semvec/pypi-packages huggingface dataset:
cz-benchmarks 0.15.1
A framework for benchmarking single-cell machine learning models
License: MIT
Requires Python: >=3.10

# CZ Benchmarks

### What is cz-benchmarks?
cz-benchmarks is a package for standardized evaluation and comparison of machine learning models for biological applications (first, in the single-cell transcriptomics domain, with future plans to expand to additional domains). The package provides a toolkit for running containerized models, executing biologically-relevant tasks, and computing performance metrics. We see this tool as a step towards ensuring that large-scale ML Models can be harnessed to deliver genuine biological insights -- by building trust, accelerating development, and bridging the gap between ML and biology communities.

### Why benchmarking? Why now?
Last year, CZI hosted a workshop focused on benchmarking and evaluation of ML Models in biology, and the [insights gained](https://virtualcellmodels.cziscience.com/micro-pub/benchmarking-workshop) have reinforced our commitment to supporting the development of a robust benchmarking infrastructure, which we see as critical to achieving our Virtual Cell vision.

### 💬 Community Feedback & Contributions
We're working to get the alpha version of cz-benchmarks stable to build with the community. In the meantime, for issues you may identify, feel free to open an issue on GitHub or reach out to us at [virtualcellmodels@chanzuckerberg.com](mailto:virtualcellmodels@chanzuckerberg.com).

## Getting Started

To get started with `cz-benchmarks`, refer to the [Quick Start Guide](https://chanzuckerberg.github.io/cz-benchmarks/quick_start.html).


### 📚 Additional Resources

- [How To Guides](https://chanzuckerberg.github.io/cz-benchmarks/how_to_guides/index.html)
    - [Add a Custom Dataset](https://chanzuckerberg.github.io/cz-benchmarks/how_to_guides/add_custom_dataset.html)
- [Developer Guides](https://chanzuckerberg.github.io/cz-benchmarks/developer_guides/index.html)
- [API Reference](https://chanzuckerberg.github.io/cz-benchmarks/api_reference.html)
- [Assets](https://chanzuckerberg.github.io/cz-benchmarks/assets.html)

📖 **Documentation**: The full documentation is available at [cz-benchmarks](https://chanzuckerberg.github.io/cz-benchmarks/)
markdown-latex-prerender 0.1.1
An extension to python-markdown that replaces inline latex with prerendered SVGs
License: MIT
Requires Python: <4.0,>=3.10

# Markdown LaTeX Prerenderer

This is a very simple [python-markdown](https://github.com/Python-Markdown/markdown) extension, similar to other markdon-latex implementations that adds support
for latex in markdown files.

The key differences to other approaches are:

1. It does not require javascript on the client
2. It does not require nodejs when building

For some reason, I could not find a way to render latex inside a markdown file without Javascript, that seemed crazy to
me. So I built this tool. No KaTeX required. No NodeJS required. Just classic latex implementations.

## How to use:

Make sure you have `dvilualatex` and `dvisvgm` installed on your system.

Then, add `LatexExtension` to your markdown extension list:

```python
import markdown
from markdown_latex_prerender import LatexExtension

markdown.markdown(
    md_str,
    extensions=[
        LatexExtension(),
        ...
    ],
)
```

## How it works:

It uses the hosts `dvilualatex` and `dvisvgm` (both usually come with TexLive and friends) to convert a latex snippet to
an svg file. **It does not ship either program itself. This is why it's less that 200 lines of code.

Do not feed it untrusted input. It may break. Or worse.

## Caching:

You may want to cache rendered things. For this, you can do something like this:

```python
from my_fav_caching_library import cache_function

import markdown_latex_prerenderer.render as render

render.render_latex = cache_function(render.render_latex)
```

Or whatever else you feel like doing.

## Inpiration:

Since I don't know how to build a python-markdown module to render inline-latex, I ~~asked chatGPT~~ looked at other
peoples solutions. I found this: [arithmatex.py](https://github.com/facelessuser/pymdown-extensions/blob/main/pymdownx/arithmatex.py)
in [facelessuser/pymdown-extensions](https://github.com/facelessuser/pymdown-extensions/blob/main/pymdownx/arithmatex.py).
I re-used a lot of their code. I guess it works.