cythonbuilder 0.0.17

CythonBuilder; automated compiling and packaging of Cython code

CythonBuilder

CythonBuilder; automated compiling and packaging of Cython code

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Installation

pip install cythonbuilder

How to use

CythonBuilder makes it easy to use Cython in your Python project by automating the building process. You can use CythonBuilder from the commandline or import it as a package in Python. Generated files can be imported in Python directly

With Commandline

Add -v (verbose) for more information

1. Listing files with and without filter

cybuilder list
cybuilder list file1 file2.pyx

2. Build with and without optional build arguments

cybuilder build
cybuilder build --include-numpy --no-annotation --no-cleanup

3. Clean

cybuilder clean 
cybuilder clean --no-cleanup

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With python

1. Listing files with and without filter

from src import cythonbuilder as cybuilder

print(cybuilder.cy_list())  # without a filter
print(cybuilder.cy_list(target_files=['some_name.pyx']))  # with a filter

2. Build with and without optional build arguments (cleans automatically afterwards)

from src import cythonbuilder as cybuilder

cybuilder.cy_build()

found_files = cybuilder.cy_build(target_files=['some_name'])
cybuilder.cy_build(target_files=found_files, include_numpy=False, create_annotations=False)

3. Clean

from src import cythonbuilder as cybuilder

cybuilder.cy_clean()

found_files = cybuilder.cy_build(target_files=['some_name'])
cybuilder.cy_clean(target_files=['some_name'])

4. Setting debug level for verbose logging

from src.cythonbuilder import logger
from src.cythonbuilder import set_logger_debug_mode

set_logger_debug_mode(logger=logger)

In-depth, step by step Explanation

I've written a few articles that explain why Python is slow, why Cython can be a solution and how CythonBuilder helps us develop fast code easily:

• Why Python is so slow and how to speed it up
• Getting started with Cython; how to perform >1.7 billion calculations per second with Python
• Cython for data science: 6 steps to make this Pandas dataframe operation over 100x faster