semvec/pypi-packages huggingface dataset:
wdg-lab 0.1.1
Winding Lab: Your powerful winding design assistant
Requires Python: >=3.12

# WDG Lab — Winding Design Laboratory
WDG Lab is a concise, developer-friendly toolkit for designing, analyzing, and visualizing electrical windings. It focuses on practical outputs for design validation, documentation, and quick iteration.

## Table of contents
- [Key features](#key-features)
- [Quick links](#quick-links)
- [Installation](#installation)
- [Quick start](#quick-start)
- [Project layout](#project-layout)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Key features

- Stranded and hairpin winding generators
- Magnetic calculations: MMF, harmonics, and winding factors
- Slot current distribution and pin/connection details
- 2D visualization utilities for winding layouts and reports
- Example scripts and a lightweight web UI (see Quick links)

## Quick links

- Documentation: [docs/](docs/)
- Examples: [examples/](examples/)
- Web UI (optional): https://github.com/DawnEver/wdg-lab-webui

## Installation

Recommended: install from source (development mode).

1. Clone the repository

```bash
git clone https://github.com/DawnEver/wdg-lab.git
cd wdg-lab
```

2. Create and activate a virtual environment, uv is recommanded:
```bash
uv venv
# or use venv
pip -m venv .venv
```

3. Install the package
```bash
uv pip install -e . # for normal user
uv pip install -e ".[web]" # for web user
uv pip install -e ".[dev,web]" # for developer
```

## Quick start

- Generate a winding from parameters (examples include basic parameter sets):

```bash
python examples/gen_winding_from_params.py
```

- Generate a winding from winding configuration files:
```bash
python examples/gen_winding_from_toml.py
```

## Project layout

- `wdg_lab/` — core library modules and APIs
- `examples/` — runnable examples and small utilities
- `tests/` — unit tests and validation cases

## Contributing

Contributions, bug reports, and feature requests are welcome. To contribute:

1. Open an issue to discuss large changes.
2. Fork the repo and create a feature branch.
3. Add tests where applicable and follow the existing code style.
4. Open a pull request describing your change.

## License

This project is licensed under the Apache License, Version 2.0. See the full license in the LICENSE file or at https://www.apache.org/licenses/LICENSE-2.0

## Contact

For questions or collaboration, open an issue or contact the maintainers through the repository.
ux-lens 0.1.1
MCP server for browser-based UX critique with Gemini
Requires Python: >=3.11

# UX Lens

An MCP server that captures browser interactions and uses Gemini 3 Flash Preview to provide comprehensive UX/frontend critique.

## What it does

1. Launches a browser and records your interaction with any website
2. Sends the video to Gemini 3 Flash Preview for analysis
3. Returns detailed UX/frontend feedback

## Installation

```bash
# Clone and install
git clone https://github.com/nice-bills/ux-lens.git
cd ux-lens
uv tool install .
```

## Configuration

This tool requires a Gemini API key. You can also optionally configure the model.

### Environment Variables

- `GEMINI_API_KEY`: Your Google Gemini API key (Required)
- `GEMINI_MODEL`: The model to use (Optional, defaults to `gemini-3-flash-preview`)

## Usage

### With MCP Clients (Claude Code, Gemini CLI)

Add to your MCP configuration (e.g., `~/.gemini/settings.json` or `claude_code_config.json`):

```json
{
  "mcpServers": {
    "ux-watcher": {
      "command": "ux-watcher",
      "env": {
        "GEMINI_API_KEY": "your-key-here",
        "GEMINI_MODEL": "gemini-3-flash-preview"
      }
    }
  }
}
```

Then simply ask the assistant:

> "Check the UX of https://example.com"

### Direct CLI

If you have installed it as a tool, you can run the server directly:

```bash
ux-watcher
```

## Requirements

- Python 3.11+
- Chromium browser (installed automatically by Playwright)
- Gemini API key

## License

MIT
cdef-pil-100126-0020 0.1.0
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.

## Config Path Aliases
You can reference `[paths]` entries directly from `registry_pattern` to avoid repeating paths.
When a pattern equals a `*_path` key, it expands to that path and automatically appends
`/*.parquet` if no glob or `.parquet` suffix is provided. You can also set `base_path`
to resolve `/path/to/...` placeholders or relative patterns in covariates.

Example:
```toml
[paths]
base_path = "E:/workdata/708245/data/registers"
bef_path = "E:/workdata/708245/data/registers/bef"
akm_path = "E:/workdata/708245/data/registers/akm"

[[covariates]]
label = "employment"
registry_pattern = "akm_path"
column = "SOCIO13"

[[covariates]]
label = "education"
registry_pattern = "/path/to/uddf/*.parquet"
column = "HFAUDD"
```

## Pipeline Cache
To speed repeat runs, cache each phase under `[output]` and enable resume:
```toml
[output]
output_dir = "output"
cache_dir = "output/cache"
resume = true
# clean_cache = true
```

CLI equivalents: `--resume`, `--clean-cache`, `--cache-dir`.

## 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`
cdef-pil-100126-0010 0.1.0
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.

## Config Path Aliases
You can reference `[paths]` entries directly from `registry_pattern` to avoid repeating paths.
When a pattern equals a `*_path` key, it expands to that path and automatically appends
`/*.parquet` if no glob or `.parquet` suffix is provided. You can also set `base_path`
to resolve `/path/to/...` placeholders or relative patterns in covariates.

Example:
```toml
[paths]
base_path = "E:/workdata/708245/data/registers"
bef_path = "E:/workdata/708245/data/registers/bef"
akm_path = "E:/workdata/708245/data/registers/akm"

[[covariates]]
label = "employment"
registry_pattern = "akm_path"
column = "SOCIO13"

[[covariates]]
label = "education"
registry_pattern = "/path/to/uddf/*.parquet"
column = "HFAUDD"
```

## Pipeline Cache
To speed repeat runs, cache each phase under `[output]` and enable resume:
```toml
[output]
output_dir = "output"
cache_dir = "output/cache"
resume = true
# clean_cache = true
```

CLI equivalents: `--resume`, `--clean-cache`, `--cache-dir`.

## 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`
image-to-poetry 0.0.1
A Python package to generate poems from images.
Requires Python: >=3.9

# Image to Poetry

Upload an image and get a rhyming poem about it!

## Prerequisites

You need a Google Gemin API key. Get one from [aistudio.google.com](https://aistudio.google.com/).

Set your API key as an environment variable:
```bash
export GEMINI_API_KEY="your_api_key_here"
```

## Installation

```bash
pip install image-to-poetry
```

## Usage

```bash
image2poetry my_image.jpg
```
reemote 0.1.3
An API for controlling remote systems.
Requires Python: >=3.13

# Reemote

An API for controlling remote systems.

For a detailed description refer to the [Reemote home page](https://reemote.org/).

## Installation

Install the module with pip:

```bash
pip install reemote
```

## Installing the ReST API

The optional ReST API can be installed with pipx.

```bash
python3 -m venv myenv
source myenv/bin/activate
pipx install reemote
```

## Starting the ReST API server

To start the server on the local host:

```bash
reemote --port=8001 
```

Parameter, such as the port number, are passed to [uvicorn](https://uvicorn.dev/#command-line-options) except:

* `--inventory`: The inventory file path (optional).
* `--logging`: The logging file path (optional).

When the server is running, the Swagger UI can be found a http://localhost:8001/docs and API documentation can be found at http://localhost:8001/redoc.