`Vg UdZddlmZddlmZddlZddlmZmZddl Z ddl Z ddl Z ddl m Z ddlmZmZmZmZmZmZmZmZmZmZddlZddlZddlmZmZdd lm Z m!Z"dd l#m$Z$dd l%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+dd l,m-Z-dd l.m/Z/ddl0m1Z1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;mZ>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEddlFmGZGddlHmIZImJZJmKZKmLZLmMZMmNZNmOZOmPZPmQZQddlRmSZSddlTmUZUmVZVmWZWddlXmYcmZZ[ddl\m]Z]m^Z^ddl_m`Z`ddlambZbddlcmdZdmeZeddlfmgZgddlhmiZimjZjerddlkmlZlmmZmmnZnddlcmoZodZpdZqd Zrd!Zsd"Zte]Zudvd#Zvd$Zwd%exd&<d'Zyd%exd(<d)Zzd%exd*<d+d+d,d,d-Z{eIdgiZ|d.Z}d%exd/<d0Z~d%exd1<ejd25ejd3d4e}ej5ejd6de~ejgd75ddddad4ad8Z dw dxd9Z dy dzd:Zd{d;ZGd<d=ZGd>d?ZGd@dAZGdBdCeZGdDdEeZGdFdGeZGdHdIeZGdJdKZGdLdMeZGdNdOeZGdPdQeZGdRdSeZGdTdUeZGdVdWeZGdXdYeZGdZd[eZGd\d]eZGd^d_eZGd`daeZGdbdceZd|d}ddZd~deZe d ddfZedddgZ d ddhZddiZddjZ ddkZddlZ ddmZddnZddoZddpZddqZddrZddsZGdtduZy#1swYxYw)zY High level interface to PyTables for reading and writing pandas data structures to disk ) annotations)suppressN)datetzinfo)dedent) TYPE_CHECKINGAnyCallableFinalHashableIteratorLiteralSequencecastoverload)config get_option)libwriters) timezones) AnyArrayLike ArrayLikeDtypeArgFilePathShapenpt)import_optional_dependency) patch_pickle)AttributeConflictWarningClosedFileErrorIncompatibilityWarningPerformanceWarningPossibleDataLossError)cache_readonly)find_stack_level) ensure_object is_bool_dtypeis_categorical_dtypeis_complex_dtypeis_datetime64_dtypeis_datetime64tz_dtypeis_extension_array_dtype is_list_likeis_string_dtypeis_timedelta64_dtypeneeds_i8_conversion)array_equivalent) DataFrame DatetimeIndexIndex MultiIndex PeriodIndexSeriesTimedeltaIndexconcatisna) Int64Index) Categorical DatetimeArray PeriodArray) PyTablesExprmaybe_expression) extract_array) ensure_index) ArrayManager BlockManager)stringify_path)adjoin pprint_thing)ColFileNode)Blockz0.15.2UTF-8c\t|tjr|jd}|S)z(if we have bytes, decode them to unicoderL) isinstancenpbytes_decode)ss S/var/www/html/hubwallet-dev/venv/lib/python3.12/site-packages/pandas/io/pytables.py_ensure_decodedrTs#!RYY HHW  Hc|t}|SN)_default_encodingencodings rS_ensure_encodingr[s$ OrUc<t|tr t|}|S)z Ensure that an index / column name is a str (python 3); otherwise they may be np.string dtype. 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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) N scope_level)rNlisttupler@Termlen)whererdlevelterms rS _ensure_termrls !OE%$' 2B$1GD519 -T Q  % U.MSZ59T9 s&A2z where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with the copy_to method) r incompatibility_doczu the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None attribute_conflict_docz your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->%s,key->%s] [items->%s] performance_docfixedtable)frptrqz; : boolean drop ALL nan rows when appending to a table dropna_docz~ : format default format writing format, if None, then put will default to 'fixed' and append will default to 'table' format_doczio.hdf dropna_tableF) validatordefault_format)rprqNct=ddl}|att5|jj dk(adddtStS#1swYtSxYw)Nrstrict) _table_modtablesrAttributeErrorfile_FILE_OPEN_POLICY!_table_file_open_policy_is_strict)r|s rS_tablesrsX n %  --9 .  :   s A  Ac  |r   f d}n   f d}t|}t|tr!t||||5}||dddy||y#1swYyxYw)z+store this object, close it if we opened itc : |j   S)N)formatindex min_itemsizenan_repdropna data_columnserrorsrZ)append storerrrZrrrkeyrrvalues rSzto_hdf.. s3%,,  %%'  rUc : |j   S)N)rrrrrrrZrputrs rSrzto_hdf..s3%))  %%$  rU)mode complevelcomplibN)rErNr]HDFStore) path_or_bufrrrrrrrrrrrrrrZrrrs `` ```````` rSto_hdfrsu$  !-K+s#  di    eH   +   s  A//A8c F|dvrtd|d| t|d}t|tr|js t d|} d} nht |}t|ts td  tjj|} | std |d t|f||d | } d } |[| j}t|dk(r td|d}|ddD]}t!||rtd|j"}| j%||||||| | S#ttf$rd} YwxYw#ttt&f$rGt|ts5t)t*5| j-ddd#1swYxYwwxYw)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']) # doctest: +SKIP >>> df.to_hdf('./store.h5', 'data') # doctest: +SKIP >>> reread = pd.read_hdf('./store.h5') # doctest: +SKIP )rr+azmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nrbrcz&The HDFStore must be open for reading.Fz5Support for generic buffers has not been implemented.zFile z does not exist)rrTrz]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.)ristartstopcolumnsiterator chunksize auto_close) ValueErrorrlrNris_openOSErrorrEr]NotImplementedErrorospathexists TypeErrorFileNotFoundErrorgroupsrh_is_metadata_of _v_pathnameselectKeyErrorrr}close)rrrrrirrrrrkwargsrrrrcandidate_only_groupgroup_to_checks rSread_hdfr2s^ ##D6- .   U2+x(""BC C $[1 +s+%G  WW^^K0F #eK=$HI II4I&I % ;\\^F6{a D$*!9 #)* &~7KL$;  '22C|| !  A:& F T  8 ,+x0.)       s=6D.8AE=0E.EE3F 8F F F  F c|j|jkry|}|jdkDr=|j}||k(r|jdk(ry|j}|jdkDr=y)zDCheck if a given group is a metadata group for a given parent_group.FrbmetaT)_v_depth _v_parent_v_name)group parent_groupcurrentparents rSrrsk ~~...G   Q "" \ !goo&?##   Q  rUc2eZdZUdZded<ded<ded<ded < d7 d8d Zd9d Zed Zed9dZ d:dZ d;dZ d;dZ ddZd9dZd?dZd@dZdAdBdZdCdZdDdZdZdEdFdZd@dZedGdZdHdIdZd:d Z dJ dKd!Z dL dMd"Z dN dOd#Z dP dQd$Z dR dSd%Z dLd;d&Z! dT dUd'Z" dV dWd(Z# dL dXd)Z$dYd*Z%dZd[d+Z&d\d,Z'd]d-Z( d^ d_d.Z)d9d/Z*d0Z+d`d1Z, da dbd2Z- dc ddd3Z.ded4Z/dfd5Z0dgd6Z1y )hraa Dict-like IO interface for storing pandas objects in PyTables. Either Fixed or Table format. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path : str File path to HDF5 file. mode : {'a', 'w', 'r', 'r+'}, default 'a' ``'r'`` Read-only; no data can be modified. ``'w'`` Write; a new file is created (an existing file with the same name would be deleted). ``'a'`` Append; an existing file is opened for reading and writing, and if the file does not exist it is created. ``'r+'`` It is similar to ``'a'``, but the file must already exist. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. fletcher32 : bool, default False If applying compression use the fletcher32 checksum. **kwargs These parameters will be passed to the PyTables open_file method. Examples -------- >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore('test.h5') >>> store['foo'] = bar # write to HDF5 >>> bar = store['foo'] # retrieve >>> store.close() **Create or load HDF5 file in-memory** When passing the `driver` option to the PyTables open_file method through **kwargs, the HDF5 file is loaded or created in-memory and will only be written when closed: >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE') >>> store['foo'] = bar >>> store.close() # only now, data is written to disk z File | None_handler]_modeint _complevelbool _fletcher32Nc d|vr tdtd}|;||jjvr#td|jjd|||jj}t ||_|d}||_d|_|r|nd|_ ||_ ||_ d|_ |jd d|i|y) Nrz-format is not a defined argument for HDFStorer|zcomplib only supports z compression.rrr)rrfilters all_complibsdefault_complibrE_pathrrr_complibr_filtersopen)selfrrrr fletcher32rr|s rS__init__zHDFStore.__init__"s v LM M+H5  7&..2M2M#M()D)D(E]S  ?y4nn44G#D) <D  '0)a %  &t&v&rUc|jSrWrrs rS __fspath__zHDFStore.__fspath__Ds zzrUcj|j|jJ|jjS)zreturn the root node)_check_if_openrrootrs rSrz HDFStore.rootGs0 ||'''||   rUc|jSrWrrs rSfilenamezHDFStore.filenameN zzrUc$|j|SrW)getrrs rS __getitem__zHDFStore.__getitem__Rsxx}rUc(|j||yrWr)rrrs rS __setitem__zHDFStore.__setitem__Us erUc$|j|SrW)removers rS __delitem__zHDFStore.__delitem__Xs{{3rUc |j|S#ttf$rYnwxYwtdt |j d|d)z$allow attribute access to get stores'z' object has no attribute ')rrr r}type__name__)rr_s rS __getattr__zHDFStore.__getattr__[sW 88D> !/*   T ##$$?vQ G  s %%c^|j|}||j}||k(s|dd|k(ryy)zx check for existence of this key can match the exact pathname or the pathnm w/o the leading '/' NrbTF)get_noder)rrnoder_s rS __contains__zHDFStore.__contains__es< }}S!  ##Ds{d12h#orUc4t|jSrW)rhrrs rS__len__zHDFStore.__len__qs4;;=!!rUcNt|j}t|d|dS)N File path:  )rGrr)rpstrs rS__repr__zHDFStore.__repr__ts'DJJ't*]4&33rUc|SrWrrs rS __enter__zHDFStore.__enter__xs rUc$|jyrW)r)rexc_type exc_value tracebacks rS__exit__zHDFStore.__exit__{s  rUc|dk(r(|jDcgc]}|jc}S|dk(rC|jJ|jjddDcgc]}|jc}St d|dcc}wcc}w)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 pandasnative/Table) classnamez8`include` should be either 'pandas' or 'native' but is 'r)rrr walk_nodesr)rincludens rSkeysz HDFStore.keys~s, h +/;;=9aAMM9 9  <<+ ++'+||'>'>sg'>'V"#  Fwiq Q  :s B B c4t|jSrW)iterrrs rS__iter__zHDFStore.__iter__sDIIK  rUc#VK|jD]}|j|fyw)' iterate on key->group N)rrrgs rSitemszHDFStore.itemss, #A--" " #s')c#Ktjdtt|j Ed{y7w)r zTiteritems is deprecated and will be removed in a future version. Use .items instead. stacklevelN)warningswarn FutureWarningr%rrs rS iteritemszHDFStore.iteritemss3   " ')  ::<s8AAAc Zt}|j|k7rP|jdvr|dvrn6|dvr2|jr&td|jd|jd||_|jr|j |j rN|j dkDr?tj|j |j|j|_ tr|jr d }t||j|j|jfi||_y ) 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. )rw)rr)rzRe-opening the file [z ] with mode [z] will delete the current file!r)rzGCannot open HDF5 file, which is already opened, even in read-only mode.N)rrrr#rrrFiltersrrrrr open_filer)rrrr|msgs rSrz HDFStore.opens :: zzZ'DK,?<<// |= U88 DJ << JJL ??t2#I--4;K;K.DM -* S/ !'v'' DJJI&I rUc^|j|jjd|_y)z0 Close the PyTables file handle N)rrrs rSrzHDFStore.closes% << # LL    rUcZ|jyt|jjS)zF return a boolean indicating whether the file is open F)rrisopenrs rSrzHDFStore.is_opens& << DLL''((rUc|jc|jj|rFtt5t j |jj dddyyy#1swYyxYw)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)rflushrrrfsyncfileno)rr s rSrzHDFStore.flushsf << # LL   g&4HHT\\002344 $44s .A11A:ct5|j|}|td|d|j|cdddS#1swYyxYw)z Retrieve pandas object stored in file. Parameters ---------- key : str Returns ------- object Same type as object stored in file. NNo object named in the file)rrr _read_grouprrrs rSrz HDFStore.get sV^ +MM#&E}!1#lCDD##E*  + + +s 3AAc  |j|} | td|dt|d}|j|  j  fd} t | | | j ||||| } | jS)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. r#r$rbrcc.j|||S)N)rrrir)read)_start_stop_whererrRs rSfunczHDFStore.select..funcWs66U&'6R RrUrinrowsrrrrr)rrrl_create_storer infer_axes TableIteratorr/ get_result) rrrirrrrrrrr-itrRs ` @rSrzHDFStore.selects\ c" =-cU,?@ @U2    &  S  ''!  }}rUct|d}|j|}t|ts t d|j |||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 rbrcz&can only read_coordinates with a tablerirr)rl get_storerrNrrread_coordinates)rrrirrtbls rSselect_as_coordinateszHDFStore.select_as_coordinatesjsL4U2ooc"#u%DE E##%u4#HHrUc|j|}t|ts td|j |||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)columnrr)r7rNrr read_column)rrr<rrr9s rS select_columnzHDFStore.select_columns>Fooc"#u%?@ @fEEErUc t|d}t|ttfrt |dk(r|d}t|t r|j ||||||| St|ttfs tdt |s td||d}|D cgc]} |j| c} |j|} d} tj| |fgt|D]d\} } | td| d | jstd | jd | | j } K| j | k7s[td Dcgc]}t|t"s|}}t|D chc]} | j$ddc} dfd }t'|| ||| |||||  }|j)dScc} wcc}wcc} w)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 rbrcr)rrirrrrrrzkeys must be a list/tuplez keys must have a non-zero lengthNzInvalid table []zobject [z>] is not a table, and cannot be used in all select as multiplez,all tables must have exactly the same nrows!c Dcgc]}|j|||}}t|djScc}w)NrirrrF)axisverify_integrity)r)r9 _consolidate)r*r+r,rsobjsrCrtblss rSr-z)HDFStore.select_as_multiple..funcsR VWFOD $TEBOOQ Q sAr.T) coordinates)rlrNrerfrhr]rrrr7 itertoolschainzipris_tablepathnamer/rnon_index_axesr2r3)rrriselectorrrrrrrkrRr/rsx_tblsr-r4rCrGs ` @@rSselect_as_multiplezHDFStore.select_as_multiples VU2 dT5M *s4yA~7D dC ;;!#%  $u .78 84y?@ @  AwH-11q"1 OOH %OOa]OSt_E QDAqy1566::qzzl+)) }E! !OPP Q !9qJq%$899U;Q%%a(+;`__. encoding : str, default None Provide an encoding for strings. track_times : bool, default True Parameter is propagated to 'create_table' method of 'PyTables'. If set to False it enables to have the same h5 files (same hashes) independent on creation time. dropna : bool, default False, optional Remove missing values. .. versionadded:: 1.1.0 Nio.hdf.default_formatrp) rrrrrrrrrZr track_timesr)r_validate_format_write_to_group)rrrrrrrrrrrrZrrVrs rSrz HDFStore.put,sgh > 78CGF&&v.   %%#  rUct|d} |j|}tj|||rjjdyjs t d|j|||S#t$rt$rt$rB}| t d||j |}||jdYd}~yYd}~d}~wwxYw) 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 rbrcNz5trying to remove a node with a non-None where clause!T recursivez7can only remove with where on objects written as tablesr6) rlr7rAssertionError Exceptionrr _f_removecomall_nonerrLdelete)rrrirrrRerrrs rSrzHDFStore.removets*U2 $A. <<ud + GG    -:: M88%u48@ @?       K ==%D.  sA??C3CCc| td| td}|tdxsd}|j|}|j||||||||| | | | ||||y)a Append to Table in file. Node must already exist and be Table format. Parameters ---------- key : str value : {Series, DataFrame} format : 'table' is the default Format to use when storing object in HDFStore. Value can be one of: ``'table'`` Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. index : bool, default True Write DataFrame index as a column. append : bool, default True Append the input data to the existing. data_columns : list of columns, or True, default None List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See `here `__. min_itemsize : dict of columns that specify minimum str sizes nan_rep : str to use as str nan representation chunksize : size to chunk the writing expectedrows : expected TOTAL row size of this table encoding : default None, provide an encoding for str dropna : bool, default False, optional Do not write an ALL nan row to the store settable by the option 'io.hdf.dropna_table'. Notes ----- Does *not* check if data being appended overlaps with existing data in the table, so be careful Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerUrq)raxesrrrrrrr expectedrowsrrrZr)rrrWrX)rrrrrdrrrrrrrrrerrrZrs rSrzHDFStore.appendsv  P  > 56F > 78CGF&&v.   %%%!  rUc | tdt|ts td||vr tdt t t jt ttz d}d} g} |jD](\} } | | td| } | j| *| Wj|} | jt| }t| j!|}| j#||| <|||}|rKfd|j%D}t'|}|D]}|j)|}j*||j-dd}|jD]e\} } | |k(r|nd}j/| | }|)|jDcic] \}}|| vs ||c}}nd}|j0| |f||d |gycc}}w) 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 dictrzz.HDFStore.append_to_multiple..Ps)ODE$K&&5&177Os),rrC)rr)rrNdictrresetrangendim _AXES_MAPrrextendrd differencer4sorted get_indexertakevaluesnext intersectionlocpopreindexr)rdrrOrrdrrrC remain_key remain_valuesrPvorderedorddidxs valid_indexrrdcvalrfiltereds ` rSappend_to_multiplezHDFStore.append_to_multiples4>  B  !T")  1 O  Cejj)*S4;1G-HHI!L  GGI (DAqy)$V $$Q' (  !jj&G%%eM&:;D'--d34D#LL.AjM  X;L OAHHJODt*K >)66u=  >IIk*Ezz.$7 GGI RDAq!"hDB----C +1=0B0B0DQ eqeQ  DKK3 QRh Q& Q RRs  G6G6ct|j|}|yt|ts t d|j |||y)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)roptlevelkind)rr7rNrr create_index)rrrrrrRs rScreate_table_indexzHDFStore.create_table_indexfsG>  OOC  9 !U#OP P wErUct|j|jJtJ|jj Dcgc]}t |tj jsYt|jdds@t|dds3t |tjjr|jdk7r|c}Scc}w)z Return a list of all the top-level nodes. Each node returned is not a pandas storage object. Returns ------- list List of objects. N pandas_typerq) rrrr{ walk_groupsrNlinkLinkgetattr_v_attrsrqrrr s rSrzHDFStore.groupss   ||'''%%%\\--/  q*//"6"67AJJ t<q'40"1j&6&6&<&<=!))wBV    s BCc#LKt|j|jJtJ|jj |D]}t |j ddg}g}|jjD]w}t |j dd}|At|tjjsA|j|j]|j|jy|jjd||fyw)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`. Nrr)rrrr{rrr _v_childrenrxrNrGrouprrrrstrip)rrir rleaveschildrs rSwalkz HDFStore.walks6   ||'''%%%))%0 >Aqzz=$7CFF--/ 1%ennmTJ &!%)9)9)?)?@ emm4MM%--0  1=='',ff= = >s CD$AD$cf|j|jdsd|z}|jJtJ |jj |j |}t|tjsJt||S#tj j$rYywxYw)z9return the node with the key or None if it does not existrN) r startswithrr{rr exceptionsNoSuchNodeErrorrNrJr)rrrs rSrzHDFStore.get_nodes ~~c")C||'''%%% <<((C8D$ 0<$t*<0 $$44  s&BB0/B0c|j|}|td|d|j|}|j|S)z !((+{{1~a'.3E"12 HA1<< H H$$#%,Q%E!" %MM!TAJJM?- @0!Is (D:DcXt|j}t|d|d}|jrt |j }t |rwg}g}|D]\} |j|}|F|jt|jxs||jt|xsd^|td||z }|S|dz }|S|d z }|S#t$rt$r;}|j|t|} |jd| dYd}~d}~wwxYw) zg Print detailed information on the store. Returns ------- str rrNzinvalid_HDFStore nodez[invalid_HDFStore node: r@ EmptyzFile is CLOSED) rGrrrrurrhr7rrMr\r]rF) rroutputlkeysrrxrPrRdetaildstrs rSinfoz HDFStore.info1s9DJJ'J<}TF"5 <<499;'E5z JA J OOA.= KK QZZ_1(EF"MM,q7S,value->z ,format->)rr)rsrrrs rSerrorz&HDFStore._create_storer..errorws4:1#Z'$u+ixA rUr table_typerq frame_table generic_tablezKcannot create a storer if the object is not existing nor a value are passedseriesframe_table)rr _STORER_MAPrZr series_tablerrbappendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)rrrrrworm _TABLE_MAP)rNr7r2rrTrrrr{rqr SeriesFixed FrameFixedrnlevels GenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable WORMTable)rrrrrZrrptttrclsrbrrs ``` rSr0zHDFStore._create_storeris  Z 7J%KFG G WU^^]DI J WU^^\4H I :} !---5'40J:++115'B(B#1 eV,!B BW$(NB " %0:FK 4!"otUXfE E : '#E7D9E( ==A-!4B"]]Q.!9B=(#E7D9E( ==A-!3B"]]Q.!8B*!6&@ 4%>   /R.C4&AAI 4M*3 4B / %3 . /s06F-G- G6 F??G G GGct|ddr|dk(s|ry|j||}|j|||||}|rQ|jr|jr|dk(r|jr t d|js!|j n|j |js |r t d|j|||||| | | | | ||| t|tr|r|j|yyy) NemptyrqrrpzCan only append to Tablesz0Compression not supported on Fixed format stores) objrdrrrrrrrerrrrV)r) r_identify_groupr0rL is_existsrset_object_infowriterNrr)rrrrrdrrrrrrrrerrrrZrrVrrRs rSrXzHDFStore._write_to_groups. 5'4 (f.?6 $$S&1   vuxPV  W ::!**71Bq{{ !<==;;!!#   zzgOP P !%%%#  a E NN5N )%* rUcd|j|}|j|jSrW)r0r1r))rrrRs rSr%zHDFStore._read_groups&    & vvxrUc|j|}|jJ|!|s|jj|dd}||j|}|S)z@Identify HDF5 group based on key, delete/create group if needed.NTrZ)rr remove_node_create_nodes_and_group)rrrrs rSrzHDFStore._identify_groupsb c"||'''  V LL $ $Ud $ ;E =005E rUc |jJ|jd}d}|D]\}t|s|}|jds|dz }||z }|j |}||jj ||}|}^S)z,Create nodes from key and return group name.r)rsplitrhendswithr create_group)rrpathsrpnew_pathrs rSrz HDFStore._create_nodes_and_groups||''' # Aq6H==%C MHMM(+E} 11$:D  rU)rNNF)rr]r int | NonerrreturnNonerr]rr])rr]rr)r_r])rr]rrrr)rrrr)r)rr]r list[str])rz Iterator[str])rzIterator[tuple[str, list]])r)rr]rrrrF)r rrr)NNNNFNF)rr]rrNNNrr]rrrrNN)rr]r<r]rrrr)NNNNNFNF)rr) NTFNNNNNNrzTF)rr]rDataFrame | Seriesrrrint | dict[str, int] | Noner Literal[True] | list[str] | Nonerr]rVrrrrr)NNTTNNNNNNNNNNrz)rr]rrrrrrr bool | Nonerrrr]rr)NNF)r~rnrr)rr]rrr str | Nonerr)rre)r)rir]rz*Iterator[tuple[str, list[str], list[str]]])rr]rz Node | None)rr]rGenericFixed | Table)rTNNNFT)rrrrrrrr)rr]rr])NNrLrz)rzDataFrame | Series | NonerZr]rr]rr)NTFNNNNNNFNNNrzT)rr]rrrrrrrr]rVrrr)rrJ)rr]rrrrJ)rr]rrJ)2r __module__ __qualname____doc____annotations__rrpropertyrrrrrrrrrrrrr rrrrrrrrr:r>rSrrrrrrrrr7rrrrWr0rXr%rrrrUrSrrs|?B JO  $ ' ' '  '  'D!!    "4  D!#  +JZ))4,+0  I II\  I I I  IH! &F &F&F &F  &FV  x/x/| $489= F F "F F 2F 7F F F F !F P7Az  $48"9=%V V "V V 2V V 7!V $%V & 'V z _R _R _RH# &F &F &F  &F  &FP 8.>`   $ ; ;;; ;z%TE+/ ZB) ZB  ZB  ZB ZBB $48 '<* <*"<*<*2<*$%<*&'<*( )<*| $rUrcreZdZUdZded<ded<ded< d dd Zd Zdd Zddd Zy)r2aa 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. rrrrrrRNc ,||_||_||_||_|jjr|d}|d}||}t ||}||_||_||_d|_ |s| | d} t| |_ | |_ yd|_ | |_ y)Nr順) rrRr-rirLminr/rrrHrrr) rrrRr-rir/rrrrrs rSrzTableIterator.__init__Hs    66??}}|ud#D    y, "  ^DN%"DN$rUc#^K|j}|j td||jkrgt ||j z|j}|j dd|j||}|}| t|sb|||jkrg|jyw)Nz*Cannot iterate until get_result is called.) rrHrrrrr-rhr)rrrrs rSr zTableIterator.__iter__rs**    #IJ J !w/;DIIdD$*:*:74*HIEG}CJK ! s BB-B-cR|jr|jjyyrW)rrrrs rSrzTableIterator.closes ?? JJ    rUc|jRt|jts t d|jj |j |_|S|rbt|jts t d|jj |j |j|j}n |j }|j|j|j|}|j|S)Nz0can only use an iterator or chunksize on a table)riz$can only read_coordinates on a tabler6) rrNrRrrr8rirHrrr-r)rrHriresultss rSr3zTableIterator.get_results >> %dffe, RSS#vv66TZZ6HD K dffe, FGGFF++jj ,EJJE))DJJ 59 rU)NNFNF) rrrRrrrrrrrrrrr)rHr) rrrrrrr rr3rrUrSr2r20sz& O  $ (%(% (%(%(%(% (%T rUr2ceZdZUdZdZded<dZded<gdZded<ded < d$ d%d Ze d&d Z e d'd Z d(dZ d'dZ d)dZd*dZe d*dZ d+dZdZe dZe dZe dZe dZdZd,d-dZd-dZd.dZd,dZd/dZd-dZd-d Zd-d!Zd0d"Z d0d#Z!y )1IndexCola 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 Tris_an_indexableis_data_indexable)freqtz index_namer]r_cnameNct|ts td||_||_||_||_|xs||_||_||_ ||_ | |_ | |_ | |_ | |_| |_||_||j#|t|j tsJt|jtsJy)Nz`name` must be a str.)rNr]rrxrtypr_r rCposr r r rrqrmetadataset_pos)rr_rxrrr rCrr r r rrqrrs rSrzIndexCol.__init__s$$$45 5   ]d   $     ? LL $))S)))$**c***rUc.|jjSrW)ritemsizers rSrzIndexCol.itemsizesxx   rUc |jdS)N_kindr^rs rS kind_attrzIndexCol.kind_attr))E""rUcT||_||j||j_yyy)z,set the position of this column in the TableN)rr_v_pos)rrs rSrzIndexCol.set_poss) ?txx3!DHHO 4?rUc ttt|j|j|j |j |jf}djtgd|Dcgc] \}}|d|c}}Scc}}w)N,)r_r rCrr->) rfmaprGr_r rCrrjoinrKrtemprrs rSrzIndexCol.__repr__s}  tyy$**dii499U V xx#&&NPT"U C%r%!    -B c0tfddDS)compare 2 col itemsc3TK|]}t|dt|dk(!ywrWrrjrotherrs rSrlz"IndexCol.__eq__..0  D!T "geQ&= = %()r_r rCrrhrr(s``rS__eq__zIndexCol.__eq__s 5   rUc&|j| SrW)r-r,s rS__ne__zIndexCol.__ne__s;;u%%%rUct|jdsyt|jj|jj S)z%return whether I am an indexed columnrkF)hasattrrqrrkr rrs rSrzIndexCol.is_indexeds4tzz6*tzz 3>>>rUct|tjsJt||jj ||j }t|j}t||||}i}t|j|d<|jt|j|d<t}t|jst|jrt}n|jdk(rd|vrd} ||fi|}t#||j$} | | fS#t $rd|vrd|d<||fi|}Y7wxYw)zV Convert the data from this selection to the appropriate pandas type. Nr_r i8ctdd|i|S)Nordinalr)r6)rQkwdss rSrz"IndexCol.convert..*s ))!)rU)rNrOndarrayrdtypefieldsr rTr_maybe_convertr r r4r*r+r3r_set_tzr ) rrxrrZrval_kindrfactory new_pd_indexfinal_pd_indexs rSconvertzIndexCol.convert s2 &"**-;tF|;- <<   *DJJ'F"499-(FC(9v 99 ,TYY7F6N5: v|| ,0Efll0S#G \\T !f&6 G  5"64V4L!tww7~-- 5!%v"64V4L  5s? D""E?Ec|jS)zreturn the valuesrxrs rS take_datazIndexCol.take_data:s {{rUc.|jjSrW)rqrrs rSattrszIndexCol.attrs>zz"""rUc.|jjSrWrq descriptionrs rSrIzIndexCol.descriptionBzz%%%rUcDt|j|jdS)z!return my current col descriptionN)rrIr rs rScolz IndexCol.colFst''T::rUc|jSzreturn my cython valuesrBrs rScvalueszIndexCol.cvaluesKs{{rUc,t|jSrW)rrxrs rSr zIndexCol.__iter__PsDKK  rUct|jdk(rst|tr|j |j }|E|j j|kr+tj||j|_yyyy)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)rr) rTrrNrnrr_rrr StringColr)rrs rSmaybe_set_sizezIndexCol.maybe_set_sizeSsu 499 % 1,-+// : 'DHH,=,= ,L"9.. $((.S-M' 2rUcyrWrrs rSvalidate_nameszIndexCol.validate_names` rUc|j|_|j|j||j||j ||j yrW)rq validate_col validate_attrvalidate_metadatawrite_metadataset_attr)rhandlerrs rSvalidate_and_setzIndexCol.validate_and_setcsL]]   6" w' G$ rUc t|jdk(r`|j}|R| |j}|j|kr)t d|d|j d|jd|jSy)z:validate this column: return the compared against itemsizerRNz#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)rTrrLrrr )rrcs rSrYzIndexCol.validate_colks 499 % 1A}##}}H::($=hZH JJ<(JJ<(<< zz!rUc|rPt|j|jd}|,||jk7rt d|d|jdyyy)Nzincompatible kind in col [ - r@)rrErrr)rr existing_kinds rSrZzIndexCol.validate_attr~s] #DJJEM(]dii-G0s499+QO.H( rUc |jD]}t||d}|j|ji}|j |}||vrp|n||k7ri|dvrCt |||fz}t j|ttd||<t||dtd|jd|d|d|d |||||<y) z set/update the info for this indexable with the key/value if there is a conflict raise/warn as needed N)r r rzinvalid info for [z] for [z], existing_value [z] conflicts with new value [r@) _info_fieldsr setdefaultr_rrnrrrr%setattrr)rrrridxexisting_valuewss rS update_infozIndexCol.update_infos $$ %CD#t,E//$))R0C WWS\Ncze/Ne4K00/32NNBMM4AQAS $CHD#t,%,TYYKwseD++9*:;&&+WA/ $(B$CH5 %rUcv|j|j}||jj|yy)z!set my state from the passed infoN)rr___dict__update)rrris rSset_infozIndexCol.set_infos0hhtyy! ? MM  % rUcZt|j|j|jy)zset the kind for this columnN)rhrErrrs rSr]zIndexCol.set_attrs DNNDII6rUc|jdk(rE|j}|j|j}||t ||s t dyyyy)z:validate that kind=category does not change the categoriescategoryNzEcannot append a categorical with different categories to the existing)rr read_metadatar r1r)rr^ new_metadata cur_metadatas rSr[zIndexCol.validate_metadatash 99 "==L"00rUrcBeZdZdZeddZ ddZddZy) GenericIndexColz:an index which is not represented in the data of the tablecyNFrrs rSrzGenericIndexCol.is_indexedrUct|tjsJt|t tj t |}||fS)z Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : str encoding : str errors : str )rNrOr7rr;arangerh)rrxrrZrrs rSr@zGenericIndexCol.convertsB&"**-;tF|;-299S[12e|rUcyrWrrs rSr]zGenericIndexCol.set_attrrWrUNr)rxryrZr]rr]rztuple[Int64Index, Int64Index]r)rrrrrrr@r]rrUrSr|r|sAD  58BE &$ rUr|c8eZdZdZdZdZddgZ d dfd ZeddZ eddZ ddZ dd Z dd Z d Zedd Zed ZeddZeddZedZedZedZedZddZd dZddZxZS)!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 rc Vt|||||||||| | |  | |_| |_y)N) r_rxrrrr r rrqrr)superrr8r)rr_rxrrr rr rrqrrr8r __class__s rSrzDataCol.__init__sH     rUc |jdS)N_dtyper^rs rS dtype_attrzDataCol.dtype_attr s))F##rUc |jdS)N_metar^rs rS meta_attrzDataCol.meta_attr rrUc ttt|j|j|j |j |jf}djtgd|Dcgc] \}}|d|c}}Scc}}w)Nr)r_r r8rshaper) rfrrGr_r r8rrrrKr s rSrzDataCol.__repr__! s tyy$**djj$))TZZX   xx#&&QSW"X C%r%!    r"c0tfddDS)r$c3TK|]}t|dt|dk(!ywrWr&r's rSrlz!DataCol.__eq__..0 r)r*)r_r r8rr+r,s``rSr-zDataCol.__eq__. s 6   rUc|J|jJt|\}}||_||_t||_yrW)r8_get_data_and_dtype_namer_dtype_to_kindr)rr dtype_names rSset_datazDataCol.set_data5 sGzz!!!3D9j  ":. rUc|jS)zreturn the datarrs rSrCzDataCol.take_data? s yyrUcn|j}|j}|j}|jdk(rd|jf}t |t r5|j}|j||jj}|St|s t|r|j|}|St|r|j|}|St|r t!j#||d}|St%|r|j'||}|S|j||j}|S)zW Get an appropriately typed and shaped pytables.Col object for values. rbrrrr)r8rrrqsizerNr<codes get_atom_datar_r*r+get_atom_datetime64r/get_atom_timedelta64r)r ComplexColr.get_atom_string)rrxr8rrratoms rS _get_atomzDataCol._get_atomC s/  >>  ;;!  $E fk *LLE$$U1A1A$BD ! '+@+G**51D "% (++E2D e $9''q'JD  U #&&uh7D $$U$tj||dS)NrrrrSrrrs rSrzDataCol.get_atom_stringc sy""HE!H"EErUc|jdr |dd}d|d}n)|jdrd}n|j}|d}tt|S)z0return the PyTables column class for this columnuintNUIntrHperiodInt64Col)r capitalizerr)rrk4col_namekcaps rSget_atom_coltypezDataCol.get_atom_coltypeg s_ ??6 "abBbT~H __X &!H??$Ds|Hwy(++rUc:|j||dS)Nrrrrrrrs rSrzDataCol.get_atom_datav s!.s###.U1X>>rUc<tj|dSNrrrrrrs rSrzDataCol.get_atom_datetime64z y!!a!11rUc<tj|dSrrrs rSrzDataCol.get_atom_timedelta64~ rrUc0t|jddS)Nr)rrrs rSrz DataCol.shape styy'400rUc|jSrNrrs rSrOzDataCol.cvalues syyrUc|rt|j|jd}|#|t|jk7r t dt|j|j d}|||jk7r t dyyy)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!)rrErrerxrrr8)rrexisting_fieldsexisting_dtypes rSrZzDataCol.validate_attr s %djj$..$GO*$t{{BS/S !WXX$TZZ$GN)n .J V/K) rUc t|tjsJt||jj ||j }|jJ|jt|\}}t|}n|}|j}|j}t|tjsJt|j}|j} |j} |j} |Jt|} | dk(rt!|| d}n| dk(rtj"|d}n| dk(r> tj"|D cgc]} t%j&| c} t(}n|dk(r| }|j/}|t1gtj2}n\t5|}|j7rA||}||d k7xx|j9t:j=j>zcc<tAjB||| }n |j9| d }t|dk(rtG||||}|jH|fScc} w#t*$rEtj"|D cgc]} t%j,| ncc} wc} t(}Y}wxYw#tD$r|j9d d }YwxYw)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 datetime64Tcoerce timedelta64m8[ns]r8rrs) categoriesrFrOrRrrZr)%rNrOr7rr8r9r rrrrrTrrrr r;asarrayr fromordinalobjectr fromtimestampravelr4float64r:anyastypercumsum_valuesr< from_codesr_unconvert_string_arrayrx)rrxrrZr convertedrrrrrr r8rrrmasks rSr@zDataCol.convert s &"**-;tF|;- <<   *DJJ'Fxx### :: %=V$D !Iz!*-DIJ99D)RZZ000tyy)==,, WW%%% + L  2d;I m # 9H=I f_ JJ2;%,,U,? 4 H ,/7XfI{{I%%Y= JJ4=>qT''*>>f  B >%,,Su,=  >sB;I&I!* I&#J7!I&&J4J J43J47KKc"t|j|j|jt|j|j|j |j Jt|j|j|j y)zset the data for this columnN)rhrErrxrrr8rrs rSr]zDataCol.set_attr sZ DNNDKK8 DNNDII6zz%%% DOOTZZ8rU) NNNNNNNNNNNN)r_r]r8zDtypeArg | Nonerrrrx)rrrr)rxrrrH)rr]rz type[Col]rr]rrHr)rxryrZr]rr])rrrrrr rfrrrrrr-rrC classmethodrrrrrrrrOrZr@r] __classcell__rs@rSrrsV O)$L      !%  @$$##   />FF , ,??222211 c&J9rUrc^eZdZdZdZd dZedZed dZedZ edZ y) DataIndexableColz+represent a data column that can be indexedTc`t|jjs tdy)N-cannot have non-object label DataIndexableCol)r4rx is_objectrrs rSrVzDataIndexableCol.validate_names s(T[[!++-LM M.rUc6tj|S)N)rrrs rSrz DataIndexableCol.get_atom_string sy""H"55rUc0|j|S)Nrrrs rSrzDataIndexableCol.get_atom_data s.s###.00rUc2tjSrWrrs rSrz$DataIndexableCol.get_atom_datetime64 y!!##rUc2tjSrWrrs rSrz%DataIndexableCol.get_atom_timedelta64 rrUNrr) rrrrr rVrrrrrrrUrSrr sa5N 6611$$$$rUrceZdZdZy)GenericDataIndexableColz(represent a generic pytables data columnN)rrrrrrUrSrr s2rUrceZdZUdZded<dZded<ded<ded <ded <d ed <d ed<ded<dZded< d- d.dZed/dZ ed0dZ edZ d1dZ d2dZ d3dZedZedZedZedZed4dZed/dZed Zd2d!Zd2d"Zed#Zed/d$Zed%Zd5d&Zd6d2d(Zd/d)Z d7 d8d*Zd+Z d9 d:d,Z y');Fixedz represent an object in my store facilitate read/write of various types of objects this is an abstract base class Parameters ---------- parent : HDFStore group : Node The group node where the table resides. r] pandas_kindrp format_typetype[DataFrame | Series]obj_typerrqrZrrrJrrFrrLct|tsJt|tJt|tjsJt|||_||_t||_||_ yrW) rNrrr{rJrrr[rZr)rrrrZrs rSrzFixed.__init__= sg&(+9T&\9+%%%%1>4;>1  (2  rUcv|jddkxr&|jddkxr|jddkS)Nrrb )versionrs rSis_old_versionzFixed.is_old_versionL s:||A!#U Q2(=U$,,q/TUBUUrUctt|jjdd} t d|j dD}t |dk(r|dz}|S#t$rd}Y|SwxYw)zcompute and set our versionpandas_versionNc32K|]}t|ywrW)r)rjrQs rSrlz Fixed.version..U s?qCF?s.r)r)rrr)rTrrrrfrrhr})rrs rSrz Fixed.versionP sx"'$***=*=?OQU"VW ?GMM#,>??G7|q !D. 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M  rUctj|||r(|jj|jdyt d)zs support fully deleting the node in its entirety (only) - where specification must be None TrZNz#cannot delete on an abstract storer)r_r`rrrr)rrirrs rSraz Fixed.delete s> <<ud + LL $ $TZZ4 $ @=>>rU)rLrz) rrrrJrZr]rr]rrr)rztuple[int, int, int]rr)rrr)rzLiteral[True] | NonerWNNNNrrrrr)rrrrrr)!rrrrrrrLrrrrrrrrrrMrrrrrErrrrr/rrr1r)rrarrUrSrr& s K&& IM  K KHd          VV  RR  2 &&##$$&&''##  55           HL ?!+ ?:D ?  ?rUrcVeZdZUdZedediZejDcic]\}}|| c}}}ZgZ de d<ddZ dZ dZ dd Zedd Zdd Zdd Zdd ZdddZ d ddZddZddZ d ddZ d d dZd!dZ d" d#dZycc}}}w)$ GenericFixedza generified fixed versiondatetimerr attributesc:|jj|dS)N)_index_type_mapr)rrs rS_class_to_aliaszGenericFixed._class_to_alias s##''R00rUcft|tr|S|jj|tSrW)rNr_reverse_index_maprr4)raliass rS_alias_to_classzGenericFixed._alias_to_class s* eT "L&&**5%88rUc R|jtt|dd}|tk(rdd}|}n|tk(rdd}|}n|}i}d|vr|d|d<|t urt }d|vr=t|dtr|djd|d<n|d|d<|tusJ||fS) N index_classr.ctj|j|}tj|d}| |j dj |}|S)Nr r^UTC)r= _simple_newrxr3 tz_localize tz_convert)rxr r dtaresults rSrrz*GenericFixed._get_index_factory..f sO#// DI&223TB>#//6AA"EF rUc^tj||}tj|dS)Nr8r^)r>r:r6)rxr r parrs rSrrz*GenericFixed._get_index_factory..f s'"..vDA"..t$??rUr r zutf-8r) r4rTrr3r6r4r8rNbytesrQ)rrEr6rrr=rs rS_get_index_factoryzGenericFixed._get_index_factory s** GE="= > - ' G K ' @G!G U?"6]F6Ne#( 5=%+u-$T{11':t  %T{t -/ //rUc8| td| tdy)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)rrris rS validate_readzGenericFixed.validate_read s=  T   Y  rUcy)NTrrs rSrzGenericFixed.is_exists& srUcp|j|j_|j|j_yr)rZrErrs rSrzGenericFixed.set_attrs* s""mm  KK rUc tt|jdd|_t t|jdd|_|j D]-}t||t t|j|d/y)retrieve our attributesrZNrrz)r[rrErZrTrr,rh)rrs rSrzGenericFixed.get_attrs/ sg(Z)NO %gdjj(H&MN  LA D!_WTZZD-IJ K LrUc $|jyrW)r)rrrs rSrzGenericFixed.write7 s  rUNcddl}t|j|}|j}t|dd}t ||j r |d||}nt t|dd} t|dd} | tj| | }n|||}| dk(rt|d d} t|| d }n| d k(rtj|d }|r |jS|S)z2read an array for the specified node (off of grouprN transposedF value_typerrrr Trrr) r|rrrrNVLArrayrTrOrr;rT) rrrrr|rrErKretr8rr s rS read_arrayzGenericFixed.read_array: stzz3' UL%8 dFNN +q'%%C#GE<$FGEE7D1E hhuE25& $UD$/c2d3-'jjH5 55LJrUctt|j|d}|dk(r|j|||S|dk(r,t|j|}|j |||}|St d|)N_varietymultirrregularzunrecognized index variety: )rTrrEread_multi_indexrread_index_noder)rrrrvarietyrrs rS read_indexzGenericFixed.read_index\ s"'$**X6F"GH g ((E(E E  !4::s+D((U(FEL:7)DE ErUc4t|tr-t|j|dd|j ||yt|j|ddt d||j |j}|j||jt|j|}|j|j_ |j|j_t|tt fr)|j#t%||j_t|tt t(fr|j*|j_t|tr2|j,%t/|j,|j_yyy)NrRrSrUr)rNr5rhrEwrite_multi_index_convert_indexrZr write_arrayrxrrrrr_r3r6r0rr6r8r r _get_tz)rrrrrs rS write_indexzGenericFixed.write_indexj s$ eZ ( DJJ3%x 0' :  " "3 . DJJ3%x 0) <&wt}}dkkRI   S)"2"2 34::s+D!*DMM !&DMM %-!=>,0,@,@e,M )%-n!MN%*ZZ "%/EHH4H#*588#4  5I/rUcpt|j|d|jtt |j |j |jD]\}\}}}t|r td|d|}t|||j|j}|j||jt|j |} |j"| j$_|| j$_t| j$|d|||d|} |j| |y)N_nlevelsz=Saving a MultiIndex with an extension dtype is not supported._level_name_label)rhrEr enumeraterKlevelsrnamesr,rr\rZrr]rxrrrrr_) rrrilev level_codesr_ level_key conv_levelr label_keys rSr[zGenericFixed.write_multi_index s se8,emm<+4  ekk5;; 7,  5 'A'[$(,)S%vaS)I' 3 t{{SJ   Y (9(9 :4::y1D!+DMM !%DMM  DMMcU%v#6 =%vaS)I   Y 4) 5rUct|j|d}g}g}g}t|D]}|d|} t|j| } |j | ||} |j | |j | j |d|} |j| ||} |j | t|||dS)NrarbrTrdT)rfrrgrD) rrErprrWrr_rPr5)rrrrrrfrrgrhrkrrirmrjs rSrVzGenericFixed.read_multi_index s$**X&67 "w &A%vaS)I4::y1D&&t5t&DC MM#  LL "%vaS)I//)5t/LK LL % &ed  rUc|||}d|jvrktj|jjdk(r?tj|jj|jj }t |jj}d}d|jvr*t|jj}t |}|j}|j|\}} |dk(r1|t|||j|jfdti| } n*|t|||j|jfi| } || _ | S)Nrrrr_rrr8)rrOprodrrrLrTrr`r_rB_unconvert_indexrZrr) rrrrrrr_rEr=rrs rSrWzGenericFixed.read_index_node s7E$ dmm # 0C0C(D(I88DMM//t}}7O7OPDt}}112 T]] "t}}112D"4(D 11%8 6> $t{{   E $t{{ E  rUc@tjd|jz}|jj |j ||t |j |}t|j|j_ |j|j_ y)zwrite a 0-len array)rbN) rOrrqr create_arrayrrr]r8rrLr)rrrarrrs rSwrite_array_emptyzGenericFixed.write_array_empty sjhhtejj() !!$**c37tzz3'#&u{{#3  #kk rUct|d}||jvr&|jj|j||jdk(}d}t |j r td|st|dr|j}d}d}|jEtt5tjj|j }ddd|[|sE|jj!|j|||j"|j}||ddn|j%||nx|j j&t(j*k(rt-j.|d} |rn6| d k(rn0t0| ||fz} t3j4| t6t9 |jj;|j|tj=} | j?|ntA|j r]|jjC|j||jEd d tG|j|jH_%n?tM|j r}|jjC|j||jNtG|j|} tQ|jR| jH_)d | jH_%ntU|j r\|jjC|j||jEd d tG|j|jH_%n<|r|j%||n'|jjC|j|||tG|j|jH_+y#1swYxYw)NT) extract_numpyrFz]Cannot store a category dtype in a HDF5 dataset that uses format="fixed". Use format="table".rN)rskipnarRrr3rr),rArrrrr(r8rr1rNrrrrAtom from_dtype create_carrayrrurrOobject_r infer_dtyperorrr"r%create_vlarray ObjectAtomrr*rsviewrrrLr+asi8r^r r/rK) rrrrr empty_arrayrKrca inferred_typerkvlarrrs rSr]zGenericFixed.write_array sc6 $**  LL $ $TZZ 5jjAo   ,%/ uc"!  == $*% >y~~00= >   \\//JJT5;; 01&&sE2 [[   + OOE%@M(*$ sE'BB b"4AQASTLL// CAUAUAWXE LL   - LL % %djj#uzz$7G H;GGDJJ $ - - 8 "5;; / LL % % C 4::s+D 'uxx0DMM '3DMM $ !%++ . LL % %djj#uzz$7G H;HGDJJ $ - - 8   " "3 . LL % %djj#u =7A C ))4w > >s ,.OO'rrrrr)rr]rrrrrr4)rr]rr4rr)rr]rr5rr)rr]rrrrrr5)rrJrrrrrr4)rr]rrrrrW)rr]rrrz Index | Nonerr)rrrrr3r6r/rr2r,rr0r4rBrDrrrrrrPrYr_r[rVrWrur])rjrPrs000rSr*r* sW$$j+xHO+:+@+@+BCC41a!Q$CJ 19 ,\ ( L FFJ F F) F8B F  F5.54FJ  ) 8B  0HL$$!+$:D$ $L*BFXBXB)XB2>XB XBIDs B$r*cbeZdZUdZdgZded<edZ d ddZd fd Z xZ S) rrr_r cn t|jjfS#ttf$rYywxYwrW)rhrrxrr}rs rSrzSeriesFixed.shapeA s6  ))*, ,>*  s "44c|j|||jd||}|jd||}t|||jS)NrrTrx)rr_)rDrYrPr7r_)rrirrrrrxs rSr)zSeriesFixed.readH sO 7E*u4@TBfE ::rUc t||fi||jd|j|j d||j |j _y)Nrrx)rrr_rr]r_rE)rrrrs rSrzSeriesFixed.writeU sG  c$V$ #)), 3'(( rUr'rrrrrr7r) rrrrr,rrrr)rrrs@rSrr; saKJ N   ; ;  ;  ;##rUrcbeZdZUddgZded<eddZ d d dZd fd ZxZ S) BlockManagerFixedrqnblocksrcn |j}d}t|jD]4}t|jd|d}t|dd}|-||dz }6|jj }t|dd}|t |d|dz }ng}|j||S#t$rYywxYw)Nrblock_itemsrrb) rqrprrr block0_valuesrerr})rrqrrhrrs rSrzBlockManagerFixed.shapea s 99DE4<<( &tzzU1#V+<=gt4$U1X%E  &::++DD'40E U1q23 LL L  sAB(AB(( B43B4c|j|||jjd}g}t|jD]8}||k(r||fnd\}} |j d||| } |j | :|d} g} t|jD]o}|j d|d} |jd|d }| | j| }t|j||d }| j |qt| dkDr"t| d }|j| d }|St|d|d S) NrrrCrTrrrrbrrrmF)rr)rDr_get_block_manager_axisrprqrYrrrPrvr2rNrhr9r})rrirrr select_axisrdrhr*r+axrdfs blk_itemsrxdfouts rSr)zBlockManagerFixed.read| sY 7E*mmo==a@ tyy! A-.+-=UDMdZ d>dZ d?dZ d@dZe dAdZ dBdZe dCdZe dAdZe dZe d Ze d!Ze d"Ze d#Ze dCd$Ze dAd%Ze dDd&ZdEd'Zd(ZdFd)ZdGd*ZdHd+ZdId,Z d@d-Z!d@d.Z"dJd@d/Z#d@d0Z$e%d1Z& dK dLd2Z' dM dNd3Z(e)dOd4Z*d5Z+ dP dQd6Z,e- dRd7Z.dJdSd8Z/ dTd9Z0 dK dUd:Z1 dK dVd;Z2xZ3S)Wraa represent a table: facilitate read/write of various types of tables Attrs in Table Node ------------------- These are attributes that are store in the main table node, they are necessary to recreate these tables when read back in. index_axes : a list of tuples of the (original indexing axis and index column) non_index_axes: a list of tuples of the (original index axis and columns on a non-indexing axis) values_axes : a list of the columns which comprise the data of this table data_columns : a list of the columns that we are allowing indexing (these become single columns in values_axes) nan_rep : the string to use for nan representations for string objects levels : the names of levels metadata : the names of the metadata columns wide_tablerqr]rrrbzint | list[Hashable]rfTzlist[IndexCol] index_axeszlist[tuple[int, Any]]rNz list[DataCol] values_axesrerrrnrc t ||||||xsg|_|xsg|_|xsg|_|xsg|_| xsi|_| |_y)Nr)rrrrNrrrr) rrrrZrrrNrrrrrs rSrzTable.__init__ s` &I$*,2&,"(.BJB  rUc>|jjddS)N_r)rrrs rStable_type_shortzTable.table_type_short s$$S)!,,rUc |jt|jrdj|jnd}d|d}d}|jr8dj|j Dcgc] }t |c}}d|d}dj|jDcgc]}|jc}}|jd|d|jd |jd |jd |d|d Scc}wcc}w) rrr.z,dc->[r@rrrz (typ->z,nrows->z,ncols->z ,indexers->[r) r1rhrrrrr]rr_rrr/ncols)rjdcrverrQjverr jindex_axess rSrzTable.__repr__ s -01B1B-Cchht(()cU!_   88T\\:SV:;DdV1+Chh@1@A &se,**+8DJJ<@jj\k]!B4q B ; As 0D(DcL|jD]}||jk(s|cSy)zreturn the axis for cN)rdr_)rrars rSrzTable.__getitem__ s* AAFF{ rUc b|y|j|jk7r&td|jd|jddD]h}t||d}t||d}||k7s#t|D]$\}}||}||k7st d|d|d|dt d|d|d|dy) z"validate against an existing tableNz'incompatible table_type with existing [rcr@)rrNrzinvalid combination of [z] on appending data [z] vs current table [)rrrrerr])rr(rasvovrhsaxoaxs rSrzTable.validate s =    t .$$%S(9<  A Aq$'B4(BRx (mFAsQ%Ccz(6qc: #u$8Q@  .qc1FrdK&&(T,% rUc6t|jtS)z@the levels attribute is 1 or a list in the case of a multi-index)rNrfrers rSis_multi_indexzTable.is_multi_index: s$++t,,rUctj|jj} |j }t |tsJ||fS#t $r}t d|d}~wwxYw)ze validate that we can store the multi-index; reset and return the new object zBduplicate names/columns in the multi-index when storing as a tableN)r_fill_missing_namesrrg reset_indexrrNr2)rrrf reset_objrbs rSvalidate_multiindexzTable.validate_multiindex? so'' 8 )I )Y///&   T  sA A+ A&&A+ctj|jDcgc]}|jjdc}Scc}w)z-based on our axes, compute the expected nrowsr)rOrprrOrrrhs rSnrows_expectedzTable.nrows_expectedP s2wwDOODq *DEEDs Acd|jvS)zhas this table been createdrqrrs rSrzTable.is_existsU s$**$$rUc0t|jddSNrqrrrs rSrzTable.storableZ stzz7D11rUc|jS)z,return the table group (this is my storable))rrs rSrqz Table.table^ s}}rUc.|jjSrW)rqr8rs rSr8z Table.dtypec szzrUc.|jjSrWrHrs rSrIzTable.descriptiong rJrUcVtj|j|jSrW)rIrJrrrs rSrdz Table.axesk st0@0@AArUc:td|jDS)z.the number of total columns in the values axesc3FK|]}t|jywrW)rhrx)rjrs rSrlzTable.ncols..r s;Q3qxx=;s!)sumrrs rSrz Table.ncolso s;$*:*:;;;rUcyr~rrs rS is_transposedzTable.is_transposedt rrUc ttj|jDcgc]}t |dc}|j Dcgc]}t |j c}Scc}wcc}w)z@return a tuple of my permutated axes, non_indexable at the frontr)rfrIrJrNrrrCrrs rSdata_orientationzTable.data_orientationx sY OO$($7$78qQqT8&*oo6QVV6   86s A/ A4clddd}|jDcgc]}|j|f}}|jDcgc] \}}||df}}}|jDcgc]2}|jt |j vs%|j|f4}}t||z|zScc}wcc}}wcc}w)z|jj}|d|dS)z)return the metadata pathname for this keyz/meta/z/metar r&s rS_get_metadata_pathzTable._get_metadata_path s$ &&se5))rUc|jj|j|t|d|j|j |j y)z Write out a metadata array to the key as a fixed-format Series. Parameters ---------- key : str values : ndarray rq)rrZrrN)rrrr7rZrr)rrrxs rSr\zTable.write_metadata sF   # #C ( 6N]];;LL  rUctt|jdd|d*|jj|j |Sy)z'return the meta data array for this keyrN)rrrrrrs rSrtzTable.read_metadata sA 74::vt4c4 @ L;;%%d&=&=c&BC CrUcBt|j|j_|j|j_|j |j_|j |j_|j |j_|j|j_|j|j_|j|j_ |j|j_ |j|j_ y)zset our table type & indexablesN) r]rrErrrNrrrZrrfrrs rSrzTable.set_attrs s #DOO 4  $ 1 !%!1!1!3 $($7$7 !"&"3"3 !\\ "mm  KK  KK )) rUct|jddxsg|_t|jddxsg|_t|jddxsi|_t|jdd|_t t|jdd|_tt|jdd|_ t|jd dxsg|_ |jDcgc]}|js|c}|_ |jDcgc]}|jr|c}|_ycc}wcc}w) rHrNNrrrrZrrzrf)rrErNrrrr[rZrTrrf indexablesrrrrs rSrzTable.get_attrs s%djj2BDIOR#DJJEKDJJ5; tzz9d; (Z)NO %gdjj(H&MN &-djj(D&I&OR &*ooK9J9J1K'+P!a>O>OAPLPs5EE!E3Ec |k|jr^tdj|jDcgc] }t |c}z}t j |ttyyycc}w)rNrr) rrmrrr]rrr!r%)rrirQrks rSrzTable.validate_version s\  ""(388T\\4RSV4R+SS */1# 4RsA/ c|yt|tsy|j}|D]}|dk(r ||vstd|dy)z validate the min_itemsize doesn't contain items that are not in the axes this needs data_columns to be defined Nrxzmin_itemsize has the key [z%] which is not an axis or data_column)rNrnrr)rrqrPs rSvalidate_min_itemsizezTable.validate_min_itemsize sf   ,-  OO  AH}z 04"" rUc ` g}jjjtjjD]k\}\}}t |}j |}|dnd}|d}t |d} t|||| |j||} |j| mtjt| fd} |jtjjD cgc]\}} | || c} }|Scc} }w)z/create/cache the indexables if they don't existNrsr)r_rCrrrrqrrc ft|tsJt}| vrt}t |}t |j }t |dd}t |dd}t|}j|}t |dd} ||||| |z|j| || } | S)Nrrr) r_r rxrrrrqrrr8) rNr]rrr_maybe_adjust_namerrrtrq)rhraklassradj_namerxr8rmdrrbase_posrdescr table_attrss rSrrzTable.indexables..fsa% %%EBw(4#D)!T\\:H[XJe*)>? *OA|d4&D##D)B!#:TD&I; 48D jj I   y )% **"" #{#! ! J  $**:P:P0QR1AaGRSSs D* c |jsy|dury||dur0|jDcgc]}|js|j}}t |t t fs|g}i}|||d<|||d<|j}|D]}t|j|d}||jr^|j} | j} | j} || |k7r|jn| |d<|| |k7r|jn| |d<|jr|jj!dr t#d|j$di|||j&dd vst)d |d |d |d ycc}w)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. NFTrrcomplexzColumns containing complex values can be stored but cannot be indexed when using table format. 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LL $ $TZZ 9!!%% "  A    ..#%) /G OO %0GM " 'EMM & &u{{ >g >!::  .A  uf - . 62rUc B|jj}|j}g}|rs|jD]d}t |j j d}t|tjsC|j|jddft|r%|d}|ddD]}||z} |j}nd}|jDcgc]}|j} }t| } | dk(sJ| |jDcgc]}|j!} }| D cgc]Q} | j#tj$tj&| j(| j(dz S} } g} t+| D]K\}} |f|j|| |zj,z}| j| |j/|M|d}tj0t3|||j }||zdz}t5|D]^}||z}t3|dz|z|}||k\ry|j7|| Dcgc]}||| c}||||nd| D cgc]} | || c} `ycc}wcc}wcc} wcc}wcc} w) z` we form the data into a 2-d including indexes,values,mask write chunk-by-chunk rrmu1FrrbNrr)indexesrrx)r8rgrrr:rrhrNrOr7rrrhrrrOrC transposerollrrqrerreshaperrrpwrite_data_chunk)rrrrgr/masksrrmrYnindexesrxrbvaluesrh new_shaperowschunksstart_iend_is rSrUzAppendableTable.write_data s   ## %% @AFF|''Q'/dBJJ/LLT!>?  @ u:8D12Y ax ::rrhrindsindrframesr index_valsrOrkrgrxindex_cols_rrs rSr)zAppendableFrameTable.reads e$ uEE4&&' IIMM$--a03R 8 )3PearT__Q=O7OPP4yA~~1gs Adii(% DAq((("() Jxx</Z(!--j9HHW%E ud3!! e'%*FG u75&$+GH{{aJvrzz$BFLLO(<=&"**-vxxfEFE*vuFC++VHe6RII-224 Oryy&,,6O O4 MM" K% N v;! BQ'Bd%u4H   rY  H oQs L,LrrKr'r()rrrrrrrqr2rrrrrrr)rrUrSrrso2K#J D)2H&2 ,,  N N  NrUrcxeZdZdZdZdZdZeZe d dZ e d dZ d fd Z d d fd ZxZS)rrQrrrcyr~rrs rSrz#AppendableSeriesTable.is_transposedrrUc|SrWrrs rSrz AppendableSeriesTable.get_objects rUc t|ts!|jxsd}|j|}t |d||j jd|S)+we are going to write this as a frame tablerxrrr)rNr2r_to_framerrrr')rrrrr_rs rSrzAppendableSeriesTable.write!sO#y)88'xD,,t$Cw}R3;;3E3E3GR6RRrUcf|j}|F|rDt|jtsJ|jD]}||vs|j d|t |||||}|r|j|jd|jdddf}|jdk(rd|_ |S)NrrBTrurx) rrNrfrer<rr) set_indexrFr_) rrirrrrrrRrs rSr)zAppendableSeriesTable.read(s,,  >dkk40 00[[ )G#NN1a( ) GLugUL N  KK TK 2 FF1a4L 66X AFrUrrKrWr'r)rrrrrrrqr7rrrrrrr)rrs@rSrrs|2 K$J DH S       rUrc*eZdZdZdZdZfdZxZS)rrQrrc |jxsd}|j|\}|_t|jtsJt |j}|j |t ||_t|$dd|i|S)rrxrr) r_rrfrNrerr4rrr)rrrr_newobjrkrs rSrz AppendableMultiSeriesTable.writeHsxxx#8"66s; $++t,,,DKK  Dtw}22622rU)rrrrrrrrrs@rSrrBs2 K)J33rUrcleZdZUdZdZdZdZeZde d<e d dZ e dZ dd Z ed Zd Zy )rz:a table that read/writes the generic pytables table formatrrrzlist[Hashable]rfc|jSrW)rrs rSrzGenericTable.pandas_type\srUcLt|jddxs |jSrrrs rSrzGenericTable.storable`stzz7D1?TZZ?rUcNg|_d|_g|_|jDcgc]}|js|c}|_|jDcgc]}|jr|c}|_|j Dcgc]}|jc}|_ycc}wcc}wcc}w)rHN) rNrrfrrrrr_rrs rSrzGenericTable.get_attrsds~   &*ooK9J9J1K'+P!a>O>OAP-1-=-=>QVV>LP>sBBB"B<B"c |j}|jd}|dnd}tdd|j||}|g}t |j D]h\}}t |tsJt||}|j|}|dnd}t|||g||j||} |j| j|S)z0create the indexables from the table descriptionrNrsr)r_rCrqrr)r_rrxrrqrr) rIrtr|rqre_v_namesrNr]rrr) rr~rrrrrhrrrs rSrzGenericTable.indexablesns       (^z#q  IR{ ajj) #DAqa% %%1a=D##A&B!#:TD(sjjB   r " #"rUc td)Nz cannot write on an generic tabler"r%s rSrzGenericTable.writes!"DEErUNrr)rrrrrrrqr2rrrrrrr$rrrrUrSrrSsgDK J DH    @@?  DFrUrceZdZdZdZeZdZejdZ e ddZ d fd Z d d fd ZxZS) rza frame with a multi-indexrrz ^level_\d+$cy)Nappendable_multirrs rSrz*AppendableMultiFrameTable.table_type_shorts!rUc (|g}n|dur|jj}|j|\}|_t |jt sJ|jD]}||vs|j d|t| d||d|S)NTrrr) rr'rrfrNrer<rr)rrrrrrs rSrzAppendableMultiFrameTable.writes  L T !;;--/L33C8T[$++t,,, *A $##Aq) *w}J<J6JJrUc,t|||||}|j|j}|jj |jj Dcgc]!}|jj|rdn|#c}|_|Scc}w)NrB) rr)rrfrrxrg _re_levelssearch)rrirrrrr_rs rSr)zAppendableMultiFrameTable.reads~W\we$\ O \\$++ &88%%HJ WT__++D1Tt ; W   Xs&BrrWr'r()rrrrrr2rrqrecompilerrrrr)rrs@rSrrsi$(JH DN+J "" K     rUrc|j|}t|}| t|}||j|r|j|r|St|j}|*t|jj |d}|j|s7t ddg|j z}|||<|jt|}|S)NF)sort) r3rBequalsuniquerzslicerqr{rf)rrCr>r(rslicers rSrrs t B & !F U# u-6==3D &--/ *F elln-::6:N == ',T4'8&9CHH&Dt ggeFm$ JrUc0tj|}|S)z+for a tz-aware type, return an encoded zone)r get_timezone)r zones rSr^r^s  ! !" %D KrUcyrWrrxr rs rSr;r;srUcyrWrrs rSr;r;srUct|tr|j|j|k(sJ|ut|tr|j}|j}nd}|j }t |}t||}|jdj|}|S|rtj|d}|S)a coerce the values to a DatetimeIndex if tz is set preserve the input shape if possible Parameters ---------- values : ndarray or Index tz : str or tzinfo coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray Nr^r9M8[ns]r) rNr3r r_rrrTr;r<rOr)rxr rr_s rSr;r;s&-(yy FIIO33 ~ fm ,;;D[[FD\\^F R vD1##E*55b9 M F(3 MrUc Zt|tsJ|j}t|\}}t |}t j |}t|ts*t|jst|jr(t||||t|ddt|dd|St|tr tdtj |d} t#j$|} | dk(rgt#j$| D cgc]} | j'c} t"j(}t||dt+j-| S| d k(rJt/| ||}|jj0} t||d t+j3| | S| d vrt||||| St|t"j4r|jt6k(sJ|d k(sJ|t+j9}t||||| Scc} w)Nr r )rxrrr r r zMultiIndex not supported here!Frxrr)r rR)integerfloating)rxrrr r)rNr]r_rrrrr;r0r8r'rrr5rrr~rOr toordinalint32r Time32Col_convert_string_arrayrrSr7rr) r_rrZrr rrrrrrxrrs rSr\r\s dC  J5U;Iz * %D  % %i 0D 5*% u{{ +  %  -udD)!  %$899OOE%8MZZ FJJv>! >bhhO  )VWY%8%8%:z   ( ")&(FC ??++    I   )!    1 1 4J  )RZZ0Y__5NNNx%%y##%it KK1 ?sH(c :|dk(r t|}|S|dk(r t|}|S|dk(r? tj|Dcgc]}t j |c}t }|S|dvrtj|}|S|dvrt|d||}|S|dk(rtj|d }|Std |cc}w#t$rFtj|Dcgc]}t j|ncc}wc}t }Y|SwxYw) Nrrrr)rfloatrrRrrrzunrecognized index type ) r3r8rOrrrrrrr)rrrZrrrs rSrqrqQs7 |d#$ L#  t$ L  TJJTB 0 0 3B&QE L - - 4  L ( ' $&  L   47# L3D6:;; C TJJtD! 2 21 5DDFSE L Ts.C C C C D'D DDc|jtk7r|Sttj|}|jj }t j|d} | dk(r td| dk(r td| dk(s|dk(s|St|} |j} || | <t j| d} | dk7rht| jd D]M} | | } t j| d} | dk7s%t|| kDr|| nd | }td |d | d t| ||j| j}|j }t#|t$r1t'|j)|xs|j)dxsd }t+|xsd |}||j-|}|||kDr|}|j/d|d}|S)NFrxrz+[date] is not implemented as a table columnr+z>too many timezones in this block, create separate data columnsrRrrzNo.zCannot serialize the column [z2] because its data contents are not [string] but [z] object dtyperxz|Sr)r8rrrOr7r_rr~rr:rrprrhrr\rrNrnrrrBrYr)r_rar rrrZrrrrrrrhrLerror_column_labelrrecis rSrrjs}}2::w'G##JOOGE:MEFF * $ L  x':+A =D <<>DDJOOD7M tzz!}% Aq'COOC>M(36w?H ::dAhZ. 1D KrUc|j}tj|jt}t |rt jt|}d|}t|dtr1t|jj||j}n(|j|djtd}|d}t j |||j#|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. rUr)rFrr)rrOrrrrhrrr&rNrAr7r]rQrr!string_array_replace_from_nan_repr\)rrrZrrrr8s rSrrs& JJE ::djjl& 1D 4y22=3FGH: d1gu %$<##**8F*CKKD;;u5;188e8LD00w? << rUct|tsJt|t|rt |||}||}|SrW)rNr]r _need_convert_get_converter)rxr<rZrconvs rSr:r:s? h $4d8n4 $Xh&9f MrUcH|dk(rdS|dk(rfdStd|)Nrc0tj|dS)Nrr)rOr)rQs rSrz _get_converter..sAX6rUrRc"t|dS)Nr)r)rQrZrs rSrz _get_converter..s0 thv rUz invalid kind )r)rrZrs ``rSrrs7 |66    =/00rUc|dvryy)N)rrRTFrrs rSrrs '' rUct|tst|dkr td|ddk(r@|ddkr8|ddk(r0t j d|}|r|j d}d|}|S) z Prior to 0.10.1, we named values blocks like: values_block_0 an the name values_0, adjust the given name if necessary. Parameters ---------- name : str version : Tuple[int, int, int] Returns ------- str z6Version is incorrect, expected sequence of 3 integers.rrbrrzvalues_block_(\d+)values_)rNr]rhrrrr)r_rr_grps rSrrsx'33w= start and < stop)rqrirr conditionr=termsrHr-rrrr~rOrr8bool_r/r issubclassrrrgenerateevaluate)rrqrirrinferreds rSrzSelection.__init__ps        *% 1??5?y(H ,AJJu-E{{bhh.&*jj$))t =$%E<#'::#3#3D+-99UD+A%+H(#EKK$4$4bjjA JJ2 8J7O7O7Q II1u 7I6N6N6P", U#,1(% 1(    #u-DJzz%.2jj.A.A.C+ & $) 1 1s D:G..G7c|y|jj} t|||jjS#t$rB}dj |j }td|d|d}t||d}~wwxYw)z'where can be a : dict,list,tuple,stringN)rrZrz- 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 ) rqrr?rZ NameErrorrrrr)rrirrbqkeysrs rSrzSelection.generates = JJ ! ! # +!djj>Q>QR R +HHQVVX&E..3W5CDI'J CS/s * +s!A B  =BB c|jT|jjj|jj|j|j S|j /|jjj|j S|jjj|j|j S)( generate the selection rT) rrq read_whererrrrHr8r)rs rSrzSelection.selects >> %::##..%%'tzz /    )::##44T5E5EF Fzz$$4::DII$FFrUc|j|j}}|jj}|d}n |dkr||z }||}n |dkr||z }|jA|jjj |jj ||dS|j |jStj||S)rrT)rrr) rrrqr/rget_where_listrrHrOr)rrrr/s rSrEzSelection.select_coordssjj$))t    =E QY UNE <D AX EMD >> %::##22%%'u4d3    )## #yy%%rUr)rqrrrrrrr)rrrrrrrrErrUrSrrdsS   -D-D -D  -D  -D^+. 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