semvec/pypi-packages huggingface dataset:
tropir-agent 0.1.1
A thin client for tracking OpenAI API calls
Requires Python: >=3.7

# Tropir Agent

A zero-configuration client for tracking OpenAI API calls and sending logs to your Tropir API.

## Installation

```bash
pip install tropir
```

## Usage

### Command Line Interface (Recommended)

Simply run your Python scripts or modules with the `tropir` command instead of `python`:

```bash
# Run a Python script with Tropir tracking
tropir python your_script.py

# Run a Python module with Tropir tracking
tropir python -m your_module
```

No code changes required! The Tropir agent automatically tracks all OpenAI API calls in your code.

### Advanced: As a Python Library

For more control, you can also use Tropir as a library:

```python
# Import and initialize the agent at the start of your program
from tropir import initialize
initialize()

# Now all your OpenAI API calls will be tracked automatically
```

## Configuration

Configuration is done via environment variables:

- `TROPIR_ENABLED`: Set to "0" to disable tracking (defaults to "1")
- `TROPIR_API_URL`: Custom API URL (defaults to "https://tropir.fly.dev/api/log")

## License

MIT
qnlab 0.0.3
L-BFGS variants for academic research
Requires Python: >=3.8

# qnlab

https://pypi.org/project/qnlab/

qnlab is a Python package that provides an implementation of the L-BFGS optimization algorithm.

## Documentation

https://docs.readthedocs.com/platform/stable/tutorial/

```bash
docformatter --in-place --style=google --recursive qnlab/
```

## References

This project is partially inspired by the following L-BFGS-related repositories.
Thanks to the developers of these projects.

* [DirL-BFGS](https://github.com/ashkansl/DirL-BFGS): Direct L-BFGS method using a low-rank plus diagonal inverse Hessian approximation.
* [LBFGSpp](https://github.com/yixuan/LBFGSpp): Header-only C++ library for L-BFGS and L-BFGS-B algorithms.
* [liblbfgs](https://github.com/chokkan/liblbfgs): C library implementing the L-BFGS optimization method.
* [mL-BFGS](https://github.com/yuehniu/mL-BFGS?tab=readme-ov-file): Momentum-based L-BFGS for distributed large-scale neural network training.
* [paper-regularized-qn-benchmark](https://github.com/dmsteck/paper-regularized-qn-benchmark): Regularized L-BFGS approach for large-scale nonconvex minimization.
* [py-owlqn](https://github.com/samson-wang/py-owlqn.git): Python implementation of the OWL-QN optimization algorithm with a logistic regression example.
* [pylbfgs-dedupeio](https://github.com/dedupeio/pylbfgs): Python/Cython wrapper for the liblbfgs library.
* [pylbfgs-larsmans](https://github.com/larsmans/pylbfgs): Alternative Python/Cython wrapper for the liblbfgs library.
* [python_lbfgsb](https://github.com/avieira/python_lbfgsb): Pure Python implementation of L-BFGS-B, though using a deprecated scipy module.

## License

This project is licensed under the MIT License. (`liblbfgs` is under the MIT License as well).

See the [LICENSE](LICENSE) file for details.
data-structures-py 0.1.0
A pedagogical Python package implementing core data structures from scratch
Requires Python: >=3.6

# # Data Structures in Python

A Python package implementing core data structures from scratch for educational purposes.

## Features

- **Array**: Enhanced array based on numpy.ndarray
- **Stack**: LIFO (Last-In-First-Out) data structure
- **Queue**: FIFO (First-In-First-Out) data structure
- **Linked List**: Singly linked list implementation
- **Binary Search Tree**: BST with traversal methods
- **Graph**: Graph with DFS and BFS traversal

## Installation

```bash
pip install data-structures-py