CythonBuilder; automated compiling and packaging of Cython code

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pip install cythonbuilder

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

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

1. Listing files with and without filter

from cythonbuilder 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 cythonbuilderr import logger
from cythonbuilderr import set_logger_debug_mode

set_logger_debug_mode(logger=logger)

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