# 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
Parameters
----------
formats : string or list
comma-separated format descriptions --- 'f8, i4, a5'
list of format description strings --- ['f8', 'i4', 'a5']
names : string or (list or tuple of strings)
comma-separated field names --- 'col1, col2, col3'
list or tuple of field names
titles : sequence
sequence of title strings or unicode
aligned : bool
align the fields by padding as the C-compiler would
byteorder :
If specified, all the fields will be changed to the
provided byteorder. Otherwise, the default byteorder is
used.
Returns
-------
object
A Python object whose dtype attribute is a data-type.
"""
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
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):
"""recarray(shape, dtype=None, buf=None, **kwds)
Subclass of ndarray that allows field access using attribute lookup.
Parameters
----------
shape : tuple
shape of record array
dtype : data-type or None
The desired data-type. If this is None, then the data-type is determined
by the *formats*, *names*, *titles*, *aligned*, and *byteorder* keywords.
buf : [buffer] or None
If this is None, then a new array is created of the given shape and data-type
If this is an object exposing the buffer interface, then the array will
use the memory from an existing buffer. In this case, the *offset* and
*strides* keywords can also be used.
See Also
--------
format_parser : determine a data-type from formats, names, titles
record : fundamental data-type for recarray
"""
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 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, obj):
try:
if issubclass(obj, ndarray):
return ndarray.view(self, obj)
except TypeError:
pass
dtype = sb.dtype(obj)
if dtype.fields is None:
return self.__array__().view(dtype)
return ndarray.view(self, obj)
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=N.array([1,2,3,4])
>>> x2=N.array(['a','dd','xyz','12'])
>>> x3=N.array([1.1,2,3,4])
>>> r = 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=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(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)
res.dtype = sb.dtype((record, res.dtype))
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 = N.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> fd.seek(0)
>>> r=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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