Skip to content

CONTRIBUTORS.md

Latest commit

 

History

History
92 lines (70 loc) · 2.47 KB

CONTRIBUTORS.md

File metadata and controls

92 lines (70 loc) · 2.47 KB

2d-plotter-art-g-code

Local Setup

  1. Install VS Code Plugins - "Black Formatter", "Mypy", and "Flake8"
  2. Run ./bootstrap.sh

Running Tests

  1. source ./venv/bin/activate && pytest
  2. If tests fail, run GENERATE_SNAPSHOTS=yes pytest to generate new snapshots, compare git diff to ensure changes are expected, and commit the new snapshots.

Local Development

  1. Make changes to code.
  2. Experiment within src/sandbox/main.py to test updated functionality.
  3. Docstrings will be generated automatically with precommit via docstrings2md.py, be sure to commit the new docs as well.
  4. Create a pull request with changes.

Deploy

  1. Add following code to ~/.pypirc. See below for filling in details.
    • PyPi Test
      1. Username: __token__
      2. Password: Password
    • PyPi
      1. Username: __token__
      2. Password: Password
[distutils]
index-servers =
    pypi
    testpypi

[testpypi]
repository = https://test.pypi.org/legacy/
username = sillysideprojects
password = foobar

[pypi]
repository = https://upload.pypi.org/legacy/ 
username = __token__
password = buzzfizz
  1. Increment package number.
  2. Build package rm -rf ./dist && python -m build
  3. Deploy package python -m twine upload --repository testpypi dist/*
  4. Navigate to TestPyPI, create new venv
  5. Install package with pip install -i https://test.pypi.org/simple/ gcode2dplotterart==2.0.3 --extra-index-url https://pypi.org/simple/
  6. Run the following test code.
from gcode2dplotterart import Plotter2D

plotter = Plotter2D(
    title="Test",
    x_min=0,
    x_max=100,
    y_min=0,
    y_max=100,
    feed_rate=1000,
    handle_out_of_bounds="Warning",
    output_directory="./output",
    include_comments=True,
    return_home_before_plotting=True,
)

plotter.add_layer("foo", "red", line_width=1)

plotter.layers["foo"].add_line(
    x_start=0,
    y_start=0,
    x_end=100,
    y_end=100,
)

plotter.preview()

plotter.save()
  1. Deploy to production python -m twine upload --repository pypi dist/*

Generating Timelapses

Setup a camera to take photos of the plotter. Output those photos to folder/file.jpg. Run the following script. Change 60 to match the desired FPS

ffmpeg -framerate 60 -pattern_type glob -i 'folder/filename_*.jpg' -c:v libx264 -r 60 -vf "pad=ceil(iw/2)*2:ceil(ih/2)*2" -pix_fmt yuv420p output.mp4