Module records

source code

Classes

[hide private]

format_parser

recarray

record

Functions

[hide private]

array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, copy=False)
Construct a record array from a wide-variety of objects.

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find_duplicate(list)
Find duplication in a list, return a list of duplicated elements

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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

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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

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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.

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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

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get_remaining_size(fd)

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Variables

[hide private]

_byteorderconv = {'<': '<', '=': '=', '>': '>', 'B': '>', 'I':...

_typestr = {<type 'numpy.int64'>: 'i8', <type 'numpy.int16'>: ...

numfmt = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2: ...

Function Details

[hide private]

fromarrays(arrayList, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None)

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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])

fromfile(fd, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None)

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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,)

fromrecords(recList, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None)

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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)]

Variables Details

[hide private]

_byteorderconv

Value:

{'<': '<',
 '=': '=',
 '>': '>',
 'B': '>',
 'I': '|',
 'L': '<',
 'N': '=',
 'S': 's',
...

_typestr

Value:

{<type 'numpy.int64'>: 'i8', <type 'numpy.int16'>: 'i2', <type 'numpy.
float32'>: 'f4', <type 'numpy.bool_'>: 'b1', <type 'numpy.uint8'>: 'u1
', <type 'numpy.object_'>: 'O4', <type 'numpy.float64'>: 'f8', <type '
numpy.uint16'>: 'u2', <type 'numpy.string_'>: 'S', <type 'numpy.int8'>
: 'i1', <type 'numpy.float96'>: 'f12', <type 'numpy.unicode_'>: 'U', <
type 'numpy.uint32'>: 'u4', <type 'numpy.complex192'>: 'c24', <type 'n
umpy.int32'>: 'i4', <type 'numpy.complex64'>: 'c8', <type 'numpy.uint3
2'>: 'u4', <type 'numpy.int32'>: 'i4', <type 'numpy.complex128'>: 'c16
...

numfmt

Value:

{0: <type 'numpy.bool_'>,
 1: <type 'numpy.int8'>,
 2: <type 'numpy.uint8'>,
 3: <type 'numpy.int16'>,
 4: <type 'numpy.uint16'>,
 5: <type 'numpy.int32'>,
 6: <type 'numpy.uint32'>,
 7: <type 'numpy.int32'>,
...