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""" 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', int), ('y', float)]) >>> a array([(1, 2.0), (1, 2.0)], dtype=[('x', '<i4'), ('y', '<f8')])
Here, each element consists of two fields: x (and int), and y (a float). This is known as a structured array. The different fields are analogous to columns in a spread-sheet. The different fields can be accessed as one would a dictionary::
>>> 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.])
"""
# All of the functions allow formats to be a dtype
'l':'<', 'n':'=', 'B':'>', 'L':'<', 'N':'=', 'S':'s', 's':'s', '>':'>', '<':'<', '=':'=', '|':'|', 'I':'|', 'i':'|'}
# formats regular expression # allows multidimension spec with a tuple syntax in front # of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 ' # are equally allowed
"""Find duplication in a list, return a list of duplicated elements""" dup = [] for i in range(len(list)): if (list[i] in list[i + 1:]): if (list[i] not in dup): dup.append(list[i]) return dup
""" 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(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... ['T1', 'T2', 'T3']).dtype dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), '|S5')])
`names` and/or `titles` can be empty lists. If `titles` is an empty list, titles will simply not appear. If `names` is empty, default field names will be used.
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], ... []).dtype dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '|S5')]) >>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', '|S5')])
"""
self._parseFormats(formats, aligned) self._setfieldnames(names, titles) self._createdescr(byteorder) self.dtype = self._descr
""" Parse the field formats """
if formats is None: raise ValueError("Need formats argument") if isinstance(formats, list): if len(formats) < 2: formats.append('') formats = ','.join(formats) dtype = sb.dtype(formats, aligned) fields = dtype.fields if fields is None: dtype = sb.dtype([('f1', dtype)], aligned) fields = dtype.fields keys = dtype.names self._f_formats = [fields[key][0] for key in keys] self._offsets = [fields[key][1] for key in keys] self._nfields = len(keys)
"""convert input field names into a list and assign to the _names attribute """
if (names): if (type(names) in [list, tuple]): pass elif isinstance(names, (str, unicode)): names = names.split(',') else: raise NameError("illegal input names %s" % repr(names))
self._names = [n.strip() for n in names[:self._nfields]] else: self._names = []
# if the names are not specified, they will be assigned as # "f0, f1, f2,..." # if not enough names are specified, they will be assigned as "f[n], # f[n+1],..." etc. where n is the number of specified names..." self._names += ['f%d' % i for i in range(len(self._names), self._nfields)] # check for redundant names _dup = find_duplicate(self._names) if _dup: raise ValueError("Duplicate field names: %s" % _dup)
if (titles): self._titles = [n.strip() for n in titles[:self._nfields]] else: self._titles = [] titles = []
if (self._nfields > len(titles)): self._titles += [None] * (self._nfields - len(titles))
descr = sb.dtype({'names':self._names, 'formats':self._f_formats, 'offsets':self._offsets, 'titles':self._titles}) if (byteorder is not None): byteorder = _byteorderconv[byteorder[0]] descr = descr.newbyteorder(byteorder)
self._descr = descr
"""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
def __repr__(self): if get_printoptions()['legacy'] == '1.13': return self.__str__() return super(record, self).__repr__()
def __str__(self): if get_printoptions()['legacy'] == '1.13': return str(self.item()) return super(record, self).__str__()
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.fields: return obj.view((self.__class__, obj.dtype.fields)) return obj else: raise AttributeError("'record' object has no " "attribute '%s'" % attr)
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)
obj = nt.void.__getitem__(self, indx)
# copy behavior of record.__getattribute__, if isinstance(obj, nt.void) and obj.dtype.fields: return obj.view((self.__class__, obj.dtype.fields)) else: # return a single element return obj
"""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)
"""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 -------- rec.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', float), ('y', int)]) >>> x array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i4')])
>>> 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', '<i4'), ('y', '<f8'), ('z', '<i4')])
"""
# manually set name and module so that this class's type shows # up as "numpy.recarray" when printed
formats=None, names=None, titles=None, byteorder=None, aligned=False, order='C'):
if dtype is not None: descr = sb.dtype(dtype) else: descr = format_parser(formats, names, titles, aligned, byteorder)._descr
if buf is None: self = ndarray.__new__(subtype, shape, (record, descr), order=order) else: self = ndarray.__new__(subtype, shape, (record, descr), buffer=buf, offset=offset, strides=strides, order=order) return self
if self.dtype.type is not record and self.dtype.fields: # if self.dtype is not np.record, invoke __setattr__ which will # convert it to a record if it is a void dtype. self.dtype = self.dtype
# See if ndarray has this attr, and return it if so. (note that this # means a field with the same name as an ndarray attr cannot be # accessed by attribute). try: return object.__getattribute__(self, attr) except AttributeError: # attr must be a fieldname pass
# look for a field with this name fielddict = ndarray.__getattribute__(self, 'dtype').fields try: res = fielddict[attr][:2] except (TypeError, KeyError): raise AttributeError("recarray has no attribute %s" % attr) obj = self.getfield(*res)
# At this point obj will always be a recarray, since (see # PyArray_GetField) the type of obj is inherited. Next, if obj.dtype is # non-structured, convert it to an ndarray. Then if obj is structured # with void type convert it to the same dtype.type (eg to preserve # numpy.record type if present), since nested structured fields do not # inherit type. Don't do this for non-void structures though. if obj.dtype.fields: if issubclass(obj.dtype.type, nt.void): return obj.view(dtype=(self.dtype.type, obj.dtype)) return obj else: return obj.view(ndarray)
# Save the dictionary. # If the attr is a field name and not in the saved dictionary # Undo any "setting" of the attribute and do a setfield # Thus, you can't create attributes on-the-fly that are field names.
# Automatically convert (void) structured types to records # (but not non-void structures, subarrays, or non-structured voids) if attr == 'dtype' and issubclass(val.type, nt.void) and val.fields: val = sb.dtype((record, val))
newattr = attr not in self.__dict__ try: ret = object.__setattr__(self, attr, val) except Exception: fielddict = ndarray.__getattribute__(self, 'dtype').fields or {} if attr not in fielddict: exctype, value = sys.exc_info()[:2] raise exctype(value) else: fielddict = ndarray.__getattribute__(self, 'dtype').fields or {} if attr not in fielddict: return ret if newattr: # We just added this one or this setattr worked on an # internal attribute. try: object.__delattr__(self, attr) except Exception: return ret try: res = fielddict[attr][:2] except (TypeError, KeyError): raise AttributeError("record array has no attribute %s" % attr) return self.setfield(val, *res)
obj = super(recarray, self).__getitem__(indx)
# copy behavior of getattr, except that here # we might also be returning a single element if isinstance(obj, ndarray): if obj.dtype.fields: obj = obj.view(type(self)) if issubclass(obj.dtype.type, nt.void): return obj.view(dtype=(self.dtype.type, obj.dtype)) return obj else: return obj.view(type=ndarray) else: # return a single element return obj
def __repr__(self):
repr_dtype = self.dtype if (self.dtype.type is record or (not issubclass(self.dtype.type, nt.void))): # If this is a full record array (has numpy.record dtype), # or if it has a scalar (non-void) dtype with no records, # represent it using the rec.array function. Since rec.array # converts dtype to a numpy.record for us, convert back # to non-record before printing if repr_dtype.type is record: repr_dtype = sb.dtype((nt.void, repr_dtype)) prefix = "rec.array(" fmt = 'rec.array(%s,%sdtype=%s)' else: # otherwise represent it using np.array plus a view # This should only happen if the user is playing # strange games with dtypes. prefix = "array(" fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
# get data/shape string. logic taken from numeric.array_repr if self.size > 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_printoptions()['legacy'] == '1.13': lf = ' ' + lf # trailing space return fmt % (lst, lf, repr_dtype)
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.fields: return obj return obj.view(ndarray) else: return self.setfield(val, *res)
names=None, titles=None, aligned=False, byteorder=None): """ create a record array from a (flat) list of arrays
>>> 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) >>> x1[1]=34 >>> r.a array([1, 2, 3, 4]) """
arrayList = [sb.asarray(x) for x in arrayList]
if shape is None or shape == 0: shape = arrayList[0].shape
if isinstance(shape, int): shape = (shape,)
if formats is None and dtype is None: # go through each object in the list to see if it is an ndarray # and determine the formats. formats = [] for obj in arrayList: if not isinstance(obj, ndarray): raise ValueError("item in the array list must be an ndarray.") formats.append(obj.dtype.str) formats = ','.join(formats)
if dtype is not None: descr = sb.dtype(dtype) _names = descr.names else: parsed = format_parser(formats, names, titles, aligned, byteorder) _names = parsed._names descr = parsed._descr
# Determine shape from data-type. if len(descr) != len(arrayList): raise ValueError("mismatch between the number of fields " "and the number of arrays")
d0 = descr[0].shape nn = len(d0) if nn > 0: shape = shape[:-nn]
for k, obj in enumerate(arrayList): nn = descr[k].ndim testshape = obj.shape[:obj.ndim - nn] if testshape != shape: raise ValueError("array-shape mismatch in array %d" % k)
_array = recarray(shape, descr)
# populate the record array (makes a copy) for i in range(len(arrayList)): _array[_names[i]] = arrayList[i]
return _array
titles=None, aligned=False, byteorder=None): """ create a recarray from a list of records in text form
The data in the same field can be heterogeneous, they will be promoted to the highest data type. This method is intended for creating smaller record arrays. If used to create large array without formats defined
r=fromrecords([(2,3.,'abc')]*100000)
it can be slow.
If formats is None, then this will auto-detect formats. Use list of tuples rather than list of lists for faster processing.
>>> 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='|S3') >>> import pickle >>> print(pickle.loads(pickle.dumps(r))) [(456, 'dbe', 1.2) (2, 'de', 1.3)] """
if formats is None and dtype is None: # slower obj = sb.array(recList, dtype=object) arrlist = [sb.array(obj[..., i].tolist()) for i in range(obj.shape[-1])] return fromarrays(arrlist, formats=formats, shape=shape, names=names, titles=titles, aligned=aligned, byteorder=byteorder)
if dtype is not None: descr = sb.dtype((record, dtype)) else: descr = format_parser(formats, names, titles, aligned, byteorder)._descr
try: retval = sb.array(recList, dtype=descr) except (TypeError, ValueError): if (shape is None or shape == 0): shape = len(recList) if isinstance(shape, (int, long)): shape = (shape,) if len(shape) > 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
names=None, titles=None, aligned=False, byteorder=None): """ create a (read-only) record array from binary data contained in a string"""
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)._descr
itemsize = descr.itemsize if (shape is None or shape == 0 or shape == -1): shape = (len(datastring) - offset) // itemsize
_array = recarray(shape, descr, buf=datastring, offset=offset) return _array
try: fn = fd.fileno() except AttributeError: return os.path.getsize(fd.name) - fd.tell() st = os.fstat(fn) size = st.st_size - fd.tell() return size
names=None, titles=None, aligned=False, byteorder=None): """Create an array from binary file data
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).
>>> 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")
if (shape is None or shape == 0): shape = (-1,) elif isinstance(shape, (int, long)): shape = (shape,)
if isfileobj(fd): # file already opened name = 0 else: # open file fd = open(os_fspath(fd), 'rb') name = 1
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)._descr
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 IOError("Didn't read as many bytes as expected") if name: fd.close()
return _array
names=None, titles=None, aligned=False, byteorder=None, copy=True): """Construct a record array from a wide-variety of objects. """
if ((isinstance(obj, (type(None), str)) or isfileobj(obj)) and (formats is None) and (dtype is None)): raise ValueError("Must define formats (or dtype) if object is " "None, string, or an open file")
kwds = {} if dtype is not None: dtype = sb.dtype(dtype) elif formats is not None: dtype = format_parser(formats, names, titles, aligned, byteorder)._descr else: kwds = {'formats': formats, 'names': names, 'titles': titles, 'aligned': aligned, 'byteorder': byteorder }
if obj is None: if shape is None: raise ValueError("Must define a shape if obj is None") return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)
elif isinstance(obj, bytes): return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)
elif isinstance(obj, (list, tuple)): if isinstance(obj[0], (tuple, list)): return fromrecords(obj, dtype=dtype, shape=shape, **kwds) else: return fromarrays(obj, dtype=dtype, shape=shape, **kwds)
elif isinstance(obj, recarray): if dtype is not None and (obj.dtype != dtype): new = obj.view(dtype) else: new = obj if copy: new = new.copy() return new
elif isfileobj(obj): return fromfile(obj, dtype=dtype, shape=shape, offset=offset)
elif isinstance(obj, ndarray): if dtype is not None and (obj.dtype != dtype): new = obj.view(dtype) else: new = obj if copy: new = new.copy() return new.view(recarray)
else: interface = getattr(obj, "__array_interface__", None) if interface is None or not isinstance(interface, dict): raise ValueError("Unknown input type") obj = sb.array(obj) if dtype is not None and (obj.dtype != dtype): obj = obj.view(dtype) return obj.view(recarray) |