semvec/pypi-packages huggingface dataset:
ChromeFetcher 2025.5.132001
Automatically fetches and downloads the appropriate Chrome version based on OS and architecture.
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/chromefetcher.svg)](https://badge.fury.io/py/chromefetcher)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/chromefetcher)](https://pepy.tech/project/chromefetcher)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

# ChromeFetcher

`ChromeFetcher` automates the process of fetching Chrome or ChromeDriver based on the operating system and architecture. It simplifies the task of downloading the appropriate version for your system.

## Installation

To install `ChromeFetcher`, use pip:

```bash
pip install ChromeFetcher
```

## Usage

Easily download ChromeDriver with:

```python
from ChromeFetcher.chrome_fetcher import fetch_chrome

fetch_chrome(product='chromedriver')
```

Specify `product` as `'chrome'` or `'chromedriver'` to download. Options allow unzipping and cleanup post-download.

The `fetch_chrome` function offers a comprehensive approach to automatically downloading Chrome or ChromeDriver based on the user's operating system and architecture, streamlining the setup process for web automation tasks. Here's an overview of all the parameters available in this function:

- **channel (default='Stable')**: This parameter allows users to specify the release channel of Chrome they wish to download. The default is set to 'Stable', but users can select other channels like 'Beta' or 'Dev' depending on their requirements.

- **product (default='chrome')**: Determines whether to download Chrome or ChromeDriver. By setting this parameter to 'chrome', the function will download the browser. If set to 'chromedriver', it will fetch the driver needed for automation.

- **download_path (default=os.getcwd())**: Specifies the directory where the downloaded file will be saved. By default, it uses the current working directory. Users can provide a custom path to suit their project structure.

- **unzip (default=True)**: A boolean parameter that, when set to True, automatically extracts the contents of the downloaded ZIP file. If False, the function leaves the ZIP file as is.

- **delete_zip (default=True)**: This parameter works in tandem with the unzip option. When set to True, it deletes the ZIP file after extraction to save space and keep the directory tidy. If unzip is False, this parameter has no effect.

## Features

- Automatically fetches Chrome or ChromeDriver.
- Supports different OS and architectures.
- Unzips and cleans up downloads optionally.

## Contributing

Contributions, issues, and feature requests are welcome! Check our [issues page](https://github.com/chigwell/ChromeFetcher/issues).

## License

Licensed under the [MIT License](https://choosealicense.com/licenses/mit/).
kafkit 0.3.1
Kafkit helps you write Kafka producers and consumers in Python with asyncio.
Requires Python: >=3.11
Keywords: rubin, lsst

# Kafkit

Kafkit helps you write Kafka producers and consumers in Python with asyncio:

- Kafkit provides a client for the Confluent Schema Registry's HTTP API.
  The `RegistryApi` client includes both high-level methods for managing subjects and schemas in a Registry, and direct low-level access to HTTP methods (GET, POST, PUT, PATCH, and DELETE).
  The high-level methods use caching so you can use the client as an integral part of your application's schema management.
  `RegistryApi` is implemented around [aiohttp](https://aiohttp.readthedocs.io/en/stable/), but since the base class is designed with a [sans IO architecture](https://sans-io.readthedocs.io), a Registry client can be implemented with any asyncio HTTP library.

- Kafkit provides Avro message serializers and deserializers that integrate with the [Confluent Schema Registry](https://docs.confluent.io/current/schema-registry/docs/index.html): `Deserializer`, `Serializer`, and `PolySerializer`.

- The `RecordNameSchemaManager` is a streamlined tool for serializing messages using the schemas maintained by your app, while also integrating with the [Confluent Schema Registry](https://docs.confluent.io/current/schema-registry/docs/index.html).

- The `kafkit.ssl` module helps you connect to SSL-secured Kafka brokers.

Learn more about Kafkit at https://kafkit.lsst.io.
ViSound 0.1.6
A library to sonify images using Python

# ViSound

Library to convert images to sounds.

Refer to the wiki [here](https://github.com/dheerajshenoy/ViSound/blob/main/WIKI.md)

## Introduction

This is just a standalone library for use in other programs that can make use of `ViSound` capabilities. On it's own `ViSound` doesn't do anything. You have to use it in some way to get the output and play the audio yourself.

## Installation

ViSound can be installed using pip:

```
pip install visound
```

## Features

* Sonify images in a number of different ways
* Output the audio to a file or stdout
* Apply effects

## Traversal Modes

You can traverse the given input images in different ways to get different audio output. The following modes are currently supported.

* Left To Right
* Right to Left
* Top to Bottom
* Bottom to Top
* Circle Inward
* Circle Outward

## Usage

`visound <path to an image> --play --verbose`

## Dependencies

* numpy
* opencv-python
* sounddevice
* soundfile

# TODO

- [X] Traversal
    - [X] Left to Right
    - [X] Right to left
    - [X] Top to bottom
    - [X] Bottom to top
    - [X] Circular inward
    - [X] Circular outward
- [X] Apply effects to the output audio
- [ ] Allow for custom pixel to frequency mapping
- [X] CLI client
- [ ] Machine learning to apply effects and custom image traversal
hyperunison-public-api-sdk 2.3.5
Python SDK to use public API of Unison
Requires Python: >=3.8

# Hyperunison Python SDK

You can use this SDK to execute UQL queries via Public API.

## Installation
If you need to install this SDK, you should add the line
```shell
hyperunison-public-api-sdk===2.3.5
```
to the file **requirements.txt**.

Or you can run the command
```shell
pip install hyperunison-public-api-sdk
```
in CLI.

## API keys

You will need to create API key to use the Public API. You can do it in Web interface of the site.

## The example of using

You can see all examples of using of this SDK here:
<a href="https://github.com/Hyperunison/unison-sdk-python/tree/master/examples">https://github.com/Hyperunison/unison-sdk-python/tree/master/examples</a>.
openedx-mongodbproxy 0.3.0
Proxy around MongoDB connection that automatically handles AutoReconnect exceptions.

MongoDBProxy
============

MongoDBProxy is used to create a proxy around a MongoDB-connection in order to automatically handle 
AutoReconnect-exceptions.
You use MongoDBProxy in the same way you would an ordinary MongoDB-connection but don't need to worry 
about handling AutoReconnects by yourself.

Usage:

import pymongo

import mongodb_proxy

safe_conn = mongodb_proxy.MongoProxy(pymongo.MongoReplicaSetClient(replicaSet='blog_rs')

safe_conn.blogs.posts.insert(post)

**See here for more details:**
http://www.arngarden.com/2013/04/29/handling-mongodb-autoreconnect-exceptions-in-python-using-a-proxy/


**Contributors**:

Jonathan Kamens (https://github.com/jikamens)

Michael Cetrulo (https://github.com/git2samus)

Richard Frank (https://github.com/richafrank)

David Lindquist (https://github.com/dlindquist)
aind-behavior-experiment-launcher 0.5.0rc1
A library for a minimal framework that can be used to build experimental interfaces.
Requires Python: >=3.11

# aind-behavior-experiment-laucher

![CI](https://github.com/AllenNeuralDynamics/Aind.Behavior.ExperimentLauncher/actions/workflows/ci.yml/badge.svg)
[![PyPI - Version](https://img.shields.io/pypi/v/aind-behavior-experiment-launcher)](https://pypi.org/project/aind-behavior-experiment-launcher/)
[![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)

Source for for a minimal framework that can be used to build experimental interfaces

---

## Introduction


The launcher module of this library provides a frontend interface to launch, monitor and manage behavior tasks using this library. It is also designed to interface with the AIND Services.

### AIND Services Module

While we will try to keep this library up to date, it is recommended to check the original repositories for the most recent updates.

A list of services relevant for this library include:

- [aind-data-schema](https://github.com/AllenNeuralDynamics/aind-data-schema)
- [aind-data-schema-models](https://github.com/AllenNeuralDynamics/aind-data-schema-models)
- [aind-watchdog-service](https://github.com/AllenNeuralDynamics/aind-watchdog-service)
- [aind-slims-api](https://github.com/AllenNeuralDynamics/aind-slims-api)
- [aind-data-mapper](https://github.com/AllenNeuralDynamics/aind-metadata-mapper)

We will generally try to wrap the services provided by these repositories into a more user-friendly and opinionated interface that can be used by the Aind Behavior Services library and other repositories.

We will also try to scope all dependencies of the related to AIND Services to its own optional dependency list in the `./pyproject.toml` file of this repository. Therefore, in order to use this module, you will need to install these optional dependencies by running:

```pip install .[aind-services]```

Feedback and contributions are welcome!

## General instructions

This repository follows the project structure laid out in the [Aind.Behavior.Services repository](https://github.com/AllenNeuralDynamics/Aind.Behavior.Services).
datasette-chronicle 0.3
Use sqlite-chronicle with tables in Datasette
Requires Python: >=3.9

# datasette-chronicle

[![PyPI](https://img.shields.io/pypi/v/datasette-chronicle.svg)](https://pypi.org/project/datasette-chronicle/)
[![Changelog](https://img.shields.io/github/v/release/datasette/datasette-chronicle?include_prereleases&label=changelog)](https://github.com/datasette/datasette-chronicle/releases)
[![Tests](https://github.com/datasette/datasette-chronicle/workflows/Test/badge.svg)](https://github.com/datasette/datasette-chronicle/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-chronicle/blob/main/LICENSE)

Use [sqlite-chronicle](https://github.com/simonw/sqlite-chronicle) with tables in Datasette

## Installation

Install this plugin in the same environment as Datasette.
```bash
datasette install datasette-chronicle
```
## Usage

Once installed, users with the `enable-chronicle` and `disable-chronicle` permissions (granted to the `root` user by default) will have access to new table action menu options for enabling and disabling row version tracking for a table.

Tables that have had row version tracking enabled will now support a new `?_since=X` query string parameter on the table page, which will filter for just rows that have been inserted or updated since the specified chronicle version.

See the [sqlite-chronicle documentation](https://github.com/simonw/sqlite-chronicle/blob/main/README.md) for more details on what this is and how it works.

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd datasette-chronicle
python3 -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
pip install -e '.[test]'
```
To run the tests:
```bash
pytest
```
image-processing-by-igorgabrig-by-igorgabrig 0.0.2
Image Processing Package Using Skimage
Requires Python: >=3.5

# package_name

Description. 
The package package_name is used to:
	Processing:
		- Histogram matching 
		- Structural similarity
		- Resize Image
	Utils:
		- Read Image
		- Save Image
		- Plot Image
		- Plot Result
		- Plost Histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_by_igorgabrig_by_igorgabrig
```

## Usage

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Author
Igor Gabrig Barboza

## License
[MIT](https://choosealicense.com/licenses/mit/)