Overview¶
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Library to handle sparse bytes within a virtual memory space.
Free software: BSD 2-Clause License
Objectives¶
This library aims to provide utilities to work with a virtual memory, which constsis in a virtual addressing space where sparse chunks of data can be stored.
In order to be easy to use, its interface should be close to that of a
bytearray, which is the closest pythonic way to store dynamic data.
The main downside of a bytearray is that it requires a contiguous data
allocation starting from address 0. This is not good when sparse data have to
be stored, such as when emulating the addressing space of a generic
microcontroller.
The main idea is to provide a bytearray-like class with the possibility to
internally hold the sparse blocks of data.
A block is ideally a tuple (start, data) where start is the start
address and data is the container of data items (e.g. bytearray).
The length of the block is len(data).
Those blocks are usually not overlapping nor contiguous, and sorted by start
address.
Python implementation¶
This library is the Cython complement to the Python implementation provided by
the bytesparse Python package.
Please refer to its own documentation for more details.
Cython implementation¶
The library provides an experimental Cython implementation. It tries to mimic the same algorithms of the Python implementation, while exploiting the speedup of compiled C code.
Beware that the Cython implementation is meant to be potentially faster than the pure Python one, but there might be even faster ad-hoc implementations of virtual memory highly optimized for the underlying hardware.
The addressing space is limited to that of an uint_fast64_t (typically
32-bit or 64-bit as per the hosting machine), so it is not possible to have
an infinite addressing space, nor negative addresses.
To keep the implementation code simple enough, the highest address (e.g.
0xFFFFFFFF on a 32-bit machine) is reserved.
Block data chunks cannot be greater than the maximum ssize_t value
(typically half of the addressing space).
The Cython implementation is optional, and potentially useful only when the Python implementation seems too slow for the user’s algorithms, within the limits stated above.
If in doubt about using the Cython implementation, just stick with the Python one, which is much easier to integrate and debug.
More details can be found within cbytesparse.c.
Documentation¶
For the full documentation, please refer to:
Installation¶
From PyPI (might not be the latest version found on github):
$ pip install cbytesparse
From the source code root directory:
$ pip install .
Development¶
To run the all the tests:
$ pip install tox
$ tox
To regenerate the Cython files manually, run the following commands:
$ python scripts/cython_build_src.py
$ python scripts/cython_build_tests.py
or alternatively:
$ tox -e cythonize