Skip to main content

CNC machine controller

Project description

Build Status


This project bring CNC control for Raspberry Pi or any ARM based Linux boards. Typically there is no way to control stepper motors from Linux runtime environment due to the lack of real time GPIO control. Even kernel based modules can not guarantee precise control of pulses for steppers. There is always way out. DMA(Direct Memory Access) is a separated hardware module which provides high precision for GPIO outputs. This module can copy bytes which represent GPIO states from RAM buffer directly to GPIO with some clock based on main chip internal oscillator without using CPU’s cores. Using such approach this project generates impulses for moving stepper motors and that is very precise way regardless CPU load and OS time jitter.
This approach also allows to use Python language for this project. Typically, Python is not good choice for real time application, but since project just needs to set up DMA buffers and hardware will do the rest, Python become the perfect choice for easy development of this project.

Video demo - YouTube video

Current command support

G0, G1, G4, G20, G21, G28, G90, G91, G92, M2, M3, M5, M30
Commands can be easily added, see file.


All configs are stored in and contain hardware properties, limitations and pin names for hardware control.
Raspberry Pi implementation should be connected to A4988, DRV8825 or any other stepper motor drivers with DIR and STEP pin inputs. Default config is created for Raspberry Pi 2-3 and this wiring diagram: image1
So having Raspberry Pi connected this way, there is no need to configure pin map for project.


Currently, this project supports Raspberry Pi 1-3. Tested with RPI2. But there is a way to add new boards. See file. Note: Current Raspberry Pi implementation uses the same resources as on board GPU(memory). So video output will not work with this project. Use ssh connection to board. And do not connect HDMI cable, otherwise project would not run. Probably, increasing of GPU dedicated memory(at least to 64 MB) could solve it and allow to work project and GPU together, but it was never tested.


Just clone this repo and run ./pycnc from repo root. It will start in interactive terminal mode where gcode commands can be entered manually.
To run file with gcode commands, just run ./pycnc filename.
Optionally, pycnc can be installed. Run
sudo pip install .

in repo root directory to install it. After than, pycnc command will be added to system path. To remove installation, just run:

sudo pip remove pycnc

Performance notice

Pure Python interpreter wouldn’t provide great performance for high speed machines. Overspeeding setting causes motors mispulses and probably lose of trajectory. According to my tests, Raspberry Pi 2 can handle axises with 400 pulses on mm with top velocity ~800 mm per min. There is always way out! :) Use JIT Python implementation like PyPy. RPi2 can handle up to 18000 mm per minute on the machine with 80 steps per millimeter motors with PyPy.
Note: Raspbian has outdated PyPy version in repositories(v4.0). Moreover v4.0 has issue with ``mmap`` module implementation. Use PyPy v5.0+, download it for your OS from `here <>`__.
PyPy installation:
wget wget
sudo mkdir /opt/pypy
sudo tar xvf pypy2-v5.7.1-linux-armhf-raspbian.tar.bz2 --directory /opt/pypy/ --strip-components=1
sudo ln -s /opt/pypy/bin/pypy /usr/local/bin/pypy

Project architecture


Nothing for runtime. Just pure Python code. For uploading to PyPi there is a need in pandoc:

sudo dnf install pandoc
sudo pip install pypandoc

GCode simulation

Just a link, mostly for myself :), to a nice web software for gcode files emulation (very helpful for manual creating of gcode files):


see LICENSE file.


Nikolay Khabarov

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pycnc-0.1.2.tar.gz (25.3 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page