d$NdZddlZddlmZmZddlmZdZGddeZ dS) z!Here is defined the EArray class.N)convert_to_np_atom2SizeType)CArrayz1.4cHeZdZdZdZ d fd ZdZdZd Zd Z xZ S) EArrayaThis class represents extendable, homogeneous datasets in an HDF5 file. The main difference between an EArray and a CArray (see :ref:`CArrayClassDescr`), from which it inherits, is that the former can be enlarged along one of its dimensions, the *enlargeable dimension*. That means that the :attr:`Leaf.extdim` attribute (see :class:`Leaf`) of any EArray instance will always be non-negative. Multiple enlargeable dimensions might be supported in the future. New rows can be added to the end of an enlargeable array by using the :meth:`EArray.append` method. Parameters ---------- parentnode The parent :class:`Group` object. .. versionchanged:: 3.0 Renamed from *parentNode* to *parentnode*. name : str The name of this node in its parent group. atom An `Atom` instance representing the *type* and *shape* of the atomic objects to be saved. shape The shape of the new array. One (and only one) of the shape dimensions *must* be 0. The dimension being 0 means that the resulting `EArray` object can be extended along it. Multiple enlargeable dimensions are not supported right now. title A description for this node (it sets the ``TITLE`` HDF5 attribute on disk). filters An instance of the `Filters` class that provides information about the desired I/O filters to be applied during the life of this object. expectedrows A user estimate about the number of row elements that will be added to the growable dimension in the `EArray` node. If not provided, the default value is ``EXPECTED_ROWS_EARRAY`` (see ``tables/parameters.py``). If you plan to create either a much smaller or a much bigger `EArray` try providing a guess; this will optimize the HDF5 B-Tree creation and management process time and the amount of memory used. chunkshape The shape of the data chunk to be read or written in a single HDF5 I/O operation. Filters are applied to those chunks of data. The dimensionality of `chunkshape` must be the same as that of `shape` (beware: no dimension should be 0 this time!). If ``None``, a sensible value is calculated based on the `expectedrows` parameter (which is recommended). byteorder The byteorder of the data *on disk*, specified as 'little' or 'big'. If this is not specified, the byteorder is that of the platform. track_times Whether time data associated with the leaf are recorded (object access time, raw data modification time, metadata change time, object birth time); default True. Semantics of these times depend on their implementation in the HDF5 library: refer to documentation of the H5O_info_t data structure. As of HDF5 1.8.15, only ctime (metadata change time) is implemented. .. versionadded:: 3.4.3 Examples -------- See below a small example of the use of the `EArray` class. The code is available in ``examples/earray1.py``:: import numpy as np import tables as tb fileh = tb.open_file('earray1.h5', mode='w') a = tb.StringAtom(itemsize=8) # Use ``a`` as the object type for the enlargeable array. array_c = fileh.create_earray(fileh.root, 'array_c', a, (0,), "Chars") array_c.append(np.array(['a'*2, 'b'*4], dtype='S8')) array_c.append(np.array(['a'*6, 'b'*8, 'c'*10], dtype='S8')) # Read the string ``EArray`` we have created on disk. for s in array_c: print('array_c[%s] => %r' % (array_c.nrow, s)) # Close the file. fileh.close() The output for the previous script is something like:: array_c[0] => 'aa' array_c[1] => 'bbbb' array_c[2] => 'aaaaaa' array_c[3] => 'bbbbbbbb' array_c[4] => 'cccccccc' EARRAYNTc ||jjd}||_ t|||||||| | | dS)NEXPECTED_ROWS_EARRAY)_v_fileparams_v_expectedrowssuper__init__) self parentnodenameatomshapetitlefilters expectedrows chunkshape byteorder_log track_times __class__s -lib/python3.11/site-packages/tables/earray.pyrzEArray.__init__si  %-45KLL+D T4w#Yk C C C C CcBtjtj|jdk}|dkrB|dkr-t |jd|_ntdtd| |j S)z+Create a new array in file (specific part).rrz6Multiple enlargeable (0-)dimensions are not supported.zZWhen creating EArrays, you need to set one of the dimensions of the Atom instance to zero.) npsumarrayrlistindexextdimNotImplementedError ValueError_g_create_commonr)rzerodimss r _g_createzEArray._g_creates6"(4:..!344 a<<1}}"4:..44Q77 )!"""?@@ @ $$T%9:::r cnt|j}t|jt|jjz }||krtd||j|fzt |D]B}||jkr5|j||j|krtd|j|fzCdS)z;Test that nparr shape is consistent with underlying EArray.zGthe ranks of the appended object (%d) and the ``%s`` EArray (%d) differz^the shapes of the appended object and the ``%s`` EArray differ in non-enlargeable dimension %dN)lenrrr) _v_pathnameranger')rnparrmyranknarankis r_check_shape_appendzEArray._check_shape_appendsTZU[!!C $8$88 V  : &(8&ABCC Cv K KADKDJqMU[^$C$C #1595Eq4I"JKKK K Kr c||jt||j}|||jdkr||dSdS)aAdd a sequence of data to the end of the dataset. The sequence must have the same type as the array; otherwise a TypeError is raised. In the same way, the dimensions of the sequence must conform to the shape of the array, that is, all dimensions must match, with the exception of the enlargeable dimension, which can be of any length (even 0!). If the shape of the sequence is invalid, a ValueError is raised. rN) _g_check_openr _check_writablerrr5size_append)rsequencer1s rappendz EArray.appends{  $$&&&$Hdi88   ''' :>> LL      >r c |||||\}}}|j} t|j} d| | <t t |||} t |||j| ||| ||  }|j}d|jD}d|_ t ||||zD][}|||zz}||kr|}t||||| <| | t|\d|_ tj|jt |jjz}||fS)z2Private part of Leaf.copy() for each kind of leaf.r)rrrrrrrc0g|]}td|dS)rr)slice).0dims r z-EArray._g_copy_with_stats..s$999s%3""999r FT)dtype)_process_range_readmaindimr%rr.r0rr nrowsinbuf _v_convertr?r: __getitem__tupler"prodritemsize)rgrouprstartstopsteprrrrkwargsrErnrowsobjectrFslicesstart2stop2nbytess r_g_copy_with_statszEArray._g_copy_with_statssY#66udDIId,TZ  gE%t,,-- 4diuE%J _ 99dj999 E4 ):;; < rcs''00000000  ` ` ` ` ` V` ` ` ` ` r