semvec/pypi-packages huggingface dataset:
psutierlist-api-wrapper 1.0.0
API Wrapper
Requires Python: >=3.11

# psutierlist-api-wrapper

A fully-typed, modern Python wrapper for the internal API used on [psutierlist.org](https://psutierlist.org/).

> [!WARNING]
> This library is not affiliated with the website or anyone behind SPL's PSU Tier List. It may break at any time if the API changes, due to this being an unofficial wrapper that relies on an undocumented API.

## Installation

This package is on PyPI, so you can use `pip`:

```bash
pip install psutierlist-api-wrapper
```

## Usage

Sync usage:
```python
import psutierlist_api_wrapper as paw

def main() -> None:
    pages = paw.get_pages()  # type is `paw.Response`

if __name__ == "__main__":
    main()
```

Async usage:
```python
import asyncio
import psutierlist_api_wrapper as paw

async def main() -> None:
    pages = await paw.get_pages_async()

if __name__ == "__main__":
    asyncio.run(main())
```

## Docs

I have not written docs yet. The code is pretty simple and self-explanatory, so you can just browse through `psutierlist_api_wrapper/__init__.py` and `psutierlist_api_wrapper/enums.py` at the [GitHub Page](https://github.com/PowerPCFan/psutierlist-api-wrapper) to see how everything is structured. Sorry for the inconvenience!
agenttrace-py 0.1.2
Deterministic execution and replay layer for AI Agents
Requires Python: >=3.8

# AgentTrace Python SDK

The deterministic execution and replay layer for AI Agents.

Stop guessing why your agents crash in production. AgentTrace records every LLM call, tool execution, and intermediate state—letting you rewind time, fork the execution, and test fixes instantly.

## Installation

```bash
pip install agenttrace
```

## Quick Start (The "Thin" SDK)

AgentTrace works as a thin data-collection layer. Simply decorate your main entry point, and use `with agenttrace.step()` to log intermediate actions. The SDK automatically bundles the events and syncs them securely to your dashboard.

```python
import agenttrace

# 1. Initialize with your API Key
agenttrace.init(api_key="at_live_...")

# 2. Add the @agenttrace.run decorator to track the entire execution
@agenttrace.run(name="checkout_agent")
def main():
    
    # 3. Log an LLM call or thought process
    with agenttrace.step("Planning Phase", type="thought"):
        plan = "Validating the order."
        
    # 4. Log a tool execution
    with agenttrace.step("Validate Order", type="tool_call", input={"order_id": 123}):
        # Your custom tool code here
        result = {"status": "valid"}
        agenttrace.set_result(result)
        
    return {"success": True}

if __name__ == "__main__":
    main()
```

When `main()` finishes, the complete trace is uploaded securely and becomes available in your AgentTrace dashboard for debugging and forking.
fepydas 0.0.75
felix' python data analysis suite
Requires Python: >=3.6

A python3 tool for data analysis and fitting.

Install from https://pypi.org/project/fepydas/ with "pip install fepydas".
View repository at https://git.tu-berlin.de/nippert/fepydas
Find usage examples at https://git.tu-berlin.de/nippert/fepydas/-/tree/master/examples

Requirements:
Python3
numpy
scipy
matplotlib
lmfit
scikit-learn
pyamg