semvec/pypi-packages huggingface dataset:
labda 0.1.0
A Python package built for health researchers, transforms raw movement data into compelling stories about human behaviour.
Requires Python: >=3.11
Keywords: accelerometers, gnss, gps, movement-behavior, physical-activity, sedentary-behaviour, transportation, wearables

[![pypi](https://img.shields.io/pypi/v/labda.svg)](https://pypi.python.org/pypi/labda)
[![versions](https://img.shields.io/pypi/pyversions/labda.svg)](https://github.com/josefheidler/labda)
[![downloads](https://static.pepy.tech/badge/labda/month)](https://pepy.tech/project/labda)
[![discord](https://img.shields.io/discord/1165947556807848016)](https://discord.gg/qu6ax5Q64j)

# LABDA: Library for Advanced Behavioural Data Analysis

LABDA, a Python package built for health researchers, transforms raw movement data into compelling stories about human behaviour.
np-vmd 0.1.1
A package for Vibrations and Machine Dynamics
Requires Python: >=3.10

# Vibrations and Machine Dynamics

This is a package for helping with the lecture of npapnet at the Mechanical Engineering department. 

Contains code for:

- SDOF system
  - Free response
    - Undamped
    - Damped
  - Forced HArmonic response
    - Undamped
    - Damped 
- TDOF systems
- MDOF systems

## project urls
- [Documentation: https://npapnet.github.io/np-vmd/](https://npapnet.github.io/np-vmd/)
- PyPI: https://pypi.org/project/np-vmd/ - not yet published
- [Source Code: https://github.com/npapnet/np-vmd](https://github.com/npapnet/np-vmd)

# Usage

## installation

Navigate to the folder of the pypkg and use

> pip install --editable .


# TODO 

Issues:
- 20240116: [ ] update rst documentation with new configuration of tdof_mck and the response classes. 
- 20240116: [ ] Inman: check forced response of system (no iv)
- 20240116: [ ] Inman: check forced response of system (with iv)
- 20240116: [ ] Gagnon: check forced response of system (single excitation - with iv)
- 20240116: [ ] Gagnon: check forced response of system (only iv - no excitation) 
- 20240116: [ ] Gagnon: check forced response of system (only iv - multipleexcitation)

## History 
### v:0.1.1
- 20251205: [x] moved to separate repository. Updated name and documentation. 

### v0.0.2
- 20240116: [x] Inman: check free response of system
- 20240116: [X] Gagnon: check forced response of system (single excitation - no iv)
cdef-pil 1.0.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
libresubtitles 1.0.3
Privacy-first AI subtitles for videos and audio
Requires Python: >=3.13

# LibreSubtitles

![PyPI](https://img.shields.io/pypi/v/libresubtitles)
![License](https://img.shields.io/badge/License-Apache%202.0-blue)
![Python](https://img.shields.io/badge/python-3.13%2B-blue)

**LibreSubtitles** is an open-source, offline subtitle generator accurate subtitles using [Whisper](https://github.com/openai/whisper). 

---
![Project Logo](https://raw.githubusercontent.com/chirag-juneja/LibreSubtitles/refs/heads/master/assets/logo.png) 
---
## Features

- Generate `.srt` subtitles from audio or video files.
- Works offline; privacy-first.
- Handles long videos efficiently by splitting on silence.
- CLI interface for quick usage.
- Cross-platform: Linux, macOS, Windows (CPU mode recommended).  

---

## Requirements

- Python 3.13 or higher
- [FFmpeg](https://ffmpeg.org/) installed and accessible from your `PATH`


## Installation

Install via pip:

```bash
pip install libresubtitles
```

## Usage

```bash
libresubtitles -i /path/to/video.mp4
```