KgdZddlmZddlZddlZddlZddlmZmZm Z m Z m Z ddl m Z ddlmZddlZddlmZdd lmZmZmZdd lmZmZmZdd lmZdd lmZdd l m!Z!m"Z"m#Z#m$Z$e rddl%m&Z&ejNe(e)ejTejVfZ,e e-e.e/e,fZ0ddZ1ddZ2edddZ3ddZ4ddZ5ddZ6Gdde Z7GddeZ8y)zHere is defined the Leaf class.) annotationsN)AnyLiteral NamedTupleUnion TYPE_CHECKING)Path) lru_cache)Node) byteorderslazyattrSizeType) check_flavorinternal_flavortoarray) alias_map)Filters)NoSuchChunkErrorNotChunkAlignedErrorNotChunkedErrorPerformanceWarning)Groupc ttjdz 5}tj|cdddS#1swYyxYw#t $ricYSwxYw)z+Load the CPU information from a cache file..pytables-cpuinfo.jsonN)openr homejsonloadFileNotFoundError)fs +lib/python3.12/site-packages/tables/leaf.pyread_cached_cpu_infor#)sG $))+ 88 9Q99Q<: 9 9  s- A A A A A A AActtjdz d5}tj||ddddy#1swYyxYw)z&Write CPU information to a cache file.rw)indentN)rr rrdump) cpu_info_dictr!s r"write_cached_cpu_infor*2s6 diik44c :a -1- ; : :s AA )maxsizect}|r|S ddl}|j} t ||S#t$ricYSwxYw#t $rY|SwxYw)z/Return a dictionary containing CPU information.rN)r#cpuinfo ImportError get_cpu_infor*OSError) cached_infor-r)s r"r/r/8ss'(K((*M m,        s2 A AA AAcNd}|tdtj|zzS)z,Return the fitted chunksize for expected_mb.i )intmathlog10) expected_mbbasesizes r" csformular9Ms' H c!tzz+667 77c&|dkrd}|S|dkDrd}|S)z:Protection against creating too small or too large chunks.r i)r7s r"limit_esr=Vs+Q   u  r:c~t|}ttj|}d|z}t |}|dzS)aCompute the optimum HDF5 chunksize for I/O purposes. Rational: HDF5 takes the data in bunches of chunksize length to write the on disk. A BTree in memory is used to map structures on disk. The more chunks that are allocated for a dataset the larger the B-tree. Large B-trees take memory and causes file storage overhead as well as more disk I/O and higher contention for the meta data cache. You have to balance between memory and I/O overhead (small B-trees) and time to access to data (big B-trees). The tuning of the chunksize parameter affects the performance and the memory consumed. This is based on my own experiments and, as always, your mileage may vary. )r=r4r5r6r9)r7zone chunksizes r"calc_chunksizerC_s> ;'K tzz+& 'Dd(K+&I q=r:c:eZdZUdZded<ded<ded<ded<y) ChunkInfoaInformation about storage for a given chunk. It may also refer to a chunk which is within the dataset's shape but that does not exist in storage, i.e. a missing chunk. An instance of this named tuple class contains the following information, in field order: .. attribute:: start The coordinates in dataset items where the chunk starts, a tuple of integers with the same rank as the dataset. These coordinates are always aligned with chunk boundaries. Also present for missing chunks. .. attribute:: filter_mask An integer where each active bit signals that the filter in its position in the pipeline was disabled when storing the chunk. For instance, ``0b10`` disables shuffling, ``0b100`` disables szip, and so on. ``None`` for missing chunks. .. attribute:: offset An integer which indicates the offset in bytes of chunk data as it exists in storage. ``None`` for missing chunks. .. attribute:: size An integer which indicates the size in bytes of chunk data as it exists in storage. ``None`` for missing chunks. ztuple[int, ...] | Nonestart int | None filter_maskoffsetsizeN)__name__ __module__ __qualname____doc____annotations__r<r:r"rErEws" D "!  r:rEceZdZdZej Z ejZ e d*dZ e d+dZ e d,dZ e d,dZ ed-dZe d.dZe d,dZe d/d Zej&d0d Zej(d1d Ze d,d Z d2 d3fd Zd,dZd*dZd1fd Z d4dZd,dZ d5 d6dZ d7 d8dZ d7 d9dZ d:dZd;dZ d1dZ!d d?dZ& d> d@dZ'dAdZ(d.d Z)dBd!Z*dCd"Z+dDd#Z,d1d$Z-dEd%Z. dF dGd&Z/ dH dId'Z0d7dJfd( Z1d7dJd)Z2xZ3S)KLeafaAbstract base class for all PyTables leaves. A leaf is a node (see the Node class in :class:`Node`) which hangs from a group (see the Group class in :class:`Group`) but, unlike a group, it can not have any further children below it (i.e. it is an end node). This definition includes all nodes which contain actual data (datasets handled by the Table - see :ref:`TableClassDescr`, Array - see :ref:`ArrayClassDescr`, CArray - see :ref:`CArrayClassDescr`, EArray - see :ref:`EArrayClassDescr`, and VLArray - see :ref:`VLArrayClassDescr` classes) and unsupported nodes (the UnImplemented class - :ref:`UnImplementedClassDescr`) these classes do in fact inherit from Leaf. .. rubric:: Leaf attributes These instance variables are provided in addition to those in Node (see :ref:`NodeClassDescr`): .. attribute:: byteorder The byte ordering of the leaf data *on disk*. It will be either ``little`` or ``big``. .. attribute:: dtype The NumPy dtype that most closely matches this leaf type. .. attribute:: extdim The index of the enlargeable dimension (-1 if none). .. attribute:: nrows The length of the main dimension of the leaf data. .. attribute:: nrowsinbuf The number of rows that fit in internal input buffers. You can change this to fine-tune the speed or memory requirements of your application. .. attribute:: shape The shape of data in the leaf. c|jS)zName of the node. The name of this node in its parent group (This is an easier-to-write alias of :attr:`Node._v_name`). )_v_nameselfs r"namez Leaf.names||r:ct|ddS)zHDF5 chunk size for chunked leaves (a tuple). This is read-only because you cannot change the chunk size of a leaf once it has been created. _v_chunkshapeN)getattrrTs r" chunkshapezLeaf.chunkshapest_d33r:c|jS)zNode identifier, which may change from run to run. (This is an easier-to-write alias of :attr:`Node._v_objectid`). .. versionchanged:: 3.0 The *objectID* property has been renamed into *object_id*. ) _v_objectidrTs r" object_idzLeaf.object_idsr:c,t|jS)zXReturn the number of dimensions of the leaf data. .. versionadded: 2.4 )lenshaperTs r"ndimz Leaf.ndims 4::r:c,tj|S)z]Filter properties for this leaf. See Also -------- Filters )r _from_leafrTs r"filtersz Leaf.filterss!!$''r:c"|jS)aReturn True if the timestamps for the leaf are recorded. If the leaf is not a dataset, this will fail with HDF5ExtError. The track times dataset creation property does not seem to survive closing and reopening as of HDF5 1.8.17. Currently, it may be more accurate to test whether the ctime for the dataset is 0: track_times = (leaf._get_obj_timestamps().ctime == 0) )_get_obj_track_timesrTs r" track_timeszLeaf.track_times s((**r:c:|jdkry|jS)zDimension along which iterators work. Its value is 0 (i.e. the first dimension) when the dataset is not extendable, and self.extdim (where available) for extendable ones. r)extdimrTs r"maindimz Leaf.maindims ;;?{{r:c|jS)a.Type of the data object read from this leaf. It can be any of 'numpy' or 'python'. You can (and are encouraged to) use this property to get, set and delete the FLAVOR HDF5 attribute of the leaf. When the leaf has no such attribute, the default flavor is used. )_flavorrTs r"flavorz Leaf.flavor%s||r:c~|jjt||x|j_|_yN)_v_file_check_writabler_v_attrsFLAVORrl)rUrms r"rmz Leaf.flavor1s- $$&V.44 t|r:c2|j`t|_yro)rrrsrrlrTs r"rmz Leaf.flavor7s MM & r:c"|jS)aASize on disk of the object. The size of this leaf's data in bytes as it is stored on disk. If the data is compressed, this shows the compressed size. In the case of uncompressed, chunked data, this may be slightly larger than the amount of data, due to partially filled chunks. )_get_storage_sizerTs r" size_on_diskzLeaf.size_on_disk<s%%''r:c||_ d|_ d|_ |r7| |j}||jd<|dvrt d|z||_ ||_t|%|||y)Nrd)NlittlebigzGthe byteorder can only take 'little' or 'big' values and you passed: %s) _v_new nrowsinbufrl _v_filters__dict__ ValueError byteorder_want_track_timessuper__init__) rU parentnoderVnewrdr_logrg __class__s r"rz Leaf.__init__Gs ?&* :> 8 $//'.DMM) $ 77 )+45'DN ?!, T40r:c|jS)a.Return the length of the main dimension of the leaf data. Please note that this may raise an OverflowError on 32-bit platforms for datasets having more than 2**31-1 rows. This is a limitation of Python that you can work around by using the nrows or shape attributes. )nrowsrTs r"__len__z Leaf.__len__rszzr:c8g}|jjr|jd|jjr|jjr|jd|jj r|jd|j|jj d|jjd|jd|jj|jdj|d|jS) zReturn the string representation of the object. The string representation for this object is its pathname in the HDF5 object tree plus some additional metainfo. fletcher32shuffle bitshuffle()z (z, z) ) rdrappend complevelrrcomplib _v_pathnamerrKr`join_v_title)rUrds r"__str__z Leaf.__str__|s  << " " NN< ( << ! !||##y)||&&|, NNdll2231T\\5K5K4LAN O 4>>#:#:";zzl499W-.b0A C r:ct||jrY|j t|_y|j j dr'|jjd|jyy|j j dr7t|jdt}tj|||_yt|_y)zCode to be run after node creation and before creation logging. This method gets or sets the flavor of the leaf. NPYTABLES_SYS_ATTRSrs) r_g_post_init_hookr{rlrrpparamsrr _g__setattrrYflavor_alias_mapget)rUrmrs r"rzLeaf._g_post_init_hooks !# ;;||#. <<&&';<MM--h E=||""#78 /J/33FFC . r:c|jdk(r tdfSd}||z|z}t|}|jj}|y|j drh|j dvrZ|dz}t}d|vr@|d} |jdd } t| turt| tur| | kDr| }|d kDrd }|j} ||z} | d k(rd} t|j} d| | <tj| t }|| kr | |z| | <nMtt!| D]1}d| |<tj| t }|| ks)| |z| |<n| | d <t#d| DS)z'Calculate the shape for the HDF5 chunk.r<r iblosc2)TABLECARRAYEARRAY l3_cache_size l2_cache_sizez Not foundirdtypec32K|]}t|ywro)r).0ss r" z(Leaf._calc_chunkshape..s5*QXa[*s)r`rrCrdr startswith _c_classidr/rtyper4rjlistnpprodranger_tuple)rU expectedrowsrowsizeitemsizeMBr7rBrcpu_inforrrj chunknitemsrZnewchunknitemsjs r"_calc_chunkshapezLeaf._calc_chunkshapes :: QK> !#g-"4 ";/ ,,&&  ""8,#@@ OI#~H(* ( 9 !) _k J '3.]+s2% 5 -I:%& ,,8+ ! K$**%  78< [ ("-"?Jw 3z?+ ! 1 !#8!D![0$/>$AJqM ,"- 25*555r:c|jj}|j}|d}|dk7r||z}nd}|dk(r:d}|d|z}||kDr+tjd|j d|dt |S)z<@, -' r:c| |j}n|j|}|r|r|dkr tdt|||j t |S)Nrzslice step cannot be negative)rr`rsliceindicesr4)rUrFstopstepdim warn_negsteprs r"_process_rangezLeaf._process_rangesV ;JJEJJsOE DTAX<= = UD$'//E ;;r:c|j}|3|1|/d|cxkr|krnntd|d|dd}|dk(r|}n|dz}|j||||\}}}|||fS)Nrzstart of range (z") is greater than number of rows (rr r)r)r IndexErrorr)rUrFrrrrs r"_process_range_readzLeaf._process_range_read's   $,5!E! -2E;D{qy // 4L0 tTtT""r:c P|jdd}|jdd}|jdd}|jd|j} |jd|j} |jd|j} |jdd} |jd d} | d k(r |j} n| d k(rd} | |j} | |j} |j|||||| | | |f i|\}}| r(|j j |j d nMd |j vr?|jjdr&|j jd |j|j|_ | | dxxdz cc<| dxx|z cc<|S)NrFrrtitlerdrZ copyuserattrsTstatskeepauto) copyclassrsrleavesr bytes) poprrdrZ_g_copy_with_statsrr_g_copyrprrrl)rU newparentnewname recursiverkwargsrFrrrrdrZrrnew_nodebytes_s r"rz Leaf._g_copy>s 7D)zz&$'zz&$' 7DMM2**Y 5ZZ doo>  ?D9  7D)  J 6 !J =MME ?llG5T44             6  MM ! !("3"3t ! D  &||""#78!!--h E<<   (Oq O 'Nf $Nr:ct|}|j||_|dvrH||jk7r7|jjs|j }|j d|Sd|_|S)z1Fix the byteorder of data passed in constructors.)ryrzT irrelevant)r rflags writeablecopybyteswap)rUdata dbyteorders r"_g_fix_byteorder_datazLeaf._g_fix_byteorder_datawsq +  >> !'DN * *T^^+zz++99;D d#  *DN r:c|}t|ttfvrLt|tr/t |t |j kDrt d| t|}n(t|tjstd|t |dk(rtjgdS|jjdk(r`|j |j k(s t dtj|j!d}tj"|}nz|jjdk(s|jjdk(r? ? s8q=88Bd+ + 99>>S 99 * CZZ T:F\\&)F YY^^s "ciinn&;399~! FSYY1$99Q<3tzz?2$JCt4f-Ct4$[[06fkkDy 1*C!DJJ.4F3Kvvfqj!RVVFdjj,@%A !677?@ @||&&[[]F e D":3' BCC Ds  KK&c2|j tdy)NzThe dataset is not chunked)rZrrTs r"_check_chunkedzLeaf._check_chunkeds ?? "!">? ? #r:ct||jk7rtd|d|jt dt ||jDrt d|d|jy)Nz.Chunk coordinates do not match dataset shape: z !~ c3:K|]\}}|dkxs||k\yw)rNr<)rcrs r"rz+Leaf._check_chunk_within..s%E-D6Aqq1uQ-Dsz,Chunk coordinates not within dataset shape: z <> )r_rarr`rziprrUrs r"_check_chunk_withinzLeaf._check_chunk_withins{ v;$)) #@($tzzl,  ES-DE E>($tzzl,  Fr:ctdt||jDr$tdt |d|jy)Nc3,K|] \}}||zywror<)rrcss r"rz+Leaf._check_chunk_coords..sB%A'1bq2v%Asz.Coordinates are not multiples of chunk shape: z !* )rrrZrrrs r"_check_chunk_coordszLeaf._check_chunk_coordssJ BS%AB B&@=/doo%68  Cr:c&|jdy)zRemove this node from the hierarchy. This method has the behavior described in :meth:`Node._f_remove`. Please note that there is no recursive flag since leaves do not have child nodes. FN) _f_removerTs r"removez Leaf.removes ur:c&|j|y)zqRename this node in place. This method has the behavior described in :meth:`Node._f_rename()`. N) _f_rename)rUrs r"renamez Leaf.renames wr:c,|j||||y)zkMove or rename this node. This method has the behavior described in :meth:`Node._f_move` N)_f_move)rUrr overwrite createparentss r"movez Leaf.moves YMBr:c 0|j|||fd|i|S)a}Copy this node and return the new one. This method has the behavior described in :meth:`Node._f_copy`. Please note that there is no recursive flag since leaves do not have child nodes. .. warning:: Note that unknown parameters passed to this method will be ignored, so may want to double check the spelling of these (i.e. if you write them incorrectly, they will most probably be ignored). Parameters ---------- title The new title for the destination. If omitted or None, the original title is used. filters : Filters Specifying this parameter overrides the original filter properties in the source node. If specified, it must be an instance of the Filters class (see :ref:`FiltersClassDescr`). The default is to copy the filter properties from the source node. copyuserattrs You can prevent the user attributes from being copied by setting this parameter to False. The default is to copy them. start, stop, step : int Specify the range of rows to be copied; the default is to copy all the rows. stats This argument may be used to collect statistics on the copy process. When used, it should be a dictionary with keys 'groups', 'leaves' and 'bytes' having a numeric value. Their values will be incremented to reflect the number of groups, leaves and bytes, respectively, that have been copied during the operation. chunkshape The chunkshape of the new leaf. It supports a couple of special values. A value of keep means that the chunkshape will be the same as original leaf (this is the default). A value of auto means that a new shape will be computed automatically in order to ensure the best performance when accessing the dataset through the main dimension. Any other value should be an integer or a tuple matching the dimensions of the leaf. r)_f_copy)rUrrrrrs r"rz Leaf.copys5jt||    (     r:cZ|jdkr td|j|y)aTruncate the main dimension to be size rows. If the main dimension previously was larger than this size, the extra data is lost. If the main dimension previously was shorter, it is extended, and the extended part is filled with the default values. The truncation operation can only be applied to *enlargeable* datasets, else a TypeError will be raised. rz,non-enlargeable datasets cannot be truncatedN)rir _g_truncate)rUrJs r"truncatez Leaf.truncateZs) ;;?JK K r:c"|jS)z~Return True if this node is visible. This method has the behavior described in :meth:`Node._f_isvisible()`. ) _f_isvisiblerTs r" isvisiblezLeaf.isvisiblejs   ""r:c$|j|S)z~Get a PyTables attribute from this node. This method has the behavior described in :meth:`Node._f_getattr`. ) _f_getattrrUrVs r"get_attrz Leaf.get_attrss t$$r:c(|j||y)zSet a PyTables attribute for this node. This method has the behavior described in :meth:`Node._f_setattr()`. N) _f_setattr)rUrVvalues r"set_attrz Leaf.set_attr{s e$r:c&|j|y)zDelete a PyTables attribute from this node. This method has the behavior described in :meth:`Node_f_delAttr`. N) _f_delattrr#s r"del_attrz Leaf.del_attrs r:c$|jy)a.Flush pending data to disk. Saves whatever remaining buffered data to disk. It also releases I/O buffers, so if you are filling many datasets in the same PyTables session, please call flush() extensively so as to help PyTables to keep memory requirements low. N)_g_flushrTs r"flushz Leaf.flushs  r:c|j|j|tj|t}|j |\}}}|j }||z}||z}tt|j|||S)aGet storage information about the chunk containing the `coords`. The coordinates `coords` are a tuple of integers with the same rank as the dataset. Return a :class:`ChunkInfo` instance with the information. The coordinates need not be aligined with chunk boundaries. This means that this method may be used to get the start coordinates of the chunk that contains the item at the given coordinates, for use with other direct chunking operations (see :attr:`ChunkInfo.start`). If the coordinates are within the dataset's shape but there is no such chunk in storage (missing chunk), a :class:`ChunkInfo` with a valid ``start`` and ``filter_mask = offset = size = None`` is returned. If the coordinates are beyond the shape, :exc:`IndexError` is raised (even if the start of the chunk would fall within the shape). Calling this method on a non-chunked dataset raises a :exc:`NotChunkedError`. r) rr rrr _g_chunk_inforZrErtolist)rUrrHrIrJrZs r" chunk_infozLeaf.chunk_infos.    (&1$($6$6v$>! VT__ :*v}}/fdKKr:ct|j|j||j||"tjt |fd|}tj |t}|j||}|tdt|||jSt|S)aiGet the raw chunk that starts at the given `coords` from storage. The coordinates `coords` are a tuple of integers with the same rank as the dataset. If they are not multiples of its chunkshape, :exc:`NotChunkAlignedError` is raised. If a buffer-like `out` argument is given, it receives chunk data. If it has insufficient storage for the chunk, :exc:`ValueError` is raised (use :meth:`chunk_info()` to get the required capacity). The obtained data is supposed to have gone at storage time through dataset filters, minus those in the chunk's filter mask (use :meth:`chunk_info()` to get it). Return the chunk's raw content, either as a `bytes` instance (if `out` is ``None``) or as a `memoryview` over the object given as `out`. Reading a chunk within the dataset's shape, but not in storage (missing chunk) raises a :exc:`NoSuchChunkError`. If the chunk is beyond the shape, :exc:`IndexError` is raised. Calling this method on a non-chunked dataset raises a :exc:`NotChunkedError`. u1rbufferrz(Can't read missing chunk at coordinates ) rr r rrr_rr _g_read_chunkrrtobytes memoryview)rUroutchunks r" read_chunkzLeaf.read_chunks<    (   ( ?**c#h[SAC&1""63/ =":uV}oN #&+u}}B:c?Br:c|j|j||j|tj|t }tj t|fd|}|j|||y)aWrite `data` to storage for the chunk starting at the given `coords`. The coordinates `coords` are a tuple of integers with the same rank as the dataset. If they are not multiples of its chunkshape, :exc:`NotChunkAlignedError` is raised. The content of the buffer-like `data` must already have gone through dataset filters, minus those in the given `filter_mask` (which is to be saved along data; see :attr:`ChunkInfo.filter_mask`). Writing a chunk which is already in storage replaces it, otherwise it is added to storage as long as it is within the dataset's shape (missing chunk). This means that you may use :meth:`truncate()` to grow an enlargeable dataset cheaply (as no chunk data is written), then sparsely write selected chunks in arbitrary order. If the chunk is beyond the dataset's shape, :exc:`IndexError` is raised. Calling this method on a non-chunked dataset raises a :exc:`NotChunkedError`. rr4r5N) rr r rrrrr__g_write_chunk)rUrrrHs r" write_chunkzLeaf.write_chunksf4    (   (&1zz3t9,d4@ FD+6r:c|jsy|rt|dr|j|jt|y)zClose this node in the tree. This method has the behavior described in :meth:`Node._f_close`. Besides that, the optional argument flush tells whether to flush pending data to disk or not before closing. N_v_iobuf) _v_isopenhasattrr._g_closer_f_close)rUr.rs r"rEz Leaf._f_close s>~~  WT:. JJL  r:c&|j|y)znClose this node in the tree. This method is completely equivalent to :meth:`Leaf._f_close`. N)rE)rUr.s r"closez Leaf.close!s er:)returnstr)rHtuple[int, ...])rHr4)rHr)rHbool)rHLiteral['numpy', 'python'])rmrLrHNone)rHrM)FNNTT)rrrVrIrrKrdzFilters | NonerzLiteral['little', 'big', None]rrKrgrKrHrM)rr4rr4rr4rHrJ)NT) rFr4rr4rr4rrGrrKrHtuple[int, int, int])T) rFr4rr4rr4rrKrHrN) rrrrIrrKrrKrHrQ)r np.ndarrayrrIrHrO)rzlist | tuple | np.ndarrayrHrO)rrJrHrM)rrIrHrM)NNFF) r Group | Noner str | NonerrKrrKrHrM) rrPrrQrrKrrKrHrQ)rJr4rHrM)rVrIrHr)rVrIr'rrHrM)rVrIrHrM)rrJrHrEro)rrJr:zbytearray | NPByteArray | NonerHzbytes | memoryview)r)rrJr BufferLikerHr4rHrM)r.rKrHrM)4rKrLrMrNr rrattrsrrpropertyrVrZr]rarrdrgrjrmsetterdeleterrwrrrrrrrrrrrrr r rrrrrr r$r(r+r.r2r<r?rErG __classcell__)rs@r"rQrQs0f MMEC MMED44    (( + +   ]]55  ^^''(("&48 )1)1)1 )1  )1 2 )1)1)1 )1V (/(I6I6*-I69<I6 I6VL! <<< <  <  < <0FJ## ##+.#>B# #8 777 7  7 7r,/ 4Rh@  #'"# C C C C  C  C #'"# ; ; ;  ;  ;  ; z #%% !LL/3+C+C,+C  +C\MN 7% 7-7 7FI 7  7D0r:rQ)rHdict[str, Any])r)rXrHrM)r7r4rHr4)9rN __future__rrr5rtypingrrrrrpathlibr functoolsr numpyrnoder utilsr rrrmrrrrrrdr exceptionsrrrrgrouprrrr4ruint8 NPByteArrayr bytearrayr9rRr#r*r/r9r=rCrErQr<r:r"res%" AA11::1jjsRXXbhh%778 5)Z< = .  1(80& &RG4Gr: