CythonBuilder: automated compiling and packaging of Cython code

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

pip install cythonbuilder

Use CythonBuilder via the CLI

Add -v (verbose) for more information

1. Listing files with and without filter

cybuilder list
cybuilder list --files 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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Use CythonBuilder via Python

1. Listing files with and without filter

import cythonbuilder

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

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

import cythonbuilder

cythonbuilder.cy_build()

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

3. Clean

import cythonbuilder

cythonbuilder.cy_clean()

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

4. Setting debug level for verbose logging

import logging
from . import logger

logger.setLevel(logging.DEBUG)

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