semvec/pypi-packages huggingface dataset:
sandlercubics 0.10.2
Cubic equations of state from Chemical, Biochemical, and Engineering Thermodynamics (5th edition) by Stan Sandler
Requires Python: >=3.7

# Sandlercubics

> Digitized cubic equations of state from Sandler's 5th ed.

Sandlercubics implements a Python interface to the cubic equations of state found in  _Chemical, Biochemical, and Engineering Thermodynamics_ (5th edition) by Stan Sandler (Wiley, USA). It should be used for educational purposes only.

For full documentation, see [https://sandlercubics.readthedocs.io/en/stable/index.html](https://sandlercubics.readthedocs.io/en/stable/index.html)
sandlersteam 0.12.2
Steam tables from Chemical, Biochemical, and Engineering Thermodynamics (5th edition) by Stan Sandler
Requires Python: >=3.7

# Sandlersteam

> Digitized steam tables from Sandler's 5th ed.

Sandlersteam implements a python interface to the steam tables found in Appendix III of _Chemical, Biochemical, and Engineering Thermodynamics_ (5th edition) by Stan Sandler (Wiley, USA). It should be used for educational purposes only.

Full documentation is available at [https://sandlersteam.readthedocs.io/en/stable/index.html](https://sandlersteam.readthedocs.io/en/stable/index.html)
bliss-audio 0.2.0
A package containing helpers to make audio playlists.
Requires Python: >=3.7

Python bindings for [bliss-rs](https://github.com/Polochon-street/bliss-rs).

Audio library used as a building block to make playlists from songs.

Installation
============

bliss-audio is available for Python 3.5+ via pip:

```
$ pip install bliss-audio
```

Usage
=====

```python
from bliss_audio import Song
import numpy as np

song1 = Song("/path/to/song1")

print(f'Song "{song1.title}" has the following analysis:')
for key, value in sorted(song1.analysis_dict.items()):
    print(f"{key}: {value}")

song2 = Song("/path/to/song2")

distance = np.linalg.norm(np.array(song1.analysis) - np.array(song2.analysis))

print(f'\nDistance between song1 and song2 is {distance}')
```

Then you most likely want to analyze a bunch of songs like that, store the
result somewhere, and generate playlists on the fly by taking a song and
finding the next one by computing the one with the smallest distance to it.
jaraco.develop 8.19.2
Development utilities by jaraco
Requires Python: >=3.9

.. image:: https://img.shields.io/pypi/v/jaraco.develop.svg
   :target: https://pypi.org/project/jaraco.develop

.. image:: https://img.shields.io/pypi/pyversions/jaraco.develop.svg

.. image:: https://github.com/jaraco/jaraco.develop/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.develop/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://readthedocs.org/projects/jaracodevelop/badge/?version=latest
   :target: https://jaracodevelop.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2025-informational
   :target: https://blog.jaraco.com/skeleton

This package includes a collection of libraries and scripts used by `jaraco <https://www.jaraco.com>`_ and many of the `projects maintained by jaraco <https://pypi.org/user/jaraco>`_.

It includes facilities for managing scores of repositories with SCM tools like Git, working with Read the Docs, determining release versions from pertinent changes, synchronizing related histories, automatically resolving known merge conflicts, and more.

Many of the modules in this package are executable using ``runpy`` (e.g. ``python -m jaraco.develop.macos-build-python``).

Although this package is built on assumptions about jaraco's workflow, these routines are provided here for shared use across those projects, transparency of operations, and for potential re-use. Contributions and feedback are welcome.
polarisdb 0.1.2
High-performance embedded vector database
Requires Python: >=3.8

# PolarisDB Python Bindings

**PolarisDB** is a high-performance, embedded vector database written in Rust. This package provides Python bindings for efficient local vector search and storage.

## Features

- **Fast**: Built on Rust with SIMD optimizations.
- **Embedded**: Runs locally without a separate server process.
- **Simple**: Easy-to-use Python API for managing collections and indexes.
- **Search**: Supports Euclidean, Cosine, and Dot Product distance metrics.
- **Durable**: WAL-based persistence for data safety (Collection API).

## Installation

```bash
pip install polarisdb
```

## Quick Start

### In-Memory Index (Brute Force)

Perfect for small datasets or exact search requirements.

```python
import numpy as np
from polarisdb import Index

# Create an index with Cosine similarity and 128 dimensions
index = Index("cosine", 128)

# Insert vectors (ID, Vector)
# Vectors can be lists or numpy arrays
v1 = np.random.rand(128).astype(np.float32)
index.insert(1, v1)

v2 = np.random.rand(128).astype(np.float32)
index.insert(2, v2)

# Search for nearest neighbors
query = np.random.rand(128).astype(np.float32)
results = index.search(query, k=5)

for id, distance in results:
    print(f"Found ID: {id}, Distance: {distance}")
```

### Persistent Collection

Store vectors on disk with crash recovery.

```python
from polarisdb import Collection

# Open or create a collection at the specified path
collection = Collection.open_or_create("./my_collection", 128, "euclidean")

# Insert vectors
v1 = [1.0] * 128
collection.insert(1, v1)

# Persist changes to disk (WAL checkpoint)
collection.flush()

# Search
results = collection.search(v1, k=1)
print(results)
```

## API Reference

### `Index(metric: str, dimension: int)`

Creates an in-memory brute-force index.

- **metric**: "cosine", "euclidean", or "dot".
- **dimension**: Dimension of vectors (e.g., 128, 768).

### `Collection.open_or_create(path: str, dimension: int, metric: str)`

Opens an existing collection or creates a new one.

- **path**: Directory path for storage.
- **dimension**: Vector dimension.
- **metric**: Distance metric.

## LangChain Integration

Use PolarisDB as a vector store in LangChain RAG pipelines:

```python
from polarisdb.langchain import PolarisDBVectorStore
from langchain_openai import OpenAIEmbeddings

# Create from texts
vectorstore = PolarisDBVectorStore.from_texts(
    texts=["Hello world", "Goodbye world"],
    embedding=OpenAIEmbeddings(),
    collection_path="./my_vectors",
)

# Similarity search
docs = vectorstore.similarity_search("Hello", k=1)

# Use as retriever
retriever = vectorstore.as_retriever()
```

**Requirements**: `pip install langchain-core langchain-openai`

## Batch Operations

For efficient bulk operations:

```python
from polarisdb import Index

index = Index("cosine", 128)

# Insert many vectors at once
ids = [1, 2, 3, 4, 5]
vectors = [[0.1] * 128 for _ in range(5)]
index.insert_batch(ids, vectors)

# Search multiple queries
queries = [[0.1] * 128, [0.2] * 128]
results = index.search_batch(queries, k=3)
```

## License

MIT
pytest-cython 0.4.0rc1
A plugin for testing Cython extension modules.
Requires Python: >=3.8
Keywords: pytest, py.test, cython, doctest

# Overview

[![PyPI Package latest release](https://img.shields.io/pypi/v/pytest-cython.svg?style=flat)](https://pypi.org/project/pytest-cython)
[![PyPI Package monthly downloads](https://img.shields.io/pypi/dm/pytest-cython.svg?style=flat)](https://pypi.org/project/pytest-cython)
[![PyPI Wheel](https://img.shields.io/pypi/wheel/pytest-cython.svg?style=flat)](https://pypi.org/project/pytest-cython)
[![Supported versions](https://img.shields.io/pypi/pyversions/pytest-cython.svg?style=flat)](https://pypi.org/project/pytest-cython)
[![Supported implementations](https://img.shields.io/pypi/implementation/pytest-cython.svg?style=flat)](https://pypi.org/project/pytest-cython)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/pytest-cython.svg)](https://anaconda.org/conda-forge/pytest-cython)
[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pytest-cython.svg)](https://anaconda.org/conda-forge/pytest-cython)

[![CI Lint Status](https://github.com/lgpage/pytest-cython/actions/workflows/lint.yml/badge.svg?branch=main)](https://github.com/lgpage/pytest-cython/actions/workflows/lint.yml?query=branch%3Amain)
[![CI Test Status](https://github.com/lgpage/pytest-cython/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/lgpage/pytest-cython/actions/workflows/test.yml?query=branch%3Amain)

This [pytest](https://github.com/pytest-dev/pytest) plugin allows for the doctesting of C/C++ extension modules for
Python created through [cython](https://cython.org/).

## Installation

You can install `pytest-cython` via [pip](https://pypi.org/project/pip/) from [PyPI](https://pypi.org):

``` shell
pip install pytest-cython
```

## Usage

Basic usage:

``` shell
pytest --doctest-cython
```

You can also run the doctests for a single `.pyx` file as such:

``` shell
pytest --doctest-cython path/to/module.pyx
```

### Notes

- The pytest option `--import-mode=importlib` is not supported, though we would like it to be.
- When using the pytest option `--import-mode=prepend` with packages that are installed in a
  non-editable fashion, you must set the environment variable `PY_IGNORE_IMPORTMISMATCH=1`.

## Compatibility

The following table describes the versions of Pytest and Cython the each version of the pytest-cython plugin is
compatible with.

| Version | Pytest | Cython  |
| ------- | ------ | ------- |
 | 0.4.x  | 9      | 3       |
| 0.3.x   | 8      | 0.29, 3 |
| 0.2.x   | 6, 7   | 0.29, 3 |

## Issues

If you encounter any problems, please [file an issue](https://github.com/lgpage/pytest-cython/issues) along with a
detailed description.

## Acknowledgements

This [pytest](https://github.com/pytest-dev/pytest) plugin was generated with
[cookiecutter](https://github.com/cookiecutter/cookiecutter) along with [\@hackebrot](https://github.com/hackebrot)'s
[cookiecutter-pytest-plugin](https://github.com/pytest-dev/cookiecutter-pytest-plugin) and
[\@ionelmc](https://github.com/ionelmc)'s [cookiecutter-pylibrary](https://github.com/ionelmc/cookiecutter-pylibrary)
templates.