/jdZddlZddlmZddlmZddlmZddlm Z dd lm Z dd l mZdd lmZgd Ze j$Zd ddd ddddd ddddddZe j(ZdZedGddZGdde j0ZGddeZdZed d#dZed d#dZed d$dZd Zed d$d!Z ed d%d"Z!y)&a Record Arrays ============= Record arrays expose the fields of structured arrays as properties. Most commonly, ndarrays contain elements of a single type, e.g. floats, integers, bools etc. However, it is possible for elements to be combinations of these using structured types, such as:: >>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', np.int64), ('y', np.float64)]) >>> a array([(1, 2.), (1, 2.)], dtype=[('x', '>> a['x'] array([1, 1]) >>> a['y'] array([2., 2.]) Record arrays allow us to access fields as properties:: >>> ar = np.rec.array(a) >>> ar.x array([1, 1]) >>> ar.y array([2., 2.]) N)Counter) nullcontext) set_module)numeric) numerictypes) os_fspath)_get_legacy_print_mode)recordrecarray format_parser fromarrays fromrecords fromstringfromfilearray><=s|)blnBLNSrrrrrIicpt|jDcgc] \}}|dkDr|c}}Scc}}w)z@Find duplication in a list, return a list of duplicated elementsr)ritems)listitemcountss U/root/repos/pocs/video-evals/.venv/lib/python3.12/site-packages/numpy/core/records.pyfind_duplicater(Ns<$DM//1  D& A:   s2numpyc,eZdZdZddZddZdZdZy) 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, a5'``, or a list of format description strings in the form ``['f8', 'i4', 'a5']``. 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 -------- dtype, typename, sctype2char Examples -------- >>> np.format_parser(['>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... []).dtype dtype([('col1', '>> np.format_parser(['G>PQ 7%,,q/7+QE HHWg.E >HHtUm_g6E\\F{{59:c6#;q>:378CQ8 D  R;8sC> ;DD c$|rt|ttfvrn9t|tr|j d}nt dt|z|d|jDcgc]}|jc}|_ ng|_ |xjtt|j|jDcgc]}d|z c}z c_ t|j}|rtd|z|r1|d|jDcgc]}|jc}|_n g|_g}|jt|kDr0|xjdg|jt|z zz c_yycc}wcc}wcc}w)zQconvert input field names into a list and assign to the _names attribute ,zillegal input names %sNzf%dzDuplicate field names: %s)typer$tupler;strsplit NameErrorreprrDstrip_namesrangerCr(r:_titles)r0r2r3rr!_dups r'r.zformat_parser._setfieldnamessP E{tUm+E3' C( 84; FGG.3NT]].CD1779DDKDK 5T[[1A15,@Aa A A dkk* 84?@ @ /5nt}}/EF!AGGIFDLDLF ==3v; & LLTFdmmc&k&AB BL '+EAGs%F FF ctj|j|j|j|j d}|t |d}|j|}||_y)N)r2r1offsetsr3r)r<r=rQrArBrS_byteorderconv newbyteorder)r0r5r=s r'r/zformat_parser._createdtypes^[[}}ll     &y|4I&&y1E r7)FN)F)__name__ __module__ __qualname____doc__r6r-r.r/r7r'rrWs8t% "*"CH r7rcLeZdZdZdZdZfdZfdZdZdZdZdZ xZ S) r zEA data-type scalar that allows field access as attribute lookup. r)cZtdkr|jSt| SNq)r __str__super__repr__r0 __class__s r'rdzrecord.__repr__s( ! #s *<<> !w!!r7cltdkrt|jSt|Sr`)r rLr%rcrbres r'rbzrecord.__str__s, ! #s *tyy{# #w  r7c|dvr tjj||S tjj||S#t$rYnwxYwtjj|dj}|j |d}|rf|j |dd} |j}n#t$r|cYSwxYw|j'|j|j|jfS|Std|z)Nsetfieldgetfieldr=r=r%'record' object has no attribute '%s') ntvoid__getattribute__AttributeErrorr@getrkr=r2viewrf)r0attr fielddictresobjdts r'rozrecord.__getattribute__s 4 477++D$7 7 77++D$7 7   GG,,T7;BB mmD$' $--Ra)C YY!   xx#xx ;<<J "%'+",- -s#A AA& B33 CCcF|dvrtd|ztjj|dj}|j |d}|r|j |g|ddSt||dr!tjj|||Std|z)NrizCannot set '%s' attributer=rrl) rprmrnror@rqrjgetattr __setattr__)r0rsvalrtrus r'rzzrecord.__setattr__ s 4 4 !z record.pprint..'s14SY1sz %% %ds: %%s )r=r2maxryjoin)r0r2maxlenfmtrrowss r'pprintz record.pprint#se   1511f$>CDdtWT4011DDyyEsA) rYrZr[r\rdrbrorzr}r __classcell__rfs@r'r r s2 HJ" ! -2 1r7r cXeZdZdZdZdZ d dZdZdZdZfdZdZ d d Z xZ S) r a-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 -------- core.records.fromrecords : Construct a record array from data. record : fundamental data-type for `recarray`. format_parser : determine a 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``: >>> 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', '>> x1=np.array([1,2,3,4]) >>> x2=np.array(['a','dd','xyz','12']) >>> x3=np.array([1.1,2,3,4]) >>> r = np.core.records.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.core.records.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"))r<asarrayrrr;rr=rr2rCr:r r>ndim) arrayListr=rr1r2r3r4r5xrvrrQd0nn_arrayk testshapers r'rrLsb)221A2I2 'u -E }! "" E3 5=)2239922 gufgyIOO [[F 5zS^#+, , qB RB Avds  eU #FI&3 1X]]IInsxx"}- ay  =aSD6LM Mt  MW33s E!E&c ^|z|xtj|t}t|jdD cgc]*} tj|d| fj ,} } t | ||||||S|tjt|f} nt|||||j} tj|| } || j|k7r|| _ | j-t }|Scc} w#ttf$rt|}| t|}t|tr|f}t|dkDr tdt!|| } t| j"D]}t%||| |<t'j(dt*d | cYSwxYw) a Create 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.core.records.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{{# *Agaj)F1I *   G a ) %s/C<5DB(F,+F,c | | td|tj|} nt|||||j} | j} t |}|dvrt ||z | z}t|| ||} | S)aCreate 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.core.records.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.core.records.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.core.records.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)Nr)rr)rr<r=ritemsizerrCr ) datastringr=rrr1r2r3r4r5rrrs r'rrsp }LMM gufgyIOO~~H 'u -E Z6)h6 eU 6 BF Mr7c|j} |jdd|j|z |j|dS#|j|dwxYw)Nrr)tellseek)fdposs r'get_remaining_sizerKsG '')C 1 wwy3 QQs $A Ac | | tdt|}|d}nt|tr|f}t |dr t |} nt t|d} | 5}|dkDr|j|dt|} |tj|} nt|||||j} | j} tj|jt j"} | | z}|dkrat%|}| | z||j'd <t)|}tj|jt j"} | | z}|| kDr t+d t-|| }|j/|j0}||k7r t3d  ddd|S#1swYSxYw) aCreate an array from binary file data Parameters ---------- fd : str or file type If file is a string or a path-like object then that file is opened, else it is assumed to be a file object. The file object must support random access (i.e. it must have tell and seek methods). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. offset : int, optional Position in the file to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation Returns ------- np.recarray record array consisting of data enclosed in file. Examples -------- >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,a5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.newbyteorder('<') >>> a.tofile(fd) >>> >>> _ = fd.seek(0) >>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10, ... byteorder='<') >>> print(r[5]) (0.5, 10, 'abcde') >>> r.shape (10,) Nz0fromfile() needs a 'dtype' or 'formats' argument)rreadintorbrrrrz:Not enough bytes left in file for specified shape and typez%Didn't read as many bytes as expected)rrr;rhasattrropenr rrr<r=rrrprodrmintpr$indexrKr:r rdataOSError)rr=rrr1r2r3r4r5ctxrrr shapeprod shapesizenbytesr nbytesreads r'rrTs\ }JKK 'u -E } E3 r:"o9R=$' C A: GGFA !"%  HHUOE!'5&'9MSSE>>HHUO((rww(7 ( q=KE%)iZ%7E%++b/ "%LE,,277,;IX% D=PR R%'[[-  AB B ;C@ MAC@ Ms "E F77Gc nt|tdtfs t|dr| | t di} |t j |}n$|t||||| j }n||||| d} || t dt|||||St|trt||f||d| St|ttfr7t|dttfrt|f||d | St|f||d | St|tr9|!|j |k7r|j|} n|} | r| j!} | St|drt#|||| St|t$rL|!|j |k7r|j|} n|} | r| j!} | jtSt'|d d} | t| t(s t d t j*|}| |j |k7r|j|}|jtS) a 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]]) array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> np.core.records.array(a) rec.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=int32) >>> b = [(1, 1), (2, 4), (3, 9)] >>> c = np.core.records.array(b, formats = ['i2', 'f2'], names = ('x', 'y')) >>> c rec.array([(1, 1.0), (2, 4.0), (3, 9.0)], dtype=[('x', '>> c.x rec.array([1, 2, 3], dtype=int16) >>> c.y rec.array([ 1.0, 4.0, 9.0], 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='rso"F" ".  ** (  GDDDNKRWWKjI,wI,X  K:>AE[[| KEI6:QQh KEIAEHHT K;??CaaH KGKGKQ"Q"r7