+ eicsRt^RIt^RIt^RIHt^RIHt^RIHt^RI H t ^RI H t ^RIHt.R%Ot] P"tR R R RRRRR RRRRRRRRR R RRRRRRRRRR/t] P&t]!R4R4t]!R4!RR 44t!RR] P.4t]!R4!RR ]44tRt]!R4R&Rl4t]!R4R&R l4t]!R4R'R!l4tR"t]!R4R'R#l4t]!R4R(R$l4t R#))z< This module contains a set of functions for record arrays. N)Counter) nullcontext) set_module)numeric) numerictypes)_get_legacy_print_moderecordrecarray format_parserZbZ>ZlZ<nZ=ZBZLZNZSZsZ|ZIiz numpy.reccsz\V4P4UUu.uFwrV^8gKVNK upp#uuppi)z@Find duplication in a list, return a list of duplicated elements)rZitems)listitemZcountss& :/usr/lib64/python3.14/site-packages/numpy/_core/records.pyfind_duplicater/sA $DM//1 1LD A: 1  s 77csDa]tRt^9toRtRRltR RltRtRtRt Vt R#) ra Class to convert formats, names, titles description to a dtype. After constructing the format_parser object, the dtype attribute is the converted data-type: ``dtype = format_parser(formats, names, titles).dtype`` Attributes ---------- dtype : dtype The converted data-type. Parameters ---------- formats : str or list of str The format description, either specified as a string with comma-separated format descriptions in the form ``'f8, i4, S5'``, or a list of format description strings in the form ``['f8', 'i4', 'S5']``. names : str or list/tuple of str The field names, either specified as a comma-separated string in the form ``'col1, col2, col3'``, or as a list or tuple of strings in the form ``['col1', 'col2', 'col3']``. An empty list can be used, in that case default field names ('f0', 'f1', ...) are used. titles : sequence Sequence of title strings. An empty list can be used to leave titles out. aligned : bool, optional If True, align the fields by padding as the C-compiler would. Default is False. byteorder : str, optional If specified, all the fields will be changed to the provided byte-order. Otherwise, the default byte-order is used. For all available string specifiers, see `dtype.newbyteorder`. See Also -------- numpy.dtype, numpy.typename Examples -------- >>> import numpy as np >>> np.rec.format_parser(['>> np.rec.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... []).dtype dtype([('col1', '>> np.rec.format_parser(['HHtUm_g6E\\F{{59:Tc#;q>>T:3784CQ48 D  ;8sD <D Dc sV'd\V4\\39dM?\V\4'dVP R4pM\ R\V4 24hVRVPUu.uFq3P4NK upVn M.Vn T;P\\VP4VP4Uu.uF pRV,NK up, un \VP4pV'd\RV 24hV'd3VRVPUu.uFq3P4NK upVnM .Vn.pVP\V48d>V;PR.VP\V4, ,, unR#R#uupiuupiuupi)zIconvert input field names into a list and assign to the _names attribute ,zillegal input names Nzf%dzDuplicate field names: )typer tuplerstrZsplitZ NameErrorreprr$Zstrip_namesranger#rr_titles)rrrr r Z_dups&&& r rZformat_parser._setfieldnamessm E{tUm+E3'' C("6tE{m DEE.3NT]].CD.C779.CDDKDK 5T[[1A15,@A,@a ,@A A dkk* 6tf=> > /5nt}}/EF/E!GGI/EFDLDLF ==3v; & LLTFdmmc&k&AB BL '+EAGs4F:F?7Gc s\P!RVPRVPRVPRVP /4pVe&\ V^,,pVPV4pW nR#)rrZoffsetsrN)rrr*r!r"r,_byteorderconvZ newbyteorder)rrr&& r rZformat_parser._createdtypesd T[[ t t}} dll     &y|4I&&y1E r)r!r*r$r"r,r)FN)F) __name__ __module__ __qualname____firstlineno____doc__rrrr__static_attributes____classdictcell__) __classdict__s@r rr9s&9v% "0"CH  rcsbaa]tRt^toRtRtRtV3RltV3RltRtRtRt Rt R t Vt V;t #) rzEA data-type scalar that allows field access as attribute lookup. Znumpycs^<\4^q8:dVP4#\SV` 4#iq)r__str__super__repr__r __class__&r r:Zrecord.__repr__s( ! #s *<<> !w!!rcsp<\4^q8:d\VP44#\SV`4#r7)rr(r r9r8r;r=r r8Zrecord.__str__s, ! #s *tyy{# #w  rcs4VR9d \PPW4#\PPW4# \dMi;i\PPTR4PpTP TR4pT'dpTP !TR,!pTPpM \dTu#i;iTPe(TPTPTP34#T#\RT R24h)setfieldrNNiN"'record' object has no attribute ''r>getfieldr) ntvoid__getattribute__AttributeErrorr getrCrrviewr<)rattr fielddictresobjZdt&& r rFZrecord.__getattribute__s 4 477++D7 7 77++D7 7   GG,,T7;BB mmD$' --R)C YY!   xx#xx ;<<J #EdV1!MN Ns#A AA4 C CCcsnVR9d\RV R24h\PPVR4PpVP VR4pV'dVP !V.VR,O5!#\WR4'd!\PPWV4#\RV R24h) r>rz Cannot set 'z ' attributeNr?r@rArB) rGrDrErFr rHr>getattr __setattr__)rrJvalrKrLs&&& r rPZrecord.__setattr__s 4 4 <v[!AB BGG,,T7;BB mmD$' ==/s2w/ / T & &77&&t37 7 #EdV1!MN Nrcs\PPW4p\V\P4'd@VPP e(VP VPVP34#V#r)rDrE __getitem__rrrrIr<)rindxrMr.r rRZrecord.__getitem__sYgg!!$- c277 # # (C88T^^SYY78 8Jrc  sVPPp\RV44pRV,pVUu.uFqCV\W43,NK ppRP V4#uupi)zPretty-print all fields.c3s8"TFp\V4xK R#5ir)r#)Z.0name& r Z Z record.pprint.. s154SYY5sz %% %ds: %%s )rrZmaxrOZjoin)rrZmaxlenfmtrTZrows& r pprintZ record.pprintse   1511f$>CDedtWT0111eDyyEsA%)r/r0r1r2r3r:r8rFrPrRrYr4r5 __classcell__r<r6@@r rrs; HJ" ! O0 Orcsbaa]tRtRtoRtR RltRtRtRtV3Rlt Rt R R lt R t Vt V;t#) ria Construct an ndarray that allows field access using attributes. Arrays may have a data-types containing fields, analogous to columns in a spread sheet. An example is ``[(x, int), (y, float)]``, where each entry in the array is a pair of ``(int, float)``. Normally, these attributes are accessed using dictionary lookups such as ``arr['x']`` and ``arr['y']``. Record arrays allow the fields to be accessed as members of the array, using ``arr.x`` and ``arr.y``. Parameters ---------- shape : tuple Shape of output array. dtype : data-type, optional The desired data-type. By default, the data-type is determined from `formats`, `names`, `titles`, `aligned` and `byteorder`. formats : list of data-types, optional A list containing the data-types for the different columns, e.g. ``['i4', 'f8', 'i4']``. `formats` does *not* support the new convention of using types directly, i.e. ``(int, float, int)``. Note that `formats` must be a list, not a tuple. Given that `formats` is somewhat limited, we recommend specifying `dtype` instead. names : tuple of str, optional The name of each column, e.g. ``('x', 'y', 'z')``. buf : buffer, optional By default, a new array is created of the given shape and data-type. If `buf` is specified and is an object exposing the buffer interface, the array will use the memory from the existing buffer. In this case, the `offset` and `strides` keywords are available. Other Parameters ---------------- titles : tuple of str, optional Aliases for column names. For example, if `names` were ``('x', 'y', 'z')`` and `titles` is ``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then ``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``. byteorder : {'<', '>', '='}, optional Byte-order for all fields. aligned : bool, optional Align the fields in memory as the C-compiler would. strides : tuple of ints, optional Buffer (`buf`) is interpreted according to these strides (strides define how many bytes each array element, row, column, etc. occupy in memory). offset : int, optional Start reading buffer (`buf`) from this offset onwards. order : {'C', 'F'}, optional Row-major (C-style) or column-major (Fortran-style) order. Returns ------- rec : recarray Empty array of the given shape and type. See Also -------- numpy.rec.fromrecords : Construct a record array from data. numpy.record : fundamental data-type for `recarray`. numpy.rec.format_parser : determine data-type from formats, names, titles. Notes ----- This constructor can be compared to ``empty``: it creates a new record array but does not fill it with data. To create a record array from data, use one of the following methods: 1. Create a standard ndarray and convert it to a record array, using ``arr.view(np.recarray)`` 2. Use the `buf` keyword. 3. Use `np.rec.fromrecords`. Examples -------- Create an array with two fields, ``x`` and ``y``: >>> import numpy as np >>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '>> x array([(1., 2), (3., 4)], dtype=[('x', '>> x['x'] array([1., 3.]) View the array as a record array: >>> x = x.view(np.recarray) >>> x.x array([1., 3.]) >>> x.y array([2, 4]) Create a new, empty record array: >>> np.recarray((2,), ... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP rec.array([(-1073741821, 1.2249118382103472e-301, 24547520), (3471280, 1.2134086255804012e-316, 0)], dtype=[('x', '*A99A>csVR8XdU\VP\P4'd+VPe\ P !\V34pWP9p\PWV4p\PVR4P;'g/pW9dV#V'd\PW4MWQ,R,pTP&!T.TO5!# \dTu#i;i \d5\PTR4P;'g/pY9dhLui;i \ \"3dp\%RT 24ThRp?ii;i)rNr?zrecord array has no attribute )rkr&rDrErrrrZ__dict__rhrPrarFr Z __delattr__Z ExceptionrirjrGr>)rrJrQZnewattrZretrKrLrls&&& r rPZrecarray.__setattr__sR GO sxx ) ) II !((FC=)Cmm+ $$T5C  00w?FFLL"I$ &&t2  /"%C }}S'3''!J 00w?FFLL"I$% "8$  07  sB+D <C7E 7 DD 1E < E E  E1E,,E1cs<\SV`V4p\V\4'dVPP eVP \V44p\VPP\P4'd3VP VPPVP3R7#V#VP \R7#V#)Nrg)r&) r9rRrrarrrIr&rkrDrE)rrSrMr<s&& r rRZrecarray.__getitem__sg!$' c7 # #yy*hhtDz*ciinnbgg66884::??CII*F8GG xxWx--JrcsdVPpVPP\Jg5\VPP\P 4'g@VP\Jd'\ P!\P V34pRpRpMRpRpVP^8gVPR 8Xd\ P!VRVRR7pMR\VP4 2pRR \V4,,p\4^q8:d R V,pW4WQ3,#) z rec.array(zrec.array(%s,%sdtype=%s)zarray(z)array(%s,%sdtype=%s).view(numpy.recarray)z, r%)Z separatorprefixZsuffixz [], shape=rVZ ) rr&rrkrDrErsizercZ array2stringr)r#r)rZ repr_dtypermrWZlstZlfrXr r:Zrecarray.__repr__sZZ JJOOv %4::??BGG44&(XXrww &;< !F,C F=C 99q=DJJ$.//VCACtDJJ/01C C#f+% % ! #s *rB2***rcs\V\4'd)\PVR4PpW1,p\PVR4P pWA,R,pVf@VP !V!pVPPeV#VP\4#VP!V.VO5!#)rr?) rintrarFrr rCrrIr>)rrJrQrrKrLrMs&&& r fieldZrecarray.fields dC ,,T7;AAE;D,,T7;BB ob! ;--%Cyy* 88G$ $==+s+ +rrg) NNroNNNNNFZCr)r/r0r1r2r3rbrfrFrPrRr:rrr4r5r[r\r]r rrs7gR,$ %D#(J""+H,,rcsRV^8Xd \P!R\^R7R#V#)rozPassing `shape=0` to have the shape be inferred is deprecated, and in future will be equivalent to `shape=(0,)`. To infer the shape and suppress this warning, pass `shape=None` instead.Z stacklevelN)warningswarn FutureWarning)rcs&r _deprecate_shape_0_as_Nonerw-s, z  N a  )  rcs&VUu.uFp\P!V4NK pp\V4pVfV^,PpM\ V\ 4'dV3pVfVfVU u.uFqP NK pp Ve\P !V4p M\W4WVV4P p V Pp \V 4\V48wd \R4hV ^,Pp \V 4p V ^8dVRV )p\W*4p\V4F\wrW,Pp V PRV PV , pW,pVV8wd\RV RV R24hWV&K^ V#uupiuup i)aCreate a record array from a (flat) list of arrays Parameters ---------- arrayList : list or tuple List of array-like objects (such as lists, tuples, and ndarrays). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional Shape of the resulting array. If not provided, inferred from ``arrayList[0]``. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.rec.format_parser` to construct a dtype. See that function for detailed documentation. Returns ------- np.recarray Record array consisting of given arrayList columns. Examples -------- >>> x1=np.array([1,2,3,4]) >>> x2=np.array(['a','dd','xyz','12']) >>> x3=np.array([1.1,2,3,4]) >>> r = np.rec.fromarrays([x1,x2,x3],names='a,b,c') >>> print(r[1]) (2, 'dd', 2.0) # may vary >>> x1[1]=34 >>> r.a array([1, 2, 3, 4]) >>> x1 = np.array([1, 2, 3, 4]) >>> x2 = np.array(['a', 'dd', 'xyz', '12']) >>> x3 = np.array([1.1, 2, 3,4]) >>> r = np.rec.fromarrays( ... [x1, x2, x3], ... dtype=np.dtype([('a', np.int32), ('b', 'S3'), ('c', np.float32)])) >>> r rec.array([(1, b'a', 1.1), (2, b'dd', 2. ), (3, b'xyz', 3. ), (4, b'12', 4. )], dtype=[('a', 'mismatch between the number of fields and the number of arrayszarray-shape mismatch in array z ("z"))rZasarrayrwrcrrqrrrr#rrrZndim)Z arrayListrrcrrrrrZxrMrer*Zd0Znn_arraykZ testshaperTs&&&&&&&& r fromarraysrz9syb)22 1A I2 'u -E }! "" E3  5=)22 99 2 gfyIOO [[F 5zS^#+, , qB RB Avds  e #FI& X]]IInsxx"}- y  =aSD6LM Mt ' MW33s F .Fc sVfVf\P!V\R7p\VPR,4U u.uF0p \P!VRV 3,P 44NK2 p p \ WW$WVVR7#Ve\P!\V34p M\W4WVV4Pp \P!W R7p VeV PV8wdW,nT P-\ 4pT#uup i \\3d\T4pTf \T4p\T\4'dT3p\T4^8d \R4h\!Y+4p \T P"4Fp\%Y,4Y&K \&P(!R\*^R7T u#i;i)avCreate a recarray from a list of records in text form. Parameters ---------- recList : sequence data in the same field may be heterogeneous - they will be promoted to the highest data type. dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. If both `formats` and `dtype` are None, then this will auto-detect formats. Use list of tuples rather than list of lists for faster processing. Returns ------- np.recarray record array consisting of given recList rows. Examples -------- >>> r=np.rec.fromrecords([(456,'dbe',1.2),(2,'de',1.3)], ... names='col1,col2,col3') >>> print(r[0]) (456, 'dbe', 1.2) >>> r.col1 array([456, 2]) >>> r.col2 array(['dbe', 'de'], dtype='>> import pickle >>> pickle.loads(pickle.dumps(r)) rec.array([(456, 'dbe', 1.2), ( 2, 'de', 1.3)], dtype=[('col1', '<= =%'v{{#Agj)FI$   G a ) %s6D DB8G  G c sVfVf \R4hVe\P!V4p M\WEWgV4Pp V Pp \ V4pVR9d\ V4V, V ,p\W)WR7p V #)a0Create a record array from binary data Note that despite the name of this function it does not accept `str` instances. Parameters ---------- datastring : bytes-like Buffer of binary data dtype : data-type, optional Valid dtype for all arrays shape : int or tuple of ints, optional Shape of each array. offset : int, optional Position in the buffer to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. Returns ------- np.recarray Record array view into the data in datastring. This will be readonly if `datastring` is readonly. See Also -------- numpy.frombuffer Examples -------- >>> a = b'\x01\x02\x03abc' >>> np.rec.fromstring(a, dtype='u1,u1,u1,S3') rec.array([(1, 2, 3, b'abc')], dtype=[('f0', 'u1'), ('f1', 'u1'), ('f2', 'u1'), ('f3', 'S3')]) >>> grades_dtype = [('Name', (np.str_, 10)), ('Marks', np.float64), ... ('GradeLevel', np.int32)] >>> grades_array = np.array([('Sam', 33.3, 3), ('Mike', 44.4, 5), ... ('Aadi', 66.6, 6)], dtype=grades_dtype) >>> np.rec.fromstring(grades_array.tobytes(), dtype=grades_dtype) rec.array([('Sam', 33.3, 3), ('Mike', 44.4, 5), ('Aadi', 66.6, 6)], dtype=[('Name', '>> s = '\x01\x02\x03abc' >>> np.rec.fromstring(s, dtype='u1,u1,u1,S3') Traceback (most recent call last): ... TypeError: a bytes-like object is required, not 'str' z2fromstring() needs a 'dtype' or 'formats' argument)rdr_)Nr{)rirrritemsizerwr#r) Z datastringrrcr_rrrrrrer~rxs &&&&&&&&& r fromstringrsp }LMM gfyIOO~~H 'u -E Z6)h6 e BF MrcsVP4pVP^^4VP4V, VPV^4# TPT^4i;irn)Ztellseek)fdZposrUr get_remaining_sizer<sG '')C 1 wwy3 QQs (A A!c sVfVf \R4h\V4pVfR pM\V\4'dV3p\ VR4'd \ V4p M \ \P!V4R4p T ;_uu_4pV^8dVPV^4\V4p Ve\P!V4p M\WEWgV4Pp V Pp \P!V4P!\"P$R7p W,pV^8de\'V4pW),W"P)R4&\+V4p\P!V4P!\"P$R7p W,pW8d \-R4h\/W+4pVP1VP24pVV8wd \5R4hRRR4V# +'giX#;i) ahCreate an array from binary file data Parameters ---------- fd : str or file type If file is a string or a path-like object then that file is opened, else it is assumed to be a file object. The file object must support random access (i.e. it must have tell and seek methods). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. offset : int, optional Position in the file to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation Returns ------- np.recarray record array consisting of data enclosed in file. Examples -------- >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,a5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.view(a.dtype.newbyteorder('<')) >>> a.tofile(fd) >>> >>> _ = fd.seek(0) >>> r=np.rec.fromfile(fd, formats='f8,i4,a5', shape=10, ... byteorder='<') >>> print(r[5]) (0.5, 10, b'abcde') >>> r.shape (10,) Nz0fromfile() needs a 'dtype' or 'formats' argumentreadintoZrbrgz;Not enough bytes left in file for specified shape and type.z%Didn't read as many bytes as expectedr{)r{)rirwrrqhasattrrZopenosZfspathrrrrrr~r|ZprodrDZintpr Zindexr'rrrZdataZOSError)rrrcr_rrrrrZctxrprer~Z shapeprodZ shapesizeZnbytesrxZ nbytesreads&&&&&&&&& r fromfilerEs\ }JKK 'u -E } E3  r:"o299R=$'  A: GGFA !"%  HHUOE!e >>HHUO((rww(7 ( q=KE%)Z%7E++b/ "%LE,,277,;I% =&  %'[[-  AB B C H MI H Ms EG-- G> c s2\V\R4\34'g\VR4'dVfVf \ R4h/p Ve\ P !V4pM(Ve\WVVW4P pM RVRVRVRVRV /p VfVf \ R 4h\W!WVR 7#\V\4'd\W3R VR V/V B#\V\\34'dD\V^,\\34'd\V3R VR V/V B#\V3R VR V/V B#\V\4'dDVe$VP V8wdVPV4p MTp V 'dV P!4p V #\VR4'd\#WW#R7#\V\$4'dWVe$VP V8wdVPV4p MTp V 'dV P!4p V P\4#\'VRR4p V e\V \(4'g \ R4h\ P*!V4pVe#VP V8wdVPV4pVP\4#)az Construct a record array from a wide-variety of objects. A general-purpose record array constructor that dispatches to the appropriate `recarray` creation function based on the inputs (see Notes). Parameters ---------- obj : any Input object. See Notes for details on how various input types are treated. dtype : data-type, optional Valid dtype for array. shape : int or tuple of ints, optional Shape of each array. offset : int, optional Position in the file or buffer to start reading from. strides : tuple of ints, optional Buffer (`buf`) is interpreted according to these strides (strides define how many bytes each array element, row, column, etc. occupy in memory). formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation. copy : bool, optional Whether to copy the input object (True), or to use a reference instead. This option only applies when the input is an ndarray or recarray. Defaults to True. Returns ------- np.recarray Record array created from the specified object. Notes ----- If `obj` is ``None``, then call the `~numpy.recarray` constructor. If `obj` is a string, then call the `fromstring` constructor. If `obj` is a list or a tuple, then if the first object is an `~numpy.ndarray`, call `fromarrays`, otherwise call `fromrecords`. If `obj` is a `~numpy.recarray`, then make a copy of the data in the recarray (if ``copy=True``) and use the new formats, names, and titles. If `obj` is a file, then call `fromfile`. Finally, if obj is an `ndarray`, then return ``obj.view(recarray)``, making a copy of the data if ``copy=True``. Examples -------- >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> a array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> np.rec.array(a) rec.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=int64) >>> b = [(1, 1), (2, 4), (3, 9)] >>> c = np.rec.array(b, formats = ['i2', 'f2'], names = ('x', 'y')) >>> c rec.array([(1, 1.), (2, 4.), (3, 9.)], dtype=[('x', '>> c.x array([1, 2, 3], dtype=int16) >>> c.y array([1., 4., 9.], dtype=float16) >>> r = np.rec.array(['abc','def'], names=['col1','col2']) >>> print(r.col1) abc >>> r.col1 array('abc', dtype='>> r.col2 array('def', dtype='rs "# .  **ssssssssssssss (  K KHHHVHRWWHf KQ,wQ,Q,h  K[[| KUUp KHHT KeeP KR"R"r