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All files under this 'examples/' directory are unsupported.
While bitarray itself supports Python 3.6+, some examples may require
a later Python versoin.
bloom.py
Demonstrates the implementation of a "Bloom filter", see:
http://en.wikipedia.org/wiki/Bloom_filter
distance.py
Implementation of distance functions and comparison to the
corresponding functions in the scipy.spatial.distance module.
double.py
Functionality to analyze double precision floating point numbers
(IEEE 754 binary64).
extend_json.py
Demonstrates how to construct a json encoder and decoder (using the
'json' standard library) which can handle extended Python data structures
containing bitarrays.
gene.py
Shows how gene sequences (ATGC) can be very easily and efficiently
represented by bitarrays.
hamming.py
Implementation of Hamming codes for error correction with send
and receive functionality.
huffman/
Directory containing a library and examples for working with Huffman
trees and codes.
lexico.py
Contains two functions, all_perm and next_perm, which are handle
lexicographical permutations of bitarrays.
lfsr.py
Linear Feedback Shift Register
mandel.py
Generates a .ppm image file of size 4000 x 3000 of the Mandelbrot set.
Despite its size, the output image file has only a size of slightly
over 1.5 Million bytes (uncompressed) because each pixel is stored in
one bit.
Requires: numba
masked.py
Illustrate masked indexing, i.e. using bitarrays to select indices
mmapped-file.py
Demonstrates how to memory map a file into a bitarray.
ndarray.py
Demonstrates how to efficiently convert boolean data from a bitarray
to a numpy.ndarray of dtype bool.
Requires: numpy
pbm.py
Defines a simple class called PBM (Portable Bit Map) which allows:
- addressing pixels by their coordinates
- storing and loading .ppm (P4), which is the same as .pbm, files
puff/
DEFLATE decompression implementation based on Mark Adler's excellent
https://github.com/madler/zlib/blob/master/contrib/puff/puff.c
sieve.py
Demonstrates the "Sieve of Eratosthenes" algorithm for finding all prime
numbers up to a specified integer.
smallints.py
A class is defined which allows efficiently storing an array of
integers represented by a specified number of bits (1 through 8).
For example, an array with 1000 5 bit integers can be created,
allowing each element in the array to take values form 0 to 31,
while the size of the object is 625 (5000/8) bytes.
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