semvec/pypi-packages huggingface dataset:
labda-parsers 0.1.0
A package designed to extract data from movement sensors such as accelerometers and GPS devices.
Requires Python: >=3.11
Keywords: accelerometer, gps, movement-behavior, parsers, sensors

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

# LABDA Parsers
## Library for Advanced Behavioural Data Analysis

A package designed to extract data from movement sensors such as accelerometers and GPS devices.

### Supported formats
- Actigraph (gt3x)
- Axivity (cwa)
- Sens (bin)
bifis 0.1.0
A CFD image sampling library.
Requires Python: >=3.9
Keywords: bifis, cfd, image, machine learning, sampling, sdf

<p align="center">
    <a>
	    <img src='https://img.shields.io/badge/python-3.10%2B-blueviolet' alt='Python' />
	</a>
    <a>
	    <img src='https://img.shields.io/badge/code%20style-black-black' />
	</a>
    <a href='https://opensource.org/license/lgpl-2-1'>
	    <img src='https://img.shields.io/badge/license-LGPLv2+-blue' />
	</a>
</p>

# 🌐 BiFIS

[**Signed Distance Function-biased flow importance sampling for implicit neural compression of flow fields**]()<br/>
[Omar A. Mures](https://omaralv.com/), [Miguel Cid Montoya](https://mcidmontoya.com/)

## Usage

### Installation

The `bifis` library is available using pip. We recommend using a virtual environment to avoid conflicts with other software on your machine.

``` bash
pip install bifis
```

### Example

```python
from bifis.utils import Config, console
from bifis.sampling import Uniform, VariableDensity, BiFIS
from scipy.interpolate import griddata

# Load configuration
config = Config("config.json")

# Build toy deck
points, deck, path = build_toy_deck()

uniform = Uniform(config, args.resolution[0], args.resolution[1], np.arange(len(points)))
uniform.show(points[:, 0], points[:, 1], write=False)

variable_density = VariableDensity(config, args.resolution[0], args.resolution[1], np.arange(len(points)))
variable_density.show(points[:, 0], points[:, 1], write=False)

bifis = BiFIS(config, args.resolution[0], args.resolution[1], np.arange(len(points)), samples=points, surface=deck, surface_idx=np.arange(len(deck)))
bifis.show(points[:, 0], points[:, 1], write=False)

# Use resulting grid
interp_var_dens = griddata((points[:, 0], points[:, 1]), p_original, (variable_density.grid_x, variable_density.grid_y), method=interpolation_method)

# BiFIS uses cell ids instead of interpolation to get pixel data
fields_b_i = p_original[bifis.idx].reshape(bifis.img_shape)
```

## 📢 News

### May 2025

- Initial code version published.

### March 2025

- Code coming soon.

## 🎯 TODO

The repo is still under construction, thanks for your patience. 

- [ ] Release pip package.
- [x] Release of the sampling code.

## 📜 Citation

```
@Article{montoya2024aeroelastic,
  author = {Mures, Omar A. and Cid Montoya, Miguel},
  title = {Signed Distance Function-biased flow importance sampling for implicit neural compression of flow fields},
  journal = {Computer-Aided Civil and Infrastructure Engineering},
  volume = {n/a},
  number = {n/a},
  pages = {},
  doi = {n/a},
  url = {n/a},
}
```