semvec/pypi-packages huggingface dataset:
aind-s3-cache 0.1.0
Small library providing a transparent local cache for S3 objects.
Requires Python: >=3.10

# AIND S3 Cache

![CI](https://github.com/AllenNeuralDynamics/aind-s3-cache/actions/workflows/ci-call.yml/badge.svg)
[![PyPI - Version](https://img.shields.io/pypi/v/aind-s3-cache)](https://pypi.org/project/aind-s3-cache/)
[![semantic-release: angular](https://img.shields.io/badge/semantic--release-angular-e10079?logo=semantic-release)](https://github.com/semantic-release/semantic-release)
[![License](https://img.shields.io/badge/license-MIT-brightgreen)](LICENSE)
[![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)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![Copier](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/copier-org/copier/master/img/badge/badge-grayscale-inverted-border.json)](https://github.com/copier-org/copier)
Small library providing a transparent local cache for S3 objects.

## Key Features

- **S3 Integration**: Built-in support for AWS S3 with caching and anonymous access
- **Multi-source JSON Reading**: Unified JSON loading from local files, HTTP URLs, and S3 URIs

## Installation

If you choose to clone the repository, you can install the package by running the following command from the root directory of the repository:

```bash
pip install .
```

Otherwise, you can use pip:

```bash
pip install aind-s3-cache
```



To develop the code, run:
```bash
uv sync
```

## Development

Please test your changes using the full linting and testing suite:

```bash
./scripts/run_linters_and_checks.sh -c
```

Or run individual commands:
```bash
uv run --frozen ruff format          # Code formatting
uv run --frozen ruff check           # Linting

uv run --frozen mypy                 # Type checking

uv run --frozen interrogate -v       # Documentation coverage
uv run --frozen codespell --check-filenames  # Spell checking
uv run --frozen pytest --cov  # Tests with coverage
```


### Documentation
```bash
sphinx-build -b html docs/source/ docs/build/html
```


## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
pymatlie 0.0.1a1
pymatlie provides an implementation of Matrix Lie Groups (MLG) used in robotics.
Requires Python: >=3.9

# PyMatLie

![workflow](https://github.com/luis-marques/pymatlie/actions/workflows/ci.yml/badge.svg)

## Installation
Either install via `pip` through
```
pip install pymatlie
```
or, for development, clone the repository and install in editable mode using
```
$ python -m venv venv
$ source venv/bin/activate
$ pip install -e ".[dev]"
```

## Usage

We use $g \in G$ for a group element, where $G$ is the Matrix Lie Group,
$\xi \in \mathbb R^m$ for a column vector in the Lie Algebra and 
$\xi^\wedge \in \mathfrak g$ for the matrix representation of said vector.

```python
from pymatlie.se2 import SE2

g = SE2.random() # Sample random group element

# Moving from the Group to the Lie Algebra
xi_hat = SE2.log(g)
xi     = SE2.vee(xi_hat)
# or alternatively
xi     = SE2.Log(g)

# Moving from the Lie Algebra to the Group
xi_hat = SE2.hat(xi) # Or SE3.wedge(xi)
g       = SE2.expm(xi_hat)
# or alternatively
g       = SE2.exp(xi)

# There are methods for generating common matrices
e       = SE2.get_identity()
Jl      = SE2.left_jacobian(xi)
Jr      = SE2.right_jacobian(xi)
Ad      = SE2.adjoint_matrix(g)
ad      = SE2.ad_operator(g)
```

## Features

- Implemented SO(2), SE(2)
- Batched
- Euler-Poincare and Euler-Poincare-Suslov
flagkaptain 0.1.0
Add your description here
Requires Python: >=3.12



<div class="title-block" style="text-align: center;" align="center">
<p><img title="flagship logo" src="static/logo.png" width="500" height="300"></p>

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Code Style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

</div>

## Table of Contents
pyflagship 0.1.0
Add your description here
Requires Python: >=3.12



<div class="title-block" style="text-align: center;" align="center">
<p><img title="flagship logo" src="static/logo.png" width="500" height="300"></p>

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Code Style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

</div>

## Table of Contents
linteum-verba 1.0.0
A rich text editor with syntax highlighting, code folding, and advanced editing features
Requires Python: >=3.7

# Linteum Verba - Rich Text Editor

A feature-rich text editor built with Tkinter and TTK Bootstrap using the Canvas widget.

## Features

- Line numbers
- Code folding
- Syntax highlighting
- Text selection and editing
- Modern UI with TTK Bootstrap

## Requirements

- Python 3.7+
- ttkbootstrap>=1.10.1
- pygments>=2.15.0

## Installation

1. Clone this repository
2. Install the required packages:
   ```
   pip install -r requirements.txt
   ```

## Usage

Run the main application:
```
python Source/Python/main.py
```
vlbisim 0.1.1
A simple simulator for VLBI
Requires Python: >=3.9

# vlbisim

`vlbisim` is a Python package for simulating Very Long Baseline Interferometry (VLBI) observations. It allows users to define telescope arrays, simulate uv-coverage for astronomical sources, visualize baseline distributions, and create synthetic dirty images. The package supports major VLBI arrays such as VLBA, EHT, ngVLA, EVN, and user-defined custom arrays.

---

## Features

- Define and configure telescopes with latitude, longitude, elevation, and visibility limits.  
- Calculate uv-coverage for arbitrary telescope arrays and sources.  
- Simulate interferometric observations with customizable observation times and scan durations.  
- Plot uv-coverage with baseline highlighting options.  
- Generate synthetic dirty images from uv-coverage.  
- Visualize telescope locations on world maps and globes.  
- Supports major VLBI arrays, including VLBA, EHT, ngVLA, GMVA, EVN, MeerKAT, SKA, and others.  

---

## Installation

```bash
pip install vlbisim
```

> **Note:** Some features require additional dependencies such as `numpy`, `matplotlib`, `astropy`, `basemap`, and `numba`.

---

## Usage

### Importing the package

```python
from vlbisim import Telescope, getArray, simulateUV, plotUVdata, plotMap
```

---

### Define telescopes

```python
# Create a single telescope
t1 = Telescope(lat=50.524778, lon=6.883972, name="Effelsberg", elev_lim=15)

# Load a pre-defined array
vlba = getArray("VLBA")
```

---

### Simulate uv-coverage

```python
# Define source coordinates [RA, Dec] in degrees
source = [180.0, 45.0]

# Simulate uv-coverage
uv_coverage = simulateUV(vlba, source, n_iter=100, obsstart=0, obsend=24, make_plot=True)
```

---

### Plot uv-coverage

```python
plotUVdata(uv_coverage, highlight_baselines=[0, 1, 2])
```

---

### Plot telescope array on a world map

```python
plotMap(vlba, llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
```

---

### Additional Utilities

- `SplitScans`: Split long observations into shorter scans.  
- `combine_telescopes`: Merge multiple arrays.  
- `getHighlightBaselines`: Highlight specific baselines in plots.  
- `plotGlobe`: Visualize telescope arrays on a 3D globe.  
- `get_elevation`: Fetch telescope elevation using Open-Meteo API.  

---

## Examples

```python
# Combine two arrays
full_array = combine_telescopes([getArray("VLBA"), getArray("EVN")])

# Split observation into scans
uv_scans = SplitScans(full_array, source=[180,45], obstart=0, obsend=24, scan_time=2, n_scans=12)

# Animate uv-coverage and save as mp4
simulateUV(full_array, source, make_movie=True, output_name="uvout.mp4")
```

---

## Dependencies

- `numpy`  
- `matplotlib`  
- `astropy`  
- `basemap`  
- `numba`  
- `requests`  

---

## License

This project is licensed under the MIT License.
uvlpy 0.1.2
UV abused for your pleasure
Requires Python: >=3.10
Keywords: constraint, solver, uv

# uvlpy
A light package for constraint solving by levereging uv's dependency resolver

## Example
Example of some basic code to show the features would be:
```py
from uvlpy.uvlpy import System

sys = System()
a, b, c = sys.make_vars([0, 1], [2, 3, 4], list(range(0,9)))
a.val_constr(1, b == 2)
c.constr(b > 1)
sys.constr(a > 0, c < 9, b.to_constr())
print(sys.execute(a, b, c))
```
Where we:
- Create 3 variables with given possible values
- Constrain `a = 1` to require `b = 2`
- Constrain all values of `c` to require `b > 1`
- Constrain the result to require a positive `a`, `c < 9`, and to include `b`
- After execution the output is e.g. `[1, 2, 8]`, `a` and `b` can only be 1 and 2, but `c` can be any of its possible values (`uv` tends to prefer higher)

You can see the `sudoku_example.py` for a sudoku solver, 
which uses lists directly instead of destructuring them into variables like here.

## Install
Simply `pip install uvlpy`, or with `uv`, `uv pip install uvlpy`.

Otherwise, you can build it locally with `uv build` and use the wheel as you see fit.

Note that `uv` being installed and visible on PATH is required for this library to function.

## Why
Can be done. 

`uvlpy` uses `uv`'s solver, which should be reliable. The performance is not great, but sufficient.

However, note that the library creates a lot temporary files and packages to utilize `uv`, and as such, should be used with care and within reason.
deepnlp-2025 0.2.0
Advanced deep learning techniques for natural language processing with modern architectures and Korean language applications
Requires Python: <4.0,>=3.9
Keywords: python

# Deep Learning for NLP 2025

[![pypi-image]][pypi-url]
[![version-image]][release-url]
[![release-date-image]][release-url]
[![license-image]][license-url]
[![codecov][codecov-image]][codecov-url]
[![jupyter-book-image]][docs-url]

<!-- Links: -->
[codecov-image]: https://codecov.io/gh/entelecheia/deepnlp-2025/branch/main/graph/badge.svg?token=EC778uVu1e
[codecov-url]: https://codecov.io/gh/entelecheia/deepnlp-2025
[pypi-image]: https://img.shields.io/pypi/v/deepnlp-2025
[license-image]: https://img.shields.io/github/license/entelecheia/deepnlp-2025
[license-url]: https://github.com/entelecheia/deepnlp-2025/blob/main/LICENSE
[version-image]: https://img.shields.io/github/v/release/entelecheia/deepnlp-2025?sort=semver
[release-date-image]: https://img.shields.io/github/release-date/entelecheia/deepnlp-2025
[release-url]: https://github.com/entelecheia/deepnlp-2025/releases
[jupyter-book-image]: https://jupyterbook.org/en/stable/_images/badge.svg

[repo-url]: https://github.com/entelecheia/deepnlp-2025
[pypi-url]: https://pypi.org/project/deepnlp-2025
[docs-url]: https://deepnlp2025.jeju.ai
[changelog]: https://github.com/entelecheia/deepnlp-2025/blob/main/CHANGELOG.md
[contributing guidelines]: https://github.com/entelecheia/deepnlp-2025/blob/main/CONTRIBUTING.md
<!-- Links: -->

Advanced deep learning techniques for natural language processing with modern architectures and Korean language applications

- Documentation: [https://deepnlp2025.jeju.ai][docs-url]
- GitHub: [https://github.com/entelecheia/deepnlp-2025][repo-url]
- PyPI: [https://pypi.org/project/deepnlp-2025][pypi-url]

This course focuses on advanced deep learning techniques for natural language processing, covering state-of-the-art architectures including Transformers, State Space Models (Mamba, RWKV), and parameter-efficient fine-tuning methods. Students will learn prompt engineering, RAG systems, RLHF alternatives, and build practical NLP applications with Korean language datasets.

## Changelog

See the [CHANGELOG] for more information.

## Contributing

Contributions are welcome! Please see the [contributing guidelines] for more information.

## License

This project is released under the [CC-BY-4.0 License][license-url].