i  hUdZddlmZddlmZddlZddlmZmZddl Z ddl Z ddl Z ddl m Z ddlmZmZmZmZmZmZmZmZddlZddlZddlmZmZmZdd lmZm Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)ddl*m+Z+ddl,m-Z-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3m4Z4ddl5m6Z6ddl7m8Z8m9Z9m:Z:m;Z;mm?Z?m@Z@mAZAmBZBddlCmDZDddlEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOmPZPddlQmRZRmSZSmTZTddlUmVZVddlWmXZXddlYmZcm[Z\ddl]m^Z^m_Z_ddl`maZbmcZcddldmeZeddlfmgZgddlhmiZimjZjer6ddlkmlZlmmZmmnZnmoZoddlpmqZqmrZrdd lsmtZtmuZumvZvdd!lwmxZxmyZymzZzm{Z{m|Z|m}Z}m~Z~dd"lmZd#Zd$Zd}d%Zd&Ze^Zd'ed(<d~d)Zd*Zd+ed,<d-Zd+ed.<d/Zd+ed0<d1d1d2d2d3ZeFdgiZd4Zd+ed5<d6Zd+ed7<ejd85ej d9d:eej";ej ded?<d:ad@Z d ddAZe4dB d ddCZddDZe4dBGdEdFZGdGdHZGdIdJZGdKdLeZGdMdNeZGdOdPeZGdQdReZGdSdTZGdUdVeZGdWdXeZGdYdZeZGd[d\eZGd]d^eZGd_d`eZGdadbeZGdcddeZGdedfeZGdgdheZGdidjeZGdkdleZ d ddmZddnZ ddoZddpZddqZ ddrZddsZ ddtZdduZddvZddwZddxZddyZddzZGd{d|Zy#1swYxYw)zY High level interface to PyTables for reading and writing pandas data structures to disk ) annotations)suppressN)datetzinfo)dedent) TYPE_CHECKINGAnyFinalLiteralSelf TypeAliascastoverload)config get_optionusing_string_dtype)libwriters)is_string_array) timezones) HAS_PYARROW)import_optional_dependency) patch_pickle)AttributeConflictWarningClosedFileErrorIncompatibilityWarningPerformanceWarningPossibleDataLossError)cache_readonly set_module)find_stack_level) ensure_object is_bool_dtypeis_complex_dtype is_list_likeis_string_dtypeneeds_i8_conversion)CategoricalDtypeDatetimeTZDtypeExtensionDtype PeriodDtype)array_equivalent) DataFrame DatetimeIndexIndex MultiIndex PeriodIndex RangeIndexSeries StringDtypeTimedeltaIndexconcatisna) Categorical DatetimeArray PeriodArray) tz_to_dtype)BaseStringArray) PyTablesExprmaybe_expression)array extract_array) ensure_index)stringify_path)adjoin pprint_thing)CallableHashableIteratorSequence) ModuleType TracebackType)ColFileNode) AnyArrayLike ArrayLikeAxisIntDtypeArgFilePathTimeUnitnpt)Blockz0.15.2UTF-8c|t}|SN)_default_encodingencodings T/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/pandas/io/pytables.py_ensure_encodingr]s$ Oc<t|tr 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 ) isinstancestrnames r\ _ensure_strrds$4y Kr^r Termc|dz}t|ttfr-|Dcgc]!}|t|rt ||dzn|#}}nt|r t ||}| t |r|SdScc}w)z Ensure that the where is a Term or a list of Term. This makes sure that we are capturing the scope of variables that are passed create the terms here with a frame_level=2 (we are 2 levels down) N scope_level)r`listtupler>relen)whererilevelterms r\ _ensure_termrps !OE%$' 2B$1GD519 -T Q  % U.MSZ59T9 s&A2z where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with the copy_to method) r incompatibility_doczu the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None attribute_conflict_docz your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->%s,key->%s] [items->%s] performance_docfixedtable)frttruz; : boolean drop ALL nan rows when appending to a table dropna_docz~ : format default format writing format, if None, then put will default to 'fixed' and append will default to 'table' format_doczio.hdf dropna_tableF) validatordefault_format)rtruNzModuleType | None _table_modct=ddl}|att5|jj dk(adddtStS#1swYtSxYw)Nrstrict)r}tablesrAttributeErrorfile_FILE_OPEN_POLICY!_table_file_open_policy_is_strict)rs r\_tablesrsX n %  --9 .  :   s A  Ac  |r   f d}n   f d}t|tr ||yt|}t||||5}||dddy#1swYyxYw)z+store this object, close it if we opened itc : |j   S)N)formatindex min_itemsizenan_repdropna data_columnserrorsr[)append storerrr[rrrkeyrrvalues r\zto_hdf..s3%,,  %%'  r^c : |j   S)N)rrrrrrr[rputrs r\rzto_hdf..(s3%))  %%$  r^)mode complevelcomplibN)r`HDFStorerB) path_or_bufrrrrrrrrrrrrrr[rvrs `` ```````` r\to_hdfrsn$  +x( +$[1  di    eH   s  A//A8pandasc F|dvrtd|d| t|d}t|tr|js t d|} d} nht |}t|ts td  tjj|} | std |d t|f||d | } d } |[| j}t|dk(r td|d}|ddD]}t!||rtd|j"}| j%||||||| | S#ttf$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 ------- object The selected object. Return type depends on the object stored. See Also -------- DataFrame.to_hdf : Write an HDF file from a DataFrame. HDFStore : Low-level access to HDF files. Notes ----- When ``errors="surrogatepass"``, ``pd.options.future.infer_string`` is true, and PyArrow is installed, if a UTF-16 surrogate is encountered when decoding to UTF-8, the resulting dtype will be ``pd.StringDtype(storage="python", na_value=np.nan)``. Examples -------- >>> df = pd.DataFrame([[1, 1.0, "a"]], columns=["x", "y", "z"]) # doctest: +SKIP >>> df.to_hdf("./store.h5", "data") # doctest: +SKIP >>> reread = pd.read_hdf("./store.h5") # doctest: +SKIP )rr+azmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nrgrhz&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.)rmstartstopcolumnsiterator chunksize auto_close) ValueErrorrpr`ris_openOSErrorrBraNotImplementedErrorospathexists TypeErrorFileNotFoundErrorgroupsrl_is_metadata_of _v_pathnameselect LookupErrorrrclose)rrrrrmrrrrrkwargsrrrrcandidate_only_groupgroup_to_checks r\read_hdfr?sn ##D6- .   U2+x(""BC C $[1 +s+%G  WW^^K0F #eK=$HI II4I&I % ;\\^F6{a D$*!9 #)* &~7KL$;  '22C|| !  A:& F T  ; /+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.FrgmetaT)_v_depth _v_parent_v_name)group parent_groupcurrentparents r\rrsk ~~...G   Q "" \ !goo&?##   Q  r^cjeZdZUdZded<ded< d2 d3dZd4dZed Zed4d Z d5d Z d6d Z d7d Z d8dZ d9dZd:dZd4dZd;dZ ddZd?dZd@dZdAdBdZdCdZedDdZdEdFdZd5dZ dG dHdZ dI dJdZ dK dLdZ dM dNdZ dO dPd ZdId7d!Z dQ dRd"Z! dS dTd#Z" dI dUd$Z#dVd%Z$dWdXd&Z%dYd'Z&dZd(Z' d[ d\d)Z(d4d*Z)dCd+Z*d]d,Z+ d^ d_d-Z, d` dad.Z-dbd/Z.dcd0Z/ddd1Z0y)eraP Dict-like IO interface for storing pandas objects in PyTables. Either Fixed or Table format. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path : str File path to HDF5 file. mode : {'a', 'w', 'r', 'r+'}, default 'a' ``'r'`` Read-only; no data can be modified. ``'w'`` Write; a new file is created (an existing file with the same name would be deleted). ``'a'`` Append; an existing file is opened for reading and writing, and if the file does not exist it is created. ``'r+'`` It is similar to ``'a'``, but the file must already exist. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. fletcher32 : bool, default False If applying compression use the fletcher32 checksum. **kwargs These parameters will be passed to the PyTables open_file method. Examples -------- >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore("test.h5") >>> store["foo"] = bar # write to HDF5 >>> bar = store["foo"] # retrieve >>> store.close() **Create or load HDF5 file in-memory** When passing the `driver` option to the PyTables open_file method through **kwargs, the HDF5 file is loaded or created in-memory and will only be written when closed: >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore("test.h5", driver="H5FD_CORE") >>> store["foo"] = bar >>> store.close() # only now, data is written to disk z File | None_handlera_modeNc 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 HDFStorerzcomplib only supports z compression.rrr)rrfilters all_complibsdefault_complibrB_pathrr _complevel_complib _fletcher32_filtersopen)selfrrrr fletcher32rrs r\__init__zHDFStore.__init__6s v LM M+H5  7&..2M2M#M()D)D(E]S  ?y4nn44G#D) <D  '0)a %  &t&v&r^c|jSrXrrs r\ __fspath__zHDFStore.__fspath__Ws zzr^cj|j|jJ|jjS)zreturn the root node)_check_if_openrrootrs r\rz HDFStore.rootZs0 ||'''||   r^c|jSrXrrs r\filenamezHDFStore.filenamea zzr^c$|j|SrX)getrrs r\ __getitem__zHDFStore.__getitem__esxx}r^c(|j||yrXr)rrrs r\ __setitem__zHDFStore.__setitem__hs er^c$|j|SrX)removers r\ __delitem__zHDFStore.__delitem__ks{{3r^c |j|S#ttf$rYnwxYwtdt |j d|d)z$allow attribute access to get stores'z' object has no attribute ')rKeyErrorrrtype__name__)rrcs r\ __getattr__zHDFStore.__getattr__nsW 88D> !/*   T ##$$?vQ G  s %%cV|j|}||j}|||ddfvryy)zx check for existence of this key can match the exact pathname or the pathnm w/o the leading '/' NrgTF)get_noder)rrnodercs r\ __contains__zHDFStore.__contains__xs< }}S!  ##DtT!"X&&r^c4t|jSrX)rlrrs r\__len__zHDFStore.__len__s4;;=!!r^cNt|j}t|d|dS)N File path:  )rDrr)rpstrs r\__repr__zHDFStore.__repr__s'DJJ't*]4&33r^c|SrXrrs r\ __enter__zHDFStore.__enter__s r^c$|jyrX)r)rexc_type exc_value tracebacks r\__exit__zHDFStore.__exit__s r^c|dk(r(|jDcgc]}|jc}S|dk(rC|jJ|jjddDcgc]}|jc}St d|dcc}wcc}w)af Return a list of keys corresponding to objects stored in HDFStore. Parameters ---------- include : str, default 'pandas' When kind equals 'pandas' return pandas objects. When kind equals 'native' return native HDF5 Table objects. Returns ------- list List of ABSOLUTE path-names (e.g. have the leading '/'). Raises ------ raises ValueError if kind has an illegal value See Also -------- HDFStore.info : Prints detailed information on the store. HDFStore.get_node : Returns the node with the key. HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> store.get("data") # doctest: +SKIP >>> print(store.keys()) # doctest: +SKIP ['/data1', '/data2'] >>> store.close() # doctest: +SKIP rnative/Table) classnamez8`include` should be either 'pandas' or 'native' but is 'r)rrr walk_nodesr)rincludens r\keysz HDFStore.keyssH h +/;;=9aAMM9 9  <<+ ++'+||'>'>sg'>'V"#  Fwiq Q  :s B B c4t|jSrX)iterr rs r\__iter__zHDFStore.__iter__sDIIK  r^c#VK|jD]}|j|fyw)z' iterate on key->group N)rrrgs r\itemszHDFStore.itemss, #A--" " #s')c 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)rrrrrrrFiltersrrrrr open_filer)rrrrmsgs r\rz HDFStore.opens :: zzZ'DK,?<<// |= U88 DJ << JJL ??t2#I--4;K;K.DM -* S/ !'v'' DJJI&I r^c^|j|jjd|_y)z0 Close the PyTables file handle N)rrrs r\rzHDFStore.closes% << # LL    r^cZ|jyt|jjS)zF return a boolean indicating whether the file is open F)rboolisopenrs r\rzHDFStore.is_opens& << DLL''((r^c|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)rrs r\rzHDFStore.flushsf << # LL   g&4HHT\\002344 $44s .A11A:ct5|j|}|td|d|j|cdddS#1swYyxYw)a Retrieve pandas object stored in file. Parameters ---------- key : str Object to retrieve from file. Raises KeyError if not found. Returns ------- object Same type as object stored in file. See Also -------- HDFStore.get_node : Returns the node with the key. HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> store.get("data") # doctest: +SKIP >>> store.close() # doctest: +SKIP NNo object named in the file)rrr _read_grouprrrs r\rz HDFStore.get$sV6^ +MM#&E}!1#lCDD##E*  + + +s 3AAc  |j|} | td|dt|d}|j|  j  fd} t | | | j ||||| } | jS)aC 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. See Also -------- HDFStore.select_as_coordinates : Returns the selection as an index. HDFStore.select_column : Returns a single column from the table. HDFStore.select_as_multiple : Retrieves pandas objects from multiple tables. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> store.get("data") # doctest: +SKIP >>> print(store.keys()) # doctest: +SKIP ['/data1', '/data2'] >>> store.select("/data1") # doctest: +SKIP A B 0 1 2 1 3 4 >>> store.select("/data1", where="columns == A") # doctest: +SKIP A 0 1 1 3 >>> store.close() # doctest: +SKIP rr rgrhc.j|||S)N)rrrmr)read)_start_stop_whererss r\funczHDFStore.select..funcs66U&'6R Rr^rmnrowsrrrrr)rrrp_create_storer infer_axes TableIteratorr, get_result) rrrmrrrrrrrr*itr)s ` @r\rzHDFStore.selectGsL c" =-cU,?@ @U2    &  S  ''!  }}r^ct|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 rgrhz&can only read_coordinates with a tablermrr)rp get_storerr`rrread_coordinates)rrrmrrtbls r\select_as_coordinateszHDFStore.select_as_coordinatessL4U2ooc"#u%DE E##%u4#HHr^c|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)r4r`rr read_column)rrr9rrr6s r\ select_columnzHDFStore.select_columns>Fooc"#u%?@ @fEEEr^c 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]d\} } | td | d | jstd | jd | | j } K| j | k7s[tdDcgc]}t|t"s|}}|D chc]} | j$ddc} j'fd}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 rgrhr)rrmrrrrrrzkeys must be a list/tuplez keys must have a non-zero lengthNTrzInvalid 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)NrmrrrF)axisverify_integrity)r%r6 _consolidate)r&r'r(rwobjsrArtblss r\r*z)HDFStore.select_as_multiple..funcPsR VWFOD $TEBOOQ Q sAr+) coordinates)rpr`rjrkrlrarrrr4 itertoolschainzipris_tablepathnamer,rnon_index_axespopr/r0)rr rmselectorrrrrrrkr)r,rwx_tblsr*r1rArEs ` @@r\select_as_multiplezHDFStore.select_as_multiplesVU2 dT5M *s4yA~7D dC ;;!#%  $u .78 84y?@ @  AwH-11q"1 OOH %OOa]OStD5QR QDAqy1566::qzzl+)) }E! !OPP Q !9qJq%$8991661  #A&6::< R  !  }}}..g2*:7s%G#G 9G G%c|tdxsd}|j|}|j||||||||| | | | | |y)aL Store object in HDFStore. This method writes a pandas DataFrame or Series into an HDF5 file using either the fixed or table format. The `table` format allows additional operations like incremental appends and queries but may have performance trade-offs. The `fixed` format provides faster read/write operations but does not support appends or queries. Parameters ---------- key : str Key of object to store in file. value : {Series, DataFrame} Value of object to store in file. format : 'fixed(f)|table(t)', default is 'fixed' Format to use when storing object in HDFStore. Value can be one of: ``'fixed'`` Fixed format. Fast writing/reading. Not-appendable, nor searchable. ``'table'`` Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. index : bool, default True Write DataFrame index as a column. append : bool, default False This will force Table format, append the input data to the existing. complib : default None This parameter is currently not accepted. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. min_itemsize : int, dict, or None Dict of columns that specify minimum str sizes. nan_rep : str Str to use as str nan representation. data_columns : list of columns or True, default None List of columns to create as data columns, or True to use all columns. See `here `__. encoding : str, default None Provide an encoding for strings. errors : str, default 'strict' The error handling scheme to use for encoding errors. The default is 'strict' meaning that encoding errors raise a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors. 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. See Also -------- HDFStore.info : Prints detailed information on the store. HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP Nio.hdf.default_formatrt) rrrrrrrrr[r track_timesr)r_validate_format_write_to_group)rrrrrrrrrrrr[rrUrs r\rz HDFStore.putksgh > 78CGF&&v.   %%#  r^ct|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 rgrhNz5trying to remove a node with a non-None where clause!T recursivez7can only remove with where on objects written as tablesr3) rpr4rAssertionError Exceptionrr _f_removecomall_nonerrJdelete)rrrmrrr)errrs r\rzHDFStore.removes*U2 $A. <<ud + GG    -zzVW Wxxe5tx<<;       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 Key of object to append. value : {Series, DataFrame} Value of object to append. 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. axes : default None This parameter is currently not accepted. index : bool, default True Write DataFrame index as a column. append : bool, default True Append the input data to the existing. complib : default None This parameter is currently not accepted. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. columns : default None This parameter is currently not accepted, try data_columns. min_itemsize : int, dict, or None Dict of columns that specify minimum str sizes. nan_rep : str Str to use as str nan representation. chunksize : int or None Size to chunk the writing. expectedrows : int Expected TOTAL row size of this table. dropna : bool, default False, optional Do not write an ALL nan row to the store settable by the option 'io.hdf.dropna_table'. 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 `__. encoding : default None Provide an encoding for str. errors : str, default 'strict' The error handling scheme to use for encoding errors. The default is 'strict' meaning that encoding errors raise a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors. See Also -------- HDFStore.append_to_multiple : Append to multiple tables. Notes ----- Does *not* check if data being appended overlaps with existing data in the table, so be careful Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df1, format="table") # doctest: +SKIP >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["A", "B"]) >>> store.append("data", df2) # doctest: +SKIP >>> store.close() # doctest: +SKIP A B 0 1 2 1 3 4 0 5 6 1 7 8 Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerTru)raxesrrrrrrr expectedrowsrrr[r)rrrVrW)rrrrrcrrrrrrrrrdrrr[rs r\rzHDFStore.append sF  P  > 56F > 78CGF&&v.   %%%!  r^c | tdt|ts td||vr tdt t t tjt ttz }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 dictzz.HDFStore.append_to_multiple..s)ODE$K&&5&177Os),rrA)rr)rr`dictrnextr setrangendim _AXES_MAPrrextendrc differencer/sorted get_indexertakevalues intersectionlocrMreindexr)rdrrNrrcrrrA remain_key remain_valuesrOvorderedorddidxs valid_indexrrdcvalrfiltereds ` r\append_to_multiplezHDFStore.append_to_multiples4>  B  !T")  1 O  DU5::./#iU 6L2MMNO  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 G<G<ct|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)rr4r`rr create_index)rrrrrr)s r\create_table_indexzHDFStore.create_table_indexsG>  OOC  9 !U#OP P wEr^ct|j|jJtJ|jj Dcgc]}t |tj jsYt|jdds@t|dds3t |tjjr|jdk7r|c}Scc}w)a Return a list of all the top-level nodes. Each node returned is not a pandas storage object. Returns ------- list List of objects. See Also -------- HDFStore.get_node : Returns the node with the key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> print(store.groups()) # doctest: +SKIP >>> store.close() # doctest: +SKIP [/data (Group) '' children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array), 'block0_items' (Array)]] N pandas_typeru) rrrr} walk_groupsr`linkLinkgetattr_v_attrsrurrrs r\rzHDFStore.groupss4   ||'''%%%\\--/  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)a Walk the pytables group hierarchy for pandas objects. This generator will yield the group path, subgroups and pandas object names for each group. Any non-pandas PyTables objects that are not a group will be ignored. The `where` group itself is listed first (preorder), then each of its child groups (following an alphanumerical order) is also traversed, following the same procedure. Parameters ---------- where : str, default "/" Group where to start walking. Yields ------ path : str Full path to a group (without trailing '/'). groups : list Names (strings) of the groups contained in `path`. leaves : list Names (strings) of the pandas objects contained in `path`. See Also -------- HDFStore.info : Prints detailed information on the store. Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df1, format="table") # doctest: +SKIP >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["A", "B"]) >>> store.append("data", df2) # doctest: +SKIP >>> store.close() # doctest: +SKIP >>> for group in store.walk(): # doctest: +SKIP ... print(group) # doctest: +SKIP >>> store.close() # doctest: +SKIP Nrr)rrrr}rrr _v_childrenrxr`rGrouprrrrstrip)rrmrrleaveschildrs r\walkz HDFStore.walk>sV   ||'''%%%))%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 exceptionsNoSuchNodeErrorr`rMr)rrrs r\rzHDFStore.get_node~s ~~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?) @,!Is (D:DcFt|j}t|d|d}|jrt |j }|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) a Print detailed information on the store. Returns ------- str A String containing the python pandas class name, filepath to the HDF5 file and all the object keys along with their respective dataframe shapes. See Also -------- HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["C", "D"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data1", df1) # doctest: +SKIP >>> store.put("data2", df2) # doctest: +SKIP >>> print(store.info()) # doctest: +SKIP >>> store.close() # doctest: +SKIP File path: store.h5 /data1 frame (shape->[2,2]) /data2 frame (shape->[2,2]) rrNzinvalid_HDFStore nodez[invalid_HDFStore node: r> EmptyzFile is CLOSED) rDrrrrur r4rrKr[r\rC) rroutputlkeysr rxrOr)detaildstrs r\infoz HDFStore.infos58DJJ'J<}TF"5 <<499;'E JA J OOA.= KK QZZ_1(EF"MM,q7SN) _FORMAT_MAPlowerrr)rrras r\rVzHDFStore._validate_formatsK V 0F  VA&KLRU U Vs 949c |!t|ttfs tdt |j dd}t |j dd}|t|St tJt |dds$t|tjjrd}d}n*tdt|trd }nd }|dk(r|d z }d|vrttd } ||} | ||||S|q|o|dk(r3t |dd} | [| jdk(rd}nI| jdkDr:d}n7|dk(r2t |dd} | #| jdk(rd}n| jdkDrd}tt t"t$t&t(d} | |} | ||||S#t$r#} td |dt|d|| d} ~ wwxYw#t$r#} td|dt|d|| d} ~ wwxYw)z"return a suitable class to operateNz(value must be None, Series, or DataFramer table_typeru frame_table generic_tablezKcannot create a storer if the object is not existing nor a value are passedseriesframe_table)rrz=cannot properly create the storer for: [_STORER_MAP] [group->,value->z ,format->r[r series_tablerrgappendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)rrrrrwormz)r`r3r-rrrrr}rur SeriesFixed FrameFixedrrnlevels GenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable WORMTable) rrrrr[rpttt _STORER_MAPclsrar _TABLE_MAPs r\r-zHDFStore._create_storersO  Z 7J%KFG G U^^]D 9 U^^\4 8 :} !---5'40J:++115'B(B#1 eV,!B BW$(NB " %0:FK !"o tUXfE E : '#E7D9E( ==A-!4B"]]Q.!9B=(#E7D9E( ==A-!3B"]]Q.!8B*!6&@ 4%>   R.C4&AAU SgXd5k])F8E H N'$u+ixA  s0FG F=F88F= G, G''G,ct|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) NemptyrurrtzCan only append to Tablesz0Compression not supported on Fixed format stores) objrcrrrrrrrdrrrrUr) r_identify_groupr-rJ is_existsrset_object_infowriter`rr)rrrrrcrrrrrrrrdrrrr[rrUrr)s r\rWzHDFStore._write_to_groupzs. 5'4 (f.?6 $$S&1   vuxPV  W ::!**71Bq{{ !<==;;!!#   zzgOP P !%%%#  a E NN5N )%* r^cd|j|}|j|jSrX)r-r.r%)rrr)s r\r!zHDFStore._read_groups&    & vvxr^c|j|}|jJ|!|s|jj|dd}||j|}|S)z@Identify HDF5 group based on key, delete/create group if needed.NTrY)rr remove_node_create_nodes_and_group)rrrrs r\rzHDFStore._identify_groupsb c"||'''  V LL $ $Ud $ ;E =005E r^c |jJ|jd}d}|D]\}t|s|}|jds|dz }||z }|j |}||jj ||}|}^S)z,Create nodes from key and return group name.r)rsplitrlendswithr create_group)rrpathsrpnew_pathrs r\rz HDFStore._create_nodes_and_groups||''' # Aq6H==%C MHMM(+E} 11$:D  r^)rNNF)rrar int | NonerrreturnNonerrarra)rrarr)rrarr)rcra)rrarrrint)rr )rztype[BaseException] | NonerzBaseException | NonerzTracebackType | Nonerr)r)rrar list[str])rz Iterator[str])rzIterator[tuple[str, list]])r)rrarrrrrrF)rrrr)NNNNFNF)rrarrrrrrNNNrrarrrrNN)rrar9rarrrr)NNNNNFNF)rrrrrr) NTFNNNNNNrTF)rrarDataFrame | Seriesrrrrrrrint | dict[str, int] | Noner Literal[True] | list[str] | NonerrarUrrrrr)NNTTNNNNNNNNNNr)rrarrrbool | list[str]rrrrrrrrr bool | Nonerrrrarr)NNF)r|rmrrrr)rrarrr str | Nonerrrrj)r)rmrarz*Iterator[tuple[str, list[str], list[str]]])rrarz Node | None)rrarGenericFixed | Table)rTNNNFT) rrarrrrrrrrrr)rrarra)NNrVr)rzDataFrame | Series | Noner[rarrarr)NTFNNNNNNFNNNrT)rrarrrrrrrrrrrrrrrrarUrrr)rrM)rrarrrrM)rrarrM)1r __module__ __qualname____doc____annotations__rrpropertyrrrrrrrrrrrr r rrrrrrrr7r;rRrrrrrrrrr4rrrrVr-rWr!rrrr^r\rrs>?B J  $ '' '  ' 'B!!    "4,((   . `!#+JZ))4,!+L  $ a aaaaL  I I I  IH! &F &F&F &F  &FV  $ w/w/w/w/z $489= f f "f  f  f f 2f 7f f f f !f P5=v "& $48 $"9=%~ ~ "~  ~ ~ ~ 2~ ~ ~ 7!~ $%~ & '~ J _R _R_R _RH# &F &F &F  &F  &FP) V>>@   $ 99 9999 9v9|E+/ YB) YB  YB  YB YB@"& $48 $ '<* <*"<*  <*<*<*2<*<*<*$%<*&'<*( )<*| $r^rcteZdZUdZded<ded<ded< d dd Zdd Zdd Zddd Zy)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. rrrrrr)Nc ,||_||_||_||_|jjr|d}|d}||}t ||}||_||_||_d|_ |s| | d} t| |_ | |_ yd|_ | |_ y)Nr順) rr)r*rmrJminr,rrrFrrr) rrr)r*rmr,rrrrrs r\rzTableIterator.__init__s    66??}}|ud#D    y, "  ^DN%"DN$r^c#^K|j}|j td||jkrgt ||j z|j}|j dd|j||}|}| t|sb|||jkrg|jyw)Nz*Cannot iterate until get_result is called.) rrFrrrrr*rlr)rrrrs r\r zTableIterator.__iter__'s**    #IJ J !w/;DIIdD$*:*:74*HIEG}CJK ! s BB-B-cR|jr|jjyyrX)rrrrs r\rzTableIterator.close7s ?? JJ    r^c|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)rmz$can only read_coordinates on a tabler3) rr`r)rrr5rmrFrrr*r)rrFrmresultss r\r0zTableIterator.get_result;s >> %dffe, RSS#vv66TZZ6HD K dffe, FGGFF++jj ,EJJE))DJJ 59 r^)NNFNF) rrr)rrrrrrrrrrrGrr)rFr) rrrrrrr rr0rr^r\r/r/sz& O  $ (%(% (%(%(%(% (%T r^r/creZdZUdZdZded<dZded<gdZ d! d"dZe d#d Z e d$d Z d%d Z d$d Z d&d Zd'dZe d'dZ d(dZdZe dZe dZe dZe dZd)dZd*d+dZd+dZd,dZd*dZd-dZd+dZd+dZd+dZd.dZ d.d Z!y)/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 Tris_an_indexableis_data_indexable)freqtz index_nameNct|ts td||_||_||_||_|xs||_||_||_ ||_ | |_ | |_ | |_ | |_| |_||_||j#|t|j tsJt|jtsJy)Nz`name` must be a str.)r`rarrxrtyprccnamerAposr r r rrurmetadataset_pos)rrcrxrrrrArr r r rrurrs r\rzIndexCol.__init__gs"$$45 5   ]d   $     ? LL $))S)))$**c***r^c.|jjSrX)ritemsizers r\rzIndexCol.itemsizesxx   r^c |jdS)N_kindrbrs r\ kind_attrzIndexCol.kind_attr))E""r^cT||_||j||j_yyy)z,set the position of this column in the TableN)rr_v_pos)rrs r\rzIndexCol.set_poss) ?txx3!DHHO 4?r^c ttt|j|j|j |j |jf}djtgd|dDcgc] \}}|d|c}}Scc}}w)N,)rcrrArrTr=->) rkmaprDrcrrArrjoinrIrtemprrs r\rzIndexCol.__repr__s  tyy$**dii499U V xx#&.0  D!T "geQ&= = %()rcrrArrgrr)s``r\__eq__zIndexCol.__eq__s 5   r^c&|j| SrX)r.r-s r\__ne__zIndexCol.__ne__s;;u%%%r^ct|jdsyt|jj|jj S)z%return whether I am an indexed columnrjF)hasattrrurrjrrrs r\rzIndexCol.is_indexeds4tzz6*tzz 3>>>r^c t|tjsJt||jj ||j j}|j}t||||}i}|j|d<|j|j|d<t}tj|jdst|jtrt }n|jdk(rd|vrd} ||fi|}|j2>t|t r.|j5d j7|j2} | | fS|} | | fS#t"$r_} |dk(rOt%rEt'| j)dr+t*r%||fd t-d tj. i|}nYd} ~ d} ~ wt0$rd|vrd|d<||fi|}YwxYw) zV Convert the data from this selection to the appropriate pandas type. Nrcr Mi8cttj||jddj|dS)Nr )r rc)r1 from_ordinalsr_rename)rPkwdss r\rz"IndexCol.convert..s2 (A(A.)gd6l#r^ surrogatepasssurrogates not alloweddtypepythonstoragena_valueUTC)r`npndarrayrr<fieldsrrr_maybe_convertr r r/r is_np_dtyper)r.UnicodeEncodeErrorrrarrr4nanrr  tz_localize tz_convert) rrxrr[rval_kindrfactory new_pd_indexrafinal_pd_indexs r\convertzIndexCol.converts &"**-;tF|;- <<   *DJJ',,.F99(FCv 99 !YYF6N/4 ??6<< - LL/2 $G \\T !f&6 $G  5"64V4L. 77 :lM#J)55e<GGPN~--*N~--5" /)&(H%%&>?& %hH     5!%v"64V4L  5s E G$AF>>GGc|jS)zreturn the valuesrxrs r\ take_datazIndexCol.take_data s {{r^c.|jjSrX)rurrs r\attrszIndexCol.attrs zz"""r^c.|jjSrXru descriptionrs r\rXzIndexCol.description zz%%%r^cDt|j|jdS)z!return my current col descriptionN)rrXrrs r\colz IndexCol.col st''T::r^c|jSzreturn my cython valuesrQrs r\cvalueszIndexCol.cvalues s{{r^c,t|jSrX)r rxrs r\r zIndexCol.__iter__ sDKK  r^c|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) rr`rmrrcrrr StringColr)rrs r\maybe_set_sizezIndexCol.maybe_set_size sp 99 ,-+// : 'DHH,=,= ,L"9.. $((.S-M' !r^cyrXrrs r\validate_nameszIndexCol.validate_names)  r^c|j|_|j|j||j||j ||j yrX)ru validate_col validate_attrvalidate_metadatawrite_metadataset_attr)rhandlerrs r\validate_and_setzIndexCol.validate_and_set, sL]]   6" w' G$ r^c |jdk(r`|j}|R| |j}|j|kr)td|d|jd|jd|jSy)z:validate this column: return the compared against itemsizeraNz#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)rr[rrr)rrcs r\rhzIndexCol.validate_col4 s 99 A}##}}H::($=hZH JJ<(JJ<(<< zz!r^c|rPt|j|jd}|,||jk7rt d|d|jdyyy)Nzincompatible kind in col [ - r>)rrTrrr)rr existing_kinds r\rizIndexCol.validate_attrG s] #DJJEM(]dii-G0s499+QO.H( r^c |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  stacklevelzinvalid info for [z] for [z], existing_value [z] conflicts with new value [r>) _info_fieldsr setdefaultrcrrrwarningswarnrr!setattrr)rrrridxexisting_valuewss r\ update_infozIndexCol.update_infoP s $$ !CD#t,E//$))R0C WWS\Ncze/Ne4K00/32NNBMM4AQAS $CHD#t,%,TYYKwseD++9*:;&&+WA/ "n&@ C1 !r^cv|j|j}||jj|yy)z!set my state from the passed infoN)rrc__dict__update)rrr|s r\set_infozIndexCol.set_infoo s0hhtyy! ? MM  % r^cZt|j|j|jy)zset the kind for this columnN)r{rTrrrs r\rlzIndexCol.set_attru s DNNDII6r^c|jdk(rH|j}|j|j}||t ||dds t dyyyy)z:validate that kind=category does not change the categoriescategoryNT strict_nan dtype_equalzEcannot append a categorical with different categories to the existing)rr read_metadatarr,r)rrm new_metadata cur_metadatas r\rjzIndexCol.validate_metadatay sp 99 "==L"00& 7">r^rcBeZdZdZeddZ ddZddZy) GenericIndexColz:an index which is not represented in the data of the tablecyNFrrs r\rzGenericIndexCol.is_indexed r^ct|tjsJt|t 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 )r`rBrCrr2rl)rrxrr[rrs r\rOzGenericIndexCol.convert s9&"**-;tF|;-3v;'e|r^cyrXrrs r\rlzGenericIndexCol.set_attr rfr^Nr)rxrr[rarrarztuple[Index, Index]r)rrrrrrrOrlrr^r\rr sAD  58BE $ r^rc<eZdZdZdZdZddgZ 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) rcrxrrrrr rrurr)superrr<r)rrcrxrrrrr rrurrr<r __class__s r\rzDataCol.__init__ sH     r^c |jdS)N_dtyperbrs r\ dtype_attrzDataCol.dtype_attr s))F##r^c |jdS)N_metarbrs r\ meta_attrzDataCol.meta_attr rr^c ttt|j|j|j |j |jf}djtgd|dDcgc] \}}|d|c}}Scc}}w)Nr)rcrr<rshapeTr=r) rkrrDrcrr<rrr rIr!s r\rzDataCol.__repr__ s tyy$**djj$))TZZX   xx#&?d# C%r%!    r#c0tfddDS)r%c3TK|]}t|dt|dk(!ywrXr'r(s r\rkz!DataCol.__eq__.. r*r+)rcrr<rr,r-s``r\r.zDataCol.__eq__ s 6   r^c|J|jJt|\}}||_||_t||_yrX)r<_get_data_and_dtype_namer_dtype_to_kindr)rr dtype_names r\set_datazDataCol.set_data sGzz!!!3D9j  ":. r^c|jS)zreturn the datarrs r\rRzDataCol.take_data s yyr^c|j}|j}|j}|jdk(rd|jf}t |t r5|j}|j||jj}|Stj|dst |tr|j|}|Stj|dr|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. rgrr4mrrr)r<rrrqsizer`r8codes get_atom_datarcrrFr)get_atom_datetime64get_atom_timedelta64r$r ComplexColr&get_atom_string)rrxr<rrratoms r\ _get_atomzDataCol._get_atom s<  >>  ;;!  $E fk *LLE$$U1A1A$BD __UC (Juo,N**51D __UC (++E2D e $9''q'JD  U #&&uh7D $$U$tj||dS)Nrrrrbrrrs r\rzDataCol.get_atom_string* sy""HE!H"EEr^c|jdr |dd}d|d}n)|jdrd}n|j}|d}tt|S)z0return the PyTables column class for this columnuintNUIntrKperiodInt64Col)r capitalizerr)rrk4col_namekcaps r\get_atom_coltypezDataCol.get_atom_coltype. s_ ??6 "abBbT~H __X &!H??$Ds|Hwy(++r^c:|j||dS)Nrrrrrrrs r\rzDataCol.get_atom_data= s!.s###.U1X>>r^c<tj|dSNrrrrrrs r\rzDataCol.get_atom_datetime64A y!!a!11r^c<tj|dSrrrs r\rzDataCol.get_atom_timedelta64E rr^c0t|jddS)Nr)rrrs r\rz DataCol.shapeI styy'400r^c|jSr]rrs r\r^zDataCol.cvaluesM syyr^c|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!)rrTrrjrxrrr<)rrexisting_fieldsexisting_dtypes r\rizDataCol.validate_attrR s %djj$..$GO*$t{{BS/S !WXX$TZZ$GN)n .J V/K) r^c t|tjsJt||jj ||j }|jJ|jt|\}}t|}n|}|j}|j}t|tjsJ|j}|j} |j} |j} |J|} | jdr| dk(rd} t!|| | }nJ| jdr7| dk(rtj"|d}ntj"|| }n| dk(r> tj"|D cgc]} t%j&| c} t(}n|dk(r| }|j/}|t1gtj2}n\t5|}|j7rA||}||dk7xx|j9t:j=j>zcc<tAjB||| d }n |j9| d }|d k(rtG||||}|jH|fScc} w#t*$rEtj"|D cgc]} t%j,| ncc} wc} t(}YtwxYw#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 datetime64datetime64[ns] timedelta64m8[ns]r<rrF) categoriesrvalidaterOrarr[r)%r`rBrCrr<rDrrrrrrrrr r_set_tzasarrayr fromordinalrr fromtimestampravelr/float64r7anyastypercumsum_valuesr8 from_codesr_unconvert_string_arrayrx)rrxrr[r convertedrrrrrr r<rrrmasks r\rOzDataCol.convert_ s &"**-;tF|;- <<   *DJJ'Fxx### :: %=V$D !Iz!*-DIJ99D)RZZ000yy==,, WW%%%   L ) $( 2u5I   m , %JJyA JJy> f_ JJ2;%,,U,? 8 /7XfI{{I%%W= JJ4=>qT''*>>f  @ >%,,Su,=  >sB%J8J JKJK$K KKK87K8c"t|j|j|jt|j|j|j |j Jt|j|j|j y)zset the data for this columnN)r{rTrrxrrr<rrs r\rlzDataCol.set_attr sZ DNNDKK8 DNNDII6zz%%% DOOTZZ8r^) NNNNNNNNNNNN)rcrarrr<zDtypeArg | Nonerrrr)rrOrr)rxrOrrK)rrarz type[Col]rrarrKr)rxrr[rarra)rrrrr r rwrrrrrr.rrR classmethodrrrrrrrr^rirOrl __classcell__rs@r\rr s` O)$L      !%    @$$##   />FF , ,??222211 h&T9r^rc^eZdZdZdZd dZedZed dZedZ edZ y) DataIndexableColz+represent a data column that can be indexedTcjtt|jjs t dy)N-cannot have non-object label DataIndexableCol)r&r/rxr<rrs r\rezDataIndexableCol.validate_names s*uT[[1778LM M9r^c6tj|S)N)rrrs r\rz DataIndexableCol.get_atom_string sy""H"55r^c0|j|S)Nrrrs r\rzDataIndexableCol.get_atom_data s.s###.00r^c2tjSrXrrs r\rz$DataIndexableCol.get_atom_datetime64 y!!##r^c2tjSrXrrs r\rz%DataIndexableCol.get_atom_timedelta64 rr^Nrr) rrrrr rerrrrrrr^r\rr sa5N 6611$$$$r^rceZdZdZy)GenericDataIndexableColz(represent a generic pytables data columnN)rrrrrr^r\rr s2r^rceZdZUdZded<dZded<ded<ded <d ed <d Zd ed< d) d*dZed+dZ ed,dZ edZ d-dZ d.dZ d/dZedZedZedZedZed0dZed+dZedZd.dZd.dZedZed+d Zed!Zd1d"Zd2d.d$Zd+d%Z d3 d4d&Zd.d'Z d5 d6d(Z y#)7Fixedz 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. ra pandas_kindrt format_typetype[DataFrame | Series]obj_typerrqrrFrrJct|tsJt|tJt|tjsJt|||_||_t||_||_ yrX) r`rrr}rMrrr]r[r)rrrr[rs r\rzFixed.__init__ sg&(+9T&\9+%%%%1>4;>1  (2  r^cv|jddkxr&|jddkxr|jddkS)Nrrg )versionrs r\is_old_versionzFixed.is_old_version s:||A!#U Q2(=U$,,q/TUBUUr^ct|jjdd}t|trHt d|j dD}t|dk(rg|d}t|dk(sJ|Sy) zcompute and set our versionpandas_versionNc32K|]}t|ywrX)r)rirPs r\rkz Fixed.version.. sC1ACs.r r)rrr)rrrr`rarkrrl)rr  version_tups r\r z Fixed.version sx$**--/?F gs #C c0BCCK;1$/ /Q/ {#q( (( r^cDt|jjddS)Nr)rrrrs r\rzFixed.pandas_type$ stzz**M4@@r^c|j|j}|Wt|ttfr.dj |Dcgc] }t |c}}d|d}|jdd|dS|jScc}w)(return a pretty representation of myselfr[r>12.12z (shape->))r.rr`rjrkr rDr)rr)rPjshapes r\rzFixed.__repr__( s  JJ =!dE]+A">q<?">?xqM&&u-Yqc; ;#?sBct|j|j_tt|j_y)zset my pandas type & versionN)rarrTr_versionrrs r\rzFixed.set_object_info3 s)!$T%5%5!6 $'M !r^c0tj|}|SrXr)rnew_selfs r\rz Fixed.copy8 s99T?r^c|jSrX)r,rs r\rz Fixed.shape< rr^c.|jjSrXrrrs r\rKzFixed.pathname@ rYr^c.|jjSrX)rrrs r\rz Fixed._handleD s{{"""r^c.|jjSrX)rrrs r\rzFixed._filtersH s{{###r^c.|jjSrX)rrrs r\rzFixed._complevelL s{{%%%r^c.|jjSrX)rrrs r\rzFixed._fletcher32P s{{&&&r^c.|jjSrX)rrrs r\rTz Fixed.attrsT rUr^cyzset our object attributesNrrs r\ set_attrszFixed.set_attrsX r^cy)zget our object attributesNrrs r\ get_attrszFixed.get_attrs[ r+r^c|jS)zreturn my storablerrs r\storablezFixed.storable^ szzr^cyrrrs r\rzFixed.is_existsc rr^c0t|jddS)Nr,)rr0rs r\r,z Fixed.nrowsg st}}gt44r^c |yy)z%validate against an existing storableNTrr-s r\rzFixed.validatek s =r^Ncy)+are we trying to operate on an old version?Nrrrms r\validate_versionzFixed.validate_versionq r+r^cB|j}|y|jy)zr infer the axes of my storer return a boolean indicating if we have a valid storer or not FT)r0r-)rr)s r\r.zFixed.infer_axest s" MM 9 r^ctd)Nz>cannot read on an abstract storer: subclasses should implementrrrmrrrs r\r%z Fixed.read s" L  r^c td)Nz?cannot write on an abstract storer: subclasses should implementr:rrrs r\rz Fixed.write s! 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T]] "t}}112D 11%8 % % $t{{   E $dT]]4;; .  '& KK?2*,C))*BC##( $t{{*(RVVL  ! E s*E G BGG c@tjd|jz}|jj |j ||t |j |}t|j|j_ |j|j_ y)zwrite a 0-len array)rgN) rBrrqr create_arrayrrrar<rr_r)rrrarrrs r\write_array_emptyzGenericFixed.write_array_empty sjhhtejj() !!$**c37tzz3'#&u{{#3  #kk r^c t|d}||jvr&|jj|j||jdk(}d}t |j tr td|st|dr|j}d}t |tr|jj|j|tj|j}|j!|j#t%|j|}t'|j |j(_nd} |jEt-t.5tj0j3|j } ddd| [|sE|jj5|j|| |j6|j} || ddn|j9||n|j j:t<j>k(rtAjB|d} |rnA| d k(rn;tEd r0tF| ||fz} tIjJ| tLtO |jj|j|tj}|j!|n tAjP|j d rp|jjS|j||jUd t'|j t%|j|j(_nzt |j tVr|jjS|j||jXt%|j|}t[|j\|j(_.d|j j^d|j(_ntAjP|j dro|jjS|j||jUd t'|j t%|j|j(_n<|r|j9||n'|jjS|j|||t%|j|j(_0y#1swYixYw)NT) extract_numpyrFz^Cannot store a category dtype in an HDF5 dataset that uses format="fixed". Use format="table".rb)rskipnaraperformance_warningsrur4r5 datetime64[r>r)1r@rrrrr`r<r(rr2rbr<create_vlarrayr ObjectAtomrrto_numpyrrarr_rrAtom from_dtype create_carrayrrrrBobject_r infer_dtyperrsryrzrr!rFrviewr)asi8rrr unitr^) rrrrr empty_arrayr^vlarrrrca inferred_typer~s r\rqzGenericFixed.write_array sc6 $**  LL $ $TZZ 5jjAo  ekk#3 4%/ uc"! e_ -LL// C!5!5!70E LL) *4::s+D'*5;;'7DMM $D}}(j)B"9>>44U[[ADB #33 Cu{{DMM4B"BqE**36!!RZZ/!$e D "h. 67(M3+FFBMM"&8EUEWX 33JJWY%9%9%; U#c2 ))$**c5::d;KL?B5;;?O C(11<EKK9  ))JJJJ tzz3/#*588#4  -89I9I8J!+L (c2 ))$**c5::d;KL?B5;;?O C(11<&&sE2 ))$**c5A7A C ))4{BBs %.S::Trrrrr)rrarrrrrr/)rrarr/rr)rrarr0rr)rrarrrrrr0)rrMrrrrrr/)rrarrOrrrX)rrarrNrz Index | Nonerr)rrrrr.r1rGrrJrDrrHrLrUrWrrr*r-rrdrmrsrorjrkrrq)rirOrs000r\rBrB sV$$j+xHO+:+@+@+BCC41a!Q$CJ 19 )V ( ;*ZFJ F F) F8B F  F5.58FJ  ) 8B  0HL44!+4:D4 4l*BFaBaB)aB2>aB aByDs B$rBcdeZdZUdZdgZded<eddZ d d dZd fd Z xZ S) rrrcrFcn t|jjfS#ttf$rYywxYwrX)rlrrxrrrs r\rzSeriesFixed.shape' s6  ))*, ,>*  s "44c |j|||jd||}|jd||} t|||jd}|S#t $ry}|j dk(r^trTt|jdr:tr4t|||jdtdtj  }nYd}~|Sd}~wwxYw) NrrhrxF)rrcrr:r;r=r>)rrcrr<)rWrmrdr3rcrGrrrarrr4rBrH) rrmrrrrrxrRras r\r%zSeriesFixed.read. s 7E*u4@TB F%diieLF" !"  .&(H%%&>?%hH  ! sA C A.CCc t||fi||jd|j|j d||j |j _y)Nrrx)rrrsrrqrcrT)rrrrs r\rzSeriesFixed.writeL sG  c$V$ #)), 3'(( r^)rztuple[int] | Noner?rrrrrr3r) rrrrrDrrrr%rrrs@r\rr! saKJ N        <##r^rcbeZdZUddgZded<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$rYywxYw)Nrblock_itemsrrg) rqrprrr block0_valuesrjrr)rrqrr|rrs r\rzBlockManagerFixed.shapeX s 99DE4<<( &tzzU1#V+<=gt4$U1X%E  &::++DD'40E U1q23 LL L  sAB(AB(( B43B4cv|j|||jjd}g}t|jD]8}||k(r||fnd\}} |j d||| } |j | :|d} g} t|jD]}|j d|d} |jd|d }| | j| }t|j||dd }trPt|tjr6t!|d r)|j#t%tj& }| j |t)| dkDr-t+| dj-}|j/| St|d|dS)NrrrArhrrrrgFrrrTr)r@rlrrr)rWr_get_block_manager_axisrprqrmrrrdrvr-rbrr`rBrCrrr4rHrlr6rr{)rrmrrr select_axisrcr|r&r'axrdfs blk_itemsrxdfouts r\r%zBlockManagerFixed.reads s 7E*mmo==a@ tyy! A-.+-=UDMdZd?d Zd@d!ZdAd"ZdBd#Z d7d$Z!d7d%Z"dCd7d&Z#d7d'Z$e%d(Z& dD dEd)Z' dF dGd*Z(e)dHd+Z*dId,Z+ dJ dKd-Z,e- dLd.Z.dCdMd/Z/ dNd0Z0 dD dOd1Z1 dD dPd2Z2xZ3S)Qraa 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_tablerurarrrgzint | list[Hashable]rzTrjrc t ||||||xsg|_|xsg|_|xsg|_|xsg|_| xsi|_| |_y)Nr)rr index_axesrL values_axesrrr) rrrr[rrrLrrrrrs r\rzTable.__init__ s` &I$*,2&,"(.BJB  r^c>|jjddS)N_r)rrrs r\table_type_shortzTable.table_type_short s$$S)!,,r^c |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) rrrFz,dc->[r>rrrz (typ->z,nrows->z,ncols->z ,indexers->[r) r.rlrr rr rarrcrrr,ncols)rjdcrverrPjverr jindex_axess r\rzTable.__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)rcrc)rrprs r\rzTable.__getitem__s* AAFF{ r^c  |y|j|jk7r&td|jd|jddD]}t||d}t||d}||k7s#t|D]x\}}||}||k7s|dk(rO|j|jk7r6t d|j dd |jd |jd t d |d |d|dtd |d |d|dy)z"validate against an existing tableNz'incompatible table_type with existing [rrr>)rrLrrCannot serialize the column [rz%] because its data contents are not [z] but [] object dtypezinvalid combination of [z] on appending data [z] vs current table [)rrrryrrrxr\)rr)rpsvovr|saxoaxs r\rzTable.validatesb =    t .$$%S(9<  A Aq$'B4(BRx(m FAsQ%Ccz -#((chh2F","? 1 OFFIhhZP((+z!A# )6qc: #u$8Q@   .qc1FrdK&&(T,/ r^c6t|jtS)z@the levels attribute is 1 or a list in the case of a multi-index)r`rzrjrs r\is_multi_indexzTable.is_multi_index/s$++t,,r^ctj|jj} |j }t |tsJ||fS#t $r}t d|d}~wwxYw)ze validate that we can store the multi-index; reset and return the new object zBduplicate names/columns in the multi-index when storing as a tableN)r^fill_missing_namesrr{ reset_indexrr`r-)rrrz reset_objras r\validate_multiindexzTable.validate_multiindex4so'' 8 )I )Y///&   T  sA A+ A&&A+ctj|jDcgc]}|jjdc}Scc}w)z-based on our axes, compute the expected nrowsr)rBrrr^rrr|s r\nrows_expectedzTable.nrows_expectedEs2wwDOODq *DEEDs Acd|jvS)zhas this table been createdrur/rs r\rzTable.is_existsJs$**$$r^c0t|jddSNrurrrs r\r0zTable.storableOstzz7D11r^c|jS)z,return the table group (this is my storable))r0rs r\ruz Table.tableSs}}r^c.|jjSrX)rur<rs r\r<z Table.dtypeXszzr^c.|jjSrXrWrs r\rXzTable.description\rYr^cVtj|j|jSrX)rGrHrrrs r\rcz Table.axes`st0@0@AAr^c:td|jDS)z.the number of total columns in the values axesc3FK|]}t|jywrX)rlrx)rirs r\rkzTable.ncols..gs;Q3qxx=;s!)sumrrs r\rz Table.ncolsds;$*:*:;;;r^cyrrrs r\ is_transposedzTable.is_transposedirr^c ttj|jDcgc]}t |dc}|j Dcgc]}t |j c}Scc}wcc}w)z@return a tuple of my permutated axes, non_indexable at the frontr)rkrGrHrLrrrArrs r\data_orientationzTable.data_orientationmsY OO$($7$78qQqT8&*oo6QVV6   86s A/ A4clddd}|jDcgc]}|j|f}}|jDcgc] \}}||df}}}|jDcgc]2}|jt |j vs%|j|f4}}t||z|zScc}wcc}}wcc}w)z|jj}|d|dS)z)return the metadata pathname for this keyz/meta/z/metar"r"s r\_get_metadata_pathzTable._get_metadata_paths$ &&se5))r^c|jj|j|t|dd|j|j |j y)z Write out a metadata array to the key as a fixed-format Series. Parameters ---------- key : str values : ndarray Frru)rr[rrN)rrrr3r[rr)rrrxs r\rkzTable.write_metadatasI   # #C ( 6 &]];;LL  r^ctt|jdd|d*|jj|j |Sy)z'return the meta data array for this keyrN)rrrrrrs r\rzTable.read_metadatasA 74::vt4c4 @ L;;%%d&=&=c&BC Cr^cBt|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) rarrTrrrLrrr[rrzrrs r\r*zTable.set_attrss #DOO 4  $ 1 !%!1!1!3 $($7$7 !"&"3"3 !\\ "mm  KK  KK )) r^ct|jddxsg|_t|jddxsg|_t|jddxsi|_t|jdd|_t t|jdd|_t|jdd|_t|jd dxsg|_ |jDcgc]}|js|c}|_ |jDcgc]}|jr|c}|_ ycc}wcc}w) r[rLNrrrr[rrrz)rrTrLrrrr]r[rrz indexablesr rrrs r\r-zTable.get_attrss%djj2BDIOR#DJJEKDJJ5; tzz9d; (Z)NO djj(H= &-djj(D&I&OR &*ooK9J9J1K'+P!a>O>OAPLPs,D7>D7D<*D<c |k|jr^tdj|jDcgc] }t |c}z}t j |ttyyycc}w)r5Nrru) rrqr r raryrzrr!)rrmrPr~s r\r7zTable.validate_versions\  ""(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)r`rmrr)rrqrOs r\validate_min_itemsizezTable.validate_min_itemsizesd   ,-  OO  AH}z 04""  r^c b g}jjjtjjD]k\}\}}t |}j |}|dnd}|d}t |d} t|||| |j||} |j| mtjt| d fd } |jtjjD cgc]\}} | || c} }|Scc} }w)z/create/cache the indexables if they don't existNrr)rcrArrrrurrc ft|tsJt}| vrt}t |}t |j }t |dd}t |dd}t|}j|}t |dd} ||||| |z|j| || } | S)Nrrr) rcrrxrrrrurrr<) r`rarrr_maybe_adjust_namer rrru)r|rpklassradj_namerxr<rmdrrbase_posrdescr table_attrss r\rvzTable.indexables..fsa% %%EBw(4#D)!T\\:H[XJe*)>? *OA|d4&D##D)B!#:TD&I; 48D jj I   y )% **"" #{#! ! J  $**:P:P0QR1AaGRSSsD+ c |jsy|dury||dur0|jDcgc]}|js|j}}t |t t fs|g}i}|||d<|||d<|j}|D]}t|j|d}||jr^|j} | j} | j} || |k7r|jn| |d<|| |k7r|jn| |d<|jr|jj!dr t#d|j$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. Either use fixed format, set index=False, or do not include the columns containing complex values to data_columns when initializing the table.rrgzcolumn 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.r)r.rcr rr`rkrjrurrjrrrr remove_indexrrrrrLr) rrrrrkwrurprr cur_optlevelcur_kinds r\rzTable.create_index0s<  e   ?go(, I1Q5H5HqwwIGI'E4=1iG   %BzN  BvJ $ A At,A}<<GGE#(>>L$zzH'H,<(%-6 + 0H()5:||vv((3'H#ANN(R(d))!,Q//$aS!//0c21123SUA$ Js E<E<c(t||||}|j}g}|jD]a}|j|j|j ||j |j|j}|j|c|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]] r3r) SelectionrrcrrrOrr[rr) rrmrr selectionrxrrress r\ _read_axeszTable._read_axess"d%u4H !!# A JJtyy !)) {{ C NN3  r^c|S)zreturn the data for this objrrrr^s r\ get_objectzTable.get_objects  r^ct|sgS|d\}}|jj|i}|jddk(r|rtd|d||dur t |}n|g}t |t rLt|}t |}|j|jDcgc] }|dk7r||vr|c}|D cgc] } | |vs|  c} Scc}wcc} w)zd take the input data_columns and min_itemize and create a data columns spec rrr0z"cannot use a multi-index on axis [z] with data_columns Trx) rlrrrrjr`rmrorsr ) rrrrLrA axis_labelsrexisting_data_columnsrOrps r\validate_data_columnszTable.validate_data_columnss  >"I*1-kyy}}T2& 88F | + 4TF; ,~/  4  ,L  !L lD )$' $5 ! -L   *..0H}2G)G (}| |_ |jAd|jC| |jE||g}t|}|d k(sJt| d k(sJ| D]}tG||d|d }|jd k(}|jI||| }|jK||jM}|jO|| | |jP|\}}g}tStU||dD]\}\}}tV}d}|r=t|d k(r/|d|vr(tX}|d}|t|tZs t!d| r|r |jP|}nd}|xsd|}!ta|!|jb||||j<|j>|}"te|!|jf}#|ji|"}$tk|"jljn}%d}&tq|"ddts|"jt}&dx}'x}(})t|"jltvr<|"jx})d}'t+jz|"j|j}(n/t|jltrt[|jl}'t|"\}*}+||#|!t||$||%|&|)|'|(|+|* },|,jC| |j7|,|d z }|D,cgc]},|,js|,jn}-},t ||j|j|j<|j>|| ||-| | }.t|dr|j|._E|.j||r| r|.j||.Scc}wcc}w#t\t^f$r"} t!d|d|jPd| d} ~ wwxYwcc},w)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->rr>NrTFr rgz.s6 16s rrr{rr=rzIncompatible appended table [z]with existing table [ values_block_) existing_colrrr[rrr r) rcrrxrrrr rrrr<r) rrr[rrrLrrrrrz)Hr`r-rrrr_get_axis_numberr.rrArjrrrrqrlrrnrcrLr,rBr?rurxr{rr_get_axis_namerpr[rrrrc _reindex_axisr!rrC_get_blocks_and_itemsrryrIrrra IndexErrorr_maybe_convert_for_string_atomrxrr rrr<rcrrrr r(rrrrr4rr rr2rzrr)/rrcrrrrrrr table_existsnew_infonew_non_index_axesr| append_axisindexer exist_axisr axis_name new_indexnew_index_axesjr^rrrvaxesr|rb_itemsrrcr%ranew_namedata_convertedr rrr rrrrrr[dcs new_tables/ ` r\ _create_axeszTable._create_axess9@#y)JJ&&EA%Is)A'  <3D266A$$Q'6 ?? 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X<X77X<c d}|j}t|j}||}t|r|d\} } t | j t |} |j | | j}t|j}||}|D]R} |j | g| j}|j|j|j||T|rt||dD cic]!\} }t|j| |f#}} }g}g}|D]N}t|j} |j|\} }|j| |j|P|}|}||fScc}} w#ttf$rC}dj!|Dcgc] }t#|ncc}wc}}t%d|d|d}~wwxYw) Nc|jDcgc]'}|jj|j)c}Scc}wrX)rrrwr)mgrrs r\ get_blk_itemsz2Table._get_blocks_and_items..get_blk_itemss+rrlTr=rz+cannot match existing table structure for [z] on appending data)rrjrrlr/rtr{rsrIrktolistrxrMrr*rr rDr)rr,r.rrr@r?rrrAr new_labelsrpbr7by_items new_blocks new_blk_itemsearraitemjitemss r\r)zTable._get_blocks_and_itemss Hjj"3::.!.s!3 |  !31 5 D+{+66u\7JKJ-- -6;;C#**%F%c*I! 5mmQCdm388 cjj)  s!34  5 #&fi"EAwgnn&'!W5H')JM! bii( !)e!4JAw%%a(!((1  F%Iy  +#H- XXe&Ld|D'9&L&LMF$EfXN,,s*&F6FG'$G"3G G""G'c| t|}|PjrDtjtsJjD]}||vs|j d|j D]\}}t |||fd}|j-|jjD]\}} } || | S)zprocess axes filtersrcjD]}j|}j|}|J||k(rP jr$|j t j }|||}j||cS||vstt|j}t|}ttrd|z }|||}j||cStd|d)Nrlrgzcannot find the field [z] for filtering!) _AXIS_ORDERSr& _get_axisrunionr/rzrzrArrxr`r-r) fieldfiltopr2 axis_number axis_valuestakersrxrrs r\process_filterz*Table.process_axes..process_filters !$!1!1AI"%"6"6y"AK"%-- ":K&222 ) ..#'::eDKK.@#AD!#K!6"wwKw8@@+-!-gc5.A.H.H!I+D1&c95*+k/K!#FD!1"wwKw8@@5A8!#:5'AQ!RSSr^) rjrr`rzinsertrLr(filterr) rrrrrrAlabelsrUrOrQrPs `` r\ process_axeszTable.process_axess  7mG  4#6#6dkk40 00[[ )G#NN1a( ) !// TLD&T67;C T TF    '#,#3#3#:#:#< 6r4$UD"5 6 r^cn|t|jd}d|d}|jDcic]}|j|jc}|d<|rC||j xsd}t j|||xs |j}||d<|S|j|j|d<|Scc}w)z:create the description of the table from the axes & valuesi'ru)rcrdrX )rrrr) maxrrcrrrrrrr)rrrrrdr|rrs r\create_descriptionzTable.create_description8s  t22E:Ll ;59II>qAGGQUUN>-   OO0q i''#%9)9)9(G #AiL]] &==AiL?sB2c|j||jsyt||||}|j}|j|jj D]m\}}}|j ||j|jdz} ||| j||jz |j}ot|dS)zf select coordinates (row numbers) from a table; return the coordinates object Fr3rgrhr) r7r.r select_coordsrWrr:rr\ilocrxr/) rrmrrrcoordsrOrQrPrs r\r5zTable.read_coordinatesWs e$ d%u4H ((*    '#,#3#3#:#:#< Sr4''FJJL14D( 499Vfjjl-B#CT J Q QR  S V%((r^cH|j|jsy| td|jD]}||jk(s|j st d|dt|jj|}|j|j|j||||j|j|j}|d}t|jj |dd} t#||d| cSt%d|d ) zj return a single column from the table, generally only indexables are interesting FNz4read_column does not currently accept a where clausezcolumn [z=] can not be extracted individually; it is not data indexablerrgr)rcrr<z] not found in the table)r7r.rrcrcr rrrurjrrrOrr[rrTr3r) rr9rmrrrrp col_valuescvsr<s r\r:zTable.read_columnqs$    RS S IA**$"6(+33 DJJOOV4 499%YYeDM LL!]];; ' !m 0 0VHE2BDIcU%HH' I*&)ABCCr^)NrNNNNNN)rrrrMr[rrrarzlist[IndexCol] | NonerLz list[tuple[AxisInt, Any]] | Nonerzlist[DataCol] | Nonerz list | Nonerz dict | Nonerrr)rprarr)rrrz tuple[DataFrame, list[Hashable]]r)rzitertools.chain[IndexCol])rztuple[int, ...])rdict[str, Any])rzlist[tuple[Any, Any]])rr)rrarra)rrarxrrrrrXr)rrrrr)rrrrrz9list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]r^rr)TNNN)rr-rr)rr-r,r)rrrr-)rrrrrdrrrerrrr)r9rarrrr)4rrrrrrrrzrJrrrrrrrrrrr0rur<rXrcrrrrrrrrkrr*r-r7rrrrrrrr!r< staticmethodr)rYr]r5r:rrs@r\rr sE.KKO#$F $HN $,0;?,0$(     * 9*" *-- $%N--!%! )!"FF%%22  &&BB<<   "< 3*  $ $ Q *IIX?CT1;T TnCG & 5?  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LL $ $TZZ 9!!%% "  A    ..#%) /G OO %0GM " 'EMM & &u{{ >g >!::  .A  uf - . 62r^c *|jj}|j}g}|rs|jD]d}t |j j d}t|tjsC|j|jddf|r%|d}|ddD]}||z} |j}nd}|jDcgc]}|j} }t| } | dk(sJ| |jDcgc]}|j!} }| D cgc]Q} | j#tj$tj&| j(| j(dz S} } g} t+| D]H\}} |g|j|| |zj,}| j| j/|J|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 rrlu1FrrgNrr)indexesrrx)r<r{rrr7rrgr`rBrCrrrrr^rlrR transposerollarangerqryrreshaperrrpwrite_data_chunk)rrrr{r,masksrrrrunindexesrxrbvaluesr| new_shaperowschunksstart_iend_is r\rqzAppendableTable.write_datas   ## %% @AFF|''Q'/dBJJ/LLT!>?  @ 8D12Y ax ::>@    %%'D$tax.../FbzR b!ayA~ a#Bf% $))%2,7!!tzz!}-DB4H14L"   JJ    r^) NFNNNNNNFNNT) rrrrrrrUrrrr)rrrrrr) rrrulist[np.ndarray]rznpt.NDArray[np.bool_] | Nonerxrrrrr@) rrrrrrrqrzr`rr^r\rrs2J  $ 93 93939393 93v9v**"*+ * ! *  *ZHL:!+::D: :r^rcheZdZUdZdZdZdZeZde d<e d dZ e d dZ d dd Zy )rrmrrr rrc:|jdjdk(S)Nrrg)rrArs r\rz"AppendableFrameTable.is_transposedsq!&&!++r^c"|r |j}|S)zthese are written transposed)rbrs r\rzAppendableFrameTable.get_objects %%C r^Nc |j||jsy|j|||}t|jr,|j j |jddini}t|jDcgc]\}}||jdus|} }}t| dk(sJ| d} || d} g} t|jD]B\}} | |jvr||\}}|j ddk7r t|}ntj|}|j d}||j|d|jr|}|}t| t!| d d }n&|j"}t| t!| d d }|}|j$dk(r:t'|t(j*r |j-d|j.df}t'|t(j*t0fr t3|j"||d }n8t'|trt3|||}nt3jD|g||}t9r|jFjHdk(sD|jJ|jFk(jMsJ|jJ|jFf|D]B}t!|jNjP|dd}|dvs,||jS|||<D| jU|Et| dk(r| d}n tW| d}tY||||}|j[|||}|Scc}}w#t4$ry}|j6d k(r^t9rTt;|j=dr:t>r4t3|j"||d tAdt(jB}nYd}~d}~wwxYw)Nr3rrgrr0r{TinplacercrbFrr:r;r=r>)rrrr<rrr)rararl)rr).r7r.rrlrLrrryrcrrr/r0 from_tuples set_namesrrrbrqr`rBrCryrr9r-rGrrrarrr4rH _from_arraysr<rdtypesrgrurTrrr6rrY)rrmrrrrRrr|rindsindrframesr index_valsr^rjr{rxindex_cols_rrar9r<rs r\r%zAppendableFrameTable.reads e$ uEE4&&' IIMM$--a03R 8 )3PearT__Q=O7OPP4yA~~1gs Adii(= DAq((("() Jxx</Z(!--j9HHW%E ud3!! e'%*FG u75&$+GH{{aJvrzz$BFLLO(<=&2::}"=>"688U&uUB"FE*vuFC++VHe6R&(V\\->->#-E V\\1668S299fll:SS8  : 0 0VHE2BDI--!#F!2!25!9BvJ : MM" {= ~ v;! BQ'Bd%u4H   rY  H ]QN* 6.0H--.FG'&"HH$)"(!&"-h"P s%M3,M3M99 O;A.O66O;rrfr?rg)rrrrrrrqr-rrrrrrr%rr^r\rrso2K#J D)2H&2 ,,  d d  dr^rczeZdZdZdZdZdZeZe d dZ e d dZ d d fd Z d dfd ZxZS)rrmrrr cyrrrs r\rz#AppendableSeriesTable.is_transposed$rr^c|SrXrrs r\rz AppendableSeriesTable.get_object(s r^c t|ts!|jxsd}|j|}t |d||j jd|y)+we are going to write this as a frame tablerxrrNr)r`r-rcto_framerrrrA)rrrrrcrs r\rzAppendableSeriesTable.write-sL#y)88'xD,,t$C  K#CKK,>,>,@KFKr^cf|j}|F|rDt|jtsJ|jD]}||vs|j d|t |||||}|r|j|jd|jdddf}|jdk(rd|_ |S)Nrr@Trrx) rr`rzrjrVrr% set_indexr`rc) rrmrrrrrr)rs r\r%zAppendableSeriesTable.read4s,,  >dkk40 00[[ )G#NN1a( ) GLugUL N  KK TK 2 FF1a4L 66X AFr^rrfrXrr?r)rrrrrrrqr3rrrrrrr%rrs@r\rrs|2 K$J DH L       r^rc,eZdZdZdZdZdfd ZxZS)rrmrrc |jxsd}|j|\}|_t|jtsJt |j}|j |t ||_t|$dd|i|y)rrxrNr) rcrrzr`rjrr/rrr)rrrrcnewobjrjrs r\rz AppendableMultiSeriesTable.writeTsuxx#8"66s; $++t,,,DKK  Dt  +&+F+r^r)rrrrrrrrrs@r\rrMs2 K)J,,r^rcneZdZUdZdZdZdZeZde d<e d dZ e dZ dd Z ed Zdd Zy )rz:a table that read/writes the generic pytables table formatrrr zlist[Hashable]rzc|jSrX)rrs r\rzGenericTable.pandas_typehsr^cLt|jddxs |jSrrrs r\r0zGenericTable.storablelstzz7D1?TZZ?r^cNg|_d|_g|_|jDcgc]}|js|c}|_|jDcgc]}|jr|c}|_|j Dcgc]}|jc}|_ycc}wcc}wcc}w)r[N) rLrrzrr rrrcrrs r\r-zGenericTable.get_attrsps~   &*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 descriptionrNrr)rcrArurr)rcrrxrrurr) rXrrrury_v_namesr`rarrr) rr|r rrrr|rrrs r\rzGenericTable.indexableszs       (^z#q  IR{ ajj) #DAqa% %%1a=D##A&B!#:TD(sjjB   r " #"r^c td)Nzcannot write on a generic tabler:)rrs r\rzGenericTable.writes!"CDDr^Nrr)rrrrrrrqr-rrrrr0r-rrrrr^r\rr_sgDK J DH    @@?  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Parameters ---------- values : ndarray[int64] tz : str, tzinfo, or None datetime64_dtype : str, e.g. "datetime64[ns]", "datetime64[25s]" r5rS)r rr)r<rB datetime_datarr;r9_from_sequence)rxr datetime64_dtyperrr<rQs r\rrsg <<4 -- /0GD!  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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. rUrFrr)rr<TrH)rrBrrrrlrrr"r`bytesr3radecoderflags writeabler!string_array_replace_from_nan_repry)rrr[rrrr<sers r\rrs& JJE ::djjl& 1D 4y22=3FGH: d1gu %E*..55x6C<<>D#'DJJ ;;u5;188e8LD00w? << r^ct|tsJt|t|rt |||}||}|SrX)r`rar _need_convert_get_converter)rxrKr[rconvs r\rErEs? h $4d8n4 $Xh&9f Mr^c\dk(rdSdvrfdSdk(rfdStd)Nrc0tj|dS)NzM8[ns]rrBr)rPs r\rz _get_converter..sAX6r^c2tj|S)Nrr)rPrs r\rz _get_converter.. sAT2r^rac"t|dS)Nr)r)rPr[rs r\rz _get_converter.. s0 thv r^z invalid kind )r)rr[rs```r\rrsE |66  22    =/00r^c|dvsd|vryy)N)rrarTFrrs r\rrs ''<4+? r^ct|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 and the name values_0, adjust the given name if necessary. 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