semvec/pypi-packages huggingface dataset:
fasttext-reducer 0.1.3
Lightweight package that allows for downloading fasttext models to any location and reducing their dimensions through a one-liner.
Requires Python: >=3.8

# fasttext-reducer
A tiny package (and standalone script) for downloading any pretrained fasttext word vector model to any desired location and then reducing its number of dimensions.

## Installation
Either run the following to install from PyPI:
```
pip install fasttext-reducer
```

Or clone the repository and install the package using `pip install .`

## Example usage
To download the English pretrained model, reduce it to 30 dimensions, and save it to `/Users/admin/`, you can run the following command:
```
fasttext-reduce --root_dir /Users/admin --lang en --dim 30
```
You can also use the package within Python:
```
from fasttext_reducer.reduce_fasttext_models import reduce_fasttext_models

reduce_fasttext_models('/Users/admin', 'en', 30)
```
cookiecutter-hydro 0.0.1
A cookiecutter template for scientific Python projects in the field of hydrology.
Requires Python: <4.0,>=3.11
Keywords: cookiecutter, template, poetry

[![Release](https://img.shields.io/github/v/release/zawadzkim/cookiecutter-hydro)](https://pypi.org/project/cookiecutter-hydro/)
[![Build status](https://img.shields.io/github/actions/workflow/status/zawadzkim/cookiecutter-hydro/main.yml?branch=main)](https://github.com/zawadzkim/cookiecutter-hydro/actions/workflows/main.yml?query=branch%3Amain)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/cookiecutter-hydro)](https://pypi.org/project/cookiecutter-hydro/)
[![Docs](https://img.shields.io/badge/docs-gh--pages-blue)](https://zawadzkim.github.io/cookiecutter-hydro/)
[![License](https://img.shields.io/github/license/zawadzkim/cookiecutter-hydro)](https://img.shields.io/github/license/zawadzkim/cookiecutter-hydro)

## Main features

- [Poetry](https://python-poetry.org/) for dependency management
- [GitHub Actions](https://github.com/features/actions)
- Pre-commit hooks with [pre-commit](https://pre-commit.com/)
- Code quality with [ruff](https://github.com/charliermarsh/ruff), [mypy](https://mypy.readthedocs.io/en/stable/), [deptry](https://github.com/zawadzkim/deptry/) and [prettier](https://prettier.io/)
- Containerization with [Docker](https://www.docker.com/)
- Development environment with [VSCode devcontainers](https://code.visualstudio.com/docs/devcontainers/containers)

## Quickstart

On your local machine, navigate to the directory in which you want to
create a project directory, and run the following two commands:

```bash
pip install cookiecutter-hydro
cchydro
```

Alternatively, install `cookiecutter` and directly pass the URL to this
Github repository to the `cookiecutter` command:

```bash
pip install cookiecutter
cookiecutter https://github.com/zawadzkim/cookiecutter-hydro.git
```

Create a repository on GitHub, and then run the following commands, replacing `<project-name>`, with the name that you gave the Github repository and
`<github_author_handle>` with your Github username.

```bash
cd <project_name>
git init -b main
git add .
git commit -m "Init commit"
git remote add origin git@github.com:<github_author_handle>/<project_name>.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!

## Acknowledgements

This project is a fork of [Cookiecutter Poetry](https://github.com/zawadzkim/cookiecutter-hydro-example) and is partly inspired by [Cookiecutter Data Science](https://github.com/drivendataorg/cookiecutter-data-science) project, but is tailored for use in hydrological research.
picproba 0.0.1
Probability distribution management
Requires Python: >=3.9

Probability distributions management
This package provides classes for Probability distributions and parametric families of probability distributions, with a particular focus on log density and Kullback--Leibler divergence.
Probability distributions and family of probability distributions can be easily transformed.
NOTE: numba requirement is currently pinned to 0.58, as other versions have not yet been fully tested. This notably impacts the version of numpy (<2). This should be fixed in future versions.