semvec/pypi-packages huggingface dataset:
slope-er 0.1.6
Python implementation of Slope Error Rate (SER) by Arsalan Rahman Mirza.
Requires Python: >=3.8

Slope Error Rate (SER)

A Python implementation of Slope Error Rate (SER), designed to provide improved sensitivity and robustness 
under class imbalance, particularly for biometric authentication systems.

Overview

Slope Error Rate (SER) is a distance-based evaluation metric that quantifies the trade-off between False Acceptance Rate (FAR) 
and False Rejection Rate (FRR) across decision thresholds. Unlike traditional metrics such as Equal Error Rate (EER), which rely 
on a single operating point, SER captures error dynamics and provides a more informative assessment of classifier behavior.

SER is especially useful in applications where:

Data is highly imbalanced
False positives and false negatives have asymmetric costs
Threshold sensitivity matters

A lower SER value indicates better overall performance.

#Key Features
Designed for binary classification
Robust under class imbalance
Threshold-sensitive evaluation
Based on FAR and FRR geometry
Lightweight and easy to integrate
Suitable for biometrics, spoof detection, anomaly detection, and security systems

# Installation

pip install slope-er==0.1.6

# To import and use

import slope_er

# Call the function

result = slope_er.ser(0.3, 0.7,True)
print(f"The Slope Error Rate is: {result}")

or 

a = np.array([1,1,1,1,1,1,0,0,1,0,1,0])
b = np.array([1,0,1,1,1,1,0,0,1,0,1,0])
result = slope_er.ser(a, b,True)
print(f"The Slope Error Rate is: {result}")
scopie 0.3.0
Authorization engine providing fine grained access while you control the data. Aimed at being easy to use and implement without overtaking other parts of your system.
Requires Python: >=3.9
Keywords: authorization

# Scopie-py

[![PyPI - Version](https://img.shields.io/pypi/v/scopie?style=for-the-badge)](https://pypi.org/project/scopie/)
[![Static Badge](https://img.shields.io/badge/Stable-black?style=for-the-badge&logo=readthedocs&label=Docs)](https://scopie-py.readthedocs.io/en/stable/)

Python implementation of [scopie](https://github.com/miniscruff/scopie).

```python
from scopie import is_allowed

users = {
    "elsa": {
        "rules": ["allow/blog/create|update"],
    },
    "bella": {
        "rules": ["allow/blog/create"],
    },
}

blogPosts = {}

def create_blog(username, blogSlug, blogContent):
    user = users[username]
    if is_allowed(["blog/create"], user["rules"]):
        blogPosts[blogSlug] = {
            "author": user,
            "content": blogContent,
        }

def update_blog(username, blogSlug, blogContent):
    user = users[username]
    if is_allowed(["blog/update"], user["rules"]):
        blogPosts[blogSlug] = {
            "author": user,
            "content": blogContent,
        }
```
wicky-vllm 0.1.6
a lightweight vLLM implementation built from scratch
Requires Python: <3.13,>=3.10

<p align="center">
<img width="300" src="assets/logo.png">
</p>

<p align="center">
<a href="https://trendshift.io/repositories/15323" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15323" alt="GeeeekExplorer%2Fnano-vllm | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>

# Nano-vLLM

A lightweight vLLM implementation built from scratch.

## Key Features

* 🚀 **Fast offline inference** - Comparable inference speeds to vLLM
* 📖 **Readable codebase** - Clean implementation in ~ 1,200 lines of Python code
* ⚡ **Optimization Suite** - Prefix caching, Tensor Parallelism, Torch compilation, CUDA graph, etc.

## Installation

```bash
pip install git+https://github.com/GeeeekExplorer/nano-vllm.git
```

## Model Download

To download the model weights manually, use the following command:
```bash
huggingface-cli download --resume-download Qwen/Qwen3-0.6B \
  --local-dir ~/huggingface/Qwen3-0.6B/ \
  --local-dir-use-symlinks False
```

## Quick Start

See `example.py` for usage. The API mirrors vLLM's interface with minor differences in the `LLM.generate` method:

**Using local model path:**
```python
from wickyvllm import LLM, SamplingParams
llm = LLM("/YOUR/MODEL/PATH", enforce_eager=True, tensor_parallel_size=1)
sampling_params = SamplingParams(temperature=0.6, max_tokens=256)
prompts = ["Hello, Nano-vLLM."]
outputs = llm.generate(prompts, sampling_params)
outputs[0]["text"]
```

**Using Hugging Face model ID (auto-download):**
```python
from wickyvllm import LLM, SamplingParams
llm = LLM("Qwen/Qwen3-0.6B", enforce_eager=True, tensor_parallel_size=1)
sampling_params = SamplingParams(temperature=0.6, max_tokens=256)
prompts = ["Hello, Nano-vLLM."]
outputs = llm.generate(prompts, sampling_params)
outputs[0]["text"]
```

## Benchmark

See `bench.py` for benchmark.

**Attention Kernel Benchmark:**

To benchmark the Triton attention kernel performance:
```bash
python benchmark_attention.py
```

**Test Configuration:**
- Hardware: RTX 4070 Laptop (8GB)
- Model: Qwen3-0.6B
- Total Requests: 256 sequences
- Input Length: Randomly sampled between 100–1024 tokens
- Output Length: Randomly sampled between 100–1024 tokens

**Performance Results:**
| Inference Engine | Output Tokens | Time (s) | Throughput (tokens/s) |
|----------------|-------------|----------|-----------------------|
| vLLM           | 133,966     | 98.37    | 1361.84               |
| Nano-vLLM      | 133,966     | 93.41    | 1434.13               |


## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=GeeeekExplorer/nano-vllm&type=Date)](https://www.star-history.com/#GeeeekExplorer/nano-vllm&Date)
timescaler 0.1.0
Monitor your timescaledb like a pro
Requires Python: >=3.8

## Timesquare

Monitor your timescaledb like a pro!

Shows you the stats of your hypertables and chunks.

Next Step:
1. Show how the table size is growing as more and more data is inserted.
2. Allow the user to be able to compress, merge and de-merge chunks from the GUI
3. Show for each chunk, what is the time range of data stored in it, and for what 
![alt text](image.png)
httpprint 1.3
A minimal package allowing printing to a http server and the console insted of just the console. Great for monitoring long running scripts!
Requires Python: >=3.9

# httpprint

This module changes python's builtin print method to one that hosts a terminal clone on a flask server.
It needs almost no changes to the code, so it can be integrated easily into your projects.

## Usage

Just add

```
from httpprint import print
```

into the import section of every file that prints.

...and you're done! When you run the code, a http server is opened on port 5580. Everything you print to the console also gets shown here. Update the page to view new changes.

## Currently supported terminal behaviours

- `\t` pads to the next multiple of **8** spaces
- `\n` for newlines
- Filtered output: `<` and `>` don't break the entire page

## Planned

- Escape sequences for coloring
crp 0.0.5
Tools for cropping images.
Requires Python: <4,>=3.12

# crp

_Tools for cropping images._

[![PyPI](https://img.shields.io/pypi/v/crp?color=success)](https://pypi.org/project/crp/)
[![coverage](https://img.shields.io/badge/coverage-100%25-brightgreen?logo=pytest&logoColor=white)](https://coverage.readthedocs.io/en/latest/)
[![ci](https://github.com/br3ndonland/crp/workflows/ci/badge.svg)](https://github.com/br3ndonland/crp/actions/workflows/ci.yml)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

## Description

Images often need to be cropped to specific aspect ratios and dimensions for upload to sites like [TheMovieDB](https://www.themoviedb.org/) (TMDB). [TheMovieDB's image upload guidelines](https://www.themoviedb.org/bible/image) explain that backdrops should be in a 16:9 aspect ratio (width x height) and posters should be in a 2:3 aspect ratio. Some sites have auto-cropping features that will offer to crop images to the required dimensions during upload, but this is often not adequate. Images often need further editing beyond just brute-force auto-cropping. It's therefore helpful to have dimensions to use as a guideline when editing images.

This project provides a command-line interface (CLI) that suggests dimensions to use for cropping.

## Installation

- [pip](https://pip.pypa.io/en/stable/cli/pip_install/)
    - Install with `python -m pip install crp` from within a virtual environment
    - Invoke with `crp` or `python -m crp`
- [pipx](https://pipx.pypa.io/stable/getting-started/)
    - Install CLI with `pipx install crp` and invoke with `crp`
    - Run without installing CLI with `pipx run crp`
- [uv](https://docs.astral.sh/uv/guides/tools/)
    - Install CLI with `uv tool install crp` and invoke with `crp`
    - Run without installing CLI with `uvx crp`

## Usage

```sh
crp suggest --width=3940 --height 2160 backdrop # Crop to 3840 x 2160 (16:9).
crp suggest --width 1652 --height 2214 poster # Crop to 1476 x 2214 (2:3).
```

To see the help text, run `crp --help`/`crp -h`.

## Related

- GitHub topics:
  [crop-image](https://github.com/topics/crop-image),
  [crop](https://github.com/topics/crop),
  [cropping](https://github.com/topics/cropping),
  [image-manipulation](https://github.com/topics/image-manipulation),
  [image-processing](https://github.com/topics/image-processing),
  [tmdb](https://github.com/topics/tmdb)
- [`react-easy-crop`](https://github.com/ValentinH/react-easy-crop)/[`svelte-easy-crop`](https://github.com/ValentinH/svelte-easy-crop)
- [`react-image-crop`](https://github.com/DominicTobias/react-image-crop)
- [`smartcrop.js`](https://github.com/jwagner/smartcrop.js)
- [`smartcrop.py`](https://github.com/smartcrop/smartcrop.py)
- [TMDB Trello: Add support for image cropping](https://trello.com/c/9nVLokdG/237-add-support-for-image-cropping)
ed-archiver 0.1.0
Archive Ed Discussion courses into RAG-ready JSON
Requires Python: >=3.11

# Ed Archiver

Archive [Ed Discussion](https://edstem.org) courses into RAG-ready JSON files.

## Features

- **RAG-optimized output** - Clean JSON with text, metadata, and quality signals
- **Multi-region support** - Works with US, EU, AU Ed instances
- **Preserves media** - Extracts image and link URLs from posts
- **Rate-limit handling** - Automatic retry with exponential backoff

## Installation

```bash
git clone https://github.com/eliemada/edstem-archiver.git
cd ed-archiver
uv sync
```

## Setup

1. Generate an API token at https://edstem.org/us/settings/api-tokens (or your region)
2. Create a `.env` file:
   ```
   ED_API_TOKEN=your_token_here
   ```

## Usage

```bash
# Interactive mode
uv run ed-archiver

# Direct course ID (defaults to US region)
uv run ed-archiver 23247

# Full URL (auto-detects region)
uv run ed-archiver https://edstem.org/eu/courses/1124/discussion/

# Custom output directory
uv run ed-archiver 1124 -o ./data
```

## Output Structure

```
out/{course_id}/
├── metadata.json
└── threads/
    └── {thread_id}.json
```

### Thread Schema

```json
{
  "id": 124296,
  "type": "question",
  "title": "How to implement X?",
  "category": "Homework 3",
  "subcategory": "",
  "created_at": "2024-03-27T20:33:03Z",
  "is_answered": true,
  "is_pinned": false,
  "is_endorsed": false,
  "vote_count": 5,
  "view_count": 219,
  "reply_count": 3,
  "content": "Plain text content...",
  "images": ["https://static.eu.edusercontent.com/files/..."],
  "links": ["https://example.com/reference"],
  "answers": [
    {
      "id": 185874,
      "content": "Answer text...",
      "images": [],
      "links": [],
      "vote_count": 2,
      "is_endorsed": true,
      "is_accepted": true,
      "created_at": "2024-03-27T21:19:56Z",
      "comments": [...]
    }
  ],
  "full_text": "Concatenated text for embedding..."
}
```

### Metadata Schema

```json
{
  "course_id": "1124",
  "region": "eu",
  "archived_at": "2026-01-25T18:30:00Z",
  "thread_count": 2000,
  "base_url": "https://edstem.org/eu/courses/1124"
}
```

## RAG Integration

The output is designed for RAG pipelines:

| Field | RAG Use |
|-------|---------|
| `full_text` | Direct embedding input |
| `vote_count`, `is_endorsed` | Quality ranking signals |
| `category`, `type` | Metadata filtering |
| `images` | Vision-language captioning |
| `base_url` + `id` | Source citation URLs |

Reconstruct source URLs:
```python
source_url = f"{metadata['base_url']}/discussion/{thread['id']}"
# → "https://edstem.org/eu/courses/1124/discussion/124296"
```

## Acknowledgments

Built on top of [edapi](https://github.com/smartspot2/edapi) by [@smartspot2](https://github.com/smartspot2). Thank you for creating and maintaining the unofficial Ed API client.

## License

MIT