semvec/pypi-packages huggingface dataset:
beetsplug 0.1.0
Add your description here
Requires Python: >=3.13

[Folkways](https://folkways.si.edu/) autotagger plugin for [beets](https://beets.io/).

It directly scrapes the html of the website which makes it prone to breakage.
The meta tags are sadly even more inconsistent than the info boxes, so for now, that's what this code is using.
faceoff 0.2.1
Terminal tool for watching hockey games
Requires Python: <4.0,>=3.10
Keywords: python

# Faceoff

<p align="center">
  <img src="faceoff_logo.png" alt="Faceoff Logo" width="200">
</p>

A terminal user interface (TUI) application for following NHL hockey games in real-time.

![Python](https://img.shields.io/badge/python-3.10+-blue.svg)
[![License](https://img.shields.io/github/license/vgreg/faceoff)](https://img.shields.io/github/license/vgreg/faceoff)

## Features

- **Live Game Schedule**: Browse NHL games by date with easy navigation (previous/next day)
- **Real-time Updates**: Auto-refreshing scores for live games
- **Game Details**: View play-by-play, box scores, and scoring summaries for in-progress and completed games
- **Pre-Game Preview**: View matchup data including goalie comparison and skater leaders for upcoming games
- **League Standings**: View current NHL standings with multiple views (Wild Card, Division, Conference, League)
- **Player Stats Leaders**: View top players in various statistical categories for skaters and goalies
- **Team Browser**: Browse all NHL teams, view rosters, and team schedules
- **Player Profiles**: View detailed player information, career stats, and game logs
- **Responsive Layout**: Game cards automatically arrange based on terminal width
- **Local Time Display**: Game times shown in your local timezone with timezone abbreviation

## Installation

### Using uvx (recommended)

```bash
uvx faceoff
```

### Using pip

```bash
pip install faceoff
```

Then run:

```bash
faceoff
```

### From source

```bash
git clone https://github.com/vgreg/faceoff.git
cd faceoff
uv run faceoff
```

## Screenshot

![Faceoff Screenshot](screenshot.png)

## Acknowledgments

This project was inspired by [Playball](https://github.com/paaatrick/playball), a similar terminal application for following MLB baseball games.

### Built With

- **[Textual](https://github.com/Textualize/textual)** - The modern TUI framework for Python that powers the user interface
- **[nhl-stats-api-client](https://github.com/nhl-stats-api-client)** - Python client for accessing NHL API data

## Disclaimer

This project is not affiliated with, endorsed by, or in any way officially connected with the National Hockey League (NHL), any of its teams, or any of its affiliates. All NHL logos, trademarks, and data are the property of the NHL and its teams.

This application uses publicly available NHL API data for informational and educational purposes only.

## Development

### Setup

```bash
git clone https://github.com/vgreg/faceoff.git
cd faceoff
make install
```

### Running Tests

```bash
uv run pytest
```

### Linting

```bash
uv run ruff check src/
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
mk10-pro 1.0.2
Deterministic pre-delivery truth infrastructure for audiovisual mastering. Produces cryptographically verifiable Master Truth Bundles (MTB).
Requires Python: >=3.9
Keywords: verification, deterministic, audiovisual, mastering, cryptographic, mtb, truth-bundle

# MK10-PRO v1.0 — Deterministic Pre‑Delivery Truth Infrastructure

> **STATUS:** FINAL / AUTHORITATIVE / CLOSED / FINISHABLE
>
> **SCOPE (HARD BOUNDARY):** Pre‑delivery truth only. Formal playability under declared specifications. No cinema playback. No devices. No operators. No trust. No exceptions.

---

## EXECUTIVE DEFINITION (NON‑MARKETING)

**MK10‑PRO is deterministic audiovisual infrastructure that converts mastering into provable, durable facts instead of trusted outputs.**

If a claim cannot be proven — how a master was produced, what transformed it, which rules governed it, who approved its promotion, or whether it is *formally playable under a declared specification* — MK10‑PRO treats that claim as invalid.

This is not a tool. It is infrastructure.

---

## SYSTEM AXIOMS (IMMUTABLE)

1. **Truth is executable** — claims emerge only from execution.
2. **Evidence is the product** — files are inputs, not outcomes.
3. **Policy is law** — configuration cannot override rules.
4. **Verification is hostile** — no engine, no trust, no authority required.
5. **Determinism is mandatory** — same inputs must yield identical outputs.
6. **Scope ends before institutions** — hardware, venues, operators are out of bounds.

If any axiom is violated, MK10‑PRO is invalid by definition.

---

## QUICK START

```bash
# Install dependencies
pip install -r requirements.txt
# OR
make install

# Ingest source assets
mk10 ingest --source /path/to/assets

# Execute mastering pipeline
mk10 execute --dag pipeline.yaml

# Promote to release
mk10 promote --title "MyTitle" --version "v1.0" --state RELEASE

# Verify an MTB
mk10 verify --mtb /path/to/mtb.zip
```

### Runtime Dependencies

**Required:**
- `pyyaml>=6.0` — YAML parsing (policy rules, config)
- `jsonschema>=4.0` — JSON schema validation (MTB, evidence, ingest)
- `click>=8.0` — CLI framework
- `cryptography>=41.0` — Cryptographic operations
- `pycryptodome>=3.19.0` — Additional crypto support

**Full list:** See `requirements.txt`

---

## THE ACTUAL PRODUCT: MASTER TRUTH BUNDLE (MTB)

Files are not the product.

The **Master Truth Bundle (MTB)** is the product.

An MTB is a sealed, self‑contained, verifiable object that represents a title/version as fact.

If the MTB validates, the title exists.
If it does not, the title is not real.

---

## GOVERNING PROMISE — "NO FILE FALLS AGAIN"

A master is considered safe only if it can always:

1. Be located
2. Be verified
3. Be explained
4. Be reproduced
5. Be proven formally playable under its specification
6. Be re‑delivered without ambiguity

If any condition fails, MK10‑PRO refuses the claim.

---

## LICENSE

See LICENSE file for details.

---

## FINAL AUTHORITY STATEMENT

If MK10‑PRO says a title exists, it exists.
If MK10‑PRO refuses a claim, the claim is invalid.

There is no appeal to trust.
There is only proof.
symclosestwannier 2.0.4
A Python library for Symmetry-Adapted Closest Wannier (SymCW) Tight-Binding model based on Plane-Wave DFT calculation.
Requires Python: >=3.9
Keywords: dft, wannier function, tight-binding model, symmetry, materials science

# [SymClosestWannier](https://cmt-mu.github.io/SymClosestWannier/)

- **Overview**:
    A Python library to create Symmetry-adapted Closest Wannier (SymCW) tight-binding models [1] based on the Symmetry-Adapted Multipole Basis (SAMB) [2] and the Closest Wannier formalism developed by Taisuke Ozaki [3].

    > [1] Rikuto Oiwa, Akane Inda, Satoru Hayami, Takuya Nomoto, Ryotaro Arita, and Hiroaki Kusunose, Symmetry-adapted closest Wannier modeling based on complete multipole basis set, Phys. Rev. B 112, 035116 (2025).
    > DOI: https://doi.org/10.1103/2s5q-p42x
    
    > [2] Hiroaki Kusunose, Rikuto Oiwa, and Satoru Hayami, Symmetry-adapted modeling for molecules and crystals, Phys. Rev. B 107, 195118 (2023).
    > DOI: https://doi.org/10.1103/PhysRevB.107.195118.<br>
  
    > [3] Taisuke Ozaki, Closest Wannier functions to a given set of localized orbitals, Phys. Rev. B 110, 125115, (2024).
    > DOI: https://doi.org/10.1103/PhysRevB.110.125115.

- **Authors**: Rikuto Oiwa

- **Installation**: SymClosestWannier can be installed from PyPI using pip on Python >= 3.9:
    ```
    pip install symclosestwannier
    ```
    You can also visit [PyPI](https://pypi.org/project/symclosestwannier/) or [GitHub](https://github.com/CMT-MU/SymClosestWannier/) to download the source.

- **Citing SymClosestWannier**: If you are using SymClosestWannier in your scientific research, please help our scientific visibility by citing our work:

    > [1] Rikuto Oiwa, Akane Inda, Satoru Hayami, Takuya Nomoto, Ryotaro Arita, and Hiroaki Kusunose, Symmetry-adapted closest Wannier modeling based on complete multipole basis set, Phys. Rev. B 112, 035116 (2025).
    > DOI: https://doi.org/10.1103/2s5q-p42x
    
    > [2] Hiroaki Kusunose, Rikuto Oiwa, and Satoru Hayami, Symmetry-adapted modeling for molecules and crystals, Phys. Rev. B 107, 195118 (2023).
    > DOI: https://doi.org/10.1103/PhysRevB.107.195118.<br>
  
    > [3] Taisuke Ozaki, Closest Wannier functions to a given set of localized orbitals, Phys. Rev. B 110, 125115, (2024).
    > DOI: https://doi.org/10.1103/PhysRevB.110.125115.

- **Requirements**:
  - Symmetry-Adapted Multipole Basis (SAMB) for molecular or crystal are optionally generated by [MultiPie](https://github.com/CMT-MU/MultiPie).
  - MultiPie library optionally requires [TeXLive](https://www.tug.org/texlive/) environment to create LaTeX and PDF files.
  - Molecular or crystal structure files are optionally generated by [QtDraw](https://github.com/CMT-MU/QtDraw).

- **See also**:
  - [user guide](https://cmt-mu.github.io/SymClosestWannier/)
pabumeasures 0.2.1
Measures on participatory budgeting elections
Requires Python: >=3.10
Keywords: participatory budgeting, social choice

# Pabumeasures

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pabumeasures)](https://pypi.org/project/pabumeasures/)
[![PyPI - Version](https://img.shields.io/pypi/v/pabumeasures)](https://pypi.org/project/pabumeasures/)
[![Test](https://github.com/mdbrnowski/pabumeasures/actions/workflows/test.yml/badge.svg)](https://github.com/mdbrnowski/pabumeasures/actions/workflows/test.yml)

## Installation
> Prerequisites
> * [CMake](https://cmake.org/download/)
> * [Google OR-Tools (C++)](https://developers.google.com/optimization/install/cpp) – Install via a package manager (e.g., `brew`) or download a binary distribution.

**Pabumeasures** uses dynamic linking to reduce build times. You might need to make the OR-Tools headers and libraries discoverable at both build-time and runtime by exporting the variables as shown below.

> **Environment Configuration**
>
> ⚠️ **Note:** The path to OR-Tools provided below must be the installation root containing the `lib` and `include` subdirectories.
>
> **Linux**
> ```shell
> export CMAKE_PREFIX_PATH="/path/to/ortools"
> export LD_LIBRARY_PATH="$CMAKE_PREFIX_PATH/lib:$LD_LIBRARY_PATH"
> ```
> **macOS**
> ```shell
> export CMAKE_PREFIX_PATH="/path/to/ortools"
> export DYLD_LIBRARY_PATH="$CMAKE_PREFIX_PATH/lib:$DYLD_LIBRARY_PATH"
> ```
> **Windows**
> ```shell
> set CMAKE_PREFIX_PATH=C:\path\to\ortools
> set PATH=%CMAKE_PREFIX_PATH%\lib;%PATH%
> ```


Then, you can simply install **pabumeasures** from PyPI:

```shell
pip install pabumeasures
```

## Documentation

Currently, there is no dedicated documentation. However, the interface is quite simple.

The general workflow is as follows: create or import PB instances using **pabutools**, then compute rule results and measures for those rules using **pabumeasures**.

```py
from pabumeasures import Measure, mes_cost, mes_cost_measure
from pabutools.election import ApprovalBallot, ApprovalProfile, Instance, Project

p1 = Project("p1", 1)
p2 = Project("p2", 1)
p3 = Project("p3", 3)

b1 = ApprovalBallot([p1, p2])
b2 = ApprovalBallot([p1, p2, p3])
b3 = ApprovalBallot([p3])

instance = Instance([p1, p2, p3], budget_limit=3)
profile = ApprovalProfile([b1, b2, b3])

mes_cost(instance, profile) # returns [p1, p2]
mes_cost_measure(instance, profile, p3, Measure.ADD_APPROVAL_OPTIMIST) # returns 1
```
grist 0.1.0.dev0
Generative Retrieval Id Semantic Transforms on top of Google Grain.
Requires Python: <3.13,>=3.10

# GRIST 🌾

Generative Retrieval ID Semantic Transforms for reproducible data pipelines.

GRIST is a focused Python library for bridging raw research datasets and generative retrieval models. It enriches datasets with Semantic Identifiers, guarantees deterministic preprocessing, and provides helpers for publishing results to public hubs like HuggingFace and Kaggle. It is designed to work smoothly with existing data pipeline tooling, including Grain.

## Why GRIST

In Generative Retrieval (GR) research, reproducibility is everything. GRIST treats a dataset not as a static file, but as a deterministic factory. Every transformation, from text cleaning to model-based ID generation, is designed to be perfectly reproducible.

## Features

- Pipeline-native: Fits into existing data pipeline tooling without new paradigms to learn.
- Semantic ID injection: Built-in MapTransform classes for UUIDs, hashes, or model-generated codes.
- Inference-ready: Wrap any pre-trained model (HuggingFace, JAX, PyTorch) as an ID generator.
- Publishing helpers: Tools to facilitate uploads to HuggingFace or Kaggle.

## Installation

```bash
uv add grist
```

## Quick Start

TODO: Quick start example for the planned public API.

## Concepts

- Semantic Identifiers: Stable, model-aware IDs that augment dataset samples for generative retrieval.
- Deterministic pipelines: Transform semantics guarantee repeatable preprocessing.
- Dataset configs: Optional, reusable configuration files for well-known datasets.

## Why the Name

In milling, grist is the grain separated from its chaff and ready to be ground. This library prepares your "raw grain" (datasets) into a refined format ready for the "mill" of generative retrieval models.