,Vh dZddlZddlZddlmZddlmZddlmZddl m Z dd l m Z dd lmZgd Ze j"Zd d dd d dddd d dddddZe j&ZeddZedGddZGdde j.ZedGddeZdZed d!dZed d!dZed d"dZdZed d"dZed d#d Z y)$z< This module contains a set of functions for record arrays. N)Counter) nullcontext) set_module)numeric) numerictypes)_get_legacy_print_mode) recordrecarray format_parser fromarrays fromrecords fromstringfromfilearrayfind_duplicate><=s|)blnBLNSrrrrrIiz numpy.reccpt|jDcgc] \}}|dkDr|c}}Scc}}w)z@Find duplication in a list, return a list of duplicated elementsr)ritems)listitemcountss C/home/dcms/DCMS/lib/python3.12/site-packages/numpy/_core/records.pyrr.s< $DM//1  D& A:   s2c,eZdZdZddZddZdZdZy) r a 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(['keyskeys r'r+zformat_parser._parseFormatszs ?45 5 gt $HH'0&8"7\\!_g. EHHWg.E >HHtUm_g6E\\F{{59:c6#;q>:378CQ8 D  ;8sC> ;DD c$|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$tupler9strsplit NameErrorreprrBstrip_namesrangerArr8_titles)r.r0r1rr!_dups r'r,zformat_parser._setfieldnamessP E{tUm+E3' C( 84; FGG.3NT]].CD1779DDKDK 5T[[1A15,@Aa A A dkk* 84?@ @ /5nt}}/EF!AGGIFDLDLF ==3v; & LLTFdmmc&k&AB BL '+EAGs%F FF ctj|j|j|j|j d}|t |d}|j|}||_y)N)r0r/offsetsr1r)r:r;rOr?r@rQ_byteorderconv newbyteorder)r.r3r;s r'r-zformat_parser._createdtypes^[[}}ll     &y|4I&&y1E r5)FN)F)__name__ __module__ __qualname____doc__r4r+r,r-r5r'r r 8s9v% "0"CH r5r cLeZdZdZdZdZfdZfdZdZdZdZdZ xZ S) r zEA data-type scalar that allows field access as attribute lookup. numpycZtdkr|jSt| SNq)r __str__super__repr__r. __class__s r'rczrecord.__repr__s( ! #s *<<> !w!!r5cltdkrt|jSt|Sr_)r rJr%rbrards r'razrecord.__str__s, ! #s *tyy{# #w  r5c|dvr tjj||S tjj||S#t$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>getrjr;r0viewre)r.attr fielddictresobjdts r'rnzrecord.__getattribute__s 4 477++D$7 7 77++D$7 7   GG,,T7;BB mmD$' $--Ra)C YY!   xx#xx ;<<J "%'+",- -s#A AA& B33 CCcF|dvrtd|ztjj|dj}|j |d}|r|j |g|ddSt||dr!tjj|||Std|z)NrhzCannot set '%s' attributer;rrk) rorlrmrnr>rprigetattr __setattr__)r.rrvalrsrts r'ryzrecord.__setattr__s 4 4 !z record.pprint.. s14SY1sz %% %ds: %%s )r;r0maxrxjoin)r.r0maxlenfmtrrowss r'pprintz record.pprintse   1511f$>CDdtWT4011DDyyEsA) rWrXrYrZrcrarnryr|r __classcell__res@r'r r s2 HJ" ! -2 1r5r cPeZdZdZ d dZdZdZdZfdZdZ d dZ xZ S) r aSConstruct 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', ' TypeErrorKeyErrorrjr;r0 issubclassrHrlrmrq)r.rrrsrterus r'rnzrecarray.__getattribute__s **46 6   ,,T7;BB  OD/"1%C8$ O !?$!FGQ N OdmmS! 99?? &#)).."''2xxtzz &BxCCJ88G$ $s$ $$AA4 A//A4c|dk(rKt|jtjr'|jt j t|f}||jv} tj|||}tj|djxsi}||vr|S|r tj|| ||dd}|j&|g|S#t$r|cYSwxYw#t$r,tj|djxsi}||vrYawxYw#t t"f$r}t%d|z|d}~wwxYw)Nr;rz record array has no attribute %s)rrHrlrmr0r:r;r __dict__rryrrnr> __delattr__ Exceptionrrrori)r.rrrznewattrretrsrtrs r'ryzrecarray.__setattr__sR GO sxx ) II !((FC=)Cdmm+ $$T45C  00w?FFL"I9$ &&tT2 D/"1%C t}}S'3''!J 00w?FFL"I9$% "8$  2T9  s< C'$C;D C$#C$'2DDE.D==Ect||}t|tr|jj ||j t|}t|jjtjr2|j |jj|jfS|S|j tS|S)Nr)rH) rbr|r9rr;r0rqrHrrlrm)r.r}rures r'r|zrecarray.__getitem__sg!$' c7 #yy*hhtDz*ciinnbgg6884::??CII*F8GG xxWx--Jr5c$|j}|jjtus.t|jjtj s<|jtur%t jtj |f}d}d}nd}d}|jdkDs|jdk(rt j|d|d }nd t|j}d d t|zz}td krd |z}||||fzS)Nz rec.array(zrec.array(%s,%sdtype=%s)zarray(z)array(%s,%sdtype=%s).view(numpy.recarray)r)rz, rG) separatorprefixsuffixz [], shape=r r`) r;rHr rrlrmr:sizer array2stringrMrAr )r. repr_dtyperrlstlfs r'rczrecarray.__repr__sZZ JJOOv %4::??BGG4&(XXrww &;< !F,C F=C 99q=DJJ$.//VCAC/ %)$46C #c&k/ ! ! #s *rBc2z***r5c^t|tr%tj|dj}||}tj|dj }||dd}|<|j |}|jj|S|jtS|j|g|S)Nr;r) r9intrrnr0r>rjr;rqri)r.rrrzr0rsrtrus r'fieldzrecarray.fields dC ,,T7;AAE;D,,T7;BB obq! ;$--%Cyy* 88G$ $ 4==+s+ +r5) NNrNNNNNFCr*) rWrXrYrZrrrnryr|rcrrrs@r'r r s=gRIM1558,$ %D#(J""+H,r5r cJ|dk(rtjdtdy|S)NrzPassing `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. stacklevel)warningswarn FutureWarning)rs r'_deprecate_shape_0_as_Noner/s, z  N a  )  r5c|Dcgc]}tj|}}t|}||dj}nt |t r|f}|||D cgc]} | j }} |tj |} nt|||||j } | j} t| t|k7r td| dj} t| } | dkDr|d| }t|| }t|D]Q\}} | |j} | jd| j| z }| |}||k7rtd|d|d| ||<S|Scc}wcc} w)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"))r:asarrayrrr9rr;r r0rAr8r r<ndim) arrayListr;rr/r0r1r2r3xrurrOd0nn_arrayk testshapers r'rr;sb)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) aCreate 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{{# *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.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)Nr)rr)rr:r;r itemsizerrAr ) datastringr;rrr/r0r1r2r3rrrs r'rrsp }LMM gufgyIOO~~H 'u -E Z6)h6 eU 6 BF Mr5c|j} |jdd|j|z |j|dS#|j|dwxYw)Nrr)tellseek)fdposs r'get_remaining_sizer>sG '')C 1 wwy3 QQs $A Ac | | tdt|}|d}nt|tr|f}t |dr t |} nt tj|d} | 5}|dkDr|j|dt|} |tj|} nt|||||j} | j} tj|j!t"j$} | | z}|dkrat'|}| | z||j)d <t+|}tj|j!t"j$} | | z}|| kDr t-d t/|| }|j1|j2}||k7r t5d  ddd|S#1swYSxYw) aCreate an array from binary file data Parameters ---------- fd : str or file type If file is a string or a path-like object then that file is opened, else it is assumed to be a file object. The file object must support random access (i.e. it must have tell and seek methods). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. offset : int, optional Position in the file to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation Returns ------- np.recarray record array consisting of data enclosed in file. Examples -------- >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,a5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.view(a.dtype.newbyteorder('<')) >>> a.tofile(fd) >>> >>> _ = fd.seek(0) >>> r=np.rec.fromfile(fd, formats='f8,i4,a5', shape=10, ... byteorder='<') >>> print(r[5]) (0.5, 10, b'abcde') >>> r.shape (10,) Nz0fromfile() needs a 'dtype' or 'formats' argument)rreadintorbrrrrz;Not enough bytes left in file for specified shape and type.z%Didn't read as many bytes as expected)rrr9rhasattrropenosfspathrrr:r;r rrprodrlintpr$indexrIr8r rdataOSError)rr;rrr/r0r1r2r3ctxrrr shapeprod shapesizenbytesr nbytesreads r'rrGs\ }JKK 'u -E } E3 r:"o299R=$' "C A: GGFA !"%  HHUOE!e >>HHUO((rww(7 ( q=KE%)iZ%7E%++b/ "%LE,,277,;IX% D=&  %'[[-  AB B C"CH MI"CH Ms ,E GG c nt|tdtfs t|dr| | t di} |t j |}n$|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]]) >>> 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 " .  ** (  K KHHHVKRWWKl KQ,wQ,Q,h  K:>AE[[| KEI6:UUp KEIAEHHT K;??CeeP KGKGKR"R"r5