/usr/lib64/python2.6/site-packages/numpy/core
Edit: /usr/lib64/python2.6/site-packages/numpy/core/records.pyo (24437B)
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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', '
>> 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.])
t recordt recarrayt
format_parseriÿÿÿÿN( t chararrayt >t bt >> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... ['T1', 'T2', 'T3']).dtype
dtype([(('T1', 'col1'), '>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... []).dtype
dtype([('col1', '>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype
dtype([('f0', '', '='}, 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', '>> 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.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])
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ƒ } x+ t | i ƒ D] } t | | ƒ | | >> 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)]
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| | | i d ƒ >> 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,)
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