o
    g                 	   @  sN  d Z ddlmZ ddlmZ ddlZddlmZmZ ddl	Z	ddl
Z
ddlZddlmZ ddlmZmZmZmZmZmZ 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& ddl'm(Z( ddl)m*Z* ddl+m,Z, ddl-m.Z. ddl/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9 ddl:m;Z; ddl<m=Z=m>Z>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZF ddlGmHZHmIZImJZJ ddlKmL  mMZN ddlOmPZPmQZQ ddlRmSZS ddlTmUZU ddlVmWZWmXZX ddlYmZZZ ddl[m\Z\m]Z] erddl^m_Z_m`Z`maZa ddlVmbZb dZcdZddd Zed d! Zfd"d# ZgePZhdd&d'ZiG d(d) d)ejZkG d*d+ d+ejZlG d,d- d-emZnd.ZoG d/d0 d0emZpd1ZqG d2d3 d3emZrd4Zsd5Ztd6d6d7d7d8Zue=dgiZvd9Zwd:Zxeyd;  ejzd<d=ewej{d> ejzd?dexe|g d@d> W d   n	1 sw   Y  da}d=a~dAdB Z	C			=		D					E	ddd[d\Z		]	E					=	dddadbZddgdhZG didj djZG dkdl dlZG dmdn dnZG dodp dpeZG dqdr dreZG dsdt dteZG dudv dveZG dwdx dxZG dydz dzeZG d{d| d|eZG d}d~ d~eZG dd 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ZddddZdddZ	=ddddZdddZdddZdddZdddZdddZdddZdddZdddZdddZdddÄZdddƄZG ddȄ dȃZdS )zY
High level interface to PyTables for reading and writing pandas data structures
to disk
    )annotations)suppressN)datetzinfo)dedent)TYPE_CHECKINGAnyCallableHashableSequencecast)config
get_option)libwriters)	timezones)	ArrayLikeDtypeArgFrameOrSeriesFrameOrSeriesUnionShape)import_optional_dependency)patch_pickle)PerformanceWarning)cache_readonly)
ensure_objectis_categorical_dtypeis_complex_dtypeis_datetime64_dtypeis_datetime64tz_dtypeis_extension_array_dtypeis_list_likeis_string_dtypeis_timedelta64_dtypeneeds_i8_conversion)array_equivalent)
	DataFrameDatetimeIndexIndex
Int64Index
MultiIndexPeriodIndex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 unicoder@   )
isinstancenpbytes_decode)s rF   P/home/ubuntu/cloudmapper/venv/lib/python3.10/site-packages/pandas/io/pytables.py_ensure_decodedu   s   
rH   c                 C  s   | d u rt } | S N)_default_encodingencodingrF   rF   rG   _ensure_encoding|   s   rM   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
    )rA   strnamerF   rF   rG   _ensure_str   s   
rQ   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 )NrT   rR   )r4   Term).0termlevelrF   rG   
<listcomp>   s
    z _ensure_term.<locals>.<listcomp>rU   N)rA   listtupler4   rV   len)whererR   rF   rY   rG   _ensure_term   s   	
r`   c                   @     e Zd ZdS )PossibleDataLossErrorN__name__
__module____qualname__rF   rF   rF   rG   rb          rb   c                   @  ra   )ClosedFileErrorNrc   rF   rF   rF   rG   rh      rg   rh   c                   @  ra   )IncompatibilityWarningNrc   rF   rF   rF   rG   ri      rg   ri   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)
c                   @  ra   )AttributeConflictWarningNrc   rF   rF   rF   rG   rj      rg   rj   zu
the [%s] attribute of the existing index is [%s] which conflicts with the new
[%s], resetting the attribute to None
c                   @  ra   )DuplicateWarningNrc   rF   rF   rF   rG   rk      rg   rk   z;
duplicate entries in table, taking most recently appended
z
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]
fixedtable)frl   trm   z;
: boolean
    drop ALL nan rows when appending to a table
z~
: format
    default format writing format, if None, then
    put will default to 'fixed' and append will default to 'table'
zio.hdfdropna_tableF)	validatordefault_format)rl   rm   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)ru   rF   rF   rG   _tables   s   


rz   aTrs   keyrN   valuer   mode	complevel
int | Nonecomplib
str | Noneappendboolformatindexmin_itemsizeint | dict[str, int] | Nonedropnabool | Nonedata_columnsbool | list[str] | NoneerrorsrL   c              
     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   rL   )r   store
r   r   rL   r   r   r   r|   r   r   r}   rF   rG   <lambda>      zto_hdf.<locals>.<lambda>c                   s   | j 	 d
S )N)r   r   r   r   r   r   rL   r   putr   r   rF   rG   r   (  r   )r~   r   r   N)r9   rA   rN   HDFStore)path_or_bufr|   r}   r~   r   r   r   r   r   r   r   r   r   r   rL   rn   r   rF   r   rG   to_hdf  s    

"r   rstartstop	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
    -------
    item : 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'])
    >>> df.to_hdf('./store.h5', 'data')
    >>> reread = pd.read_hdf('./store.h5')
    )r   r+r{   zmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.NrT   rU   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.)r_   r   r   columnsiteratorr   
auto_close)
ValueErrorr`   rA   r   is_openOSErrorr9   rN   NotImplementedErrorospathexists	TypeErrorFileNotFoundErrorgroupsr^   _is_metadata_of_v_pathnameselectKeyErrorr   rv   close)r   r|   r~   r   r_   r   r   r   r   r   kwargsr   r   r   r   candidate_only_groupgroup_to_checkrF   rF   rG   read_hdf?  sv   O








r   groupr>   parent_groupreturnc                 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.FrT   metaT)_v_depth	_v_parent_v_name)r   r   currentparentrF   rF   rG   r     s   

r   c                   @  s  e Zd ZU dZded< ded< ded< ded	< 	
			ddddZdd Zedd Zedd Z	dddZ
dddZdddZdd!d"Zdd$d%Zdd&d'Zdd(d)Zd*d+ Zd,d- Zddd1d2Zd3d4 Zd5d6 ZeZddd7d8Zd9d: Zedd;d<Zddd>d?Zdd@dAZ							dddCdDZ			dddGdHZ		dddJdKZ								dddLdMZ		N								O	N	dddYdZZ ddd[d\Z!			N	N											Oddd^d_Z"			dddbdcZ#			dddgdhZ$didj Z%ddldmZ&ddodpZ'ddrdsZ(	t	N					NdddvdwZ)ddxdyZ*dzd{ Z+dd}d~Z,				OddddZ-		N												O	NddddZ.dddZ/dddZ0dddZ1d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_handlerN   _moderS   
_complevelr   _fletcher32r{   NFr~   r   r   
fletcher32c                 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 HDFStoreru   zcomplib only supports z compression.r{   r   r~   rF   )r   r   filtersall_complibsdefault_complibr9   _pathr   r   r   _complibr   _filtersopen)selfr   r~   r   r   r   r   ru   rF   rF   rG   __init__/  s&   

zHDFStore.__init__c                 C     | j S rI   r   r   rF   rF   rG   
__fspath__Q  s   zHDFStore.__fspath__c                 C  s   |    | jdusJ | jjS )zreturn the root nodeN)_check_if_openr   rootr   rF   rF   rG   r   T  s   zHDFStore.rootc                 C  r   rI   r   r   rF   rF   rG   filename[     zHDFStore.filenamer|   c                 C  
   |  |S rI   )getr   r|   rF   rF   rG   __getitem___     
zHDFStore.__getitem__c                 C  s   |  || d S rI   r   )r   r|   r}   rF   rF   rG   __setitem__b  s   zHDFStore.__setitem__c                 C  r   rI   )remover   rF   rF   rG   __delitem__e  r   zHDFStore.__delitem__rP   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   rh   rv   typerd   )r   rP   rF   rF   rG   __getattr__h  s   zHDFStore.__getattr__r   c                 C  s8   |  |}|dur|j}||ks|dd |krdS dS )zx
        check for existence of this key
        can match the exact pathname or the pathnm w/o the leading '/'
        NrT   TF)get_noder   )r   r|   noderP   rF   rF   rG   __contains__r  s   
zHDFStore.__contains__c                 C     t |  S rI   )r^   r   r   rF   rF   rG   __len__~     zHDFStore.__len__c                 C  s   t | j}t|  d| dS )N
File path: 
)r;   r   r   )r   pstrrF   rF   rG   __repr__  s   
zHDFStore.__repr__c                 C  s   | S rI   rF   r   rF   rF   rG   	__enter__     zHDFStore.__enter__c                 C     |    d S rI   )r   )r   exc_type	exc_value	tracebackrF   rF   rG   __exit__  r   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 rF   r   rW   nrF   rF   rG   r[         z!HDFStore.keys.<locals>.<listcomp>nativeNc                 S  r   rF   r   r   rF   rF   rG   r[     s    /Table)	classnamez8`include` should be either 'pandas' or 'native' but is 'r   )r   r   
walk_nodesr   )r   r   rF   rF   rG   keys  s   
zHDFStore.keysc                 C  r   rI   )iterr   r   rF   rF   rG   __iter__  r   zHDFStore.__iter__c                 c  s     |   D ]}|j|fV  qdS )z'
        iterate on key->group
        N)r   r   )r   grF   rF   rG   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)rz   r   r   rb   r   r   r   Filtersr   r   r   ry   r   	open_filer   )r   r~   r   ru   msgrF   rF   rG   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   rF   rF   rG   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   rF   rF   rG   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  rF   rF   rG   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   rF   rF   rG   r     s   
$zHDFStore.getr   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
  rT   rU   c                   s   j | || dS )N)r   r   r_   r   read_start_stop_wherer   rE   rF   rG   funcZ  s   zHDFStore.select.<locals>.funcr_   nrowsr   r   r   r   r   )r   r   r`   _create_storer
infer_axesTableIteratorr  
get_result)r   r|   r_   r   r   r   r   r   r   r   r  itrF   r  rG   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
        rT   rU   z&can only read_coordinates with a tabler_   r   r   )r`   
get_storerrA   r   r   read_coordinates)r   r|   r_   r   r   tblrF   rF   rG   select_as_coordinatesm  s
   

zHDFStore.select_as_coordinatescolumnc                 C  s,   |  |}t|tstd|j|||dS )a~  
        return a single column from the table. This is generally only useful to
        select an indexable

        .. warning::

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

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

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

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

        z!can only read_column with a table)r!  r   r   )r  rA   r   r   read_column)r   r|   r!  r   r   r  rF   rF   rG   select_column  s   
#
zHDFStore.select_columnc
                   sx  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 }t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
        rT   rU   r   )r|   r_   r   r   r   r   r   r   zkeys must be a list/tuplez keys must have a non-zero lengthNc                      g | ]}  |qS rF   )r  rW   kr   rF   rG   r[         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 rF   )rA   r   rW   xrF   rF   rG   r[         c                 S  s   h | ]	}|j d  d  qS r   )non_index_axesrW   ro   rF   rF   rG   	<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 )r_   r   r   r   r  r.  )r  r  r  r   rF   rG   r[     s    z=HDFStore.select_as_multiple.<locals>.func.<locals>.<listcomp>F)axisverify_integrity)r.   _consolidate)r  r  r  objs)r1  r   tblsr  rG   r    s   z)HDFStore.select_as_multiple.<locals>.funcr  T)coordinates)r`   rA   r\   r]   r^   rN   r   r   r   r  	itertoolschainzipr   is_tablepathnamer  r  r  )r   r   r_   selectorr   r   r   r   r   r   rE   r  ro   r&  _tblsr  r  rF   )r1  r   r   r5  rG   select_as_multiple  sf   +

 
zHDFStore.select_as_multipleTrs   r}   r   r   r   r   list[str] | Noner   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.
        append : bool, default False
            This will force Table format, append the input data to the existing.
        data_columns : list, 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.

            .. versionadded:: 1.1.0
        Nio.hdf.default_formatrl   )r   r   r   r   r   r   r   r   rL   r   r@  r   )r   _validate_format_write_to_group)r   r|   r}   r   r   r   r   r   r   r   r   rL   r   r@  r   rF   rF   rG   r   /  s&   0

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

        rT   rU   Nz5trying to remove a node with a non-None where clause!T	recursivez7can only remove with where on objects written as tablesr  )r`   r  r   AssertionError	Exceptionr   r   	_f_removecomall_noner   r:  delete)r   r|   r_   r   r   rE   errr   rF   rF   rG   r   s  s8   
zHDFStore.remover   c                 C  sl   |	durt d|du rtd}|du rtdpd}| |}| j|||||||||
|||||||d dS )a6  
        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.
        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
            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_tablerA  rm   )r   axesr   r   r   r   r   r   r   expectedrowsr   r   rL   r   )r   r   rB  rC  )r   r|   r}   r   rM  r   r   r   r   r   r   r   r   rN  r   r   rL   r   rF   rF   rG   r     s6   8

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   )rW   cols)r}   rF   rG   	<genexpr>L  s     z.HDFStore.append_to_multiple.<locals>.<genexpr>r   r1  c                   s   i | ]\}}| v r||qS rF   rF   rW   r|   r}   )vrF   rG   
<dictcomp>\  s    z/HDFStore.append_to_multiple.<locals>.<dictcomp>)r   r   )r   rA   rP  r   r\   setrangendim	_AXES_MAPr   r   extendrM  
differencer(   sortedget_indexertakevaluesnextintersectionlocpopreindexr   )r   rO  r}   r<  r   rM  r   r   r1  
remain_keyremain_valuesr&  orderedorddidxsvalid_indexr   r   dcvalfilteredrF   )rW  r}   rG   append_to_multiple  s\   
&

zHDFStore.append_to_multipleoptlevelkindr   c                 C  sB   t   | |}|du rdS t|tstd|j|||d dS )a  
        Create a pytables index on the table.

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

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

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

        Raises
        ------
        TypeError: raises if the node is not a table
        Nz1cannot create table index on a Fixed format store)r   rr  rs  )rz   r  rA   r   r   create_index)r   r|   r   rr  rs  rE   rF   rF   rG   create_table_indexb  s   

zHDFStore.create_table_indexc                 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_typeNrm   )	rA   rt   linkLinkgetattr_v_attrsrm   r   r   )rW   r   rF   rF   rG   r[     s    

z#HDFStore.groups.<locals>.<listcomp>)rz   r   r   rt   walk_groupsr   rF   rF   rG   r     s   zHDFStore.groupsr   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`.
        Nrv  r   )rz   r   r   rt   r{  ry  rz  _v_childrenrb  rA   r   Groupr   r   r   rstrip)r   r_   r   r   leaveschildrv  rF   rF   rG   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   rt   r   r   
exceptionsNoSuchNodeErrorrA   r>   r   )r   r|   r   rF   rF   rG   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   rE   rF   rF   rG   r    s   

zHDFStore.get_storerr  propindexesc	              	   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 rF   )
is_indexedrP   rW   r{   rF   rF   rG   r[          z!HDFStore.copy.<locals>.<listcomp>r   )r   r   rL   rK   )r   r\   r   rA   r]   r  r   r   r   rM  r   ry  rL   r   )r   rw   r~   r  r   r   r   r   	overwrite	new_storer&  rE   datar   rF   rF   rG   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)r;   r   r   r   r_  r   r^   r  r   r;  rF  rG  r:   )
r   r   outputlkeysr   rb  r&  rE   detaildstrrF   rF   rG   info-  s8   


zHDFStore.infoc                 C  s   | j st| j dd S )Nz file is not open!)r   rh   r   r   rF   rF   rG   r   W  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   rL  rF   rF   rG   rB  [  s   zHDFStore._validate_formatr@   FrameOrSeries | NonerL   c              
     s  durt ttfstd fdd}ttjdd}ttjdd}|du rbdu rPt  tdus:J tddsGt tj	j
rLd}d	}ntd
t trXd}nd} dkrb|d7 }d|vrttd}	z|	| }
W n ty } z|d|d}~ww |
| ||dS |du rΈ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|d|d}~ww |
| ||dS )z"return a suitable class to operateNz(value must be None, Series, or DataFramec              	     s$   t d|  d dt d  S )Nz(cannot properly create the storer for: [z
] [group->,value->z	,format->)r   r   )ro   r   r   r}   rF   rG   errors  s   z&HDFStore._create_storer.<locals>.errorrv  
table_typerm   frame_tablegeneric_tablezKcannot create a storer if the object is not existing nor a value are passedseriesframe_table)r  r  _STORER_MAPrL   r   series_tabler   rT   appendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)r  r  r  r  r  worm
_TABLE_MAP)rA   r,   r&   r   rH   ry  rz  rz   rt   rm   r   SeriesFixed
FrameFixedr   nlevelsGenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable	WORMTable)r   r   r   r}   rL   r   r  ptttr  clsrL  r   r  rF   r  rG   r  e  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emptyrm   r  rl   zCan only append to Tablesz0Compression not supported on Fixed format stores)objrM  r   r   r   r   r   r   rN  r   r   r   r@  )r   )ry  _identify_groupr  r:  	is_existsr   set_object_infowriterA   r   rt  )r   r|   r}   r   rM  r   r   r   r   r   r   r   rN  r   r   r   rL   r   r@  r   rE   rF   rF   rG   rC    s>   
zHDFStore._write_to_groupr   r>   c                 C  s   |  |}|  | S rI   )r  r  r  )r   r   rE   rF   rF   rG   r    s   
zHDFStore._read_groupr   c                 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.NTrD  )r   r   remove_node_create_nodes_and_group)r   r|   r   r   rF   rF   rG   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   splitr^   endswithr   create_group)r   r|   pathsr   pnew_pathr   rF   rF   rG   r    s   


z HDFStore._create_nodes_and_group)r{   NNF)r~   rN   r   r   r   r   r|   rN   )rP   rN   )r|   rN   r   r   r   rS   r   rN   )r   )r   rN   r   r   )r{   )r~   rN   r   r   F)r  r   )NNNNFNF)r|   rN   r   r   NNNr|   rN   r   r   r   r   NN)r|   rN   r!  rN   r   r   r   r   )NNNNNFNF)r   r   )NTFNNNNNNrs   TF)r|   rN   r}   r   r   r   r   r   r   r?  r   rN   r@  r   r   r   )NNTTNNNNNNNNNNrs   )r|   rN   r}   r   r   r   r   r   r   r   r   r?  r   rN   )NNF)rO  rP  )r|   rN   rr  r   rs  r   )r   )r|   rN   r   r  )r|   rN   r   r  )r  TNNNFT)r  r   r   r   r   r   )r   rN   r   rN   )NNr@   rs   )r}   r  rL   rN   r   rN   r   r  )NTFNNNNNNFNNNrs   T)r|   rN   r}   r   r   r   r   r   r   rN   r@  r   )r   r>   )r|   rN   r   r   r   r>   )r|   rN   r   r>   )2rd   re   rf   __doc____annotations__r   r   propertyr   r   r   r   r   r   r   r   r   r   r   r   r   r   	iteritemsr   r   r   r  r   r   r   r#  r>  r   r   r   rq  ru  r   r  r   r  r  r  r   rB  r  rC  r  r  r  rF   rF   rF   rG   r     s  
 A"









"-
N$+~D=Zd(

0

=*
a
>
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 Z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  rE   NFr   r   r   c                 C  s   || _ || _|| _|| _| jjr'|d u rd}|d u rd}|d u r"|}t||}|| _|| _|| _d | _	|s9|	d urE|	d u r?d}	t
|	| _nd | _|
| _d S )Nr   順 )r   rE   r  r_   r:  minr  r   r   r6  rS   r   r   )r   r   rE   r  r_   r  r   r   r   r   r   rF   rF   rG   r   D  s,   

zTableIterator.__init__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   r6  r   r   r  r   r  r^   r   )r   r   r   r}   rF   rF   rG   r   n  s   


	zTableIterator.__iter__c                 C  s   | j r
| j  d S d S rI   )r   r   r   r   rF   rF   rG   r   ~  s   zTableIterator.closer6  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)r_   z$can only read_coordinates on a tabler  )r   rA   rE   r   r   r  r_   r6  r   r   r  r   )r   r6  r_   resultsrF   rF   rG   r    s   
zTableIterator.get_result)NNFNF)
r   r   rE   r  r   r   r   r   r   r   r  )r6  r   )	rd   re   rf   r  r  r   r   r   r  rF   rF   rF   rG   r  ,  s   
 	*r  c                   @  sP  e Zd ZU dZdZdZg dZded< ded< 													dHdId	d
Ze	dJddZ
e	dKddZdLddZdKddZdMddZdNddZe	dNddZdOd#d$Zd%d& Ze	d'd( Ze	d)d* Ze	d+d, Ze	d-d. Zd/d0 ZdPd1d2Zd3d4 ZdQd8d9ZdPd:d;ZdRd<d=Zd>d? Zd@dA ZdBdC ZdSdDdEZ dSdFdGZ!dS )T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

    T)freqtz
index_namerN   rP   cnameNr   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.)rA   rN   r   rb  rs  typrP   r  r1  posr  r  r  rj  rm   r   metadataset_pos)r   rP   rb  rs  r  r  r1  r  r  r  r  rj  rm   r   r  rF   rF   rG   r     s(   


zIndexCol.__init__r   rS   c                 C     | j jS rI   )r  itemsizer   rF   rF   rG   r    s   zIndexCol.itemsizec                 C     | j  dS )N_kindrO   r   rF   rF   rG   	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  rF   rF   rG   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  "    | ]\}}| d | V  qdS z->NrF   rV  rF   rF   rG   rT    
    
z$IndexCol.__repr__.<locals>.<genexpr>)rP   r  r1  r  rs  )
r]   mapr;   rP   r  r1  r  rs  joinr9  r   temprF   rF   rG   r     s   zIndexCol.__repr__otherr   r   c                      t  fdddD S )compare 2 col itemsc                 3  (    | ]}t |d t  |d kV  qd S rI   ry  r  r  r   rF   rG   rT    
    
z"IndexCol.__eq__.<locals>.<genexpr>)rP   r  r1  r  rQ  r   r  rF   r  rG   __eq__     zIndexCol.__eq__c                 C  s   |  | S rI   )r  r  rF   rF   rG   __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 columnrS  F)hasattrrm   ry  rS  r  r  r   rF   rF   rG   r    s   zIndexCol.is_indexedrb  
np.ndarrayrL   r   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r8t| j|d< t}t|jsDt|jrFt}z
||fi |}W n tyi   d|v r_d|d< ||fi |}Y nw t|| j}||fS )zV
        Convert the data from this selection to the appropriate pandas type.
        NrP   r  )rA   rB   ndarrayr   dtypefieldsr  rH   rs  _maybe_convertr  r  r(   r   r   r'   r   _set_tzr  )	r   rb  r   rL   r   val_kindr   factorynew_pd_indexrF   rF   rG   convert	  s*   


	zIndexCol.convertc                 C  r   )zreturn the valuesrb  r   rF   rF   rG   	take_data/  r   zIndexCol.take_datac                 C  r  rI   )rm   rz  r   rF   rF   rG   attrs3     zIndexCol.attrsc                 C  r  rI   rm   descriptionr   rF   rF   rG   r
  7  r  zIndexCol.descriptionc                 C  s   t | j| jdS )z!return my current col descriptionN)ry  r
  r  r   rF   rF   rG   col;     zIndexCol.colc                 C  r   zreturn my cython valuesr  r   rF   rF   rG   cvalues@     zIndexCol.cvaluesc                 C  s
   t | jS rI   )r   rb  r   rF   rF   rG   r   E  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  )rH   rs  rA   rP  r   rP   r  r  rz   	StringColr  )r   r   rF   rF   rG   maybe_set_sizeH  s   
zIndexCol.maybe_set_sizec                 C     d S rI   rF   r   rF   rF   rG   validate_namesU  r   zIndexCol.validate_nameshandlerAppendableTabler   c                 C  s:   |j | _ |   | | | | | | |   d S rI   )rm   validate_colvalidate_attrvalidate_metadatawrite_metadataset_attr)r   r  r   rF   rF   rG   validate_and_setX  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)rH   rs  r  r  r   r  )r   r  crF   rF   rG   r  `  s   
zIndexCol.validate_colc                 C  sJ   |rt | j| jd }|d ur!|| jkr#td| d| j dd S d S d S )Nzincompatible kind in col [ - r(  )ry  r  r  rs  r   )r   r   existing_kindrF   rF   rG   r  s  s   zIndexCol.validate_attrc                 C  s   | j D ]\}t| |d}|| ji }||}||v rS|durS||krS|dv rAt|||f }tj|td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_fieldsry  
setdefaultrP   r   attribute_conflict_docwarningswarnrj   setattrr   )r   r  r|   r}   idxexisting_valuewsrF   rF   rG   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   rP   __dict__update)r   r  r)  rF   rF   rG   set_info  s   zIndexCol.set_infoc                 C  s   t | j| j| j dS )zset the kind for this columnN)r(  r  r  rs  r   rF   rF   rG   r       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  r%   r   )r   r  new_metadatacur_metadatarF   rF   rG   r    s   
zIndexCol.validate_metadatac                 C  s"   | j dur|| j| j  dS dS )zset the meta dataN)r  r  r  )r   r  rF   rF   rG   r    s   
zIndexCol.write_metadata)NNNNNNNNNNNNN)rP   rN   r  r   r  r  )r  rS   r  r   r   r   r  rb  r  rL   rN   r   rN   rI   )r  r  r   r   )r   r   )r  r  )"rd   re   rf   r  is_an_indexableis_data_indexabler#  r  r   r  r  r  r  r   r  r  r  r  r  r  r
  r  r  r   r  r  r  r  r  r,  r/  r  r  r  rF   rF   rF   rG   r    sh   
 ,




&







	
r  c                   @  s0   e Zd ZdZedddZdddZdd ZdS )GenericIndexColz:an index which is not represented in the data of the tabler   r   c                 C     dS NFrF   r   rF   rF   rG   r       zGenericIndexCol.is_indexedrb  r  rL   rN   r   c                 C  s2   t |tjsJ t|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
        )rA   rB   r  r   r)   aranger^   )r   rb  r   rL   r   rF   rF   rG   r    s   zGenericIndexCol.convertc                 C  r  rI   rF   r   rF   rF   rG   r    r   zGenericIndexCol.set_attrNr  r6  )rd   re   rf   r  r  r  r  r  rF   rF   rF   rG   r9    s    
r9  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d>ddZd?ddZdd Zed@d d!Zed"d# ZedAd&d'ZedBd(d)Zed*d+ Zed,d- Zed.d/ Zed0d1 Zd2d3 ZdCd7d8Zd9d: Z  ZS )D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  rj  NrP   rN   r  DtypeArg | Nonec                   s2   t  j|||||||||	|
|d || _|| _d S )N)rP   rb  rs  r  r  r  r  rj  rm   r   r  )superr   r  r  )r   rP   rb  rs  r  r  r  r  rj  rm   r   r  r  r  	__class__rF   rG   r     s   
zDataCol.__init__r   c                 C  r  )N_dtyperO   r   rF   rF   rG   
dtype_attr	  r  zDataCol.dtype_attrc                 C  r  )N_metarO   r   rF   rF   rG   	meta_attr	  r  zDataCol.meta_attrc                 C  r  )Nr  c                 s  r  r  rF   rV  rF   rF   rG   rT  	  r  z#DataCol.__repr__.<locals>.<genexpr>)rP   r  r  rs  shape)
r]   r  r;   rP   r  r  rs  rG  r  r9  r  rF   rF   rG   r   	  s   zDataCol.__repr__r  r   r   c                   r  )r  c                 3  r  rI   r  r  r  rF   rG   rT  	  r  z!DataCol.__eq__.<locals>.<genexpr>)rP   r  r  r  r  r  rF   r  rG   r  	  r  zDataCol.__eq__r  r   c                 C  s@   |d usJ | j d u sJ t|\}}|| _|| _ t|| _d S rI   )r  _get_data_and_dtype_namer  _dtype_to_kindrs  )r   r  
dtype_namerF   rF   rG   set_data$	  s   zDataCol.set_datac                 C  r   )zreturn the datar  r   rF   rF   rG   r  .	  r   zDataCol.take_datarb  r<   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.
        rT   rs  r   r  rG  )r  r  rG  r[  sizerA   r0   codesget_atom_datarP   r   r   get_atom_datetime64r#   get_atom_timedelta64r   rz   
ComplexColr"   get_atom_string)r  rb  r  r  rG  rP  atomrF   rF   rG   	_get_atom2	  s.   





zDataCol._get_atomc                 C  s   t  j||d dS )Nr   rN  rz   r  r  rG  r  rF   rF   rG   rU  R	     zDataCol.get_atom_stringrs  	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UIntr<   periodInt64Col)r  
capitalizery  rz   )r  rs  k4col_namekcaprF   rF   rG   get_atom_coltypeV	  s   


zDataCol.get_atom_coltypec                 C  s   | j |d|d dS )NrM  r   rG  re  r  rG  rs  rF   rF   rG   rQ  e	  r0  zDataCol.get_atom_datac                 C     t  j|d dS Nr   rf  rz   r`  r  rG  rF   rF   rG   rR  i	     zDataCol.get_atom_datetime64c                 C  ri  rj  rk  rl  rF   rF   rG   rS  m	  rm  zDataCol.get_atom_timedelta64c                 C     t | jdd S )NrG  )ry  r  r   rF   rF   rG   rG  q	     zDataCol.shapec                 C  r   r  rL  r   rF   rF   rG   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!)ry  r  r  r\   rb  r   rD  r  )r   r   existing_fieldsexisting_dtyperF   rF   rG   r  z	  s   zDataCol.validate_attrr  rL   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 rF   r   fromordinalrW   rW  rF   rF   rG   r[   	  r'  z#DataCol.convert.<locals>.<listcomp>c                 S  rx  rF   r   fromtimestampr{  rF   rF   rG   r[   	  r'  r1  )
categoriesrj  Fr  Or  r   rL   r   )"rA   rB   r  r   r  r  r  r  rH  rI  rs  rH   r   r  rj  r  r   asarrayobjectr   ravelr(   float64r/   anyastyperS   cumsum_valuesr0   
from_codesr   _unconvert_string_arrayrb  )r   rb  r   rL   r   	convertedrJ  rs  r   r  rj  r  r  r  rP  maskrF   rF   rG   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)r(  r  r  rb  rF  r   r  rD  r   rF   rF   rG   r  	  s   zDataCol.set_attr)NNNNNNNNNNNN)rP   rN   r  r?  r  r5  r  r   )rb  r   r   r<   )rs  rN   r   r[  rs  rN   r   r<   r6  )rd   re   rf   r  r7  r8  r#  r   r  rD  rF  r   r  rK  r  classmethodrW  rU  re  rQ  rR  rS  rG  r  r  r  r  __classcell__rF   rF   rA  rG   r>    sZ     









er>  c                   @  sN   e Zd ZdZdZ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 indexedTc                 C  s   t | j stdd S )N-cannot have non-object label DataIndexableCol)r(   rb  	is_objectr   r   rF   rF   rG   r  	  s   zDataIndexableCol.validate_namesc                 C  s   t  j|dS )N)r  rX  rY  rF   rF   rG   rU  	  ro  z DataIndexableCol.get_atom_stringrs  rN   r   r<   c                 C  s   | j |d S )NrM  rg  rh  rF   rF   rG   rQ  
  ro  zDataIndexableCol.get_atom_datac                 C  
   t   S rI   rk  rl  rF   rF   rG   rR  
     
z$DataIndexableCol.get_atom_datetime64c                 C  r  rI   rk  rl  rF   rF   rG   rS  

  r  z%DataIndexableCol.get_atom_timedelta64Nr  )rd   re   rf   r  r8  r  r  rU  rQ  rR  rS  rF   rF   rF   rG   r  	  s    

r  c                   @  s   e Zd ZdZdS )GenericDataIndexableColz(represent a generic pytables data columnN)rd   re   rf   r  rF   rF   rF   rG   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ed< ded< ded< dZ		dLdMddZedNddZ	edOddZ
edd ZdPddZd d! Zd"d# Zed$d% Zed&d' Zed(d) Zed*d+ ZedQd,d-ZedNd.d/Zed0d1 Zd2d3 Zd4d5 Zed6d7 ZedNd8d9Zed:d; Zd<d= ZdRd?d@ZdAdB Z	>	>	>	>dSdTdFdGZdHdI ZdUdTdJdKZ d>S )V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.
    rN   pandas_kindrl   format_typetype[FrameOrSeriesUnion]obj_typerS   r[  rL   r   r   r>   r   r   Fr@   rs   c                 C  sZ   t |tsJ t|td usJ t |tjsJ t||| _|| _t|| _|| _	d S rI   )
rA   r   r   rt   r>   r   r   rM   rL   r   )r   r   r   rL   r   rF   rF   rG   r   ,
  s   

zFixed.__init__r   r   c                 C  s*   | j d dko| j d dko| j d dk S )Nr   rT   
      )versionr   rF   rF   rG   is_old_version;
  s   *zFixed.is_old_versiontuple[int, int, int]c                 C  sf   t t| jjdd}ztdd |dD }t|dkr$|d }W |S W |S  ty2   d}Y |S w )	zcompute and set our versionpandas_versionNc                 s      | ]}t |V  qd S rI   rS   r)  rF   rF   rG   rT  D
      z Fixed.version.<locals>.<genexpr>.r  r,  )r   r   r   )rH   ry  r   rz  r]   r  r^   rv   )r   r  rF   rF   rG   r  ?
  s   
zFixed.versionc                 C  s   t t| jjdd S )Nrv  )rH   ry  r   rz  r   rF   rF   rG   rv  K
  rZ  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  r  rI   r;   r)  rF   rF   rG   rT  U
  r  z!Fixed.__repr__.<locals>.<genexpr>[r(  12.12z	 (shape->))r  rG  rA   r\   r]   r  rv  )r   rE   jshaperF   rF   rG   r   O
  s   zFixed.__repr__c                 C  s   t | j| j_t t| j_dS )zset my pandas type & versionN)rN   r  r  rv  _versionr  r   rF   rF   rG   r  Z
  s   zFixed.set_object_infoc                 C  s   t  | }|S rI   r  )r   new_selfrF   rF   rG   r  _
  s   
z
Fixed.copyc                 C  r   rI   )r  r   rF   rF   rG   rG  c
  r   zFixed.shapec                 C  r  rI   r   r   r   rF   rF   rG   r;  g
  r  zFixed.pathnamec                 C  r  rI   )r   r   r   rF   rF   rG   r   k
  r  zFixed._handlec                 C  r  rI   )r   r   r   rF   rF   rG   r   o
  r  zFixed._filtersc                 C  r  rI   )r   r   r   rF   rF   rG   r   s
  r  zFixed._complevelc                 C  r  rI   )r   r   r   rF   rF   rG   r   w
  r  zFixed._fletcher32c                 C  r  rI   )r   rz  r   rF   rF   rG   r  {
  r  zFixed.attrsc                 C  r:  zset our object attributesNrF   r   rF   rF   rG   	set_attrs
  r<  zFixed.set_attrsc                 C  r:  )zget our object attributesNrF   r   rF   rF   rG   	get_attrs
  r<  zFixed.get_attrsc                 C  r   )zreturn my storabler   r   rF   rF   rG   storable
  r  zFixed.storablec                 C  r:  r;  rF   r   rF   rF   rG   r  
  r<  zFixed.is_existsc                 C  rn  )Nr  )ry  r  r   rF   rF   rG   r  
  ro  zFixed.nrowsc                 C  s   |du rdS dS )z%validate against an existing storableNTrF   r  rF   rF   rG   validate
  s   zFixed.validateNc                 C  r:  )+are we trying to operate on an old version?TrF   )r   r_   rF   rF   rG   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   rE   rF   rF   rG   r  
  s
   zFixed.infer_axesr   r   r   c                 C     t d)Nz>cannot read on an abstract storer: subclasses should implementr   r   r_   r   r   r   rF   rF   rG   r  
  s   z
Fixed.readc                 K  r  )Nz?cannot write on an abstract storer: subclasses should implementr  r   r   rF   rF   rG   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
        TrD  Nz#cannot delete on an abstract storer)rI  rJ  r   r  r   r   )r   r_   r   r   rF   rF   rG   rK  
  s   zFixed.delete)r@   rs   )r   r   r   r>   rL   rN   r   rN   r  )r   r  r  r  rI   NNNNr   r   r   r   r  )!rd   re   rf   r  r  r  r:  r   r  r  r  rv  r   r  r  rG  r;  r   r   r   r   r  r  r  r  r  r  r  r  r  r  r  rK  rF   rF   rF   rG   r  
  sn   
 









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 Zed<ddZdd Zdd Zdd Zd=d>d d!Z	d=d?d#d$Zd@d&d'ZdAd)d*Z	d=dBd+d,Z	d=dCd/d0ZdDd3d4ZdEdFd9d:ZdS )GGenericFixedza generified fixed versiondatetimer_  c                 C  s   i | ]\}}||qS rF   rF   )rW   r&  rW  rF   rF   rG   rX  
  r'  zGenericFixed.<dictcomp>r   
attributesr   rN   c                 C  s   | j |dS )N )_index_type_mapr   )r   r  rF   rF   rG   _class_to_alias
  s   zGenericFixed._class_to_aliasc                 C  s   t |tr|S | j|tS rI   )rA   r   _reverse_index_mapr   r(   )r   aliasrF   rF   rG   _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  rO   UTC)r1   _simple_newrb  r'   tz_localize
tz_convert)rb  r  r  dtaresultrF   rF   rG   rn   
  s
   z*GenericFixed._get_index_factory.<locals>.fc                 S  s   t j| |d}tj|d dS )Nr  rO   )r2   r  r+   )rb  r  r  parrrF   rF   rG   rn   
  s   r  r  zutf-8r  )
r  rH   ry  r'   r+   r(   r-   rA   bytesrD   )r   r  r  rn   r  r   rF   rF   rG   _get_index_factory
  s*   

zGenericFixed._get_index_factoryc                 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   r_   rF   rF   rG   validate_read  s   zGenericFixed.validate_readr   c                 C  r:  )NTrF   r   rF   rF   rG   r    r<  zGenericFixed.is_existsc                 C  s   | j | j_ | j| j_dS r  )rL   r  r   r   rF   rF   rG   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 attributesrL   Nr   rs   )rM   ry  r  rL   rH   r   r  r(  )r   r   rF   rF   rG   r    s
   
zGenericFixed.get_attrsc                 K  r   rI   )r  r   r  r   rF   rF   rG   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_typerG  rw  rr  r  Trs  ru  rv  )ru   ry  r   rz  rA   VLArrayrH   rB   r  r   r  T)r   r|   r   r   ru   r   r  r  retr  rG  r  rF   rF   rG   
read_array&  s&   zGenericFixed.read_arrayr(   c                 C  sd   t t| j| d}|dkr| j|||dS |dkr+t| j|}| j|||d}|S td| )N_varietymultir   r   regularzunrecognized index variety: )rH   ry  r  read_multi_indexr   read_index_noder   )r   r|   r   r   varietyr   r   rF   rF   rG   
read_indexH  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   )rA   r*   r(  r  write_multi_index_convert_indexrL   r   write_arrayrb  ry  r   rs  rz  rP   r'   r+   r  r   r  r-   r  r  _get_tz)r   r|   r   r  r   rF   rF   rG   write_indexV  s    



zGenericFixed.write_indexr*   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)r(  r  r  	enumerater9  levelsrP  namesr    r   r  rL   r   r  rb  ry  r   rs  rz  rP   )r   r|   r   ilevlevel_codesrP   	level_key
conv_levelr   	label_keyrF   rF   rG   r  m  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  rP  r  r2  )	ry  r  rZ  r   r  r   rP   r  r*   )r   r|   r   r   r  r  rP  r  r  r  r   r  r  r  rF   rF   rG   r    s    
zGenericFixed.read_multi_indexr   r>   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krW|t||| j| jdfdti|	}
n|t||| j| jdfi |	}
||
_	|
S )NrG  r   rw  rP   r   r  r  )rz  rB   prodrG  r  r  rH   rs  rQ   rP   r  _unconvert_indexrL   r   r  )r   r   r   r   r  rs  rP   r  r  r   r   rF   rF   rG   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rT   N)rB   r  r[  r   create_arrayr   ry  rN   r  rz  r  rG  )r   r|   r}   arrr   rF   rF   rG   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d
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!  i8rr  ru  )+r5   r   r   r  rO  r   r  r   r  r  r   r   r   rz   Atom
from_dtypecreate_carrayrG  r  r   rB   object_r   infer_dtypeperformance_docr&  r'  r   create_vlarray
ObjectAtomr   r   r  viewry  rz  r  r   asi8r  r  r#   r  )r   r|   r  r   r}   empty_arrayr  rV  cainferred_typer+  vlarrr   rF   rF   rG   r    sh   









zGenericFixed.write_arrayr  r  r  r  )r|   rN   r   r   r   r   r   r(   )r|   rN   r   r(   )r|   rN   r   r*   )r|   rN   r   r   r   r   r   r*   )r   r>   r   r   r   r   r   r(   )r|   rN   r}   r   rI   )r|   rN   r  r   r   r  )rd   re   rf   r  r'   r+   r  r   r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  rF   rF   rF   rG   r  
  s2   
 
.#


&	r  c                      sP   e Zd ZU dZdgZded< edd Z				ddd
dZ fddZ	  Z
S )r  r  rP   r
   c              	   C  s*   zt | jjfW S  ttfy   Y d S w rI   )r^   r   rb  r   rv   r   rF   rF   rG   rG  +  s
   zSeriesFixed.shapeNr   r   r   c                 C  s<   |  || | jd||d}| jd||d}t||| jdS )Nr   r  rb  )r   rP   )r  r  r  r,   rP   )r   r_   r   r   r   r   rb  rF   rF   rG   r  2  s   zSeriesFixed.readc                   s<   t  j|fi | | d|j | d| |j| j_d S )Nr   rb  )r@  r  r  r   r  rP   r  r  rA  rF   rG   r  >  s   zSeriesFixed.writer  r  )rd   re   rf   r  r  r  r  rG  r  r  r  rF   rF   rA  rG   r  %  s   
 
r  c                      sP   e Zd ZU ddgZded< edddZ				ddddZ fddZ  Z	S )BlockManagerFixedr[  nblocksrS   r   Shape | Nonec                 C  s   zJ| j }d}t| jD ]}t| jd| d}t|dd }|d ur'||d 7 }q| jj}t|dd }|d urAt|d|d  }ng }|| |W S  tyT   Y d S w )Nr   block_itemsrG  rT   )	r[  rZ  r  ry  r   block0_valuesr\   r   rv   )r   r[  r   r  r   rG  rF   rF   rG   rG  J  s&   
zBlockManagerFixed.shapeNr   r   r   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	}|| q>t|dkrt|dd
}|j|dd}|S t
|d |d d	S )Nr   r  r1  r  r  r  r  rT   r   r   rU  F)r   r  )r  r  _get_block_manager_axisrZ  r[  r  r   r  r  r`  r&   r  r^   r.   rg  )r   r_   r   r   r   select_axisrM  r  r  r  axr   dfs	blk_itemsrb  dfoutrF   rF   rG   r  e  s(   zBlockManagerFixed.readc                   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 formatr1  r  )r   r  )r@  r  rA   _mgrr7   _as_manageris_consolidatedconsolidater[  r  r  rM  	is_uniquer   r  r^   blocksr  r   ra  mgr_locsr  rb  )r   r  r   r  r  r  blkr  rA  rF   rG   r    s"   

zBlockManagerFixed.write)r   r  r  r  )
rd   re   rf   r  r  r  rG  r  r  r  rF   rF   rA  rG   r  E  s   
 %r  c                   @  s   e Zd ZdZeZdS )r  r  N)rd   re   rf   r  r&   r  rF   rF   rF   rG   r    s    r  c                      s@  e Zd ZU dZdZdZded< ded< dZded	< d
Zded< ded< ded< ded< ded< ded< 								dd fddZ	e
dd d!Zdd"d#Zdd%d&Zd'd( Ze
dd*d+Zdd/d0Ze
dd2d3Ze
dd4d5Ze
d6d7 Ze
d8d9 Ze
d:d; Ze
d<d= Ze
d>d? Ze
dd@dAZe
ddBdCZe
dDdE ZddGdHZdIdJ ZddLdMZddOdPZddSdTZddUdVZ dWdX Z!dYdZ Z"dd[d\Z#d]d^ Z$e%d_d` Z&dddcddZ'	dddidjZ(e)ddldmZ*dndo Z+	
			dddrdsZ,e-ddvdwZ.dddzd{Z/dddZ0	ddddZ1			ddddZ2  Z3S )r   a  
    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), or True to force all
        columns
    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_tablerm   rN   r  r  rT   zint | list[Hashable]r  Tzlist[IndexCol]
index_axeszlist[tuple[int, Any]]r-  zlist[DataCol]values_axesr\   r   r  rP  r  Nrs   r   r   r   r>   r   c                   sP   t  j||||d |pg | _|pg | _|pg | _|pg | _|	p!i | _|
| _d S )Nr  )r@  r   r'  r-  r(  r   r  r   )r   r   r   rL   r   r'  r-  r(  r   r  r   rA  rF   rG   r     s   





zTable.__init__r   c                 C  s   | j dd S )N_r   )r  r  r   rF   rF   rG   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  rI   rN   r)  rF   rF   rG   rT    r  z!Table.__repr__.<locals>.<genexpr>r  c                 s  s    | ]}|j V  qd S rI   rO   r  rF   rF   rG   rT    s    r  z (typ->z,nrows->z,ncols->z,indexers->[r  )r  r^   r   r  r  r  r'  rv  r*  r  ncols)r   jdcrn  verjverjindex_axesrF   rF   rG   r     s(   zTable.__repr__r  c                 C  s"   | j D ]}||jkr|  S qdS )zreturn the axis for cN)rM  rP   )r   r  r{   rF   rF   rG   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'  r-  r(  zinvalid combination of [z] on appending data [z] vs current table [)r  r   ry  r  r   rG  )r   r  r  svovr  saxoaxrF   rF   rG   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)rA   r  r\   r   rF   rF   rG   is_multi_index   s   zTable.is_multi_indexr  r    tuple[DataFrame, list[Hashable]]c              
   C  s\   dd t |jjD }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
        c                 S  s&   g | ]\}}|d ur|nd| qS )Nlevel_rF   )rW   r  lrF   rF   rG   r[   ,  s    z-Table.validate_multiindex.<locals>.<listcomp>zBduplicate names/columns in the multi-index when storing as a tableN)r  r   r  reset_indexr   rA   r&   )r   r  r  	reset_objrL  rF   rF   rG   validate_multiindex%  s   
zTable.validate_multiindexrS   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  rG  rW   r  rF   rF   rG   r[   ;  r  z(Table.nrows_expected.<locals>.<listcomp>)rB   r  r'  r   rF   rF   rG   nrows_expected8  s   zTable.nrows_expectedc                 C  s
   d| j v S )zhas this table been createdrm   r  r   rF   rF   rG   r  =  s   
zTable.is_existsc                 C  rn  Nrm   ry  r   r   rF   rF   rG   r  B  ro  zTable.storablec                 C  r   )z,return the table group (this is my storable))r  r   rF   rF   rG   rm   F  r  zTable.tablec                 C  r  rI   )rm   r  r   rF   rF   rG   r  K  r  zTable.dtypec                 C  r  rI   r	  r   rF   rF   rG   r
  O  r  zTable.descriptionc                 C  s   t | j| jS rI   )r7  r8  r'  r(  r   rF   rF   rG   rM  S  r+  z
Table.axesc                 C  s   t dd | jD S )z.the number of total columns in the values axesc                 s  s    | ]}t |jV  qd S rI   )r^   rb  r  rF   rF   rG   rT  Z  s    zTable.ncols.<locals>.<genexpr>)sumr(  r   rF   rF   rG   r-  W  s   zTable.ncolsc                 C  r:  r;  rF   r   rF   rF   rG   is_transposed\  r<  zTable.is_transposedc                 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  rF   rF   rG   r[   e  r  z*Table.data_orientation.<locals>.<listcomp>c                 S  s   g | ]}t |jqS rF   )rS   r1  r  rF   rF   rG   r[   f  r'  )r]   r7  r8  r-  r'  r   rF   rF   rG   data_orientation`  s   zTable.data_orientationdict[str, Any]c                   sR   ddd dd j D } fddjD }fddjD }t|| | S )z<return a dict of the kinds allowable columns for this objectr   r   r   rT   c                 S  s   g | ]}|j |fqS rF   r  r  rF   rF   rG   r[   p  r'  z$Table.queryables.<locals>.<listcomp>c                   s   g | ]
\}} | d fqS rI   rF   )rW   r1  rb  )
axis_namesrF   rG   r[   q  s    c                   s&   g | ]}|j t jv r|j|fqS rF   )rP   rY  r   r  r{  r   rF   rG   r[   r  s     )r'  r-  r(  rP  )r   d1d2d3rF   )rG  r   rG   
queryablesj  s   

zTable.queryablesc                 C     dd | j D S )zreturn a list of my index colsc                 S  s   g | ]}|j |jfqS rF   )r1  r  r=  rF   rF   rG   r[   }  r  z$Table.index_cols.<locals>.<listcomp>r'  r   rF   rF   rG   
index_colsz  r  zTable.index_colsr   c                 C  rL  )zreturn a list of my values colsc                 S  r   rF   rF  r=  rF   rF   rG   r[     r   z%Table.values_cols.<locals>.<listcomp>)r(  r   rF   rF   rG   values_cols  r+  zTable.values_colsr|   c                 C  s   | j j}| d| dS )z)return the metadata pathname for this keyz/meta/z/metar  r  rF   rF   rG   _get_metadata_path  s   zTable._get_metadata_pathrb  r  c                 C  s0   t |}| jj| ||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
        rm   )r   rL   r   r   N)r,   r   r   rP  rL   r   r   )r   r|   rb  rF   rF   rG   r    s   
zTable.write_metadatac                 C  s0   t t | jdd|ddur| j| |S dS )z'return the meta data array for this keyr   N)ry  r   r   r   rP  r   rF   rF   rG   r2    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)rN   r  r  rN  rO  r-  r   r   rL   r   r  r  r   rF   rF   rG   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  r-  Nr   r  r   rL   r   rs   r  c                 S     g | ]}|j r|qS rF   r7  r  rF   rF   rG   r[     r'  z#Table.get_attrs.<locals>.<listcomp>c                 S     g | ]}|j s|qS rF   rR  r  rF   rF   rG   r[     r'  )ry  r  r-  r   r  r   rM   rL   rH   r   r  
indexablesr'  r(  r   rF   rF   rG   r    s   zTable.get_attrsc                 C  sl   |dur.| j d dkr0| j d dkr2| j d dk r4tddd | j D  }t|t dS dS dS dS dS )	r  Nr   rT   r  r  r  c                 s  r  rI   r,  r)  rF   rF   rG   rT    r  z)Table.validate_version.<locals>.<genexpr>)r  incompatibility_docr  r&  r'  ri   )r   r_   r+  rF   rF   rG   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
        Nrb  zmin_itemsize has the key [z%] which is not an axis or data_column)rA   rP  rK  r   )r   r   qr&  rF   rF   rG   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 existNr1  r  )rP   r1  r  rs  r  rm   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  rC  rE  )
rP   r  rb  rs  r  r  rm   r   r  r  )
rA   rN   r>  r  ry  _maybe_adjust_namer  rI  r2  rm   )r  r  klassrV  adj_namerb  r  rs  mdr   r  )base_posrn  descr   table_attrsrF   rG   rn     s0   

zTable.indexables.<locals>.fc                   s   g | ]	\}} ||qS rF   rF   )rW   r  r  )rn   rF   rG   r[   !  r+  z$Table.indexables.<locals>.<listcomp>)r
  rm   r  r  rN  ry  r2  r  r   rY  r   r^   r]  rO  )r   _indexablesr  r1  rP   rV  r[  r   r  rs  	index_colrF   )r\  rn  r]  rn   r   r^  rG   rT    s2   




 #zTable.indexablesrs  r   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  rF   )r8  r  r  rF   rF   rG   r[   H  r  z&Table.create_index.<locals>.<listcomp>rr  rs  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   rT   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.rF   )r  rM  rA   r]   r\   rm   ry  rS  r  r   rr  rs  remove_indexr   r  r   rt  r-  rv   )r   r   rr  rs  kwrm   r  rW  r   cur_optlevelcur_kindrF   rF   rG   rt  %  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]]
        r  r  )
	Selectionr   rM  r/  r  r  r   rL   r   r   )	r   r_   r   r   	selectionrb  r  r{   resrF   rF   rG   
_read_axesy  s   
zTable._read_axesr  c                 C     |S )zreturn the data for this objrF   r  r  r  rF   rF   rG   
get_object  s   zTable.get_objectc                   s   t |sg S |d \} | j|i }|ddkr&|r&td| d| |du r/t }n|du r5g }t|trPt|t|}|fdd	|	 D   fd
d	|D S )zd
        take the input data_columns and min_itemize and create a data
        columns spec
        r   r   r*   z"cannot use a multi-index on axis [z] with data_columns TNc                   s    g | ]}|d kr| vr|qS r  rF   r%  )existing_data_columnsrF   rG   r[     s
    z/Table.validate_data_columns.<locals>.<listcomp>c                   s   g | ]}| v r|qS rF   rF   )rW   r  )axis_labelsrF   rG   r[     r  )
r^   r  r   r   r\   rA   rP  rY  r]  r   )r   r   r   r-  r1  r  rF   )ro  rn  rG   validate_data_columns  s.   


	zTable.validate_data_columnsr&   r  c           /        sr  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}| "|||
}| #|$ }| %|||
| j&|\}}g }t't(||D ]\}\}}t)}d}|r\t|dkr\|d |v r\t*}|d }|du s\t |t+s\td|r|rz| j&| }W n t,t-fy }  ztd| d| j& d| d} ~ ww d}|pd| }!t.|!||||| j| j|d}"t/|!| j0}#|1|"}$t2|"j3j4}%d}&t5|"dddurt6|"j7}&d }' }(})t8|"j3r|"j9})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?|._?|.@| |r7|r7|.A|  |.S )a0  
        Create and return the axes.

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

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

        min_itemsize: Dict[str, int] or None, default None
            The min itemsize for a column in bytes.
        z/cannot properly create the storer for: [group->r  r(  Nr   c                   r$  rF   )_get_axis_numberr  )r  rF   rG   r[     r'  z&Table._create_axes.<locals>.<listcomp>Tc                 S  r   rF   rU  r  rF   rF   rG   r[     r   Fr  rT   z<currently only support ndim-1 indexers in an AppendableTablenanc                   s   g | ]}| vr|qS rF   rF   r)  )rM  rF   rG   r[     r  rE  r  r   r  zIncompatible appended table [z]with existing table [values_block_)existing_colr   r   rL   r   r   r  r1  r  )rP   r  rb  r  r  rs  r  rj  r   r  r  r  c                 S  r  rF   )r8  rP   )rW   r  rF   rF   rG   r[     r  )
r   r   rL   r   r'  r-  r(  r   r  r   r  )BrA   r&   r   r   r   r   r  r'  r\   r   r   r  r[  r^   r   rM  r-  r%   rB   arrayr_  r$  r  rd   r   _get_axis_namer  rL   r   r1  r  r,  r  _reindex_axisrp  rm  r3  _get_blocks_and_itemsr(  r  r9  r>  r  rN   
IndexErrorr   _maybe_convert_for_string_atomrX  r  rW  rI  r  rP   ry  r  r  r   rj  r  r  rH  r   r  r  rW  r  )/r   rM  r  r  r   r   r   r   table_existsnew_infonew_non_index_axesr)  r{   append_axisindexer
exist_axisr  	axis_name	new_indexnew_index_axesjr  r  r#  r  vaxesr  r%  b_itemsrY  rP   rt  rL  new_namedata_convertedrZ  r  rs  r  r   r  rj  r  rJ  r  dcs	new_tablerF   )rM  r  rG   _create_axes  s   
 







"






zTable._create_axesr  r{  c                 C  sj  t | jtr| d} dd }| j}tt|}t|j}||}t|r_|d \}	}
t	|

t	|}| j||	dj}t|j}||}|D ]}| j|g|	dj}||j ||| qF|rdd t||D }g }g }|D ];}t|j}z||\}}|| || W qq 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 rF   )r   ra  r$  )rW   r%  mgrrF   rG   r[     r+  zFTable._get_blocks_and_items.<locals>.get_blk_items.<locals>.<listcomp>)r#  r  rF   r  rG   get_blk_items  s   z2Table._get_blocks_and_items.<locals>.get_blk_itemsr   rU  c                 S  s"   i | ]\}}t | ||fqS rF   )r]   tolist)rW   br  rF   rF   rG   rX    s    z/Table._get_blocks_and_items.<locals>.<dictcomp>r  c                 s  r  rI   r  )rW   itemrF   rF   rG   rT    r  z.Table._get_blocks_and_items.<locals>.<genexpr>z+cannot match existing table structure for [z] on appending data)rA   r  r7   r  r   r8   r\   r#  r^   r(   r^  rg  r]  r9  r]   rb  rf  r   ry  r   r  r   )r  r{  r}  r(  r   r  r  r#  r  r1  ro  
new_labelsr  by_items
new_blocksnew_blk_itemsear   r  r  rL  jitemsrF   rF   rG   rx    sR   






zTable._get_blocks_and_itemsrh  rg  c           
        s   |durt |}|dur'jr'tjt sJ jD ]}||vr&|d| qjD ]\}}t ||| q*|jdurS|j D ]\}} fdd}	|	|| q@ S )zprocess axes filtersNr   c                   s    j D ]X} |} |}|d usJ | |kr3jr$|tj}||} j|d|   S | |v r[tt	 | j
}t|}t trLd| }||} j|d|   S qtd|  d)NrU  rT   zcannot find the field [z] for filtering!)_AXIS_ORDERSrq  	_get_axisr6  unionr(   r  re  r6   ry  rb  rA   r&   r   )fieldfiltr  axis_numberaxis_valuestakersrb  r  opr   rF   rG   process_filter  s$   





z*Table.process_axes.<locals>.process_filter)	r\   r6  rA   r  insertr-  rw  filterr   )
r   r  rh  r   r   r1  labelsr  r  r  rF   r  rG   process_axes  s   

!zTable.process_axesr   r   rN  c                 C  s   |du r
t | jd}d|d}dd | jD |d< |r6|du r$| 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'  rm   )rP   rN  c                 S  s   i | ]}|j |jqS rF   )r  r  r  rF   rF   rG   rX  8  r'  z,Table.create_description.<locals>.<dictcomp>r
  	   )r   r   r   r   )maxr>  rM  r   rz   r  r   r   )r   r   r   r   rN  rO  r   rF   rF   rG   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
        Fr  NrT   r  )r  r  rg  select_coordsr  r   r"  r  r  ilocrb  r(   )
r   r_   r   r   rh  coordsr  r  r  r  rF   rF   rG   r  H  s   

 zTable.read_coordinatesr!  c                 C  s   |    |  s
dS |durtd| jD ]=}||jkrR|js'td| dt| jj	|}|
| j |j||| | j| j| jd}tt|d |j|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  rT   rO   z] not found in the table)r  r  r   rM  rP   r8  r   ry  rm   rS  r/  r  r  r   rL   r   r,   r   r  r   )r   r!  r_   r   r   r{   r  
col_valuesrF   rF   rG   r"  b  s,   



zTable.read_column)Nrs   NNNNNN)r   r   r   r>   r   rN   r  )r  rN   r  )r  r   r   r7  r  )r   rD  )r   r   )r|   rN   r   rN   )r|   rN   rb  r  r  rI   r  )rs  r   r  )r   r   r   r   r   rf  r  r   )TNNN)r  r&   r  r   )r  r&   r{  r   )rh  rg  )r   r   r   r   rN  r   r   rD  r  )r!  rN   r   r   r   r   )4rd   re   rf   r  r  r  r  r  r:  r   r  r*  r   r   r  r6  r<  r>  r  r  rm   r  r
  rM  r-  rB  rC  rK  rN  rO  rP  r  r2  r  r  r  rW  r   rT  rt  rj  r  rm  rp  r  staticmethodrx  r  r  r  r"  r  rF   rF   rA  rG   r     s   
 









	





IU"* k>
: r   c                   @  s0   e Zd ZdZdZ				ddddZ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  rF   rF   rG   r    s   
zWORMTable.readc                 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  rF   rF   rG   r    s   zWORMTable.writer  r  )rd   re   rf   r  r  r  r  rF   rF   rF   rG   r    s    r  c                   @  sX   e Zd ZdZdZ												dddZddddZdddZdd ddZdS )!r  (support the new appendable table formats
appendableNFTc                 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 )Nrm   )rM  r  r  r   r   r   )r   r   r   rN  r@  )r   )r  r   r  r   r  rM  r  r  r  create_tabler  r  r  
write_data)r   r  rM  r   r   r   r   r   r   rN  r   r   r   r@  rm   r{   optionsrF   rF   rG   r    s4   

	


zAppendableTable.writer   r   r   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   rU  u1Fr  rT   Nc                 S  r   rF   )r  r  rF   rF   rG   r[     r   z.AppendableTable.write_data.<locals>.<listcomp>c                 S     g | ]}|  qS rF   )r  r  rF   rF   rG   r[         c              	   S  s,   g | ]}| tt|j|jd  qS r  )	transposerB   rollr=  r[  r{  rF   rF   rG   r[     s   , r  rw  c                      g | ]}|  qS rF   rF   r  end_istart_irF   rG   r[   #  r  c                   r  rF   rF   r{  r  rF   rG   r[   %  r  )indexesr  rb  )r  r  r>  r(  r/   r  rQ  rA   rB   r  r   r  r^   r  r'  r  rG  reshaper  r  rZ  write_data_chunk)r   r   r   r  r  masksr{   r  mr  nindexesrb  bvaluesr  rW  	new_shaperowschunksrF   r  rG   r    sP   


zAppendableTable.write_datar  r  r  list[np.ndarray]r  np.ndarray | Nonerb  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   rw  Fr  )rB   r  rG  r^   r  r  r  r  r  r  r   rQ  rm   r   r  )r   r  r  r  rb  rW  r  r  r  r  r)  r  rF   rF   rG   r  (  s*   z AppendableTable.write_data_chunkr   r   c                 C  s^  |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| }t |}	|	r| }
t|
|
dk j}t |sidg}|d |	krt||	 |d dkr|dd | }t|D ]}|t||}|j||jd  ||jd  d d |}q| j  |	S )NTrD  r  rT   r   r~  )r^   r  r   r  r   rm   remove_rowsr  r  rg  r  r,   sort_valuesdiffr\   r   r   r  rf  reversedra  rZ  )r   r_   r   r   r  rm   rh  rb  sorted_serieslnr  r   pgr   r  rF   rF   rG   rK  T  sF   


zAppendableTable.delete)NFNNNNNNFNNTr  )r   r   r   r   )r  r  r  r  r  r  rb  r  r  r  )	rd   re   rf   r  r  r  r  r  rK  rF   rF   rF   rG   r    s&    
<
;,r  c                   @  sZ   e Zd ZU dZdZdZdZeZde	d< e
dd	d
ZedddZ				ddddZdS )r  r  r  r  r  r  r  r   r   c                 C  s   | j d jdkS )Nr   rT   )r'  r1  r   rF   rF   rG   rB    r+  z"AppendableFrameTable.is_transposedr  c                 C  s   |r|j }|S )zthese are written transposed)r  rl  rF   rF   rG   rm    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rht
|}nt|}|d}|d ur||j|d	d
  jr|}|}t|	t|	dd d}n|j}t|	t|	dd d}|}|jdkrt|tjr|d|jd f}t|tjrt|j||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 )Nr  r   c                   s"   g | ]\}}| j d  u r|qS r,  rM  )rW   r  r  r   rF   rG   r[     s   " z-AppendableFrameTable.read.<locals>.<listcomp>rT   r   r*   r  TinplacerP   rO   r  rU  )rh  r   ) r  r  rj  r^   r-  r  r   r  rM  r(  r*   from_tuplesr(   	set_namesrB  ry  r  r[  rA   rB   r  r  rG  r&   _from_arraysdtypesr  rQ  r   r.   rg  r  )r   r_   r   r   r   r  r  indsindr   framesr  r{   
index_valsr  rS  r  rb  index_cols_r  rh  rF   r   rG   r    sZ   
	


 
zAppendableFrameTable.readr  r  r  r  )rd   re   rf   r  r  r  r[  r&   r  r  r  rB  r  rm  r  rF   rF   rF   rG   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  r   r   c                 C  r:  r;  rF   r   rF   rF   rG   rB    r<  z#AppendableSeriesTable.is_transposedr  c                 C  rk  rI   rF   rl  rF   rF   rG   rm    r<  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 tablerb  r  r   NrF   )rA   r&   rP   to_framer@  r  r   r  )r   r  r   r   rP   rA  rF   rG   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   r0  Tr  rb  )
r6  rA   r  r\   r  r@  r  	set_indexr  rP   )r   r_   r   r   r   r6  r   rE   rA  rF   rG   r    s   

zAppendableSeriesTable.readr  r  rI   r  )r   r   r   r   r   r,   )rd   re   rf   r  r  r  r[  r,   r  r  rB  r  rm  r  r  r  rF   rF   rA  rG   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  rb  r  NrF   )
rP   r<  r  rA   r\   r   r(   r   r@  r  )r   r  r   rP   newobjrS  rA  rF   rG   r  ,  s   



z AppendableMultiSeriesTable.write)rd   re   rf   r  r  r  r  r  rF   rF   rA  rG   r  &  s
    r  c                   @  s`   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 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  r   rN   c                 C  r   rI   )r  r   rF   rF   rG   rv  @  r   zGenericTable.pandas_typec                 C  s   t | jdd p	| jS r?  r@  r   rF   rF   rG   r  D  rZ  zGenericTable.storablec                 C  sL   g | _ d| _g | _dd | jD | _dd | jD | _dd | jD | _dS )r  Nc                 S  rQ  rF   rR  r  rF   rF   rG   r[   N  r'  z*GenericTable.get_attrs.<locals>.<listcomp>c                 S  rS  rF   rR  r  rF   rF   rG   r[   O  r'  c                 S  r   rF   rO   r  rF   rF   rG   r[   P  r   )r-  r   r  rT  r'  r(  r   r   rF   rF   rG   r  H  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   Nr1  r   )rP   r1  rm   r   r  )rP   r  rb  r  rm   r   r  )r
  r2  r9  rm   r  _v_namesrA   rN   ry  r  r   )
r   rO  r[  r   r`  r_  r  r   rV  rn  rF   rF   rG   rT  R  s.   


	zGenericTable.indexablesc                 K  r  )Nz cannot write on an generic tabler  r  rF   rF   rG   r  u  s   zGenericTable.writeNr  )rd   re   rf   r  r  r  r[  r&   r  r  r  rv  r  r  r   rT  r  rF   rF   rF   rG   r  7  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+$r   rN   c                 C  r:  )Nappendable_multirF   r   rF   rF   rG   r*    r<  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  rF   )	r   r  r<  r  rA   r\   r  r@  r  )r   r  r   r   r   rA  rF   rG   r    s   

zAppendableMultiFrameTable.writer   r   r   c                   sD   t  j||||d}| j}|j fdd|jjD |_|S )Nr0  c                   s    g | ]} j |rd n|qS rI   )
_re_levelssearch)rW   rP   r   rF   rG   r[     s     z2AppendableMultiFrameTable.read.<locals>.<listcomp>)r@  r  r  r  r   r  r  )r   r_   r   r   r   r  rA  r   rG   r    s   zAppendableMultiFrameTable.readr  rI   r  r  )rd   re   rf   r  r  r&   r  r[  recompiler  r  r*  r  r  r  rF   rF   rA  rG   r  y  s    
r  r  r&   r1  r  r(   c                 C  s   |  |}t|}|d urt|}|d u s||r!||r!| S t| }|d ur6t| j|dd}||sOtd d g| j }|||< | jt| } | S )NF)sort)	r  r6   equalsuniquerd  slicer[  re  r]   )r  r1  r  r  r  slicerrF   rF   rG   rw    s   

rw  r  r   str | tzinfoc                 C  s   t | }|S )z+for a tz-aware type, return an encoded zone)r   get_timezone)r  zonerF   rF   rG   r    s   
r  rb  np.ndarray | Indexstr | tzinfo | Nonert  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
    NrO   r  M8[ns]rw  )rA   r'   r  rP   r  r  rH   r  r  rB   r  )rb  r  rt  rP   rF   rF   rG   r     s   

r   rP   c              
   C  sf  t | tsJ |j}t|\}}t|}t|}t |ts#t|j	r6t
| |||t|dd t|dd |dS t |tr?tdtj|dd}	t|}
|	dkrht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  )rb  rs  r  r  r  r  zMultiIndex not supported here!Fr  r   c                 S  r  rF   )	toordinalr{  rF   rF   rG   r[     r  z"_convert_index.<locals>.<listcomp>rw  )r  r  )integerfloating)rb  rs  r  r  r  )rA   rN   rP   rH  rI  r  rW  r)   r$   r  r  ry  r*   r   r   r  rB   r  int32rz   	Time32Col_convert_string_arrayr  r  r  r  r	  )rP   r   rL   r   r  r  rJ  rs  rV  r  rb  r  rF   rF   rG   r    sT   








r  rs  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| )Nrr  ru  r   c                 S  rx  rF   ry  r{  rF   rF   rG   r[   0  r'  z$_unconvert_index.<locals>.<listcomp>rw  c                 S  rx  rF   r|  r{  rF   rF   rG   r[   2  r'  )r  floatr  r  r  r   zunrecognized index type )r'   r-   rB   r  r  r   r  )r  rs  rL   r   r   rF   rF   rG   r  '  s4   
	r  r  r?   r   r   c                 C  s  |j }|jtkr
|S |jj}	tj|dd}
|
dkrtd|
dkr%td|
dks/|	dks/|S |j|dd	}t|d
ks>J |d }|j }tj|dd}
|
dkrt	|j
d D ],}||}tj|dd}
|
dkrt||krs|| nd| }td| d|
 dqWt||||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  )downcastrT   r   zNo.zCannot serialize the column [z2]
because its data contents are not [string] but [z] object dtyperb  z|Sr  )rb  r  r  rP   r   r  r   fillnar^   rZ  rG  igetr  r  r  rA   rP  rS   r   r  r  r  )rP   r  rt  r   r   rL   r   r   r  rJ  r  r#  r  r  r  error_column_labelr  r  ecirF   rF   rG   rz  @  sZ   




rz  r  r  c                 C  s\   t | rt|  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]
    rT   Srw  )r^   r,   r  rN   encoder  r  rG  r   r  
libwritersmax_len_string_arrayrB   r  )r  rL   r   ensuredr  rF   rF   rG   r    s   


r  c                 C  s   | j }tj|  td} t| r;tt| }d| }t	| d t
r/t| jj||dj} n| j|ddjtdd} |du rAd}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.
    rw  Ur   )r   Fr  Nrr  )rG  rB   r  r  r  r^   r  r  r   rA   r  r,   rN   rD   r  r  !string_array_replace_from_nan_repr  )r  r   rL   r   rG  r  r  rF   rF   rG   r    s   

r  r  c                 C  s6   t |tsJ t|t|rt|||}|| } | S rI   )rA   rN   r   _need_convert_get_converter)rb  r  rL   r   convrF   rF   rG   r    s
   r  c                   s4   | dkrdd S | dkr fddS t d|  )Nrr  c                 S  s   t j| ddS )Nr  rw  )rB   r  r*  rF   rF   rG   r     s    z _get_converter.<locals>.<lambda>r  c                   s   t | d  dS )Nr  )r  r
  r  rF   rG   r     s    zinvalid kind )r   )rs  rL   r   rF   r  rG   r    s
   r  c                 C  s   | dv rdS dS )N)rr  r  TFrF   rM  rF   rF   rG   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   rT   r  r  zvalues_block_(\d+)values_)rA   rN   r^   r   r  r  r   )rP   r  r  grprF   rF   rG   rX    s   $
rX  	dtype_strc                 C  s   t | } | ds| drd}|S | drd}|S | dr$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  ra  rS   r\  r  rr  	timedeltaru  r   r1  r_  r  zcannot interpret dtype of [r(  )rH   r  r   )r  rs  rF   rF   rG   rI  
  s@   





	
rI  r   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  )rA   r0   rP  r  rP   r  rs  rB   r  r
  r+   r  )r  rJ  rF   rF   rG   rH  +  s   


rH  c                   @  s:   e Zd ZdZ			dddd	Zd
d Zdd Zdd ZdS )rg  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

    Nrm   r   r   r   r   c                 C  s^  || _ || _|| _|| _d | _d | _d | _d | _t|rt	t
h tj|dd}|dks0|dkrt|}|jtjkrZ| j| j}}|d u rHd}|d u rP| j j}t||| | _n't|jjtjr| jd urn|| jk  sz| jd ur~|| jk r~t
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)rm   r_   r   r   	conditionr  termsr6  r!   r   r   r   r  rB   r  r  bool_r  r=  
issubclassr   r  r  generateevaluate)r   rm   r_   r   r   inferredrF   rF   rG   r   M  sF   



zSelection.__init__c              
   C  sr   |du rdS | j  }z
t||| j jdW S  ty8 } zd| }td| d| d}t||d}~ww )z'where can be a : dict,list,tuple,stringN)rK  rL   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
                )	rm   rK  r3   rL   	NameErrorr  r   r   r   )r   r_   rV  rL  qkeysr  rF   rF   rG   r  |  s"   

	zSelection.generatec                 C  sX   | j dur| jjj| j  | j| jdS | jdur!| jj| jS | jjj| j| jdS )(
        generate the selection
        Nr  )	r  rm   
read_wherer   r   r   r6  r  r  r   rF   rF   rG   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 )r  Nr   T)r   r   r  )
r   r   rm   r  r  get_where_listr   r6  rB   r=  )r   r   r   r  rF   rF   rG   r    s"   

zSelection.select_coordsr  )rm   r   r   r   r   r   )rd   re   rf   r  r   r  r   r  rF   rF   rF   rG   rg  A  s    /rg  )rR   rS   )r{   NNFNTNNNNrs   r@   )r|   rN   r}   r   r~   rN   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   rN   rL   rN   )	Nr   rs   NNNNFN)
r~   rN   r   rN   r   r   r   r   r   r   )r   r>   r   r>   r   r   rI   )r  r&   r1  rS   r  r(   r   r&   )r  r   r   r  r  )rb  r  r  r  rt  r   r   r  )
rP   rN   r   r(   rL   rN   r   rN   r   r  )rs  rN   rL   rN   r   rN   r   r  )rP   rN   r  r?   r   r   )r  r  rL   rN   r   rN   r   r  )rb  r  r  rN   rL   rN   r   rN   )rs  rN   rL   rN   r   rN   )rs  rN   r   r   )rP   rN   r  r  r   rN   )r  rN   r   rN   r  )r  
__future__r   
contextlibr   r  r  r   r   r7  r   r  textwrapr   typingr   r   r	   r
   r   r   r&  numpyrB   pandas._configr   r   pandas._libsr   r   r  pandas._libs.tslibsr   pandas._typingr   r   r   r   r   pandas.compat._optionalr   pandas.compat.pickle_compatr   pandas.errorsr   pandas.util._decoratorsr   pandas.core.dtypes.commonr   r   r   r   r   r    r!   r"   r#   r$   pandas.core.dtypes.missingr%   r   r&   r'   r(   r)   r*   r+   r,   r-   r.   r/   pandas.core.arraysr0   r1   r2   pandas.core.commoncorecommonrI   pandas.core.computation.pytablesr3   r4   pandas.core.constructionr5   pandas.core.indexes.apir6   pandas.core.internalsr7   r8   pandas.io.commonr9   pandas.io.formats.printingr:   r;   ru   r<   r=   r>   r?   r  rJ   rH   rM   rQ   rV   r`   rG  rb   rh   Warningri   rU  rj   r%  rk   duplicate_docr  r  r\  
dropna_doc
format_docconfig_prefixregister_optionis_boolis_one_of_factoryrt   ry   rz   r   r   r   r   r  r  r9  r>  r  r  r  r  r  r  r  r   r  r  r  r  r  r  r  rw  r  r   r  r  rz  r  r  r  r  r  rX  rI  rH  rg  rF   rF   rF   rG   <module>   s0    00


: 
          Np      1  b _       l fd1B+
	
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