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bitstream 1.0.0-alpha.15

A Binary Data Type with a Stream Interface

Latest Version: 2.0.3

About this document. It is originally a plain text file using the Markdown syntax, but you may be reading a HTML, PDF or ReST version instead. In any case, the contents are subject to a Creative Commons Attribution 3.0 license.

What is Bitstream ?

Bitstream provides a binary data type with a stream interface for Python.

  • Binary Data: the BitStream class is a linearly ordered container of bits. The standard library is only convenient to manage binary data at the byte level. Consider using BitStream instead, especially you need to address the bit level.
  • Stream Interface: you can only read data at the start of a stream and write data at its end. This is a very simple way to interact with binary data, but it is also the pattern that comes naturally in many applications. To manage binary codes and formats, in my experience, random data access is not a requirement.
  • Python and NumPy Types. BitStream has built-in readers and writers for the common data types with a standard binary layout: bools, ASCII strings, fixed-size integers and floating-point integers.
  • User-Defined Types. The list of supported types and binary representation may be enlarged at will: new readers and writers can be implemented and associated to specific data types.
  • Performance. Bitstream is a Python C-extension module that has been optimized for the common use cases. Hopefully, it will be fast enough for your needs ! Under the hood, the Cython language and compiler are used to generate this extension module.
  • Open-Source: the Bitstream software is distributed under a MIT license, its documentation under a Creative Commons Attribution 3.0 license. The development takes place on GitHub and releases are also available on PyPi.

Requirements & Installation

Bitstream targets Python 2.7, you will need to install it first.

TODO: move NumPy dependency here (? Dunno …), talk about Linux-only platform.

Then, several installation options are available: TODO: state clearly what one should do depending on the aim.

  • Easy install: if the pip package manager is available, execute the following command as root:

    $ pip install bitstream
    

    The dependencies of Bitstream will be handled automatically. If you don’t have root privileges, use virtualenv.

  • Install from source: the releases of Bitstream are available on the Python Package Index (PyPi). Once you have downloaded and unpacked the archive, to build the Bitstream module, you need setuptools. You also need to install the NumPy package, version 1.6.1 or later.

    TODO: test if numpy is automatically download if needed.

    Then, as root, execute

    $ python setup.py install
    
  • Hack with git: to experiment with the latest version of Bitstream, clone the GitHub repository:

    $ git clone git://github.com/boisgera/bitstream.git
    

    To actually build the module, you will need everything you need to build from source and will execute the same command. If in addition, you want to edit the source files, you will also need the Cython compiler, version 0.15.1 or later and will execute instead:

    $ python setup.py install --with-cython
    

Getting Started

Most of the features of bitstream are available via the BitStream class.

>>> from bitstream import BitStream

The module is tightly integrated with the NumPy library. For convenience, we import all symbols from its top-level module.

>>> from numpy import *

Overview of Bitstream Features

>>> stream = BitStream()
>>> stream
<BLANKLINE>
>>> stream.write(True, bool)
>>> stream
1
>>> stream.write(False, bool)
>>> stream
10
>>> stream.write(-128, int8)
>>> stream
1010000000
>>> stream.write("AB", str)
>>> stream
10100000000100000101000010
>>> stream.read(bool, 2)
[True, False]
>>> stream
100000000100000101000010
>>> stream.read(int8, 1)
array([-128], dtype=int8)
>>> stream
0100000101000010
>>> stream.read(str, 2)
'AB'
>>> stream
<BLANKLINE>

Built-in Readers and Writers

Bools

Write single bits to a bitstream with the arguments True and False:

>>> stream = BitStream()
>>> stream.write(False, bool)
>>> stream.write(True , bool)
>>> stream
01

Lists of booleans may be used too write multiple bits at once:

>>> stream = BitStream()
>>> stream.write([], bool)
>>> stream
<BLANKLINE>
>>> stream.write([False], bool)
>>> stream.write([True] , bool)
>>> stream
01
>>> stream.write([False, True], bool)
>>> stream
0101

The second argument to the write method – the type information – can also be specified with the keyword argument type:

>>> stream = BitStream()
>>> stream.write(False, type=bool)
>>> stream.write(True , type=bool)
>>> stream
01

For single bools or lists of bools, the type information is optional:

>>> stream = BitStream()
>>> stream.write(False)
>>> stream.write(True)
>>> stream.write([])
>>> stream.write([False])
>>> stream.write([True])
>>> stream.write([False, True])
>>> stream
010101

Numpy bool_ scalars or one-dimensional arrays can be used instead:

>>> bool_
<type 'numpy.bool_'>
>>> stream = BitStream()
>>> stream.write(bool_(False)  , bool)
>>> stream.write(bool_(True)   , bool)
>>> stream
01

>>> stream = BitStream()
>>> empty = array([], dtype=bool)
>>> stream.write(empty, bool)
>>> stream
<BLANKLINE>
>>> stream.write(array([False]), bool)
>>> stream.write(array([True]) , bool)
>>> stream.write(array([False, True]), bool)
>>> stream
0101

For such data, the type information is also optional:

>>> stream = BitStream()
>>> stream.write(bool_(False))
>>> stream.write(bool_(True))
>>> stream.write(array([], dtype=bool))
>>> stream.write(array([False]))
>>> stream.write(array([True]))
>>> stream.write(array([False, True]))
>>> stream
010101

Python and Numpy numeric types are also valid arguments: zero is considered false and nonzero numbers are considered true.

Q: Use a predicate instead (non-zero) ? and check iff ?

>>> small_integers = range(0, 64)
>>> stream = BitStream()
>>> for integer in small_integers:
...     stream.write(integer, bool)
>>> stream
0111111111111111111111111111111111111111111111111111111111111111
>>> stream = BitStream()
>>> for integer in small_integers:
...     stream.write(-integer, bool)
>>> stream
0111111111111111111111111111111111111111111111111111111111111111

>>> large_integers = [2**i for i in range(6, 64)]
>>> stream = BitStream()
>>> for integer in large_integers:
...     stream.write(integer, bool)
>>> stream
1111111111111111111111111111111111111111111111111111111111
>>> stream = BitStream()
>>> for integer in large_integers:
...     stream.write(-integer, bool)
>>> stream
1111111111111111111111111111111111111111111111111111111111

TODO: use iinfo(type).min/max

TODO: write sample(type, r) iterator.

>>> def irange(start, stop, r=1.0):
...     i = 0
...     while i < stop:
...         yield i
...         i = max(i+1, int(i*r))

>>> unsigned = [uint8, uint16, uint32]
>>> for integer_type in unsigned:
...     _min, _max = iinfo(integer_type).min, iinfo(integer_type).max
...     for i in irange(_min, _max + 1, r=1.001):
...         integer = integer_type(i)
...         if integer and BitStream(integer, bool) != BitStream(True):
...             type_name = integer_type.__name__
...             print "Failure for {0}({1})".format(type_name, integer)





>>> stream = BitStream()
>>> stream.write(0.0, bool)
>>> stream.write(1.0, bool)
>>> stream.write(pi , bool)
>>> stream.write(float64(0.0), bool)
>>> stream.write(float64(1.0), bool)
>>> stream.write(float64(pi) , bool)
>>> stream
011011

TODO: arrays of numeric type (non-bools), written as bools


TODO: Mark all following behaviors as undefined ? Probably safer …

Actually, any single data written as a bool, is conceptually cast into a bool first, with the semantics of the bool constructor. List and one-dimensional numpy array arguments are considered holders of multiple data, each of which is converted to bool. Any other sequence type (strings, tuples, etc.) is considered single data.

>>> bool("")
False
>>> bool(" ")
True
>>> bool("A")
True
>>> bool("AAA")
True

>>> stream = BitStream()
>>> stream.write("", bool)
>>> stream.write(" ", bool)
>>> stream.write("A", bool)
>>> stream.write("AAA", bool)
>>> stream
0111
>>> stream = BitStream()
>>> stream.write(["", " " , "A", "AAA"], bool)
>>> stream
0111
>>> stream = BitStream()
>>> stream.write(array(["", " " , "A", "AAA"]), bool)
>>> stream
0111

>>> stream = BitStream()
>>> stream.write(    (), bool)
>>> stream.write(  (0,), bool)
>>> stream.write((0, 0), bool)
>>> stream
011

>>> stream = BitStream()
>>> stream.write([[], [0], [0, 0]], bool)
>>> stream
011

>>> class BoolLike(object):
...     def __init__(self, value):
...         self.value = bool(value)
...     def __nonzero__(self):
...         return self.value
>>> false = BoolLike(False)
>>> true = BoolLike(True)
>>> stream = BitStream()
>>> stream.write(false, bool)
>>> stream.write(true, bool)
>>> stream.write([false, true], bool)
>>> stream
0101

TODO:

  • direct call to write_bool (import the symbol first)
  • reader tests

Integers

TODO

Floating-Point Numbers

>>> import struct
>>> struct.pack(">d", pi)
'@\t!\xfbTD-\x18'

>>> stream = BitStream()
>>> stream.write(0.0)
>>> stream.write([1.0, 2.0, 3.0])
>>> stream.write(arange(4.0, 10.0))
>>> len(stream)
640
>>> output = stream.read(float, 10)
>>> type(output)
<type 'numpy.ndarray'>
>>> all(output == arange(10.0))
True

>>> BitStream(1.0) == BitStream(1.0, float) == BitStream(1.0, float64)
True
>>> BitStream(1.0) == BitStream([1.0]) == BitStream(ones(1))
True

The byte order is big endian:

>>> BitStream(struct.pack(">d", pi)) == BitStream(pi)
True

Extra Methods

TODO::

  • length
  • str, repr
  • _extend ? Make it public ? This is low-level … but may be necesssary to implement new readers/writers. Don’t specify it now, as we don’t specify the offsets / stream state, let the user only rely on the high-level methods.
  • copy
  • hash, comparison.

Custom Writers and Readers

>>> import bitstream

Definition and Registration of Writers and Readers

Let’s define a writer for the binary representation of natural numbers:

>>> def write_integer(stream, data):
...     if isinstance(data, list):
...         for integer in data:
...             write_integer(stream, integer)
...     else:
...         integer = int(data)
...         if integer < 0:
...             error = "negative integers cannot be encoded"
...             raise ValueError(error)
...         bools = []
...         while integer:
...             bools.append(integer & 1)
...             integer = integer >> 1
...         bools.reverse()
...         stream.write(bools, bool)

We can check that this writer behaves as expected:

>>> stream = BitStream()
>>> write_integer(stream, 42)
>>> stream
101010
>>> write_integer(stream, [1, 2, 3])
>>> stream
10101011011

Then, we can associate it to the type int:

>>> bitstream.register(int, writer=write_integer)

After this step, BitStream will redirect all data of type int to this writer:

>>> BitStream(42)
101010
>>> BitStream([1, 2, 3])
11011

If the type information is explicit, other kind of data can use this writer too:

>>> BitStream(uint8(42), int)
101010
>>> BitStream("42", int)
101010

A possible implementation of the corresponding reader is given by:

>>> def read_integer(stream, n=None):
...     if n is not None:
...         error = "unsupported argument n"
...         raise NotImplementedError(error)
...     else:
...         integer = 0
...         for _ in range(len(stream)):
...             integer = integer << 1
...             if stream.read(bool):
...                 integer += 1
...     return integer

>>> read_integer(BitStream(42))
42

Once this reader is registered with

>>> bitstream.register(int, reader=read_integer)

the calls to read_integer can be made through the read method of BitStream.

>>> BitStream(42).read(int)
42

In all readers, the second argument of readers, named n, represents the number of values to read from the stream. Here, this argument is not supported, instead any call to this reader interprets the complete stream content as a single value.

Writer and Reader Factories

We actually had a legitimate reason not to support the number of values argument in the binary representation reader. Indeed, when the binary representation is used to code sequence of integers instead of a single integer, it becomes ambiguous: the same bitstream may represent several sequences of integers. For example, we have:

>>> BitStream(255)
11111111
>>> BitStream([15, 15])
11111111
>>> BitStream([3, 7, 3, 1])
11111111
>>> BitStream([3, 3, 3, 3])
11111111

We say that this code is not self-delimiting, as there is no way to know where is the boundary between the bits coding for different integers.

For natural numbers with known bounds, we may solve this problem by setting a number of bits to be used for each integer. However, to do that, we would have to define and register a new writer for every possible number of bits. Instead, we register a single but configurable writer, defined by a writer factory.

Let’s define a type tag uint whose instances hold a number of bits:

>>> class uint(object):
...     def __init__(self, num_bits):
...         self.num_bits = num_bits

Then, we define a factory that given a uint instance, returns a stream writer:

>>> def write_uint_factory(instance):
...     num_bits = instance.num_bits
...     def write_uint(stream, data):
...         if isinstance(data, list):
...             for integer in data:
...                 write_uint(stream, integer)
...         else:
...             integer = int(data)
...             if integer < 0:
...                 error = "negative integers cannot be encoded"
...                 raise ValueError(error)
...             bools = []
...             for _ in range(num_bits):
...                 bools.append(integer & 1)
...                 integer = integer >> 1
...             bools.reverse()
...             stream.write(bools, bool)
...     return write_uint

Finally, we register this writer factory with bitstream:

>>> bitstream.register(uint, writer=write_uint_factory)

To select a writer, we use the proper instance of type tag:

>>> BitStream(255, uint(8))
11111111
>>> BitStream(255, uint(16))
0000000011111111
>>> BitStream(42, uint(8))
00101010
>>> BitStream(0, uint(16))
0000000000000000

TODO: reader, give details, comment.

>>> def read_uint_factory(instance): # use the name factory ?
...     num_bits = instance.num_bits
...     def read_uint(stream, n=None):
...         if n is None:
...             integer = 0
...             for _ in range(num_bits):
...                 integer = integer << 1
...                 if stream.read(bool):
...                     integer += 1
...             return integer
...         else:
...             integers = [read_uint(stream) for _ in range(n)]
...             return integers
...     return read_uint

>>> bitstream.register(uint, reader=read_uint_factory)

>>> stream = BitStream([0, 1, 2, 3, 4], uint(8))
>>> stream.read(uint(8))
0
>>> stream.read(uint(8), 1)
[1]
>>> stream.read(uint(8), 3)
[2, 3, 4]

Unit Tests

The text version of the document you are reading is also an executable specification. Check that the code examples produce the expected results with

$ python -m doctest -v manual.txt

Examples

Unary coder / Rice coder ? Huffman tree/table coder ?

 
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