semvec/pypi-packages huggingface dataset:
multiverse-simulators 0.0.14
A collection of simulators for the Multiverse Framework.
Requires Python: >=3.10

# Multiverse Simulators

The **Multiverse Simulators** is a collection of simulators for the [Multiverse Framework](https://github.com/Multiverse-Framework/Multiverse).

---

## 📋 Prerequisites

- **Python** ≥ 3.10 (Linux), 3.12 (Windows)

Each simulator connector will have different dependencies, e.g.:
  - Python packages for MuJoCo listed in [requirements.txt](https://github.com/Multiverse-Framework/Multiverse-Simulators-Connector/blob/main/src/mujoco_connector/requirements.txt)
  - Python packages for Isaac Sim listed in [requirements.txt](https://github.com/Multiverse-Framework/Multiverse-Simulators-Connector/blob/main/src/isaac_sim_connector/requirements.txt)

Install the required packages:

```bash
pip install -r src/mujoco_connector/requirements.txt
```

---

## ⚙️ Setup

First, clone the repository:

```bash
git clone https://github.com/Multiverse-Framework/Multiverse-Simulators-Connector --depth 1
```
---

Then install it as a local Python package using a symbolic link (editable mode):
Currently, only MuJoCo is installed via pip; support for Isaac Sim and others will be added later.

```bash
pip install -e .
```

This allows you to make changes to the source code and immediately reflect them without reinstalling.

You can then test it in a Python shell:

```python
from mujoco_connector import MultiverseMujocoConnector
from multiverse_simulator import MultiverseSimulatorState, MultiverseSimulatorConstraints

simulator = MultiverseMujocoConnector(file_path=os.path.join(resources_path, "mjcf/unitree/h1_scene.xml"))
constraints = MultiverseSimulatorConstraints(max_simulation_time=10.0)
simulator.start(constraints=constraints)
while simulator.state != MultiverseSimulatorState.STOPPED:
    time.sleep(1)
```
jambashrimp 0.1.2
JambaShrimp (jash): playful CLI to chat with PDFs or crawled docs using Jamba2 3B
Requires Python: >=3.12

## JambaShrimp (`jash`)

Playful command-line interface for chatting with any PDF or crawled documentation site using AI21's Jamba2 3B (GGUF build). The CLI can:

- load entire PDFs directly into the prompt for ad-hoc question answering
- crawl documentation sites (recursively) and ground answers with a local FAISS index (RAG)
- cache indexes per URL so you can come back later—even offline—and keep chatting

The product name is **JambaShrimp**, and its CLI entry point is the shortened `jash`.

### Requirements

- [uv](https://docs.astral.sh/uv/latest/) for environment management
- macOS with Metal (recommended) or CPU fallback
- Optional `HF_TOKEN` environment variable if your Hugging Face account is required for downloads

### Install with `pipx`

Publishing the package (named `jambashrimp`) to PyPI lets anyone install the CLI with an isolated virtual environment:

```bash
pipx install jambashrimp

# run it
jash --help

# download the sample PDF + model
jash-setup
```

While developing locally you can point pipx directly at the repo:

```bash
pipx install --spec . jambashrimp
```

Assets are stored under `~/.jambashrimp/assets` when the package is installed globally. You can override `--model`/`--pdf` if you keep files elsewhere.

### Dev Setup

```bash
uv sync
uv run scripts/setup_assets.py     # downloads the default OECD PDF + GGUF model
uv run python -m jamba_cli.cli
```

Pass `--pdf` or `--model` to point at different files. Run `uv run jamba-chat --help` to see all knobs (context window, temperature, GPU layers, etc.).

### CLI Shortcuts

- `/quit` or `/exit` – leave the chat
- `/history` – print the running conversation
- `/reload` – reload the PDF from disk (useful when editing)
- `/help` – show the command list

By default the CLI hides `<think>` traces but still streams answers token-by-token. Useful switches:

- `--history-turns N` – keep N previous Q/A pairs inside the prompt (defaults to 0 to maximize context for the document itself).
- `--cache-prompt` – reuse the KV cache between turns if you *really* need faster follow ups. Leave it off (default) if you encounter `llama_decode returned -1`; clearing the cache between turns prevents those context overflows.
- `--show-thinking` / `--no-stream` – reveal hidden reasoning traces or print whole answers at once.
procode 0.1.0
ProcCode Builder Assistant
Requires Python: >=3.10
Keywords: procode

=======
ProCode
=======






ProcCode Builder Assistant



Features
----------------

* TODO

Before Coding
----------------

* create a virtual env

.. code-block:: 
  
  python3 -m venv .venv
  source .venv/bin/activate
  make install-testing-requisites

Credits
----------------

This package was created with Cookiecutter_ .

.. _Cookiecutter: https://github.com/audreyr/cookiecutter


=======
History
=======

0.1.0 (2026-01-11)
------------------

* First release on PyPI.
dccpath 0.1.3
Utility library for locating common DCC (Digital Content Creation) software executables
Requires Python: >=3.10
Keywords: Blender, DCC, Maya, MotionBuilder

# dccpath

Small utility library for locating common DCC (Digital Content Creation) software executables

Currently supported DCCs:

- Autodesk Maya
- Autodesk MotionBuilder
- Blender

```python
from dccpath import get_blender, get_mayapy

blender = get_blender(version="4.4")
mayapy = get_mayapy(version="2025")
```

## Development

This project uses uv for development, to initialize the project and sync all dependencies run:

```sh
uv sync --dev
```
expectfs 0.1.3
Declarative validation for files, directories, and system expectations
Requires Python: >=3.12
Keywords: validation, filesystem, testing, expectations

# expectfs

Declarative validation for things that are **not data models**.

`expectfs` lets you define clear, readable expectations about files, directories,
and system state — without schemas, test frameworks, or boilerplate.

This is for validating *the world*, not validating Python objects.

---

## Installation

```bash
pip install expectfs
```

---

## Quick Example

```python
from expect import expect, validate

expect.file("metrics.json") \
    .is_json() \
    .size_gt(100)

result = validate()

result.print()
```

If any expectation fails, a clear, actionable error message will be displayed after running validation.

---

## Why expect?

Most validation tools focus on:

* JSON schemas
* Data models
* API payloads
* Function inputs

But engineers constantly need to validate things like:

* “Does this file exist?”
* “Is this directory populated?”
* “Is this JSON valid?”
* “Did my pipeline actually produce output?”

`expectfs` exists for that gap.

---

## Key Features

* Declarative, chainable expectations
* Automatic dependency resolution (rules run in the correct order)
* Rule caching (each rule runs at most once)
* Clear failure messages

---

## How it Works (Conceptually)

```python
expect.file("output.json").is_json().size_gt(1024)
```

* `is_json` automatically ensures the file exists
* `size_gt` reuses prior results instead of re-running checks
* Dependencies are resolved recursively

Users don’t need to think about ordering.

---

## Supported Targets

Currently supported:

* Files
* Directories

Planned:

* Globs
* Environment variables
* Command outputs

---

## Status

This project is in **alpha**.

* APIs may evolve
* Backwards compatibility is not yet guaranteed
* Feedback and contributions are welcome

---

## License

MIT