semvec/pypi-packages huggingface dataset:
dyadic-tiling 0.2.0
Morton encoded dyadic cubes and points for efficient space decomposition.
Requires Python: >=3.8
Keywords: dyadic cube, Morton code, spatial indexing, stopping time

# DyadicTiling

[![PyPI version](https://badge.fury.io/py/dyadic-tiling.svg)](https://badge.fury.io/py/dyadic-tiling)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

> **Morton encoded dyadic cubes and points for efficient space decomposition.**

DyadicTiling is a lightweight Python package for tiling spaces of arbitrary dimension with dyadic cubes. Points are organised using Morton encodings, dyadic cubes are points with a truncation level, and tilings are collections of dyadic cubes. The natural ordering from the Morton encoding allows for efficient operations, even in high-dimensions.

---

## ✨ Features

* **Pure‑Python**: Only one light-weight dependency, `sortedcontainers`.
* **Arbitrary dimension**: The same code works from 1‑D to 17‑D and beyond.
* **PointSet & DyadicCubeSet**: Membership operations in *O(log n)*.
* **Stopping Times**: Define a (consistent) rule τ(x) that assigns a level to each point and automatically get a disjoint set of dyadic cubes.
* **Fully tested**: 100% coverage across 100+ unit tests.

---

## 🚀 Installation

```bash
pip install dyadic-tiling            # From PyPI (recommended)
# or
pip install git+https://github.com/Benjamin-Walker/DyadicTiling.git
```

Requires Python ⩾ 3.8 and sortedcontainers ⩾ 2.4.

---

## ⚡ Quick‑start

```python
from dyadic_tiling import Point
from dyadic_tiling import DyadicCube

# 1. Encode any point in [range[0][0], range[0][1]] x [range[1][0], range[1][1]]
range = [[-3, -1], [1.1, 1.3]]
p = Point([-2.3, 1.2], coordinate_ranges=range)
print(p.get_morton_string(4))      # → '00110111' (morton code at level 4)

# 2. Grab the dyadic cube that contains it at level k
cube = p.get_containing_cube(level=2)
print(cube)                        # DyadicCube(dim=2, level=2, morton_code=0011)

# 3. Slice a PointSet by cube
from dyadic_tiling import PointSet
cloud = PointSet([p, Point([-2.5, 1.18], coordinate_ranges=range), Point([-1.1, 1.28], coordinate_ranges=range)])
print(cloud.in_cube(cube))         # Only points inside that square

# 4. Build a stopping using DyadicCubes
from dyadic_tiling import DyadicCubeSetStoppingTime
rule = DyadicCubeSetStoppingTime()
rule.add(cube)
print(rule(p))                     # 2  (the level for the point p)
```

---


## 📄 License

DyadicTiling is distributed under the MIT license — see `LICENSE` for details.

---

## ✏️ Citation

If this package saved you some time, please cite it:

```bibtex
@software{dyadictiling,
  author       = {Benjamin Walker},
  title        = {DyadicTiling: Morton encoded dyadic cubes and points for efficient space decomposition.},
  year         = {2025},
  url          = {https://github.com/Benjamin-Walker/DyadicTiling},
  license      = {MIT}
}
```
labdesk 0.4.0
Labdesk is a library created to support rapid and reproducible experimentation.
Requires Python: <4.0,>=3.8

# Labdesk

Labdesk is a library created to support rapid and reproducible experimentation.

## Installation

```bash
pip install labdesk
```

## Usage

Labdesk provides command-line tools for managing experiments:

- `labd` - Main CLI interface
- `labd-manager` - Experiment manager
- `labd-runner` - Experiment runner

## Requirements

- Python ^3.8
- typer

## Development

This project uses Poetry for dependency management.

```bash
poetry install
poetry run labd --help
```

## License

See LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
cherry-sql 0.2.0
Библиотека для работы с SQL и вспомогательными файлами
Requires Python: >=3.7

# CherrySQL

Библиотека для работы с SQL и вспомогательными файлами.

## Установка

```bash
pip install cherry-sql
```

## Использование

```python
from CherrySQL import ... # ваш импорт
```
teklia-accounts-client 0.1.6
Official API client for Teklia account system
Requires Python: >=3.10
Keywords: python

# Accounts API Client

Official API client for Teklia account system

### Development

For development and tests purpose it may be useful to install the project as a editable package with pip.

* Use a virtualenv (e.g. with virtualenvwrapper `mkvirtualenv -a . accounts-api-client`)
* Install accounts-api-client as a package (e.g. `pip install -e .`)

### Linter

Code syntax is analyzed before submitting the code.\
To run the linter tools suite you may use pre-commit.

```shell
pip install pre-commit
pre-commit run -a
```

### Run tests

Tests are executed with `tox` using [pytest](https://pytest.org).

```shell
pip install tox
tox
```

To recreate tox virtual environment (e.g. a dependencies update), you may run `tox -r`.

Run a single test module: `tox -- <test_path>`
Run a single test: `tox -- <test_path>::<test_function>`
ete-ts 0.2.1
A package for analysing different caractheristics of time series data.
Requires Python: >=3.9

# Welcome to "Easy to Explain: Time-Series Features"

This Python library offers diverse solution for advanced time-series analysis. This library is built to empower developers and data scientists by simplifying complex time-series tasks.

To acess the full documentation, visit our official website: https://franciscovmacieira.github.io/ete_ts/

---

## What It Does

`ete_ts` equips you with a robust set of features to master your time-series data:

**Trend Analysis:** Quantify the direction, strength, and stability of the trend in your time-series.

**Noise & Volatility Modeling:** Characterize the randomness, complexity, and predictability of your time-series.

**Seasonality Detection:** Identify and measure the strength of recurring, cyclical patterns.

**Model Selection:** Extract key statistical properties to guide your choice of forecasting models.

**Clustering & Classification:** Generate unique fingerprints for your time-series to use in machine learning tasks.

---

## Installation

Get started in seconds.

```bash
pip install ete_ts
```

## Context

This library was developed as the focus of a research initiative by Francisco Macieira, an undergraduate student of Artificial Intelligence and Data Science at FCUP. The project was supervised by Professor Moisés Santos, affiliated with both FCUP and FEUP.