ledstudio VS Poetry

That’s pretty cool.

I did something similar once, where I wrote a Qt application that let you add effects to a timeline, which would get saved as json and could then be run on a Raspberry Pi to control a grid of NeoPixel LEDs. Each effect on the timeline has a start and length, which pixels it affected, RGBW colors, easing function in and out and effect type (fade, blink etc — there were a few basic effect types that could be combined to create more elaborate effects). The Qt application was able to simulate the LEDs in a GUI using the same code that ran on the RPi (just the GUI was replaced with LED driver code there). It was used to play animations on a t-shirt that was covered in a grid of LEDs made for a local musician. It was a fun project. The code is on GitHub but it’s terrible quality due to having been written over the space of 3 days and then never touched again: https://github.com/danielytics/ledstudio

I have the code on GitHub but I haven’t built the code in 4 years so don’t have a screenshot. Like I said, the code was written in about 3 days and QML allowed me to iterate quickly, but the resulting code quality isn’t great (since it was a once off project, built and used in the same week and then never used again, I didn’t clean it up or anything). One key feature was that the LED strip could be connected to a Raspberry Pi and the tool could be run directly on the Pi to control the LEDs directly from the editor, or it could be run on my laptop and simulated.

Anyway, the code is here: https://github.com/danielytics/ledstudio

And the QML specifically is here: https://github.com/danielytics/ledstudio/blob/master/main.qm...

If I were to clean it up, I would at least split the different labels into their own QML files but hey, shortcuts were taken over those few days :)

You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.

Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:

Poetry

The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:

Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?

https://github.com/python-poetry/poetry/issues/6409

Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.

Not sure where I would stand if I fully investigated it tho.

[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...

We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.

Here are the two main packaging issues I run into, specifically when using Poetry:

1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.

2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.

I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.

Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.

[0] https://github.com/python-poetry/poetry/issues/2740

[1] https://github.com/python-poetry/poetry/issues/2184

[2] https://pypi.org/project/pypiserver/

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github