semvec/pypi-packages huggingface dataset:
reasoning-core 0.2.1
A RL env with procedurally generated symbolic reasoning data

# Reasoning core ◉


reasoning-core is a text-based RLVR for LLM reasoning training.
It is centered on expressive symbolic tasks, including full fledged FOL, formal mathematics with TPTP, formal planning with novel domains, and syntax tasks.

🤗 https://hf.co/datasets/reasoning-core/rc1

# Prime Environment Hub
```python
#!pip install uv #install uv if needed
!uv tool install prime --with openai  -q
!uv tool run prime -- env install sileod/reasoning-core-env

from verifiers import load_environment
import os; from openai import OpenAI

env = load_environment("reasoning-core-env")

client = OpenAI( base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPENROUTER_API_KEY")) #🔑
results = env.evaluate(client=client, model="gpt-4.1-mini", num_examples=20, rollouts_per_example=1)
df=env.make_dataset(results).to_pandas()
```

# Standalone
```python
pip install reasoning_core

from reasoning_core import list_tasks, get_task, score_answer

T = get_task('arithmetics')()
x = T.generate_example()
assert score_answer(x.answer, x)==1
```

# Generation
Run `bash run_generate.sh` for multi-threaded generation to json files (readable by Huggingface Datasets).


# Reasoning gym

We use a custom interface, leaner than reasoning-gym (RG). But our tasks, which are all orthogonal to RG, can be imported in it.

```python
import reasoning_gym

from reasoning_core import register_to_reasoning_gym
register_to_reasoning_gym()

specs = [
    # here, leg_counting tasks will make up two thirds of tasks
    DatasetSpec(name='leg_counting', weight=2, config={}),  #from reasoning_gym 🏋
    DatasetSpec(name='arithmetics', weight=2, config={}),  #from reasoning_core ◉
]
D=reasoning_gym.create_dataset('composite', size=10, seed=42, datasets=specs)

```

## Citation
```
@misc{reasoningcore2025,
      title={Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning}, 
      author={Valentin Lacombe and Valentin Quesnel and Damien Sileo},
      year={2025},
      eprint={2509.18083},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2509.18083}, 
}
```
kleinkram 0.58.0.dev20260220150241
give me your bags
Requires Python: >=3.8

# Kleinkram: CLI

Install the package

```bash
pip install kleinkram
```

Run the CLI

```bash
klein
```

## Usage

Here are some basic examples of how to use the CLI.

### Listing Files

To list all files in a mission:

```bash
klein list -p project-name -m mission-name
```

### Uploading Files

To upload all `*.bag` files in the current directory to a mission:

```bash
klein upload -p project-name -m mission-name *.bag
```

If you would like to create a new mission on upload use the `--create` flag.

### Downloading Files

To download all files from a mission and save them `out`:

```bash
klein download -p project-name -m mission-name --dest out
```

You can additionally specify filenames or ids if you only want to download specific files.

Instead of downloading files from a specified mission you can download arbitrary files by specifying their ids:

```bash
klein download --dest out *id1* *id2* *id3*
```

For more information consult the [documentation](https://docs.datasets.leggedrobotics.com//usage/python/setup).

## Development

Clone the repo

```bash
git clone git@github.com:leggedrobotics/kleinkram.git
cd kleinkram/cli
```

Setup the environment

```bash
virtualenv -ppython3.8 .venv
source .venv/bin/activate
pip install -e . -r requirements.txt
```

Install `pre-commit` hooks

```bash
pre-commit install
```

Run the CLI

```bash
klein --help
```

### Run Tests
to run unit tests:
```bash
pytest -m "not slow"
```
to run all tests (including e2e and integration tests):
```bash
pytest
```
For the latter you need to have an instance of the backend running locally.
See instructions in the root of the repository for this.
On top of that these tests require particular files to be present in the `cli/tests/data` directory.
These files are automatically generated by the `cli/tests/generate_test_data.py` script.

You also need to make sure to be logged in with the cli with `klein login`.
treeIDW 1.0.0
A package for KD-tree optimized inverse distance weighting interpolation.
Requires Python: >=3.9

# treeIDW

<p align="center">
  <img src="https://raw.githubusercontent.com/Dorian210/treeIDW/main/docs/logo.png" width="500" />
</p>

**treeIDW** is a Python library for performing **Inverse Distance Weighting (IDW)** interpolation using an efficient **KD-tree-based selection strategy**.  
It is designed to be easy to use for newcomers while offering fine-grained control and performance-oriented options for advanced users in numerical methods and spatial data analysis.

---

## Key Features

- Efficient IDW interpolation using KD-tree nearest-neighbor selection
- Automatic exclusion of boundary nodes with negligible contribution
- Optimized numerical kernels powered by `numba`
- Scalable to large datasets (millions of interpolation points)
- Simple API with expert-level tunable parameters

---

## Installation

**treeIDW is available on PyPI.**

```bash
pip install treeIDW
```

### Development installation (from source)

```bash
git clone https://github.com/Dorian210/treeIDW
cd treeIDW
pip install -e .
```

---

## Dependencies

The core dependencies are:

- `numpy`
- `scipy`
- `numba`

These are automatically installed when using `pip`.

---

## Package Structure

- **treeIDW.treeIDW**  
  Core IDW interpolation engine.  
  Uses a KD-tree to select only boundary nodes with significant influence, improving both accuracy and performance.

- **treeIDW.helper_functions**  
  Low-level, performance-critical routines for IDW weight computation.  
  Implemented with `numba`, including vectorized and parallelized variants.

- **treeIDW.weight_function**  
  Definition of the IDW weight function.  
  The default implementation uses inverse squared distance, but custom weight laws can be implemented if needed.

---

## Examples

Example scripts are provided in the `examples/` directory:

- **Graphical demonstration**  
  Interpolation of a rotating vector field inside a square domain.

- **Large-scale computation**  
  Propagation of a scalar field from 1,000 boundary nodes to 1,000,000 internal points, highlighting scalability.

- **Logo generation**  
  The generation process of the *treeIDW* logo itself, where the letters “IDW” are encoded as a vector field and interpolated on a 2D grid.

---

## Documentation

- Online documentation: https://dorian210.github.io/treeIDW/
- API reference is also available in the `docs/` directory of the repository.

---

## Contributions

This project is currently not open to active development contributions.  
However, bug reports and suggestions are welcome via the issue tracker.

---

## License

This project is distributed under the **CeCILL License**.  
See [LICENSE.txt](LICENSE.txt) for details.