ddl=m>Z>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJ ddlKmLZL ddlMmNZNmOZOmPZPmQZQmRZRmSZSmTZTmUZUmVZVmWZW ddlXmYZYmZZZm[Z[ ddl\m]  m^Z_ ddl`maZambZb ddlcmdZd ddlemfZf ddlgmhZhmiZi ddljmkZk ddllmmZmmnZn er-ddlompZpmqZqmrZr ddlgmsZs dZtdZud d! Zvdd&d'Zwd(d) ZxeaZydd,d-Zzd.Z{d/e|d0< d1Z}d/e|d2< d3Z~d/e|d4< d5d5d6d6d7ZeNdgiZd8Zd/e|d9< d:Zd/e|d;< ed<  ejd=d>eejd? ejd@deeg dAd? W d   n	1 sw   Y  dad>adBdC Z	D			>		E					F	ddd\d]Z		^	F					>	dddgdhZddldmZG dndo doZG dpdq dqZG drds dsZG dtdu dueZG dvdw dweZG dxdy dyeZG dzd{ d{eZG d|d} d}ZG d~d de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	>ddddZeddddZ	>ddddZdddZdddZdddZdddZdddZdddZdddZdddĄZdddȄZddd˄Zddd̈́ZG ddτ dσZdS )zY
High level interface to PyTables for reading and writing pandas data structures
to disk
    )annotations)suppressN)datetzinfo)dedent)TracebackType)
TYPE_CHECKINGAnyCallableFinalHashableIteratorLiteralSequencecastoverload)config
get_option)libwriters)	timezones)AnyArrayLike	ArrayLikeAxisIntDtypeArgFilePathShapenpt)import_optional_dependency)patch_pickle)AttributeConflictWarningClosedFileErrorIncompatibilityWarningPerformanceWarningPossibleDataLossError)cache_readonly)find_stack_level)ensure_objectis_bool_dtypeis_categorical_dtypeis_complex_dtypeis_datetime64_dtypeis_datetime64tz_dtypeis_extension_array_dtypeis_integer_dtypeis_list_likeis_object_dtypeis_string_dtypeis_timedelta64_dtypeneeds_i8_conversion)array_equivalent)
	DataFrameDatetimeIndexIndex
MultiIndexPeriodIndex
RangeIndexSeriesTimedeltaIndexconcatisna)CategoricalDatetimeArrayPeriodArray)PyTablesExprmaybe_expression)extract_array)ensure_index)ArrayManagerBlockManager)stringify_path)adjoinpprint_thing)ColFileNode)Blockz0.15.2UTF-8c                 C  s   t | tjr| d} | S )z(if we have bytes, decode them to unicoderO   )
isinstancenpbytes_decode)s rU   W/var/www/html/visualizacion-main/env/lib/python3.10/site-packages/pandas/io/pytables.py_ensure_decoded   s   
rW   encoding
str | Nonereturnstrc                 C  s   | d u rt } | S N)_default_encodingrX   rU   rU   rV   _ensure_encoding   s   r_   c                 C  s   t | tr	t| } | S )z
    Ensure that an index / column name is a str (python 3); otherwise they
    may be np.string dtype. Non-string dtypes are passed through unchanged.

    https://github.com/pandas-dev/pandas/issues/13492
    )rP   r[   namerU   rU   rV   _ensure_str   s   
rb   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 )Nre   rc   )rC   Term).0termlevelrU   rV   
<listcomp>   s    z _ensure_term.<locals>.<listcomp>rf   N)rP   listtuplerC   rg   len)whererc   rU   rj   rV   _ensure_term   s   	
rq   z
where criteria is being ignored as this version [%s] is too old (or
not-defined), read the file in and write it out to a new file to upgrade (with
the copy_to method)
r   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)fru   trv   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)ru   rv   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   rU   rU   rV   _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   rX   )r   store
r   r   rX   r   r   r   r   r   r   r   rU   rV   <lambda>      zto_hdf.<locals>.<lambda>c                   s   | j 	 d
S )N)r   r   r   r   r   r   rX   r   putr   r   rU   rV   r     r   )r   r   r   N)rH   rP   r[   HDFStore)r   r   r   r   r   r   r   r   r   r   r   r   r   r   rX   rw   r   rU   r   rV   to_hdf   s    

"r   rrp   str | list | Nonestartstopcolumnslist[str] | Noneiterator	chunksizec
                 K  s  |dvrt d| d|durt|dd}t| tr'| js"td| }d}n:t| } t| ts4td	zt	j
| }W n tt fyI   d}Y nw |sTtd
|  dt| f||d|
}d}z9|du r| }t|dkrtt d|d }|dd D ]}t||st dq~|j}|j|||||||	|dW S  t ttfy   t| tstt |  W d    1 sw   Y   w )a"	  
    Read from the store, close it if we opened it.

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

    .. warning::

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

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

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

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

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

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

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

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

    Examples
    --------
    >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])  # doctest: +SKIP
    >>> df.to_hdf('./store.h5', 'data')  # doctest: +SKIP
    >>> reread = pd.read_hdf('./store.h5')  # doctest: +SKIP
    )r   r+r   zmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nre   rf   z&The HDFStore must be open for reading.Fz5Support for generic buffers has not been implemented.zFile z does not exist)r   r   Tr   z]Dataset(s) incompatible with Pandas data types, not table, or no datasets found in HDF5 file.z?key must be provided when HDF5 file contains multiple datasets.)rp   r   r   r   r   r   
auto_close)
ValueErrorrq   rP   r   is_openOSErrorrH   r[   NotImplementedErrorospathexists	TypeErrorFileNotFoundErrorgroupsro   _is_metadata_of_v_pathnameselectKeyErrorr   r   close)r   r   r   r   rp   r   r   r   r   r   kwargsr   r   r   r   candidate_only_groupgroup_to_checkrU   rU   rV   read_hdf6  sv   O








r   grouprM   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.Fre   metaT)_v_depth	_v_parent_v_name)r   r   currentparentrU   rU   rV   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dd1d2Zddd6d7Zdd9d:Zdd<d=Zddd>d?Zdd@dAZeddBdCZdddEdFZddGdHZ							dddKdLZ			dddOdPZ		dddRdSZ								dddTdUZ		V								W	V	dddcddZdddedfZ 			V	V											WdddidjZ!			dddmdnZ"			dddrdsZ#ddudvZ$dddzd{Z%dd}d~Z&dddZ'		V					VddddZ(dddZ)dd Z*dddZ+				WddddZ,		V												W	VddddZ-dddZ.dddZ/dddZ0dS )r   aa	  
    Dict-like IO interface for storing pandas objects in PyTables.

    Either Fixed or Table format.

    .. warning::

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

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

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

        ``'r'``
            Read-only; no data can be modified.
        ``'w'``
            Write; a new file is created (an existing file with the same
            name would be deleted).
        ``'a'``
            Append; an existing file is opened for reading and writing,
            and if the file does not exist it is created.
        ``'r+'``
            It is similar to ``'a'``, but the file must already exist.
    complevel : int, 0-9, default None
        Specifies a compression level for data.
        A value of 0 or None disables compression.
    complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
        Specifies the compression library to be used.
        As of v0.20.2 these additional compressors for Blosc are supported
        (default if no compressor specified: 'blosc:blosclz'):
        {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
         'blosc:zlib', 'blosc:zstd'}.
        Specifying a compression library which is not available issues
        a ValueError.
    fletcher32 : bool, default False
        If applying compression use the fletcher32 checksum.
    **kwargs
        These parameters will be passed to the PyTables open_file method.

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

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

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

    >>> bar = pd.DataFrame(np.random.randn(10, 4))
    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
    >>> store['foo'] = bar
    >>> store.close()   # only now, data is written to disk
    zFile | None_handler[   _moder   NFr   r   r   
fletcher32r   rZ   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   rU   )r   r   filtersall_complibsdefault_complibrH   _pathr   r   
_complevel_complib_fletcher32_filtersopen)selfr   r   r   r   r   r   r   rU   rU   rV   __init__$  s&   	
zHDFStore.__init__c                 C     | j S r\   r   r   rU   rU   rV   
__fspath__E  s   zHDFStore.__fspath__c                 C  s   |    | jdusJ | jjS )zreturn the root nodeN)_check_if_openr   rootr   rU   rU   rV   r   H  s   zHDFStore.rootc                 C  r   r\   r   r   rU   rU   rV   filenameO     zHDFStore.filenamer   c                 C  
   |  |S r\   )getr   r   rU   rU   rV   __getitem__S     
zHDFStore.__getitem__c                 C  s   |  || d S r\   r   )r   r   r   rU   rU   rV   __setitem__V  s   zHDFStore.__setitem__c                 C  r   r\   )remover   rU   rU   rV   __delitem__Y  r   zHDFStore.__delitem__ra   c              	   C  s@   z|  |W S  ttfy   Y nw tdt| j d| d)z$allow attribute access to get stores'z' object has no attribute ')r   r   r!   r   type__name__)r   ra   rU   rU   rV   __getattr__\  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 '/'
        Nre   TF)get_noder   )r   r   nodera   rU   rU   rV   __contains__f  s   
zHDFStore.__contains__rd   c                 C     t |  S r\   )ro   r   r   rU   rU   rV   __len__r     zHDFStore.__len__c                 C  s   t | j}t|  d| dS )N
File path: 
)rJ   r   r   )r   pstrrU   rU   rV   __repr__u  s   
zHDFStore.__repr__c                 C  s   | S r\   rU   r   rU   rU   rV   	__enter__y     zHDFStore.__enter__exc_typetype[BaseException] | None	exc_valueBaseException | None	tracebackTracebackType | Nonec                 C     |    d S r\   )r   )r   r   r   r   rU   rU   rV   __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.

                .. versionadded:: 1.1.0

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

        Raises
        ------
        raises ValueError if kind has an illegal value
        r  c                 S     g | ]}|j qS rU   r   rh   nrU   rU   rV   rl         z!HDFStore.keys.<locals>.<listcomp>nativeNc                 S  r  rU   r  r  rU   rU   rV   rl     s    /Table)	classnamez8`include` should be either 'pandas' or 'native' but is 'r   )r   r   
walk_nodesr   )r   r  rU   rU   rV   keys  s   
zHDFStore.keysIterator[str]c                 C  r   r\   )iterr  r   rU   rU   rV   __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   grU   rU   rV   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$   r   r   r   Filtersr   r   r   r   r   	open_filer   )r   r   r   r   msgrU   rU   rV   r     s*   

zHDFStore.openc                 C  s   | j dur
| j   d| _ dS )z0
        Close the PyTables file handle
        N)r   r   r   rU   rU   rV   r     s   


zHDFStore.closec                 C  s   | j du rdS t| j jS )zF
        return a boolean indicating whether the file is open
        NF)r   r   isopenr   rU   rU   rV   r     s   
zHDFStore.is_openfsyncc                 C  s^   | j dur+| j   |r-tt t| j   W d   dS 1 s$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  rU   rU   rV   r    s   


"zHDFStore.flushc                 C  sV   t   | |}|du rtd| d| |W  d   S 1 s$w   Y  dS )z
        Retrieve pandas object stored in file.

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

        Returns
        -------
        object
            Same type as object stored in file.
        NNo object named  in the file)r   r   r   _read_groupr   r   r   rU   rU   rV   r     s   
$zHDFStore.getr   r   c	                   st   |  |}	|	du rtd| dt|dd}| |	   fdd}
t| |
|j|||||d
}| S )	a  
        Retrieve pandas object stored in file, optionally based on where criteria.

        .. warning::

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

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

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

        Returns
        -------
        object
            Retrieved object from file.
        Nr  r  re   rf   c                   s   j | || dS )N)r   r   rp   r   read_start_stop_wherer   rT   rU   rV   funcQ  s   zHDFStore.select.<locals>.funcrp   nrowsr   r   r   r   r   )r   r   rq   _create_storer
infer_axesTableIteratorr*  
get_result)r   r   rp   r   r   r   r   r   r   r   r(  itrU   r'  rV   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
        re   rf   z&can only read_coordinates with a tablerp   r   r   )rq   
get_storerrP   r  r   read_coordinates)r   r   rp   r   r   tblrU   rU   rV   select_as_coordinatesd  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)r5  r   r   )r1  rP   r  r   read_column)r   r   r5  r   r   r3  rU   rU   rV   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
        re   rf   r   )r   rp   r   r   r   r   r   r   zkeys must be a list/tuplez keys must have a non-zero lengthNc                      g | ]}  |qS rU   )r1  rh   kr   rU   rV   rl         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 rU   )rP   r  rh   xrU   rU   rV   rl         c                 S  s   h | ]	}|j d  d  qS r   )non_index_axesrh   rx   rU   rU   rV   	<setcomp>  r?  z.HDFStore.select_as_multiple.<locals>.<setcomp>c                   s*    fddD }t |dd S )Nc                   s   g | ]}|j  d qS )rp   r   r   r   r!  rB  )r$  r%  r&  r   rU   rV   rl     s    z=HDFStore.select_as_multiple.<locals>.func.<locals>.<listcomp>F)axisverify_integrity)r=   _consolidate)r$  r%  r&  objs)rE  r   tblsr#  rV   r(  
  s   z)HDFStore.select_as_multiple.<locals>.funcr)  T)coordinates)rq   rP   rm   rn   ro   r[   r   r   r   r1  	itertoolschainzipr   is_tablepathnamer*  popr-  r.  )r   r  rp   selectorr   r   r   r   r   r   rT   r*  rx   r:  _tblsr(  r/  rU   )rE  r   r   rI  rV   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.

            .. versionadded:: 1.1.0
        Nio.hdf.default_formatru   )r   r   r   r   r   r   r   r   rX   r   rT  r   )r   _validate_format_write_to_group)r   r   r   r   r   r   r   r   r   r   r   rX   r   rT  r   rU   rU   rV   r   %  s&   4

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

        re   rf   Nz5trying to remove a node with a non-None where clause!T	recursivez7can only remove with where on objects written as tablesr0  )rq   r1  r   AssertionError	Exceptionr   r   	_f_removecomall_noner   rN  delete)r   r   rp   r   r   rT   errr   rU   rU   rV   r   m  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
        Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerU  rv   )r   axesr   r   r   r   r   r   r   expectedrowsr   r   rX   r   )r   r   rV  rW  )r   r   r   r   rb  r   r   r   r   r   r   r   r   rc  r   r   rX   r   rU   rU   rV   r     s6   ;

zHDFStore.appendddictc                   s  |durt dt|tstd||vrtdtttjttt	  d }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 dictr   z<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   )rh   cols)r   rU   rV   	<genexpr>I  s     z.HDFStore.append_to_multiple.<locals>.<genexpr>r   rE  c                   s   i | ]\}}| v r||qS rU   rU   rh   r   r   )vrU   rV   
<dictcomp>Y  s    z/HDFStore.append_to_multiple.<locals>.<dictcomp>)r   r   )r   rP   re  r   rm   setrangendim	_AXES_MAPr   r  extendrb  
differencer7   sortedget_indexertakevaluesnextintersectionlocrP  reindexr   )r   rd  r   rQ  r   rb  r   r   rE  
remain_keyremain_valuesr:  orderedorddidxsvalid_indexr   r   dcvalfilteredrU   )rl  r   rV   append_to_multiple  s\   
&

zHDFStore.append_to_multipleoptlevelkindrY   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   r1  rP   r  r   create_index)r   r   r   r  r  rT   rU   rU   rV   create_table_index_  s   

zHDFStore.create_table_indexrm   c                 C  s<   t   |   | jdusJ tdusJ dd | j D S )z
        Return a list of all the top-level nodes.

        Each node returned is not a pandas storage object.

        Returns
        -------
        list
            List of objects.
        Nc                 S  sP   g | ]$}t |tjjst|jd ds$t|dds$t |tjjr|jdkr|qS )pandas_typeNrv   )	rP   r   linkLinkgetattr_v_attrsrv   r  r   )rh   r  rU   rU   rV   rl     s    
z#HDFStore.groups.<locals>.<listcomp>)r   r   r   r   walk_groupsr   rU   rU   rV   r     s   zHDFStore.groupsr
  rp   *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 )aS  
        Walk the pytables group hierarchy for pandas objects.

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

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

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

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

        Yields
        ------
        path : str
            Full path to a group (without trailing '/').
        groups : list
            Names (strings) of the groups contained in `path`.
        leaves : list
            Names (strings) of the pandas objects contained in `path`.
        Nr  r
  )r   r   r   r   r  r  r  _v_childrenrw  rP   r   Groupr   r   r   rstrip)r   rp   r  r   leaveschildr  rU   rU   rV   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NoSuchNodeErrorrP   rM   r   )r   r   r   rU   rU   rV   r     s   
zHDFStore.get_nodeGenericFixed | Tablec                 C  s8   |  |}|du rtd| d| |}|  |S )z<return the storer object for a key, raise if not in the fileNr  r  )r   r   r+  r,  )r   r   r   rT   rU   rU   rV   r1    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 rU   )
is_indexedra   rh   r   rU   rU   rV   rl         z!HDFStore.copy.<locals>.<listcomp>r   )r   r   rX   r^   )r   rm   r  rP   rn   r1  r   r   r  rb  r   r  rX   r   )r   r   r   r  r  r   r   r   r  	new_storer:  rT   datar   rU   rU   rV   copy  s8   





zHDFStore.copyc           
      C  s  t | j}t|  d| d}| jr~t|  }t|rxg }g }|D ]K}z| |}|durA|t |j	p5| |t |p>d W q" t
yJ     tym } z|| t |}	|d|	 d W Y d}~q"d}~ww |td||7 }|S |d7 }|S |d	7 }|S )
zg
        Print detailed information on the store.

        Returns
        -------
        str
        r   r   Nzinvalid_HDFStore nodez[invalid_HDFStore node: r<     EmptyzFile is CLOSED)rJ   r   r   r   rt  r  ro   r1  r   rO  rZ  r[  rI   )
r   r   outputlkeysr  rw  r:  rT   detaildstrrU   rU   rV   info(  s8   


zHDFStore.infoc                 C  s   | j st| j dd S )Nz file is not open!)r   r!   r   r   rU   rU   rV   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 [r<  N)_FORMAT_MAPlowerr   r   )r   r   r`  rU   rU   rV   rV  V  s   zHDFStore._validate_formatrO   DataFrame | Series | NonerX   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_typerv   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->rX   r   series_tabler   re   appendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)r  r  r  r  r  wormz<cannot properly create the storer for: [_TABLE_MAP] [group->)rP   r;   r5   r   rW   r  r  r   r   rv   r  SeriesFixed
FrameFixedr   r   nlevelsGenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable	WORMTable)r   r   r   r   rX   r   pttt_STORER_MAPclsr`  r   
_TABLE_MAPrU   rU   rV   r+  `  s   





zHDFStore._create_storerc                 C  s   t |dd r|dks|rd S | ||}| j|||||d}|r9|jr-|jr1|dkr1|jr1td|js8|  n|  |jsF|rFtd|j||||||	|
||||||d t|t	rg|ri|j
|d d S d S d S )	Nemptyrv   r  ru   zCan only append to Tablesz0Compression not supported on Fixed format stores)objrb  r   r   r   r   r   r   rc  r   r   r   rT  )r   )r  _identify_groupr+  rN  	is_existsr   set_object_infowriterP   r  r  )r   r   r   r   rb  r   r   r   r   r   r   r   rc  r   r   r   rX   r   rT  r   rT   rU   rU   rV   rW    s>   
zHDFStore._write_to_groupr   rM   c                 C  s   |  |}|  | S r\   )r+  r,  r"  )r   r   rT   rU   rU   rV   r    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.NTrX  )r   r   remove_node_create_nodes_and_group)r   r   r   r   rU   rU   rV   r    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   splitro   endswithr   create_group)r   r   pathsr   pnew_pathr   rU   rU   rV   r    s   


z HDFStore._create_nodes_and_group)r   NNF)r   r[   r   r   r   r   rZ   r   rZ   r[   r   r[   )r   r[   rZ   r   )ra   r[   )r   r[   rZ   r   rZ   rd   )rZ   r   )r   r   r   r   r   r   rZ   r   )r  )r  r[   rZ   r  )rZ   r  )rZ   r  )r   )r   r[   rZ   r   rZ   r   rZ   r   F)r  r   rZ   r   )NNNNFNF)r   r[   r   r   r   r   NNNr   r[   r   r   r   r   NN)r   r[   r5  r[   r   r   r   r   )NNNNNFNF)r   r   r   r   )NTFNNNNNNr~   TF)r   r[   r   r   r   r   r   r   r   r   r   r   r   r   r   r[   rT  r   r   r   rZ   r   )NNTTNNNNNNNNNNr~   )r   r[   r   r   r   ra  r   r   r   r   r   r   r   r   r   r   r   r[   rZ   r   )NNF)rd  re  r   r   rZ   r   )r   r[   r  r   r  rY   rZ   r   )rZ   rm   )r
  )rp   r[   rZ   r  )r   r[   rZ   r  )r   r[   rZ   r  )r  TNNNFT)r   r[   r  r   r   r   r   r   r  r   rZ   r   )r   r[   rZ   r[   )NNrO   r~   )r   r  rX   r[   r   r[   rZ   r  )NTFNNNNNNFNNNr~   T)r   r[   r   r   r   ra  r   r   r   r   r   r   r   r   r   r[   rT  r   rZ   r   )r   rM   )r   r[   r   r   rZ   rM   )r   r[   rZ   rM   )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   r4  r7  rS  r   r   r   r  r  r   r  r   r1  r  r  r   rV  r+  rW  r  r  r  rU   rU   rU   rV   r     s
  
 A
!











"

-
N$+}H=]d
(
0

;*
`
>
r   c                   @  s`   e Zd ZU dZded< ded< ded< 							ddddZdddZdddZddddZdS )r-  aa  
    Define the iteration interface on a table

    Parameters
    ----------
    store : HDFStore
    s     : the referred storer
    func  : the function to execute the query
    where : the where of the query
    nrows : the rows to iterate on
    start : the passed start value (default is None)
    stop  : the passed stop value (default is None)
    iterator : bool, default False
        Whether to use the default iterator.
    chunksize : the passed chunking value (default is 100000)
    auto_close : bool, default False
        Whether to automatically close the store at the end of iteration.
    r   r   r   r   r  rT   NFr   r   r   rZ   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   rT   r(  rp   rN  minr*  r   r   rJ  rd   r   r   )r   r   rT   r(  rp   r*  r   r   r   r   r   rU   rU   rV   r   >  s,   

zTableIterator.__init__r   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   rJ  r   r   r  r   r(  ro   r   )r   r   r   r   rU   rU   rV   r  h  s   


	zTableIterator.__iter__c                 C  s   | j r
| j  d S d S r\   )r   r   r   r   rU   rU   rV   r   x  s   zTableIterator.closerJ  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)rp   z$can only read_coordinates on a tabler0  )r   rP   rT   r  r   r2  rp   rJ  r   r   r(  r   )r   rJ  rp   resultsrU   rU   rV   r.  |  s   
zTableIterator.get_result)NNFNF)r   r   rT   r  r   r   r   r   r   r   rZ   r   rZ   r   r  r  )rJ  r   )	r   r  r  r  r  r   r  r   r.  rU   rU   rU   rV   r-  &  s   
 	
*
r-  c                   @  s\  e Zd ZU dZdZded< dZded< g dZ													dMdNddZe	dOddZ
e	dPddZdQddZdPddZdRddZdSddZe	dSd d!ZdTd'd(Zd)d* Ze	d+d, Ze	d-d. Ze	d/d0 Ze	d1d2 ZdUd4d5ZdVdWd6d7ZdWd8d9ZdXd=d>ZdVd?d@ZdYdAdBZdWdCdDZdWdEdFZdWdGdHZdZdIdJZ dZdKdLZ!dS )[IndexCola  
    an index column description class

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

    Tr   is_an_indexableis_data_indexable)freqtz
index_nameNra   r[   cnamerY   rZ   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.)rP   r[   r   rw  r  typra   r  rE  posr  r  r  r~  rv   r   metadataset_pos)r   ra   rw  r  r  r  rE  r  r  r  r  r~  rv   r   r  rU   rU   rV   r     s(   


zIndexCol.__init__rd   c                 C     | j jS r\   )r  itemsizer   rU   rU   rV   r    s   zIndexCol.itemsizec                 C     | j  dS )N_kindr`   r   rU   rU   rV   	kind_attr     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  rU   rU   rV   r    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->rU   rk  rU   rU   rV   rl         z%IndexCol.__repr__.<locals>.<listcomp>)ra   r  rE  r  r  )
rn   maprJ   ra   r  rE  r  r  joinrM  r   temprU   rU   rV   r     s   zIndexCol.__repr__otherr	   c                      t  fdddD S )compare 2 col itemsc                 3  (    | ]}t |d t  |d kV  qd S r\   r  r  r  r   rU   rV   ri    
    
z"IndexCol.__eq__.<locals>.<genexpr>)ra   r  rE  r  rf  r   r  rU   r  rV   __eq__     zIndexCol.__eq__c                 C  s   |  | S r\   )r  r  rU   rU   rV   __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 columnrh  F)hasattrrv   r  rh  r  r  r   rU   rU   rV   r    s   zIndexCol.is_indexedrw  
np.ndarrayrX   r   3tuple[np.ndarray, np.ndarray] | tuple[Index, Index]c           
      C  s  t |tjsJ t||jjdur|| j  }t| j	}t
||||}i }t| j|d< | jdur:t| j|d< t}t|jsFt|jrIt}n|jdkrVd|v rVdd }z
||fi |}W n tyy   d|v rod|d< ||fi |}Y nw t|| j}	|	|	fS )zV
        Convert the data from this selection to the appropriate pandas type.
        Nra   r  i8c                 [  s   t dd| i|S )NordinalrU   )r9   )r>  kwdsrU   rU   rV   r     s
    z"IndexCol.convert.<locals>.<lambda>)rP   rQ   ndarrayr   dtypefieldsr  r  rW   r  _maybe_convertr  r  r7   r+   r,   r6   r   _set_tzr  )
r   rw  r   rX   r   val_kindr   factorynew_pd_indexfinal_pd_indexrU   rU   rV   convert  s.   

zIndexCol.convertc                 C  r   )zreturn the valuesrw  r   rU   rU   rV   	take_data.  r   zIndexCol.take_datac                 C  r  r\   )rv   r  r   rU   rU   rV   attrs2     zIndexCol.attrsc                 C  r  r\   rv   descriptionr   rU   rU   rV   r'  6  r%  zIndexCol.descriptionc                 C  s   t | j| jdS )z!return my current col descriptionN)r  r'  r  r   rU   rU   rV   col:     zIndexCol.colc                 C  r   zreturn my cython valuesr"  r   rU   rU   rV   cvalues?     zIndexCol.cvaluesr   c                 C  s
   t | jS r\   )r  rw  r   rU   rU   rV   r  D  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  )rW   r  rP   re  r   ra   r  r  r   	StringColr  )r   r   rU   rU   rV   maybe_set_sizeG  s   
zIndexCol.maybe_set_sizec                 C     d S r\   rU   r   rU   rU   rV   validate_namesT  r   zIndexCol.validate_nameshandlerAppendableTabler   c                 C  s:   |j | _ |   | | | | | | |   d S r\   )rv   validate_colvalidate_attrvalidate_metadatawrite_metadataset_attr)r   r2  r   rU   rU   rV   validate_and_setW  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)rW   r  r(  r  r   r  )r   r  crU   rU   rV   r4  _  s   
zIndexCol.validate_colc                 C  sJ   |rt | j| jd }|d ur!|| jkr#td| d| j dd S d S d S )Nzincompatible kind in col [ - r<  )r  r$  r  r  r   )r   r   existing_kindrU   rU   rV   r5  r  s   zIndexCol.validate_attrc                 C  s   | j D ]]}t| |d}|| ji }||}||v rT|durT||krT|dv rBt|||f }tj|tt	 d d||< t
| |d qtd| j d| d| d| d	|dus\|dur`|||< qdS )	z
        set/update the info for this indexable with the key/value
        if there is a conflict raise/warn as needed
        N)r  r  
stacklevelzinvalid info for [z] for [z], existing_value [z] conflicts with new value [r<  )_info_fieldsr  
setdefaultra   r   rs   warningswarnr    r&   setattrr   )r   r  r   r   idxexisting_valuewsrU   rU   rV   update_info{  s.   

zIndexCol.update_infoc                 C  s(   | | j}|dur| j| dS dS )z!set my state from the passed infoN)r   ra   __dict__update)r   r  rD  rU   rU   rV   set_info  s   zIndexCol.set_infoc                 C  s   t | j| j| j dS )zset the kind for this columnN)rC  r$  r  r  r   rU   rU   rV   r8       zIndexCol.set_attrc                 C  sN   | j dkr| j}|| j}|dur!|dur#t||s%tddS dS dS dS )z:validate that kind=category does not change the categoriescategoryNzEcannot append a categorical with different categories to the existing)r   r  read_metadatar  r4   r   )r   r2  new_metadatacur_metadatarU   rU   rV   r6    s   
zIndexCol.validate_metadatac                 C  s"   | j dur|| j| j  dS dS )zset the meta dataN)r  r7  r  )r   r2  rU   rU   rV   r7    s   
zIndexCol.write_metadata)NNNNNNNNNNNNN)ra   r[   r  rY   rZ   r   r  r  )r  rd   rZ   r   r  r	   rZ   r   r  )rw  r  rX   r[   r   r[   rZ   r  r  r\   r  )r2  r3  r   r   rZ   r   )r   r   rZ   r   )r2  r3  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/  r1  r9  r4  r5  rG  rJ  r8  r6  r7  rU   rU   rU   rV   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 tablerZ   r   c                 C     dS NFrU   r   rU   rU   rV   r       zGenericIndexCol.is_indexedrw  r  rX   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
        )rP   rQ   r  r   r:   ro   )r   rw  r   rX   r   r   rU   rU   rV   r!    s   zGenericIndexCol.convertr   c                 C  r0  r\   rU   r   rU   rU   rV   r8    r   zGenericIndexCol.set_attrNr  )rw  r  rX   r[   r   r[   rZ   rU  r  )r   r  r  r  r  r  r!  r8  rU   rU   rU   rV   rQ    s    
rQ  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~  Nra   r[   r  rY   r  DtypeArg | NonerZ   r   c                   s2   t  j|||||||||	|
|d || _|| _d S )N)ra   rw  r  r  r  r  r  r~  rv   r   r  )superr   r  r  )r   ra   rw  r  r  r  r  r  r~  rv   r   r  r  r  	__class__rU   rV   r     s   
zDataCol.__init__c                 C  r  )N_dtyper`   r   rU   rU   rV   
dtype_attr	  r  zDataCol.dtype_attrc                 C  r  )N_metar`   r   rU   rU   rV   	meta_attr	  r  zDataCol.meta_attrc                 C  r  )Nr  c                 S  r  r   rU   rk  rU   rU   rV   rl   	  r  z$DataCol.__repr__.<locals>.<listcomp>)ra   r  r  r  shape)
rn   r  rJ   ra   r  r  r  r_  r  rM  r  rU   rU   rV   r   	  s   zDataCol.__repr__r  r	   r   c                   r  )r  c                 3  r	  r\   r
  r  r  rU   rV   ri  	  r  z!DataCol.__eq__.<locals>.<genexpr>)ra   r  r  r  r  r  rU   r  rV   r  	  r  zDataCol.__eq__r  r   c                 C  s@   |d usJ | j d u sJ t|\}}|| _|| _ t|| _d S r\   )r  _get_data_and_dtype_namer  _dtype_to_kindr  )r   r  
dtype_namerU   rU   rV   set_data$	  s   zDataCol.set_datac                 C  r   )zreturn the datar  r   rU   rU   rV   r#  .	  r   zDataCol.take_datarw  rK   c                 C  s   |j }|j}|j}|jdkrd|jf}t|tr&|j}| j||j j	d}|S t
|s.t|r5| |}|S t|r@| |}|S t|rPt j||d d}|S t|r\| ||}|S | j||j	d}|S )zW
        Get an appropriately typed and shaped pytables.Col object for values.
        re   r  r   r  r_  )r  r  r_  rp  sizerP   r?   codesget_atom_datara   r+   r,   get_atom_datetime64r2   get_atom_timedelta64r*   r   
ComplexColr1   get_atom_string)r  rw  r  r  r_  rh  atomrU   rU   rV   	_get_atom2	  s.   





zDataCol._get_atomc                 C  s   t  j||d dS )Nr   rf  r   r.  r  r_  r  rU   rU   rV   rm  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UIntrK   periodInt64Col)r  
capitalizer  r   )r  r  k4col_namekcaprU   rU   rV   get_atom_coltypeV	  s   


zDataCol.get_atom_coltypec                 C  s   | j |d|d dS )Nre  r   r_  r}  r  r_  r  rU   rU   rV   ri  e	  rK  zDataCol.get_atom_datac                 C     t  j|d dS Nr   r~  r   rx  r  r_  rU   rU   rV   rj  i	     zDataCol.get_atom_datetime64c                 C  r  r  r  r  rU   rU   rV   rk  m	  r  zDataCol.get_atom_timedelta64c                 C     t | jdd S )Nr_  )r  r  r   rU   rU   rV   r_  q	     zDataCol.shapec                 C  r   r*  rd  r   rU   rU   rV   r+  u	  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  r$  r  rm   rw  r   r\  r  )r   r   existing_fieldsexisting_dtyperU   rU   rV   r5  z	  s   zDataCol.validate_attrr  rX   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krbt||dd}n|dkrntj|dd}n~|dkrztjd	d
 |D td}W nk ty   tjdd
 |D td}Y nWw |dkr|	}| }|du rtg tjd}nt|}| r||  }||dk  |t j8  < tj|||
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 rU   r   fromordinalrh   rl  rU   rU   rV   rl   	  r;  z#DataCol.convert.<locals>.<listcomp>c                 S  r  rU   r   fromtimestampr  rU   rU   rV   rl   	  r;  rL  )
categoriesr~  Fr  Or-  r   rX   r   )"rP   rQ   r  r   r  r  r  r  r`  ra  r  rW   r   r  r~  r  r  asarrayobjectr   ravelr7   float64r>   anyastyperd   cumsum_valuesr?   
from_codesr   _unconvert_string_arrayrw  )r   rw  r   rX   r   	convertedrb  r  r   r  r~  r  r  r  rh  maskrU   rU   rV   r!  	  sj   






 
zDataCol.convertc                 C  sH   t | j| j| j t | j| j| j | jdusJ t | j| j| j dS )zset the data for this columnN)rC  r$  r  rw  r^  r   r  r\  r   rU   rU   rV   r8  	  s   zDataCol.set_attr)NNNNNNNNNNNN)ra   r[   r  rY   r  rW  rZ   r   r  rP  )r  r   rZ   r   )rw  r   rZ   rK   )r  r[   rZ   rs  r  r[   rZ   rK   r  )rw  r  rX   r[   r   r[   )r   r  r  r  r  r  r?  r   r  r\  r^  r   r  rc  r#  classmethodro  rm  r}  ri  rj  rk  r_  r+  r5  r!  r8  __classcell__rU   rU   rY  rV   rV    sZ     










drV  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 indexedTrZ   r   c                 C  s   t t| jstdd S )N-cannot have non-object label DataIndexableCol)r0   r7   rw  r   r   rU   rU   rV   r1  	  s   zDataIndexableCol.validate_namesc                 C  s   t  j|dS )N)r  rp  rq  rU   rU   rV   rm  	  r  z DataIndexableCol.get_atom_stringr  r[   rK   c                 C  s   | j |d S )Nre  r  r  rU   rU   rV   ri  
  r  zDataIndexableCol.get_atom_datac                 C  
   t   S r\   r  r  rU   rU   rV   rj  
     
z$DataIndexableCol.get_atom_datetime64c                 C  r  r\   r  r  rU   rU   rV   rk  	
  r  z%DataIndexableCol.get_atom_timedelta64Nr  r  )r   r  r  r  r  r1  r  rm  ri  rj  rk  rU   rU   rU   rV   r  	  s    


r  c                   @  s   e Zd ZdZdS )GenericDataIndexableColz(represent a generic pytables data columnN)r   r  r  r  rU   rU   rU   rV   r  
  s    r  c                   @  s|  e Zd ZU dZded< dZded< ded< ded	< d
ed< dZded< 		dPdQddZedRddZ	edSddZ
edd  ZdTd!d"ZdUd#d$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dLdM 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_kindru   format_typetype[DataFrame | Series]obj_typerd   rp  r   r   Fr   rN  rO   r~   r   rM   rX   rY   r   rZ   r   c                 C  sZ   t |tsJ t|td usJ t |tjsJ t||| _|| _t|| _|| _	d S r\   )
rP   r   r   r   rM   r   r   r_   rX   r   )r   r   r   rX   r   rU   rU   rV   r   &
  s   

zFixed.__init__c                 C  s*   | j d dko| j d dko| j d dk S )Nr   re   
      )versionr   rU   rU   rV   is_old_version5
  s   *zFixed.is_old_versiontuple[int, int, int]c                 C  sf   t t| jjdd}ztdd |dD }t|dkr$|d }W |S W |S  ty2   d}Y |S w )	zcompute and set our versionpandas_versionNc                 s  s    | ]}t |V  qd S r\   rd   r=  rU   rU   rV   ri  >
  s    z Fixed.version.<locals>.<genexpr>.r  r@  )r   r   r   )rW   r  r   r  rn   r  ro   r   )r   r  rU   rU   rV   r  9
  s   
zFixed.versionc                 C  s   t t| jjdd S )Nr  )rW   r  r   r  r   rU   rU   rV   r  E
  rr  zFixed.pandas_typec                 C  s^   |    | j}|dur,t|ttfr"ddd |D }d| d}| jdd| d	S | jS )
(return a pretty representation of myselfNr  c                 S     g | ]}t |qS rU   rJ   r=  rU   rU   rV   rl   O
      z"Fixed.__repr__.<locals>.<listcomp>[r<  12.12z	 (shape->))r,  r_  rP   rm   rn   r  r  )r   rT   jshaperU   rU   rV   r   I
  s   zFixed.__repr__c                 C  s   t | j| j_t t| j_dS )zset my pandas type & versionN)r[   r  r$  r  _versionr  r   rU   rU   rV   r  T
  s   zFixed.set_object_infoc                 C  s   t  | }|S r\   r  )r   new_selfrU   rU   rV   r  Y
  s   
z
Fixed.copyc                 C  r   r\   )r*  r   rU   rU   rV   r_  ]
  r   zFixed.shapec                 C  r  r\   r   r   r   rU   rU   rV   rO  a
  r%  zFixed.pathnamec                 C  r  r\   )r   r   r   rU   rU   rV   r   e
  r%  zFixed._handlec                 C  r  r\   )r   r   r   rU   rU   rV   r   i
  r%  zFixed._filtersc                 C  r  r\   )r   r   r   rU   rU   rV   r   m
  r%  zFixed._complevelc                 C  r  r\   )r   r   r   rU   rU   rV   r   q
  r%  zFixed._fletcher32c                 C  r  r\   )r   r  r   rU   rU   rV   r$  u
  r%  zFixed.attrsc                 C  rR  zset our object attributesNrU   r   rU   rU   rV   	set_attrsy
      zFixed.set_attrsc                 C  rR  )zget our object attributesNrU   r   rU   rU   rV   	get_attrs|
  r  zFixed.get_attrsc                 C  r   )zreturn my storabler   r   rU   rU   rV   storable
  r,  zFixed.storablec                 C  rR  rS  rU   r   rU   rU   rV   r  
  rT  zFixed.is_existsc                 C  r  )Nr*  )r  r  r   rU   rU   rV   r*  
  r  zFixed.nrowsLiteral[True] | Nonec                 C  s   |du rdS dS )z%validate against an existing storableNTrU   r  rU   rU   rV   validate
  s   zFixed.validateNc                 C  rR  )+are we trying to operate on an old version?NrU   )r   rp   rU   rU   rV   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   rT   rU   rU   rV   r,  
  s
   zFixed.infer_axesr   r   r   c                 C     t d)Nz>cannot read on an abstract storer: subclasses should implementr   r   rp   r   r   r   rU   rU   rV   r"  
  s   z
Fixed.readc                 K  r  )Nz?cannot write on an abstract storer: subclasses should implementr  r   r   rU   rU   rV   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
        TrX  Nz#cannot delete on an abstract storer)r]  r^  r   r  r   r   )r   rp   r   r   rU   rU   rV   r_  
  s   zFixed.delete)rO   r~   )
r   r   r   rM   rX   rY   r   r[   rZ   r   r  )rZ   r  r  r  )rZ   r  r  )rZ   r  r\   NNNNr   r   r   r   r  )r   r   r   r   rZ   r   )!r   r  r  r  r  r  rN  r   r  r  r  r  r   r  r  r_  rO  r   r   r   r   r$  r  r  r  r  r*  r  r  r,  r"  r  r_  rU   rU   rU   rV   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$d%ZdBd'd(ZdCd*d+Z	d?dDd,d-Z	d?dEd0d1ZdFd4d5Z	dGdHd:d;ZdS )IGenericFixedza generified fixed versiondatetimerw  c                 C  s   i | ]\}}||qS rU   rU   )rh   r:  rl  rU   rU   rV   rm  
  r;  zGenericFixed.<dictcomp>r  
attributesrZ   r[   c                 C  s   | j |dS )N )_index_type_mapr   )r   r  rU   rU   rV   _class_to_alias
  s   zGenericFixed._class_to_aliasc                 C  s   t |tr|S | j|tS r\   )rP   r   _reverse_index_mapr   r7   )r   aliasrU   rU   rV   _alias_to_class
  s   
zGenericFixed._alias_to_classc                 C  s   |  tt|dd}|tkrd	dd}|}n|tkr#d	dd}|}n|}i }d|v r7|d |d< |tu r7t}d|v rXt|d trL|d 	d|d< n|d |d< |tu sXJ ||fS )
Nindex_classr  c                 S  s:   t j| j|d}tj|d d}|d ur|d|}|S )Nr  r`   UTC)r@   _simple_newrw  r6   tz_localize
tz_convert)rw  r  r  dtaresultrU   rU   rV   rw   
  s
   z*GenericFixed._get_index_factory.<locals>.fc                 S  s   t j| |d}tj|d dS )Nr  r`   )rA   r  r9   )rw  r  r  parrrU   rU   rV   rw   
  s   r  r  zutf-8r  )
r  rW   r  r6   r9   r7   r<   rP   bytesrS   )r   r$  r  rw   r  r   rU   rU   rV   _get_index_factory
  s*   

zGenericFixed._get_index_factoryr   c                 C  s$   |durt d|durt ddS )zE
        raise if any keywords are passed which are not-None
        Nzqcannot pass a column specification when reading a Fixed format store. this store must be selected in its entiretyzucannot pass a where specification when reading from a Fixed format store. this store must be selected in its entirety)r   )r   r   rp   rU   rU   rV   validate_read
  s   zGenericFixed.validate_readr   c                 C  rR  )NTrU   r   rU   rU   rV   r    rT  zGenericFixed.is_existsc                 C  s   | j | j_ | j| j_dS r  )rX   r$  r   r   rU   rU   rV   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 attributesrX   Nr   r~   )r_   r  r$  rX   rW   r   r  rC  )r   r  rU   rU   rV   r    s
   
zGenericFixed.get_attrsc                 K  r   r\   )r  r   r  r   rU   rU   rV   r    r   zGenericFixed.writeNr   r   r   r   c                 C  s   ddl }t| j|}|j}t|dd}t||jr"|d || }n=tt|dd}	t|dd}
|
dur<tj|
|	d}n||| }|	dkrTt|d	d}t	||d
d}n|	dkr_tj
|dd}|rd|jS |S )z2read an array for the specified node (off of groupr   N
transposedF
value_typer_  r  r  r  Tr  r  r  )r   r  r   r  rP   VLArrayrW   rQ   r  r  r  T)r   r   r   r   r   r   r$  r  retr  r_  r  rU   rU   rV   
read_array   s&   zGenericFixed.read_arrayr7   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: )rW   r  r$  read_multi_indexr   read_index_noder   )r   r   r   r   varietyr   r   rU   rU   rV   
read_indexB  s   zGenericFixed.read_indexr   c                 C  s   t |trt| j| dd | || d S t| j| dd td|| j| j}| ||j	 t
| j|}|j|j_|j|j_t |ttfrQ| t||j_t |tttfr^|j|j_t |trq|jd urst|j|j_d S d S d S )Nr  r  r  r   )rP   r8   rC  r$  write_multi_index_convert_indexrX   r   write_arrayrw  r  r   r  r  ra   r6   r9   r  r   r  r<   r  r  _get_tz)r   r   r   r  r   rU   rU   rV   write_indexP  s    



zGenericFixed.write_indexr8   c                 C  s   t | j| d|j tt|j|j|jD ]N\}\}}}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)rC  r$  r  	enumeraterM  levelsrh  namesr-   r   r  rX   r   r  rw  r  r   r  r  ra   )r   r   r   ilevlevel_codesra   	level_key
conv_levelr   	label_keyrU   rU   rV   r  g  s$   
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  rh  r  rF  )	r  r$  ro  r   r  r   ra   r  r8   )r   r   r   r   r  r  rh  r  r  r  r   r	  r  r
  rU   rU   rV   r    s    
zGenericFixed.read_multi_indexr   rM   c                 C  s   ||| }d|j v rt|j jdkrtj|j j|j jd}t|j j}d }d|j v r6t|j j	}t|}|j }| 
|\}}	|dv rW|t||| j| jdfdti|	}
n|t||| j| jdfi |	}
||
_	|
S )Nr_  r   r  ra   )r   r  r  r  )r  rQ   prodr_  r  r  rW   r  rb   ra   r  _unconvert_indexrX   r   r  )r   r   r   r   r  r  ra   r$  r  r   r   rU   rU   rV   r    s:   
zGenericFixed.read_index_noder   r   c                 C  sJ   t d|j }| j| j|| t| j|}t|j|j	_
|j|j	_dS )zwrite a 0-len arrayre   N)rQ   r  rp  r   create_arrayr   r  r[   r  r  r  r_  )r   r   r   arrr   rU   rU   rV   write_array_empty  s
   zGenericFixed.write_array_emptyr  r   r  Index | Nonec                 C  s>  t |dd}|| jv r| j| j| |jdk}d}t|jr#td|s/t|dr/|j	}d}d }| j
d urRtt t j|j}W d    n1 sMw   Y  |d urt|sm| 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  }|| nit |jr| j!| j||"d dt#| j|j$_%nOt&|jr| j!| j||j' t#| j|}t(|j)|j$_)d|j$_%n.t*|jr| j!| j||"d dt#| j|j$_%n|r| || n	| j!| j|| |t#| j|j$_+d S )NT)extract_numpyr   Fz]Cannot store a category dtype in a HDF5 dataset that uses format="fixed". Use format="table".r  )r   skipnar-  r=  r  r  r  ),rD   r   r   r  rg  r)   r  r   r  r  r   r   r   r   Atom
from_dtypecreate_carrayr_  r  r   rQ   object_r   infer_dtypert   rA  rB  r#   r&   create_vlarray
ObjectAtomr   r+   r  viewr  r  r  r,   asi8r  r  r2   r  )r   r   r  r  r   empty_arrayr  rn  cainferred_typerF  vlarrr   rU   rU   rV   r    sh   









zGenericFixed.write_arrayr  r  r  r  r  )r   r[   r   r   r   r   rZ   r7   )r   r[   r   r7   rZ   r   )r   r[   r   r8   rZ   r   )r   r[   r   r   r   r   rZ   r8   )r   rM   r   r   r   r   rZ   r7   )r   r[   r   r   rZ   r   r\   )r   r[   r  r   r  r  rZ   r   )r   r  r  r  r6   r9   r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r   r  r  r  r  r  rU   rU   rU   rV   r  
  s4   
 

.


#


&
r  c                      sR   e Zd ZU dZdgZded< edd Z				ddddZd fddZ	  Z
S )r  r  ra   r   c              	   C  s*   zt | jjfW S  ttfy   Y d S w r\   )ro   r   rw  r   r   r   rU   rU   rV   r_  '  s
   zSeriesFixed.shapeNr   r   r   rZ   r;   c                 C  s>   |  || | jd||d}| jd||d}t||| jddS )Nr   r  rw  F)r   ra   r  )r  r  r  r;   ra   )r   rp   r   r   r   r   rw  rU   rU   rV   r"  .  s   zSeriesFixed.readr   c                   s<   t  j|fi | | d|j | d| |j| j_d S )Nr   rw  )rX  r  r   r   r  ra   r$  r  rY  rU   rV   r  ;  s   zSeriesFixed.writer  r   r   r   r   rZ   r;   r  )r   r  r  r  r  r  r  r_  r"  r  r  rU   rU   rY  rV   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 )BlockManagerFixedrp  nblocksrd   rZ   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_itemsr_  re   )	rp  ro  r'  r  r   block0_valuesrm   r   r   )r   rp  r  r  r   r_  rU   rU   rV   r_  G  s&   
zBlockManagerFixed.shapeNr   r   r   r5   c                 C  s  |  || |  d}g }t| jD ]}||kr||fnd\}}	| jd| ||	d}
||
 q|d }g }t| jD ].}| d| d}| jd| d||	d}||	| }t
|j||d d	d
}|| q>t|dkrt|ddd}|j|d	d}|S t
|d |d dS )Nr   r  rE  r  r)  r*  r  re   Fr   r   r  T)rE  r  )r   r  r   r   )r  r  _get_block_manager_axisro  rp  r  r   r'  r  ru  r5   r  ro   r=   r{  )r   rp   r   r   r   select_axisrb  r  r$  r%  axr  dfs	blk_itemsrw  dfoutrU   rU   rV   r"  b  s(   zBlockManagerFixed.readr   c                   s   t  j|fi | t|jtr|d}|j}| s | }|j| j	_t
|jD ]\}}|dkr9|js9td| d| | q*t|j| j	_t
|jD ]"\}}|j|j}| jd| d|j|d | d| d| qOd S )Nr)  r   z/Columns index has to be unique for fixed formatrE  r  )r  r*  )rX  r  rP   _mgrrF   _as_manageris_consolidatedconsolidaterp  r$  r  rb  	is_uniquer   r   ro   blocksr'  r  rv  mgr_locsr  rw  )r   r  r   r  r  r0  blkr2  rY  rU   rV   r    s"   

zBlockManagerFixed.write)rZ   r(  r  )r   r   r   r   rZ   r5   r  )
r   r  r  r  r  r  r_  r"  r  r  rU   rU   rY  rV   r&  B  s   
 $r&  c                   @  s   e Zd ZdZeZdS )r  r  N)r   r  r  r  r5   r  rU   rU   rU   rV   r    s    r  c                      s&  e Zd ZU dZdZdZded< ded< dZded	< d
Zded< 								dd fd"d#Z	e
dd$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
dBdC Ze
ddDdEZe
ddFdGZe
ddIdJZddLdMZdNdO ZddQdRZddTdUZddXdYZddZd[Z dd\d]Z!dd^d_Z"ddd`daZ#ddbdcZ$e%ddde Z&	dddgdhZ'	dddmdnZ(e)ddpdqZ*drds Z+	
			dddvdwZ,e-ddzd{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_tablerv   r[   r  r  re   zint | list[Hashable]r  Trm   r  Nr~   r   r   r   rM   rX   rY   r   
index_axeslist[IndexCol] | NonerA   list[tuple[AxisInt, Any]] | Nonevalues_axeslist[DataCol] | Noner   list | Noner  dict | NonerZ   r   c                   sP   t  j||||d |pg | _|pg | _|pg | _|pg | _|	p!i | _|
| _d S )Nr  )rX  r   r>  rA  rA  r   r  r   )r   r   r   rX   r   r>  rA  rA  r   r  r   rY  rU   rV   r     s   





zTable.__init__c                 C  s   | j dd S )N_r   )r  r  r   rU   rU   rV   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->[r<  r  c                 S  r  rU   r[   r=  rU   rU   rV   rl     r  z"Table.__repr__.<locals>.<listcomp>r  c                 S  r  rU   r`   r  rU   rU   rV   rl     r  r  z (typ->z,nrows->z,ncols->z,indexers->[r  )r,  ro   r   r  r  r  r>  r  rF  r*  ncols)r   jdcr  verjverjindex_axesrU   rU   rV   r     s(   zTable.__repr__r:  c                 C  s"   | j D ]}||jkr|  S qdS )zreturn the axis for cN)rb  ra   )r   r:  r   rU   rU   rV   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rZt|D ]\}}|| }||krKtd| d| d| dq1td| d| d| dqdS )	z"validate against an existing tableNz'incompatible table_type with existing [r;  r<  )r>  rA  rA  zinvalid combination of [z] on appending data [z] vs current table [)r  r   r  r  r   r[  )r   r  r:  svovr  saxoaxrU   rU   rV   r    s@   zTable.validater   c                 C  s   t | jtS )z@the levels attribute is 1 or a list in the case of a multi-index)rP   r  rm   r   rU   rU   rV   is_multi_index  s   zTable.is_multi_indexr  r    tuple[DataFrame, list[Hashable]]c              
   C  sT   t |jj}z| }W n ty } ztd|d}~ww t|ts&J ||fS )ze
        validate that we can store the multi-index; reset and return the
        new object
        zBduplicate names/columns in the multi-index when storing as a tableN)r]  fill_missing_namesr   r  reset_indexr   rP   r5   )r   r  r  	reset_objr`  rU   rU   rV   validate_multiindex  s   zTable.validate_multiindexrd   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 r@  )r+  r_  rh   r  rU   rU   rV   rl   1  r  z(Table.nrows_expected.<locals>.<listcomp>)rQ   r  r>  r   rU   rU   rV   nrows_expected.  s   zTable.nrows_expectedc                 C  s
   d| j v S )zhas this table been createdrv   r  r   rU   rU   rV   r  3  s   
zTable.is_existsc                 C  r  Nrv   r  r   r   rU   rU   rV   r  8  r  zTable.storablec                 C  r   )z,return the table group (this is my storable))r  r   rU   rU   rV   rv   <  r,  zTable.tablec                 C  r  r\   )rv   r  r   rU   rU   rV   r  A  r%  zTable.dtypec                 C  r  r\   r&  r   rU   rU   rV   r'  E  r%  zTable.descriptionc                 C  s   t | j| jS r\   )rK  rL  r>  rA  r   rU   rU   rV   rb  I  rG  z
Table.axesc                 C  s   t dd | jD S )z.the number of total columns in the values axesc                 s  s    | ]}t |jV  qd S r\   )ro   rw  r  rU   rU   rV   ri  P  s    zTable.ncols.<locals>.<genexpr>)sumrA  r   rU   rU   rV   rI  M  s   zTable.ncolsc                 C  rR  rS  rU   r   rU   rU   rV   is_transposedR  rT  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 r@  r  r  rU   rU   rV   rl   [  r  z*Table.data_orientation.<locals>.<listcomp>c                 S  s   g | ]}t |jqS rU   )rd   rE  r  rU   rU   rV   rl   \  r;  )rn   rK  rL  rA  r>  r   rU   rU   rV   data_orientationV  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   re   c                 S  s   g | ]}|j |fqS rU   r  r  rU   rU   rV   rl   f  r;  z$Table.queryables.<locals>.<listcomp>c                   s   g | ]
\}} | d fqS r\   rU   )rh   rE  rw  )
axis_namesrU   rV   rl   g  s    c                   s&   g | ]}|j t jv r|j|fqS rU   )ra   rn  r   r  r  r   rU   rV   rl   h  s
    )r>  rA  rA  re  )r   d1d2d3rU   )rc  r   rV   
queryables`  s   

zTable.queryablesc                 C     dd | j D S )zreturn a list of my index colsc                 S  s   g | ]}|j |jfqS rU   )rE  r  rX  rU   rU   rV   rl   q  r  z$Table.index_cols.<locals>.<listcomp>r>  r   rU   rU   rV   
index_colsn  r)  zTable.index_colsr  c                 C  rh  )zreturn a list of my values colsc                 S  r  rU   rb  rX  rU   rU   rV   rl   u  r  z%Table.values_cols.<locals>.<listcomp>)rA  r   rU   rU   rV   values_colss  rG  zTable.values_colsr   c                 C  s   | j j}| d| dS )z)return the metadata pathname for this keyz/meta/z/metar  r   rU   rU   rV   _get_metadata_pathw  s   zTable._get_metadata_pathrw  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  rv   )r   rX   r   r   N)r   r   rl  r;   rX   r   r   )r   r   rw  rU   rU   rV   r7  |  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   rl  r   rU   rU   rV   rM    s   zTable.read_metadatac                 C  sp   t | j| j_|  | j_|  | j_| j| j_| j| j_| j| j_| j| j_| j	| j_	| j
| j_
| j| j_dS )zset our table type & indexablesN)r[   r  r$  rj  rk  rA  r   r   rX   r   r  r  r   rU   rU   rV   r    s   





zTable.set_attrsc                 C  s   t | jddpg | _t | jddpg | _t | jddpi | _t | jdd| _tt | jdd| _tt | jdd| _	t | jd	dpBg | _
d
d | jD | _dd | jD | _dS )r  rA  Nr   r  r   rX   r   r~   r  c                 S     g | ]}|j r|qS rU   r  r  rU   rU   rV   rl     r;  z#Table.get_attrs.<locals>.<listcomp>c                 S     g | ]}|j s|qS rU   rn  r  rU   rU   rV   rl     r;  )r  r$  rA  r   r  r   r_   rX   rW   r   r  
indexablesr>  rA  r   rU   rU   rV   r    s   zTable.get_attrsc                 C  sF   |dur| j r!tddd | jD  }tj|tt d dS dS dS )r  Nr  c                 S  r  rU   rH  r=  rU   rU   rV   rl     r  z*Table.validate_version.<locals>.<listcomp>r=  )r  rr   r  r  rA  rB  r"   r&   )r   rp   rF  rU   rU   rV   r    s   
zTable.validate_versionc                 C  sR   |du rdS t |tsdS |  }|D ]}|dkrq||vr&td| dqdS )z
        validate the min_itemsize doesn't contain items that are not in the
        axes this needs data_columns to be defined
        Nrw  zmin_itemsize has the key [z%] which is not an axis or data_column)rP   re  rg  r   )r   r   qr:  rU   rU   rV   validate_min_itemsize  s   

zTable.validate_min_itemsizec                   s   g }j jjtjjD ]5\}\}}t|}|}|dur%dnd}| d}t|d}	t||||	|j||d}
||
 qt	j
t|  fdd|fddtjjD  |S )	z/create/cache the indexables if they don't existNrL  r  )ra   rE  r  r  r  rv   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  r[  r]  )
ra   r  rw  r  r  r  rv   r   r  r  )
rP   r[   rV  r  r  _maybe_adjust_namer  ra  rM  rv   )r  r:  klassrn  adj_namerw  r  r  mdr   r  )base_posr  descr   table_attrsrU   rV   rw     s0   

zTable.indexables.<locals>.fc                   s   g | ]	\}} ||qS rU   rU   )rh   r  r:  )rw   rU   rV   rl     r?  z$Table.indexables.<locals>.<listcomp>)r'  rv   r$  r  rj  r  rM  r  r   rn  r   ro   rr  rk  )r   _indexablesr  rE  ra   rn  rv  r   r  r  	index_colrU   )rw  r  rx  rw   r   ry  rV   rp    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  rU   )r  r  r  rU   rU   rV   rl   >  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   re   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.rU   )r,  rb  rP   rn   rm   rv   r  rh  r  r   r  r  remove_indexr   r  r   r  rA  r   )r   r   r  r  kwrv   r:  rl  r   cur_optlevelcur_kindrU   rU   rV   r    sX   

zTable.create_indexr   r   r   !list[tuple[ArrayLike, ArrayLike]]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]]
        r0  r  )
	Selectionr   rb  rJ  r  r!  r   rX   r   r   )	r   rp   r   r   	selectionrw  r  r   resrU   rU   rV   
_read_axeso  s   
zTable._read_axesr  c                 C     |S )zreturn the data for this objrU   r  r  r  rU   rU   rV   
get_object  s   zTable.get_objectc                   s   t |sg S |d \} | j|i }|ddkr&|r&td| d| |du r/t }n|du r5g }t|trPt|t|}|fdd	|	 D   fd
d	|D S )zd
        take the input data_columns and min_itemize and create a data
        columns spec
        r   r   r8   z"cannot use a multi-index on axis [z] with data_columns TNc                   s    g | ]}|d kr| vr|qS r"  rU   r9  )existing_data_columnsrU   rV   rl     s    z/Table.validate_data_columns.<locals>.<listcomp>c                   s   g | ]}| v r|qS rU   rU   )rh   r:  )axis_labelsrU   rV   rl     r  )
ro   r  r   r   rm   rP   re  rn  rr  r  )r   r   r   rA  rE  r  rU   )r  r  rV   validate_data_columns  s.   


	zTable.validate_data_columnsr5   r  c           /        st  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} fdddD d }j| }t|}|rt|
}| j| d }tt|t|sttt|tt|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}| "|||
}| #|$ }| %|||
| j&|\}}g }t't(||D ]\}\}}t)}d}|r\t|dkr\|d |v r\t*}|d }|du s\t |t+s\td|r|rz| j&| }W n t,t-fy }  ztd| d| j& d| d} ~ ww d}|pd| }!t.|!|j/|||| j| j|d}"t0|!| j1}#|2|"}$t3|"j4j5}%d}&t6|"dddurt7|"j8}&d }' }(})t9|"j4r|"j:})d}'tj|"j;d
d< }(t=|"\}*}+||#|!t||$||%|&|)|'|(|+|*d},|,|	 ||, |d7 }q,dd |D }-t| | j>| j| j| j||
||-|	|d
}.t?| dr(| j@|._@|.A| |r8|r8|.B|  |.S )a0  
        Create and return the axes.

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

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

        min_itemsize: Dict[str, int] or None, default None
            The min itemsize for a column in bytes.
        z/cannot properly create the storer for: [group->r  r<  Nr   c                   r8  rU   )_get_axis_numberr  )r  rU   rV   rl     r;  z&Table._create_axes.<locals>.<listcomp>Tc                 S  r  rU   rj  r  rU   rU   rV   rl     r  Fr  re   z<currently only support ndim-1 indexers in an AppendableTablenanc                   s   g | ]}| vr|qS rU   rU   r=  )rb  rU   rV   rl     r  ra  r  r   r  zIncompatible appended table [z]with existing table [values_block_)existing_colr   r   rX   r   r   r  rL  r  )ra   r  rw  r  r  r  r  r~  r   r  r  r  c                 S  r  rU   )r  ra   )rh   r(  rU   rU   rV   rl     r  )
r   r   rX   r   r>  rA  rA  r   r  r   r  )CrP   r5   r   r   r   r   r,  r>  rm   r   r   r  rp  ro   r   rb  rA  r4   rQ   arrayrt  r@  r  r   r   _get_axis_namer  rX   r   rE  r  rG  r/  _reindex_axisr  r  rG  _get_blocks_and_itemsrA  r  rM  rV  r  r[   
IndexErrorr   _maybe_convert_for_string_atomrw  rs  r  ro  ra  r  ra   r  r  r  r)   r~  r  r  r`  r   r  r  rr  r  )/r   rb  r  r  r   r   r   r   table_existsnew_infonew_non_index_axesrD  r   append_axisindexer
exist_axisr  	axis_name	new_indexnew_index_axesjr  r  r:  r2  vaxesr  r<  b_itemsrt  ra   r  r`  new_namedata_convertedru  r  r  r  r   r  r~  r  rb  r(  dcs	new_tablerU   )rb  r  rV   _create_axes  s   
 







"






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 )Nr)  c                   s    fdd j D S )Nc                   s   g | ]	} j |jqS rU   )r  rv  r;  )rh   r<  mgrrU   rV   rl     r?  zFTable._get_blocks_and_items.<locals>.get_blk_items.<locals>.<listcomp>)r:  r  rU   r  rV   get_blk_items  s   z2Table._get_blocks_and_items.<locals>.get_blk_itemsr   rj  c                 S  s"   i | ]\}}t | ||fqS rU   )rn   tolist)rh   br  rU   rU   rV   rm    s    z/Table._get_blocks_and_items.<locals>.<dictcomp>r  c                 S  r  rU   r  )rh   itemrU   rU   rV   rl     r  z/Table._get_blocks_and_items.<locals>.<listcomp>z+cannot match existing table structure for [z] on appending data)rP   r5  rF   r6  r   rG   rm   r:  ro   r7   rs  r{  rr  rM  rn   rw  rP  r   r  r   r  r   )r  r  r  rA  r   r  r  r:  r2  rE  r  
new_labelsr:  by_items
new_blocksnew_blk_itemsear  r  r  r`  jitemsrU   rU   rV   r    sV   








zTable._get_blocks_and_itemsr  r  c                   s   |durt |}|dur'jr'tjt sJ jD ]}||vr&|d| qjD ]\}}t |||  fdd}q*|jdurS|j D ]\}}	}
|||
|	 qG S )zprocess axes filtersNr   c                   s    j D ]X} |} |}|d usJ | |kr3jr$|tj}|||} j|d|   S | |v r[tt	 | j
}t|}t trLd| }|||} j|d|   S qtd|  d)Nrj  re   zcannot find the field [z] for filtering!)_AXIS_ORDERSr  	_get_axisrR  unionr7   r  rz  rE   r  rw  rP   r5   r   )fieldfiltopr  axis_numberaxis_valuestakersrw  r  r   rU   rV   process_filter  s$   





z*Table.process_axes.<locals>.process_filter)	rm   rR  rP   r  insertrA  r  filterr   )r   r  r  r   r  rE  labelsr  r  r  r  rU   r  rV   process_axes  s   

 zTable.process_axesr   r   rc  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'  rv   )ra   rc  c                 S  s   i | ]}|j |jqS rU   )r  r  r  rU   rU   rV   rm  .  r;  z,Table.create_description.<locals>.<dictcomp>r'  	   )r   r   r   r   )maxrY  rb  r   r   r  r   r   )r   r   r   r   rc  rd  r   rU   rU   rV   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
        Fr0  Nre   r  )r  r,  r  select_coordsr  r   r6  r  r  ilocrw  r7   )
r   rp   r   r   r  coordsr  r  r  r  rU   rU   rV   r2  >  s   

 zTable.read_coordinatesr5  c                 C  s   |    |  s
dS |durtd| jD ]>}||jkrS|js'td| dt| jj	|}|
| j |j||| | j| j| jd}tt|d |j|dd  S qtd| d	)
zj
        return a single column from the table, generally only indexables
        are interesting
        FNz4read_column does not currently accept a where clausezcolumn [z=] can not be extracted individually; it is not data indexabler  re   )ra   r  z] not found in the table)r  r,  r   rb  ra   r  r   r  rv   rh  rJ  r  r!  r   rX   r   r;   r  r  r   )r   r5  rp   r   r   r   r:  
col_valuesrU   rU   rV   r6  X  s,   



zTable.read_column)Nr~   NNNNNN)r   r   r   rM   rX   rY   r   r[   r>  r?  rA  r@  rA  rB  r   rC  r  rD  rZ   r   r  )r:  r[   r  r  )r  r   rZ   rS  r  )rZ   r^  )rZ   r`  )rZ   r  )r   r[   rZ   r[   )r   r[   rw  r  rZ   r   r  r\   r  )r  rY   rZ   r   r  )r   r   r   r   rZ   r  r  r   )TNNN)r  r5   r  r   )r  r5   r  r   )r  r  rZ   r5   )r   r   r   r   rc  r   rZ   r`  r  )r5  r[   r   r   r   r   )4r   r  r  r  r  r  r  r  rN  r   r  rF  r   r   r  rR  rW  rY  r  r  rv   r  r'  rb  rI  r]  r_  rg  rj  rk  rl  r7  rM  r  r  r  rr  r%   rp  r  r  r  r  r  r  staticmethodr  r  r  r2  r6  r  rU   rU   rY  rV   r    s   
 


!






	







LW"* i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  rU   rU   rV   r"    s   
zWORMTable.readrZ   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  rU   rU   rV   r    s   zWORMTable.writer  r  r  )r   r  r  r  r  r"  r  rU   rU   rU   rV   r    s    r  c                   @  sZ   e Zd ZdZdZ												dd ddZd!d"ddZd#ddZd$d%ddZdS )&r3  (support the new appendable table formats
appendableNFTr   r   r   rT  rZ   r   c                 C  s   |s| j r| j| jd | j||||||d}|jD ]}|  q|j sA|j||||	d}|  ||d< |jj	|jfi | |j
|j_
|jD ]}||| qI|j||
d d S )Nrv   )rb  r  r  r   r   r   )r   r   r   rc  rT  )r   )r  r   r  r   r  rb  r1  r  r  create_tabler  r$  r9  
write_data)r   r  rb  r   r   r   r   r   r   rc  r   r   r   rT  rv   r   optionsrU   rU   rV   r    s4   

	


zAppendableTable.writer   r   c                   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   rj  u1Fr  re   Nc                 S  r  rU   )r+  r  rU   rU   rV   rl     r  z.AppendableTable.write_data.<locals>.<listcomp>c                 S     g | ]}|  qS rU   )r#  r  rU   rU   rV   rl     r  c              	   S  s,   g | ]}| tt|j|jd  qS r  )	transposerQ   rollarangerp  r  rU   rU   rV   rl     s   , r  r  c                      g | ]}|  qS rU   rU   r  end_istart_irU   rV   rl     r  c                   r  rU   rU   r  r  rU   rV   rl     r  )indexesr  rw  )r  r  rY  rA  r>   r  rf  rP   rQ   r  r   r  ro   r  r>  r  r_  reshaper  r  ro  write_data_chunk)r   r   r   r  r*  masksr   r  mr  nindexesrw  bvaluesr  rl  	new_shaperowschunksrU   r  rV   r    sP   


zAppendableTable.write_datar  r  r  list[np.ndarray]r  npt.NDArray[np.bool_] | Nonerw  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  )rQ   r  r_  ro   r  r  r  r  r  r  r   rf  rv   r   r  )r   r  r  r  rw  rl  r*  r  r  r  rD  r  rU   rU   rV   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 )	NTrX  r  Fr  re   r   r  )ro   r*  r   r  r   rv   remove_rowsr  r,  r  r  r;   sort_valuesdiffrm   r   r   r  rP  reversedrv  ro  )r   rp   r   r   r*  rv   r  rw  sorted_serieslnr  r   pgr  r  rU   rU   rV   r_  J  sF   


zAppendableTable.delete)NFNNNNNNFNNT)r   r   r   r   rT  r   rZ   r   r  )r   r   r   r   rZ   r   )
r  r  r  r  r  r  rw  r  rZ   r   r  r  )	r   r  r  r  r  r  r  r  r_  rU   rU   rU   rV   r3    s&    ;
;,r3  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  rZ   r   c                 C  s   | j d jdkS )Nr   re   )r>  rE  r   rU   rU   rV   r]    rG  z"AppendableFrameTable.is_transposedr  c                 C  s   |r|j }|S )zthese are written transposed)r  r  rU   rU   rV   r    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rUqK|| \}}|ddkrgt
|}nt|}|d}|d ur||j|d	d
  jr|}|}t
|	t|	dd d}n|j}t
|	t|	dd d}|}|jdkrt|tjr|d|jd f}t|tjrt|j||dd}nt|t
rt|||d}n	tj|g||d}|j|jk sJ |j|jf|
| qKt|
dkr|
d }nt|
dd}t |||d} j|||d}|S )Nr0  r   c                   s"   g | ]\}}| j d  u r|qS r@  ri  )rh   r  r0  r   rU   rV   rl     s   " z-AppendableFrameTable.read.<locals>.<listcomp>re   r   r8   r  Tinplacera   r`   Fr,  r-  rj  )r  r   ) r  r,  r  ro   rA  r  r   r  rb  rA  r7   r8   from_tuples	set_namesr]  r  r  rp  rP   rQ   r  r  r_  r5   _from_arraysdtypesr  rf  r   r=   r  r  )r   rp   r   r   r   r  r  indsindr   framesr  r   
index_valsr+  rh  r  rw  index_cols_r3  r  rU   r   rV   r"    sZ   





 
zAppendableFrameTable.readr  r  r  r  )r   r  r  r  r  r  rp  r5   r  r  r  r]  r  r  r"  rU   rU   rU   rV   r    s   
 r  c                      sf   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 fdd	Z				dd fddZ  ZS )r  r  r  r  r  rZ   r   c                 C  rR  rS  rU   r   rU   rU   rV   r]    rT  z#AppendableSeriesTable.is_transposedr  c                 C  r  r\   rU   r  rU   rU   rV   r    rT  z AppendableSeriesTable.get_objectNc                   s<   t |ts|jp	d}||}t jd||j d|S )+we are going to write this as a frame tablerw  r  r   NrU   )rP   r5   ra   to_framerX  r  r   r  )r   r  r   r   ra   rY  rU   rV   r    s   


zAppendableSeriesTable.writer   r   r   r;   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   rD  Tr  rw  )
rR  rP   r  rm   r  rX  r"  	set_indexr  ra   )r   rp   r   r   r   rR  r  rT   rY  rU   rV   r"    s   

zAppendableSeriesTable.readr  r  r\   r  r%  )r   r  r  r  r  r  rp  r;   r  r  r]  r  r  r  r"  r  rU   rU   rY  rV   r    s     	r  c                      s(   e Zd ZdZdZdZ fddZ  ZS )r  r  r  r  c                   s^   |j pd}| |\}| _t| jtsJ t| j}|| t||_t j	dd|i|S )r  rw  r  NrU   )
ra   rW  r  rP   rm   r   r7   r   rX  r  )r   r  r   ra   newobjrh  rY  rU   rV   r    s   



z AppendableMultiSeriesTable.write)r   r  r  r  r  r  r  r  rU   rU   rY  rV   r    s
    r  c                   @  sb   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 ZdS )r  z:a table that read/writes the generic pytables table formatr  r  r  zlist[Hashable]r  rZ   r[   c                 C  r   r\   )r  r   rU   rU   rV   r  2  r   zGenericTable.pandas_typec                 C  s   t | jdd p	| jS rZ  r[  r   rU   rU   rV   r  6  rr  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  rm  rU   rn  r  rU   rU   rV   rl   @  r;  z*GenericTable.get_attrs.<locals>.<listcomp>c                 S  ro  rU   rn  r  rU   rU   rV   rl   A  r;  c                 S  r  rU   r`   r  rU   rU   rV   rl   B  r  )rA  r   r  rp  r>  rA  r   r   rU   rU   rV   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   NrL  r   )ra   rE  rv   r   r  )ra   r  rw  r  rv   r   r  )r'  rM  rQ  rv   r  _v_namesrP   r[   r  r  r   )
r   rd  rv  r   r{  rz  r  r  rn  r  rU   rU   rV   rp  D  s.   


	zGenericTable.indexablesc                 K  r  )Nz cannot write on an generic tabler  r  rU   rU   rV   r  g  s   zGenericTable.writeNr  r  )r   r  r  r  r  r  rp  r5   r  r  r  r  r  r  r%   rp  r  rU   rU   rU   rV   r  )  s   
 



"r  c                      s^   e Zd ZdZdZeZdZe	dZ
edddZ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+$rZ   r[   c                 C  rR  )Nappendable_multirU   r   rU   rU   rV   rF  s  rT  z*AppendableMultiFrameTable.table_type_shortNc                   sx   |d u rg }n	|du r|j  }| |\}| _t| jts J | jD ]}||vr/|d| q#t jd||d|S )NTr   r  rU   )	r   r  rW  r  rP   rm   r  rX  r  )r   r  r   r   r  rY  rU   rV   r  w  s   

zAppendableMultiFrameTable.writer   r   r   c                   sD   t  j||||d}| j}|j fdd|jjD |_|S )NrD  c                   s    g | ]} j |rd n|qS r\   )
_re_levelssearch)rh   ra   r   rU   rV   rl     s     z2AppendableMultiFrameTable.read.<locals>.<listcomp>)rX  r"  r  r  r   r  r  )r   rp   r   r   r   r3  rY  r   rV   r"    s   zAppendableMultiFrameTable.readr  r\   r  r  )r   r  r  r  r  r5   r  rp  recompiler  r  rF  r  r"  r  rU   rU   rY  rV   r  k  s    
r  r  r5   rE  r   r  r7   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  rE   equalsuniquery  slicerp  rz  rn   )r  rE  r  r  r0  slicerrU   rU   rV   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  zonerU   rU   rV   r    s   
r  rw  np.ndarray | Indexr  r6   c                 C  r0  r\   rU   rw  r  r  rU   rU   rV   r    s   r  r  c                 C  r0  r\   rU   r  rU   rU   rV   r    rT  str | tzinfo | Nonenp.ndarray | DatetimeIndexc                 C  s   t | tr| jdu s| j|ksJ |dur;t | tr!| j}| j} nd}|  } t|}t| |d} | d|} | S |rDt	j
| dd} | S )a  
    coerce the values to a DatetimeIndex if tz is set
    preserve the input shape if possible

    Parameters
    ----------
    values : ndarray or Index
    tz : str or tzinfo
    coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
    Nr`   r  M8[ns]r  )rP   r6   r  ra   r   r  rW   r  r  rQ   r  )rw  r  r  ra   rU   rU   rV   r    s   

ra   c              
   C  s|  t | tsJ |j}t|\}}t|}t|}t |jtjr$t	|s.t
|js.t|jrAt| |||t|dd t|dd |dS t |trJtdtj|dd}	t|}
|	dkrst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 )Nr  r  )rw  r  r  r  r  r  zMultiIndex not supported here!Fr  r   c                 S  r  rU   )	toordinalr  rU   rU   rV   rl     r  z"_convert_index.<locals>.<listcomp>r  )r  r-  )integerfloating)rw  r  r  r  r  )rP   r[   ra   r`  ra  r  ro  r  rQ   r.   r3   r(   r  r  r8   r   r   r  r  int32r   	Time32Col_convert_string_arrayr  r.  r  r  r  )ra   r   rX   r   r  r  rb  r  rn  r#  rw  r  rU   rU   rV   r    sb   








r  r  c                 C  s   |dkr
t | }|S |dkrt| }|S |dkr>ztjdd | D td}W |S  ty=   tjdd | D td}Y |S w |dv rIt| }|S |d	v rWt| d ||d
}|S |dkrdt| d }|S td| )Nr  r  r   c                 S  r  rU   r  r  rU   rU   rV   rl   0  r;  z$_unconvert_index.<locals>.<listcomp>r  c                 S  r  rU   r  r  rU   rU   rV   rl   2  r;  )r  floatr   r-  r  r  r   zunrecognized index type )r6   r<   rQ   r  r  r   r  )r  r  rX   r   r   rU   rU   rV   r  '  s4   
	r  r  r   r  c                 C  s  |j tkr|S ttj|}|j j}tj|dd}	|	dkr td|	dkr(td|	dks2|dks2|S t	|}
|
 }|||
< tj|dd}	|	dkr|t|jd	 D ]+}|| }tj|dd}	|	dkr{t||krk|| nd
| }td| d|	 dqPt||||j}|j}t|trt|| p|dpd	}t|pd	|}|d ur||}|d ur||kr|}|jd| dd}|S )NFr  r   z+[date] is not implemented as a table columnr  z>too many timezones in this block, create separate data columnsr-  r  r   zNo.zCannot serialize the column [z2]
because its data contents are not [string] but [z] object dtyperw  z|Sr  )r  r  r   rQ   r  ra   r   r  r   r>   r  ro  r_  ro   r  r  r  rP   re  rd   r   r  r4  r  )ra   r  r  r   r   rX   r   r   rb  r#  r  r  r  r(  error_column_labelr  r  ecirU   rU   rV   r  @  sP   

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

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

    Returns
    -------
    np.ndarray[fixed-length-string]
    Fr  re   Sr  )ro   r;   r  r[   encoder  r  r_  r'   r  
libwritersmax_len_string_arrayrQ   r  )r  rX   r   ensuredr  rU   rU   rV   r    s   

r  c                 C  s   | j }tj|  td} t| r=tt| }d| }t	| d t
r1t| ddjj||dj} n| j|ddjtdd} |du rCd}t| | | |S )	a*  
    Inverse of _convert_string_array.

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

    Returns
    -------
    np.ndarray[object]
        Decoded data.
    r  Ur   Fr  )r   Nr  )r_  rQ   r  r  r  ro   r  r  r'   rP   r  r;   r[   rS   r  r  !string_array_replace_from_nan_repr  )r  r   rX   r   r_  r  r  rU   rU   rV   r    s   

r  r  c                 C  s6   t |tsJ t|t|rt|||}|| } | S r\   )rP   r[   r   _need_convert_get_converter)rw  r  rX   r   convrU   rU   rV   r    s
   r  c                   s4   | dkrdd S | dkr fddS t d|  )Nr  c                 S  s   t j| ddS )Nr  r  )rQ   r  r>  rU   rU   rV   r     s    z _get_converter.<locals>.<lambda>r-  c                   s   t | d  dS )Nr  )r  r#  r  rU   rV   r     s    zinvalid kind )r   )r  rX   r   rU   r  rV   r!    s
   r!  c                 C  s   | dv rdS dS )N)r  r-  TFrU   re  rU   rU   rV   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   re   r  r  zvalues_block_(\d+)values_)rP   r[   ro   r   r  r   r   )ra   r  r  grprU   rU   rV   rs    s   $
rs  	dtype_strc                 C  s   t | } | ds| drd}|S | drd}|S | dr$d}|S | ds.| dr2d}|S | dr;d}|S | d	rDd
}|S | drMd}|S | drVd}|S | dr_d}|S | dkrgd}|S td|  d)zA
    Find the "kind" string describing the given dtype name.
    r-  r  r  r|  rd   rt  r  r  	timedeltar  r   rL  rw  r  zcannot interpret dtype of [r<  )rW   r  r   )r(  r  rU   rU   rV   ra     s@   





	
ra  c                 C  sb   t | tr| j} | jjdd }| jjdv r t| 	d} nt | t
r(| j} t| } | |fS )zJ
    Convert the passed data into a storable form and a dtype string.
    r  r   )r  Mr  )rP   r?   rh  r  ra   r  r  rQ   r  r  r9   r   )r  rb  rU   rU   rV   r`  !  s   


r`  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

    Nrv   r  r   r   r   rZ   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)rv   rp   r   r   	conditionr  termsrJ  r/   r   r   r   r  rQ   r  r  bool_r*  r  
issubclassr   r  r  generateevaluate)r   rv   rp   r   r   inferredrU   rU   rV   r   C  sL   



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)rg  rX   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
                )	rv   rg  rB   rX   	NameErrorr  r  r   r   )r   rp   rq  r`  qkeysr  rU   rU   rV   r0  p  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  )	r,  rv   
read_wherer   r   r   rJ  r2  r"  r   rU   rU   rV   r     s   

zSelection.selectc                 C  s   | j | j}}| jj}|du rd}n|dk r||7 }|du r!|}n|dk r)||7 }| jdur<| jjj| j ||ddS | jdurD| jS t	||S )r5  Nr   T)r   r   r  )
r   r   rv   r*  r,  get_where_listr   rJ  rQ   r  )r   r   r   r*  rU   rU   rV   r    s"   

zSelection.select_coordsr  )rv   r  r   r   r   r   rZ   r   )r   r  r  r  r   r0  r   r  rU   rU   rU   rV   r  7  s    -r  )rX   rY   rZ   r[   )rc   rd   )r   NNFNTNNNNr~   rO   )r   r   r   r[   r   r   r   r[   r   r   r   rY   r   r   r   rY   r   r   r   r   r   r   r   r   r   r[   rX   r[   rZ   r   )	Nr   r~   NNNNFN)r   r   r   r[   r   r[   rp   r   r   r   r   r   r   r   r   r   r   r   )r   rM   r   rM   rZ   r   r\   )r  r5   rE  r   r  r7   rZ   r5   )r  r   rZ   r  r  )rw  r  r  r  r  r   rZ   r6   )rw  r  r  r   r  r   rZ   r  )rw  r  r  r  r  r   rZ   r  )
ra   r[   r   r7   rX   r[   r   r[   rZ   r  )r  r[   rX   r[   r   r[   rZ   r  )ra   r[   r  r   r   r  )r  r  rX   r[   r   r[   rZ   r  )rw  r  r  r[   rX   r[   r   r[   )r  r[   rX   r[   r   r[   )r  r[   rZ   r   )ra   r[   r  r$  rZ   r[   )r(  r[   rZ   r[   )r  r   )r  
__future__r   
contextlibr   r  r  r   r   rK  r   r  textwrapr   typesr   typingr   r	   r
   r   r   r   r   r   r   r   rA  numpyrQ   pandas._configr   r   pandas._libsr   r   r  pandas._libs.tslibsr   pandas._typingr   r   r   r   r   r   r   pandas.compat._optionalr   pandas.compat.pickle_compatr   pandas.errorsr    r!   r"   r#   r$   pandas.util._decoratorsr%   pandas.util._exceptionsr&   pandas.core.dtypes.commonr'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   pandas.core.dtypes.missingr4   r  r5   r6   r7   r8   r9   r:   r;   r<   r=   r>   pandas.core.arraysr?   r@   rA   pandas.core.commoncorecommonr]   pandas.core.computation.pytablesrB   rC   pandas.core.constructionrD   pandas.core.indexes.apirE   pandas.core.internalsrF   rG   pandas.io.commonrH   pandas.io.formats.printingrI   rJ   r   rK   rL   rM   rN   r  r]   rW   r_   rb   rg   rq   rr   r  rs   rt   r  rq  ry   rz   config_prefixregister_optionis_boolis_one_of_factoryr   r   r   r   r   r   r   r-  r  rQ  rV  r  r  r  r  r  r&  r  r  r  r3  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r!  r   rs  ra  r`  r  rU   rU   rU   rV   <module>   s4   0$	<0



: 
          Qp  &   -  e!^       f dc0B+

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@

I

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



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