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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, 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 = a.view(np.recarray)
>>> ar.x array([1, 1])
>>> ar.y array([ 2., 2.])
""" # All of the functions allow formats to be a dtype __all__ = ['record', 'recarray', 'format_parser']
import numeric as sb from defchararray import chararray import numerictypes as nt import types import os import sys
ndarray = sb.ndarray
_byteorderconv = {'b':'>', '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
numfmt = nt.typeDict _typestr = nt._typestr
def find_duplicate(list): """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 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(['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')])
""" def __init__(self, formats, names, titles, aligned=False, byteorder=None): self._parseFormats(formats, aligned) self._setfieldnames(names, titles) self._createdescr(byteorder) self.dtype = self._descr
def _parseFormats(self, formats, aligned=0): """ 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)
def _setfieldnames(self, names, titles): """convert input field names into a list and assign to the _names attribute """
if (names): if (type(names) in [types.ListType, types.TupleType]): pass elif (type(names) == types.StringType): names = names.split(',') else: raise NameError, "illegal input names %s" % `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))
def _createdescr(self, byteorder): 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
class record(nt.void): """A data-type scalar that allows field access as attribute lookup. """ def __repr__(self): return self.__str__()
def __str__(self): return str(self.item())
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 recarray, # if it's a string ('SU') return a chararray # otherwise return the object try: dt = obj.dtype except AttributeError: return obj if dt.fields: return obj.view(obj.__class__) if dt.char in 'SU': return obj.view(chararray) 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 pprint(self): """Pretty-print all fields.""" # pretty-print all fields names = self.dtype.names maxlen = max([len(name) for name in names]) rows = [] fmt = '%% %ds: %%s' % maxlen for name in names: rows.append(fmt % (name, getattr(self, name))) 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, analagous 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.
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 reccord 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')])
""" def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False):
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)) else: self = ndarray.__new__(subtype, shape, (record, descr), buffer=buf, offset=offset, strides=strides) return self
def __getattribute__(self, attr): try: return object.__getattribute__(self, attr) except AttributeError: # attr must be a fieldname pass fielddict = ndarray.__getattribute__(self, 'dtype').fields try: res = fielddict[attr][:2] except (TypeError, KeyError): raise AttributeError, "record array has no attribute %s" % attr obj = self.getfield(*res) # if it has fields return a recarray, otherwise return # normal array if obj.dtype.fields: return obj if obj.dtype.char in 'SU': return obj.view(chararray) 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.
def __setattr__(self, attr, val): newattr = attr not in self.__dict__ try: ret = object.__setattr__(self, attr, val) except: 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 try: # or this setattr worked on an internal # attribute. object.__delattr__(self, attr) except: return ret try: res = fielddict[attr][:2] except (TypeError, KeyError): raise AttributeError, "record array has no attribute %s" % attr return self.setfield(val, *res)
def __getitem__(self, indx): obj = ndarray.__getitem__(self, indx) if (isinstance(obj, ndarray) and obj.dtype.isbuiltin): return obj.view(ndarray) return obj
def __repr__(self) : ret = ndarray.__repr__(self) return ret.replace("recarray", "rec.array", 1)
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.fields: return obj if obj.dtype.char in 'SU': return obj.view(chararray) return obj.view(ndarray) else: return self.setfield(val, *res)
def view(self, dtype=None, type=None): if dtype is None: return ndarray.view(self, type) elif type is None: try: if issubclass(dtype, ndarray): return ndarray.view(self, dtype) except TypeError: pass dtype = sb.dtype(dtype) if dtype.fields is None: return self.__array__().view(dtype) return ndarray.view(self, dtype) else: return ndarray.view(self, dtype, type)
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
>>> 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 += _typestr[obj.dtype.type] if issubclass(obj.dtype.type, nt.flexible): formats += `obj.itemsize` formats += ',' formats = formats[:-1]
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 = len(descr[k].shape) testshape = obj.shape[:len(obj.shape) - 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
# shape must be 1-d if you use list of lists... 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
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 chararray(['dbe', 'de'], dtype='|S3') >>> import cPickle >>> print cPickle.loads(cPickle.dumps(r)) [(456, 'dbe', 1.2) (2, 'de', 1.3)] """
nfields = len(recList[0]) if formats is None and dtype is None: # slower obj = sb.array(recList, dtype=object) arrlist = [sb.array(obj[..., i].tolist()) for i in xrange(nfields)] 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: # list of lists instead of list of tuples 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 xrange(_array.size): _array[k] = tuple(recList[k]) return _array else: if shape is not None and retval.shape != shape: retval.shape = shape
res = retval.view(recarray)
return res
def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None, 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 ValueError, "Must have dtype= or formats="
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
def get_remaining_size(fd): 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
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
If file is a string then that file is opened, else it is assumed to be a file object.
>>> 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 (shape is None or shape == 0): shape = (-1,) elif isinstance(shape, (int, long)): shape = (shape,)
name = 0 if isinstance(fd, str): name = 1 fd = open(fd, 'rb') 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() shapesize = shapeprod * itemsize if shapesize < 0: shape = list(shape) shape[ shape.index(-1) ] = size / -shapesize shape = tuple(shape) shapeprod = sb.array(shape).prod()
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
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. """
if isinstance(obj, (type(None), str, file)) 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, str): 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 isinstance(obj, file): 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() res = new.view(recarray) if issubclass(res.dtype.type, nt.void): res.dtype = sb.dtype((record, res.dtype)) return res
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) res = obj.view(recarray) if issubclass(res.dtype.type, nt.void): res.dtype = sb.dtype((record, res.dtype)) return res
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