&jݕdZddlZddlZddlmZddlmZddlmZddl m Z m Z ddlmZgd Ze j"Zd d d d d d d d d d d ddddZe j&ZeddZedGddZGdde j.ZedGddeZdZed ddZed ddZed d dZdZed d dZed d!dZ 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 5C:\FundamentalWorkbench\vendor\numpy\_core\records.pyrr.sA $DM//1 1LD& A: 1  s2c,eZdZdZddZddZdZdZy) r a Class to convert formats, names, titles description to a dtype. After constructing the format_parser object, the dtype attribute is the converted data-type: ``dtype = format_parser(formats, names, titles).dtype`` Attributes ---------- dtype : dtype The converted data-type. Parameters ---------- formats : str or list of str The format description, either specified as a string with comma-separated format descriptions in the form ``'f8, i4, S5'``, or a list of format description strings in the form ``['f8', 'i4', 'S5']``. names : str or list/tuple of str The field names, either specified as a comma-separated string in the form ``'col1, col2, col3'``, or as a list or tuple of strings in the form ``['col1', 'col2', 'col3']``. An empty list can be used, in that case default field names ('f0', 'f1', ...) are used. titles : sequence Sequence of title strings. An empty list can be used to leave titles out. aligned : bool, optional If True, align the fields by padding as the C-compiler would. Default is False. byteorder : str, optional If specified, all the fields will be changed to the provided byte-order. Otherwise, the default byte-order is used. For all available string specifiers, see `dtype.newbyteorder`. See Also -------- numpy.dtype, numpy.typename Examples -------- >>> import numpy as np >>> np.rec.format_parser(['>> np.rec.format_parser(['f8', 'i4', 'S5'], ['col1', 'col2', 'col3'], ... []).dtype dtype([('col1', '>> np.rec.format_parser(['HHtUm_g6E\\F{{59:Tc6#;q>T:3784CQ48 D  ;8sC2 /C8 C=c$|rt|ttfvrn9t|tr|j d}nt dt||d|jDcgc]}|jc}|_ ng|_ |xjtt|j|jDcgc]}d| c}z c_ t|j}|rtd||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 Nr6zDuplicate field names: )typer#tupler9strsplit NameErrorreprrAstrip_namesranger@rr8_titles)r-r/r0rr _dups r&r+zformat_parser._setfieldnamessn E{tUm+E3' C("6tE{m DEE.3NT]].CD.C1779.CDDKDK s4;;/?15*@A*@A!A3*@A A dkk* 6tf=> > /5nt}}/EF/E!AGGI/EFDLDLF ==3v; & LLTFdmmc&k&AB BL '+EAGs%F FF ctj|j|j|j|j d}|t |d}|j|}||_y)N)r/r.offsetsr0r)r:r;rNr>r?rP_byteorderconv newbyteorder)r-r2r;s r&r,zformat_parser._createdtypes^[[}}ll     &y|4I&&y1E r4)FN)F)__name__ __module__ __qualname____doc__r3r*r+r,r4r&r r 8s9v% "0"CH r4r 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&rbzrecord.__repr__s( ! #s *<<> !w!!r4cltdkrt|jSt|Sr^)r rIr$rar`rcs r&r`zrecord.__str__s, ! #s *tyy{# #w  r4c|dvr tjj||S tjj||S#t$rYnwxYwtjj|dj}|j |d}|rf|j |dd} |j}n#t$r|cYSwxYw|j'|j|j|jfS|Std|d)Nsetfieldgetfieldr;r;"'record' object has no attribute '') ntvoid__getattribute__AttributeErrorr=getrir;r/viewrd)r-attr fielddictresobjdts r&rozrecord.__getattribute__s 4 477++D$7 7 77++D$7 7   GG,,T7;BB mmD$' $--Ra)C YY!   xx#xx ;<<J #EdV1!MN Ns#A AA& B33 CCcJ|dvrtd|dtjj|dj}|j |d}|r|j |g|ddSt||dr!tjj|||Std|d)Nrgz Cannot set 'z ' attributer;rjrkrl) rprmrnror=rqrhgetattr __setattr__)r-rsvalrtrus r&rzzrecord.__setattr__s 4 4 <v[!AB BGG,,T7;BB mmD$'  4==/s2Aw/ / T4 &77&&tT37 7 #EdV1!MN Nr4ctjj||}t|tjr=|jj '|j |j|jfS|Sr))rmrn __getitem__r9r;r/rrrd)r-indxrvs r&r}zrecord.__getitem__sXgg!!$- c277 # (C88T^^SYY78 8Jr4c |jj}td|D}|Dcgc]}|d|dt||}}dj |Scc}w)zPretty-print all fields.c32K|]}t|ywr))r@).0names r& z record.pprint.. s154SY5srz:  )r;r/maxryjoin)r-r/maxlenrrowss r&pprintz record.pprintsg   1511GLMut4&z""WT4%8$9:uMyyNsA) rVrWrXrYrbr`rorzr}r __classcell__rds@r&r r s4 HJ" ! O0 Or4r cTeZdZdZ d dZdZdZdZdZfdZ dZ d d Z 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', '>> 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 r/r@r8r r<ndim) arrayListr;rr.r/r0r1r2xrvrrNd0nn_arrayk testshapers r&r r 9sb)22 1A I2 'u -E }! "" E3 5=)22 399 2 gufgyIOO [[F 5zS^#+, , qB RB Avds  eU #FI&3 1X]]IInsxx"}- ay  =aSD6LM Mt ' MW33s E!E&c r|z|xtj|t}t|jdD cgc]*} tj|d| fj ,} } t | ||||||S|tjt|f} nt|||||j} tj|| } |!| j|k7r| j|} | j/t"}|Scc} w#ttf$rt|}| t|}t|t r|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/D5D B(F65F6c 2| | td|tj|} nt|||||j} | jrt d| d| 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%Cannot create record array for dtype 1. Arrays containing references are not supported.)Nr)rr) rr:r;r hasobjectr8itemsizerr@r ) datastringr;rrr.r/r0r1r2rrrs r&rrsp }LMM gufgyIOO 3E7;? ?  ~~H 'u -E Z6)h6 eU 6 BF Mr4c|j} |jdd|j|z |j|dS#|j|dwxYw)Nrrj)tellseek)fdposs r&get_remaining_sizerBsG '')C 1 wwy3 QQs $A Ac | | tdt|}|d}nt|tr|f}t |dr t |} nt tj|d} | 5}|dkDr|j|dt|} |tj|} nt|||||j} | jrtd| d | j } tj"|j%t&j( } | | z}|dkrat+|}| | z||j-d <t/|}tj"|j%t&j( } | | z}|| kDr td t1|| }|j3|j4}||k7r t7d  ddd|S#1swYSxYw)aCreate an array from binary file data Parameters ---------- fd : str or file type If file is a string or a path-like object then that file is opened, else it is assumed to be a file object. The file object must support random access (i.e. it must have tell and seek methods). dtype : data-type, optional valid dtype for all arrays shape : int or tuple of ints, optional shape of each array. offset : int, optional Position in the file to start reading from. formats, names, titles, aligned, byteorder : If `dtype` is ``None``, these arguments are passed to `numpy.format_parser` to construct a dtype. See that function for detailed documentation Returns ------- np.recarray record array consisting of data enclosed in file. Examples -------- >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,S5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.view(a.dtype.newbyteorder('<')) >>> a.tofile(fd) >>> >>> _ = fd.seek(0) >>> r=np.rec.fromfile(fd, formats='f8,i4,S5', shape=10, ... byteorder='<') >>> print(r[5]) (0.5, 10, b'abcde') >>> r.shape (10,) Nz0fromfile() needs a 'dtype' or 'formats' argument)rreadintorbrrrrrrz;Not enough bytes left in file for specified shape and type.z%Didn't read as many bytes as expected)rrr9rhasattrropenosfspathrrr:r;r rr8rrprodrmintpr#indexrHr rdataOSError)rr;rrr.r/r0r1r2ctxrrr shapeprod shapesizenbytesr nbytesreads r&rrKs\ }JKK 'u -E } E3 r:"o299R=$'  A: GGFA !"%  HHUOE!e  ??7w?BB  >>HHUO((rww(7 ( q=KE%)iZ%7E%++b/ "%LE,,277,;IX% D=&  %'[[-  AB B O T MU T 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 KHHHVGRWWGd KS,wS,S,l  K:>AE[[| KEI6:UUp KEIAENN` K;??Ckk\ KGKGKR"R"r4