semvec/pypi-packages huggingface dataset:
mjviewer 0.3.23
The mjviewer project, based on kscale-mujoco-viewer
Requires Python: >=3.11

# kscale-mujoco-viewer

Mujoco viewer maintained by K-Scale Labs.

https://github.com/user-attachments/assets/5312fa03-7215-4c1b-9d66-acaa18b96a3c

## Installation

```bash
pip install kmv
```

## Examples

Run the humanoid example script to see the viewer in action:

```bash
# cd to the root of the repo
python examples/default_humanoid.py
```


## Usage

```python
import mujoco, time
from kmv.app.viewer import QtViewer

model  = mujoco.MjModel.from_xml_path("path/to/model.xml")
data   = mujoco.MjData(model)
viewer = QtViewer(model)

while viewer.is_open:
    mujoco.mj_step(model, data)

    # send state
    viewer.push_state(data.qpos, data.qvel, sim_time=data.time)

    # send a few scalars to the “Debug” plot group
    viewer.push_plot_metrics(
        scalars={
            "qpos0": float(data.qpos[0]),
            "qvel0": float(data.qvel[0]),
        },
        group="Debug",
    )

    time.sleep(model.opt.timestep)

viewer.close()
```

# Troubleshooting

If you have issues with linux machines that have both integrated and nvidia GPUs, you may need to set these environment variables to force use of the nvidia GPU.

```bash
__NV_PRIME_RENDER_OFFLOAD=1 __GLX_VENDOR_LIBRARY_NAME=nvidia python examples/default_humanoid.py
```

You may also have to install the following utilities:
```bash
sudo apt install libxcb-cursor0 libxcb-cursor-dev
```

Additionally, depending on your setup, you may need to set the right environment variables to run the viewer properly. See [this issue](https://github.com/kscalelabs/ksim/issues/431#issuecomment-2982603066) for more details.
```bash
QT_QPA_PLATFORM=wayland python examples/default_humanoid.py
```

# Acknowledgements

Originally referenced from [mujoco-python-viewer](https://github.com/gaolongsen/mujoco-python-viewer).
models-package-vasmoul 0.1.1
Django models for Project Management Dashboard

# models_package_vasmoul

A reusable Django models module containing the core data models for a Project Management Dashboard application.

This package can be used in any Django project to manage projects, their milestones, team members, and events.

## Features

- `Project` model with title, description, owner, tags, progress, status, health, and deletion status.
- `Milestone` model to track project milestones and progress.
- `TeamMember` model for project team assignments.
- `Event` model to log actions related to projects.
- Easily integrated into existing Django projects.

## Installation

You can install the package from PyPI:

```bash
pip install models_package_vasmoul

# Usage
# Add the app to your Django INSTALLED_APPS:

INSTALLED_APPS = [
    ...
    'models_package_vasmoul',
]

# Run migrations
```bash
python manage.py makemigrations models_package_vasmoul
python manage.py migrate

# Usage of models
from models_package_vasmoul.models import Project, Milestone, TeamMember, Event

# Create a new project
project = Project.objects.create(
    title="Project Alpha",
    short_description="A short description",
    owner="John Doe",
    status="in_progress",
    health="green"
)

# Add a milestone
Milestone.objects.create(
    project=project,
    title="Milestone 1",
    description="Initial phase",
    due_date="2025-12-01",
    progress=50
)
pyslingshot 0.2.0
Python implementation of the Slingshot pseudotime algorithm
Requires Python: >=3.9

# Slingshot in Python

This is a Python implementation of the Slingshot pseudotime algorithm (Street et al., 2018).
The original implementation is written in R: https://github.com/kstreet13/slingshot.

A complete working example is located in `slingshot.ipynb`, using a synthetically generated dataset.

## Installation:

- `pip install pyslingshot`


### Deprecated version
Older versions if pyslingshot (before v0.1.0) requires manually installing `numpy`, `scipy`, `sklearn`, and my fork
of `pcurvepy` (https://github.com/mossjacob/pcurvepy).


<img src=readme_example.png height="200">

## Contributing

- Fork & download source
- Install requirements with `poetry install`

[1] Street, K., Risso, D., Fletcher, R.B., Das, D., Ngai, J., Yosef, N., Purdom, E. and Dudoit, S., 2018. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC genomics, 19(1), pp.1-16.
nyckel 0.4.23
Python package for the Nyckel API
Requires Python: >=3.8

# Nyckel

Python package for [www.nyckel.com](www.nyckel.com)

![build](https://github.com/NyckelAI/python-sdk/actions/workflows/build.yml/badge.svg)
![test](https://github.com/NyckelAI/python-sdk/actions/workflows/test.yml/badge.svg)
![docs](https://github.com/NyckelAI/python-sdk/actions/workflows/docs.yml/badge.svg)
[![PyPi version](https://img.shields.io/pypi/v/nyckel.svg)](https://pypi.python.org/pypi/nyckel/)

## Docs

Visit our [Docs pages](https://nyckelai.github.io/python-sdk/)

## Quickstart

```python
from nyckel import Credentials, TextClassificationFunction

# Get credentials from https://www.nyckel.com/console/keys
credentials = Credentials(client_id=..., client_secret=...)

# Create a new text classification function.
func = TextClassificationFunction.create(credentials=credentials, name="IsToxic")

# Provide a few examples.
func.create_samples([
    ("This is a nice comment", "not toxic"),
    ("Hello friend", "not toxic"),
    ("This is a bad comment", "toxic"),
    ("Who is this? Go away!", "toxic"),
])

# Nyckel trains and deploys your model in a few seconds...
# ...and you can start classifying text right away!

# Classify a new piece of text.
predictions = func.invoke(["New text"])
```

## Contributors

### Setup dev environment

Create a `python3.11` venv and run:

```bash
pip install -r requirements.txt
```

### Building docs locally

Install additional packages

```bash
pip install -r docs/requirements.txt
```

Run

```bash
mkdocs build
```

### Testing

Testing locally requires a local server running the API. CI will run tests against a staging environment.

1. Ensure the local server is running
2. Ensure the `nyckel` package is installed in the venv using `pip install -e .`
3. Run `pytest` from the root directory

### Deploying

#### One time

1. `pip install hatch`

#### Each time

`hatch publish`