semvec/pypi-packages huggingface dataset:
activitypub-linked-data 0.0.1
JSON-LD context loader and RDF schema definitions for ActivityPub
Requires Python: <4,>=3.9
Keywords: activitypub, jsonld

# activitypub-linked-data

**Local JSON-LD context loader and RDF namespace definitions for
ActivityPub and the Fediverse**

`activitypub-linked-data` is a Python utility package that provides
preloaded JSON-LD context documents and RDF namespace constants for
use in ActivityPub implementations and other Fediverse-compatible
applications. It includes a built-in JSON-LD document loader optimized
for speed, reliability, and offline use.

## Features

- Local resolution of common JSON-LD context URLs used in the
  Fediverse
- Built-in `rdflib` namespaces for ActivityStreams, Security and
  Identity vocabularies
- Secure document loader to replace remote HTTP fetching
- Compatible with `rdflib` and `pyld` JSON-LD processing
- Designed for use in Fediverse applications, including servers and
  identity tools

---

## Installation

```sh
pip install activitypub-linked-data
```


## Usage

```python
from pyld import jsonld

from ap.linked_data import AS2, setup_rdflib_local_handler, jsonld_local_document_loader

if __name__ == '__main__':
    setup_rdflib_local_handler() # Patches rdflib to use locally defined contexts, when available.

    # Sets up jsonld to use local context documents
    jsonld.set_document_loader(jsonld_local_document_loader)

    # Use a namespace
    print(AS2.Person)  # -> https://www.w3.org/ns/activitystreams#Person
```

Note: pyld is not declared as a dependency. You need to include in your own project if you
wish to use `jsonld_local_document_loader`

## Attribution

This package is derived from the
[Takahē](https://github.com/jointakahe/takahe) project, which is
licensed under the [BSD 3-Clause
License](https://opensource.org/licenses/BSD-3-Clause).
uniform-vlm 0.1.3
Easy one‑liner training + inference tool for the uniform LoRA VLM.
Requires Python: >=3.9

# uniform‑vlm
A lightweight wrapper Python package for **training** and **inference** with NuExtract‑style LoRA adapters.

```bash
pip install uniform-vlm
```

### Inference
```bash
# CLI
uniform-vlm infer images/ --csv preds.csv

# Python
from uniform_vlm.infer import images_to_csv
images_to_csv("images", "preds.csv")
```

### Training (continue fine‑tuning existing adapter by default)
```bash
uniform-vlm train data/train.csv --image-col path --label-col label_json \
             --output-dir output/my_adapter
```

See the Colab walkthrough ➜ <https://colab.research.google.com/drive/1ndRcS9EMcunvrLorQdP97InvCvkEETjJ?usp=sharing>

---
blendipose 0.1.2
Render human motion sequences with Blender, with or without overlaying on a video
Requires Python: >=3.10

# BlendiPose

This is a Python library for rendering human motion videos, mainly to visualize the output of 3D human pose and shape estimation models. It's similar to my other project [PoseViz](https://github.com/isarandi/poseviz), but optimized for beautiful rendering quality instead of speed.

BlendiPose is built on top of Blender, and the [Blendify](https://github.com/ptrvilya/blendify) package (specifically my fork of it, which is vendored in this repository).

## References

Regarding Blendify, see: 

```bibtex
@article{blendify2024,
  title={Blendify -- Python rendering framework for Blender},
  author={Guzov, Vladimir and Petrov, Ilya A and Pons-Moll, Gerard},
  journal={arXiv preprint arXiv:2410.17858},
  year={2024}
}
```