semvec/pypi-packages huggingface dataset:
uniparser-yawarana 0.0.6
A UniParser implementation for morphologically parsing and annotating Yawarana material.
Keywords: linguistics,morphology

# uniparser-yawarana

A morphological parser for Yawarana ([yaba1248](https://glottolog.org/resource/languoid/id/yaba1248), [yar](https://iso639-3.sil.org/code/yar)).

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6510179.svg)](https://doi.org/10.5281/zenodo.6510179)
![License](https://img.shields.io/github/license/fmatter/uniparser-yawarana)
[![Tests](https://img.shields.io/github/actions/workflow/status/fmatter/uniparser-yawarana/tests.yml?label=tests&branch=main)](https://github.com/fmatter/uniparser-yawarana/actions/workflows/tests.yml)
[![Linting](https://img.shields.io/github/actions/workflow/status/fmatter/uniparser-yawarana/lint.yml?label=linting&branch=main)](https://github.com/fmatter/uniparser-yawarana/actions/workflows/lint.yml)
[![Codecov](https://img.shields.io/codecov/c/github/fmatter/uniparser-yawarana)](https://app.codecov.io/gh/fmatter/uniparser-yawarana/)
[![PyPI](https://img.shields.io/pypi/v/uniparser-yawarana.svg)](https://pypi.org/project/uniparser-yawarana)
![Versions](https://img.shields.io/pypi/pyversions/uniparser-yawarana)

This project uses the excellent [uniparser-morph](https://github.com/timarkh/uniparser-morph/) package.
It is currently under development.
cutepandas 0.3.2
Qt Widgets and Objects for Pandas datastructures
Requires Python: >=3.10,<3.12

# CutePandas: PandasModels and Widgets for Qt

## What is it?

**CutePandas** is a Python package that provides Qt-based widgets related to pandas data structures.


## What does it include?
The official documentation is hosted on Github Pages: https://phil65.github.io/CutePandas/


## Where to get it?
The source code is currently hosted on GitHub at:
https://github.com/phil65/CutePandas

The latest released version are available at the [Python
package index](https://pypi.org/project/cutepandas).

```sh
# or PyPI
pip install cutepandas
```

## Installation from sources

This project uses poetry for dependency management and packaging. Install this first.
In the `cutepandas` directory (same one where you found this file after
cloning the git repo), execute:

```sh
poetry install
```

## License
[MIT](LICENSE)
EfficientSceneDetector 0.0.12
An Efficient Scene Detector
Requires Python: >=3.7

# EfficientSceneDetector
A python package for detecting scenes from a video using audio features.
The generated scenes should be with different context.
## Quick Start
```
from EfficientSceneDetector.sceneDetection import SceneDetection
objScene = SceneDetection()
objScene.detect_scenes("input_video.mp4", 4, "True")
```
#### Details
* input_video.mp4 : your input video path.
* 4 : size of window to be considered while generating scenes. If window is large then large scenes could be generated. If the window size is small then small scenes could be generated
* "True" : If you want to save generated scenes using ffmpeg. Make it "False" if you don't want to generate the scenes.

#### Output
* You will get all the scenes, and a csv file in the "Scenes" folder.
* The csv file columns are : SceneNumber, StartTime (Seconds), StartTime (TimeStamp), EndTime (Seconds), EndTime (TimeStamp) and Path

#### CSV Columns
* SceneNumber : Number of scenes, starting from 1.
* StartTime (Seconds) : Start time of a scene in seconds.
* StartTime (TimeStamp) : Start time of a scene in "%d:%02d:%02d" format.
* EndTime (Seconds) : End time of a scene in seconds.
* EndTime (TimeStamp) : End time of a scene in "%d:%02d:%02d" format.
* Path : Path of generated scenes.

# Help & Contributing
Please submit any bugs/issues or feature requests to the Issue Tracker. Before submission, ensure you search through existing issues (both open and closed) to avoid creating duplicate entries. Pull requests are welcome and encouraged. 

# Repository
```
https://github.com/akewarmayur/EfficientSceneDetector
```
stocks-cli 0.3.1
Command Line Interface to the STOCKS server

# STOCKS CLI

**stocks-cli** - CLI tool for interacting with the STOCKS server.

## Description

stocks-cli is a command-line interface (CLI) tool designed to facilitate seamless communication and interaction with the STOCKS server. The tool provides various commands and options to perform common operations and tasks related to the STOCKS server, including fetching information of items, data registration, and export data to repositories.

## Features

- **Config Command**: Use the `config` command to manage the configuration settings of the stocks-cli tool. Set up server details, authentication credentials, and other parameters to customize the tool according to your needs.

- **Export Command**: The `export` command allows you to export data from the STOCKS server. Choose specific datasets or define filters to extract the desired data in various formats for further analysis and processing.

- **Fetch Command**: With the `fetch` command, you can retrieve data from the STOCKS server. Specify the datasets or filters to fetch the required information and receive the data in real-time for immediate use.

- **Register Command**: The `register` command enables you to register new data or resources with the STOCKS server. Use this command to provide relevant details and metadata for the resources you want to add to the server.

## Installation

You can install stocks-cli using pip, the Python package installer.

```shell
pip install stocks-cli
```

## Usage

Once installed, you can use the stocks-cli tool with various commands and options. Here are some examples:

- To display the available commands and options, use the `stocks --help` command.

- To configure the stocks-cli tool, run `stocks config` and follow the interactive prompts to set up the necessary server details and authentication credentials.

- To export data from the STOCKS server, use the `stocks export` command followed by specific parameters and filters as needed.

- To fetch data from the STOCKS server, execute the `stocks fetch` command with appropriate dataset or filter specifications.

- To register new data or resources with the STOCKS server, utilize the `stocks register` command and provide the required details and metadata.

## Additional Information

For more information, detailed usage instructions, and advanced options, please refer to the [stocks-cli documentation](https://stocks.embl.de/docs/training/topics/cli/).

## License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
tesla-ce-provider-pt-urkund 0.0.6
TeSLA CE Urkund Plagiarism Provider
Requires Python: >=3.8

[![PyPi Version](https://img.shields.io/pypi/v/tesla-ce-provider-pt-urkund.svg)](https://pypi.python.org/pypi/tesla-ce-provider-pt-urkund/)
[![codecov](https://codecov.io/gh/tesla-ce/provider-pt-urkund/branch/main/graph/badge.svg?token=PJJQMW981P)](https://codecov.io/gh/tesla-ce/provider-pt-urkund)
[![AGPL License](https://img.shields.io/badge/license-AGPL-blue.svg)](http://www.gnu.org/licenses/agpl-3.0)
[![FOSSA Status](https://app.fossa.com/api/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-pt-urkund.svg?type=shield)](https://app.fossa.com/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-pt-urkund?ref=badge_shield)
[![Quality gate](https://sonar.sunai.uoc.edu/api/project_badges/quality_gate?project=tesla-ce_provider-pt-urkund)](https://sonar.sunai.uoc.edu/dashboard?id=tesla-ce_provider-pt-urkund)


# TeSLA CE Plagiarism Tool based on URKUND

| :warning: This repository is **under construction**. Final release of TeSLA Community Edition source code is expected by the **end of 2021** |
| --- |

## Description
Description: A description of your project follows. A good description is clear, short, and to the point. Describe the importance of your project, and what it does.

## Installation
Installation: Installation is the next section in an effective README. Tell other users how to install your project locally. Optionally, include a gif to make the process even more clear for other people.

## Usage
Usage: The next section is usage, in which you instruct other people on how to use your project after they’ve installed it. This would also be a good place to include screenshots of your project in action.

## Contributing
Contributing: Larger projects often have sections on contributing to their project, in which contribution instructions are outlined. Sometimes, this is a separate file. If you have specific contribution preferences, explain them so that other developers know how to best contribute to your work. To learn more about how to help others contribute, check out the guide for setting guidelines for repository contributors.

## Credits
Credits: Include a section for credits in order to highlight and link to the authors of your project.

## License
This project is licensed under [AGPL v3 licence](http://www.gnu.org/licenses/agpl-3.0).

[![FOSSA Status](https://app.fossa.com/api/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-pt-urkund.svg?type=large)](https://app.fossa.com/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-pt-urkund?ref=badge_large)
tbwk-opener 0.1.1
An tool to open tbwk files generated by NanoDrop 2000
Requires Python: >=3.6

# tbwk-opener
A python package to open and read tbwk files generated from the NanoDrop 2000 software.

Testing: ![Python application](https://github.com/Gillingham-Lab/tbwk-opener/workflows/Python%20application/badge.svg)

## Features
This library has so far only been tested with Nucleic Acid worksheets.

- Can import tbwk files to read measurements contained within the file
- Successfully reads y- and x-values of a recorded spectrum
- Also reports on tabled data contained in the worksheet (such as A260 for nucleic acids, 
or direct nucleic concentration as chosen by the method)

A protein worksheet (no example provided) showed similar behaviour and should work without any issues.

## Installation

Make sure you've installed numpy and scipy before attempting to install tbwk-opener from pip, then use:

```shell script
pip install tbwk-opener
```

## Usage

Get all measurements from a worksheet and report on concentration using the absorption at 260 nm:

```python
from tbwk import Worksheet

worksheet = Worksheet.import_worksheet("examples/nanodrop-dna-measurements-01.twbk")

factor = (2.05 + 2.30)/2 # Î¼M per absorption unit
for measurement in worksheet:
    print(f"{measurement.title:20}{measurement.get_absorption_at(260)*factor:.2f} Î¼M")
```

```text
wash                0.18 Î¼M
blank               0.02 Î¼M
BSD01               61.47 Î¼M
BSD01               61.11 Î¼M
BSD01 cntl A1       37.87 Î¼M
wash                0.57 Î¼M
BSD01 cntl A2       33.94 Î¼M
wash                0.27 Î¼M
BSD01 cntl A3       44.39 Î¼M
BSD01 cntl A3       0.35 Î¼M
BSD01 cntl A4       40.00 Î¼M
wash                0.05 Î¼M
wash                0.02 Î¼M
```
oalg 0.1.1
Collection of on-line algorithms.
Requires Python: >=2.5

oalg
====

Collection of on-line algorithms.

Most of them are on-line versions of their
offline counterparts in numpy, but because of their on-line nature, they are
formulated as classes instead of functions.

For further information about on-line algorithms, please check Wikipedia:
http://en.wikipedia.org/wiki/On-line_algorithm


Copyright notice
----------------

Copyright (C) 2013-2023 Daniel Jung
Contact: proggy-contact@mailbox.org

This program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation; either version 2 of the License, or (at your option)
any later version.

This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
more details.

You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
SmartDriveML 0.0.12
SmartDriveML: Driving Industrial Projects with Affordable ML Solutions, Frameworks and Cloud Choices
Keywords: python,machine learning,pyspark,cloud



With SmartDriveML, industries can drive their projects with confidence, leveraging affordable ML solutions, making informed cloud service choices, and selecting optimal frameworks. This powerful Python library empowers users to harness the potential of machine learning while ensuring cost-effectiveness, enabling industrial projects to thrive in the era of data-driven decision-making. Key Features of SmartDriveML: 1. Affordable ML Solutions: Access a comprehensive set of functions and algorithms for cost-effective machine learning project execution. 2. Cloud Service Cost Comparison: Evaluate and compare pricing options among various cloud service providers to optimize cost-efficiency. 3. Framework Suitability Assessment: Analyze and select the most suitable ML frameworks based on project requirements and constraints. 4. Cutting-Edge Machine Learning Techniques: Leverage advanced ML techniques and algorithms to drive industrial projects with confidence. 5. Python Library: SmartDriveML is a versatile and user-friendly Python library, providing seamless integration and ease of use.