""" 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.]) """ import warnings from collections import Counter from contextlib import nullcontext from .._utils import set_module from . import numeric as sb from . import numerictypes as nt from numpy.compat import os_fspath from .arrayprint import _get_legacy_print_mode # All of the functions allow formats to be a dtype __all__ = [ 'record', 'recarray', 'format_parser', 'fromarrays', 'fromrecords', 'fromstring', 'fromfile', 'array', ] ndarray = sb.ndarray _byteorderconv = {'b':'>', 'l':'<', 'n':'=', 'B':'>', 'L':'<', 'N':'=', 'S':'s', 's':'s', '>':'>', '<':'<', '=':'=', '|':'|', 'I':'|', 'i':'|'} # formats regular expression # allows multidimensional spec with a tuple syntax in front # of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 ' # are equally allowed numfmt = nt.sctypeDict def find_duplicate(list): """Find duplication in a list, return a list of duplicated elements""" return [ item for item, counts in Counter(list).items() if counts > 1 ] @set_module('numpy') class format_parser: """ 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([' len(titles): self._titles += [None] * (self._nfields - len(titles)) def _createdtype(self, byteorder): dtype = sb.dtype({ 'names': self._names, 'formats': self._f_formats, 'offsets': self._offsets, 'titles': self._titles, }) if byteorder is not None: byteorder = _byteorderconv[byteorder[0]] dtype = dtype.newbyteorder(byteorder) self.dtype = dtype class record(nt.void): """A data-type scalar that allows field access as attribute lookup. """ # manually set name and module so that this class's type shows up # as numpy.record when printed __name__ = 'record' __module__ = 'numpy' def __repr__(self): if _get_legacy_print_mode() <= 113: return self.__str__() return super().__repr__() def __str__(self): if _get_legacy_print_mode() <= 113: return str(self.item()) return super().__str__() def __getattribute__(self, attr): if attr in ('setfield', 'getfield', 'dtype'): return nt.void.__getattribute__(self, attr) try: return nt.void.__getattribute__(self, attr) except AttributeError: pass fielddict = nt.void.__getattribute__(self, 'dtype').fields res = fielddict.get(attr, None) if res: obj = self.getfield(*res[:2]) # if it has fields return a record, # otherwise return the object try: dt = obj.dtype except AttributeError: #happens if field is Object type return obj if dt.names is not None: return obj.view((self.__class__, obj.dtype)) return obj else: raise AttributeError("'record' object has no " "attribute '%s'" % attr) def __setattr__(self, attr, val): if attr in ('setfield', 'getfield', 'dtype'): raise AttributeError("Cannot set '%s' attribute" % attr) fielddict = nt.void.__getattribute__(self, 'dtype').fields res = fielddict.get(attr, None) if res: return self.setfield(val, *res[:2]) else: if getattr(self, attr, None): return nt.void.__setattr__(self, attr, val) else: raise AttributeError("'record' object has no " "attribute '%s'" % attr) def __getitem__(self, indx): obj = nt.void.__getitem__(self, indx) # copy behavior of record.__getattribute__, if isinstance(obj, nt.void) and obj.dtype.names is not None: return obj.view((self.__class__, obj.dtype)) else: # return a single element return obj def pprint(self): """Pretty-print all fields.""" # pretty-print all fields names = self.dtype.names maxlen = max(len(name) for name in names) fmt = '%% %ds: %%s' % maxlen rows = [fmt % (name, getattr(self, name)) for name in names] return "\n".join(rows) # The recarray is almost identical to a standard array (which supports # named fields already) The biggest difference is that it can use # attribute-lookup to find the fields and it is constructed using # a record. # If byteorder is given it forces a particular byteorder on all # the fields (and any subfields) class recarray(ndarray): """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', ' 0 or self.shape == (0,): lst = sb.array2string( self, separator=', ', prefix=prefix, suffix=',') else: # show zero-length shape unless it is (0,) lst = "[], shape=%s" % (repr(self.shape),) lf = '\n'+' '*len(prefix) if _get_legacy_print_mode() <= 113: lf = ' ' + lf # trailing space return fmt % (lst, lf, repr_dtype) def field(self, attr, val=None): if isinstance(attr, int): names = ndarray.__getattribute__(self, 'dtype').names attr = names[attr] fielddict = ndarray.__getattribute__(self, 'dtype').fields res = fielddict[attr][:2] if val is None: obj = self.getfield(*res) if obj.dtype.names is not None: return obj return obj.view(ndarray) else: return self.setfield(val, *res) def _deprecate_shape_0_as_None(shape): if shape == 0: warnings.warn( "Passing `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.", FutureWarning, stacklevel=3) return None else: return shape @set_module("numpy.rec") def fromarrays(arrayList, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None): """Create 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.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.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', ' 0: shape = shape[:-nn] _array = recarray(shape, descr) # populate the record array (makes a copy) for k, obj in enumerate(arrayList): nn = descr[k].ndim testshape = obj.shape[:obj.ndim - nn] name = _names[k] if testshape != shape: raise ValueError(f'array-shape mismatch in array {k} ("{name}")') _array[name] = obj return _array @set_module("numpy.rec") def fromrecords(recList, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None): """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', ' 1: raise ValueError("Can only deal with 1-d array.") _array = recarray(shape, descr) for k in range(_array.size): _array[k] = tuple(recList[k]) # list of lists instead of list of tuples ? # 2018-02-07, 1.14.1 warnings.warn( "fromrecords expected a list of tuples, may have received a list " "of lists instead. In the future that will raise an error", FutureWarning, stacklevel=2) return _array else: if shape is not None and retval.shape != shape: retval.shape = shape res = retval.view(recarray) return res @set_module("numpy.rec") def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None): r"""Create 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' """ if dtype is None and formats is None: raise TypeError("fromstring() needs a 'dtype' or 'formats' argument") if dtype is not None: descr = sb.dtype(dtype) else: descr = format_parser(formats, names, titles, aligned, byteorder).dtype itemsize = descr.itemsize # NumPy 1.19.0, 2020-01-01 shape = _deprecate_shape_0_as_None(shape) if shape in (None, -1): shape = (len(datastring) - offset) // itemsize _array = recarray(shape, descr, buf=datastring, offset=offset) return _array def get_remaining_size(fd): pos = fd.tell() try: fd.seek(0, 2) return fd.tell() - pos finally: fd.seek(pos, 0) @set_module("numpy.rec") def fromfile(fd, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None): """Create 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,) """ if dtype is None and formats is None: raise TypeError("fromfile() needs a 'dtype' or 'formats' argument") # NumPy 1.19.0, 2020-01-01 shape = _deprecate_shape_0_as_None(shape) if shape is None: shape = (-1,) elif isinstance(shape, int): shape = (shape,) if hasattr(fd, 'readinto'): # GH issue 2504. fd supports io.RawIOBase or io.BufferedIOBase interface. # Example of fd: gzip, BytesIO, BufferedReader # file already opened ctx = nullcontext(fd) else: # open file ctx = open(os_fspath(fd), 'rb') with ctx as fd: if offset > 0: fd.seek(offset, 1) size = get_remaining_size(fd) if dtype is not None: descr = sb.dtype(dtype) else: descr = format_parser(formats, names, titles, aligned, byteorder).dtype itemsize = descr.itemsize shapeprod = sb.array(shape).prod(dtype=nt.intp) shapesize = shapeprod * itemsize if shapesize < 0: shape = list(shape) shape[shape.index(-1)] = size // -shapesize shape = tuple(shape) shapeprod = sb.array(shape).prod(dtype=nt.intp) nbytes = shapeprod * itemsize if nbytes > size: raise ValueError( "Not enough bytes left in file for specified shape and type") # create the array _array = recarray(shape, descr) nbytesread = fd.readinto(_array.data) if nbytesread != nbytes: raise OSError("Didn't read as many bytes as expected") return _array @set_module("numpy.rec") def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, copy=True): """ 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='